System

Abstract

An object of the system according to the embodiment is to realize a sensory home theater even at home.SOLUTION: A system includes an image authentication unit, a sound authentication unit, a vibration conversion unit, and an image conversion unit. The video authentication unit analyzes video data. The sound authentication section analyzes voice data. The vibration conversion unit converts sound data into vibration data. The image conversion unit converts the vibration data into image data to realize a sensory home theater.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] Conventional technology has had the problem of making it difficult to provide a immersive viewing experience in a home theater.

[0005] The system according to the embodiment aims to realize a home theater experience at home. [Means for solving the problem]

[0006] The system according to the embodiment includes a video authentication unit, a sound authentication unit, a vibration conversion unit, and a video conversion unit. The video authentication unit analyzes video data. The sound authentication unit analyzes audio data. The vibration conversion unit converts audio data into vibration data. The video conversion unit converts vibration data into video data. [Effects of the Invention]

[0007] The system according to the embodiment can realize a home theater experience at home. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example 1) The immersive home theater system according to an embodiment of the present invention analyzes video data, analyzes audio data, converts the audio data into vibration data, and converts the vibration data into video data, thereby providing an immersive entertainment experience that integrates the visual, auditory, and tactile senses.

[0029] An immersive home theater system according to an embodiment includes a video authentication unit, a sound authentication unit, a vibration conversion unit, and a video conversion unit. The video authentication unit analyzes video data. For example, the video authentication unit uses a generation AI to analyze the video data and recognize specific scenes or objects. The video authentication unit can also use the generation AI to extract scene features based on the video data and generate a corresponding vibration pattern. The sound authentication unit analyzes audio data. For example, the sound authentication unit uses a generation AI to analyze audio data and recognize specific sounds or music. The sound authentication unit can also use the generation AI to extract sound features based on the audio data and generate a corresponding vibration pattern. The vibration conversion unit converts audio data into vibration data. For example, the vibration conversion unit generates vibration data according to the frequency and rhythm of the audio data. The vibration conversion unit can also adjust the intensity of the vibration according to the strength of the audio data. The video conversion unit converts the vibration data into video data. For example, the video conversion unit changes the color tone and brightness of the video based on the vibration data. The video conversion unit can also adjust the movement of the video based on the vibration data. As a result, the immersive home theater system according to the embodiment can provide an immersive entertainment experience that integrates the senses of sight, hearing, and touch. For example, when an explosion occurs in a movie, the video authentication unit and the sound authentication unit recognize the scene, the vibration conversion unit generates vibrations, and the video conversion unit reflects the vibrations in the video, allowing the viewer to experience a sense of realism as if they were actually there.

[0030] The video authentication unit can train the generative AI on a database of past movies to improve the accuracy of scene prediction. For example, the video authentication unit trains the generative AI on a database of past movies to improve the accuracy of scene prediction. For example, by training the AI ​​on movie data from a specific genre, scenes specific to that genre can be recognized with high accuracy. The video authentication unit also trains the generative AI on a database of past movies to improve the accuracy of scene prediction. For example, by training the AI ​​on movie data from a specific director or actor, scenes characteristic of that director or actor can be recognized. The video authentication unit also trains the generative AI on a database of past movies to improve the accuracy of scene prediction. For example, by training the AI ​​on movie data containing specific historical backgrounds or cultural elements, scenes unique to that era or culture can be recognized. In this way, by training the AI ​​on past data, the accuracy of scene recognition can be improved.

[0031] The video authentication unit may visually display the results of the video authentication in real time, allowing the user to check the recognized scene. For example, the video authentication unit may visually display the results of the video authentication in real time, allowing the user to check the recognized scene. For example, the video authentication unit may pop up information about the recognized scene in a corner of the screen. The video authentication unit may also visually display the results of the video authentication in real time, allowing the user to check the recognized scene. For example, the video authentication unit may overlay an icon or text corresponding to the recognized scene on the screen. The video authentication unit may also visually display the results of the video authentication in real time, allowing the user to check the recognized scene. For example, the video authentication unit may provide an interactive widget for displaying detailed information about the recognized scene. This allows the user to check the recognized scene in real time.

[0032] The video authentication unit can recognize specific scenes not only in movies but also in sporting events and documentary programs, and generate corresponding vibrations. The video authentication unit can recognize specific scenes not only in movies but also in sporting events, and generate corresponding vibrations. For example, strong vibrations are generated for goal scenes and highlight scenes. The video authentication unit can also recognize specific scenes in documentary programs and generate corresponding vibrations. For example, appropriate vibrations are generated for tense and moving scenes. The video authentication unit can also recognize specific scenes in live concerts and generate corresponding vibrations. For example, different vibrations are generated for exciting and quiet scenes. This makes it possible to provide an immersive viewing experience even for content other than movies.

[0033] The video authentication unit transmits the results of the video authentication to a mobile device such as a smartphone or tablet, thereby providing an immersive viewing experience even when the user is out and about. The video authentication unit, for example, transmits the results of the video authentication to a smartphone, thereby providing an immersive viewing experience even when the user is out and about. For example, vibrations corresponding to specific scenes in a movie are reproduced on the smartphone. The video authentication unit also transmits the results of the video authentication to a tablet, thereby providing an immersive viewing experience even when the user is out and about. For example, vibrations corresponding to specific scenes in a sporting event are reproduced on the tablet. The video authentication unit also transmits the results of the video authentication to a wearable device, thereby providing an immersive viewing experience even when the user is out and about. For example, vibrations corresponding to specific scenes in a live concert are reproduced on the wearable device. This makes it possible to provide an immersive viewing experience even when the user is out and about.

[0034] The sound authentication unit can improve the accuracy of sound prediction by having the generation AI learn from a past music database. For example, the sound authentication unit can improve the accuracy of sound prediction by having the generation AI learn from a past music database. For example, by having the generation AI learn from music data of a specific genre, it can recognize sounds unique to that genre with high accuracy. The sound authentication unit can also have the generation AI learn from a past music database to improve the accuracy of sound prediction. For example, by having the generation AI learn from music data of a specific artist or band, it can recognize the characteristic sounds of that artist or band. The sound authentication unit can also have the generation AI learn from a past music database to improve the accuracy of sound prediction. For example, by having the generation AI learn from music data that includes specific historical backgrounds and cultural elements, it can recognize sounds unique to that era or culture. In this way, by learning from past data, it is possible to improve the accuracy of sound recognition.

[0035] The sound authentication unit may visually display the results of the sound authentication in real time, allowing the user to check the recognized sound. For example, the sound authentication unit may visually display the results of the sound authentication in real time, allowing the user to check the recognized sound. For example, the sound authentication unit may pop up information about the recognized sound in a corner of the screen. The sound authentication unit may also visually display the results of the sound authentication in real time, allowing the user to check the recognized sound. For example, the sound authentication unit may overlay an icon or text corresponding to the recognized sound on the screen. The sound authentication unit may also visually display the results of the sound authentication in real time, allowing the user to check the recognized sound. For example, an interactive widget may be provided to display detailed information about the recognized sound. This allows the user to check the recognized sound in real time.

[0036] The sound authentication unit can recognize specific scenes not only in movies but also in podcasts and audiobooks, and generate corresponding vibrations. The sound authentication unit can recognize specific scenes not only in movies but also in podcasts, and generate corresponding vibrations. For example, it generates appropriate vibrations for moving or tense scenes. The sound authentication unit can also recognize specific scenes in audiobooks and generate corresponding vibrations. For example, it can generate strong vibrations for the climax or important scenes of a story. The sound authentication unit can also recognize specific scenes in radio dramas and generate corresponding vibrations. For example, it can generate different vibrations for action scenes or moving scenes. This makes it possible to provide an immersive viewing experience for content other than movies.

[0037] The sound authentication unit can transmit the result of the sound authentication to an audio device such as a smart speaker or headphones, allowing the user to experience not only sound but also vibration. The sound authentication unit, for example, transmits the result of the sound authentication to a smart speaker, allowing the user to experience not only sound but also vibration. For example, vibrations corresponding to specific sounds are reproduced on the smart speaker. The sound authentication unit can also transmit the result of the sound authentication to headphones, allowing the user to experience not only sound but also vibration. For example, vibrations corresponding to the rhythm or beat of music are reproduced on the headphones. The sound authentication unit can also transmit the result of the sound authentication to a wearable device, allowing the user to experience not only sound but also vibration. For example, vibrations corresponding to specific scenes in a podcast or audiobook are reproduced on the wearable device. This allows the user to experience not only sound but also vibration.

[0038] The vibration converter unit generates different vibration patterns according to the frequency or rhythm of the sound, thereby providing a more diverse tactile experience. The vibration converter unit generates different vibration patterns according to, for example, the frequency of the sound, thereby providing a more diverse tactile experience. For example, it generates fine vibrations for high-frequency sounds and strong vibrations for low-frequency sounds. The vibration converter unit also generates different vibration patterns according to the rhythm of the sound, thereby providing a more diverse tactile experience. For example, it generates continuous vibrations for sounds with a fast rhythm and intermittent vibrations for sounds with a slow rhythm. The vibration converter unit also generates different vibration patterns according to the strength of the sound, thereby providing a more diverse tactile experience. For example, it generates strong vibrations for loud sounds and gentle vibrations for soft sounds. In this way, by generating vibration patterns according to the frequency or rhythm of the sound, a more diverse tactile experience can be provided.

[0039] The vibration conversion unit can improve the technology for converting sound into vibrations, allowing the user to customize the intensity and pattern of vibrations. For example, the vibration conversion unit can improve the technology for converting sound into vibrations, allowing the user to customize the intensity and pattern of vibrations. For example, a function can be provided that allows the user to change vibration settings through an application. The vibration conversion unit can also improve the technology for converting sound into vibrations, allowing the user to customize the intensity and pattern of vibrations. For example, a function can be provided that allows the user to select a preset vibration pattern. The vibration conversion unit can also improve the technology for converting sound into vibrations, allowing the user to customize the intensity and pattern of vibrations. For example, an editor can be provided that allows the user to create their own vibration pattern. This allows the user to customize the intensity and pattern of vibrations.

[0040] The vibration converter uses a technology for converting sound into vibrations to convert the sounds of not only movies but also live music concerts and sporting events into vibrations, thereby providing a sense of realism. The vibration converter, for example, uses the technology for converting sound into vibrations to convert the sounds of live music concerts into vibrations, thereby providing a sense of realism. For example, vibrations are generated in time with the beat of the live concert. The vibration converter also uses the technology for converting sound into vibrations to convert the sounds of sporting events into vibrations, thereby providing a sense of realism. For example, vibrations are generated in time with goal scenes and cheers. The vibration converter also uses the technology for converting sound into vibrations to convert the sounds of movies into vibrations, thereby providing a sense of realism. For example, vibrations are generated in time with action scenes and moving scenes. This makes it possible to provide a sense of realism even with content other than movies.

[0041] The vibration conversion unit incorporates technology that converts sound into vibrations into smart home devices, allowing for the provision of immersive entertainment throughout the home. The vibration conversion unit, for example, incorporates technology that converts sound into vibrations into smart home devices, allowing for the provision of immersive entertainment throughout the home. For example, it may be linked to a smart speaker or smart light. The vibration conversion unit may also incorporate technology that converts sound into vibrations into smart home devices, allowing for the provision of immersive entertainment throughout the home. For example, it may be linked to a smart bed or smart sofa. The vibration conversion unit may also incorporate technology that converts sound into vibrations into smart home devices, allowing for the provision of immersive entertainment throughout the home. For example, it may be linked to a smart wall or smart floor. This allows for the provision of immersive entertainment throughout the home.

[0042] The image conversion unit can change the color tone and brightness of the image according to the intensity or pattern of the vibration to enhance the visual effect. The image conversion unit, for example, changes the color tone of the image according to the intensity of the vibration to enhance the visual effect. For example, the image is made vivid for strong vibrations and pale for weak vibrations. The image conversion unit also changes the brightness of the image according to the vibration pattern to enhance the visual effect. For example, the image is made brighter for continuous vibrations and darker for intermittent vibrations. The image conversion unit also changes the color tone and brightness of the image according to the intensity and pattern of the vibration to enhance the visual effect. For example, the image is made more contrasty for strong vibrations and softer for weak vibrations. In this way, the visual effect can be enhanced by converting the image according to the intensity and pattern of the vibration.

[0043] The image conversion unit can improve the technology for converting images using vibrations to enable a user to customize the image conversion pattern. For example, the image conversion unit improves the technology for converting images using vibrations to enable a user to customize the image conversion pattern. For example, the image conversion unit provides a function for changing image settings through an application. The image conversion unit also improves the technology for converting images using vibrations to enable a user to customize the image conversion pattern. For example, the image conversion unit provides a function for selecting a preset image conversion pattern. The image conversion unit also improves the technology for converting images using vibrations to enable a user to customize the image conversion pattern. For example, the image conversion unit provides an editor that allows a user to create their own image conversion pattern. This allows a user to customize the image conversion pattern.

[0044] The image conversion unit uses the technology to convert images using vibration to convert images not only of movies but also of games and virtual reality, thereby providing a sense of immersion. The image conversion unit, for example, uses the technology to convert images using vibration to convert game images to provide a sense of immersion. For example, the image changes according to actions in the game. The image conversion unit also uses the technology to convert images using vibration to convert virtual reality images to provide a sense of immersion. For example, the image changes according to vibrations during a VR experience. The image conversion unit also uses the technology to convert images using vibration to convert movie images to provide a sense of immersion. For example, the image changes according to specific scenes in a movie. This makes it possible to provide a sense of immersion with content other than movies.

[0045] The image conversion unit can incorporate technology that converts images using vibration into smart glasses or a head-mounted display to provide an integrated visual and tactile experience. For example, the image conversion unit can incorporate technology that converts images using vibration into smart glasses to provide an integrated visual and tactile experience. For example, the image can be changed in response to the vibration of the smart glasses. The image conversion unit can also incorporate technology that converts images using vibration into a head-mounted display to provide an integrated visual and tactile experience. For example, the image can be changed in response to the vibration of the HMD. The image conversion unit can also incorporate technology that converts images using vibration into a wearable device to provide an integrated visual and tactile experience. For example, the image can be changed in response to the vibration of the wearable device. This makes it possible to provide an integrated visual and tactile experience using smart glasses or a head-mounted display.

[0046] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0047] The immersive home theater system can further include an eye-tracking unit that tracks the user's gaze. The eye-tracking unit analyzes in real time which part of the video the user is looking at and adjusts the system's operation based on that information. For example, if the user is paying attention to a particular scene, the vibration corresponding to that scene can be strengthened. Furthermore, if the user moves their gaze, the vibration pattern can be changed. This makes it possible to provide an immersive entertainment experience that is tailored to the user's gaze.

[0048] The immersive home theater system may further include a voice recognition unit that recognizes user voice commands. The voice recognition unit analyzes the user's voice commands and controls the operation of the system. For example, if the user says "Increase the vibration," the system can increase the intensity of the vibration. Also, if the user says "Go to the next scene," the system can switch the video to the next scene. This makes it possible to provide an immersive entertainment experience according to the user's voice commands.

[0049] The immersive home theater system may further include a gesture recognition unit that recognizes user gestures. The gesture recognition unit analyzes the user's hand and body movements and controls the system's operation. For example, the system can pause a video when the user waves their hand. Also, the system can change the vibration pattern when the user performs a specific gesture. This allows for a immersive entertainment experience that responds to the user's gestures.

[0050] The immersive home theater system may further include a learning unit that learns user preferences. The learning unit analyzes the user's viewing history and operation history to learn the user's preferences. For example, if the user likes action movies, the system may strengthen vibrations during action scenes. On the other hand, if the user wants to relax, the system may provide gentle vibrations. This allows for a immersive entertainment experience tailored to the user's preferences.

[0051] The processing flow of the first embodiment will be briefly explained below.

[0052] Step 1: The video authentication unit analyzes the video data. For example, the video authentication unit uses a generation AI to analyze the video data and recognize specific scenes or objects. The video authentication unit can also use the generation AI to extract scene features from the video data and generate corresponding vibration patterns. Step 2: The sound recognition unit analyzes the audio data. For example, the sound recognition unit uses the generation AI to analyze the audio data and recognize specific sounds or music. The sound recognition unit can also use the generation AI to extract sound characteristics from the audio data and generate corresponding vibration patterns. Step 3: The vibration conversion unit converts the audio data into vibration data. For example, the vibration conversion unit generates vibration data according to the frequency and rhythm of the audio data. The vibration conversion unit can also adjust the strength of the vibration according to the strength of the audio data. Step 4: The image conversion unit converts the vibration data into image data. For example, the image conversion unit changes the color tone and brightness of the image based on the vibration data. The image conversion unit can also adjust the movement of the image based on the vibration data.

[0053] (Example 2) The immersive home theater system according to an embodiment of the present invention analyzes video data, analyzes audio data, converts the audio data into vibration data, and converts the vibration data into video data, thereby providing an immersive entertainment experience that integrates the visual, auditory, and tactile senses.

[0054] An immersive home theater system according to an embodiment includes a video authentication unit, a sound authentication unit, a vibration conversion unit, and a video conversion unit. The video authentication unit analyzes video data. For example, the video authentication unit uses a generation AI to analyze the video data and recognize specific scenes or objects. The video authentication unit can also use the generation AI to extract scene features based on the video data and generate a corresponding vibration pattern. The sound authentication unit analyzes audio data. For example, the sound authentication unit uses a generation AI to analyze audio data and recognize specific sounds or music. The sound authentication unit can also use the generation AI to extract sound features based on the audio data and generate a corresponding vibration pattern. The vibration conversion unit converts audio data into vibration data. For example, the vibration conversion unit generates vibration data according to the frequency and rhythm of the audio data. The vibration conversion unit can also adjust the intensity of the vibration according to the strength of the audio data. The video conversion unit converts the vibration data into video data. For example, the video conversion unit changes the color tone and brightness of the video based on the vibration data. The video conversion unit can also adjust the movement of the video based on the vibration data. As a result, the immersive home theater system according to the embodiment can provide an immersive entertainment experience that integrates the senses of sight, hearing, and touch. For example, when an explosion occurs in a movie, the video authentication unit and the sound authentication unit recognize the scene, the vibration conversion unit generates vibrations, and the video conversion unit reflects the vibrations in the video, allowing the viewer to experience a sense of realism as if they were actually there.

[0055] The video authentication unit can use generation AI to analyze the emotional tone of a scene and generate a vibration pattern that corresponds to the emotion. For example, the video authentication unit can use generation AI to analyze the emotional tone of a scene and generate a vibration pattern that corresponds to the emotion. For example, it can generate gentle vibrations for sad scenes and strong vibrations for tense scenes. The video authentication unit can also use generation AI to analyze the emotional tone of a scene and generate a vibration pattern that corresponds to the emotion. For example, it can generate rhythmic vibrations for happy scenes and irregular vibrations for scary scenes. The video authentication unit can also use generation AI to analyze the emotional tone of a scene and generate a vibration pattern that corresponds to the emotion. For example, it can generate subtle vibrations for romantic scenes and intense vibrations for action scenes. This allows for a more realistic experience by generating vibration patterns that correspond to the emotions of the scene.

[0056] The video authentication unit can train the generative AI on a database of past movies to improve the accuracy of scene prediction. For example, the video authentication unit trains the generative AI on a database of past movies to improve the accuracy of scene prediction. For example, by training the AI ​​on movie data from a specific genre, scenes specific to that genre can be recognized with high accuracy. The video authentication unit also trains the generative AI on a database of past movies to improve the accuracy of scene prediction. For example, by training the AI ​​on movie data from a specific director or actor, scenes characteristic of that director or actor can be recognized. The video authentication unit also trains the generative AI on a database of past movies to improve the accuracy of scene prediction. For example, by training the AI ​​on movie data containing specific historical backgrounds or cultural elements, scenes unique to that era or culture can be recognized. In this way, by training the AI ​​on past data, the accuracy of scene recognition can be improved.

[0057] The video authentication unit may visually display the results of the video authentication in real time, allowing the user to check the recognized scene. For example, the video authentication unit may visually display the results of the video authentication in real time, allowing the user to check the recognized scene. For example, the video authentication unit may pop up information about the recognized scene in a corner of the screen. The video authentication unit may also visually display the results of the video authentication in real time, allowing the user to check the recognized scene. For example, the video authentication unit may overlay an icon or text corresponding to the recognized scene on the screen. The video authentication unit may also visually display the results of the video authentication in real time, allowing the user to check the recognized scene. For example, the video authentication unit may provide an interactive widget for displaying detailed information about the recognized scene. This allows the user to check the recognized scene in real time.

[0058] The video authentication unit can recognize specific scenes not only in movies but also in sporting events and documentary programs, and generate corresponding vibrations. The video authentication unit can recognize specific scenes not only in movies but also in sporting events, and generate corresponding vibrations. For example, strong vibrations are generated for goal scenes and highlight scenes. The video authentication unit can also recognize specific scenes in documentary programs and generate corresponding vibrations. For example, appropriate vibrations are generated for tense and moving scenes. The video authentication unit can also recognize specific scenes in live concerts and generate corresponding vibrations. For example, different vibrations are generated for exciting and quiet scenes. This makes it possible to provide an immersive viewing experience even for content other than movies.

[0059] The video authentication unit transmits the results of the video authentication to a mobile device such as a smartphone or tablet, thereby providing an immersive viewing experience even when the user is out and about. The video authentication unit, for example, transmits the results of the video authentication to a smartphone, thereby providing an immersive viewing experience even when the user is out and about. For example, vibrations corresponding to specific scenes in a movie are reproduced on the smartphone. The video authentication unit also transmits the results of the video authentication to a tablet, thereby providing an immersive viewing experience even when the user is out and about. For example, vibrations corresponding to specific scenes in a sporting event are reproduced on the tablet. The video authentication unit also transmits the results of the video authentication to a wearable device, thereby providing an immersive viewing experience even when the user is out and about. For example, vibrations corresponding to specific scenes in a live concert are reproduced on the wearable device. This makes it possible to provide an immersive viewing experience even when the user is out and about.

[0060] The video authentication unit can use the emotion estimation function to analyze the emotion the user feels while watching in real time and generate a vibration pattern corresponding to that emotion. The video authentication unit, for example, uses the emotion estimation function to analyze the emotion the user feels while watching in real time and generate a vibration pattern corresponding to that emotion. For example, if the user is excited, a strong vibration is generated. The video authentication unit also uses the emotion estimation function to analyze the emotion the user feels while watching in real time and generate a vibration pattern corresponding to that emotion. For example, if the user is relaxed, a gentle vibration is generated. The video authentication unit also uses the emotion estimation function to analyze the emotion the user feels while watching in real time and generate a vibration pattern corresponding to that emotion. For example, if the user is surprised, an irregular vibration is generated. In this way, a vibration pattern corresponding to the user's emotion can be generated, providing a more personalized experience.

[0061] The sound authentication unit can use generation AI to analyze the emotional tone of the sound and generate a vibration pattern corresponding to the emotion. For example, the sound authentication unit can use generation AI to analyze the emotional tone of the sound and generate a vibration pattern corresponding to the emotion. For example, it can generate gentle vibrations for sad music and strong vibrations for intense music. The sound authentication unit can also use generation AI to analyze the emotional tone of the sound and generate a vibration pattern corresponding to the emotion. For example, it can generate rhythmic vibrations for joyful music and irregular vibrations for scary music. The sound authentication unit can also use generation AI to analyze the emotional tone of the sound and generate a vibration pattern corresponding to the emotion. For example, it can generate subtle vibrations for romantic music and strong vibrations for action scene sounds. This allows for the generation of vibration patterns corresponding to the emotion of the sound, providing a more realistic experience.

[0062] The sound authentication unit can improve the accuracy of sound prediction by having the generation AI learn from a past music database. For example, the sound authentication unit can improve the accuracy of sound prediction by having the generation AI learn from a past music database. For example, by having the generation AI learn from music data of a specific genre, it can recognize sounds unique to that genre with high accuracy. The sound authentication unit can also have the generation AI learn from a past music database to improve the accuracy of sound prediction. For example, by having the generation AI learn from music data of a specific artist or band, it can recognize the characteristic sounds of that artist or band. The sound authentication unit can also have the generation AI learn from a past music database to improve the accuracy of sound prediction. For example, by having the generation AI learn from music data that includes specific historical backgrounds and cultural elements, it can recognize sounds unique to that era or culture. In this way, by learning from past data, it is possible to improve the accuracy of sound recognition.

[0063] The sound authentication unit may visually display the results of the sound authentication in real time, allowing the user to check the recognized sound. For example, the sound authentication unit may visually display the results of the sound authentication in real time, allowing the user to check the recognized sound. For example, the sound authentication unit may pop up information about the recognized sound in a corner of the screen. The sound authentication unit may also visually display the results of the sound authentication in real time, allowing the user to check the recognized sound. For example, the sound authentication unit may overlay an icon or text corresponding to the recognized sound on the screen. The sound authentication unit may also visually display the results of the sound authentication in real time, allowing the user to check the recognized sound. For example, an interactive widget may be provided to display detailed information about the recognized sound. This allows the user to check the recognized sound in real time.

[0064] The sound authentication unit can recognize specific scenes not only in movies but also in podcasts and audiobooks, and generate corresponding vibrations. The sound authentication unit can recognize specific scenes not only in movies but also in podcasts, and generate corresponding vibrations. For example, it generates appropriate vibrations for moving or tense scenes. The sound authentication unit can also recognize specific scenes in audiobooks and generate corresponding vibrations. For example, it can generate strong vibrations for the climax or important scenes of a story. The sound authentication unit can also recognize specific scenes in radio dramas and generate corresponding vibrations. For example, it can generate different vibrations for action scenes or moving scenes. This makes it possible to provide an immersive viewing experience for content other than movies.

[0065] The sound authentication unit can transmit the result of the sound authentication to an audio device such as a smart speaker or headphones, allowing the user to experience not only sound but also vibration. The sound authentication unit, for example, transmits the result of the sound authentication to a smart speaker, allowing the user to experience not only sound but also vibration. For example, vibrations corresponding to specific sounds are reproduced on the smart speaker. The sound authentication unit can also transmit the result of the sound authentication to headphones, allowing the user to experience not only sound but also vibration. For example, vibrations corresponding to the rhythm or beat of music are reproduced on the headphones. The sound authentication unit can also transmit the result of the sound authentication to a wearable device, allowing the user to experience not only sound but also vibration. For example, vibrations corresponding to specific scenes in a podcast or audiobook are reproduced on the wearable device. This allows the user to experience not only sound but also vibration.

[0066] The sound authentication unit can use the emotion estimation function to analyze the emotion the user feels while listening in real time and generate a vibration pattern corresponding to the emotion. The sound authentication unit, for example, uses the emotion estimation function to analyze the emotion the user feels while listening in real time and generate a vibration pattern corresponding to the emotion. For example, if the user is excited, a strong vibration is generated. The sound authentication unit also uses the emotion estimation function to analyze the emotion the user feels while listening in real time and generate a vibration pattern corresponding to the emotion. For example, if the user is relaxed, a gentle vibration is generated. The sound authentication unit also uses the emotion estimation function to analyze the emotion the user feels while listening in real time and generate a vibration pattern corresponding to the emotion. For example, if the user is surprised, an irregular vibration is generated. This makes it possible to provide a more personalized experience by generating a vibration pattern corresponding to the user's emotion.

[0067] The vibration conversion unit can use the generation AI to analyze the emotional tone of the sound and generate a vibration pattern corresponding to the emotion. For example, the vibration conversion unit can use the generation AI to analyze the emotional tone of the sound and generate a vibration pattern corresponding to the emotion. For example, it can generate gentle vibrations for sad music and strong vibrations for intense music. The vibration conversion unit can also use the generation AI to analyze the emotional tone of the sound and generate a vibration pattern corresponding to the emotion. For example, it can generate rhythmic vibrations for joyful music and irregular vibrations for scary music. The vibration conversion unit can also use the generation AI to analyze the emotional tone of the sound and generate a vibration pattern corresponding to the emotion. For example, it can generate subtle vibrations for romantic music and intense vibrations for action scene sounds. This allows for the generation of vibration patterns corresponding to the emotion of the sound, providing a more realistic experience.

[0068] The vibration converter unit generates different vibration patterns according to the frequency or rhythm of the sound, thereby providing a more diverse tactile experience. The vibration converter unit generates different vibration patterns according to, for example, the frequency of the sound, thereby providing a more diverse tactile experience. For example, it generates fine vibrations for high-frequency sounds and strong vibrations for low-frequency sounds. The vibration converter unit also generates different vibration patterns according to the rhythm of the sound, thereby providing a more diverse tactile experience. For example, it generates continuous vibrations for sounds with a fast rhythm and intermittent vibrations for sounds with a slow rhythm. The vibration converter unit also generates different vibration patterns according to the strength of the sound, thereby providing a more diverse tactile experience. For example, it generates strong vibrations for loud sounds and gentle vibrations for soft sounds. In this way, by generating vibration patterns according to the frequency or rhythm of the sound, a more diverse tactile experience can be provided.

[0069] The vibration conversion unit can improve the technology for converting sound into vibrations, allowing the user to customize the intensity and pattern of vibrations. For example, the vibration conversion unit can improve the technology for converting sound into vibrations, allowing the user to customize the intensity and pattern of vibrations. For example, a function can be provided that allows the user to change vibration settings through an application. The vibration conversion unit can also improve the technology for converting sound into vibrations, allowing the user to customize the intensity and pattern of vibrations. For example, a function can be provided that allows the user to select a preset vibration pattern. The vibration conversion unit can also improve the technology for converting sound into vibrations, allowing the user to customize the intensity and pattern of vibrations. For example, an editor can be provided that allows the user to create their own vibration pattern. This allows the user to customize the intensity and pattern of vibrations.

[0070] The vibration converter uses a technology for converting sound into vibrations to convert the sounds of not only movies but also live music concerts and sporting events into vibrations, thereby providing a sense of realism. The vibration converter, for example, uses the technology for converting sound into vibrations to convert the sounds of live music concerts into vibrations, thereby providing a sense of realism. For example, vibrations are generated in time with the beat of the live concert. The vibration converter also uses the technology for converting sound into vibrations to convert the sounds of sporting events into vibrations, thereby providing a sense of realism. For example, vibrations are generated in time with goal scenes and cheers. The vibration converter also uses the technology for converting sound into vibrations to convert the sounds of movies into vibrations, thereby providing a sense of realism. For example, vibrations are generated in time with action scenes and moving scenes. This makes it possible to provide a sense of realism even with content other than movies.

[0071] The vibration conversion unit incorporates technology that converts sound into vibrations into smart home devices, allowing for the provision of immersive entertainment throughout the home. The vibration conversion unit, for example, incorporates technology that converts sound into vibrations into smart home devices, allowing for the provision of immersive entertainment throughout the home. For example, it may be linked to a smart speaker or smart light. The vibration conversion unit may also incorporate technology that converts sound into vibrations into smart home devices, allowing for the provision of immersive entertainment throughout the home. For example, it may be linked to a smart bed or smart sofa. The vibration conversion unit may also incorporate technology that converts sound into vibrations into smart home devices, allowing for the provision of immersive entertainment throughout the home. For example, it may be linked to a smart wall or smart floor. This allows for the provision of immersive entertainment throughout the home.

[0072] The vibration conversion unit can use the emotion estimation function to analyze the emotion felt by the user while listening in real time and generate a vibration pattern corresponding to the emotion. The vibration conversion unit, for example, uses the emotion estimation function to analyze the emotion felt by the user while listening in real time and generate a vibration pattern corresponding to the emotion. For example, if the user is excited, a strong vibration is generated. The vibration conversion unit also uses the emotion estimation function to analyze the emotion felt by the user while listening in real time and generate a vibration pattern corresponding to the emotion. For example, if the user is relaxed, a gentle vibration is generated. The vibration conversion unit also uses the emotion estimation function to analyze the emotion felt by the user while listening in real time and generate a vibration pattern corresponding to the emotion. For example, if the user is surprised, an irregular vibration is generated. In this way, a vibration pattern corresponding to the user's emotion can be generated, providing a more personalized experience.

[0073] The image conversion unit can use the generation AI to analyze the emotional tone of the vibrations and perform image conversion according to the emotion. For example, the image conversion unit can use the generation AI to analyze the emotional tone of the vibrations and perform image conversion according to the emotion. For example, the image can be darkened for sad vibrations and brightened for happy vibrations. The image conversion unit can also use the generation AI to analyze the emotional tone of the vibrations and perform image conversion according to the emotion. For example, the image can be wobbly for tense vibrations and smoothed for relaxed vibrations. The image conversion unit can also use the generation AI to analyze the emotional tone of the vibrations and perform image conversion according to the emotion. For example, the image can be darkened for fearful vibrations and softened for romantic vibrations. This makes it possible to provide a more realistic visual experience by converting the image according to the emotion of the vibrations.

[0074] The image conversion unit can change the color tone and brightness of the image according to the intensity or pattern of the vibration to enhance the visual effect. The image conversion unit, for example, changes the color tone of the image according to the intensity of the vibration to enhance the visual effect. For example, the image is made vivid for strong vibrations and pale for weak vibrations. The image conversion unit also changes the brightness of the image according to the vibration pattern to enhance the visual effect. For example, the image is made brighter for continuous vibrations and darker for intermittent vibrations. The image conversion unit also changes the color tone and brightness of the image according to the intensity and pattern of the vibration to enhance the visual effect. For example, the image is made more contrasty for strong vibrations and softer for weak vibrations. In this way, the visual effect can be enhanced by converting the image according to the intensity and pattern of the vibration.

[0075] The image conversion unit can improve the technology for converting images using vibrations to enable a user to customize the image conversion pattern. For example, the image conversion unit improves the technology for converting images using vibrations to enable a user to customize the image conversion pattern. For example, the image conversion unit provides a function for changing image settings through an application. The image conversion unit also improves the technology for converting images using vibrations to enable a user to customize the image conversion pattern. For example, the image conversion unit provides a function for selecting a preset image conversion pattern. The image conversion unit also improves the technology for converting images using vibrations to enable a user to customize the image conversion pattern. For example, the image conversion unit provides an editor that allows a user to create their own image conversion pattern. This allows a user to customize the image conversion pattern.

[0076] The image conversion unit uses the technology to convert images using vibration to convert images not only of movies but also of games and virtual reality, thereby providing a sense of immersion. The image conversion unit, for example, uses the technology to convert images using vibration to convert game images to provide a sense of immersion. For example, the image changes according to actions in the game. The image conversion unit also uses the technology to convert images using vibration to convert virtual reality images to provide a sense of immersion. For example, the image changes according to vibrations during a VR experience. The image conversion unit also uses the technology to convert images using vibration to convert movie images to provide a sense of immersion. For example, the image changes according to specific scenes in a movie. This makes it possible to provide a sense of immersion with content other than movies.

[0077] The image conversion unit can incorporate technology that converts images using vibration into smart glasses or a head-mounted display to provide an integrated visual and tactile experience. For example, the image conversion unit can incorporate technology that converts images using vibration into smart glasses to provide an integrated visual and tactile experience. For example, the image can be changed in response to the vibration of the smart glasses. The image conversion unit can also incorporate technology that converts images using vibration into a head-mounted display to provide an integrated visual and tactile experience. For example, the image can be changed in response to the vibration of the HMD. The image conversion unit can also incorporate technology that converts images using vibration into a wearable device to provide an integrated visual and tactile experience. For example, the image can be changed in response to the vibration of the wearable device. This makes it possible to provide an integrated visual and tactile experience using smart glasses or a head-mounted display.

[0078] The image conversion unit can use the emotion estimation function to analyze the emotion felt by the user while watching in real time and perform image conversion according to the emotion. The image conversion unit, for example, uses the emotion estimation function to analyze the emotion felt by the user while watching in real time and perform image conversion according to the emotion. For example, if the user is excited, the image is made vivid. The image conversion unit can also use the emotion estimation function to analyze the emotion felt by the user while watching in real time and perform image conversion according to the emotion. For example, if the user is relaxed, the image is made softer. The image conversion unit can also use the emotion estimation function to analyze the emotion felt by the user while watching in real time and perform image conversion according to the emotion. For example, if the user is surprised, the image is shaken. This makes it possible to provide a more personalized visual experience by performing image conversion according to the user's emotion.

[0079] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0080] The immersive home theater system may further include a biometric information acquisition unit that acquires biometric information of the user. The biometric information acquisition unit, for example, measures heart rate and electrodermal response to monitor the user's physiological reactions in real time. This allows the system to grasp the user's level of excitement or relaxation and adjust the system's operation accordingly. For example, if the user's heart rate increases, the system can increase the intensity of vibrations. On the other hand, if the user is relaxed, the system can soften the vibrations. This makes it possible to provide a immersive entertainment experience that corresponds to the user's physiological state.

[0081] The immersive home theater system can further include an eye-tracking unit that tracks the user's gaze. The eye-tracking unit analyzes in real time which part of the video the user is looking at and adjusts the system's operation based on that information. For example, if the user is paying attention to a particular scene, the vibration corresponding to that scene can be strengthened. Furthermore, if the user moves their gaze, the vibration pattern can be changed. This makes it possible to provide an immersive entertainment experience that is tailored to the user's gaze.

[0082] The immersive home theater system may further include a voice recognition unit that recognizes user voice commands. The voice recognition unit analyzes the user's voice commands and controls the operation of the system. For example, if the user says "Increase the vibration," the system can increase the intensity of the vibration. Also, if the user says "Go to the next scene," the system can switch the video to the next scene. This makes it possible to provide an immersive entertainment experience according to the user's voice commands.

[0083] The immersive home theater system may further include a gesture recognition unit that recognizes user gestures. The gesture recognition unit analyzes the user's hand and body movements and controls the system's operation. For example, the system can pause a video when the user waves their hand. Also, the system can change the vibration pattern when the user performs a specific gesture. This allows for a immersive entertainment experience that responds to the user's gestures.

[0084] The immersive home theater system may further include an emotion estimation unit that estimates the user's emotion. The emotion estimation unit analyzes the user's facial expression and tone of voice to estimate the user's emotion in real time. For example, if the user is laughing, the system may make the vibrations rhythmic. If the user is surprised, the system may make the vibrations stronger. This makes it possible to provide a immersive entertainment experience that corresponds to the user's emotion.

[0085] The immersive home theater system may further include a learning unit that learns user preferences. The learning unit analyzes the user's viewing history and operation history to learn the user's preferences. For example, if the user likes action movies, the system may strengthen vibrations during action scenes. On the other hand, if the user wants to relax, the system may provide gentle vibrations. This allows for a immersive entertainment experience tailored to the user's preferences.

[0086] The immersive home theater system can also estimate the user's emotions and generate visual effects according to those emotions. For example, if the user feels sad, the color tone of the image can be darkened. If the user feels excited, the contrast of the image can be increased. This allows for a visually immersive entertainment experience that corresponds to the user's emotions.

[0087] The immersive home theater system can further estimate the user's emotions and generate audio effects according to those emotions. For example, if the user is relaxed, the tone of the audio can be softened. If the user is nervous, the tone of the audio can be strengthened. This makes it possible to provide an auditory immersive entertainment experience that corresponds to the user's emotions.

[0088] The immersive home theater system can also estimate the user's emotions and generate vibration patterns according to those emotions. For example, if the user is happy, the vibrations can be rhythmic. If the user is scared, the vibrations can be irregular. This allows for a tactile entertainment experience that corresponds to the user's emotions.

[0089] The immersive home theater system can also estimate the user's emotions and recommend content according to those emotions. For example, if the user wants to relax, relaxing movies and music can be recommended. If the user wants to get excited, action movies and energetic music can be recommended. This makes it possible to provide content according to the user's emotions.

[0090] The processing flow of the second embodiment will be briefly explained below.

[0091] Step 1: The video authentication unit analyzes the video data. For example, the video authentication unit uses a generation AI to analyze the video data and recognize specific scenes or objects. The video authentication unit can also use the generation AI to extract scene features from the video data and generate corresponding vibration patterns. Step 2: The sound recognition unit analyzes the audio data. For example, the sound recognition unit uses the generation AI to analyze the audio data and recognize specific sounds or music. The sound recognition unit can also use the generation AI to extract sound characteristics from the audio data and generate corresponding vibration patterns. Step 3: The vibration conversion unit converts the audio data into vibration data. For example, the vibration conversion unit generates vibration data according to the frequency and rhythm of the audio data. The vibration conversion unit can also adjust the strength of the vibration according to the strength of the audio data. Step 4: The image conversion unit converts the vibration data into image data. For example, the image conversion unit changes the color tone and brightness of the image based on the vibration data. The image conversion unit can also adjust the movement of the image based on the vibration data.

[0092] 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.

[0093] 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 generative AIs include the data generation model 58, such as a neural network model (e.g., a neural network model), and a neural network model (e.g., a neural network model). The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating speech, text data indicating text, and image data indicating an image is also input to the data generation model 58. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0094] 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.

[0095] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0096] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0097] 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.

[0098] 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.

[0099] 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.

[0100] 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).

[0101] 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.

[0102] 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.

[0103] 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.

[0104] 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.

[0105] 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.

[0106] 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.

[0107] 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.

[0108] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0109] 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.

[0110] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0111] 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.

[0112] 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.

[0113] 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.

[0114] 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.

[0115] 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).

[0116] 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.

[0117] 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.

[0118] 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.

[0119] 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.

[0120] In the headset type terminal 314, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. Note that the headset type terminal 314 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the specific processing unit 290 using these models.

[0121] 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.

[0122] 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.

[0123] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0124] 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.

[0125] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0126] 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0127] 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.

[0128] 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.

[0129] 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.

[0130] 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).

[0131] 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.

[0132] 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.

[0133] 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.

[0134] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

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

[0136] In the robot 414, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. The robot 414 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the specific processing unit 290 using these models.

[0137] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0138] The specific processing unit 290 transmits the result of the specific processing to the 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.

[0139] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0140] 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.

[0141] 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.

[0142] 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.

[0143] 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.

[0144] 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).

[0145] 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.

[0146] 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."

[0147] 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.

[0148] 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.

[0149] 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.

[0150] 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.

[0151] 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.

[0152] 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.

[0153] 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.

[0154] 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.

[0155] 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.

[0156] 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.

[0157] 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.

[0158] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference. [Explanation of symbols]

[0159] 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 video authentication unit that analyzes video data; a sound authentication unit that analyzes the voice data; a vibration conversion unit that converts audio data into vibration data; an image conversion unit that converts vibration data into image data; A system characterized by:

2. The video authentication unit Uses generative AI to analyze the emotional tone of a scene and generate vibration patterns based on that emotion 2. The system of claim 1.

3. The video authentication unit The generative AI learns from a database of past movies to improve scene prediction accuracy.

2. The system of claim 1.

4. The video authentication unit Visually display the results of video authentication in real time, allowing users to confirm the recognized scene 2. The system of claim 1.

5. The video authentication unit It can recognize specific scenes in movies, sporting events, and documentaries, and generate corresponding vibrations.

2. The system of claim 1.

6. The video authentication unit Video authentication results are sent to mobile devices such as smartphones or tablets, providing an immersive viewing experience on the go.

2. The system of claim 1.

7. The video authentication unit Analyzes the emotions felt by the user while watching in real time and generates vibration patterns according to those emotions 2. The system of claim 1.

8. The sound authentication unit Generative AI is used to analyze the emotional tone of the sound and generate vibration patterns according to the emotion.

2. The system of claim 1.

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