System

Abstract

An object of the system according to the embodiment is to convert a user's thought into a specific image and display the image in real time.SOLUTION: A system includes a thought analysis part, an image generation part, and a display part. The thought analyzer analyzes a brain wave or a nerve signal of the user. The image generation unit generates a specific image based on the thought content analyzed by the thought analysis unit. The display unit displays the image generated by the image generation unit in real time.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 convert a user's thoughts into concrete images and display them in real time.

[0005] The system according to the embodiment aims to convert a user's thoughts into concrete images and display them in real time. [Means for solving the problem]

[0006] The system according to the embodiment includes a thought analysis unit, an image generation unit, and a display unit. The thought analysis unit analyzes the user's brain waves or neural signals. The image generation unit generates a specific image based on the thought content analyzed by the thought analysis unit. The display unit displays the image generated by the image generation unit in real time. [Effects of the Invention]

[0007] The system according to the embodiment can convert a user's thoughts into concrete images and display them in real time. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example 1) The thought-image conversion system according to the embodiment of the present invention is a system that converts a user's thoughts into concrete images and displays them in real time, thereby providing a new means of communication by converting the user's thoughts into concrete images.

[0029] A thought-image conversion system according to an embodiment includes a thought analysis unit, an image generation unit, and a display unit. The thought analysis unit analyzes a user's brain waves or neural signals. For example, the thought analysis unit collects brain wave data using EEG and analyzes it using signal processing technology. The thought analysis unit can also collect brain wave data using MEG and analyze it using a machine learning algorithm. The thought analysis unit can also collect and analyze brain wave data using fMRI. For example, EEG measures brain waves by attaching electrodes to the scalp. MEG analyzes brain activity by measuring magnetic fields. fMRI analyzes brain activity by measuring changes in blood flow. The image generation unit generates specific images based on the thought content analyzed by the thought analysis unit. For example, the image generation unit converts the thought content into specific images using CG technology. The image generation unit can also generate images using deep learning technology. The image generation unit can also generate images using generative AI. For example, CG technology generates images using computer graphics. Deep learning technology generates images by learning from large amounts of data. The generation AI generates images using a text generation AI (e.g., LLM) or a multimodal generation AI. The display unit displays the images generated by the image generation unit in real time. For example, the display unit displays images using a display. The display unit can also display images using a projector. The display unit can also display images using a VR headset. For example, the display uses an LCD display or an OLED display. The projector projects images onto a screen. The VR headset displays images in a virtual reality space. As a result, the thought-image conversion system according to the embodiment can convert a user's thoughts into concrete images and display them in real time. For example, by displaying a landscape imagined by the user in real time, the relaxation effect can be enhanced. Furthermore, by displaying an image imagined by the user as an image, communication with a doctor or family can be facilitated. As a result, the user's feelings and wishes can be more accurately conveyed.

[0030] The thought analysis unit can perform more accurate thought analysis by referring to the user's past thought patterns and history when analyzing brain waves or neural signals. The thought analysis unit, for example, stores the user's past brain wave data in a database and refers to it when analyzing thoughts. For example, the analysis is performed based on the brain wave patterns when a specific image was thought of in the past. The thought analysis unit also analyzes the user's thought history and develops an algorithm to extract specific patterns. For example, it predicts the content of current thoughts based on past thought patterns. The thought analysis unit also builds a system that improves the accuracy of thought analysis by referring to past thought patterns. For example, it compares images that the user thought of in the past with current brain wave patterns. In this way, the accuracy of thought analysis is improved by referring to past thought patterns and history.

[0031] The thought analysis unit can combine audio or visual stimuli with the user's thought analysis to analyze the thought content from multiple angles. For example, the thought analysis unit provides audio or visual stimuli in response to an image that the user has in mind and analyzes the reaction. For example, it displays specific music or images and analyzes changes in brain waves. The thought analysis unit also builds a system that analyzes thought content from multiple angles by combining audio and visual stimuli. For example, it analyzes brain wave patterns when a user listens to specific music. The thought analysis unit also improves the accuracy of the analysis by using audio or visual stimuli in the user's thought analysis. For example, it analyzes thought content based on brain wave patterns when a user views a specific image. In this way, by combining audio and visual stimuli, the thought content can be analyzed from multiple angles.

[0032] The thought analysis unit can collect EEG data from users of different age groups or cultural backgrounds and diversify the analysis algorithm. The thought analysis unit, for example, collects EEG data from users of different age groups and diversifies the analysis algorithm. For example, the EEG patterns of children and the elderly are analyzed and reflected in the algorithm. The thought analysis unit also collects EEG data from users of different cultural backgrounds and diversifies the analysis algorithm. For example, the EEG patterns of users from different cultural spheres are analyzed and reflected in the algorithm. The thought analysis unit also builds a system that improves the analysis algorithm based on EEG data from users of different age groups and cultural backgrounds. For example, the accuracy of thought analysis is improved by using diverse data. In this way, the analysis algorithm can be diversified by collecting data from users of different age groups and cultural backgrounds.

[0033] The image generation unit can generate continuous images by reflecting the user's past thought history in the generated images. For example, the image generation unit stores the user's past thought history in a database, and the generation AI generates continuous images based on that data. For example, it generates a new landscape based on a landscape imagined in the past. The image generation unit also develops an algorithm that allows the generation AI to generate continuous images by reflecting the user's past thought history. For example, it generates a new image based on an image the user imagined in the past. The image generation unit also analyzes the user's thought history and builds a system that generates continuous images. For example, it generates a new image by combining a past image with current thought content. In this way, continuous images can be generated by reflecting the user's past thought history.

[0034] The image generation unit can combine audio or text information with the images it generates to create multimedia expressions. For example, the image generation unit builds a system that combines audio and text information with images generated by the generation AI. For example, it adds natural sounds or explanatory text to landscape images. The image generation unit also develops an algorithm that enables the generation AI to create multimedia expressions by combining audio and text information. For example, it adds audio guidance related to images. The image generation unit also builds a system that combines audio and text information in real time with images generated by the generation AI. For example, it adds explanatory text for an image that the user has in mind. In this way, by combining audio and text information, multimedia expressions become possible.

[0035] The image generation unit can generate images in different styles or art forms, enabling customization according to the user's preferences. The image generation unit, for example, develops an algorithm that enables a generation AI to generate images in different styles or art forms. For example, the image generation unit allows a user to select a style such as classical art or modern art according to their preferences. The image generation unit also builds a system that enables a generation AI to generate images in different art forms to enable customization according to the user's preferences. For example, the image generation unit generates images based on a style selected by the user. The image generation unit also develops a system that enables customization according to the user's preferences by generating images in different styles or art forms. For example, the image generation unit reflects the user's preferred color tones and composition. This enables customization according to the user's preferences by generating images in different styles or art forms.

[0036] The display unit can refer to the user's past thought history when displaying in real time, and provide a continuous display. The display unit, for example, stores the user's past thought history in a database and builds a system that references it when displaying in real time. For example, a continuous display is provided based on images displayed in the past. The display unit also develops an algorithm that maintains the continuity of the real-time display by referencing the past thought history. For example, the current display is adjusted based on images that the user had in the past. The display unit also analyzes the user's thought history and builds a system that provides a continuous real-time display. For example, past images and current thought content are combined and displayed. In this way, a continuous display is possible by referencing the user's past thought history.

[0037] The display unit can combine audio or haptic feedback with real-time display to provide a multimodal experience. The display unit, for example, builds a system that combines audio feedback with real-time display. For example, it plays natural sounds corresponding to the displayed scenery. The display unit also develops an algorithm that improves the real-time display experience by combining haptic feedback. For example, it provides vibrations corresponding to the displayed scenery. The display unit also builds a system that provides a multimodal experience by combining audio and haptic feedback. For example, it provides audio and haptic feedback corresponding to an image that the user has in mind. This makes it possible to provide a multimodal experience by combining audio and haptic feedback.

[0038] The display unit can enable real-time display on different devices. The display unit, for example, uses a VR headset to build a system that displays a user's thoughts in real time. For example, it displays a landscape imagined by the user in a VR space. The display unit also uses smart glasses to develop an algorithm that displays a user's thoughts in real time. For example, it displays an image imagined by the user on the display of the smart glasses. The display unit also builds a system that enables real-time display on different devices. For example, it adjusts the display method depending on the device selected by the user. This makes it possible to display in real time on different devices.

[0039] The system can refer to a user's past thought history as a means of communication and generate continuous messages. For example, the system builds a system that stores a user's past thought history in a database and references it as a means of communication. For example, it generates continuous messages based on messages sent in the past. The system also develops an algorithm that generates continuous messages by referencing the past thought history. For example, it adjusts a current message based on images that the user had in the past. The system also builds a system that analyzes a user's thought history and generates continuous messages. For example, it combines past messages with current thought content to generate a new message. In this way, continuous messages can be generated by referencing the user's past thought history.

[0040] The system can provide multimodal communication by combining audio or text information as a means of communication. For example, the system builds a system that combines audio and text information as a means of communication. For example, audio guidance is added to a message. The system also develops an algorithm that provides multimodal communication by combining audio and text information. For example, audio explanations related to a message are added. The system also builds a system that combines audio and text information in real time as a means of communication. For example, explanatory text is added for an image that a user has in mind. This makes multimodal communication possible by combining audio and text information.

[0041] The system can add a translation function that enables communication between users with different languages ​​or cultural backgrounds. For example, the system builds a system that adds a translation function that enables communication between users who speak different languages. For example, it translates messages in real time. The system also develops a translation function that takes cultural nuances into account to facilitate communication between users with different cultural backgrounds. For example, it appropriately translates culture-specific expressions. The system also builds a system that uses the translation function to support communication between users with different languages ​​and cultural backgrounds. For example, it provides cultural background information at the same time as translating a message. This enables communication between users with different languages ​​and cultural backgrounds.

[0042] As an application in the medical field, the system can refer to a patient's past thought history to provide a continuous treatment plan. For example, the system stores a patient's past thought history in a database and builds a system that references it as an application in the medical field. For example, a continuous treatment plan is provided based on past treatment plans. The system also develops an algorithm that provides a continuous treatment plan by referencing the past thought history. For example, the current treatment plan is adjusted based on images that the patient had in the past. The system also builds a system that analyzes the patient's thought history to provide a continuous treatment plan. For example, a new treatment plan is provided by combining past treatment plans with current thought content. In this way, a continuous treatment plan can be provided by referencing the patient's past thought history.

[0043] The system can provide multimodal treatment by combining audio or haptic feedback as an application in the medical field. For example, the system builds a system that combines audio feedback as an application in the medical field. For example, it plays music that has a relaxing effect during treatment. The system also develops an algorithm that provides multimodal treatment by combining haptic feedback. For example, it provides vibration that has a relaxing effect during treatment. The system also builds a system that provides multimodal treatment by combining audio and haptic feedback. For example, it provides audio and haptic feedback that corresponds to an image that the patient has in mind. This makes multimodal treatment possible by combining audio and haptic feedback.

[0044] The system can add functions that enable applications in different medical fields. For example, a system is constructed that adds functions that enable applications in different medical fields. For example, a system that displays scenery that has a relaxing effect as an application in psychiatry. Also, the system is developed an algorithm that adds functions that enable applications in rehabilitation. For example, the system visualizes the patient's target movements and displays them in real time. Also, the system improves the versatility of the system by adding functions that enable applications in different medical fields. For example, a function that can be used in both psychiatry and rehabilitation is provided. This enables applications in different medical fields.

[0045] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0046] The thought analysis unit can perform more accurate thought analysis by referring to the user's past thought patterns and history when analyzing the user's brain waves or neural signals. For example, the user's past brain wave data is stored in a database and referenced during thought analysis. Analysis is performed based on the brain wave patterns when a specific image was thought of in the past. An algorithm is also developed to analyze the user's thought history and extract specific patterns. Current thoughts are predicted based on past thought patterns. Furthermore, a system is constructed that improves the accuracy of thought analysis by referring to past thought patterns. Images that the user has thought of in the past are compared with the user's current brain wave pattern. In this way, the accuracy of thought analysis is improved by referring to past thought patterns and history.

[0047] The thought analysis unit can combine audio or visual stimuli with the user's thought analysis to analyze the content of thoughts from multiple angles. For example, audio or visual stimuli are provided in response to an image the user has in mind, and the reaction is analyzed. Specific music or images are displayed, and changes in brain waves are analyzed. Furthermore, by combining audio and visual stimuli, a system can be constructed that analyzes the content of thoughts from multiple angles. The brain wave patterns when the user listens to specific music are analyzed. Furthermore, by using audio and visual stimuli in the user's thought analysis, the accuracy of the analysis is improved. The content of thoughts is analyzed based on the brain wave patterns when the user views a specific image. In this way, by combining audio and visual stimuli, the content of thoughts can be analyzed from multiple angles.

[0048] The thought analysis unit can collect EEG data from users of different age groups or cultural backgrounds to diversify the analysis algorithm. For example, EEG data from users of different age groups can be collected to diversify the analysis algorithm. The EEG patterns of children and the elderly can be analyzed and reflected in the algorithm. EEG data from users of different cultural backgrounds can also be collected to diversify the analysis algorithm. The EEG patterns of users from different cultural backgrounds can be analyzed and reflected in the algorithm. Furthermore, a system can be constructed that improves the analysis algorithm based on EEG data from users of different age groups and cultural backgrounds. The accuracy of thought analysis can be improved using diverse data. In this way, the analysis algorithm can be diversified by collecting data from users of different age groups and cultural backgrounds.

[0049] The image generation unit can reflect the user's past thought history in the generated image, generating continuous images. For example, the user's past thought history is stored in a database, and the generation AI generates continuous images based on that data. A new landscape is generated based on a landscape imagined in the past. In addition, an algorithm will be developed that allows the generation AI to generate continuous images by reflecting past thought history. A new image will be generated based on an image the user has imagined in the past. Furthermore, a system will be built that analyzes the user's thought history and generates continuous images. A new image will be generated by combining past images and current thoughts. In this way, continuous images can be generated by reflecting the user's past thought history.

[0050] The image generation unit can combine audio or text information with the images it generates to create multimedia expressions. For example, we will build a system that combines audio and text information with images generated by the generation AI. Natural sounds and explanatory text will be added to landscape images. We will also develop an algorithm that allows the generation AI to create multimedia expressions by combining audio and text information. We will add audio guides related to images. We will also build a system that combines audio and text information in real time with images generated by the generation AI. We will add explanatory text for the image that the user has in mind. This will enable multimedia expressions by combining audio and text information.

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

[0052] Step 1: The thought analysis unit analyzes the user's brain waves or neural signals. For example, EEG is used to collect brain wave data and then the data is analyzed using signal processing techniques. Alternatively, MEG can be used to collect brain wave data and then analyzed using machine learning algorithms. Furthermore, fMRI can be used to collect and analyze brain wave data. EEG measures brain waves by attaching electrodes to the scalp, MEG measures magnetic fields to analyze brain activity, and fMRI measures changes in blood flow to analyze brain activity. Step 2: The image generation unit generates a specific image based on the thought content analyzed by the thought analysis unit. For example, CG technology is used to convert the thought content into a specific image. Images can also be generated using deep learning technology or generative AI. CG technology generates images using computer graphics, deep learning technology generates images by learning from large amounts of data, and generative AI generates images using text generation AI (e.g., LLM) or multimodal generative AI. Step 3: The display unit displays the image generated by the image generation unit in real time. For example, the image is displayed using a display. The image can also be displayed using a projector or a VR headset. The display uses an LCD or OLED display, the projector projects the image onto a screen, and the VR headset displays the image in a virtual reality space.

[0053] (Example 2) The thought-image conversion system according to the embodiment of the present invention is a system that converts a user's thoughts into concrete images and displays them in real time, thereby providing a new means of communication by converting the user's thoughts into concrete images.

[0054] A thought-image conversion system according to an embodiment includes a thought analysis unit, an image generation unit, and a display unit. The thought analysis unit analyzes a user's brain waves or neural signals. For example, the thought analysis unit collects brain wave data using EEG and analyzes it using signal processing technology. The thought analysis unit can also collect brain wave data using MEG and analyze it using a machine learning algorithm. The thought analysis unit can also collect and analyze brain wave data using fMRI. For example, EEG measures brain waves by attaching electrodes to the scalp. MEG analyzes brain activity by measuring magnetic fields. fMRI analyzes brain activity by measuring changes in blood flow. The image generation unit generates specific images based on the thought content analyzed by the thought analysis unit. For example, the image generation unit converts the thought content into specific images using CG technology. The image generation unit can also generate images using deep learning technology. The image generation unit can also generate images using generative AI. For example, CG technology generates images using computer graphics. Deep learning technology generates images by learning from large amounts of data. The generation AI generates images using a text generation AI (e.g., LLM) or a multimodal generation AI. The display unit displays the images generated by the image generation unit in real time. For example, the display unit displays images using a display. The display unit can also display images using a projector. The display unit can also display images using a VR headset. For example, the display uses an LCD display or an OLED display. The projector projects images onto a screen. The VR headset displays images in a virtual reality space. As a result, the thought-image conversion system according to the embodiment can convert a user's thoughts into concrete images and display them in real time. For example, by displaying a landscape imagined by the user in real time, the relaxation effect can be enhanced. Furthermore, by displaying an image imagined by the user as an image, communication with a doctor or family can be facilitated. As a result, the user's feelings and wishes can be more accurately conveyed.

[0055] The thought analysis unit can detect specific emotional states when analyzing the user's brain waves and neural signals and complement the thoughts based on those emotions. For example, the thought analysis unit analyzes the user's brain wave data in real time to detect a relaxed or excited state. For example, if an increase in alpha waves indicates a relaxed state, the thought analysis unit complements the thoughts based on that data. The thought analysis unit also develops an algorithm for detecting specific emotional states in analyzing the neural signals. For example, if a specific brain wave pattern indicates an excited state, the thought analysis unit complements the thoughts based on that pattern. The thought analysis unit also collects the user's brain wave data over a long period of time to identify individual emotional patterns in order to detect the emotional state. For example, the thought analysis unit complements the thoughts based on the brain wave pattern when the user is relaxed. This enables more accurate thought analysis by complementing the thoughts taking the user's emotional state into consideration.

[0056] The thought analysis unit can perform more accurate thought analysis by referring to the user's past thought patterns and history when analyzing brain waves or neural signals. The thought analysis unit, for example, stores the user's past brain wave data in a database and refers to it when analyzing thoughts. For example, the analysis is performed based on the brain wave patterns when a specific image was thought of in the past. The thought analysis unit also analyzes the user's thought history and develops an algorithm to extract specific patterns. For example, it predicts the content of current thoughts based on past thought patterns. The thought analysis unit also builds a system that improves the accuracy of thought analysis by referring to past thought patterns. For example, it compares images that the user thought of in the past with current brain wave patterns. In this way, the accuracy of thought analysis is improved by referring to past thought patterns and history.

[0057] The thought analysis unit can use the emotion estimation function to analyze the emotion of the user in response to the image that has been conjured up in their mind, and correct the thought content based on that emotion. The thought analysis unit, for example, uses the emotion estimation function to analyze the emotion of the user in response to the image that has been conjured up in real time. For example, it analyzes the user's facial expressions and voice and calculates an emotion score. The thought analysis unit also develops an algorithm to correct the user's thought content based on the emotion estimation data. For example, if the user has a strong positive emotion, it corrects the thought content based on that emotion. The thought analysis unit also analyzes the user's emotional response, and builds a system to correct the thought content based on the results. For example, it corrects the thought content based on emotional data when the user is relaxed. This enables more accurate thought analysis by correcting the thought content based on the user's emotion.

[0058] The thought analysis unit can combine audio or visual stimuli with the user's thought analysis to analyze the thought content from multiple angles. For example, the thought analysis unit provides audio or visual stimuli in response to an image that the user has in mind and analyzes the reaction. For example, it displays specific music or images and analyzes changes in brain waves. The thought analysis unit also builds a system that analyzes thought content from multiple angles by combining audio and visual stimuli. For example, it analyzes brain wave patterns when a user listens to specific music. The thought analysis unit also improves the accuracy of the analysis by using audio or visual stimuli in the user's thought analysis. For example, it analyzes thought content based on brain wave patterns when a user views a specific image. In this way, by combining audio and visual stimuli, the thought content can be analyzed from multiple angles.

[0059] The thought analysis unit can collect EEG data from users of different age groups or cultural backgrounds and diversify the analysis algorithm. The thought analysis unit, for example, collects EEG data from users of different age groups and diversifies the analysis algorithm. For example, the EEG patterns of children and the elderly are analyzed and reflected in the algorithm. The thought analysis unit also collects EEG data from users of different cultural backgrounds and diversifies the analysis algorithm. For example, the EEG patterns of users from different cultural spheres are analyzed and reflected in the algorithm. The thought analysis unit also builds a system that improves the analysis algorithm based on EEG data from users of different age groups and cultural backgrounds. For example, the accuracy of thought analysis is improved by using diverse data. In this way, the analysis algorithm can be diversified by collecting data from users of different age groups and cultural backgrounds.

[0060] The thought analysis unit can use the emotion estimation function to analyze the emotion of the user in response to an image that has been conjured up in real time, and provide feedback according to the emotion. For example, the thought analysis unit can use the emotion estimation function to analyze the emotion of the user in response to an image that has been conjured up in real time, and provide feedback based on the results. For example, if the emotion is strong, an encouraging message can be displayed. The thought analysis unit can also analyze the user's emotional response in real time, and build a system that provides feedback based on the results. For example, when the user is relaxing, music with a relaxing effect can be played. The thought analysis unit can also provide feedback to the user in real time based on the emotion estimation data. For example, when the user is excited, an image with a relaxing effect can be displayed. This makes it possible to provide feedback according to the user's emotion in real time.

[0061] The image generation unit can reflect the user's emotional state in the generated image and adjust the color tone or composition according to the emotion. For example, the image generation unit develops an algorithm that reflects the user's emotional state in the image generated by the generation AI. For example, soft colors are used when the user is relaxed. The image generation unit also builds a system that adjusts the color tone and composition of the generated image according to the user's emotional state. For example, vivid colors are used when the user is excited. The image generation unit also adjusts the color tone and composition of the image generated by the generation AI in real time based on the emotion estimation data. For example, a calm landscape is generated when the user is relaxed. This makes it possible to adjust the color tone and composition of the image according to the user's emotional state.

[0062] The image generation unit can generate continuous images by reflecting the user's past thought history in the generated images. For example, the image generation unit stores the user's past thought history in a database, and the generation AI generates continuous images based on that data. For example, it generates a new landscape based on a landscape imagined in the past. The image generation unit also develops an algorithm that allows the generation AI to generate continuous images by reflecting the user's past thought history. For example, it generates a new image based on an image the user imagined in the past. The image generation unit also analyzes the user's thought history and builds a system that generates continuous images. For example, it generates a new image by combining a past image with current thought content. In this way, continuous images can be generated by reflecting the user's past thought history.

[0063] The image generation unit can use the emotion estimation function to analyze the user's emotion toward the image they have in mind and adjust the details of the image based on that emotion. For example, the image generation unit uses the emotion estimation function to analyze the user's emotion toward the image they have in mind and adjust the details of the image based on the results. For example, if the user has a strong positive emotion, bright colors are used. The image generation unit also builds a system that analyzes the user's emotional response and adjusts the details of the image based on the results. For example, soft lines are used when the user is relaxed. The image generation unit also adjusts the details of the image generated by the generation AI in real time based on the emotion estimation data. For example, vivid colors are used when the user is excited. This makes it possible to adjust the details of the image based on the user's emotions.

[0064] The image generation unit can combine audio or text information with the images it generates to create multimedia expressions. For example, the image generation unit builds a system that combines audio and text information with images generated by the generation AI. For example, it adds natural sounds or explanatory text to landscape images. The image generation unit also develops an algorithm that enables the generation AI to create multimedia expressions by combining audio and text information. For example, it adds audio guidance related to images. The image generation unit also builds a system that combines audio and text information in real time with images generated by the generation AI. For example, it adds explanatory text for an image that the user has in mind. In this way, by combining audio and text information, multimedia expressions become possible.

[0065] The image generation unit can generate images in different styles or art forms, enabling customization according to the user's preferences. The image generation unit, for example, develops an algorithm that enables a generation AI to generate images in different styles or art forms. For example, the image generation unit allows a user to select a style such as classical art or modern art according to their preferences. The image generation unit also builds a system that enables a generation AI to generate images in different art forms to enable customization according to the user's preferences. For example, the image generation unit generates images based on a style selected by the user. The image generation unit also develops a system that enables customization according to the user's preferences by generating images in different styles or art forms. For example, the image generation unit reflects the user's preferred color tones and composition. This enables customization according to the user's preferences by generating images in different styles or art forms.

[0066] The image generation unit can use the emotion estimation function to analyze the emotion of the image the user has in mind in real time and generate an image according to the emotion. For example, the image generation unit uses the emotion estimation function to analyze the emotion of the image the user has in mind in real time and generate an image based on the results. For example, if the emotion is strong, an image with bright colors is generated. The image generation unit also analyzes the user's emotional response in real time and builds a system that generates images based on the results. For example, when the user is relaxed, a calm scene is generated. The image generation unit also develops an algorithm that uses the emotion estimation data to enable the generation AI to generate images in real time. For example, when the user is excited, an image with vivid colors is generated. This makes it possible to generate images according to the user's emotions in real time.

[0067] The display unit can analyze the user's emotional state during real-time display and adjust the display speed or effects according to the emotion. For example, the display unit analyzes the user's emotional state during real-time display and builds a system that adjusts the display speed based on the results. For example, the display speed is slowed when the user is relaxed. The display unit also develops an algorithm that adjusts the effects of the real-time display according to the user's emotional state. For example, the effects are emphasized when the user is excited. The display unit also builds a system that adjusts the speed and effects of the real-time display in real time based on the emotion estimation data. For example, a gentle effect is used when the user is relaxed. This makes it possible to adjust the display speed and effects according to the user's emotional state.

[0068] The display unit can refer to the user's past thought history when displaying in real time, and provide a continuous display. The display unit, for example, stores the user's past thought history in a database and builds a system that references it when displaying in real time. For example, a continuous display is provided based on images displayed in the past. The display unit also develops an algorithm that maintains the continuity of the real-time display by referencing the past thought history. For example, the current display is adjusted based on images that the user had in the past. The display unit also analyzes the user's thought history and builds a system that provides a continuous real-time display. For example, past images and current thought content are combined and displayed. In this way, a continuous display is possible by referencing the user's past thought history.

[0069] The display unit can use the emotion estimation function to analyze the emotion of the image the user has in mind and correct the display content based on that emotion. For example, the display unit uses the emotion estimation function to analyze the emotion of the image the user has in mind in real time and correct the display content based on the results. For example, if the user has a strong positive emotion, bright colors are used. The display unit also builds a system that analyzes the user's emotional response and corrects the display content based on the results. For example, a calm scene is displayed when the user is relaxed. The display unit also develops an algorithm that corrects the real-time display content based on the emotion estimation data. For example, vivid colors are used when the user is excited. This allows the display content to be corrected based on the user's emotion.

[0070] The display unit can combine audio or haptic feedback with real-time display to provide a multimodal experience. The display unit, for example, builds a system that combines audio feedback with real-time display. For example, it plays natural sounds corresponding to the displayed scenery. The display unit also develops an algorithm that improves the real-time display experience by combining haptic feedback. For example, it provides vibrations corresponding to the displayed scenery. The display unit also builds a system that provides a multimodal experience by combining audio and haptic feedback. For example, it provides audio and haptic feedback corresponding to an image that the user has in mind. This makes it possible to provide a multimodal experience by combining audio and haptic feedback.

[0071] The display unit can enable real-time display on different devices. The display unit, for example, uses a VR headset to build a system that displays a user's thoughts in real time. For example, it displays a landscape imagined by the user in a VR space. The display unit also uses smart glasses to develop an algorithm that displays a user's thoughts in real time. For example, it displays an image imagined by the user on the display of the smart glasses. The display unit also builds a system that enables real-time display on different devices. For example, it adjusts the display method depending on the device selected by the user. This makes it possible to display in real time on different devices.

[0072] The display unit can use the emotion estimation function to analyze the emotion of the user in response to the image that is brought to mind in real time, and display the content according to the emotion. For example, the display unit uses the emotion estimation function to analyze the emotion of the user in response to the image that is brought to mind in real time, and adjust the display content based on the results. For example, if the emotion is strong, bright colors are used. The display unit also builds a system that analyzes the user's emotional response in real time, and adjusts the display content based on the results. For example, a calm scene is displayed when the user is relaxed. The display unit also develops an algorithm that adjusts the content of the real-time display based on the emotion estimation data. For example, vivid colors are used when the user is excited. This makes it possible to display the content according to the user's emotions in real time.

[0073] As a means of communication, the system can analyze a user's emotional state and generate a message or image corresponding to the emotion. For example, the system analyzes a user's emotional state and builds a system that generates a message corresponding to the emotion based on the results. For example, when the user is relaxed, a calm message is generated. The system also develops an algorithm that generates an image corresponding to the user's emotion based on the emotion estimation data. For example, when the user has a strong positive emotion, an image with bright colors is generated. The system also builds a system that generates a message or image corresponding to the user's emotional state. For example, when the user is excited, an image with vivid colors is generated. This enables effective communication by generating messages or images corresponding to the user's emotional state.

[0074] The system can refer to a user's past thought history as a means of communication and generate continuous messages. For example, the system builds a system that stores a user's past thought history in a database and references it as a means of communication. For example, it generates continuous messages based on messages sent in the past. The system also develops an algorithm that generates continuous messages by referencing the past thought history. For example, it adjusts a current message based on images that the user had in the past. The system also builds a system that analyzes a user's thought history and generates continuous messages. For example, it combines past messages with current thought content to generate a new message. In this way, continuous messages can be generated by referencing the user's past thought history.

[0075] The system can use the emotion estimation function to analyze the emotion a user feels in response to an image that has been conjured up in their mind, and correct the content of communication based on that emotion. For example, the system uses the emotion estimation function to analyze the emotion a user feels in response to an image that has been conjured up in their mind in real time, and correct the content of communication based on the results. For example, if the emotion is strong, bright colors are used. The system also analyzes the user's emotional response, and builds a system that corrects the content of communication based on the results. For example, a calm message is generated when the user is relaxed. The system also develops an algorithm to correct the content of communication based on the emotion estimation data. For example, a message with bright colors is generated when the user is excited. This makes it possible to correct the content of communication based on the user's emotions.

[0076] The system can provide multimodal communication by combining audio or text information as a means of communication. For example, the system builds a system that combines audio and text information as a means of communication. For example, audio guidance is added to a message. The system also develops an algorithm that provides multimodal communication by combining audio and text information. For example, audio explanations related to a message are added. The system also builds a system that combines audio and text information in real time as a means of communication. For example, explanatory text is added for an image that a user has in mind. This makes multimodal communication possible by combining audio and text information.

[0077] The system can add a translation function that enables communication between users with different languages ​​or cultural backgrounds. For example, the system builds a system that adds a translation function that enables communication between users who speak different languages. For example, it translates messages in real time. The system also develops a translation function that takes cultural nuances into account to facilitate communication between users with different cultural backgrounds. For example, it appropriately translates culture-specific expressions. The system also builds a system that uses the translation function to support communication between users with different languages ​​and cultural backgrounds. For example, it provides cultural background information at the same time as translating a message. This enables communication between users with different languages ​​and cultural backgrounds.

[0078] The system uses an emotion estimation function to analyze the user's emotions in response to an image that comes to mind in real time, and can communicate according to the emotions. For example, the system uses the emotion estimation function to analyze the user's emotions in response to an image that comes to mind in real time, and adjusts the content of communication based on the results. For example, if the user has a strong positive emotion, bright colors are used. The system also analyzes the user's emotional response in real time, and builds a system to adjust the content of communication based on the results. For example, a calm message is generated when the user is relaxed. The system also develops an algorithm to adjust the content of communication based on the emotion estimation data. For example, a message in bright colors is generated when the user is excited. This makes it possible to communicate according to the user's emotions in real time.

[0079] As an application in the medical field, the system can analyze a patient's emotional state and provide a rehabilitation program appropriate to the emotion. For example, the system analyzes a patient's emotional state and, based on the results, builds a system that provides a rehabilitation program appropriate to the emotion. For example, a gentle program is provided when the patient is relaxed. The system also develops an algorithm that provides a rehabilitation program appropriate to the patient's emotion based on the emotion estimation data. For example, an active exercise program is provided when the patient has strong positive emotions. The system also builds a system that provides a rehabilitation program appropriate to the patient's emotional state. For example, relaxing music is played when the patient is relaxed. This makes it possible to provide a rehabilitation program appropriate to the patient's emotional state.

[0080] As an application in the medical field, the system can refer to a patient's past thought history to provide a continuous treatment plan. For example, the system stores a patient's past thought history in a database and builds a system that references it as an application in the medical field. For example, a continuous treatment plan is provided based on past treatment plans. The system also develops an algorithm that provides a continuous treatment plan by referencing the past thought history. For example, the current treatment plan is adjusted based on images that the patient had in the past. The system also builds a system that analyzes the patient's thought history to provide a continuous treatment plan. For example, a new treatment plan is provided by combining past treatment plans with current thought content. In this way, a continuous treatment plan can be provided by referencing the patient's past thought history.

[0081] The system uses an emotion estimation function to analyze the patient's emotions in response to the image they have in mind, and can correct the treatment content based on that emotion. For example, the system uses the emotion estimation function to analyze the patient's emotions in response to the image they have in mind in real time, and corrects the treatment content based on the results. For example, if the patient has strong positive emotions, bright colors are used. The system also analyzes the patient's emotional response and builds a system that corrects the treatment content based on the results. For example, a gentle treatment plan is provided when the patient is relaxed. The system also develops an algorithm that corrects the treatment content based on the emotion estimation data. For example, bright colors are used when the patient is excited. This makes it possible to correct the treatment content based on the patient's emotions.

[0082] The system can provide multimodal treatment by combining audio or haptic feedback as an application in the medical field. For example, the system builds a system that combines audio feedback as an application in the medical field. For example, it plays music that has a relaxing effect during treatment. The system also develops an algorithm that provides multimodal treatment by combining haptic feedback. For example, it provides vibration that has a relaxing effect during treatment. The system also builds a system that provides multimodal treatment by combining audio and haptic feedback. For example, it provides audio and haptic feedback that corresponds to an image that the patient has in mind. This makes multimodal treatment possible by combining audio and haptic feedback.

[0083] The system can add functions that enable applications in different medical fields. For example, a system is constructed that adds functions that enable applications in different medical fields. For example, a system that displays scenery that has a relaxing effect as an application in psychiatry. Also, the system is developed an algorithm that adds functions that enable applications in rehabilitation. For example, the system visualizes the patient's target movements and displays them in real time. Also, the system improves the versatility of the system by adding functions that enable applications in different medical fields. For example, a function that can be used in both psychiatry and rehabilitation is provided. This enables applications in different medical fields.

[0084] The system uses an emotion estimation function to analyze in real time the emotions a patient feels in response to an image that comes to mind, and can provide treatment that corresponds to those emotions. For example, the system uses the emotion estimation function to analyze in real time the emotions a patient feels in response to an image that comes to mind, and adjusts the treatment content based on the results. For example, if positive emotions are strong, bright colors are used. The system also builds a system that analyzes a patient's emotional response in real time and adjusts the treatment content based on the results. For example, a gentle treatment plan is provided when the patient is relaxed. The system also develops an algorithm that adjusts the treatment content based on the emotion estimation data. For example, bright colors are used when the patient is excited. This makes it possible to provide treatment that corresponds to the patient's emotions in real time.

[0085] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0086] The thought analysis unit can detect specific emotional states when analyzing the user's brain waves and neural signals and complement the thoughts based on those emotions. For example, the unit analyzes the user's brain wave data in real time to detect a state of relaxation or excitement. If an increase in alpha waves indicates a state of relaxation, the thought contents are complemented based on that data. Furthermore, an algorithm is developed to detect specific emotional states in the analysis of neural signals. If a specific brain wave pattern indicates a state of excitement, the thought contents are complemented based on that pattern. Furthermore, to detect the emotional state, the unit collects the user's brain wave data over a long period of time and identifies individual emotional patterns. The thought contents are complemented based on the brain wave pattern when the user is relaxed. This allows for more accurate thought analysis by complementing the thought contents taking the user's emotional state into consideration.

[0087] The thought analysis unit can perform more accurate thought analysis by referring to the user's past thought patterns and history when analyzing the user's brain waves or neural signals. For example, the user's past brain wave data is stored in a database and referenced during thought analysis. Analysis is performed based on the brain wave patterns when a specific image was thought of in the past. An algorithm is also developed to analyze the user's thought history and extract specific patterns. Current thoughts are predicted based on past thought patterns. Furthermore, a system is constructed that improves the accuracy of thought analysis by referring to past thought patterns. Images that the user has thought of in the past are compared with the user's current brain wave pattern. In this way, the accuracy of thought analysis is improved by referring to past thought patterns and history.

[0088] The thought analysis unit can use the emotion estimation function to analyze the emotion the user feels in response to the image that comes to mind, and correct the thought content based on that emotion. For example, the emotion estimation function is used to analyze the emotion the user feels in response to the image that comes to mind in real time. The user's facial expressions and voice are analyzed to calculate an emotion score. An algorithm is also developed to correct the user's thought content based on the emotion estimation data. If the emotion is strong, the thought content is corrected based on that emotion. Furthermore, a system is constructed that analyzes the user's emotional response and corrects the thought content based on the results. The thought content is corrected based on emotional data when the user is relaxed. This makes it possible to perform more accurate thought analysis by correcting the thought content based on the user's emotions.

[0089] The thought analysis unit can combine audio or visual stimuli with the user's thought analysis to analyze the content of thoughts from multiple angles. For example, audio or visual stimuli are provided in response to an image the user has in mind, and the reaction is analyzed. Specific music or images are displayed, and changes in brain waves are analyzed. Furthermore, by combining audio and visual stimuli, a system can be constructed that analyzes the content of thoughts from multiple angles. The brain wave patterns when the user listens to specific music are analyzed. Furthermore, by using audio and visual stimuli in the user's thought analysis, the accuracy of the analysis is improved. The content of thoughts is analyzed based on the brain wave patterns when the user views a specific image. In this way, by combining audio and visual stimuli, the content of thoughts can be analyzed from multiple angles.

[0090] The thought analysis unit can collect EEG data from users of different age groups or cultural backgrounds to diversify the analysis algorithm. For example, EEG data from users of different age groups can be collected to diversify the analysis algorithm. The EEG patterns of children and the elderly can be analyzed and reflected in the algorithm. EEG data from users of different cultural backgrounds can also be collected to diversify the analysis algorithm. The EEG patterns of users from different cultural backgrounds can be analyzed and reflected in the algorithm. Furthermore, a system can be constructed that improves the analysis algorithm based on EEG data from users of different age groups and cultural backgrounds. The accuracy of thought analysis can be improved using diverse data. In this way, the analysis algorithm can be diversified by collecting data from users of different age groups and cultural backgrounds.

[0091] The thought analysis unit can use the emotion estimation function to analyze the emotion of the user in response to an image that comes to mind in real time and provide feedback according to the emotion. For example, the emotion estimation function can be used to analyze the emotion of the user in response to an image that comes to mind in real time and provide feedback based on the results. If the emotion is strong, an encouraging message can be displayed. Also, a system can be constructed that analyzes the user's emotional response in real time and provides feedback based on the results. When the user is relaxed, music with a relaxing effect can be played. Furthermore, feedback to the user can be provided in real time based on the emotion estimation data. When the user is excited, an image with a relaxing effect can be displayed. This makes it possible to provide feedback according to the user's emotions in real time.

[0092] The image generation unit can reflect the user's emotional state in the image it generates, adjusting the color tone or composition according to the emotion. For example, we will develop an algorithm that reflects the user's emotional state in the images generated by the generation AI. When the user is relaxed, soft colors will be used. We will also build a system that adjusts the color tone and composition of the image it generates according to the user's emotional state. When the user is excited, vivid colors will be used. Furthermore, based on the emotion estimation data, the color tone and composition of the image it generates will be adjusted in real time. When the user is relaxed, a calm scene will be generated. This will allow the color tone and composition of the image to be adjusted according to the user's emotional state.

[0093] The image generation unit can reflect the user's past thought history in the generated image, generating continuous images. For example, the user's past thought history is stored in a database, and the generation AI generates continuous images based on that data. A new landscape is generated based on a landscape imagined in the past. In addition, an algorithm will be developed that allows the generation AI to generate continuous images by reflecting past thought history. A new image will be generated based on an image the user has imagined in the past. Furthermore, a system will be built that analyzes the user's thought history and generates continuous images. A new image will be generated by combining past images and current thoughts. In this way, continuous images can be generated by reflecting the user's past thought history.

[0094] The image generation unit can use the emotion estimation function to analyze the emotion the user has in mind when thinking about an image, and adjust the image details based on that emotion. For example, the emotion estimation function can be used to analyze the emotion the user has in mind when thinking about an image, and the image details can be adjusted based on the results. If the emotion is strong, bright colors can be used. A system can also be built that analyzes the user's emotional response and adjusts the image details based on the results. Soft lines are used when the user is relaxed. Furthermore, the details of the image generated by the generation AI can be adjusted in real time based on the emotion estimation data. Bright colors are used when the user is excited. This makes it possible to adjust the image details based on the user's emotions.

[0095] The image generation unit can combine audio or text information with the images it generates to create multimedia expressions. For example, we will build a system that combines audio and text information with images generated by the generation AI. Natural sounds and explanatory text will be added to landscape images. We will also develop an algorithm that allows the generation AI to create multimedia expressions by combining audio and text information. We will add audio guides related to images. We will also build a system that combines audio and text information in real time with images generated by the generation AI. We will add explanatory text for the image that the user has in mind. This will enable multimedia expressions by combining audio and text information.

[0096] The processing flow of the second embodiment will be briefly explained below.

[0097] Step 1: The thought analysis unit analyzes the user's brain waves or neural signals. For example, EEG is used to collect brain wave data and then the data is analyzed using signal processing techniques. Alternatively, MEG can be used to collect brain wave data and then analyzed using machine learning algorithms. Furthermore, fMRI can be used to collect and analyze brain wave data. EEG measures brain waves by attaching electrodes to the scalp, MEG measures magnetic fields to analyze brain activity, and fMRI measures changes in blood flow to analyze brain activity. Step 2: The image generation unit generates a specific image based on the thought content analyzed by the thought analysis unit. For example, CG technology is used to convert the thought content into a specific image. Images can also be generated using deep learning technology or generative AI. CG technology generates images using computer graphics, deep learning technology generates images by learning from large amounts of data, and generative AI generates images using text generation AI (e.g., LLM) or multimodal generative AI. Step 3: The display unit displays the image generated by the image generation unit in real time. For example, the image is displayed using a display. The image can also be displayed using a projector or a VR headset. The display uses an LCD or OLED display, the projector projects the image onto a screen, and the VR headset displays the image in a virtual reality space.

[0098] 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.

[0099] 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.

[0100] 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.

[0101] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0102] 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.

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

[0104] The 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.

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

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

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

[0108] Fig. 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.

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

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

[0111] 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.

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

[0113] The specific processing unit 290 transmits the result of the specific processing to the 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.

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

[0115] 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.

[0116] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0117] 5, the data processing system 310 includes the data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

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

[0119] The 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.

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

[0121] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (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).

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

[0123] 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.

[0124] 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.

[0125] 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.

[0126] 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.

[0127] 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.

[0128] 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.

[0129] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AI other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0130] 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.

[0131] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0132] 7, a data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0133] 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.

[0134] 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.

[0135] 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.

[0136] 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).

[0137] 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.

[0138] 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.

[0139] 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.

[0140] 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.

[0141] 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.

[0142] In the robot 414, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. The robot 414 also has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0143] 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.

[0144] 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.

[0145] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AI other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0146] 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.

[0147] 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.

[0148] 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.

[0149] 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.

[0150] 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).

[0151] 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 "reaction" domain, where sensation is dominant. The right half of the emotion map lists emotions belonging to the "situation" domain, where situational awareness is dominant.

[0152] 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."

[0153] 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.

[0154] 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.

[0155] 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.

[0156] 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.

[0157] 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.

[0158] 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.

[0159] 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.

[0160] 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.

[0161] 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.

[0162] 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.

[0163] 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.

[0164] 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]

[0165] 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 thought analysis unit that analyzes the user's brain waves or neural signals; an image generation unit that generates a specific image based on the thought content analyzed by the thought analysis unit; a display unit that displays the image generated by the image generation unit in real time. A system characterized by:

2. The thought analysis unit Detecting a specific emotional state when analyzing the user's brain waves or neural signals and completing the thought content based on that emotion.

2. The system of claim 1.

3. The thought analysis unit When analyzing the brain waves or nerve signals, the user's past thought patterns and history are referenced to perform more accurate thought analysis.

2. The system of claim 1.

4. The thought analysis unit Analyzing the user's feelings about the image they have in mind and correcting the thought content based on the feelings.

2. The system of claim 1.

5. The thought analysis unit The user's thought analysis is combined with audio or visual stimuli to analyze the thought content from multiple angles.

2. The system of claim 1.

Images