System

Abstract

An object of the system according to the embodiment is to enable a learner to deeply understand and experience a historical incident or person.SOLUTION: A system according to an embodiment includes an interactive map, a historical incident search part, an explanation part, a conversation part, and an experience part. The interactive map makes it possible to search on a map of Japan where different historical events have occurred. The Historical Incident Searcher allows an interactive map to search for where different historical incidents have occurred on a map of Japan. The explanation part displays details of the event related to the place searched by the historical event search part, introduction of the related person, and explanation about the society and culture at that time. The interactor enables the learner to interact with the historical person by interacting with the historical person. The experience part enables the learner to experience the historical event by reproducing the historical event.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] Conventional technology has had the problem of making it difficult for learners to deeply understand and experience historical events and people in history education.

[0005] The system according to the embodiment aims to enable learners to deeply understand and experience historical events and people. [Means for solving the problem]

[0006] The system according to the embodiment includes an interactive map, a historical event search unit, an explanation unit, a dialogue unit, and an experience unit. The interactive map allows a learner to search for locations where different historical events occurred on a map of Japan. The historical event search unit allows a learner to search for locations where different historical events occurred on a map of Japan using the interactive map. The explanation unit displays details of events related to the locations searched by the historical event search unit, introductions to related people, and commentary on the society and culture of the time. The dialogue unit allows a learner to interact with historical figures by interacting with them. The experience unit allows a learner to experience historical events by recreating them. [Effects of the Invention]

[0007] Systems according to embodiments can enable learners to gain a deeper understanding and experience of historical events and people. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0025] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

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

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

[0028] (Example 1) A Japanese History Metaverse Experience System according to an embodiment of the present invention is a system that provides an interactive and immersive learning environment for learners to explore, experience, and understand important periods and events in Japanese history. As a result, the Japanese History Metaverse Experience System allows learners to explore, experience, and understand important periods and events in Japanese history.

[0029] A Japanese history metaverse experience system according to an embodiment includes an interactive map, interaction with historical figures, and reenactment of historical events. The interactive map allows learners to explore locations where different historical events occurred on a map of Japan. For example, learners can click on each location on the map to view details of the event, introductions to related figures, and commentary on the society and culture of the time. Interaction with historical figures allows learners to interact with historical figures. For example, learners can ask questions of historical figures such as Oda Nobunaga and Prince Shotoku and participate in discussions. Reenactment of historical events allows learners to experience historical events. For example, learners can experience important historical events such as the Battle of Sekigahara and the Restoration of Imperial Rule as if they were there. In this way, the Japanese history metaverse experience system according to an embodiment allows learners to explore, experience, and understand important periods and events in Japanese history.

[0030] The historical event search unit uses AI to automatically search for documents and archived footage related to historical events at each location and provide it to learners. For example, the historical event search unit can automatically search for documents related to historical events at each location and provide it to learners. For example, it can automatically collect ancient documents and research papers on battles from the Sengoku period and make them available for learners to view. The historical event search unit also uses AI to automatically search for archived footage related to historical events at each location and provide it to learners. For example, it can automatically collect historical video materials and news footage and make it available for learners to watch. This makes it easy for learners to obtain relevant documents and archived footage.

[0031] The historical event search unit uses AI to predict and suggest the next place to visit and related events based on the learner's behavioral history. For example, the historical event search unit uses AI to predict and suggest the next place to visit based on the learner's behavioral history. For example, a learner who is interested in battles during the Sengoku period will be suggested the location of the next battle to visit. The historical event search unit also uses AI to predict and suggest related events to visit next based on the learner's behavioral history. For example, it will suggest other events related to the historical event that the learner is interested in. This makes it possible to suggest the next learning content based on the learner's interests.

[0032] The historical event exploration unit allows students to visually understand geographical changes over time by overlaying a modern map on a historical map. For example, the historical event exploration unit allows students to visually understand geographical changes over time by overlaying a modern map on a historical map. For example, a map of the Edo period and a map of modern Tokyo can be overlaid. This allows students to visually understand geographical changes.

[0033] The historical event exploration module adds a function to compare the same location in different time periods, allowing students to see geographical transitions and historical changes. The historical event exploration module adds a function to compare the same location in different time periods, allowing students to see geographical transitions and historical changes. For example, comparing Tokyo in the Edo period with modern Tokyo. This allows students to understand geographical transitions and historical changes across different time periods.

[0034] The dialogue section uses AI to simulate the voices and speaking styles of historical figures, providing a more realistic dialogue experience. For example, the dialogue section can recreate the voice and speaking styles of Oda Nobunaga, allowing learners to engage in dialogue. This allows learners to have a more realistic dialogue experience.

[0035] In the dialogue section, AI can present multiple relevant perspectives and interpretations in response to learners' questions, encouraging discussion. For example, in the dialogue section, AI can present multiple relevant perspectives and interpretations in response to learners' questions, encouraging discussion. For example, it can present the views of different historians on the policies of Oda Nobunaga. This allows learners to learn history from multiple perspectives.

[0036] The dialogue unit can add a multi-user function that allows other learners to participate in a dialogue with a historical figure, thereby promoting collaborative learning.The dialogue unit can add a multi-user function that allows other learners to participate in a dialogue with a historical figure, thereby promoting collaborative learning.For example, multiple learners can simultaneously converse with Oda Nobunaga.This allows learners to study together.

[0037] The dialogue unit can add a function that allows a learner to record a dialogue with a historical figure and play it back later to review it. The dialogue unit can add a function that allows a learner to record a dialogue with a historical figure and play it back later to review it. For example, a dialogue with Oda Nobunaga can be recorded and played back later to review it. This allows a learner to record a dialogue and review it later.

[0038] The experience section can provide a more realistic experience when recreating historical events by using AI to simulate the weather and environmental conditions of the time. For example, the experience section can recreate the weather at the time of the Battle of Sekigahara, allowing learners to feel the atmosphere of the event. The experience section can also provide a more realistic experience when recreating historical events by using AI to simulate the environmental conditions of the time. For example, the experience section can recreate the terrain and environmental sounds, allowing learners to feel the atmosphere of the event. This allows learners to have a more realistic experience.

[0039] The experience section can add a function that allows learners to move freely within the recreated scene and observe events from different perspectives. For example, the experience section can add a function that allows learners to move freely within the recreated scene and observe events from different perspectives. For example, learners can freely walk around the battlefield of the Battle of Sekigahara and observe the battle situation from different perspectives. This allows learners to observe historical events from different perspectives.

[0040] The experience section can add a function that allows learners to use their own avatars to participate in the reenactment of historical events and experience part of the events. For example, the experience section can add a function that allows learners to use their own avatars to participate in the reenactment of historical events and experience part of the events. For example, learners can participate in the Battle of Sekigahara with their own avatars and experience the battle. This allows learners to experience historical events using their own avatars.

[0041] The experience section adds a multi-user function that allows other learners to join in on the recreated scene, allowing them to collaboratively experience the event. For example, the experience section adds a multi-user function that allows other learners to join in on the recreated scene, allowing them to collaboratively experience the event. For example, multiple learners can simultaneously participate in the Battle of Sekigahara and experience the battle. This allows learners to collaboratively experience a historical event.

[0042] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0043] The Japanese History Metaverse Experience System can add a feature that allows learners to virtually recreate and explore historical buildings and ruins. For example, learners can virtually recreate Heian-kyo or Edo Castle and freely walk around inside them. Learners can also learn about the structure and construction techniques of virtually recreated buildings. Furthermore, learners can explore the virtually recreated excavation sites of ruins and learn about the artifacts that were excavated. This allows learners to gain a deeper understanding of historical buildings and ruins.

[0044] The Japanese History Metaverse Experience System can add a feature that allows learners to virtually try on historical costumes and learn about the culture and fashion of those eras. For example, learners can virtually try on the costumes of aristocrats from the Heian period or samurai armor from the Edo period and learn about the fashion and culture of those eras. Learners can also learn about the materials and manufacturing methods of the costumes they virtually try on. Furthermore, learners can share the costumes they virtually try on with other learners and hold discussions. This allows learners to gain a deeper understanding of historical costumes and culture.

[0045] The Japanese History Metaverse Experience System can add a function that allows learners to virtually create historical dishes and learn about the food culture of those times. For example, learners can virtually create meals for aristocrats in the Heian period or commoners in the Edo period to learn about the food culture of those times. Learners can also learn about the ingredients and cooking methods of the dishes they virtually create. Furthermore, learners can share and discuss the dishes they virtually create with other learners. This allows learners to gain a deeper understanding of historical food culture.

[0046] The Japanese History Metaverse Experience System can add a function that allows learners to virtually experience historical music and dance and learn about the art and culture of that era. For example, learners can virtually experience gagaku from the Heian period or kabuki from the Edo period and learn about the art and culture of those times. Learners can also learn about the history and background of the music and dance they virtually experience. Furthermore, learners can share and discuss the music and dance they virtually experience with other learners. This allows learners to gain a deeper understanding of historical art and culture.

[0047] The Japanese History Metaverse Experience System can add a function that allows learners to virtually experience historical sports and games and learn about the entertainment culture of that era. For example, learners can virtually experience kemari (Japanese football) from the Heian period or sumo (sumo) from the Edo period and learn about the entertainment culture of that era. Learners can also learn about the rules and history of the sports and games they virtually experience. Furthermore, learners can share and discuss the sports and games they virtually experience with other learners. This allows learners to gain a deeper understanding of historical entertainment culture.

[0048] The processing flow of the first embodiment will be briefly explained below.

[0049] Step 1: The interactive map allows students to explore the locations of different historical events on a map of Japan. By clicking on each location on the map, students can view details of the events that occurred there, introductions to the people involved, and commentary on the society and culture of the time. Step 2: The explanation section displays details of events related to the locations explored by the historical event exploration section, introduces relevant people, and provides commentary on the society and culture of the time, allowing learners to gain a deeper understanding of the historical background. Step 3: The dialogue section allows learners to interact with historical figures. For example, learners can ask questions to historical figures such as Oda Nobunaga and Prince Shotoku and participate in discussions. Step 4: The experience section allows students to experience historical events. For example, students can experience important historical events such as the Battle of Sekigahara and the Restoration of Imperial Rule as if they were there.

[0050] (Example 2) A Japanese History Metaverse Experience System according to an embodiment of the present invention is a system that provides an interactive and immersive learning environment for learners to explore, experience, and understand important periods and events in Japanese history. As a result, the Japanese History Metaverse Experience System allows learners to explore, experience, and understand important periods and events in Japanese history.

[0051] A Japanese history metaverse experience system according to an embodiment includes an interactive map, interaction with historical figures, and reenactment of historical events. The interactive map allows learners to explore locations where different historical events occurred on a map of Japan. For example, learners can click on each location on the map to view details of the event, introductions to related figures, and commentary on the society and culture of the time. Interaction with historical figures allows learners to interact with historical figures. For example, learners can ask questions of historical figures such as Oda Nobunaga and Prince Shotoku and participate in discussions. Reenactment of historical events allows learners to experience historical events. For example, learners can experience important historical events such as the Battle of Sekigahara and the Restoration of Imperial Rule as if they were there. In this way, the Japanese history metaverse experience system according to an embodiment allows learners to explore, experience, and understand important periods and events in Japanese history.

[0052] The historical event search unit uses AI to automatically search for documents and archived footage related to historical events at each location and provide it to learners. For example, the historical event search unit can automatically search for documents related to historical events at each location and provide it to learners. For example, it can automatically collect ancient documents and research papers on battles from the Sengoku period and make them available for learners to view. The historical event search unit also uses AI to automatically search for archived footage related to historical events at each location and provide it to learners. For example, it can automatically collect historical video materials and news footage and make it available for learners to watch. This makes it easy for learners to obtain relevant documents and archived footage.

[0053] The historical event search unit uses AI to predict and suggest the next place to visit and related events based on the learner's behavioral history. For example, the historical event search unit uses AI to predict and suggest the next place to visit based on the learner's behavioral history. For example, a learner who is interested in battles during the Sengoku period will be suggested the location of the next battle to visit. The historical event search unit also uses AI to predict and suggest related events to visit next based on the learner's behavioral history. For example, it will suggest other events related to the historical event that the learner is interested in. This makes it possible to suggest the next learning content based on the learner's interests.

[0054] The historical event search unit can use the emotion estimation function to identify historical events that are likely to interest learners and display those points with priority. The historical event search unit can, for example, use the emotion estimation function to identify historical events that are likely to interest learners and display those points with priority. For example, it can display events that make learners feel excited or surprised with priority. This makes it possible to display learning content with priority based on the learner's interests.

[0055] The historical event exploration unit allows students to visually understand geographical changes over time by overlaying a modern map on a historical map. For example, the historical event exploration unit allows students to visually understand geographical changes over time by overlaying a modern map on a historical map. For example, a map of the Edo period and a map of modern Tokyo can be overlaid. This allows students to visually understand geographical changes.

[0056] The historical event exploration module adds a function to compare the same location in different time periods, allowing students to see geographical transitions and historical changes. The historical event exploration module adds a function to compare the same location in different time periods, allowing students to see geographical transitions and historical changes. For example, comparing Tokyo in the Edo period with modern Tokyo. This allows students to understand geographical transitions and historical changes across different time periods.

[0057] The historical event search unit can use the emotion estimation function to analyze the emotion a learner has toward a specific location and suggest other related locations based on the emotion. The historical event search unit can, for example, use the emotion estimation function to analyze the emotion a learner has toward a specific location and suggest other related locations based on the emotion. For example, it can suggest other moving locations related to a location where the learner felt moved. In this way, it is possible to suggest other related locations based on the learner's emotion.

[0058] The dialogue section uses AI to simulate the voices and speaking styles of historical figures, providing a more realistic dialogue experience. For example, the dialogue section can recreate the voice and speaking styles of Oda Nobunaga, allowing learners to engage in dialogue. This allows learners to have a more realistic dialogue experience.

[0059] In the dialogue section, AI can present multiple relevant perspectives and interpretations in response to learners' questions, encouraging discussion. For example, in the dialogue section, AI can present multiple relevant perspectives and interpretations in response to learners' questions, encouraging discussion. For example, it can present the views of different historians on the policies of Oda Nobunaga. This allows learners to learn history from multiple perspectives.

[0060] The dialogue unit uses an emotion estimation function to enable the AI ​​to generate responses that correspond to the learner's emotions, thereby increasing the depth of the dialogue. For example, if the learner is excited, the dialogue unit can provide more interesting information. This allows the dialogue unit to increase the depth of the dialogue with responses that correspond to the learner's emotions.

[0061] The dialogue unit can add a multi-user function that allows other learners to participate in a dialogue with a historical figure, thereby promoting collaborative learning.The dialogue unit can add a multi-user function that allows other learners to participate in a dialogue with a historical figure, thereby promoting collaborative learning.For example, multiple learners can simultaneously converse with Oda Nobunaga.This allows learners to study together.

[0062] The dialogue unit can add a function that allows a learner to record a dialogue with a historical figure and play it back later to review it. The dialogue unit can add a function that allows a learner to record a dialogue with a historical figure and play it back later to review it. For example, a dialogue with Oda Nobunaga can be recorded and played back later to review it. This allows a learner to record a dialogue and review it later.

[0063] The dialogue unit can use the emotion estimation function to analyze the emotion felt by the learner during the dialogue and adjust the next dialogue content based on that emotion. For example, the dialogue unit can use the emotion estimation function to analyze the emotion felt by the learner during the dialogue and adjust the next dialogue content based on that emotion. For example, if the learner is excited, a more interesting topic can be provided. In this way, the next dialogue content can be adjusted based on the learner's emotion.

[0064] The experience section can provide a more realistic experience when recreating historical events by using AI to simulate the weather and environmental conditions of the time. For example, the experience section can recreate the weather at the time of the Battle of Sekigahara, allowing learners to feel the atmosphere of the event. The experience section can also provide a more realistic experience when recreating historical events by using AI to simulate the environmental conditions of the time. For example, the experience section can recreate the terrain and environmental sounds, allowing learners to feel the atmosphere of the event. This allows learners to have a more realistic experience.

[0065] The experience section can add a function that allows learners to move freely within the recreated scene and observe events from different perspectives. For example, the experience section can add a function that allows learners to move freely within the recreated scene and observe events from different perspectives. For example, learners can freely walk around the battlefield of the Battle of Sekigahara and observe the battle situation from different perspectives. This allows learners to observe historical events from different perspectives.

[0066] The experience unit can use the emotion estimation function to analyze the emotion felt by the learner in the recreated scene and adjust the details of the scene based on that emotion. For example, the experience unit can use the emotion estimation function to analyze the emotion felt by the learner in the recreated scene and adjust the details of the scene based on that emotion. For example, if the learner is excited, elements that attract more interest can be added. In this way, the details of the scene can be adjusted based on the learner's emotion.

[0067] The experience section can add a function that allows learners to use their own avatars to participate in the reenactment of historical events and experience part of the events. For example, the experience section can add a function that allows learners to use their own avatars to participate in the reenactment of historical events and experience part of the events. For example, learners can participate in the Battle of Sekigahara with their own avatars and experience the battle. This allows learners to experience historical events using their own avatars.

[0068] The experience section adds a multi-user function that allows other learners to join in on the recreated scene, allowing them to collaboratively experience the event. For example, the experience section adds a multi-user function that allows other learners to join in on the recreated scene, allowing them to collaboratively experience the event. For example, multiple learners can simultaneously participate in the Battle of Sekigahara and experience the battle. This allows learners to collaboratively experience a historical event.

[0069] The experience section uses the emotion estimation function to allow learners to share the emotions they felt in the recreated scenes and hold emotion-based discussions with other learners. For example, the experience section uses the emotion estimation function to allow learners to share the emotions they felt in the recreated scenes and hold emotion-based discussions with other learners. For example, learners can share and discuss the excitement they felt at the Battle of Sekigahara. This allows learners to share their emotions and hold discussions.

[0070] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0071] The Japanese History Metaverse Experience System can add a feature that allows learners to virtually recreate and explore historical buildings and ruins. For example, learners can virtually recreate Heian-kyo or Edo Castle and freely walk around inside them. Learners can also learn about the structure and construction techniques of virtually recreated buildings. Furthermore, learners can explore the virtually recreated excavation sites of ruins and learn about the artifacts that were excavated. This allows learners to gain a deeper understanding of historical buildings and ruins.

[0072] The Japanese History Metaverse Experience System can add a feature that allows learners to virtually try on historical costumes and learn about the culture and fashion of those eras. For example, learners can virtually try on the costumes of aristocrats from the Heian period or samurai armor from the Edo period and learn about the fashion and culture of those eras. Learners can also learn about the materials and manufacturing methods of the costumes they virtually try on. Furthermore, learners can share the costumes they virtually try on with other learners and hold discussions. This allows learners to gain a deeper understanding of historical costumes and culture.

[0073] The Japanese History Metaverse Experience System can add a function that allows learners to virtually create historical dishes and learn about the food culture of those times. For example, learners can virtually create meals for aristocrats in the Heian period or commoners in the Edo period to learn about the food culture of those times. Learners can also learn about the ingredients and cooking methods of the dishes they virtually create. Furthermore, learners can share and discuss the dishes they virtually create with other learners. This allows learners to gain a deeper understanding of historical food culture.

[0074] The Japanese History Metaverse Experience System can add a function that allows learners to virtually experience historical music and dance and learn about the art and culture of that era. For example, learners can virtually experience gagaku from the Heian period or kabuki from the Edo period and learn about the art and culture of those times. Learners can also learn about the history and background of the music and dance they virtually experience. Furthermore, learners can share and discuss the music and dance they virtually experience with other learners. This allows learners to gain a deeper understanding of historical art and culture.

[0075] The Japanese History Metaverse Experience System can add a function that allows learners to virtually experience historical sports and games and learn about the entertainment culture of that era. For example, learners can virtually experience kemari (Japanese football) from the Heian period or sumo (sumo) from the Edo period and learn about the entertainment culture of that era. Learners can also learn about the rules and history of the sports and games they virtually experience. Furthermore, learners can share and discuss the sports and games they virtually experience with other learners. This allows learners to gain a deeper understanding of historical entertainment culture.

[0076] The Japanese History Metaverse Experience System uses emotion estimation to adjust scene details based on the learner's emotions as they re-enact historical events. For example, if the learner is excited, the system can add more engaging elements. Alternatively, if the learner is feeling scared, the system can add elements to ease the tension in the scene. This allows the system to adjust scene details based on the learner's emotions, providing a more immersive experience.

[0077] The Japanese History Metaverse Experience System uses an emotion estimation function to generate responses based on the learner's emotions when they interact with historical figures, enhancing the depth of the dialogue. For example, if the learner is excited, the system can provide more interesting information. If the learner is confused, the system can provide a more understandable explanation. This increases the depth of the dialogue with responses that reflect the learner's emotions, providing a more effective learning experience.

[0078] The Japanese History Metaverse Experience System uses emotion estimation functionality to enable learners to share their emotions as they re-enact historical events and engage in emotion-based discussions with other learners. For example, learners can share and discuss the excitement they felt at the Battle of Sekigahara. They can also share and discuss the emotions they felt at the Meiji Restoration. This allows learners to share their emotions and engage in discussions, leading to a deeper understanding.

[0079] The Japanese History Metaverse Experience System uses an emotion estimation function to suggest the next learning content based on the learner's emotions as they re-enact historical events. For example, if the learner is excited, the system can suggest more interesting historical events. Also, if the learner is moved, the system can suggest related, moving historical events. This allows the system to suggest the next learning content based on the learner's emotions, providing a more effective learning experience.

[0080] The Japanese History Metaverse Experience System uses an emotion estimation function to analyze the emotions felt by learners during dialogue with historical figures, and can adjust the content of the next dialogue based on those emotions. For example, if the learner is excited, the system can provide a more interesting topic. Also, if the learner is confused, the system can provide a more understandable explanation. This allows the system to adjust the content of the next dialogue based on the learner's emotions, providing a more effective learning experience.

[0081] The processing flow of the second embodiment will be briefly explained below.

[0082] Step 1: The interactive map allows students to explore the locations of different historical events on a map of Japan. By clicking on each location on the map, students can view details of the events that occurred there, introductions to the people involved, and commentary on the society and culture of the time. Step 2: The explanation section displays details of events related to the locations explored by the historical event exploration section, introduces relevant people, and provides commentary on the society and culture of the time, allowing learners to gain a deeper understanding of the historical background. Step 3: The dialogue section allows learners to interact with historical figures. For example, learners can ask questions to historical figures such as Oda Nobunaga and Prince Shotoku and participate in discussions. Step 4: The experience section allows students to experience historical events. For example, students can experience important historical events such as the Battle of Sekigahara and the Restoration of Imperial Rule as if they were there.

[0083] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0084] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> Examples of generative AIs include the data generation model 58, such as a neural network model (e.g., a neural network model), and a neural network model (e.g., a neural network model). The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating speech, text data indicating text, and image data indicating an image is also input to the data generation model 58. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0085] Furthermore, the processing by the data processing system 10 described above is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0086] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0087] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0088] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0089] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, and the camera 42 are also connected to the bus 52.

[0090] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0091] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0092] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0093] Fig. 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Fig. 4, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0094] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0095] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0096] In the smart glasses 214, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. Note that the smart glasses 214 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0097] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0098] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0099] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0100] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the smart glasses 214 or an external device, etc., and the smart glasses 214 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0101] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0102] 5, the data processing system 310 includes the data processing device 12 and a headset type terminal 314. An example of the data processing device 12 is a server.

[0103] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0104] The headset type terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a display 343. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the display 343 are also connected to the bus 52.

[0105] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0106] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0107] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0108] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset type terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0109] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0110] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0111] In the headset type terminal 314, the identification process is performed by the processor 46. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. Note that the headset type terminal 314 may also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0112] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0113] The specific processing unit 290 transmits the result of the specific processing to the headset type terminal 314. In the headset type terminal 314, the control unit 46A causes the speaker 240 and the display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0114] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0115] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset type terminal 314, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset type terminal 314. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the headset type terminal 314 or an external device, etc., and the headset type terminal 314 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0116] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0117] 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.

[0118] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0119] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a control target 443. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the control target 443 are also connected to the bus 52.

[0120] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0121] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS image sensor or a CCD image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0122] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0123] The control object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

[0124] Fig. 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Fig. 8, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0125] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0126] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0127] In the robot 414, the processor 46 performs the identification process. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. The robot 414 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0128] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0129] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the control target 443 to output the result of the specific processing. The microphone 238 acquires voice indicating a user input regarding the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[0130] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0131] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the robot 414 or an external device, etc., and the robot 414 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0132] The emotion identification model 59 as an emotion engine may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0133] FIG. 9 illustrates an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. Emotions closer to the center of the concentric circles are more primitive. Emotions representing states and behaviors arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion encompasses both emotions and mental states. Emotions generally generated from reactions occurring in the brain are arranged on the left side of the concentric circles. Emotions generally induced by situational judgment are arranged on the right side of the concentric circles. Emotions generally generated from reactions occurring in the brain and induced by situational judgment are arranged on the upper and lower sides of the concentric circles. Furthermore, the emotion of "pleasure" is arranged on the upper side of the concentric circles, and the emotion of "discomfort" is arranged on the lower side. In this way, in the emotion map 400, multiple emotions are mapped based on the structure by which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[0134] These emotions are distributed in the 3 o'clock direction on emotion map 400, and typically fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

[0135] The inside of emotion map 400 represents what is going on in the mind, and the outside of emotion map 400 represents behavior, so the further you go outside emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[0136] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. Emotions can also be created for robots, cars, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on speech emotion recognition and brain physiological signal analysis systems for emotions, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the area called "reaction," where sensation is dominant. The right half of the emotion map lists emotions belonging to the area called "situation," where situational awareness is dominant.

[0137] The emotion map defines two emotions that promote learning. One is a negative emotion on the situation side, around the middle of "repentance" or "reflection." In other words, this occurs when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is a positive emotion on the response side, around "desire." In other words, this occurs when the robot experiences positive feelings such as "I want more" or "I want to know more."

[0138] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple pieces of training data that are combinations of user input and emotion values ​​indicating each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions that are located close to each other have similar values, as in the emotion map 900 shown in FIG. 10. FIG. 10 shows an example in which multiple emotions, "relieved," "calm," and "reassuring," have similar emotion values.

[0139] In the above embodiment, an example was given in which a specific process is performed by one computer 22, but the technology disclosed herein is not limited to this, and distributed processing of the specific process may be performed by multiple computers including computer 22.

[0140] In the above embodiment, an example in which the specific processing program 56 is stored in the storage 32 has been described, but the technology of the present disclosure is not limited to this. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-transitory storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-transitory storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes the specific processing in accordance with the specific processing program 56.

[0141] 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.

[0142] It is not necessary to store all of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store all of the specific processing program 56 in the storage 32; only a portion of the specific processing program 56 may be stored.

[0143] The hardware resource for executing a specific process can be any of the following types of processors: A processor, for example, is a CPU, which is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. A processor also includes a dedicated electrical circuit, such as an FPGA (Field-Programmable Gate Array), a PLD (Programmable Logic Device), or an ASIC (Application Specific Integrated Circuit), which is a processor with a circuit configuration designed specifically for executing a specific process. Each processor has built-in or connected memory, and each processor uses the memory to execute the specific process.

[0144] The hardware resource that executes the specific process may be configured with one of these various processors, or may be configured with a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Also, the hardware resource that executes the specific process may be a single processor.

[0145] As an example of a system configured with a single processor, first, one processor is configured by combining one or more CPUs and software, and this processor functions as a hardware resource that executes a specific process. Second, there is a system that uses a processor that realizes the functions of an entire system including multiple hardware resources that execute a specific process on a single IC chip, as typified by SoC (System-on-a-chip). In this way, a specific process is realized using one or more of the above-mentioned various processors as hardware resources.

[0146] Furthermore, the hardware structure of these various processors can be, more specifically, an electric circuit that combines circuit elements such as semiconductor devices. The specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps may be deleted, new steps may be added, or the processing order may be rearranged, without departing from the spirit of the invention.

[0147] In the above example, the first to fourth embodiments have been described separately, but some or all of these embodiments may be combined. The smart device 14, smart glasses 214, headset terminal 314, and robot 414 are merely examples, and they may be combined, or other devices may be used. In the above example, the first and second embodiments have been described separately, but they may be combined.

[0148] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, to avoid confusion and facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[0149] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference. [Explanation of symbols]

[0150] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. Interactive maps and a historical event search unit that allows users to search for locations where different historical events occurred on a map of Japan using the interactive map; an explanation section that displays details of events related to the places searched by the historical event search section, introductions to related people, and explanations about the society and culture of the time; Interacting with historical figures, a dialogue unit that allows learners to have a dialogue with historical figures through interaction with the historical figures; Reenacting historical events, and an experience unit that allows learners to experience historical events by reenacting the historical events. A system characterized by:

2. The historical event search unit AI automatically searches for documents and archived footage related to historical events at each location and provides them to learners 2. The system of claim 1.

3. The historical event search unit Overlay modern and historical maps to visually understand geographical changes over time 2. The system of claim 1.

4. The dialogue unit AI simulates the voices and speaking styles of historical figures to provide a more realistic dialogue experience 2. The system of claim 1.

5. The experience section includes: AI will simulate weather and environmental conditions during historical reenactments to provide a more realistic experience.

2. The system of claim 1.

6. The historical event search unit Identify historical events that are likely to interest learners and prioritize their display 2. The system of claim 1.

7. The dialogue unit AI generates responses based on the learner's emotions, enhancing the depth of the dialogue 2. The system of claim 1.

8. The experience section includes: Analyze the emotions experienced by the learner in the recreated scene and adjust the details of the scene based on those emotions.

2. The system of claim 1.

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