system

Abstract

The system according to this embodiment aims to provide visitors with an interactive experience by utilizing museum data. [Solution] The system according to this embodiment comprises a reception unit, a provision unit, and a generation unit. The reception unit receives input from visitors. The provision unit provides information based on the information received by the reception unit. The generation unit generates quizzes and games based on the information provided by the provision unit.

Description

Technical Field

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

Background Art

[0002] Patent Document 1 discloses a method for controlling a persona chatbot, which is performed by at least one processor, including steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to an explanation of the chatbot's character, encoding the prompt, and inputting the encoded prompt into a language model to generate a chatbot utterance that responds 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 it is difficult to sufficiently utilize the rich data of museums to provide visitors with an interactive experience.

[0005] The system according to the embodiment aims to provide visitors with an interactive experience by utilizing museum data.

Means for Solving the Problems

[0006] The system according to the embodiment includes a reception unit, a provision unit, and a generation unit. The reception unit receives the input of visitors. The provision unit provides information based on the information received by the reception unit. The generation unit generates quizzes and games based on the information provided by the provision unit. [Effects of the Invention]

[0007] The system according to this embodiment can utilize museum data to provide visitors with an interactive experience. [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 experiential museum system according to an embodiment of the present invention is a system that transforms museums into experiential museums that are more interactive and enjoyable for visitors by utilizing the abundant data held by the museum. This experiential museum system collects specialized data held by the museum and trains a generating AI, so that when a visitor inputs what they would like to experience in the museum, the generating AI provides an interactive experience based on that input. For example, if a visitor wants to learn more about a particular exhibit, the generating AI provides information related to that exhibit and further generates quizzes or games based on that information. This allows visitors to enjoy the exhibits while gaining a deeper understanding of them. The generating AI can also suggest exhibits that visitors should visit next based on their interests. This allows visitors to efficiently view exhibits that interest them while touring the museum at their own pace. Furthermore, the generating AI can collect visitor feedback and use it to improve museum operations. For example, it can collect information such as which exhibits visitors were interested in and which experiences they enjoyed, and reflect this in the planning of future exhibitions and events. In this way, by utilizing the generating AI, museums can provide visitors with more attractive experiences and improve operational efficiency. This allows the experiential museum system to provide visitors with interactive experiences and streamline museum operations.

[0029] The interactive museum system according to this embodiment comprises a reception unit, a provision unit, and a generation unit. The reception unit receives input from visitors. Visitor input includes, but is not limited to, touch panel input, voice input, and scanning of a two-dimensional code (e.g., QR Code®). The reception unit receives visitor input using, for example, a touch panel. The reception unit can also receive voice input from visitors using voice recognition technology. Furthermore, the reception unit can also receive visitor input using two-dimensional code scanning. For example, the reception unit provides information about an exhibit when a visitor scans the exhibit's two-dimensional code. The provision unit provides information based on the information received by the reception unit. The information provided includes, but is not limited to, text information, images, videos, and audio. The provision unit provides, for example, text information. The provision unit can also provide images and videos. Furthermore, the provision unit can also provide audio information. For example, the provision unit provides a detailed explanation of an exhibit as text information. The providing unit can also provide images and videos of the exhibits. Furthermore, the providing unit can also provide audio guides about the exhibits. The generating unit generates quizzes and games based on the information provided by the providing unit. The generated quizzes and games include, but are not limited to, multiple-choice quizzes, puzzle games, and action games. For example, the generating unit can generate multiple-choice quizzes. The generating unit can also generate puzzle games. Furthermore, the generating unit can also generate action games. For example, the generating unit can generate multiple-choice quizzes about the exhibits. The generating unit can also generate puzzle games about the exhibits. Furthermore, the generating unit can also generate action games about the exhibits. Thus, the experiential museum system according to the embodiment provides an interactive experience based on visitor input, and by generating quizzes and games, visitors can learn while having fun.

[0030] An interactive museum system includes a collection unit for gathering feedback. This unit collects visitor feedback, which may include, but is not limited to, questionnaire responses, comments, and rating scores. For example, the collection unit could collect visitor feedback through questionnaires. It could also collect visitor comments. Furthermore, it could collect visitor rating scores. For instance, the collection unit could conduct questionnaires for visitors and collect their responses. It could also allow visitors to leave comments about exhibits. Furthermore, it could allow visitors to assign rating scores to exhibits. This allows the collection unit to gather visitor feedback and use it to improve museum operations and exhibits.

[0031] The interactive museum system includes an analysis unit that analyzes collected feedback. The analysis unit analyzes the collected feedback. Analysis includes, but is not limited to, text mining, sentiment analysis, and statistical analysis. For example, the analysis unit can analyze feedback using text mining techniques. The analysis unit can also analyze feedback using sentiment analysis techniques. Furthermore, the analysis unit can analyze feedback using statistical analysis techniques. For example, the analysis unit can analyze visitor comments using text mining techniques to extract common opinions and impressions. The analysis unit can also analyze visitor comments using sentiment analysis techniques to classify positive and negative opinions. Furthermore, the analysis unit can analyze visitor evaluation scores using statistical analysis techniques to quantify the evaluation of exhibits. In this way, the analysis unit can analyze the collected feedback, gain a detailed understanding of visitors' opinions and impressions, and reflect this in the planning of future exhibitions and events.

[0032] The interactive museum system includes a planning department that plans future exhibitions and events based on analysis results. The planning department plans future exhibitions and events based on the analysis results. Planning includes, but is not limited to, selecting exhibition themes, scheduling events, and allocating budgets. For example, the planning department can select exhibition themes based on analysis results. It can also create event schedules. Furthermore, it can determine budget allocations. For instance, the planning department can select the next exhibition theme based on visitor feedback. It can also create event schedules based on visitor feedback. Furthermore, it can determine budget allocations based on visitor feedback. This allows the planning department to plan future exhibitions and events based on analysis results, providing exhibitions and events that meet the interests and concerns of visitors.

[0033] The reception desk can analyze visitors' past visit history and select the appropriate input method. For example, based on data of exhibits visitors have previously visited, the reception desk can suggest relevant new exhibits and process their input. The reception desk can also prioritize suggesting input methods (voice, touch panel, etc.) that visitors have used in the past to ensure smooth input. Furthermore, the reception desk can analyze visitor trends based on their past visit history and process input accordingly. This allows for the provision of more appropriate input methods by analyzing visitors' past visit history. Visitor history includes, for example, the date and time of visit, the number of visits, and past feedback. For example, the reception desk can record visitors' visit dates and times and analyze visit trends based on that data. The reception desk can also record visitors' visits and analyze visit frequency based on that data. Furthermore, the reception desk can record visitors' past feedback and identify exhibits of interest based on that data. This allows the reception desk to analyze visitors' past visit history and select the appropriate input method for registration.

[0034] The reception desk can filter visitor information based on their current interests and preferences during the input process. For example, if a visitor shows interest in a particular exhibit, the reception desk will prioritize information related to that exhibit. It can also filter and provide information on exhibits related to a specific theme if the visitor is interested in that theme. Furthermore, if a visitor is interested in a particular artist or era, the reception desk can prioritize information on exhibits related to that artist or era. This allows for the provision of more relevant information by filtering it based on the visitor's interests. A visitor's current interests and preferences can be identified through methods such as real-time surveys, behavioral tracking, and social media analysis. For example, the reception desk can conduct real-time surveys with visitors and identify their interests based on the results. It can also track visitors' behavior and identify their interests based on that data. Additionally, it can analyze visitors' social media activity and identify their interests based on that data. This allows the reception desk to filter visitors based on their current interests and preferences.

[0035] The reception desk can prioritize receiving highly relevant information based on the visitor's geographical location information during the input process. For example, if a visitor is in a specific exhibition area, the reception desk will prioritize receiving information related to that area. Furthermore, if a visitor is on a specific floor, the reception desk can prioritize receiving information related to exhibits on that floor. Additionally, if a visitor is inside a specific building, the reception desk can prioritize receiving information related to exhibits within that building. This allows for the provision of more relevant information by considering the visitor's geographical location information. Visitor geographical location information is obtained using technologies such as GPS data, beacons, and Wi-Fi location information. For example, the reception desk can determine the visitor's current location based on their GPS data and provide information related to that area. The reception desk can also use beacons to determine the visitor's location and provide information related to that floor. Furthermore, the reception desk can use Wi-Fi location information to determine the visitor's location and provide information related to exhibits within that building. This allows the reception desk to prioritize receiving highly relevant information based on the visitor's geographical location.

[0036] The reception desk can analyze visitors' social media activity and receive relevant information when they submit their information. For example, if a visitor mentions a specific exhibit on social media, the reception desk will prioritize receiving information related to that exhibit. It can also prioritize receiving information about exhibits related to a specific theme if the visitor mentions that theme on social media. Furthermore, if a visitor mentions a specific artist or era on social media, the reception desk can prioritize receiving information about exhibits related to that artist or era. This allows the reception desk to provide more relevant information by analyzing visitors' social media activity. Visitor social media activity is analyzed based on data such as post content, likes, and follower count. For example, the reception desk analyzes the content of a visitor's social media posts and provides relevant information based on that content. It can also identify exhibits of interest based on the visitor's likes and provide information about them. Furthermore, it can identify influential exhibits based on the visitor's follower count and provide information about them. This allows the reception desk to analyze visitors' social media activity and receive relevant information.

[0037] The information provider can adjust the level of detail provided based on the importance of the exhibits. For example, they can provide detailed information for important exhibits, while providing concise information for general exhibits. Furthermore, they can provide information with special effects for special exhibits. This allows for optimal information provision for visitors by adjusting the level of detail based on the importance of the exhibits. The importance of an exhibit can be evaluated based on criteria such as its historical value, popularity, or educational value. For example, the information provider can evaluate the importance of an exhibit based on its historical value and provide information accordingly. They can also evaluate the importance of an exhibit based on its popularity and provide information accordingly. Furthermore, they can evaluate the importance of an exhibit based on its educational value and provide information accordingly. This allows the information provider to adjust the level of detail provided based on the importance of the exhibits.

[0038] The information provider can apply different information provision algorithms depending on the category of the exhibit. For example, for historical exhibits, the provider can provide information in chronological order. For scientific exhibits, the provider can also provide information based on experimental results and theories. Furthermore, for artistic exhibits, the provider can provide information on the artist's background and explanations of the works. This allows for more appropriate information provision by applying different information provision algorithms depending on the category of the exhibit. Exhibit categories are classified based on criteria such as history, science, and art. For example, the provider can classify historical exhibits chronologically and provide information accordingly. The provider can also classify scientific exhibits based on experimental results and theories and provide information accordingly. Furthermore, the provider can classify artistic exhibits based on the artist's background and explanations of the works and provide information accordingly. This allows the provider to apply different information provision algorithms depending on the category of the exhibit.

[0039] The information provider can determine the priority of information provision based on the exhibition period of the exhibits. For example, the information provider can prioritize information on new exhibits. They can also provide concise information on exhibits that have been on display for a long time. Furthermore, they can provide information with special effects for special exhibits. This allows for more appropriate information provision by prioritizing information provision based on the exhibition period of the exhibits. The exhibition period of an exhibit is determined by criteria such as season, special events, and the condition of the exhibit. For example, the information provider can determine priority based on the exhibition period of the exhibits and provide information accordingly. They can also provide information based on the timing of special events. Furthermore, they can provide information considering the condition of the exhibits. This allows the information provider to determine the priority of information provision based on the exhibition period of the exhibits.

[0040] The information provider can adjust the order of information provision based on the relevance of the exhibits. For example, the provider can provide information consecutively for related exhibits. Alternatively, it can provide information with gaps between exhibits that are less relevant. Furthermore, the provider can provide information on exhibits related to a specific theme, theme by theme. This allows for more effective information provision by adjusting the order of information provision based on the relevance of the exhibits. The relevance of exhibits can be evaluated based on criteria such as thematic commonality, historical background, and technological relevance. For example, the provider can evaluate relevance based on the thematic commonality of the exhibits and provide that information. It can also evaluate relevance based on the historical background of the exhibits and provide that information. Furthermore, it can evaluate relevance based on the technological relevance of the exhibits and provide that information. This allows the provider to adjust the order of information provision based on the relevance of the exhibits.

[0041] The generation unit can improve the accuracy of quiz and game generation by considering the interrelationships between exhibits. For example, the generation unit can generate quizzes that combine related exhibits. It can also generate games with continuity based on the interrelationships between exhibits. Furthermore, the generation unit can generate quizzes and games related to multiple exhibits, taking into account the interrelationships between exhibits. This allows for the generation of more accurate quizzes and games by considering the interrelationships between exhibits. The interrelationships between exhibits are evaluated based on criteria such as historical relevance, technological relevance, and thematic commonality. For example, the generation unit can evaluate interrelationships based on the historical relevance of exhibits and generate quizzes based on that information. It can also evaluate interrelationships based on the technological relevance of exhibits and generate games based on that information. Furthermore, the generation unit can evaluate interrelationships based on thematic commonality of exhibits and generate quizzes and games based on that information. This allows the generation unit to improve the accuracy of generation by considering the interrelationships between exhibits.

[0042] The generation unit can consider the attribute information of exhibits when generating quizzes and games. For example, the generation unit can generate quizzes based on the age and region of the exhibits. It can also generate games based on the scientific attributes of the exhibits. Furthermore, the generation unit can generate quizzes and games based on the artistic attributes of the exhibits. This allows for the generation of more relevant quizzes and games by considering the attribute information of the exhibits. The attribute information of exhibits is evaluated based on criteria such as the size, material, and production date of the exhibits. For example, the generation unit can evaluate attribute information based on the size of the exhibits and generate quizzes based on that information. It can also evaluate attribute information based on the material of the exhibits and generate games based on that information. Furthermore, the generation unit can evaluate attribute information based on the production date of the exhibits and generate quizzes and games based on that information. This allows the generation unit to consider the attribute information of exhibits when generating.

[0043] The generation unit can generate quizzes and games while considering the geographical distribution of exhibits. For example, the generation unit can generate quizzes based on exhibits related to a specific region. It can also generate games that combine geographically related exhibits. Furthermore, the generation unit can generate quizzes and games related to multiple regions while considering geographical distribution. This allows for the generation of more relevant quizzes and games by considering the geographical distribution of exhibits. The geographical distribution of exhibits can be obtained using technologies such as exhibit layout maps, GPS data, and beacons. For example, the generation unit can evaluate the geographical distribution based on exhibit layout maps and generate quizzes based on that information. It can also evaluate the geographical distribution based on GPS data and generate games based on that information. Furthermore, the generation unit can identify the location of exhibits using beacons and generate quizzes and games based on that information. This allows the generation unit to generate content while considering the geographical distribution of exhibits.

[0044] The generation unit can improve the accuracy of quizzes and games by referring to relevant literature related to the exhibits. For example, the generation unit can generate quizzes based on literature related to the exhibits. It can also generate games based on research papers related to the exhibits. Furthermore, the generation unit can generate quizzes and games based on books related to the exhibits. This allows the generation unit to generate more accurate quizzes and games by referring to relevant literature related to the exhibits. Relevant literature related to exhibits can include academic papers, books, and online articles. For example, the generation unit can refer to academic papers related to the exhibits and generate quizzes based on that information. It can also refer to books related to the exhibits and generate games based on that information. Furthermore, the generation unit can refer to online articles related to the exhibits and generate quizzes and games based on that information. This allows the generation unit to improve the accuracy of generation by referring to relevant literature related to the exhibits.

[0045] The data collection unit can select the optimal collection method by referring to the visitor's past feedback history when collecting feedback. For example, the data collection unit can collect feedback in a similar format based on the format of feedback previously provided by the visitor. The data collection unit can also select whether to request detailed feedback or concise feedback based on the visitor's past feedback history. Furthermore, the data collection unit can analyze the visitor's past feedback history and select the most effective collection method. This makes it possible to collect feedback more effectively by referring to the visitor's past feedback history. The visitor's past feedback history includes, for example, past survey results, comment history, and evaluation scores. For example, the data collection unit can collect feedback based on the visitor's past survey results. The data collection unit can also collect feedback based on the visitor's comment history. Furthermore, the data collection unit can also collect feedback based on the visitor's evaluation score. This allows the data collection unit to select the optimal collection method by referring to the visitor's past feedback history when collecting feedback.

[0046] The data collection unit can select the optimal data collection method when collecting feedback, taking into account the visitor's device information. For example, if a visitor is using a smartphone, the data collection unit can easily collect feedback through touch operations. Furthermore, if a visitor is using a tablet, the data collection unit can provide a feedback collection method optimized for larger screens. Additionally, if a visitor is using a smartwatch, the data collection unit can provide a concise and highly visible feedback collection method. This allows for more effective feedback collection by considering the visitor's device information. Visitor device information includes, for example, information on devices such as smartphones, tablets, and PCs. For example, if a visitor is using a smartphone, the data collection unit selects a feedback collection method based on that device information. It can also select a feedback collection method based on the device information of a tablet. Furthermore, if a visitor is using a smartwatch, the data collection unit can select a feedback collection method based on that device information. This allows the data collection unit to select the optimal data collection method when collecting feedback, taking into account the visitor's device information.

[0047] The analysis unit can optimize its analysis algorithm by referring to past analysis data during analysis. For example, the analysis unit can select the optimal analysis algorithm based on past analysis data. It can also extract specific patterns from past analysis data and optimize the analysis algorithm accordingly. Furthermore, the analysis unit can analyze past analysis data and select algorithms to improve analysis accuracy. This allows for more accurate analysis by referring to past analysis data. Past analysis data includes, for example, past analysis results, datasets, and analysis reports. For example, the analysis unit can select an analysis algorithm based on past analysis results. It can also optimize the analysis algorithm based on past datasets. Furthermore, it can select algorithms to improve analysis accuracy based on past analysis reports. This allows the analysis unit to optimize its analysis algorithm by referring to past analysis data during analysis.

[0048] The analysis unit can weight the analysis data based on the timing of feedback submission during the analysis. For example, the analysis unit can perform the analysis immediately after feedback is submitted and weight the data in real time. The analysis unit can also adjust the weighting based on the time period in which the feedback was submitted. Furthermore, the analysis unit can weight the data before and after the event in which the feedback was submitted. This allows for more appropriate data analysis by weighting the analysis data based on the timing of feedback submission. The timing of feedback submission can be evaluated based on criteria such as submission date and time, feedback after the event, and real-time feedback. For example, the analysis unit can weight the data based on the submission date and time. The analysis unit can also weight the data based on feedback after the event. Furthermore, the analysis unit can also weight the data based on real-time feedback. This allows the analysis unit to weight the analysis data based on the timing of feedback submission during the analysis.

[0049] The planning department can select the optimal planning method by referring to data from past exhibitions and events during the planning stage. For example, the planning department can select the optimal planning method based on data from past exhibitions and events. Furthermore, the planning department can extract specific patterns from the data from past exhibitions and events and optimize the planning method. In addition, the planning department can analyze the data from past exhibitions and events and select methods to improve planning accuracy. This allows for more effective planning by referring to data from past exhibitions and events. Data from past exhibitions and events includes, for example, visitor numbers, feedback results, and exhibit evaluations. For example, the planning department can select a planning method based on past visitor numbers. Furthermore, the planning department can optimize the planning method based on past feedback results. Furthermore, the planning department can select methods to improve planning accuracy based on evaluations of past exhibits. This allows the planning department to select the optimal planning method by referring to data from past exhibitions and events during the planning stage.

[0050] The planning department can select the most suitable planning method when planning, taking into account the geographical location information of visitors. For example, if visitors are coming from a specific region, the planning department can plan exhibits and events related to that region. Furthermore, if visitors are on a specific floor, the planning department can plan exhibits and events related to that floor. In addition, if visitors are inside a specific building, the planning department can implement plans related to exhibits and events within that building. This allows for more effective planning by considering the geographical location information of visitors. Visitor geographical location information is obtained using technologies such as GPS data, beacons, and Wi-Fi location information. For example, the planning department can determine a visitor's current location based on their GPS data and plan exhibits and events related to that region. The planning department can also use beacons to pinpoint a visitor's location and plan exhibits and events related to that floor. Furthermore, the planning department can use Wi-Fi location information to pinpoint a visitor's location and implement plans related to exhibits and events within that building. This allows the planning department to select the most suitable planning method while considering the geographical location information of visitors.

[0051] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0052] The reception desk can analyze visitors' past visit history when receiving their information and select the appropriate input method. For example, based on data of exhibits a visitor has previously visited, it can suggest related new exhibits and process the input. The reception desk can also prioritize suggesting input methods (voice, touch panel, etc.) that visitors have used in the past to ensure a smooth input process. Furthermore, the reception desk can analyze visitors' past visit history to see if they tend to visit at specific times and tailor the input process to those times. In this way, by analyzing visitors' past visit history, the reception desk can provide a more appropriate input method.

[0053] The data collection unit can select the optimal data collection method by considering the visitor's device information when collecting visitor feedback. For example, if a visitor is using a smartphone, feedback can be easily collected with touch operations. The data collection unit can also provide a feedback collection method optimized for larger screens if the visitor is using a tablet. Furthermore, if the visitor is using a smartwatch, the data collection unit can provide a simple and highly visible feedback collection method. This allows for more effective feedback collection by considering the visitor's device information.

[0054] Based on the analysis results, the planning department can select the most suitable planning method for future exhibitions and events, taking into account the geographical location of visitors. For example, if visitors are coming from a specific region, the department can plan exhibitions and events related to that region. Furthermore, if visitors are on a specific floor, the planning department can plan exhibitions and events related to that floor. In addition, if visitors are inside a specific building, the planning department can implement plans related to exhibitions and events within that building. This allows for more effective planning by considering the geographical location of visitors.

[0055] The information provider can adjust the level of detail provided based on the importance of the exhibits. For example, they can provide detailed information for important exhibits. They can also provide concise information for general exhibits. Furthermore, they can provide information with special effects for special exhibits. By adjusting the level of detail based on the importance of the exhibits, they can provide visitors with the most optimal information.

[0056] The data collection unit can select the optimal collection method by referring to the visitor's past feedback history when collecting feedback. For example, it can collect feedback in a similar format based on the format of feedback previously provided by the visitor. The data collection unit can also select whether to request detailed feedback or concise feedback based on the visitor's past feedback history. Furthermore, the data collection unit can analyze the visitor's past feedback history and select the most effective collection method. This makes it possible to collect feedback more effectively by referring to the visitor's past feedback history.

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

[0058] Step 1: The reception desk receives visitor input. Visitor input can include touch panel input, voice input, and 2D code scanning. For example, the reception desk can receive visitor input using a touch panel. It can also receive visitor voice input using voice recognition technology. Furthermore, it can also receive visitor input using 2D code scanning. Step 2: The information provider provides information based on the information received by the reception department. The information provided may include text, images, videos, and audio. For example, the information provider may provide detailed descriptions of the exhibits as text. They may also provide images, videos, and audio guides. Step 3: The generation unit generates quizzes and games based on the information provided by the supply unit. The generated quizzes and games include multiple-choice quizzes, puzzle games, and action games. For example, the generation unit generates multiple-choice quizzes, puzzle games, and action games related to the exhibits.

[0059] (Example of form 2) The experiential museum system according to an embodiment of the present invention is a system that transforms museums into experiential museums that are more interactive and enjoyable for visitors by utilizing the abundant data held by the museum. This experiential museum system collects specialized data held by the museum and trains a generating AI, so that when a visitor inputs what they would like to experience in the museum, the generating AI provides an interactive experience based on that input. For example, if a visitor wants to learn more about a particular exhibit, the generating AI provides information related to that exhibit and further generates quizzes or games based on that information. This allows visitors to enjoy the exhibits while gaining a deeper understanding of them. The generating AI can also suggest exhibits that visitors should visit next based on their interests. This allows visitors to efficiently view exhibits that interest them while touring the museum at their own pace. Furthermore, the generating AI can collect visitor feedback and use it to improve museum operations. For example, it can collect information such as which exhibits visitors were interested in and which experiences they enjoyed, and reflect this in the planning of future exhibitions and events. In this way, by utilizing the generating AI, museums can provide visitors with more attractive experiences and improve operational efficiency. This allows the experiential museum system to provide visitors with interactive experiences and streamline museum operations.

[0060] The interactive museum system according to this embodiment comprises a reception unit, a provision unit, and a generation unit. The reception unit receives input from visitors. Visitor input includes, but is not limited to, touch panel input, voice input, and scanning of a two-dimensional code (e.g., a QR code). The reception unit receives visitor input using, for example, a touch panel. The reception unit can also receive voice input from visitors using voice recognition technology. Furthermore, the reception unit can also receive visitor input using two-dimensional code scanning. For example, the reception unit provides information about an exhibit when a visitor scans the exhibit's two-dimensional code. The provision unit provides information based on the information received by the reception unit. The information provided includes, but is not limited to, text information, images, videos, and audio. The provision unit provides, for example, text information. The provision unit can also provide images and videos. Furthermore, the provision unit can also provide audio information. For example, the provision unit provides a detailed explanation of an exhibit as text information. The providing unit can also provide images and videos of the exhibits. Furthermore, the providing unit can also provide audio guides about the exhibits. The generating unit generates quizzes and games based on the information provided by the providing unit. The generated quizzes and games include, but are not limited to, multiple-choice quizzes, puzzle games, and action games. For example, the generating unit can generate multiple-choice quizzes. The generating unit can also generate puzzle games. Furthermore, the generating unit can also generate action games. For example, the generating unit can generate multiple-choice quizzes about the exhibits. The generating unit can also generate puzzle games about the exhibits. Furthermore, the generating unit can also generate action games about the exhibits. Thus, the experiential museum system according to the embodiment provides an interactive experience based on visitor input, and by generating quizzes and games, visitors can learn while having fun.

[0061] An interactive museum system includes a collection unit for gathering feedback. This unit collects visitor feedback, which may include, but is not limited to, questionnaire responses, comments, and rating scores. For example, the collection unit could collect visitor feedback through questionnaires. It could also collect visitor comments. Furthermore, it could collect visitor rating scores. For instance, the collection unit could conduct questionnaires for visitors and collect their responses. It could also allow visitors to leave comments about exhibits. Furthermore, it could allow visitors to assign rating scores to exhibits. This allows the collection unit to gather visitor feedback and use it to improve museum operations and exhibits.

[0062] The interactive museum system includes an analysis unit that analyzes collected feedback. The analysis unit analyzes the collected feedback. Analysis includes, but is not limited to, text mining, sentiment analysis, and statistical analysis. For example, the analysis unit can analyze feedback using text mining techniques. The analysis unit can also analyze feedback using sentiment analysis techniques. Furthermore, the analysis unit can analyze feedback using statistical analysis techniques. For example, the analysis unit can analyze visitor comments using text mining techniques to extract common opinions and impressions. The analysis unit can also analyze visitor comments using sentiment analysis techniques to classify positive and negative opinions. Furthermore, the analysis unit can analyze visitor evaluation scores using statistical analysis techniques to quantify the evaluation of exhibits. In this way, the analysis unit can analyze the collected feedback, gain a detailed understanding of visitors' opinions and impressions, and reflect this in the planning of future exhibitions and events.

[0063] The interactive museum system includes a planning department that plans future exhibitions and events based on analysis results. The planning department plans future exhibitions and events based on the analysis results. Planning includes, but is not limited to, selecting exhibition themes, scheduling events, and allocating budgets. For example, the planning department can select exhibition themes based on analysis results. It can also create event schedules. Furthermore, it can determine budget allocations. For instance, the planning department can select the next exhibition theme based on visitor feedback. It can also create event schedules based on visitor feedback. Furthermore, it can determine budget allocations based on visitor feedback. This allows the planning department to plan future exhibitions and events based on analysis results, providing exhibitions and events that meet the interests and concerns of visitors.

[0064] The reception desk can estimate the emotions of visitors and adjust the timing of information entry based on those estimates. For example, if a visitor is excited, the reception desk can immediately process the information entry and quickly provide information about exhibits that interest them. If a visitor is tired, the reception desk can allow a break before processing the information entry and provide information in a relaxed state. Furthermore, if a visitor is lost, the reception desk can indicate that a staff member is nearby to provide reassurance before processing the information entry. By adjusting the timing of information entry according to the visitor's emotions, information can be provided at a more appropriate time. Visitor emotions are estimated using technologies such as facial recognition, voice analysis, and survey results. For example, the reception desk can photograph the visitor's face with a camera and estimate their emotions using facial recognition technology. The reception desk can also record the visitor's voice and estimate their emotions using voice analysis technology. Furthermore, the reception desk can conduct surveys with visitors and estimate their emotions based on the results. This allows the reception desk to estimate the emotions of visitors and adjust the timing of their input based on those estimated emotions.

[0065] The reception desk can analyze visitors' past visit history and select the appropriate input method. For example, based on data of exhibits visitors have previously visited, the reception desk can suggest relevant new exhibits and process their input. The reception desk can also prioritize suggesting input methods (voice, touch panel, etc.) that visitors have used in the past to ensure smooth input. Furthermore, the reception desk can analyze visitor trends based on their past visit history and process input accordingly. This allows for the provision of more appropriate input methods by analyzing visitors' past visit history. Visitor history includes, for example, the date and time of visit, the number of visits, and past feedback. For example, the reception desk can record visitors' visit dates and times and analyze visit trends based on that data. The reception desk can also record visitors' visits and analyze visit frequency based on that data. Furthermore, the reception desk can record visitors' past feedback and identify exhibits of interest based on that data. This allows the reception desk to analyze visitors' past visit history and select the appropriate input method for registration.

[0066] The reception desk can filter visitor information based on their current interests and preferences during the input process. For example, if a visitor shows interest in a particular exhibit, the reception desk will prioritize information related to that exhibit. It can also filter and provide information on exhibits related to a specific theme if the visitor is interested in that theme. Furthermore, if a visitor is interested in a particular artist or era, the reception desk can prioritize information on exhibits related to that artist or era. This allows for the provision of more relevant information by filtering it based on the visitor's interests. A visitor's current interests and preferences can be identified through methods such as real-time surveys, behavioral tracking, and social media analysis. For example, the reception desk can conduct real-time surveys with visitors and identify their interests based on the results. It can also track visitors' behavior and identify their interests based on that data. Additionally, it can analyze visitors' social media activity and identify their interests based on that data. This allows the reception desk to filter visitors based on their current interests and preferences.

[0067] The reception desk can estimate the emotions of visitors and determine the priority of input requests based on those estimates. For example, if a visitor is excited, the reception desk will prioritize information about exhibits that interest them. If a visitor is relaxed, the reception desk can also adjust the priority of input requests to provide more detailed information. Furthermore, if a visitor is in a hurry, the reception desk can prioritize and quickly provide concise information. This allows for more appropriate information to be provided by determining the priority of input requests according to the visitor's emotions. Visitor emotions can be estimated using technologies such as facial recognition, voice analysis, and survey results. For example, the reception desk can capture the visitor's facial expressions with a camera and estimate their emotions using facial recognition technology. The reception desk can also record the visitor's voice and estimate their emotions using voice analysis technology. Furthermore, the reception desk can conduct surveys with visitors and estimate their emotions based on the results. This allows the reception desk to estimate the emotions of visitors and determine the priority of their input based on those estimated emotions.

[0068] The reception desk can prioritize receiving highly relevant information based on the visitor's geographical location information during the input process. For example, if a visitor is in a specific exhibition area, the reception desk will prioritize receiving information related to that area. Furthermore, if a visitor is on a specific floor, the reception desk can prioritize receiving information related to exhibits on that floor. Additionally, if a visitor is inside a specific building, the reception desk can prioritize receiving information related to exhibits within that building. This allows for the provision of more relevant information by considering the visitor's geographical location information. Visitor geographical location information is obtained using technologies such as GPS data, beacons, and Wi-Fi location information. For example, the reception desk can determine the visitor's current location based on their GPS data and provide information related to that area. The reception desk can also use beacons to determine the visitor's location and provide information related to that floor. Furthermore, the reception desk can use Wi-Fi location information to determine the visitor's location and provide information related to exhibits within that building. This allows the reception desk to prioritize receiving highly relevant information based on the visitor's geographical location.

[0069] The reception desk can analyze visitors' social media activity and receive relevant information when they submit their information. For example, if a visitor mentions a specific exhibit on social media, the reception desk will prioritize receiving information related to that exhibit. It can also prioritize receiving information about exhibits related to a specific theme if the visitor mentions that theme on social media. Furthermore, if a visitor mentions a specific artist or era on social media, the reception desk can prioritize receiving information about exhibits related to that artist or era. This allows the reception desk to provide more relevant information by analyzing visitors' social media activity. Visitor social media activity is analyzed based on data such as post content, likes, and follower count. For example, the reception desk analyzes the content of a visitor's social media posts and provides relevant information based on that content. It can also identify exhibits of interest based on the visitor's likes and provide information about them. Furthermore, it can identify influential exhibits based on the visitor's follower count and provide information about them. This allows the reception desk to analyze visitors' social media activity and receive relevant information.

[0070] The information provider can estimate the emotions of viewers and adjust the way information is presented based on those estimated emotions. For example, if a viewer is excited, the provider can provide information with visually stimulating effects. If a viewer is relaxed, the provider can also provide information in a calm tone. Furthermore, if a viewer is tired, the provider can provide concise and easily readable information. By adjusting the way information is presented according to the viewer's emotions, more effective information delivery becomes possible. The estimation of viewers' emotions is performed using technologies such as facial recognition, voice analysis, and survey results. For example, the provider can capture the viewer's facial expressions with a camera and estimate their emotions using facial recognition technology. The provider can also record the viewer's voice and estimate their emotions using voice analysis technology. Furthermore, the provider can conduct surveys with viewers and estimate their emotions based on the results. This allows the provider to estimate the emotions of viewers and adjust the way information is presented based on those estimated emotions.

[0071] The information provider can adjust the level of detail provided based on the importance of the exhibits. For example, they can provide detailed information for important exhibits, while providing concise information for general exhibits. Furthermore, they can provide information with special effects for special exhibits. This allows for optimal information provision for visitors by adjusting the level of detail based on the importance of the exhibits. The importance of an exhibit can be evaluated based on criteria such as its historical value, popularity, or educational value. For example, the information provider can evaluate the importance of an exhibit based on its historical value and provide information accordingly. They can also evaluate the importance of an exhibit based on its popularity and provide information accordingly. Furthermore, they can evaluate the importance of an exhibit based on its educational value and provide information accordingly. This allows the information provider to adjust the level of detail provided based on the importance of the exhibits.

[0072] The information provider can apply different information provision algorithms depending on the category of the exhibit. For example, for historical exhibits, the provider can provide information in chronological order. For scientific exhibits, the provider can also provide information based on experimental results and theories. Furthermore, for artistic exhibits, the provider can provide information on the artist's background and explanations of the works. This allows for more appropriate information provision by applying different information provision algorithms depending on the category of the exhibit. Exhibit categories are classified based on criteria such as history, science, and art. For example, the provider can classify historical exhibits chronologically and provide information accordingly. The provider can also classify scientific exhibits based on experimental results and theories and provide information accordingly. Furthermore, the provider can classify artistic exhibits based on the artist's background and explanations of the works and provide information accordingly. This allows the provider to apply different information provision algorithms depending on the category of the exhibit.

[0073] The information provider can estimate the emotions of viewers and adjust the length of the information provided based on those estimated emotions. For example, if a viewer is in a hurry, the provider can provide short, concise information. If a viewer is relaxed, the provider can provide longer information including detailed explanations. Furthermore, if a viewer is excited, the provider can provide information with visually stimulating effects. By adjusting the length of the information provided according to the viewer's emotions, more effective information delivery becomes possible. The estimation of viewers' emotions is performed using technologies such as facial recognition, voice analysis, and survey results. For example, the provider can capture the viewer's facial expressions with a camera and estimate their emotions using facial recognition technology. The provider can also record the viewer's voice and estimate their emotions using voice analysis technology. Furthermore, the provider can conduct surveys with viewers and estimate their emotions based on the results. This allows the provider to estimate the viewers' emotions and adjust the length of the information provided based on those estimated emotions.

[0074] The information provider can determine the priority of information provision based on the exhibition period of the exhibits. For example, the information provider can prioritize information on new exhibits. They can also provide concise information on exhibits that have been on display for a long time. Furthermore, they can provide information with special effects for special exhibits. This allows for more appropriate information provision by prioritizing information provision based on the exhibition period of the exhibits. The exhibition period of an exhibit is determined by criteria such as season, special events, and the condition of the exhibit. For example, the information provider can determine priority based on the exhibition period of the exhibits and provide information accordingly. They can also provide information based on the timing of special events. Furthermore, they can provide information considering the condition of the exhibits. This allows the information provider to determine the priority of information provision based on the exhibition period of the exhibits.

[0075] The information provider can adjust the order of information provision based on the relevance of the exhibits. For example, the provider can provide information consecutively for related exhibits. Alternatively, it can provide information with gaps between exhibits that are less relevant. Furthermore, the provider can provide information on exhibits related to a specific theme, theme by theme. This allows for more effective information provision by adjusting the order of information provision based on the relevance of the exhibits. The relevance of exhibits can be evaluated based on criteria such as thematic commonality, historical background, and technological relevance. For example, the provider can evaluate relevance based on the thematic commonality of the exhibits and provide that information. It can also evaluate relevance based on the historical background of the exhibits and provide that information. Furthermore, it can evaluate relevance based on the technological relevance of the exhibits and provide that information. This allows the provider to adjust the order of information provision based on the relevance of the exhibits.

[0076] The generation unit can estimate the emotions of the audience and adjust the method of generating quizzes and games based on the estimated emotions. For example, if the audience is excited, the generation unit can generate visually stimulating quizzes and games. If the audience is relaxed, the generation unit can also generate quizzes and games in a calm tone. Furthermore, if the audience is tired, the generation unit can generate concise and highly visual quizzes and games. In this way, by adjusting the method of generating quizzes and games according to the emotions of the audience, a more enjoyable experience can be provided. The estimation of audience emotions is performed using technologies such as facial recognition, voice analysis, and survey results. For example, the generation unit can capture the audience's facial expressions with a camera and estimate their emotions using facial recognition technology. The generation unit can also record the audience's voice and estimate their emotions using voice analysis technology. Furthermore, the generation unit can conduct surveys with the audience and estimate their emotions based on the results. In this way, the generation unit can estimate the emotions of the audience and adjust the method of generating quizzes and games based on the estimated emotions.

[0077] The generation unit can improve the accuracy of quiz and game generation by considering the interrelationships between exhibits. For example, the generation unit can generate quizzes that combine related exhibits. It can also generate games with continuity based on the interrelationships between exhibits. Furthermore, the generation unit can generate quizzes and games related to multiple exhibits, taking into account the interrelationships between exhibits. This allows for the generation of more accurate quizzes and games by considering the interrelationships between exhibits. The interrelationships between exhibits are evaluated based on criteria such as historical relevance, technological relevance, and thematic commonality. For example, the generation unit can evaluate interrelationships based on the historical relevance of exhibits and generate quizzes based on that information. It can also evaluate interrelationships based on the technological relevance of exhibits and generate games based on that information. Furthermore, the generation unit can evaluate interrelationships based on thematic commonality of exhibits and generate quizzes and games based on that information. This allows the generation unit to improve the accuracy of generation by considering the interrelationships between exhibits.

[0078] The generation unit can consider the attribute information of exhibits when generating quizzes and games. For example, the generation unit can generate quizzes based on the age and region of the exhibits. It can also generate games based on the scientific attributes of the exhibits. Furthermore, the generation unit can generate quizzes and games based on the artistic attributes of the exhibits. This allows for the generation of more relevant quizzes and games by considering the attribute information of the exhibits. The attribute information of exhibits is evaluated based on criteria such as the size, material, and production date of the exhibits. For example, the generation unit can evaluate attribute information based on the size of the exhibits and generate quizzes based on that information. It can also evaluate attribute information based on the material of the exhibits and generate games based on that information. Furthermore, the generation unit can evaluate attribute information based on the production date of the exhibits and generate quizzes and games based on that information. This allows the generation unit to consider the attribute information of exhibits when generating.

[0079] The generation unit can estimate the emotions of viewers and adjust the display method of quizzes and games based on the estimated emotions. For example, if a viewer is excited, the generation unit can provide a visually stimulating display method. It can also display in a calm tone if the viewer is relaxed. Furthermore, if the viewer is tired, the generation unit can provide a concise and easily readable display method. This allows for a more enjoyable experience by adjusting the display method of quizzes and games according to the viewer's emotions. The estimation of viewer emotions is performed using technologies such as facial recognition, voice analysis, and survey results. For example, the generation unit can capture the viewer's facial expressions with a camera and estimate their emotions using facial recognition technology. It can also record the viewer's voice and estimate their emotions using voice analysis technology. Furthermore, the generation unit can conduct surveys with viewers and estimate their emotions based on the results. This allows the generation unit to estimate the emotions of viewers and adjust the display method of quizzes and games based on the estimated emotions.

[0080] The generation unit can generate quizzes and games while considering the geographical distribution of exhibits. For example, the generation unit can generate quizzes based on exhibits related to a specific region. It can also generate games that combine geographically related exhibits. Furthermore, the generation unit can generate quizzes and games related to multiple regions while considering geographical distribution. This allows for the generation of more relevant quizzes and games by considering the geographical distribution of exhibits. The geographical distribution of exhibits can be obtained using technologies such as exhibit layout maps, GPS data, and beacons. For example, the generation unit can evaluate the geographical distribution based on exhibit layout maps and generate quizzes based on that information. It can also evaluate the geographical distribution based on GPS data and generate games based on that information. Furthermore, the generation unit can identify the location of exhibits using beacons and generate quizzes and games based on that information. This allows the generation unit to generate content while considering the geographical distribution of exhibits.

[0081] The generation unit can improve the accuracy of quizzes and games by referring to relevant literature related to the exhibits. For example, the generation unit can generate quizzes based on literature related to the exhibits. It can also generate games based on research papers related to the exhibits. Furthermore, the generation unit can generate quizzes and games based on books related to the exhibits. This allows the generation unit to generate more accurate quizzes and games by referring to relevant literature related to the exhibits. Relevant literature related to exhibits can include academic papers, books, and online articles. For example, the generation unit can refer to academic papers related to the exhibits and generate quizzes based on that information. It can also refer to books related to the exhibits and generate games based on that information. Furthermore, the generation unit can refer to online articles related to the exhibits and generate quizzes and games based on that information. This allows the generation unit to improve the accuracy of generation by referring to relevant literature related to the exhibits.

[0082] The data collection unit can estimate the emotions of visitors and adjust the feedback collection method based on the estimated emotions. For example, if a visitor is excited, the data collection unit can collect feedback in the form of a simple questionnaire. If a visitor is relaxed, the data collection unit can also provide a questionnaire requesting more detailed feedback. Furthermore, if a visitor is tired, the data collection unit can provide a feedback format that can be completed in a short time. This allows for the collection of more appropriate feedback by adjusting the feedback collection method according to the emotions of the visitors. The estimation of visitors' emotions is performed using technologies such as facial recognition, voice analysis, and questionnaire results. For example, the data collection unit can capture visitors' facial expressions with a camera and estimate their emotions using facial recognition technology. The data collection unit can also record visitors' voices and estimate their emotions using voice analysis technology. Furthermore, the data collection unit can conduct questionnaires with visitors and estimate their emotions based on the results. This allows the data collection unit to estimate visitors' emotions and adjust the feedback collection method based on the estimated emotions.

[0083] The data collection unit can select the optimal collection method by referring to the visitor's past feedback history when collecting feedback. For example, the data collection unit can collect feedback in a similar format based on the format of feedback previously provided by the visitor. The data collection unit can also select whether to request detailed feedback or concise feedback based on the visitor's past feedback history. Furthermore, the data collection unit can analyze the visitor's past feedback history and select the most effective collection method. This makes it possible to collect feedback more effectively by referring to the visitor's past feedback history. The visitor's past feedback history includes, for example, past survey results, comment history, and evaluation scores. For example, the data collection unit can collect feedback based on the visitor's past survey results. The data collection unit can also collect feedback based on the visitor's comment history. Furthermore, the data collection unit can also collect feedback based on the visitor's evaluation score. This allows the data collection unit to select the optimal collection method by referring to the visitor's past feedback history when collecting feedback.

[0084] The data collection unit can estimate the emotions of visitors and determine the priority of feedback based on those estimated emotions. For example, if a visitor is excited, the data collection unit will prioritize collecting positive feedback. If a visitor is relaxed, the data collection unit can also prioritize collecting detailed feedback. Furthermore, if a visitor is tired, the data collection unit can also prioritize collecting concise feedback. This allows for the collection of more appropriate feedback by prioritizing feedback according to the visitor's emotions. Visitor emotions are estimated using technologies such as facial recognition, voice analysis, and survey results. For example, the data collection unit can capture visitors' facial expressions with a camera and estimate their emotions using facial recognition technology. The data collection unit can also record visitors' voices and estimate their emotions using voice analysis technology. Furthermore, the data collection unit can conduct surveys with visitors and estimate their emotions based on the results. This allows the data collection unit to estimate visitors' emotions and determine the priority of feedback based on those estimated emotions.

[0085] The data collection unit can select the optimal data collection method when collecting feedback, taking into account the visitor's device information. For example, if a visitor is using a smartphone, the data collection unit can easily collect feedback through touch operations. Furthermore, if a visitor is using a tablet, the data collection unit can provide a feedback collection method optimized for larger screens. Additionally, if a visitor is using a smartwatch, the data collection unit can provide a concise and highly visible feedback collection method. This allows for more effective feedback collection by considering the visitor's device information. Visitor device information includes, for example, information on devices such as smartphones, tablets, and PCs. For example, if a visitor is using a smartphone, the data collection unit selects a feedback collection method based on that device information. It can also select a feedback collection method based on the device information of a tablet. Furthermore, if a visitor is using a smartwatch, the data collection unit can select a feedback collection method based on that device information. This allows the data collection unit to select the optimal data collection method when collecting feedback, taking into account the visitor's device information.

[0086] The analysis unit can estimate the emotions of visitors and select analysis data based on the estimated emotions. For example, if a visitor is excited, the analysis unit will prioritize analyzing positive feedback. If a visitor is relaxed, the analysis unit can also prioritize analyzing detailed feedback. Furthermore, if a visitor is tired, the analysis unit can also prioritize analyzing concise feedback. This allows for more appropriate data analysis by selecting analysis data according to the emotions of the visitors. The estimation of visitors' emotions is performed using technologies such as facial recognition, voice analysis, and survey results. For example, the analysis unit can capture visitors' facial expressions with a camera and estimate their emotions using facial recognition technology. The analysis unit can also record visitors' voices and estimate their emotions using voice analysis technology. Furthermore, the analysis unit can conduct surveys with visitors and estimate their emotions based on the results. This allows the analysis unit to estimate visitors' emotions and select analysis data based on the estimated emotions.

[0087] The analysis unit can optimize its analysis algorithm by referring to past analysis data during analysis. For example, the analysis unit can select the optimal analysis algorithm based on past analysis data. It can also extract specific patterns from past analysis data and optimize the analysis algorithm accordingly. Furthermore, the analysis unit can analyze past analysis data and select algorithms to improve analysis accuracy. This allows for more accurate analysis by referring to past analysis data. Past analysis data includes, for example, past analysis results, datasets, and analysis reports. For example, the analysis unit can select an analysis algorithm based on past analysis results. It can also optimize the analysis algorithm based on past datasets. Furthermore, it can select algorithms to improve analysis accuracy based on past analysis reports. This allows the analysis unit to optimize its analysis algorithm by referring to past analysis data during analysis.

[0088] The analysis unit can estimate the emotions of visitors and adjust the frequency of analysis based on the estimated emotions. For example, if visitors are excited, the analysis unit can perform analyses frequently and reflect feedback in real time. If visitors are relaxed, the analysis unit can perform analyses periodically and reflect detailed feedback. Furthermore, if visitors are tired, the analysis unit can reduce the frequency of analysis and reflect concise feedback. By adjusting the frequency of analysis according to the emotions of visitors, more appropriate data analysis becomes possible. Visitor emotions are estimated using technologies such as facial recognition, voice analysis, and survey results. For example, the analysis unit can capture visitors' facial expressions with a camera and estimate their emotions using facial recognition technology. The analysis unit can also record visitors' voices and estimate their emotions using voice analysis technology. Furthermore, the analysis unit can conduct surveys with visitors and estimate their emotions based on the results. This allows the analysis unit to estimate visitors' emotions and adjust the frequency of analysis based on the estimated emotions.

[0089] The analysis unit can weight the analysis data based on the timing of feedback submission during the analysis. For example, the analysis unit can perform the analysis immediately after feedback is submitted and weight the data in real time. The analysis unit can also adjust the weighting based on the time period in which the feedback was submitted. Furthermore, the analysis unit can weight the data before and after the event in which the feedback was submitted. This allows for more appropriate data analysis by weighting the analysis data based on the timing of feedback submission. The timing of feedback submission can be evaluated based on criteria such as submission date and time, feedback after the event, and real-time feedback. For example, the analysis unit can weight the data based on the submission date and time. The analysis unit can also weight the data based on feedback after the event. Furthermore, the analysis unit can also weight the data based on real-time feedback. This allows the analysis unit to weight the analysis data based on the timing of feedback submission during the analysis.

[0090] The planning department can estimate the emotions of visitors and adjust the content of future exhibitions and events based on these estimations. For example, if visitors are excited, the planning department can plan visually stimulating exhibitions and events. If visitors are relaxed, the planning department can plan exhibitions and events with a calm tone. Furthermore, if visitors are tired, the planning department can plan concise and easily understandable exhibitions and events. By adjusting the content of future exhibitions and events according to visitors' emotions, more engaging exhibitions and events can be provided. Visitor emotions can be estimated using technologies such as facial recognition, voice analysis, and survey results. For example, the planning department can photograph visitors' faces with a camera and estimate their emotions using facial recognition technology. The planning department can also record visitors' voices and estimate their emotions using voice analysis technology. Furthermore, the planning department can conduct surveys with visitors and estimate their emotions based on the results. This allows the planning department to estimate the emotions of visitors and adjust the content of future exhibitions and events based on those estimated emotions.

[0091] The planning department can select the optimal planning method by referring to data from past exhibitions and events during the planning stage. For example, the planning department can select the optimal planning method based on data from past exhibitions and events. Furthermore, the planning department can extract specific patterns from the data from past exhibitions and events and optimize the planning method. In addition, the planning department can analyze the data from past exhibitions and events and select methods to improve planning accuracy. This allows for more effective planning by referring to data from past exhibitions and events. Data from past exhibitions and events includes, for example, visitor numbers, feedback results, and exhibit evaluations. For example, the planning department can select a planning method based on past visitor numbers. Furthermore, the planning department can optimize the planning method based on past feedback results. Furthermore, the planning department can select methods to improve planning accuracy based on evaluations of past exhibits. This allows the planning department to select the optimal planning method by referring to data from past exhibitions and events during the planning stage.

[0092] The planning department can estimate the emotions of visitors and prioritize events based on those estimated emotions. For example, if visitors are excited, the planning department can prioritize visually stimulating events. If visitors are relaxed, the planning department can prioritize events with a calm tone. Furthermore, if visitors are tired, the planning department can prioritize concise and easily understandable events. By prioritizing events according to visitors' emotions, more engaging exhibits and events can be provided. Visitor emotions can be estimated using technologies such as facial recognition, voice analysis, and survey results. For example, the planning department can photograph visitors' faces and estimate their emotions using facial recognition technology. The planning department can also record visitors' voices and estimate their emotions using voice analysis technology. Furthermore, the planning department can conduct surveys with visitors and estimate their emotions based on the results. This allows the planning department to estimate visitors' emotions and prioritize events based on those estimated emotions.

[0093] The planning department can select the most suitable planning method when planning, taking into account the geographical location information of visitors. For example, if visitors are coming from a specific region, the planning department can plan exhibits and events related to that region. Furthermore, if visitors are on a specific floor, the planning department can plan exhibits and events related to that floor. In addition, if visitors are inside a specific building, the planning department can implement plans related to exhibits and events within that building. This allows for more effective planning by considering the geographical location information of visitors. Visitor geographical location information is obtained using technologies such as GPS data, beacons, and Wi-Fi location information. For example, the planning department can determine a visitor's current location based on their GPS data and plan exhibits and events related to that region. The planning department can also use beacons to pinpoint a visitor's location and plan exhibits and events related to that floor. Furthermore, the planning department can use Wi-Fi location information to pinpoint a visitor's location and implement plans related to exhibits and events within that building. This allows the planning department to select the most suitable planning method while considering the geographical location information of visitors. === Hard Collateral 1-1 === Each of the multiple elements described above, including the reception unit, provision unit, generation unit, collection unit, analysis unit, and planning unit, is implemented by, for example, at least one of the smart device 14 and the data processing unit 12. For example, the reception unit receives input from visitors using the touch panel 38A and microphone 38B of the smart device 14. The provision unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12 and provides information to visitors. The generation unit generates quizzes and games by, for example, the specific processing unit 290 of the data processing unit 12. The collection unit collects visitor feedback by, for example, the control unit 46A of the smart device 14. The analysis unit analyzes the feedback by, for example, the specific processing unit 290 of the data processing unit 12. The planning unit plans the next exhibition or event based on the analysis results by, for example, the specific processing unit 290 of the data processing unit 12. === Hard Collateral 1-2 === Each of the multiple elements described above, including the reception unit, provision unit, generation unit, collection unit, analysis unit, and planning unit, is implemented, for example, by at least one of the smart glasses 214 and the data processing unit 12. For example, the reception unit receives voice input from visitors using the microphone 238 of the smart glasses 214. The provision unit is implemented, for example, by the specific processing unit 290 of the data processing unit 12 and provides information to visitors. The generation unit generates quizzes and games, for example, by the specific processing unit 290 of the data processing unit 12. The collection unit collects visitor feedback, for example, by the control unit 46A of the smart glasses 214. The analysis unit analyzes the feedback, for example, by the specific processing unit 290 of the data processing unit 12. The planning unit plans the next exhibition or event based on the analysis results, for example, by the specific processing unit 290 of the data processing unit 12. === Hard Collateral 1-3 === Each of the multiple elements described above, including the reception unit, provision unit, generation unit, collection unit, analysis unit, and planning unit, is implemented by, for example, at least one of the headset terminal 314 and the data processing unit 12. For example, the reception unit receives voice input from visitors using the microphone 238 of the headset terminal 314. The provision unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12 and provides information to visitors. The generation unit generates quizzes and games by, for example, the specific processing unit 290 of the data processing unit 12. The collection unit collects visitor feedback by, for example, the control unit 46A of the headset terminal 314. The analysis unit analyzes the feedback by, for example, the specific processing unit 290 of the data processing unit 12. The planning unit plans the next exhibition or event based on the analysis results by, for example, the specific processing unit 290 of the data processing unit 12. === Hard Collateral 1-4 === Each of the multiple elements described above, including the reception unit, provision unit, generation unit, collection unit, analysis unit, and planning unit, is implemented by, for example, at least one of the robot 414 and the data processing unit 12. For example, the reception unit receives voice input from visitors using the microphone 238 of the robot 414. The provision unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12 and provides information to visitors. The generation unit generates quizzes and games by, for example, the specific processing unit 290 of the data processing unit 12. The collection unit collects visitor feedback by, for example, the control unit 46A of the robot 414. The analysis unit analyzes the feedback by, for example, the specific processing unit 290 of the data processing unit 12. The planning unit plans the next exhibition or event based on the analysis results by, for example, the specific processing unit 290 of the data processing unit 12.

[0094] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0095] The reception desk can analyze visitors' past visit history when receiving their information and select the appropriate input method. For example, based on data of exhibits a visitor has previously visited, it can suggest related new exhibits and process the input. The reception desk can also prioritize suggesting input methods (voice, touch panel, etc.) that visitors have used in the past to ensure a smooth input process. Furthermore, the reception desk can analyze visitors' past visit history to see if they tend to visit at specific times and tailor the input process to those times. In this way, by analyzing visitors' past visit history, the reception desk can provide a more appropriate input method.

[0096] The data collection unit can select the optimal data collection method by considering the visitor's device information when collecting visitor feedback. For example, if a visitor is using a smartphone, feedback can be easily collected with touch operations. The data collection unit can also provide a feedback collection method optimized for larger screens if the visitor is using a tablet. Furthermore, if the visitor is using a smartwatch, the data collection unit can provide a simple and highly visible feedback collection method. This allows for more effective feedback collection by considering the visitor's device information.

[0097] The analysis unit can estimate the emotions of visitors when analyzing collected feedback and select analysis data based on the estimated emotions. For example, if visitors are excited, it will prioritize analyzing positive feedback. The analysis unit can also prioritize analyzing detailed feedback if visitors are relaxed. Furthermore, if visitors are tired, it can prioritize analyzing concise feedback. This allows for more appropriate data analysis by selecting analysis data according to the emotions of the visitors.

[0098] Based on the analysis results, the planning department can select the most suitable planning method for future exhibitions and events, taking into account the geographical location of visitors. For example, if visitors are coming from a specific region, the department can plan exhibitions and events related to that region. Furthermore, if visitors are on a specific floor, the planning department can plan exhibitions and events related to that floor. In addition, if visitors are inside a specific building, the planning department can implement plans related to exhibitions and events within that building. This allows for more effective planning by considering the geographical location of visitors.

[0099] The reception desk can estimate the visitor's emotions and adjust the timing of input based on those estimates. For example, if a visitor is excited, the reception desk can immediately process the input and quickly provide information about exhibits that interest them. If a visitor is tired, the reception desk can allow a break before processing the input and provide information in a relaxed state. Furthermore, if a visitor is lost, the reception desk can indicate that a staff member is nearby to provide reassurance before processing the input. By adjusting the timing of input according to the visitor's emotions, information can be provided at a more appropriate time.

[0100] The information provider can adjust the level of detail provided based on the importance of the exhibits. For example, they can provide detailed information for important exhibits. They can also provide concise information for general exhibits. Furthermore, they can provide information with special effects for special exhibits. By adjusting the level of detail based on the importance of the exhibits, they can provide visitors with the most optimal information.

[0101] The generation unit can estimate the emotions of the audience and adjust the way quizzes and games are generated based on those estimated emotions. For example, if the audience is excited, it can generate visually stimulating quizzes and games. It can also generate quizzes and games with a calm tone if the audience is relaxed. Furthermore, if the audience is tired, it can generate concise and easily understandable quizzes and games. This allows for a more enjoyable experience by adjusting the generation method of quizzes and games according to the audience's emotions.

[0102] The data collection unit can select the optimal collection method by referring to the visitor's past feedback history when collecting feedback. For example, it can collect feedback in a similar format based on the format of feedback previously provided by the visitor. The data collection unit can also select whether to request detailed feedback or concise feedback based on the visitor's past feedback history. Furthermore, the data collection unit can analyze the visitor's past feedback history and select the most effective collection method. This makes it possible to collect feedback more effectively by referring to the visitor's past feedback history.

[0103] The information provider can estimate the emotions of the audience and adjust the way information is presented based on those estimated emotions. For example, if the audience is excited, the information can be presented with visually stimulating effects. If the audience is relaxed, the information can be presented in a calm tone. Furthermore, if the audience is tired, the information can be presented in a concise and easily readable manner. By adjusting the way information is presented according to the audience's emotions, more effective information delivery becomes possible.

[0104] The planning department can estimate the emotions of visitors and adjust the content of future exhibitions and events based on those estimates. For example, if visitors are excited, they can plan visually stimulating exhibitions and events. If visitors are relaxed, the planning department can plan exhibitions and events with a calm tone. Furthermore, if visitors are tired, the planning department can plan exhibitions and events that are concise and easy to understand. In this way, by adjusting the content of future exhibitions and events according to the emotions of visitors, more engaging exhibitions and events can be provided.

[0105] The following briefly describes the processing flow for example form 2.

[0106] Step 1: The reception desk receives visitor input. Visitor input can include touch panel input, voice input, and 2D code scanning. For example, the reception desk can receive visitor input using a touch panel. It can also receive visitor voice input using voice recognition technology. Furthermore, it can also receive visitor input using 2D code scanning. Step 2: The information provider provides information based on the information received by the reception department. The information provided may include text, images, videos, and audio. For example, the information provider may provide detailed descriptions of the exhibits as text. They may also provide images, videos, and audio guides. Step 3: The generation unit generates quizzes and games based on the information provided by the supply unit. The generated quizzes and games include multiple-choice quizzes, puzzle games, and action games. For example, the generation unit generates multiple-choice quizzes, puzzle games, and action games related to the exhibits.

[0107] 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.

[0108] 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.

[0109] 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.

[0110] The correspondence between each part and the device or control unit is not limited to the examples described above, and various modifications are possible.

[0111] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0112] 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.

[0113] 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.

[0114] 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.

[0115] 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.

[0116] 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).

[0117] 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.

[0118] 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.

[0119] 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.

[0120] 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.

[0121] 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.

[0122] 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.).

[0123] 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.

[0124] 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.

[0125] 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.

[0126] The correspondence between each part and the device or control unit is not limited to the examples described above, and various modifications are possible.

[0127] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0128] 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.

[0129] 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.

[0130] 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.

[0131] 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.

[0132] 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).

[0133] 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.

[0134] 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.

[0135] 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.

[0136] 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.

[0137] 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.

[0138] 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.).

[0139] 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.

[0140] 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.

[0141] 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.

[0142] The correspondence between each part and the device or control unit is not limited to the examples described above, and various modifications are possible.

[0143] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0144] 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.

[0145] 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.

[0146] 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.

[0147] 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.

[0148] 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).

[0149] 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.

[0150] 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.

[0151] 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.

[0152] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0153] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0154] In 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.

[0155] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0156] The specific processing unit 290 transmits the result of the specific processing to the 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.

[0157] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0158] The data processing system 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.

[0159] The correspondence between each part and the device or control unit is not limited to the examples described above, and various modifications are possible.

[0160] 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.

[0161] 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.

[0162] 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.

[0163] 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.

[0164] 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.

[0165] 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."

[0166] 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.

[0167] 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.

[0168] 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.

[0169] 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.

[0170] 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.

[0171] 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.

[0172] 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.

[0173] 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.

[0174] 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.

[0175] 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.

[0176] 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.

[0177] 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.

[0178] [Explanation of Symbols]

[0179] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots

Claims

1. The reception area where visitors register their information, A provisioning unit that provides information based on the information received by the aforementioned reception unit, The system includes a generation unit that generates quizzes and games based on the information provided by the aforementioned provisioning unit. A system characterized by the following features.

2. It includes a collection unit for gathering feedback. The system according to feature 1.

3. It includes an analysis unit that analyzes the collected feedback. The system according to feature 2.

4. The company has a planning department that plans future exhibitions and events based on the analysis results. The system according to claim 3.

5. The aforementioned reception unit is The system estimates the emotions of visitors and adjusts the timing of input requests based on the estimated emotions. The system according to feature 1.

6. The aforementioned reception unit is Analyze visitors' past visit history to select the appropriate input method. The system according to feature 1.

7. The aforementioned reception unit is When receiving input, the system filters visitors based on their current interests and preferences. The system according to feature 1.

8. The aforementioned reception unit is The system estimates the emotions of visitors and determines the priority of input requests based on those estimated emotions. The system according to feature 1.

9. The aforementioned reception unit is When receiving user input, the system prioritizes processing highly relevant information based on the visitor's geographical location. The system according to feature 1.

10. The aforementioned reception unit is When receiving input, the system analyzes the visitor's social media activity and collects relevant information. The system according to feature 1.

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