system

Abstract

The system according to the embodiment aims to create ghosts that react interactively based on the user's home environment, bringing fear into the real world. [Solution] A system according to an embodiment includes a collection unit, a generation unit, a reflection unit, and a linking unit. The collection unit collects information on the layout and interior of a home. The generation unit generates an appropriate ghost based on the information collected by the collection unit. The reflection unit interactively reflects the ghost generated by the generation unit in a user's behavior scenario. The linking unit links with a messaging app to cause the ghost reflected by the reflection unit to invade the real world.

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 technologies have had a problem in that they have not done enough to generate ghosts that react interactively based on the user's home environment and bring fear to the real world.

[0005] The system according to the embodiment aims to create ghosts that react interactively based on the user's home environment, bringing fear into the real world. [Means for solving the problem]

[0006] The system according to the embodiment includes a collection unit, a generation unit, a reflection unit, and a linking unit. The collection unit collects information on the layout and interior of the home. The generation unit generates an appropriate ghost based on the information collected by the collection unit. The reflection unit interactively reflects the ghost generated by the generation unit in the user's behavior scenario. The linking unit links with a messaging app to allow the ghost reflected by the reflection unit to invade the real world. [Effects of the Invention]

[0007] The system according to the embodiment can generate ghosts that react interactively based on the user's home environment, bringing fear into the real world. [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) A horror experience system according to an embodiment of the present invention combines mixed reality (MR) and a generation AI to create an unprecedented horror experience. This horror experience system collects information about the layout and interior of a home, and the generation AI generates an optimal ghost based on that information. The generated ghost is interactively reflected in the user's behavioral scenario and changes in real time according to the user's movements. Furthermore, by linking with messaging apps such as LINE (registered trademark), the horror experience can infiltrate the real world, further terrifying the user. First, the horror experience system collects information about the layout and interior of a home. Detailed data is acquired using cameras and sensors and input into the generation AI. For example, information such as the layout of living room furniture, wall color, and lighting position is collected. This allows the generation AI to generate a ghost that is optimal for the home environment. Next, the generation AI analyzes the collected information and generates an optimal ghost. The generated ghost is displayed realistically in accordance with the layout and interior of the home. For example, a ghost appearing in a dark hallway or a ghost sitting on the sofa in the living room provides a horror experience optimal for the user's home environment. Furthermore, the generated ghost is interactively reflected in the user's behavioral scenario. As the user moves from room to room, the ghost changes in real time, and the terrifying experience progresses according to the user's movements. For example, a ghost could chase the user as they walk down a hallway, or suddenly appear as they enter a room. Finally, by integrating with messaging apps such as LINE, the terrifying experience can extend into the real world. For example, a user could suddenly receive a message from a ghost while chatting with a friend on LINE, or the LINE notification sound could change to a ghost's voice. This allows the user to feel fear in the real world as well. In this way, the combination of mixed reality and generative AI can create an unprecedented terrifying experience. Users can experience realistic fear from the comfort of their own home and enjoy a new form of entertainment that blends the real and virtual worlds.This allows the horror experience system to generate ghosts that are best suited to the user's home environment, reflect them interactively, and allow the horror experience to invade the real world.

[0029] A horror experience system according to an embodiment includes a collection unit, a generation unit, a reflection unit, and a linking unit. The collection unit collects information about the layout and interior of a home. The collection unit acquires detailed data using, for example, a camera or a sensor. For example, the collection unit collects information such as the furniture layout, wall color, and lighting position in the living room. The collection unit can acquire images of the entire room using a camera and detect the furniture layout and wall color using a sensor. For example, the collection unit installs a fixed camera and periodically acquires images of the entire room. The collection unit can also detect the furniture layout and wall color in real time using a motion sensor. The generation unit generates an optimal ghost based on the information collected by the collection unit. The generation unit analyzes the collected information and generates the optimal ghost using a generation AI. For example, the generation unit determines the appearance and movement of the ghost based on the collected information. The generation unit inputs a prompt to the generation AI, such as, "Please generate a ghost that is optimal for the layout and interior of your home," and the generation AI generates the ghost's appearance and movement. The generation unit can also adjust the frequency of ghost appearances and the level of fear based on the collected information. The reflection unit interactively reflects the ghost generated by the generation unit in the user's behavior scenario. The reflection unit displays the ghost according to the user's actions. For example, the reflection unit changes the ghost in real time as the user moves from room to room. The reflection unit detects the user's actions and adjusts the display of the ghost according to the actions. The reflection unit realizes, for example, a scenario in which a ghost chases a user while walking down a hallway, or a scenario in which a ghost suddenly appears when the user enters a room. The linking unit links the ghost reflected by the reflection unit with a messaging app such as LINE to invade the real world. For example, the linking unit realizes a scenario in which a user suddenly receives a message from a ghost while chatting with a friend on LINE, or a scenario in which the LINE notification sound changes to the voice of a ghost. The linking unit links with the messaging app to invade the real world with a frightening experience. In this way, the frightening experience system according to the embodiment generates a ghost optimal for the user's home environment, interactively reflects the ghost, and invades the real world with a frightening experience.

[0030] The collection unit can collect information about the floor plan and interior of the home using a camera or a sensor. The collection unit, for example, uses a camera to acquire images of the entire room. The collection unit can install a fixed camera and periodically acquire images of the entire room. The collection unit can also use a motion sensor to detect the furniture arrangement and wall color in real time. For example, the collection unit can use a motion sensor to detect the furniture arrangement and determine the room layout based on that information. The collection unit can also use a camera to detect the wall color and collect interior information based on that information. In this way, detailed floor plan and interior information can be collected using a camera or a sensor. Some or all of the above-mentioned processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input image data acquired by a camera to a generation AI and cause the generation AI to execute a process of extracting information about the floor plan and interior from the image data.

[0031] The generation unit can generate an optimal ghost based on the collected information. For example, the generation unit determines the ghost's appearance and movement based on the collected information. The generation unit inputs a prompt to the generation AI, such as "Please generate a ghost that is optimal for the layout and interior of my home," and the generation AI generates the ghost's appearance and movement. The generation unit can also adjust the ghost's appearance frequency and fear level based on the collected information. For example, the generation unit sets the ghost's appearance frequency to be high based on the collected information. The generation unit can also adjust the ghost's fear level based on the collected information. For example, the generation unit adjusts the ghost's appearance to be more frightening based on the collected information. This allows the generation of an optimal ghost based on the collected information, thereby providing a terrifying experience optimal for the user's home environment. Some or all of the above-described processing in the generation unit may be performed using, or without, the generation AI. For example, the generation unit can input the collected information to the generation AI, causing the generation AI to execute processing to generate the ghost's appearance and movement.

[0032] The reflection unit can display a ghost in response to a specific user action. For example, the reflection unit changes the ghost in real time as the user moves around the room. The reflection unit detects the user's action and adjusts the display of the ghost in response to the action. For example, the reflection unit can realize a scenario in which a ghost chases a user as they walk down a hallway. The reflection unit can also realize a scenario in which a ghost suddenly appears when the user enters a room. For example, the reflection unit displays the ghost as if it were to appear as soon as the user enters the room. The reflection unit can also adjust the display of the ghost in response to a specific user action. For example, the reflection unit displays the ghost as if it were to disappear when the user waves their hand. This allows for an interactive horror experience by displaying the ghost in response to the user's action. Some or all of the above-described processing in the reflection unit may be performed using, or without, AI. For example, the reflection unit can input user action data to a generation AI, causing the generation AI to execute a process to adjust the display of the ghost.

[0033] The linking unit can link with messaging apps including LINE to extend the frightening experience to the real world. For example, the linking unit can realize a scenario in which a user suddenly receives a message from a ghost while chatting with a friend on LINE. The linking unit can link with the messaging app to extend the frightening experience to the real world. For example, the linking unit can realize a scenario in which a LINE notification sound is changed to the voice of a ghost. The linking unit can also realize a scenario in which a message from a ghost is received while the user is using a messaging app. For example, the linking unit sets a setting so that a message from a ghost is suddenly received while the user is chatting with a friend on the messaging app. The linking unit can also change the notification sound of the messaging app to the voice of a ghost, thereby further frightening the user. In this way, by linking with the messaging app, the frightening experience can extend to the real world. Some or all of the above-described processing in the linking unit may be performed using, or without, AI. For example, the linking unit can input notification data from the messaging app to a generation AI, causing the generation AI to execute a process of adjusting the notification content.

[0034] The linking unit can link with other messaging apps or social networking sites. For example, the linking unit can link with other messaging apps or social networking sites to provide a horror experience. For example, the linking unit can link with WhatsApp or Facebook Messenger to send messages from ghosts. The linking unit can also link with social networking sites such as Twitter or Instagram to post images and videos of ghosts. For example, the linking unit can automatically send replies from ghosts when a user posts using a specific hashtag on Twitter. The linking unit can also post images and videos of ghosts as stories on Instagram. This allows for linking with other messaging apps and social networking sites to provide a wider range of horror experiences. Some or all of the above-described processing in the linking unit may be performed using, for example, AI, or may be performed without using AI. For example, the linking unit can input SNS post data into a generation AI, causing the generation AI to execute a process of generating content related to the horror experience.

[0035] When collecting data, the collection unit can select the optimal collection method by referring to the user's past interior change history. For example, the collection unit selects the optimal collection method by referring to the user's past interior change history. The collection unit can determine the scope of information to collect based on the past interior change history. For example, the collection unit determines the scope of information to collect based on the interior style changed by the user in the past. The collection unit can also prioritize collecting interior elements that the user frequently changes. For example, the collection unit prioritizes collecting information on furniture arrangements and decorative items that the user frequently changes. Furthermore, the collection unit can collect interior information tailored to a specific season from the user's past change history. For example, the collection unit collects information suitable for summer based on interior information changed by the user in the summer in the past. This allows the optimal collection method to be selected by referring to the user's past interior change history. Some or all of the above-described processing by the collection unit may be performed using, for example, AI, or may be performed without AI. For example, the collection unit can input past interior change history data to a generation AI, causing the generation AI to execute a process of selecting the optimal collection method.

[0036] The collection unit can adjust the collection timing based on the user's lifestyle pattern during collection. The collection unit adjusts the collection timing based on, for example, the user's lifestyle pattern. The collection unit can collect information based on the time of day the user is at home. For example, if the user is at home at night, the collection unit collects information at night. The collection unit can also collect interior information while the user is out. For example, the collection unit collects room layouts and furniture arrangements while the user is out. Furthermore, the collection unit can collect detailed information during times when the user is relaxing. For example, the collection unit collects detailed interior information during times when the user is relaxing. This allows information to be collected efficiently by adjusting the collection timing based on the user's lifestyle pattern. Some or all of the above-described processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit inputs the user's lifestyle pattern data to a generation AI, which then executes a process of adjusting the collection timing.

[0037] When collecting data, the collection unit can prioritize collecting highly relevant information by taking into account the user's geographical location information. For example, the collection unit prioritizes collecting highly relevant information by taking into account the user's geographical location information. If the user lives in an urban area, the collection unit can collect interior information suitable for the urban area. For example, if the user lives in an urban area, the collection unit collects interior information tailored to the characteristics of the urban area. Furthermore, if the user lives in a suburban area, the collection unit can collect interior information suitable for the suburban area. For example, if the user lives in a suburban area, the collection unit collects interior information tailored to the characteristics of the suburban area. Furthermore, if the user lives in a specific area, the collection unit can collect interior information tailored to the characteristics of the area. For example, if the user lives in a specific area, the collection unit collects interior information tailored to the characteristics of the area. In this way, highly relevant information can be prioritized by taking the user's geographical location information into account. Some or all of the above-described processing by the collection unit may be performed using AI, for example, or may be performed without using AI. For example, the collection unit can input the user's geographical location information data to a generation AI, causing the generation AI to execute a process of preferentially collecting highly relevant information.

[0038] The collection unit can analyze the user's social media activities and collect related information during collection. For example, the collection unit analyzes the user's social media activities and collects related information. The collection unit can determine the information to collect based on interior images shared by the user on social media. For example, the collection unit analyzes interior images shared by the user on social media and determines the information to collect based on the information. The collection unit can also collect information about interior designers followed by the user. For example, the collection unit analyzes posts by the interior designers followed by the user and determines the information to collect based on the information. The collection unit can also select the information to collect based on interior posts that the user has "liked." For example, the collection unit analyzes interior posts that the user has "liked" and selects the information to collect based on the information. In this way, related information can be collected by analyzing the user's social media activities. Some or all of the above-described processing by the collection unit may be performed using, or without, AI. For example, the collection unit can input the user's social media activity data into a generation AI and cause the generation AI to execute a process of collecting related information.

[0039] The generation unit can adjust the realism of the ghost based on the level of detail of the collected information during generation. For example, the generation unit adjusts the realism of the ghost based on the level of detail of the collected information. When detailed floor plan information is collected, the generation unit can realistically reproduce the movements of the ghost. For example, the generation unit realistically reproduces the movements of the ghost based on the detailed floor plan information. Furthermore, when simple floor plan information is collected, the generation unit can simplify the movements of the ghost. For example, the generation unit simplifies the movements of the ghost based on the simple floor plan information. Furthermore, when detailed interior information is collected, the generation unit can realistically reproduce the appearance of the ghost. For example, the generation unit realistically reproduces the appearance of the ghost based on detailed interior information. This allows for adjusting the realism of the ghost based on the level of detail of the collected information to provide a more realistic horror experience. Some or all of the above-described processing in the generation unit may be performed using, or without, a generation AI. For example, the generation unit may input level of detail data of the collected information into the generation AI, causing the generation AI to execute a process of adjusting the realism of the ghost.

[0040] The generation unit can generate an optimal ghost by referring to the user's past history of fear experiences. For example, the generation unit generates an optimal ghost by referring to the user's past history of fear experiences. The generation unit can generate an optimal ghost based on the characteristics of ghosts that the user has been scared of in the past. For example, the generation unit determines the appearance and movement of the ghost based on the characteristics of ghosts that the user has been scared of in the past. The generation unit can also set a ghost appearance pattern based on a fear scenario that the user has experienced in the past. For example, the generation unit sets a ghost appearance pattern based on a fear scenario that the user has experienced in the past. The generation unit can also select the most effective ghost appearance from the user's past fear experiences. For example, the generation unit selects the most effective ghost appearance from the user's past fear experiences. In this way, the optimal ghost can be generated by referring to the user's past history of fear experiences. Some or all of the above-described processing in the generation unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the generation unit can input the user's past fear experience history data into the generation AI, causing the generation AI to execute a process of generating an optimal ghost.

[0041] The generation unit can adjust the timing of the ghost's appearance based on the user's lifestyle pattern during generation. The generation unit adjusts the timing of the ghost's appearance based on, for example, the user's lifestyle pattern. The generation unit can set the timing of the ghost's appearance to night if the user is at home at night. For example, the generation unit sets the timing of the ghost's appearance to night if the user is at home at night. The generation unit can also set the timing of the ghost's appearance to day if the user is at home during the day. For example, the generation unit sets the timing of the ghost's appearance to day if the user is at home during the day. Furthermore, the generation unit can set the ghost to appear during a specific time period if the user is at home during that time period. For example, the generation unit sets the ghost to appear during a specific time period if the user is at home during that time period. This allows for adjusting the timing of the ghost's appearance based on the user's lifestyle pattern, thereby providing a more effective horror experience. Some or all of the above-described processing in the generation unit may be performed using, or without, a generation AI. For example, the generation unit may input the user's lifestyle pattern data into the generation AI, causing the generation AI to execute a process of adjusting the timing of the ghost's appearance.

[0042] During generation, the generation unit can analyze the user's social media activity and generate a related ghost. For example, the generation unit analyzes the user's social media activity and generates a related ghost. The generation unit can generate a ghost based on a scary experience the user shared on social media. For example, the generation unit determines the ghost's appearance and movement based on the scary experience the user shared on social media. The generation unit can also generate a ghost based on information about horror accounts the user follows. For example, the generation unit analyzes posts from horror accounts the user follows and generates a ghost based on that information. The generation unit can also generate a ghost based on horror posts the user "liked." For example, the generation unit analyzes horror posts the user "liked" and generates a ghost based on that information. In this way, related ghosts can be generated by analyzing the user's social media activity. Some or all of the above-described processing in the generation unit may be performed using, or without, a generation AI. For example, the generation unit can input the user's social media activity data into the generation AI and cause the generation AI to execute a process of generating a related ghost.

[0043] The reflection unit can adjust the ghost's movement in real time based on the user's specific actions during reflection. For example, the reflection unit adjusts the ghost's movement in real time based on the user's specific actions. The reflection unit can display the ghost as if it is chasing the user from behind when the user is walking. For example, the reflection unit displays the ghost as if it is chasing the user from behind when the user is walking. The reflection unit can also display the ghost as if it is approaching the user when the user is sitting. For example, the reflection unit displays the ghost as if it is approaching the user when the user is sitting. Furthermore, the reflection unit can adjust the ghost's movement in accordance with the user's specific actions when the user performs the specific actions. For example, the reflection unit displays the ghost as if it is disappearing when the user waves their hand. This allows for a more interactive horror experience by adjusting the ghost's movement in real time based on the user's specific actions. Some or all of the above-described processing in the reflection unit may be performed using, or without, AI. For example, the reflection unit can input the user's action data to the generation AI, causing the generation AI to execute a process of adjusting the ghost's movement.

[0044] The reflection unit can select the optimal reflection method by referring to the user's past behavioral patterns when reflecting. For example, the reflection unit can select the optimal reflection method by referring to the user's past behavioral patterns. The reflection unit can select the ghost display method based on behavioral patterns that frightened the user in the past. For example, the reflection unit selects the ghost display method based on behavioral patterns that frightened the user in the past. The reflection unit can also select the most effective ghost movement from the user's past behavioral patterns. For example, the reflection unit analyzes the user's past behavioral patterns and selects the optimal reflection method. In this way, the optimal reflection method can be selected by referring to the user's past behavioral patterns. Some or all of the above-mentioned processing in the reflection unit may be performed using, for example, AI, or may be performed without using AI. For example, the reflection unit can input the user's past behavioral pattern data to the generation AI, causing the generation AI to execute processing to select the optimal reflection method.

[0045] The reflection unit can adjust the display location of the ghost by taking into account the user's geographical location information when reflecting the data. The reflection unit, for example, adjusts the display location of the ghost by taking into account the user's geographical location information. The reflection unit can display the ghost in a specific room when the user is in that room. For example, the reflection unit displays the ghost in that room when the user is in that room. The reflection unit can also refrain from displaying the ghost when the user is outside the house. For example, the reflection unit refrains from displaying the ghost when the user is outside the house. The reflection unit can also display the ghost in a specific location when the user is in that location. For example, the reflection unit displays the ghost in that location when the user is in a specific location. This makes it possible to appropriately adjust the display location of the ghost by taking into account the user's geographical location information. Some or all of the above-described processing in the reflection unit may be performed using, or without, AI. For example, the reflection unit can input the user's geographical location information data to the generation AI, causing the generation AI to execute processing to adjust the display location of the ghost.

[0046] The reflection unit can analyze the user's social media activity at the time of reflection and display related ghosts. The reflection unit, for example, analyzes the user's social media activity and displays related ghosts. The reflection unit can display ghosts based on scary experiences shared by the user on social media. For example, the reflection unit determines the appearance and movement of the ghost based on the scary experiences shared by the user on social media. The reflection unit can also display ghosts based on information about horror accounts followed by the user. For example, the reflection unit analyzes posts from horror accounts followed by the user and displays ghosts based on that information. The reflection unit can also display ghosts based on horror posts that the user has "liked." For example, the reflection unit analyzes horror posts that the user has "liked" and displays ghosts based on that information. In this way, related ghosts can be displayed by analyzing the user's social media activity. Some or all of the above-described processing in the reflection unit may be performed using, or without, AI. For example, the reflection unit can input the user's social media activity data to the generation AI and cause the generation AI to execute processing to display related ghosts.

[0047] When linking, the linking unit can select the optimal linking method by referring to the user's past messaging app usage history. For example, the linking unit selects the optimal linking method by referring to the user's past messaging app usage history. The linking unit can prioritize linking of messaging apps that the user has used frequently in the past. For example, the linking unit prioritizes linking of messaging apps that the user has used frequently in the past. The linking unit can also adjust the linking timing based on the messaging apps that the user uses during a specific time period. For example, the linking unit adjusts the linking timing based on the messaging apps that the user uses during a specific time period. Furthermore, the linking unit can select the most effective linking method from the user's past messaging app usage history. For example, the linking unit analyzes the user's past messaging app usage history and selects the optimal linking method. In this way, the optimal linking method can be selected by referring to the user's past messaging app usage history. Some or all of the above-described processing in the linking unit may be performed using, for example, AI, or may be performed without using AI. For example, the linking unit can input the user's past messaging app usage history data to the generation AI, causing the generation AI to execute processing to select the optimal linking method.

[0048] The linking unit can adjust the link timing based on the user's lifestyle pattern during linking. The linking unit adjusts the link timing based on the user's lifestyle pattern, for example. If the user uses a messaging app at night, the linking unit can link at that time of day. For example, if the user uses a messaging app at night, the linking unit links at that time of day. The linking unit can also link at that time of day if the user uses a messaging app during the day. For example, if the user uses a messaging app during the day, the linking unit links at that time of day. The linking unit can also link at that time of day if the user uses a messaging app during a specific time of day. For example, if the user uses a messaging app during a specific time of day, the linking unit links at that time of day. This allows for adjusting the link timing based on the user's lifestyle pattern to provide a more effective horror experience. Some or all of the above-described processing in the linking unit may be performed using AI, for example, or may be performed without using AI. For example, the linking unit may input the user's lifestyle pattern data to the generation AI, causing the generation AI to execute processing to adjust the link timing.

[0049] During collaboration, the collaboration unit can adjust the location of notifications from the messaging app taking into account the user's geographical location information. For example, the collaboration unit adjusts the location of notifications from the messaging app taking into account the user's geographical location information. When the user is in a specific location, the collaboration unit can send notification content tailored to the location. For example, when the user is in a specific location, the collaboration unit sends notification content tailored to the location. Furthermore, when the user is at home, the collaboration unit can send notification content related to the home. For example, when the user is at home, the collaboration unit sends notification content related to the home. Furthermore, when the user is out, the collaboration unit can send notification content related to the user's destination. For example, when the user is out, the collaboration unit sends notification content related to the user's destination. In this way, the location of notifications from the messaging app can be appropriately adjusted by taking into account the user's geographical location information. Some or all of the above-described processing in the collaboration unit may be performed using AI, for example, or may be performed without using AI. For example, the collaboration unit can input the user's geographical location information data to the generation AI, causing the generation AI to execute processing to adjust the location of notifications from the messaging app.

[0050] During linking, the linking unit can analyze the user's social media activity and provide related notifications. For example, the linking unit analyzes the user's social media activity and provides related notifications. The linking unit can determine the content of the notification based on a horror experience shared by the user on social media. For example, the linking unit determines the content of the notification based on a horror experience shared by the user on social media. The linking unit can also determine the content of the notification based on information about horror accounts followed by the user. For example, the linking unit analyzes posts from horror accounts followed by the user and determines the content of the notification based on the information. The linking unit can also determine the content of the notification based on horror posts that the user has "liked." For example, the linking unit analyzes horror posts that the user has "liked" and determines the content of the notification based on the information. In this way, related notifications can be provided by analyzing the user's social media activity. Some or all of the above-described processing in the linking unit may be performed using, or without, AI. For example, the linking unit can input the user's social media activity data into the generation AI, causing the generation AI to execute processing to provide related notifications.

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

[0052] The horror experience system may further include a health management unit that monitors the user's health condition. The health management unit collects health data such as the user's heart rate, blood pressure, and stress level, and can adjust the intensity of the horror experience based on this data. For example, if the user's heart rate is high, the generation unit can reduce the frequency of ghost appearances. Also, if the user's stress level is high, the reflection unit can slow down the ghost's movements. Furthermore, the health management unit can temporarily suspend the horror experience depending on the user's health condition. This allows the user to enjoy a horror experience safely while maintaining their health.

[0053] The horror experience system may further include a behavior analysis unit that analyzes the user's behavioral history. The behavior analysis unit collects the user's past behavioral data and can optimize the horror experience scenario based on this data. For example, if the user has felt strong fear in a particular scenario in the past, the generation unit can recreate that scenario. Also, if the user tends to repeat a particular behavior, the reflection unit can adjust the ghost appearance pattern to match that behavior. Furthermore, the behavior analysis unit can analyze the user's behavioral data in real time and adjust the intensity of the horror experience based on this. This makes it possible to provide a more personalized horror experience by utilizing the user's behavioral history.

[0054] The horror experience system may further include an environmental sound collection unit that collects the user's environmental sounds. The environmental sound collection unit collects sounds around the user and can adjust the horror experience scenario based on the collected sounds. For example, if the user is in a quiet environment, the generation unit can emphasize the sound of a ghost appearing. Alternatively, if the user is in a noisy environment, the reflection unit can maintain the sense of fear by visually emphasizing the ghost's movement. Furthermore, the environmental sound collection unit can analyze the sounds around the user in real time and adjust the intensity of the horror experience based on the analysis. This makes it possible to provide a more realistic and immersive horror experience by utilizing the user's environmental sounds.

[0055] The horror experience system may further include a temperature measurement unit that measures the user's body temperature. The temperature measurement unit may measure the user's body temperature in real time and adjust the intensity of the horror experience based on the measured temperature. For example, if the user's body temperature rises, the generation unit may reduce the frequency of ghost appearances. Alternatively, if the user's body temperature drops, the reflection unit may slow down the ghosts' movements. Furthermore, the temperature measurement unit may analyze the user's body temperature data and optimize the horror experience scenario based on the analysis. This allows the user to safely enjoy a horror experience while monitoring their body temperature.

[0056] The horror experience system may further include a sleep management unit that monitors the user's sleep patterns. The sleep management unit collects the user's sleep data and adjusts the timing of the horror experience based on the collected data. For example, if the user is not getting enough sleep, the generation unit may reduce the frequency of ghost appearances. Alternatively, if the user is in deep sleep, the reflection unit may slow down the ghost's movements. Furthermore, the sleep management unit may analyze the user's sleep data and optimize the horror experience scenario based on the analysis. This allows the user to enjoy a horror experience safely while taking into account the user's sleep patterns.

[0057] The horror experience system may further include a diet management unit that monitors the user's eating patterns. The diet management unit may collect the user's eating data and adjust the intensity of the horror experience based on the collected data. For example, if the user is hungry, the generation unit may reduce the frequency of ghost appearances. Alternatively, if the user is full, the reflection unit may slow down the ghost's movements. Furthermore, the diet management unit may analyze the user's eating data and optimize the horror experience scenario based on the analysis. This allows the user to enjoy a horror experience safely while taking into account the user's eating patterns.

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

[0059] Step 1: The collection unit collects information about the layout and interior of the home. The collection unit acquires detailed data using, for example, cameras and sensors. Specifically, it collects information such as the layout of furniture in the living room, the color of the walls, and the position of lighting. The collection unit can use cameras to acquire images of the entire room and sensors to detect the layout of furniture and the color of the walls. It is also possible to install fixed cameras and periodically acquire images of the entire room, or to use motion sensors to detect the layout of furniture and the color of the walls in real time. Step 2: The generation unit generates an optimal ghost based on the information collected by the collection unit. The generation unit analyzes the collected information using the generation AI and determines the ghost's appearance and movement. For example, the generation AI can be given a prompt such as "Please generate a ghost that best suits the layout and interior of your home," and the generation AI will generate the ghost's appearance and movement. The frequency of ghost appearances and the level of fear can also be adjusted based on the collected information. Step 3: The reflection unit interactively reflects the ghosts generated by the generation unit into the user's behavior scenario. The reflection unit displays the ghosts according to the user's actions, and the ghosts change in real time as the user moves from room to room. For example, it can realize a scenario in which a ghost chases the user from behind as they walk down a hallway, or a scenario in which a ghost suddenly appears when the user enters a room. Step 4: The linking unit links the ghost reflected by the reflection unit with messaging apps such as LINE to invade the real world. For example, it can realize a scenario in which a user suddenly receives a message from a ghost while chatting with a friend on LINE, or a scenario in which the LINE notification sound changes to the voice of a ghost. This allows the terrifying experience to invade the real world.

[0060] (Example 2) A horror experience system according to an embodiment of the present invention combines mixed reality (MR) and a generation AI to create an unprecedented horror experience. This horror experience system collects information about the layout and interior of a user's home, and the generation AI generates an optimal ghost based on that information. The generated ghost is interactively reflected in the user's behavioral scenario and changes in real time according to the user's movements. Furthermore, by linking with messaging apps such as LINE, the horror experience can infiltrate the real world, further terrifying the user. First, the horror experience system collects information about the layout and interior of a user's home. Detailed data is acquired using cameras and sensors and input into the generation AI. For example, information such as the furniture arrangement, wall color, and lighting position in the living room is collected. This allows the generation AI to generate a ghost that is optimal for the home environment. Next, the generation AI analyzes the collected information and generates an optimal ghost. The generated ghost is displayed realistically in accordance with the layout and interior of the home. For example, a ghost appearing in a dark hallway or a ghost sitting on the sofa in the living room provides a horror experience optimal for the user's home environment. Furthermore, the generated ghost is interactively reflected in the user's behavioral scenario. As the user moves from room to room, the ghost changes in real time, and the terrifying experience progresses according to the user's movements. For example, a ghost could chase the user as they walk down a hallway, or suddenly appear as they enter a room. Finally, by integrating with messaging apps such as LINE, the terrifying experience can extend into the real world. For example, a user could suddenly receive a message from a ghost while chatting with a friend on LINE, or the LINE notification sound could change to a ghost's voice. This allows the user to feel fear in the real world as well. In this way, the combination of mixed reality and generative AI can create an unprecedented terrifying experience. Users can experience realistic fear from the comfort of their own home and enjoy a new form of entertainment that blends the real and virtual worlds.This allows the horror experience system to generate ghosts that are best suited to the user's home environment, reflect them interactively, and allow the horror experience to invade the real world.

[0061] A horror experience system according to an embodiment includes a collection unit, a generation unit, a reflection unit, and a linking unit. The collection unit collects information about the layout and interior of a home. The collection unit acquires detailed data using, for example, a camera or a sensor. For example, the collection unit collects information such as the furniture layout, wall color, and lighting position in the living room. The collection unit can acquire images of the entire room using a camera and detect the furniture layout and wall color using a sensor. For example, the collection unit installs a fixed camera and periodically acquires images of the entire room. The collection unit can also detect the furniture layout and wall color in real time using a motion sensor. The generation unit generates an optimal ghost based on the information collected by the collection unit. The generation unit analyzes the collected information and generates the optimal ghost using a generation AI. For example, the generation unit determines the appearance and movement of the ghost based on the collected information. The generation unit inputs a prompt to the generation AI, such as, "Please generate a ghost that is optimal for the layout and interior of your home," and the generation AI generates the ghost's appearance and movement. The generation unit can also adjust the frequency of ghost appearances and the level of fear based on the collected information. The reflection unit interactively reflects the ghost generated by the generation unit in the user's behavior scenario. The reflection unit displays the ghost according to the user's actions. For example, the reflection unit changes the ghost in real time as the user moves from room to room. The reflection unit detects the user's actions and adjusts the display of the ghost according to the actions. The reflection unit realizes, for example, a scenario in which a ghost chases a user while walking down a hallway, or a scenario in which a ghost suddenly appears when the user enters a room. The linking unit links the ghost reflected by the reflection unit with a messaging app such as LINE to invade the real world. For example, the linking unit realizes a scenario in which a user suddenly receives a message from a ghost while chatting with a friend on LINE, or a scenario in which the LINE notification sound changes to the voice of a ghost. The linking unit links with the messaging app to invade the real world with a frightening experience. In this way, the frightening experience system according to the embodiment generates a ghost optimal for the user's home environment, interactively reflects the ghost, and invades the real world with a frightening experience.

[0062] The collection unit can collect information about the floor plan and interior of the home using a camera or a sensor. The collection unit, for example, uses a camera to acquire images of the entire room. The collection unit can install a fixed camera and periodically acquire images of the entire room. The collection unit can also use a motion sensor to detect the furniture arrangement and wall color in real time. For example, the collection unit can use a motion sensor to detect the furniture arrangement and determine the room layout based on that information. The collection unit can also use a camera to detect the wall color and collect interior information based on that information. In this way, detailed floor plan and interior information can be collected using a camera or a sensor. Some or all of the above-mentioned processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input image data acquired by a camera to a generation AI and cause the generation AI to execute a process of extracting information about the floor plan and interior from the image data.

[0063] The generation unit can generate an optimal ghost based on the collected information. For example, the generation unit determines the ghost's appearance and movement based on the collected information. The generation unit inputs a prompt to the generation AI, such as "Please generate a ghost that is optimal for the layout and interior of my home," and the generation AI generates the ghost's appearance and movement. The generation unit can also adjust the ghost's appearance frequency and fear level based on the collected information. For example, the generation unit sets the ghost's appearance frequency to be high based on the collected information. The generation unit can also adjust the ghost's fear level based on the collected information. For example, the generation unit adjusts the ghost's appearance to be more frightening based on the collected information. This allows the generation of an optimal ghost based on the collected information, thereby providing a terrifying experience optimal for the user's home environment. Some or all of the above-described processing in the generation unit may be performed using, or without, the generation AI. For example, the generation unit can input the collected information to the generation AI, causing the generation AI to execute processing to generate the ghost's appearance and movement.

[0064] The reflection unit can display a ghost in response to a specific user action. For example, the reflection unit changes the ghost in real time as the user moves around the room. The reflection unit detects the user's action and adjusts the display of the ghost in response to the action. For example, the reflection unit can realize a scenario in which a ghost chases a user as they walk down a hallway. The reflection unit can also realize a scenario in which a ghost suddenly appears when the user enters a room. For example, the reflection unit displays the ghost as if it were to appear as soon as the user enters the room. The reflection unit can also adjust the display of the ghost in response to a specific user action. For example, the reflection unit displays the ghost as if it were to disappear when the user waves their hand. This allows for an interactive horror experience by displaying the ghost in response to the user's action. Some or all of the above-described processing in the reflection unit may be performed using, or without, AI. For example, the reflection unit can input user action data to a generation AI, causing the generation AI to execute a process to adjust the display of the ghost.

[0065] The linking unit can link with messaging apps including LINE to extend the frightening experience to the real world. For example, the linking unit can realize a scenario in which a user suddenly receives a message from a ghost while chatting with a friend on LINE. The linking unit can link with the messaging app to extend the frightening experience to the real world. For example, the linking unit can realize a scenario in which a LINE notification sound is changed to the voice of a ghost. The linking unit can also realize a scenario in which a message from a ghost is received while the user is using a messaging app. For example, the linking unit sets a setting so that a message from a ghost is suddenly received while the user is chatting with a friend on the messaging app. The linking unit can also change the notification sound of the messaging app to the voice of a ghost, thereby further frightening the user. In this way, by linking with the messaging app, the frightening experience can extend to the real world. Some or all of the above-described processing in the linking unit may be performed using, or without, AI. For example, the linking unit can input notification data from the messaging app to a generation AI, causing the generation AI to execute a process of adjusting the notification content.

[0066] The linking unit can link with other messaging apps or social networking sites. For example, the linking unit can link with other messaging apps or social networking sites to provide a horror experience. For example, the linking unit can link with WhatsApp or Facebook Messenger to send messages from ghosts. The linking unit can also link with social networking sites such as Twitter or Instagram to post images and videos of ghosts. For example, the linking unit can automatically send replies from ghosts when a user posts using a specific hashtag on Twitter. The linking unit can also post images and videos of ghosts as stories on Instagram. This allows for linking with other messaging apps and social networking sites to provide a wider range of horror experiences. Some or all of the above-described processing in the linking unit may be performed using, for example, AI, or may be performed without using AI. For example, the linking unit can input SNS post data into a generation AI, causing the generation AI to execute a process of generating content related to the horror experience.

[0067] The collection unit can estimate the user's emotions and adjust the level of detail of the floor plan and interior information to be collected based on the estimated user's emotions. For example, the collection unit can estimate the user's emotions and adjust the level of detail of the information to be collected based on the estimated emotions. The collection unit can estimate the user's emotions using facial expression recognition technology. For example, the collection unit can capture the user's facial expressions with a camera and estimate the emotions using a facial expression recognition algorithm. The collection unit can also estimate the user's emotions using voice analysis technology. For example, the collection unit can analyze the tone and speed of the user's voice to estimate the emotions. The collection unit can also estimate the user's emotions by measuring the user's heart rate. For example, the collection unit can measure the user's heart rate using a heart rate sensor and estimate the emotions based on the heart rate fluctuations. This allows the level of detail of the information to be collected to be adjusted according to the user's emotions, thereby collecting more appropriate information. Some or all of the above-described processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input the user's emotional data into the generation AI and cause the generation AI to execute a process to adjust the level of detail of the information it collects.

[0068] When collecting data, the collection unit can select the optimal collection method by referring to the user's past interior change history. For example, the collection unit selects the optimal collection method by referring to the user's past interior change history. The collection unit can determine the scope of information to collect based on the past interior change history. For example, the collection unit determines the scope of information to collect based on the interior style changed by the user in the past. The collection unit can also prioritize collecting interior elements that the user frequently changes. For example, the collection unit prioritizes collecting information on furniture arrangements and decorative items that the user frequently changes. Furthermore, the collection unit can collect interior information tailored to a specific season from the user's past change history. For example, the collection unit collects information suitable for summer based on interior information changed by the user in the summer in the past. This allows the optimal collection method to be selected by referring to the user's past interior change history. Some or all of the above-described processing by the collection unit may be performed using, for example, AI, or may be performed without AI. For example, the collection unit can input past interior change history data to a generation AI, causing the generation AI to execute a process of selecting the optimal collection method.

[0069] The collection unit can adjust the collection timing based on the user's lifestyle pattern during collection. The collection unit adjusts the collection timing based on, for example, the user's lifestyle pattern. The collection unit can collect information based on the time of day the user is at home. For example, if the user is at home at night, the collection unit collects information at night. The collection unit can also collect interior information while the user is out. For example, the collection unit collects room layouts and furniture arrangements while the user is out. Furthermore, the collection unit can collect detailed information during times when the user is relaxing. For example, the collection unit collects detailed interior information during times when the user is relaxing. This allows information to be collected efficiently by adjusting the collection timing based on the user's lifestyle pattern. Some or all of the above-described processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit inputs the user's lifestyle pattern data to a generation AI, which then executes a process of adjusting the collection timing.

[0070] The collection unit can estimate the user's emotions and determine the priority of information to be collected based on the estimated user emotions. For example, the collection unit can estimate the user's emotions and determine the priority of information to be collected based on the estimated emotions. The collection unit can estimate the user's emotions using facial expression recognition technology. For example, the collection unit can capture the user's facial expressions with a camera and estimate the emotions using a facial expression recognition algorithm. The collection unit can also estimate the user's emotions using voice analysis technology. For example, the collection unit can analyze the tone and speed of the user's voice to estimate the emotions. The collection unit can also estimate the user's emotions by measuring the user's heart rate. For example, the collection unit can measure the user's heart rate using a heart rate sensor and estimate the emotions based on the fluctuations in the heart rate. This allows the priority of information to be collected based on the user's emotions, thereby enabling more appropriate information to be collected. Some or all of the above-described processing by the collection unit may be performed using, for example, AI, or may be performed without AI. For example, the collection unit can input the user's emotion data to the generation AI and cause the generation AI to execute a process of determining the priority of information to be collected.

[0071] When collecting data, the collection unit can prioritize collecting highly relevant information by taking into account the user's geographical location information. For example, the collection unit prioritizes collecting highly relevant information by taking into account the user's geographical location information. If the user lives in an urban area, the collection unit can collect interior information suitable for the urban area. For example, if the user lives in an urban area, the collection unit collects interior information tailored to the characteristics of the urban area. Furthermore, if the user lives in a suburban area, the collection unit can collect interior information suitable for the suburban area. For example, if the user lives in a suburban area, the collection unit collects interior information tailored to the characteristics of the suburban area. Furthermore, if the user lives in a specific area, the collection unit can collect interior information tailored to the characteristics of the area. For example, if the user lives in a specific area, the collection unit collects interior information tailored to the characteristics of the area. In this way, highly relevant information can be prioritized by taking the user's geographical location information into account. Some or all of the above-described processing by the collection unit may be performed using AI, for example, or may be performed without using AI. For example, the collection unit can input the user's geographical location information data to a generation AI, causing the generation AI to execute a process of preferentially collecting highly relevant information.

[0072] The collection unit can analyze the user's social media activities and collect related information during collection. For example, the collection unit analyzes the user's social media activities and collects related information. The collection unit can determine the information to collect based on interior images shared by the user on social media. For example, the collection unit analyzes interior images shared by the user on social media and determines the information to collect based on the information. The collection unit can also collect information about interior designers followed by the user. For example, the collection unit analyzes posts by the interior designers followed by the user and determines the information to collect based on the information. The collection unit can also select the information to collect based on interior posts that the user has "liked." For example, the collection unit analyzes interior posts that the user has "liked" and selects the information to collect based on the information. In this way, related information can be collected by analyzing the user's social media activities. Some or all of the above-described processing by the collection unit may be performed using, or without, AI. For example, the collection unit can input the user's social media activity data into a generation AI and cause the generation AI to execute a process of collecting related information.

[0073] The generation unit can estimate the user's emotions and adjust the characteristics of the generated ghost based on the estimated user emotions. For example, the generation unit can estimate the user's emotions and adjust the characteristics of the generated ghost based on the estimated emotions. The generation unit can estimate the user's emotions using facial expression recognition technology. For example, the generation unit can capture the user's facial expressions with a camera and estimate the emotions using a facial expression recognition algorithm. The generation unit can also estimate the user's emotions using voice analysis technology. For example, the generation unit can analyze the tone and speed of the user's voice to estimate the emotions. The generation unit can also estimate the user's emotions using heart rate measurements. For example, the generation unit can measure the user's heart rate using a heart rate sensor and estimate the emotions based on the heart rate fluctuations. This allows the ghost's characteristics to be adjusted according to the user's emotions, providing a more effective horror experience. Some or all of the above-described processing in the generation unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the generation unit can input the user's emotion data into the generation AI, causing the generation AI to execute a process of adjusting the ghost's characteristics.

[0074] The generation unit can adjust the realism of the ghost based on the level of detail of the collected information during generation. For example, the generation unit adjusts the realism of the ghost based on the level of detail of the collected information. When detailed floor plan information is collected, the generation unit can realistically reproduce the movements of the ghost. For example, the generation unit realistically reproduces the movements of the ghost based on the detailed floor plan information. Furthermore, when simple floor plan information is collected, the generation unit can simplify the movements of the ghost. For example, the generation unit simplifies the movements of the ghost based on the simple floor plan information. Furthermore, when detailed interior information is collected, the generation unit can realistically reproduce the appearance of the ghost. For example, the generation unit realistically reproduces the appearance of the ghost based on detailed interior information. This allows for adjusting the realism of the ghost based on the level of detail of the collected information to provide a more realistic horror experience. Some or all of the above-described processing in the generation unit may be performed using, or without, a generation AI. For example, the generation unit may input level of detail data of the collected information into the generation AI, causing the generation AI to execute a process of adjusting the realism of the ghost.

[0075] The generation unit can generate an optimal ghost by referring to the user's past history of fear experiences. For example, the generation unit generates an optimal ghost by referring to the user's past history of fear experiences. The generation unit can generate an optimal ghost based on the characteristics of ghosts that the user has been scared of in the past. For example, the generation unit determines the appearance and movement of the ghost based on the characteristics of ghosts that the user has been scared of in the past. The generation unit can also set a ghost appearance pattern based on a fear scenario that the user has experienced in the past. For example, the generation unit sets a ghost appearance pattern based on a fear scenario that the user has experienced in the past. The generation unit can also select the most effective ghost appearance from the user's past fear experiences. For example, the generation unit selects the most effective ghost appearance from the user's past fear experiences. In this way, the optimal ghost can be generated by referring to the user's past history of fear experiences. Some or all of the above-described processing in the generation unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the generation unit can input the user's past fear experience history data into the generation AI, causing the generation AI to execute a process of generating an optimal ghost.

[0076] The generation unit can estimate the user's emotions and adjust the frequency of appearance of the generated ghosts based on the estimated user emotions. The generation unit, for example, estimates the user's emotions and adjusts the frequency of appearance of the generated ghosts based on the estimated emotions. The generation unit can estimate the user's emotions using facial expression recognition technology. For example, the generation unit captures the user's facial expression with a camera and estimates the emotion using a facial expression recognition algorithm. The generation unit can also estimate the user's emotions using voice analysis technology. For example, the generation unit analyzes the tone and speed of the user's voice to estimate the emotion. The generation unit can also estimate the user's emotions using heart rate measurement. For example, the generation unit measures the user's heart rate using a heart rate sensor and estimates the emotion based on the heart rate fluctuations. This allows the frequency of appearance of ghosts to be adjusted according to the user's emotions, thereby providing a more effective horror experience. Some or all of the above-described processing in the generation unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the generation unit can input the user's emotional data into the generation AI, causing the generation AI to execute a process of adjusting the frequency of ghost appearances.

[0077] The generation unit can adjust the timing of the ghost's appearance based on the user's lifestyle pattern during generation. The generation unit adjusts the timing of the ghost's appearance based on, for example, the user's lifestyle pattern. The generation unit can set the timing of the ghost's appearance to night if the user is at home at night. For example, the generation unit sets the timing of the ghost's appearance to night if the user is at home at night. The generation unit can also set the timing of the ghost's appearance to day if the user is at home during the day. For example, the generation unit sets the timing of the ghost's appearance to day if the user is at home during the day. Furthermore, the generation unit can set the ghost to appear during a specific time period if the user is at home during that time period. For example, the generation unit sets the ghost to appear during a specific time period if the user is at home during that time period. This allows for adjusting the timing of the ghost's appearance based on the user's lifestyle pattern, thereby providing a more effective horror experience. Some or all of the above-described processing in the generation unit may be performed using, or without, a generation AI. For example, the generation unit may input the user's lifestyle pattern data into the generation AI, causing the generation AI to execute a process of adjusting the timing of the ghost's appearance.

[0078] During generation, the generation unit can analyze the user's social media activity and generate a related ghost. For example, the generation unit analyzes the user's social media activity and generates a related ghost. The generation unit can generate a ghost based on a scary experience the user shared on social media. For example, the generation unit determines the ghost's appearance and movement based on the scary experience the user shared on social media. The generation unit can also generate a ghost based on information about horror accounts the user follows. For example, the generation unit analyzes posts from horror accounts the user follows and generates a ghost based on that information. The generation unit can also generate a ghost based on horror posts the user "liked." For example, the generation unit analyzes horror posts the user "liked" and generates a ghost based on that information. In this way, related ghosts can be generated by analyzing the user's social media activity. Some or all of the above-described processing in the generation unit may be performed using, or without, a generation AI. For example, the generation unit can input the user's social media activity data into the generation AI and cause the generation AI to execute a process of generating a related ghost.

[0079] The reflection unit can estimate the user's emotions and adjust the display method of the ghost based on the estimated user's emotions. For example, the reflection unit can estimate the user's emotions and adjust the display method of the ghost based on the estimated emotions. The reflection unit can estimate the user's emotions using facial expression recognition technology. For example, the reflection unit can capture the user's facial expressions with a camera and estimate the emotions using a facial expression recognition algorithm. The reflection unit can also estimate the user's emotions using voice analysis technology. For example, the reflection unit can analyze the tone and speed of the user's voice to estimate the emotions. Furthermore, the reflection unit can estimate the user's emotions using heart rate measurements. For example, the reflection unit can measure the user's heart rate using a heart rate sensor and estimate the emotions based on the heart rate fluctuations. This allows the display method of the ghost to be adjusted according to the user's emotions, providing a more effective horror experience. Some or all of the above-described processing in the reflection unit may be performed using, for example, AI, or may be performed without AI. For example, the reflection unit can input the user's emotional data to the generation AI, causing the generation AI to execute a process of adjusting the display method of the ghost.

[0080] The reflection unit can adjust the ghost's movement in real time based on the user's specific actions during reflection. For example, the reflection unit adjusts the ghost's movement in real time based on the user's specific actions. The reflection unit can display the ghost as if it is chasing the user from behind when the user is walking. For example, the reflection unit displays the ghost as if it is chasing the user from behind when the user is walking. The reflection unit can also display the ghost as if it is approaching the user when the user is sitting. For example, the reflection unit displays the ghost as if it is approaching the user when the user is sitting. Furthermore, the reflection unit can adjust the ghost's movement in accordance with the user's specific actions when the user performs the specific actions. For example, the reflection unit displays the ghost as if it is disappearing when the user waves their hand. This allows for a more interactive horror experience by adjusting the ghost's movement in real time based on the user's specific actions. Some or all of the above-described processing in the reflection unit may be performed using, or without, AI. For example, the reflection unit can input the user's action data to the generation AI, causing the generation AI to execute a process of adjusting the ghost's movement.

[0081] The reflection unit can select the optimal reflection method by referring to the user's past behavioral patterns when reflecting. For example, the reflection unit can select the optimal reflection method by referring to the user's past behavioral patterns. The reflection unit can select the ghost display method based on behavioral patterns that frightened the user in the past. For example, the reflection unit selects the ghost display method based on behavioral patterns that frightened the user in the past. The reflection unit can also select the most effective ghost movement from the user's past behavioral patterns. For example, the reflection unit analyzes the user's past behavioral patterns and selects the optimal reflection method. In this way, the optimal reflection method can be selected by referring to the user's past behavioral patterns. Some or all of the above-mentioned processing in the reflection unit may be performed using, for example, AI, or may be performed without using AI. For example, the reflection unit can input the user's past behavioral pattern data to the generation AI, causing the generation AI to execute processing to select the optimal reflection method.

[0082] The reflection unit can estimate the user's emotions and adjust the timing of displaying the ghost based on the estimated user's emotions. For example, the reflection unit can estimate the user's emotions and adjust the timing of displaying the ghost based on the estimated emotions. The reflection unit can estimate the user's emotions using facial expression recognition technology. For example, the reflection unit can capture the user's facial expressions with a camera and estimate the emotions using a facial expression recognition algorithm. The reflection unit can also estimate the user's emotions using voice analysis technology. For example, the reflection unit can analyze the tone and speed of the user's voice to estimate the emotions. The reflection unit can also estimate the user's emotions using heart rate measurements. For example, the reflection unit can measure the user's heart rate using a heart rate sensor and estimate the emotions based on the heart rate fluctuations. This allows the timing of displaying the ghost to be adjusted according to the user's emotions, providing a more effective horror experience. Some or all of the above-described processing in the reflection unit may be performed using, for example, AI, or may be performed without AI. For example, the reflection unit can input the user's emotion data to the generation AI, causing the generation AI to execute a process of adjusting the timing of displaying the ghost.

[0083] The reflection unit can adjust the display location of the ghost by taking into account the user's geographical location information when reflecting the data. The reflection unit, for example, adjusts the display location of the ghost by taking into account the user's geographical location information. The reflection unit can display the ghost in a specific room when the user is in that room. For example, the reflection unit displays the ghost in that room when the user is in that room. The reflection unit can also refrain from displaying the ghost when the user is outside the house. For example, the reflection unit refrains from displaying the ghost when the user is outside the house. The reflection unit can also display the ghost in a specific location when the user is in that location. For example, the reflection unit displays the ghost in that location when the user is in a specific location. This makes it possible to appropriately adjust the display location of the ghost by taking into account the user's geographical location information. Some or all of the above-described processing in the reflection unit may be performed using, or without, AI. For example, the reflection unit can input the user's geographical location information data to the generation AI, causing the generation AI to execute processing to adjust the display location of the ghost.

[0084] The reflection unit can analyze the user's social media activity at the time of reflection and display related ghosts. The reflection unit, for example, analyzes the user's social media activity and displays related ghosts. The reflection unit can display ghosts based on scary experiences shared by the user on social media. For example, the reflection unit determines the appearance and movement of the ghost based on the scary experiences shared by the user on social media. The reflection unit can also display ghosts based on information about horror accounts followed by the user. For example, the reflection unit analyzes posts from horror accounts followed by the user and displays ghosts based on that information. The reflection unit can also display ghosts based on horror posts that the user has "liked." For example, the reflection unit analyzes horror posts that the user has "liked" and displays ghosts based on that information. In this way, related ghosts can be displayed by analyzing the user's social media activity. Some or all of the above-described processing in the reflection unit may be performed using, or without, AI. For example, the reflection unit can input the user's social media activity data to the generation AI and cause the generation AI to execute processing to display related ghosts.

[0085] The linking unit can estimate the user's emotion and adjust the linking method with the messaging app based on the estimated user's emotion. For example, the linking unit can estimate the user's emotion and adjust the linking method with the messaging app based on the estimated emotion. The linking unit can estimate the user's emotion using facial expression recognition technology. For example, the linking unit can capture the user's facial expression with a camera and estimate the emotion using a facial expression recognition algorithm. The linking unit can also estimate the user's emotion using voice analysis technology. For example, the linking unit can analyze the tone and speed of the user's voice to estimate the emotion. The linking unit can also estimate the user's emotion using heart rate measurement. For example, the linking unit can measure the user's heart rate using a heart rate sensor and estimate the emotion based on the heart rate fluctuations. This allows the linking method with the messaging app to be adjusted according to the user's emotion, thereby providing a more effective horror experience. Some or all of the above-described processing in the linking unit may be performed using, for example, AI, or may be performed without using AI. For example, the collaboration unit can input user emotion data into the generation AI, causing the generation AI to execute a process to adjust the method of collaboration with a messaging app.

[0086] When linking, the linking unit can select the optimal linking method by referring to the user's past messaging app usage history. For example, the linking unit selects the optimal linking method by referring to the user's past messaging app usage history. The linking unit can prioritize linking of messaging apps that the user has used frequently in the past. For example, the linking unit prioritizes linking of messaging apps that the user has used frequently in the past. The linking unit can also adjust the linking timing based on the messaging apps that the user uses during a specific time period. For example, the linking unit adjusts the linking timing based on the messaging apps that the user uses during a specific time period. Furthermore, the linking unit can select the most effective linking method from the user's past messaging app usage history. For example, the linking unit analyzes the user's past messaging app usage history and selects the optimal linking method. In this way, the optimal linking method can be selected by referring to the user's past messaging app usage history. Some or all of the above-described processing in the linking unit may be performed using, for example, AI, or may be performed without using AI. For example, the linking unit can input the user's past messaging app usage history data to the generation AI, causing the generation AI to execute processing to select the optimal linking method.

[0087] The linking unit can adjust the link timing based on the user's lifestyle pattern during linking. The linking unit adjusts the link timing based on the user's lifestyle pattern, for example. If the user uses a messaging app at night, the linking unit can link at that time of day. For example, if the user uses a messaging app at night, the linking unit links at that time of day. The linking unit can also link at that time of day if the user uses a messaging app during the day. For example, if the user uses a messaging app during the day, the linking unit links at that time of day. The linking unit can also link at that time of day if the user uses a messaging app during a specific time of day. For example, if the user uses a messaging app during a specific time of day, the linking unit links at that time of day. This allows for adjusting the link timing based on the user's lifestyle pattern to provide a more effective horror experience. Some or all of the above-described processing in the linking unit may be performed using AI, for example, or may be performed without using AI. For example, the linking unit may input the user's lifestyle pattern data to the generation AI, causing the generation AI to execute processing to adjust the link timing.

[0088] The linking unit can estimate the user's emotion and adjust the notification content of the messaging app based on the estimated user's emotion. For example, the linking unit can estimate the user's emotion and adjust the notification content of the messaging app based on the estimated emotion. The linking unit can estimate the user's emotion using facial expression recognition technology. For example, the linking unit can capture the user's facial expression with a camera and estimate the emotion using a facial expression recognition algorithm. The linking unit can also estimate the user's emotion using voice analysis technology. For example, the linking unit can analyze the tone and speed of the user's voice to estimate the emotion. The linking unit can also estimate the user's emotion by measuring the user's heart rate. For example, the linking unit can measure the user's heart rate using a heart rate sensor and estimate the emotion based on the heart rate fluctuations. This allows the notification content of the messaging app to be adjusted according to the user's emotion, thereby providing a more effective horror experience. Some or all of the above-described processing in the linking unit may be performed using, for example, AI, or may be performed without using AI. For example, the collaboration unit can input user emotion data into the generation AI, causing the generation AI to execute a process to adjust the notification content of a messaging app.

[0089] During collaboration, the collaboration unit can adjust the location of notifications from the messaging app taking into account the user's geographical location information. For example, the collaboration unit adjusts the location of notifications from the messaging app taking into account the user's geographical location information. When the user is in a specific location, the collaboration unit can send notification content tailored to the location. For example, when the user is in a specific location, the collaboration unit sends notification content tailored to the location. Furthermore, when the user is at home, the collaboration unit can send notification content related to the home. For example, when the user is at home, the collaboration unit sends notification content related to the home. Furthermore, when the user is out, the collaboration unit can send notification content related to the user's destination. For example, when the user is out, the collaboration unit sends notification content related to the user's destination. In this way, the location of notifications from the messaging app can be appropriately adjusted by taking into account the user's geographical location information. Some or all of the above-described processing in the collaboration unit may be performed using AI, for example, or may be performed without using AI. For example, the collaboration unit can input the user's geographical location information data to the generation AI, causing the generation AI to execute processing to adjust the location of notifications from the messaging app.

[0090] During linking, the linking unit can analyze the user's social media activity and provide related notifications. For example, the linking unit analyzes the user's social media activity and provides related notifications. The linking unit can determine the content of the notification based on a horror experience shared by the user on social media. For example, the linking unit determines the content of the notification based on a horror experience shared by the user on social media. The linking unit can also determine the content of the notification based on information about horror accounts followed by the user. For example, the linking unit analyzes posts from horror accounts followed by the user and determines the content of the notification based on the information. The linking unit can also determine the content of the notification based on horror posts that the user has "liked." For example, the linking unit analyzes horror posts that the user has "liked" and determines the content of the notification based on the information. In this way, related notifications can be provided by analyzing the user's social media activity. Some or all of the above-described processing in the linking unit may be performed using, or without, AI. For example, the linking unit can input the user's social media activity data into the generation AI, causing the generation AI to execute processing to provide related notifications. === Hard Collateral 1-1 === Each of the multiple elements including the collection unit, generation unit, reflection unit, and linking unit described above is realized, for example, by at least one of the smart device 14 and the data processing device 12. For example, the collection unit collects information about the layout and interior of the home using the camera 42 and sensors of the smart device 14. The generation unit analyzes the collected information by the specific processing unit 290 of the data processing device 12 and generates an optimal ghost. The reflection unit interactively reflects the ghost generated by the control unit 46A of the smart device 14 in the user's behavior scenario. The linking unit links with a messaging app via the specific processing unit 290 of the data processing device 12 to infiltrate the terrifying experience into the real world. === Hard Collateral 1-2 === Each of the multiple elements including the collection unit, generation unit, reflection unit, and linking unit described above is realized, for example, by at least one of the smart glasses 214 and the data processing device 12. For example, the collection unit collects information about the layout and interior of the home using the camera 42 and sensors of the smart glasses 214. The generation unit analyzes the collected information by the specific processing unit 290 of the data processing device 12 and generates an optimal ghost. The reflection unit interactively reflects the ghost generated by the control unit 46A of the smart glasses 214 in the user's behavior scenario. The linking unit links with a messaging app via the specific processing unit 290 of the data processing device 12 to infiltrate the terrifying experience into the real world. === Hard Collateral 1-3 === Each of the multiple elements including the collection unit, generation unit, reflection unit, and linking unit described above is realized, for example, by at least one of the headset type terminal 314 and the data processing device 12. For example, the collection unit collects information about the layout and interior of the home using the camera 42 and sensors of the headset type terminal 314. The generation unit analyzes the information collected by the specific processing unit 290 of the data processing device 12 and generates an optimal ghost. The reflection unit interactively reflects the ghost generated by the control unit 46A of the headset type terminal 314 in the user's behavior scenario. The linking unit links with a messaging app via the specific processing unit 290 of the data processing device 12, and infiltrates the terrifying experience into the real world. === Hard Collateral 1-4 === Each of the multiple elements including the collection unit, generation unit, reflection unit, and linking unit described above is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the collection unit collects information about the layout and interior of the home using the camera 42 and sensors of the robot 414. The generation unit analyzes the information collected by the specific processing unit 290 of the data processing device 12 and generates an optimal ghost. The reflection unit interactively reflects the ghost generated by the control unit 46A of the robot 414 in the user's behavior scenario. The linking unit links with a messaging app via the specific processing unit 290 of the data processing device 12, and infiltrates the terrifying experience into the real world.

[0091] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0092] The horror experience system may further include a health management unit that monitors the user's health condition. The health management unit collects health data such as the user's heart rate, blood pressure, and stress level, and can adjust the intensity of the horror experience based on this data. For example, if the user's heart rate is high, the generation unit can reduce the frequency of ghost appearances. Also, if the user's stress level is high, the reflection unit can slow down the ghost's movements. Furthermore, the health management unit can temporarily suspend the horror experience depending on the user's health condition. This allows the user to enjoy a horror experience safely while maintaining their health.

[0093] The horror experience system may further include a scent providing unit for stimulating the user's sense of smell. The scent providing unit may provide an appropriate scent depending on the user's emotions and the scenario. For example, if the user is feeling scared, the scent providing unit may provide a lavender scent to ease anxiety. In a scenario where a ghost appears, the scent providing unit may provide the scent of old wood or damp earth to create a more realistic horror experience. Furthermore, the scent providing unit may learn the user's past scent preferences and provide individually customized scents. This may enhance the horror experience not only through the sense of sight and hearing but also through the sense of smell.

[0094] The horror experience system may further include a haptic feedback unit for stimulating the user's sense of touch. The haptic feedback unit may provide vibrations or pressure that make the user feel as if they are being touched by a ghost. For example, in a scenario in which the user is being chased by a ghost, a more realistic horror experience can be provided by making the user feel a slight vibration on the back. The sense of fear can also be amplified by applying pressure to the user's hands or arms at the moment the user feels as if they are being touched by a ghost. Furthermore, the haptic feedback unit may adjust the intensity of the haptic feedback according to the user's emotions and actions. This allows the horror experience to be intensified not only through sight and hearing but also through touch.

[0095] The horror experience system may further include an eye-gaze tracking unit that tracks the user's gaze. The eye-gaze tracking unit can track the user's gaze movements in real time and adjust the ghost's appearance position and movement based on the tracking. For example, if the user is looking at a specific location, the generation unit can make the ghost appear at that location. Also, when the user moves their gaze, the ghost moves to follow their gaze, providing a more interactive horror experience. Furthermore, the eye-gaze tracking unit can estimate the user's emotions from their gaze movements and adjust the intensity of the horror experience based on the estimation. This allows the user to experience a more realistic and personalized horror experience through their gaze.

[0096] The horror experience system may further include a sound effects unit for stimulating the user's hearing. The sound effects unit may provide appropriate sound effects depending on the user's emotions and the scenario. For example, if the user is feeling scared, the sound effects unit may provide unsettling low-pitched sound effects. In a scenario in which a ghost appears, realistic sound effects such as footsteps and whispers may be provided to further amplify the sense of fear. Furthermore, the sound effects unit may learn the user's past sound effect preferences and provide individually customized sound effects. This may enhance the horror experience not only through the sense of sight and smell but also through the sense of hearing.

[0097] The horror experience system may further include a behavior analysis unit that analyzes the user's behavioral history. The behavior analysis unit collects the user's past behavioral data and can optimize the horror experience scenario based on this data. For example, if the user has felt strong fear in a particular scenario in the past, the generation unit can recreate that scenario. Also, if the user tends to repeat a particular behavior, the reflection unit can adjust the ghost appearance pattern to match that behavior. Furthermore, the behavior analysis unit can analyze the user's behavioral data in real time and adjust the intensity of the horror experience based on this. This makes it possible to provide a more personalized horror experience by utilizing the user's behavioral history.

[0098] The horror experience system may further include an environmental sound collection unit that collects the user's environmental sounds. The environmental sound collection unit collects sounds around the user and can adjust the horror experience scenario based on the collected sounds. For example, if the user is in a quiet environment, the generation unit can emphasize the sound of a ghost appearing. Alternatively, if the user is in a noisy environment, the reflection unit can maintain the sense of fear by visually emphasizing the ghost's movement. Furthermore, the environmental sound collection unit can analyze the sounds around the user in real time and adjust the intensity of the horror experience based on the analysis. This makes it possible to provide a more realistic and immersive horror experience by utilizing the user's environmental sounds.

[0099] The horror experience system may further include a temperature measurement unit that measures the user's body temperature. The temperature measurement unit may measure the user's body temperature in real time and adjust the intensity of the horror experience based on the measured temperature. For example, if the user's body temperature rises, the generation unit may reduce the frequency of ghost appearances. Alternatively, if the user's body temperature drops, the reflection unit may slow down the ghosts' movements. Furthermore, the temperature measurement unit may analyze the user's body temperature data and optimize the horror experience scenario based on the analysis. This allows the user to safely enjoy a horror experience while monitoring their body temperature.

[0100] The horror experience system may further include a sleep management unit that monitors the user's sleep patterns. The sleep management unit collects the user's sleep data and adjusts the timing of the horror experience based on the collected data. For example, if the user is not getting enough sleep, the generation unit may reduce the frequency of ghost appearances. Alternatively, if the user is in deep sleep, the reflection unit may slow down the ghost's movements. Furthermore, the sleep management unit may analyze the user's sleep data and optimize the horror experience scenario based on the analysis. This allows the user to enjoy a horror experience safely while taking into account the user's sleep patterns.

[0101] The horror experience system may further include a diet management unit that monitors the user's eating patterns. The diet management unit may collect the user's eating data and adjust the intensity of the horror experience based on the collected data. For example, if the user is hungry, the generation unit may reduce the frequency of ghost appearances. Alternatively, if the user is full, the reflection unit may slow down the ghost's movements. Furthermore, the diet management unit may analyze the user's eating data and optimize the horror experience scenario based on the analysis. This allows the user to enjoy a horror experience safely while taking into account the user's eating patterns.

[0102] The processing flow of the second embodiment will be briefly explained below.

[0103] Step 1: The collection unit collects information about the layout and interior of the home. The collection unit acquires detailed data using, for example, cameras and sensors. Specifically, it collects information such as the layout of furniture in the living room, the color of the walls, and the position of lighting. The collection unit can use cameras to acquire images of the entire room and sensors to detect the layout of furniture and the color of the walls. It is also possible to install fixed cameras and periodically acquire images of the entire room, or to use motion sensors to detect the layout of furniture and the color of the walls in real time. Step 2: The generation unit generates an optimal ghost based on the information collected by the collection unit. The generation unit analyzes the collected information using the generation AI and determines the ghost's appearance and movement. For example, the generation AI can be given a prompt such as "Please generate a ghost that best suits the layout and interior of your home," and the generation AI will generate the ghost's appearance and movement. The frequency of ghost appearances and the level of fear can also be adjusted based on the collected information. Step 3: The reflection unit interactively reflects the ghosts generated by the generation unit into the user's behavior scenario. The reflection unit displays the ghosts according to the user's actions, and the ghosts change in real time as the user moves from room to room. For example, it can realize a scenario in which a ghost chases the user from behind as they walk down a hallway, or a scenario in which a ghost suddenly appears when the user enters a room. Step 4: The linking unit links the ghost reflected by the reflection unit with messaging apps such as LINE to invade the real world. For example, it can realize a scenario in which a user suddenly receives a message from a ghost while chatting with a friend on LINE, or a scenario in which the LINE notification sound changes to the voice of a ghost. This allows the terrifying experience to invade the real world.

[0104] 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.

[0105] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search engine).<URL: https: / / openai.com / blog / chatgpt> Examples of the generative AI include a neural network (NN) and a neural network (NN). 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 (e.g., still image data or video data). 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 one or more data formats of voice data, text data, image data, etc. 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 may perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-mentioned parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. The processing performed by an AI including the generative AI may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI including the generative AI.

[0106] 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.

[0107] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0108] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0109] 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0110] 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.

[0111] 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.

[0112] 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.

[0113] 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).

[0114] 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.

[0115] 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.

[0116] 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.

[0117] 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.

[0118] 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.

[0119] 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.

[0120] 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.

[0121] 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 including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). 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 one or more data formats, such as audio data, text data, and image data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The 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 models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[0122] 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.

[0123] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0124] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0125] 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.

[0126] 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.

[0127] 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.

[0128] 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.

[0129] 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).

[0130] 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.

[0131] 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.

[0132] 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.

[0133] 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.

[0134] In the headset type terminal 314, the identification process is performed by the processor 46. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. Note that the headset type terminal 314 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the identification processing unit 290 using these models.

[0135] 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.

[0136] 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.

[0137] 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 including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). 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 one or more data formats, such as audio data, text data, and image data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The 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 models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[0138] 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.

[0139] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0140] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0141] 7, a data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0142] 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.

[0143] 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.

[0144] 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.

[0145] 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).

[0146] 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.

[0147] 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.

[0148] 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.

[0149] 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.

[0150] 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.

[0151] In the robot 414, the processor 46 performs the identification process. The storage 50 stores the identification program 60. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as the control unit 46A in accordance with the identification program 60 executed on the RAM 48. The robot 414 also has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform the same process as the identification processing unit 290 using these models.

[0152] 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.

[0153] 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.

[0154] 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 including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). 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 one or more data formats, such as audio data, text data, and image data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The 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 models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[0155] 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.

[0156] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[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.

[0175] [Explanation of symbols]

[0176] 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 collection department that collects information on home layouts and interior design, a generator for generating an appropriate ghost based on the information collected by the collector; a reflection unit that interactively reflects the ghost generated by the generation unit in a user's behavior scenario; a linking unit that links the ghost reflected by the reflection unit with a messaging app to invade the real world. A system characterized by:

2. The collecting unit Collect information about the layout and interior of the home using cameras or sensors The system of claim 1 .

3. The generation unit Generate optimal ghosts based on collected information The system of claim 1 .

4. The reflection unit Displaying ghosts in response to specific user actions The system of claim 1 .

5. The linking unit is Linking with messaging apps to bring scary experiences into the real world The system of claim 1 .

6. The linking unit is Integrate with other messaging apps or social networks The system of claim 1 .

7. The collecting unit Estimate the user's emotions and adjust the level of detail of the floor plan and interior information collected based on the estimated user emotions. The system of claim 1 .

8. The collecting unit When collecting, the optimal collection method is selected by referring to the user's past interior modification history. The system of claim 1 .

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