system

Abstract

An object of the system according to the embodiment is to enable a user to experience a historical event or an entertainment event in real time.SOLUTION: A system according to an embodiment includes a data collection unit, an analysis unit, a VR generation unit, and an experience provision unit. The data collector collects data about historical events and entertainment events. The analysis unit analyzes the data collected by the data collection unit. The VR generation unit generates a VR experience based on the data analyzed by the analysis unit. The experience providing unit provides the VR experience generated by the VR generation unit to the user.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 made it difficult to experience historical events or entertainment events in real time.

[0005] The system according to the embodiment aims to enable historical events and entertainment events to be experienced in real time. [Means for solving the problem]

[0006] The system according to the embodiment includes a data collection unit, an analysis unit, a VR generation unit, and an experience provision unit. The data collection unit collects data related to historical events and entertainment events. The analysis unit analyzes the data collected by the data collection unit. The VR generation unit generates a VR experience based on the data analyzed by the analysis unit. The experience provision unit provides the VR experience generated by the VR generation unit to a user. [Effects of the Invention]

[0007] Systems according to embodiments may enable historical events and entertainment events to be experienced in real time. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example 1) The memorial AI service according to an embodiment of the present invention is a system that allows users to experience historical events and memorable moments from past entertainment in real time using VR. This allows users to experience historical events and entertainment events in real time.

[0029] The memorial AI service according to the embodiment includes a data collection unit, an analysis unit, a VR generation unit, and an experience provision unit. The data collection unit collects data related to historical events and entertainment events. For example, the data collection unit collects data from public databases on the Internet. The data collection unit can also collect archived video and audio data. The data collection unit can also collect specific data based on a user request. The analysis unit analyzes the data collected by the data collection unit. For example, the analysis unit can analyze text data using natural language processing technology. The analysis unit can also analyze video data using image recognition technology. The analysis unit can also analyze audio data using voice recognition technology. The VR generation unit generates a VR experience based on the data analyzed by the analysis unit. For example, the VR generation unit can recreate scenes of historical events using 3D modeling technology. The VR generation unit can also provide a realistic audio experience using audio technology. The VR generation unit can also generate a VR experience including interactive elements. The experience provision unit provides the VR experience generated by the VR generation unit to a user. For example, the experience providing unit provides a VR experience to a user using a VR headset. The experience providing unit can also provide a VR experience using a smartphone or a tablet. The experience providing unit can also provide a VR experience through a web browser. This allows the memorial AI service according to the embodiment to allow users to experience historical events or entertainment events in real time. For example, a user can experience the Normandy Landings, the 1985 Live Aid Concert, or an ancient Egyptian pyramid construction site.

[0030] The analysis unit can analyze background and environmental sounds of historical events to provide a more realistic audio experience. For example, the analysis unit uses a generation AI to analyze background and environmental sounds of historical events and reproduce them in real time. For example, the sounds of a battlefield or the voices of crowds can be realistically reproduced to provide the user with a sense of presence. In addition, audio data of historical events is collected, and the generation AI performs audio simulations based on that data. For example, the sounds of an ancient market or natural sounds can be reproduced to provide the user with an experience that makes them feel as if they are actually there. In addition, the generation AI dynamically adjusts the audio experience based on the user's location information. For example, the direction and intensity of sounds can change as the user moves, providing a more realistic experience. This provides a more realistic audio experience, improving the user's sense of immersion.

[0031] The analysis unit can analyze the movements and facial expressions of characters in historical events, allowing the user to interact with them. For example, the generation AI can analyze the movements and facial expressions of characters in historical events, allowing the user to interact with the characters. For example, when a user asks a question, the character responds in real time. The generation AI can also analyze the movements and facial expressions of characters in real time, dynamically changing them according to the user's actions. For example, the character reacts when the user approaches. The generation AI can also analyze the emotions of characters, allowing the user to interact with them according to their emotions. For example, if a character is angry, the generation AI can provide the user with options to ease their emotions. This allows the user to interact with the characters, providing a deeper experience.

[0032] The VR generation unit can enable experiences from different perspectives. For example, the generation AI collects data from different perspectives to allow the user to experience historical events from the perspectives of civilians, soldiers, leaders, etc. For example, experiencing the Normandy Landings from a soldier's perspective. The VR generation unit also allows the user to select the perspective from which to experience the event and provides experiences from different perspectives. For example, experiencing the same event from a leader's perspective deepens understanding from a different perspective. The generation AI also generates scenarios from different perspectives and allows the user to experience them from that perspective. For example, experiencing the effects of war from the perspective of civilians. This allows the user to deepen their understanding by experiencing them from different perspectives.

[0033] The VR generation unit can be expanded to include events from different eras and cultural spheres, allowing users to learn about diverse histories. For example, the generation AI collects data from different eras and cultural spheres, allowing users to experience diverse histories. For example, experiencing events from ancient Rome or medieval Europe. The generation AI also allows users to select the era or cultural sphere they want to experience, and provides a VR experience that meets their request. For example, experiencing historical events from Asia. The generation AI also generates scenarios from different eras and cultural spheres, providing an experience that makes users feel as if they are actually there. For example, experiencing Renaissance Florence. This allows users to deepen their understanding of history by experiencing events from different eras and cultural spheres.

[0034] When analyzing data on historical events, the analysis unit can integrate multiple data sources (documents, video, audio) to generate a more detailed VR experience. For example, the analysis unit uses a generation AI to integrate multiple data sources, such as documents, video, and audio, to more precisely recreate historical events. For example, it can combine video and audio of a battlefield to provide a realistic experience. It can also analyze multiple data sources, and the generation AI can generate a VR experience based on that data. For example, it can combine a scenario based on historical documents with video data. The generation AI can also integrate different data sources to provide an experience that makes the user feel as if they are actually there. For example, it can combine audio data and video data to enhance the sense of realism. In this way, by integrating multiple data sources, a more detailed VR experience can be provided.

[0035] When analyzing data on historical events, the analysis unit can incorporate perspectives from different cultures and regions to provide an experience from a global perspective. For example, the generation AI collects data from different cultures and regions and recreates historical events from a global perspective. For example, the same event can be experienced from the perspective of different cultural spheres. The generation AI also incorporates perspectives from different cultures and regions and generates a VR experience based on that data. For example, the same historical event can be experienced from the perspective of Asia and Europe. The generation AI also generates a scenario from a global perspective, allowing users to experience it from the perspectives of diverse cultures and regions. For example, a historical event can be recreated from the perspective of a different cultural sphere. This provides an experience from a global perspective by incorporating perspectives from different cultures and regions.

[0036] When analyzing data on historical events, the analysis unit can provide a customized experience based on the user's interests and concerns. For example, the analysis unit uses a generation AI to analyze the user's interests and concerns and provide a customized VR experience based on that data. For example, it may focus on specific events or people that interest the user. The generation AI may also generate a customized scenario based on the user's profile data. For example, it may select historical events that correspond to the user's interests. The generation AI may also build a system that provides a customized experience based on user feedback. For example, it may recommend events related to themes that interest the user. This provides a customized experience based on the user's interests and concerns.

[0037] When analyzing data from key entertainment moments, the VR generation unit can integrate multiple data sources (video, audio, audience reactions) to generate a more sophisticated VR experience. For example, the generation AI in the VR generation unit integrates multiple data sources, such as video, audio, and audience reactions, to more precisely recreate an entertainment event. For example, it combines video and audio from a live concert to provide a realistic experience. It also analyzes multiple data sources, and the generation AI generates a VR experience based on that data. For example, it combines video of a sporting event with audience reactions. The generation AI also integrates different data sources to provide an experience that makes the user feel as if they are actually there. For example, it combines audio data and video data to enhance the sense of realism. In this way, by integrating multiple data sources, a more sophisticated VR experience is provided.

[0038] When analyzing data on key moments in entertainment, the VR generation unit can simulate the temporal progression of the event in real time, providing an experience that makes the user feel as if they are actually there. For example, the generation AI in the VR generation unit simulates the temporal progression of an entertainment event in real time, providing an experience that makes the user feel as if they are actually there. For example, the progress of a concert and important moments are reproduced in real time. The generation AI also analyzes the temporal progression of an event, and generates a VR experience based on that data. For example, the flow of a sporting event from start to finish is reproduced in real time. The generation AI also simulates the temporal progression, allowing the user to experience it as if they were actually there. For example, the progression of an important event is relived in real time. In this way, by simulating the temporal progression in real time, the user is provided with an experience that makes the user feel as if they are actually there.

[0039] When analyzing data on entertainment moments, the VR generation unit can incorporate perspectives from different cultures and regions to provide an experience from a global perspective. For example, the generation AI collects data from different cultures and regions and recreates an entertainment event from a global perspective. For example, the same event can be experienced from the perspective of different cultural spheres. The generation AI can also incorporate perspectives from different cultures and regions and generate a VR experience based on that data. For example, the same entertainment event can be experienced from the perspective of Asia and Europe. The generation AI can also generate a scenario from a global perspective, allowing users to experience it from the perspectives of diverse cultures and regions. For example, an entertainment event can be recreated from the perspective of different cultural spheres. This provides an experience from a global perspective by incorporating perspectives from different cultures and regions.

[0040] When analyzing data on entertainment moments, the VR generation unit can provide a customized experience based on the user's interests and concerns. For example, the generation AI analyzes the user's interests and concerns and provides a customized VR experience based on that data. For example, it may focus on specific events or artists that interest the user. The generation AI may also generate a customized scenario based on the user's profile data. For example, it may select entertainment events that match the user's interests. The generation AI may also build a system that provides a customized experience based on user feedback. For example, it may recommend events related to themes that interest the user. This provides a customized experience based on the user's interests and concerns.

[0041] The analysis unit can analyze the movements and facial expressions of characters in historical events to support education, allowing users to interact with them. For example, the analysis unit allows the generation AI to analyze the movements and facial expressions of characters in historical events, allowing users to interact with the characters. For example, when a user asks a question, the character responds in real time. The generation AI can also analyze the movements and facial expressions of characters in real time and dynamically change them according to the user's actions. For example, the character reacts when the user approaches. The generation AI can also analyze the emotions of characters, allowing users to interact with them according to their emotions. For example, if a character is happy, the generation AI can provide an option for the user to share that emotion. This allows users to interact with characters, improving the effectiveness of educational support.

[0042] The VR generation unit can enable experiences from different perspectives (e.g., civilians, soldiers, leaders) for educational support. For example, the generation AI collects data from different perspectives and allows users to experience historical events from the perspectives of civilians, soldiers, leaders, etc. For example, experiencing the Normandy Landings from a soldier's perspective. The VR generation unit also allows users to select the perspective from which to experience and provides experiences from different perspectives. For example, experiencing the same event from a leader's perspective deepens understanding from a different perspective. The generation AI also generates scenarios from different perspectives and allows users to experience them from that perspective. For example, experiencing the effects of war from the perspective of civilians. This improves the effectiveness of educational support by allowing users to experience events from different perspectives.

[0043] The VR generation unit can be expanded to include events from different eras and cultural spheres for educational support, allowing users to learn about diverse histories. For example, the generation AI collects data from different eras and cultural spheres to allow users to experience diverse histories. For example, experiencing events from ancient Rome or medieval Europe. The generation AI can also allow users to select the era or cultural sphere they wish to experience, and provide a VR experience that meets their request. For example, experiencing historical events from Asia. The generation AI can also generate scenarios from different eras and cultural spheres, providing an experience that makes users feel as if they are actually there. For example, experiencing Renaissance Florence. This improves the effectiveness of educational support by allowing users to experience events from different eras and cultural spheres.

[0044] The analysis unit analyzes background and environmental sounds of key moments in entertainment to relive them, providing a more realistic audio experience. For example, the analysis unit uses a generation AI to analyze background and environmental sounds of key moments in entertainment and reproduce them in real time. For example, cheers at a concert or cheers at a sporting event can be realistically reproduced to provide the user with a sense of presence. Furthermore, the generation AI collects audio data of key moments in entertainment, and performs audio simulations based on that data. For example, it can reproduce the audio of a live concert, providing the user with an experience that makes them feel as if they are actually there. Furthermore, the generation AI dynamically adjusts the audio experience based on the user's location information. For example, it can change the direction and intensity of sound as the user moves, providing a more realistic experience. This provides a more realistic audio experience, improving the effectiveness of re-experiencing entertainment.

[0045] The analysis unit can analyze the actions and facial expressions of characters at key moments in entertainment to allow users to relive the entertainment, thereby enabling interactive involvement by the user. For example, the analysis unit allows the generation AI to analyze the actions and facial expressions of characters at key moments in entertainment, allowing users to interactively engage with the characters. For example, when a user asks a question, the character responds in real time. The generation AI can also analyze the actions and facial expressions of characters in real time and dynamically change them according to the user's actions. For example, the character reacts when the user approaches. The generation AI can also analyze the emotions of the character, allowing the user to interact according to those emotions. For example, if the character is happy, the generation AI can provide the user with an option to share that emotion. This allows users to interactively engage with the character, improving the effectiveness of reliving the entertainment.

[0046] The VR generation unit can enable experiences from different perspectives (e.g., audience, performers, staff) to relive entertainment. For example, the generation AI collects data from different perspectives, allowing users to experience memorable moments of entertainment from the perspectives of audience, performers, staff, etc. For example, experiencing a live concert from the performer's perspective. The VR generation unit also allows users to select the perspective from which to experience, providing experiences from different perspectives. For example, experiencing the same event from the perspective of a staff member deepens understanding from a different perspective. The generation AI also generates scenarios from different perspectives, allowing users to experience them from that perspective. For example, experiencing a sporting event from the perspective of a spectator. This improves the effectiveness of reliving entertainment by allowing users to experience it from different perspectives.

[0047] The VR generation unit can be expanded to different genres (music, sports, theater) to relive entertainment, allowing users to enjoy a variety of entertainment. For example, the generation AI collects data from different genres to allow users to experience a variety of entertainment. For example, users can experience music concerts, sporting events, theater, etc. The VR generation unit also allows users to select the genre they want to experience, and the generation AI provides a VR experience that meets their request. For example, if a user selects a music concert, an experience of that genre is provided. The generation AI also generates scenarios of different genres, allowing users to experience the experience as if they were actually there. For example, they can experience behind the scenes of a theater play. This diversifies the user's enjoyment by allowing them to experience entertainment of different genres.

[0048] The analysis unit can analyze background and environmental sounds from historical events for VR experiences of world history and culture, providing a more realistic audio experience. For example, the analysis unit uses a generation AI to analyze background and environmental sounds from historical events and reproduce them in real time. For example, realistic reproduction of battlefield sounds and crowd noises provides users with a sense of presence. The generation AI also collects audio data from historical events, and performs audio simulations based on that data. For example, it can reproduce the sounds of an ancient market or natural sounds, providing users with an experience that makes them feel as if they are actually there. The generation AI also dynamically adjusts the audio experience based on the user's location information. For example, it can change the direction and intensity of sounds as the user moves, providing a more realistic experience. This provides a more realistic audio experience, improving the effectiveness of VR experiences of world history and culture.

[0049] The analysis unit can analyze the movements and facial expressions of characters in historical events for a VR experience of world history and culture, allowing users to interact with them. For example, the analysis unit uses a generation AI to analyze the movements and facial expressions of characters in historical events, allowing users to interact with the characters. For example, when a user asks a question, the character responds in real time. The analysis unit can also analyze the movements and facial expressions of characters in real time and dynamically change them according to the user's actions. For example, the character reacts when the user approaches. The generation AI can also analyze the emotions of characters, allowing users to interact with them according to their emotions. For example, if a character is happy, the analysis unit can provide an option for the user to share that emotion. This allows users to interact with characters, improving the effectiveness of the VR experience of world history and culture.

[0050] The VR generation unit can enable VR experiences of world history and culture from different perspectives (e.g., civilians, soldiers, leaders). For example, the generation AI collects data from different perspectives and allows users to experience historical events from the perspectives of civilians, soldiers, leaders, etc. For example, experiencing the Normandy Landings from a soldier's perspective. The VR generation unit also allows users to select the perspective from which to experience and provides experiences from different perspectives. For example, experiencing the same event from a leader's perspective deepens understanding from a different perspective. The generation AI also generates scenarios from different perspectives and allows users to experience them from that perspective. For example, experiencing the effects of war from the perspective of civilians. This improves the effectiveness of VR experiences of world history and culture by allowing users to experience them from different perspectives.

[0051] The VR generation unit can expand the VR experience of world history and culture to include events from different eras and cultural spheres, allowing users to learn about diverse histories. For example, the generation AI collects data from different eras and cultural spheres to allow users to experience diverse histories. For example, experiencing events from ancient Rome or medieval Europe. The generation AI also allows users to select the era or cultural sphere they wish to experience, and provides a VR experience that meets their request. For example, experiencing historical events from Asia. The generation AI also generates scenarios from different eras and cultural spheres, providing an experience that makes users feel as if they are actually there. For example, experiencing Florence during the Renaissance. This improves the effectiveness of the VR experience of world history and culture by allowing users to experience events from different eras and cultural spheres.

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

[0053] The analysis unit can monitor the user's health condition in real time and adjust the load during the experience. For example, it can monitor the user's heart rate and blood pressure and adjust the difficulty of the scenario to avoid excessive stress. Furthermore, based on the user's health data, the generative AI can customize the content of the experience and provide an optimal experience according to the user's health condition. For example, it can add scenes that have a relaxing effect. It can also analyze the user's health condition in real time and display notifications encouraging them to take a break as needed. This provides a safe experience that takes user health into consideration.

[0054] The VR generation unit analyzes the user's movements in real time, making interactions during the experience more natural. For example, it analyzes the user's hand movements to allow smoother operation within the virtual space. It also tracks the user's gaze and provides interactions according to the direction of the gaze. For example, when a user looks at a specific object, information about that object is displayed. It also analyzes the user's posture and reflects the user's movements during the experience in real time. For example, if the user crouches, the same movement is reproduced in the virtual space. This provides a more natural and immersive experience.

[0055] To further personalize the user's experience, the VR generation unit can analyze the user's past experience data and provide scenarios tailored to their preferences. For example, it can suggest new experiences based on historical events that the user was interested in in the past. The generation AI can also generate customized scenarios based on the user's past experience data, providing an optimal experience tailored to the user's preferences. For example, if the user is interested in a particular era or culture, it can provide a scenario related to that theme. Furthermore, it can continuously improve the experience content based on user feedback, providing a more satisfying experience. This provides a personalized experience tailored to the user's preferences.

[0056] The VR generation unit can enhance the physical simulation in the virtual space to make the user's experience more interactive. For example, when a user lifts an object, its weight and texture are realistically reproduced. Also, as the user moves through the virtual space, the effect of terrain and obstacles is simulated in real time. For example, when a user climbs a slope, the user feels actual gravity. Also, objects in the virtual space dynamically react according to the user's movements. For example, when a user throws a ball, the trajectory of the ball is realistically reproduced. This provides a more interactive and realistic experience.

[0057] The VR generation unit can simulate weather and time changes within the virtual space to enrich the user's experience. For example, when a user experiences a historical event, the weather at that time can be realistically reproduced. It can also simulate changes in light within the virtual space depending on the time of day the user is experiencing. For example, it can realistically reproduce the passage of time from morning to night. It can also dynamically reflect changes in weather and time according to the user's actions. For example, when a user moves to a specific location, the weather at that location changes. This provides a more realistic and immersive experience.

[0058] The processing flow of the first embodiment will be briefly explained below.

[0059] Step 1: The data collection unit collects data about historical events or entertainment events. For example, the data collection unit may collect data from public databases on the Internet. The data collection unit may also collect archival video or audio data. Additionally, the data collection unit may collect specific data based on a user request. Step 2: The analysis unit analyzes the data collected by the data collection unit. For example, the analysis unit analyzes text data using natural language processing technology. The analysis unit can also analyze video data using image recognition technology. The analysis unit can also analyze audio data using voice recognition technology. Step 3: The VR generation unit generates a VR experience based on the data analyzed by the analysis unit. For example, the VR generation unit may recreate a scene from a historical event using 3D modeling technology. The VR generation unit may also provide a realistic audio experience using audio technology. Furthermore, the VR generation unit may generate a VR experience that includes interactive elements. Step 4: The experience providing unit provides the user with the VR experience generated by the VR generating unit. For example, the experience providing unit provides the user with the VR experience using a VR headset. The experience providing unit can also provide the VR experience using a smartphone or tablet. Furthermore, the experience providing unit can also provide the VR experience through a web browser.

[0060] (Example 2) The memorial AI service according to an embodiment of the present invention is a system that allows users to experience historical events and memorable moments from past entertainment in real time using VR. This allows users to experience historical events and entertainment events in real time.

[0061] The memorial AI service according to the embodiment includes a data collection unit, an analysis unit, a VR generation unit, and an experience provision unit. The data collection unit collects data related to historical events and entertainment events. For example, the data collection unit collects data from public databases on the Internet. The data collection unit can also collect archived video and audio data. The data collection unit can also collect specific data based on a user request. The analysis unit analyzes the data collected by the data collection unit. For example, the analysis unit can analyze text data using natural language processing technology. The analysis unit can also analyze video data using image recognition technology. The analysis unit can also analyze audio data using voice recognition technology. The VR generation unit generates a VR experience based on the data analyzed by the analysis unit. For example, the VR generation unit can recreate scenes of historical events using 3D modeling technology. The VR generation unit can also provide a realistic audio experience using audio technology. The VR generation unit can also generate a VR experience including interactive elements. The experience provision unit provides the VR experience generated by the VR generation unit to a user. For example, the experience providing unit provides a VR experience to a user using a VR headset. The experience providing unit can also provide a VR experience using a smartphone or a tablet. The experience providing unit can also provide a VR experience through a web browser. This allows the memorial AI service according to the embodiment to allow users to experience historical events or entertainment events in real time. For example, a user can experience the Normandy Landings, the 1985 Live Aid Concert, or an ancient Egyptian pyramid construction site.

[0062] The analysis unit can estimate the user's emotions in real time and provide scenario branching according to the emotions. For example, while the user is experiencing a historical event, the analysis unit uses the generation AI to analyze the user's facial expressions and voice and estimate emotions in real time. For example, if the user feels surprise or fear, the scenario branches according to that emotion, allowing the user to experience different perspectives and events. The generation AI also analyzes the user's emotions in real time and dynamically adjusts the scenario according to changes in emotion. For example, if the user is excited, it provides a more tense scene. Furthermore, based on the user's emotional data, the generation AI generates scenario branching in real time, providing an experience in which the user can become emotionally immersed. For example, if the user feels sad, it unfolds a scenario according to that emotion. This allows for a more immersive experience by providing scenario branching according to the user's emotions.

[0063] The analysis unit can analyze background and environmental sounds of historical events to provide a more realistic audio experience. For example, the analysis unit uses a generation AI to analyze background and environmental sounds of historical events and reproduce them in real time. For example, the sounds of a battlefield or the voices of crowds can be realistically reproduced to provide the user with a sense of presence. In addition, audio data of historical events is collected, and the generation AI performs audio simulations based on that data. For example, the sounds of an ancient market or natural sounds can be reproduced to provide the user with an experience that makes them feel as if they are actually there. In addition, the generation AI dynamically adjusts the audio experience based on the user's location information. For example, the direction and intensity of sounds can change as the user moves, providing a more realistic experience. This provides a more realistic audio experience, improving the user's sense of immersion.

[0064] The analysis unit can analyze the movements and facial expressions of characters in historical events, allowing the user to interact with them. For example, the generation AI can analyze the movements and facial expressions of characters in historical events, allowing the user to interact with the characters. For example, when a user asks a question, the character responds in real time. The generation AI can also analyze the movements and facial expressions of characters in real time, dynamically changing them according to the user's actions. For example, the character reacts when the user approaches. The generation AI can also analyze the emotions of characters, allowing the user to interact with them according to their emotions. For example, if a character is angry, the generation AI can provide the user with options to ease their emotions. This allows the user to interact with the characters, providing a deeper experience.

[0065] The VR generation unit can enable experiences from different perspectives. For example, the generation AI collects data from different perspectives to allow the user to experience historical events from the perspectives of civilians, soldiers, leaders, etc. For example, experiencing the Normandy Landings from a soldier's perspective. The VR generation unit also allows the user to select the perspective from which to experience the event and provides experiences from different perspectives. For example, experiencing the same event from a leader's perspective deepens understanding from a different perspective. The generation AI also generates scenarios from different perspectives and allows the user to experience them from that perspective. For example, experiencing the effects of war from the perspective of civilians. This allows the user to deepen their understanding by experiencing them from different perspectives.

[0066] The VR generation unit can be expanded to include events from different eras and cultural spheres, allowing users to learn about diverse histories. For example, the generation AI collects data from different eras and cultural spheres, allowing users to experience diverse histories. For example, experiencing events from ancient Rome or medieval Europe. The generation AI also allows users to select the era or cultural sphere they want to experience, and provides a VR experience that meets their request. For example, experiencing historical events from Asia. The generation AI also generates scenarios from different eras and cultural spheres, providing an experience that makes users feel as if they are actually there. For example, experiencing Renaissance Florence. This allows users to deepen their understanding of history by experiencing events from different eras and cultural spheres.

[0067] The analysis unit can use the emotion estimation function to recommend related historical events based on the emotions the user feels during the experience. For example, the analysis unit uses a generation AI to analyze the user's emotions in real time and recommend related historical events based on those emotions. For example, if the user is excited, it recommends similarly exciting events. In addition, a system can be built in which the generation AI dynamically recommends related historical events based on the user's emotion data. For example, if the user is moved, it recommends moving events. In addition, the emotion estimation function can be used to provide detailed information about related events and people based on the emotions the user feels during the experience. For example, it can display information related to events that the user is interested in. This provides a more interesting experience by recommending related historical events based on the user's emotions.

[0068] When analyzing data on historical events, the analysis unit can use the emotion estimation function to evaluate the emotional impact of the event and reflect it in the reenactment. For example, when the generation AI analyzes data on historical events, the analysis unit uses the emotion estimation function to evaluate the emotional impact of the event. For example, the tragedy of war or the joy of victory can be evaluated as an emotion score and reflected in the VR experience. The emotion estimation function can also be used to analyze the emotional aspects of historical events and generate a VR experience based on the results. For example, moving speeches or important moments can be emphasized. The generation AI can also analyze the emotion data and reproduce the emotional impact of the event in real time. For example, it can allow the user to experience the emotions as if they were there. This allows the user to evaluate the emotional impact and reflect it in the reenactment, providing a more emotionally immersive experience.

[0069] When analyzing data on historical events, the analysis unit can integrate multiple data sources (documents, video, audio) to generate a more detailed VR experience. For example, the analysis unit uses a generation AI to integrate multiple data sources, such as documents, video, and audio, to more precisely recreate historical events. For example, it can combine video and audio of a battlefield to provide a realistic experience. It can also analyze multiple data sources, and the generation AI can generate a VR experience based on that data. For example, it can combine a scenario based on historical documents with video data. The generation AI can also integrate different data sources to provide an experience that makes the user feel as if they are actually there. For example, it can combine audio data and video data to enhance the sense of realism. In this way, by integrating multiple data sources, a more detailed VR experience can be provided.

[0070] When analyzing data on historical events, the analysis unit can incorporate perspectives from different cultures and regions to provide an experience from a global perspective. For example, the generation AI collects data from different cultures and regions and recreates historical events from a global perspective. For example, the same event can be experienced from the perspective of different cultural spheres. The generation AI also incorporates perspectives from different cultures and regions and generates a VR experience based on that data. For example, the same historical event can be experienced from the perspective of Asia and Europe. The generation AI also generates a scenario from a global perspective, allowing users to experience it from the perspectives of diverse cultures and regions. For example, a historical event can be recreated from the perspective of a different cultural sphere. This provides an experience from a global perspective by incorporating perspectives from different cultures and regions.

[0071] When analyzing data on historical events, the analysis unit can provide a customized experience based on the user's interests and concerns. For example, the analysis unit uses a generation AI to analyze the user's interests and concerns and provide a customized VR experience based on that data. For example, it may focus on specific events or people that interest the user. The generation AI may also generate a customized scenario based on the user's profile data. For example, it may select historical events that correspond to the user's interests. The generation AI may also build a system that provides a customized experience based on user feedback. For example, it may recommend events related to themes that interest the user. This provides a customized experience based on the user's interests and concerns.

[0072] The analysis unit can use the emotion estimation function to provide detailed information about related events and people based on the emotions the user feels during the experience. For example, the analysis unit uses a generation AI to analyze the user's emotions in real time and provide detailed information about related events and people based on those emotions. For example, if the user is excited, events related to that emotion are displayed. Furthermore, using the emotion estimation function, a system is built that dynamically provides detailed information about related events and people based on the emotions the user feels during the experience. For example, if the user is moved, background information about people related to that emotion is displayed. Furthermore, the generation AI analyzes the user's emotion data and provides detailed information about related events and people based on the results. For example, detailed historical background related to an event that the user is interested in is displayed. This provides detailed information about related events and people based on the user's emotions.

[0073] When analyzing data of key entertainment moments, the VR generation unit can use the emotion estimation function to evaluate the emotional impact of the event and reflect it in the reproduction. For example, when the generation AI analyzes data of an entertainment event, the VR generation unit uses the emotion estimation function to evaluate the emotional impact of the event. For example, the emotion of a concert or the excitement of a sporting event can be evaluated as an emotion score and reflected in the VR experience. The emotion estimation function can also be used to analyze the emotional aspects of an entertainment event and generate a VR experience based on the results. For example, moving performances or important moments can be emphasized. The generation AI can also analyze the emotion data and reproduce the emotional impact of the event in real time. For example, it can allow the user to experience emotions as if they were there. This allows the user to evaluate the emotional impact and reflect it in the reproduction, providing a more emotionally immersive experience.

[0074] When analyzing data from key entertainment moments, the VR generation unit can integrate multiple data sources (video, audio, audience reactions) to generate a more sophisticated VR experience. For example, the generation AI in the VR generation unit integrates multiple data sources, such as video, audio, and audience reactions, to more precisely recreate an entertainment event. For example, it combines video and audio from a live concert to provide a realistic experience. It also analyzes multiple data sources, and the generation AI generates a VR experience based on that data. For example, it combines video of a sporting event with audience reactions. The generation AI also integrates different data sources to provide an experience that makes the user feel as if they are actually there. For example, it combines audio data and video data to enhance the sense of realism. In this way, by integrating multiple data sources, a more sophisticated VR experience is provided.

[0075] When analyzing data on key moments in entertainment, the VR generation unit can simulate the temporal progression of the event in real time, providing an experience that makes the user feel as if they are actually there. For example, the generation AI in the VR generation unit simulates the temporal progression of an entertainment event in real time, providing an experience that makes the user feel as if they are actually there. For example, the progress of a concert and important moments are reproduced in real time. The generation AI also analyzes the temporal progression of an event, and generates a VR experience based on that data. For example, the flow of a sporting event from start to finish is reproduced in real time. The generation AI also simulates the temporal progression, allowing the user to experience it as if they were actually there. For example, the progression of an important event is relived in real time. In this way, by simulating the temporal progression in real time, the user is provided with an experience that makes the user feel as if they are actually there.

[0076] When analyzing data on entertainment moments, the VR generation unit can incorporate perspectives from different cultures and regions to provide an experience from a global perspective. For example, the generation AI collects data from different cultures and regions and recreates an entertainment event from a global perspective. For example, the same event can be experienced from the perspective of different cultural spheres. The generation AI can also incorporate perspectives from different cultures and regions and generate a VR experience based on that data. For example, the same entertainment event can be experienced from the perspective of Asia and Europe. The generation AI can also generate a scenario from a global perspective, allowing users to experience it from the perspectives of diverse cultures and regions. For example, an entertainment event can be recreated from the perspective of different cultural spheres. This provides an experience from a global perspective by incorporating perspectives from different cultures and regions.

[0077] When analyzing data on entertainment moments, the VR generation unit can provide a customized experience based on the user's interests and concerns. For example, the generation AI analyzes the user's interests and concerns and provides a customized VR experience based on that data. For example, it may focus on specific events or artists that interest the user. The generation AI may also generate a customized scenario based on the user's profile data. For example, it may select entertainment events that match the user's interests. The generation AI may also build a system that provides a customized experience based on user feedback. For example, it may recommend events related to themes that interest the user. This provides a customized experience based on the user's interests and concerns.

[0078] The VR generation unit can use the emotion estimation function to provide detailed information on related events and people based on the emotions the user feels during the experience. For example, the generation AI in the VR generation unit analyzes the user's emotions in real time and provides detailed information on related events and people based on those emotions. For example, if the user is excited, events related to that emotion are displayed. Furthermore, using the emotion estimation function, a system is constructed that dynamically provides detailed information on related events and people based on the emotions the user feels during the experience. For example, if the user is moved, background information on people related to that emotion is displayed. Furthermore, the generation AI analyzes the user's emotion data and provides detailed information on related events and people based on the results. For example, detailed background information related to an event that the user is interested in is displayed. This provides detailed information on related events and people based on the user's emotions.

[0079] The analysis unit can estimate the user's emotions in real time for educational support and provide scenario branching according to the emotions. For example, the analysis unit uses a generation AI to analyze the user's emotions in real time and provide scenario branching according to the emotions. For example, if the user is excited, a more interesting scene is provided. Furthermore, based on the user's emotional data, the generation AI generates scenario branching in real time, providing an experience in which the user can immerse themselves emotionally. For example, if the user is moved, a scenario will unfold according to that emotion. Furthermore, the generation AI analyzes the user's facial expressions and voice to estimate emotions in real time. For example, if the user feels surprise or joy, the scenario will branch according to that emotion, allowing the user to experience different perspectives and events. This improves the effectiveness of educational support by providing scenario branching according to the user's emotions.

[0080] The analysis unit can analyze the movements and facial expressions of characters in historical events to support education, allowing users to interact with them. For example, the analysis unit allows the generation AI to analyze the movements and facial expressions of characters in historical events, allowing users to interact with the characters. For example, when a user asks a question, the character responds in real time. The generation AI can also analyze the movements and facial expressions of characters in real time and dynamically change them according to the user's actions. For example, the character reacts when the user approaches. The generation AI can also analyze the emotions of characters, allowing users to interact with them according to their emotions. For example, if a character is happy, the generation AI can provide an option for the user to share that emotion. This allows users to interact with characters, improving the effectiveness of educational support.

[0081] The VR generation unit can enable experiences from different perspectives (e.g., civilians, soldiers, leaders) for educational support. For example, the generation AI collects data from different perspectives and allows users to experience historical events from the perspectives of civilians, soldiers, leaders, etc. For example, experiencing the Normandy Landings from a soldier's perspective. The VR generation unit also allows users to select the perspective from which to experience and provides experiences from different perspectives. For example, experiencing the same event from a leader's perspective deepens understanding from a different perspective. The generation AI also generates scenarios from different perspectives and allows users to experience them from that perspective. For example, experiencing the effects of war from the perspective of civilians. This improves the effectiveness of educational support by allowing users to experience events from different perspectives.

[0082] The VR generation unit can be expanded to include events from different eras and cultural spheres for educational support, allowing users to learn about diverse histories. For example, the generation AI collects data from different eras and cultural spheres to allow users to experience diverse histories. For example, experiencing events from ancient Rome or medieval Europe. The generation AI can also allow users to select the era or cultural sphere they wish to experience, and provide a VR experience that meets their request. For example, experiencing historical events from Asia. The generation AI can also generate scenarios from different eras and cultural spheres, providing an experience that makes users feel as if they are actually there. For example, experiencing Renaissance Florence. This improves the effectiveness of educational support by allowing users to experience events from different eras and cultural spheres.

[0083] The analysis unit can use the emotion estimation function to support education and recommend related historical events based on the emotions felt by the user during the experience. For example, the analysis unit uses a generation AI to analyze the user's emotions in real time and recommend related historical events based on those emotions. For example, if the user is excited, it recommends similarly exciting events. Furthermore, a system can be constructed in which the generation AI dynamically recommends related historical events based on the user's emotion data. For example, if the user is moved, it recommends moving events. Furthermore, the emotion estimation function can be used to provide detailed information about related events and people based on the emotions felt by the user during the experience. For example, information related to an event that the user is interested in can be displayed. This improves the effectiveness of educational support by recommending related historical events based on the user's emotions.

[0084] The analysis unit can estimate the user's emotions in real time to relive entertainment and provide branching scenarios according to the emotions. For example, the analysis unit uses a generation AI to analyze the user's emotions in real time and provide branching scenarios according to the emotions. For example, if the user is excited, a more exciting scene is provided. Furthermore, based on the user's emotional data, the generation AI generates branching scenarios in real time, providing an experience in which the user can become emotionally immersed. For example, if the user is moved, a scenario will unfold according to that emotion. Furthermore, the generation AI analyzes the user's facial expressions and voice to estimate emotions in real time. For example, if the user feels surprise or joy, the scenario will branch according to that emotion, allowing the user to experience different perspectives and events. This improves the effectiveness of re-experiencing entertainment by providing branching scenarios according to the user's emotions.

[0085] The analysis unit analyzes background and environmental sounds of key moments in entertainment to relive them, providing a more realistic audio experience. For example, the analysis unit uses a generation AI to analyze background and environmental sounds of key moments in entertainment and reproduce them in real time. For example, cheers at a concert or cheers at a sporting event can be realistically reproduced to provide the user with a sense of presence. Furthermore, the generation AI collects audio data of key moments in entertainment, and performs audio simulations based on that data. For example, it can reproduce the audio of a live concert, providing the user with an experience that makes them feel as if they are actually there. Furthermore, the generation AI dynamically adjusts the audio experience based on the user's location information. For example, it can change the direction and intensity of sound as the user moves, providing a more realistic experience. This provides a more realistic audio experience, improving the effectiveness of re-experiencing entertainment.

[0086] The analysis unit can analyze the actions and facial expressions of characters at key moments in entertainment to allow users to relive the entertainment, thereby enabling interactive involvement by the user. For example, the analysis unit allows the generation AI to analyze the actions and facial expressions of characters at key moments in entertainment, allowing users to interactively engage with the characters. For example, when a user asks a question, the character responds in real time. The generation AI can also analyze the actions and facial expressions of characters in real time and dynamically change them according to the user's actions. For example, the character reacts when the user approaches. The generation AI can also analyze the emotions of the character, allowing the user to interact according to those emotions. For example, if the character is happy, the generation AI can provide the user with an option to share that emotion. This allows users to interactively engage with the character, improving the effectiveness of reliving the entertainment.

[0087] The VR generation unit can enable experiences from different perspectives (e.g., audience, performers, staff) to relive entertainment. For example, the generation AI collects data from different perspectives, allowing users to experience memorable moments of entertainment from the perspectives of audience, performers, staff, etc. For example, experiencing a live concert from the performer's perspective. The VR generation unit also allows users to select the perspective from which to experience, providing experiences from different perspectives. For example, experiencing the same event from the perspective of a staff member deepens understanding from a different perspective. The generation AI also generates scenarios from different perspectives, allowing users to experience them from that perspective. For example, experiencing a sporting event from the perspective of a spectator. This improves the effectiveness of reliving entertainment by allowing users to experience it from different perspectives.

[0088] The VR generation unit can be expanded to different genres (music, sports, theater) to relive entertainment, allowing users to enjoy a variety of entertainment. For example, the generation AI collects data from different genres to allow users to experience a variety of entertainment. For example, users can experience music concerts, sporting events, theater, etc. The VR generation unit also allows users to select the genre they want to experience, and the generation AI provides a VR experience that meets their request. For example, if a user selects a music concert, an experience of that genre is provided. The generation AI also generates scenarios of different genres, allowing users to experience the experience as if they were actually there. For example, they can experience behind the scenes of a theater play. This diversifies the user's enjoyment by allowing them to experience entertainment of different genres.

[0089] The analysis unit can use the emotion estimation function to relive entertainment and recommend related entertainment events based on the emotions felt by the user during the experience. For example, the analysis unit uses a generation AI to analyze the user's emotions in real time and recommend related entertainment events based on those emotions. For example, if the user is excited, similarly exciting events are recommended. Furthermore, a system is constructed in which the generation AI dynamically recommends related entertainment events based on the user's emotion data. For example, if the user is moved, moving events are recommended. Furthermore, the emotion estimation function is used to provide detailed information about related events and people based on the emotions felt by the user during the experience. For example, information related to an event in which the user is interested is displayed. This improves the effectiveness of re-experiencing entertainment by recommending related entertainment events based on the user's emotions.

[0090] The analysis unit can analyze background and environmental sounds from historical events for VR experiences of world history and culture, providing a more realistic audio experience. For example, the analysis unit uses a generation AI to analyze background and environmental sounds from historical events and reproduce them in real time. For example, realistic reproduction of battlefield sounds and crowd noises provides users with a sense of presence. The generation AI also collects audio data from historical events, and performs audio simulations based on that data. For example, it can reproduce the sounds of an ancient market or natural sounds, providing users with an experience that makes them feel as if they are actually there. The generation AI also dynamically adjusts the audio experience based on the user's location information. For example, it can change the direction and intensity of sounds as the user moves, providing a more realistic experience. This provides a more realistic audio experience, improving the effectiveness of VR experiences of world history and culture.

[0091] The analysis unit can analyze the movements and facial expressions of characters in historical events for a VR experience of world history and culture, allowing users to interact with them. For example, the analysis unit uses a generation AI to analyze the movements and facial expressions of characters in historical events, allowing users to interact with the characters. For example, when a user asks a question, the character responds in real time. The analysis unit can also analyze the movements and facial expressions of characters in real time and dynamically change them according to the user's actions. For example, the character reacts when the user approaches. The generation AI can also analyze the emotions of characters, allowing users to interact with them according to their emotions. For example, if a character is happy, the analysis unit can provide an option for the user to share that emotion. This allows users to interact with characters, improving the effectiveness of the VR experience of world history and culture.

[0092] The VR generation unit can enable VR experiences of world history and culture from different perspectives (e.g., civilians, soldiers, leaders). For example, the generation AI collects data from different perspectives and allows users to experience historical events from the perspectives of civilians, soldiers, leaders, etc. For example, experiencing the Normandy Landings from a soldier's perspective. The VR generation unit also allows users to select the perspective from which to experience and provides experiences from different perspectives. For example, experiencing the same event from a leader's perspective deepens understanding from a different perspective. The generation AI also generates scenarios from different perspectives and allows users to experience them from that perspective. For example, experiencing the effects of war from the perspective of civilians. This improves the effectiveness of VR experiences of world history and culture by allowing users to experience them from different perspectives.

[0093] The VR generation unit can expand the VR experience of world history and culture to include events from different eras and cultural spheres, allowing users to learn about diverse histories. For example, the generation AI collects data from different eras and cultural spheres to allow users to experience diverse histories. For example, experiencing events from ancient Rome or medieval Europe. The generation AI also allows users to select the era or cultural sphere they wish to experience, and provides a VR experience that meets their request. For example, experiencing historical events from Asia. The generation AI also generates scenarios from different eras and cultural spheres, providing an experience that makes users feel as if they are actually there. For example, experiencing Florence during the Renaissance. This improves the effectiveness of the VR experience of world history and culture by allowing users to experience events from different eras and cultural spheres.

[0094] The analysis unit uses an emotion estimation function for a VR experience of world history and culture to recommend related historical events based on the emotions felt by the user during the experience. For example, the analysis unit uses a generation AI to analyze the user's emotions in real time and recommend related historical events based on those emotions. For example, if the user is excited, it recommends similarly exciting events. The analysis unit also builds a system in which the generation AI dynamically recommends related historical events based on the user's emotion data. For example, if the user is moved, it recommends moving events. The emotion estimation function also provides detailed information about related events and people based on the emotions felt by the user during the experience. For example, it displays information related to events that the user is interested in. This improves the effectiveness of the VR experience of world history and culture by recommending related historical events based on the user's emotions.

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

[0096] The analysis unit can monitor the user's health condition in real time and adjust the load during the experience. For example, it can monitor the user's heart rate and blood pressure and adjust the difficulty of the scenario to avoid excessive stress. Furthermore, based on the user's health data, the generative AI can customize the content of the experience and provide an optimal experience according to the user's health condition. For example, it can add scenes that have a relaxing effect. It can also analyze the user's health condition in real time and display notifications encouraging them to take a break as needed. This provides a safe experience that takes user health into consideration.

[0097] The analysis unit can estimate the user's emotions and dynamically change the music and sound effects being experienced based on the estimated emotions. For example, if the user is relaxed, calm music can be played. If the user is excited, fast-paced music can be provided. Furthermore, based on the user's emotional data, the generation AI dynamically adjusts the sound effects to provide the optimal sound experience according to the emotion. For example, if the user is surprised, sound effects can be emphasized. Furthermore, the user's emotions can be analyzed in real time, and the music and sound effects can be seamlessly switched according to changes in emotion. This provides an audio experience that matches the user's emotions.

[0098] The VR generation unit analyzes the user's movements in real time, making interactions during the experience more natural. For example, it analyzes the user's hand movements to allow smoother operation within the virtual space. It also tracks the user's gaze and provides interactions according to the direction of the gaze. For example, when a user looks at a specific object, information about that object is displayed. It also analyzes the user's posture and reflects the user's movements during the experience in real time. For example, if the user crouches, the same movement is reproduced in the virtual space. This provides a more natural and immersive experience.

[0099] The analysis unit can estimate the user's emotions and dynamically change the character's reactions during the experience based on the estimated emotions. For example, if the user is happy, the character will also show a happy expression. If the user is sad, the character will show a comforting reaction. Furthermore, based on the user's emotional data, the generation AI dynamically adjusts the character's behavior to provide optimal interactions according to the emotion. For example, if the user is surprised, the character will also show a surprised reaction. Furthermore, the system analyzes the user's emotions in real time and seamlessly switches the character's reaction according to changes in emotion. This allows the character's reaction to be tailored to the user's emotions.

[0100] To further personalize the user's experience, the VR generation unit can analyze the user's past experience data and provide scenarios tailored to their preferences. For example, it can suggest new experiences based on historical events that the user was interested in in the past. The generation AI can also generate customized scenarios based on the user's past experience data, providing an optimal experience tailored to the user's preferences. For example, if the user is interested in a particular era or culture, it can provide a scenario related to that theme. Furthermore, it can continuously improve the experience content based on user feedback, providing a more satisfying experience. This provides a personalized experience tailored to the user's preferences.

[0101] The analysis unit can estimate the user's emotions and dynamically change the visual effects being experienced based on the estimated emotions. For example, if the user is excited, the visual effects are emphasized. If the user is relaxed, calm visual effects are provided. Furthermore, based on the user's emotional data, the generation AI dynamically adjusts the visual effects to provide the optimal visual experience according to the emotion. For example, if the user is surprised, the visual effects are emphasized to amplify the surprise. Furthermore, the system analyzes the user's emotions in real time and seamlessly switches visual effects according to changes in emotion. This provides a visual experience that matches the user's emotions.

[0102] The VR generation unit can enhance the physical simulation in the virtual space to make the user's experience more interactive. For example, when a user lifts an object, its weight and texture are realistically reproduced. Also, as the user moves through the virtual space, the effect of terrain and obstacles is simulated in real time. For example, when a user climbs a slope, the user feels actual gravity. Also, objects in the virtual space dynamically react according to the user's movements. For example, when a user throws a ball, the trajectory of the ball is realistically reproduced. This provides a more interactive and realistic experience.

[0103] The analysis unit can estimate the user's emotions and dynamically change the narration and guide content during the experience based on the estimated emotions. For example, if the user is excited, the narration can be made more energetic. If the user is relaxed, the guide can be provided in a calmer tone. Furthermore, based on the user's emotional data, the generation AI dynamically adjusts the content of the narration and guide to provide an optimal guide experience according to the user's emotions. For example, if the user is surprised, content explaining the surprise can be added. The system also analyzes the user's emotions in real time and seamlessly switches the content of the narration and guide according to changes in emotion. This allows the system to provide a narration and guide experience that suits the user's emotions.

[0104] The VR generation unit can simulate weather and time changes within the virtual space to enrich the user's experience. For example, when a user experiences a historical event, the weather at that time can be realistically reproduced. It can also simulate changes in light within the virtual space depending on the time of day the user is experiencing. For example, it can realistically reproduce the passage of time from morning to night. It can also dynamically reflect changes in weather and time according to the user's actions. For example, when a user moves to a specific location, the weather at that location changes. This provides a more realistic and immersive experience.

[0105] The analysis unit can estimate the user's emotions and dynamically change the speed at which the scenario is progressing based on the estimated emotions. For example, if the user is excited, the scenario will progress faster. If the user is relaxed, the scenario will progress slower. Furthermore, based on the user's emotional data, the generation AI dynamically adjusts the speed at which the scenario progresses, providing an optimal experience according to the user's emotions. For example, if the user is surprised, the scenario will temporarily slow down to emphasize that surprise. Furthermore, the user's emotions are analyzed in real time, and the speed at which the scenario progresses is seamlessly changed according to changes in emotion. This allows the scenario to progress in accordance with the user's emotions.

[0106] The processing flow of the second embodiment will be briefly explained below.

[0107] Step 1: The data collection unit collects data about historical events or entertainment events. For example, the data collection unit may collect data from public databases on the Internet. The data collection unit may also collect archival video or audio data. Additionally, the data collection unit may collect specific data based on a user request. Step 2: The analysis unit analyzes the data collected by the data collection unit. For example, the analysis unit analyzes text data using natural language processing technology. The analysis unit can also analyze video data using image recognition technology. The analysis unit can also analyze audio data using voice recognition technology. Step 3: The VR generation unit generates a VR experience based on the data analyzed by the analysis unit. For example, the VR generation unit may recreate a scene from a historical event using 3D modeling technology. The VR generation unit may also provide a realistic audio experience using audio technology. Furthermore, the VR generation unit may generate a VR experience that includes interactive elements. Step 4: The experience providing unit provides the user with the VR experience generated by the VR generating unit. For example, the experience providing unit provides the user with the VR experience using a VR headset. The experience providing unit can also provide the VR experience using a smartphone or tablet. Furthermore, the experience providing unit can also provide the VR experience through a web browser.

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

[0109] 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.

[0110] 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.

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

[0112] 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.

[0113] 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.

[0114] 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.

[0115] 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.

[0116] 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).

[0117] 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.

[0118] 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.

[0119] 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.

[0120] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0121] In the smart glasses 214, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0122] 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.

[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 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.

[0124] 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 AI 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.

[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 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.

[0126] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0127] 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.

[0128] 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.

[0129] 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.

[0130] 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.

[0131] 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).

[0132] 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.

[0133] 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.

[0134] 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.

[0135] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0136] In the headset type terminal 314, 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 headset type terminal 314 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the specific processing unit 290 using these models.

[0137] 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.

[0138] 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.

[0139] 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 AI 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.

[0140] 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.

[0141] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0142] 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.

[0143] 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.

[0144] 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.

[0145] 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.

[0146] 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).

[0147] 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.

[0148] 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.

[0149] 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.

[0150] 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.

[0151] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0152] In the robot 414, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. The robot 414 also has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0153] 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.

[0154] 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.

[0155] 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 AI 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.

[0156] 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.

[0157] 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.

[0158] 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.

[0159] 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.

[0160] 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).

[0161] 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.

[0162] 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."

[0163] 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.

[0164] 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.

[0165] 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.

[0166] 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.

[0167] 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.

[0168] 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.

[0169] 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.

[0170] 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.

[0171] 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.

[0172] 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.

[0173] 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.

[0174] 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]

[0175] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. a data collection department that collects data on historical events and entertainment events; an analysis unit that analyzes the data collected by the data collection unit; a VR generation unit that generates a VR experience based on the data analyzed by the analysis unit; an experience providing unit that provides a user with the VR experience generated by the VR generating unit; A system characterized by:

2. The analysis unit The emotion of the user is estimated in real time, and a branching scenario is provided in accordance with the emotion.

2. The system of claim 1.

3. The analysis unit Analyze the background and environmental sounds of the historical events to provide a more realistic audio experience 2. The system of claim 1.

4. The analysis unit Analyzing the actions and expressions of characters in the historical events and allowing the user to participate interactively 2. The system of claim 1.

5. The VR generation unit Allowing you to experience things from different perspectives 2. The system of claim 1.

Images