system

Abstract

The system according to the embodiment aims to accurately record the contents of dreams and share them with others. [Solution] The system according to the embodiment includes a collection unit, an analysis unit, a generation unit, a consent unit, and a sharing unit. The collection unit collects EEG data. The analysis unit analyzes the EEG data collected by the collection unit. The generation unit generates VR video based on the dream content identified by the analysis unit. The consent unit obtains the user's consent. The sharing unit shares the dream content with a third party based on the consent obtained by the consent unit.

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 accurately record the content of dreams and share them with others.

[0005] The system according to the embodiment aims to accurately record the contents of dreams and share them with others. [Means for solving the problem]

[0006] The system according to the embodiment includes a collection unit, an analysis unit, a generation unit, a consent unit, and a sharing unit. The collection unit collects EEG data. The analysis unit analyzes the EEG data collected by the collection unit. The generation unit generates VR video based on the dream content identified by the analysis unit. The consent unit obtains consent from the user. The sharing unit shares the dream content with a third party based on the consent obtained by the consent unit. [Effects of the Invention]

[0007] The system according to the embodiment allows the user to accurately record the contents of their dreams and share them with others. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example 1) A dream replay system according to an embodiment of the present invention reads brainwaves, analyzes them with AI, and replays the dreams of that day in VR. In this dream replay system, a user wears a device to read brainwaves while sleeping, collects brainwave data in real time, and transmits it to AI. Next, AI analyzes the collected brainwave data to identify the content of the dream. Based on the analyzed dream content, VR video is generated. The generated VR video is used to replay the dream after the user wakes up. Furthermore, with the user's consent, the content can be shared with others or re-edited into a video of their choice. This allows users to replay their dreams in detail and share them with others. Furthermore, the ability to customize and enjoy the content of dreams provides a richer experience. For example, a user wears a device to read brainwaves while sleeping. This device collects brainwave data in real time and transmits it to AI. For example, a headband-type device can be used, allowing the user to wear it comfortably while sleeping. Next, AI analyzes the collected brainwave data. The AI ​​analyzes the brainwave data to identify the content of the dream. For example, when a specific brainwave pattern appears, it is possible to identify the dream content corresponding to that pattern. This allows for a detailed understanding of the user's dream. VR video is generated based on the analyzed dream content. Based on the identified dream content, AI generates VR video to recreate the user's dream. For example, if a user dreams of flying, VR video is generated to recreate that dream. This allows the user to relive the dream after waking up. Furthermore, with the user's consent, the content can be shared with others. For example, a user can show the content of their dream to family and friends. They can also re-edit the video to their liking. For example, specific scenes can be added or the color tone of the video can be changed based on the content of the dream. This allows users to share their dreams with others and customize them for enjoyment. This service allows users to recreate their dreams in detail and share them with others.Furthermore, the user can customize and enjoy the content of their dreams, providing a richer experience. This allows the dream reproduction system to reproduce the user's dreams in detail and share them with others. Furthermore, the user can customize and enjoy the content of their dreams, providing a richer experience.

[0029] A dream reproduction system according to an embodiment includes a collection unit, an analysis unit, a generation unit, a consent unit, and a sharing unit. The collection unit collects EEG data. The collection unit collects EEG data using, for example, a headband-type device. The headband-type device is designed to be comfortably worn by a user while sleeping. For example, the collection unit collects EEG data in real time and transmits it to an AI. The analysis unit analyzes the EEG data collected by the collection unit. For example, the analysis unit detects a specific EEG pattern and identifies the dream content corresponding to the pattern. The specific EEG patterns include alpha waves, beta waves, theta waves, etc. The analysis unit analyzes the EEG data using AI to identify the dream content. For example, the analysis unit uses an algorithm to analyze the pattern of EEG data and identify the dream content. The generation unit generates VR video based on the dream content identified by the analysis unit. For example, the generation unit generates VR video to recreate the dream the user had based on the identified dream content. The generation unit generates VR video using AI. For example, if the user had a dream about flying in the sky, the generation unit generates VR video to recreate that dream. The consent unit obtains the user's consent. For example, the consent unit allows the user to choose whether to share the content of the dream with others after waking up. The consent unit obtains consent using AI. For example, the consent unit provides an interface that asks the user whether to share the content of the dream with others. The sharing unit shares the content of the dream with others based on the consent obtained by the consent unit. For example, the sharing unit shares the content of the dream with others based on the user's consent. The sharing unit shares the content of the dream using AI. For example, the sharing unit provides an interface for sharing the content of the user's dream with family and friends. This allows the dream reproduction system according to the embodiment to recreate the user's dream in detail and share it with others. Furthermore, the content of the dream can be customized and enjoyed, providing a richer experience.

[0030] The collection unit can collect EEG data using a headband-type device. The headband-type device is designed, for example, to be worn comfortably by the user while sleeping. The collection unit, for example, collects EEG data in real time using the headband-type device and transmits the data to an AI. For example, the collection unit collects EEG data using the headband-type device and transmits the data to an AI. As a result, by using the headband-type device, EEG data can be collected comfortably by the user while sleeping. Some or all of the above-described processing in the collection unit may be performed, for example, using an AI, or may be performed without using an AI. For example, the collection unit can input EEG data collected using the headband-type device to a generation AI and cause the generation AI to analyze the EEG data.

[0031] The analysis unit can detect specific brainwave patterns and identify the dream content corresponding to those patterns. For example, the analysis unit can detect specific brainwave patterns and identify the dream content corresponding to those patterns. Specific brainwave patterns include, for example, alpha waves, beta waves, and theta waves. The analysis unit uses AI to analyze the brainwave data and identify the dream content. For example, the analysis unit uses an algorithm that analyzes the patterns of brainwave data and identifies the dream content. By detecting specific brainwave patterns, the dream content can be understood in detail. Some or all of the above-mentioned processing in the analysis unit may be performed using AI, for example, or may be performed without using AI. For example, the analysis unit can input the brainwave data to a generation AI and have the generation AI identify the dream content.

[0032] The generation unit can generate VR video based on the content of the identified dream. For example, the generation unit generates VR video for recreating the dream the user had based on the content of the identified dream. The generation unit generates VR video using AI. For example, if the user had a dream of flying in the sky, the generation unit generates VR video for recreating that dream. In this way, the dream the user had can be reproduced by generating VR video based on the content of the identified dream. Some or all of the above-described processing in the generation unit may be performed using AI, for example, or may be performed without using AI. For example, the generation unit can input the content of the identified dream into a generation AI and cause the generation AI to generate VR video.

[0033] The consent unit can enable the user to choose whether to share the content of their dreams with a third party after waking up. The consent unit, for example, enables the user to choose whether to share the content of their dreams with other people after waking up. The consent unit obtains consent using AI. For example, the consent unit provides the user with an interface that asks the user whether to share the content of their dreams with other people. This allows the user to choose whether to share the content of their dreams with other people after waking up. Some or all of the above-mentioned processing in the consent unit may be performed using AI, for example, or may be performed without using AI. For example, the consent unit can input the user's consent into the generation AI and cause the generation AI to obtain consent.

[0034] The sharing unit can share the content of the dream with other people based on the user's consent. The sharing unit, for example, shares the content of the dream with other people based on the user's consent. The sharing unit shares the content of the dream using AI. For example, the sharing unit provides an interface for sharing the content of the user's dream with family and friends. This allows the content of the dream to be shared with other people based on the user's consent. Some or all of the above-mentioned processing in the sharing unit may be performed using AI, for example, or may be performed without using AI. For example, the sharing unit can input the content of the user's dream into a generation AI and have the generation AI execute the sharing.

[0035] The sharing unit can prevent personal identifying information from being included when sharing, in order to protect privacy. For example, the sharing unit prevents personal identifying information from being included when sharing, in order to protect privacy. The sharing unit removes personal identifying information using AI. For example, the sharing unit uses an algorithm that automatically removes personal identifying information when sharing the content of the user's dream. This prevents personal information from being included when sharing, in order to protect privacy. Some or all of the above-mentioned processing in the sharing unit may be performed using AI, for example, or may be performed without using AI. For example, the sharing unit may input the content of the user's dream into a generation AI and have the generation AI remove personal identifying information.

[0036] The generation unit can add specific scenes or change the color tone of the image based on the content of the dream. For example, the generation unit can add specific scenes or change the color tone of the image based on the content of the dream. The generation unit customizes the VR image using AI. For example, the generation unit uses an algorithm that adds specific scenes or changes the color tone of the image based on the content of the dream the user had. This allows the user's experience to be customized by adding specific scenes or changing the color tone of the image based on the content of the dream. Some or all of the above-mentioned processing in the generation unit may be performed using AI, for example, or may be performed without using AI. For example, the generation unit can input the content of the dream into the generation AI and have the generation AI add specific scenes or change the color tone of the image.

[0037] The collection unit can analyze the user's past EEG data and select the optimal collection method. For example, the collection unit analyzes the user's past EEG data and selects the optimal collection method. The collection unit analyzes the past EEG data using AI. For example, the collection unit selects the most stable collection method based on the user's past EEG data. The collection unit can also perform collection at a specific time period from the user's past EEG data. The collection unit can also analyze the user's past EEG data and select optimal device settings. In this way, the optimal collection method can be selected by analyzing the user's past EEG data. Some or all of the above-mentioned processing in the collection unit may be performed using AI, for example, or may be performed without using AI. For example, the collection unit can input the past EEG data into a generation AI and cause the generation AI to select the optimal collection method.

[0038] The collection unit can filter the EEG data based on the user's sleep stage when collecting the EEG data. For example, the collection unit can filter the EEG data based on the user's sleep stage when collecting the EEG data. The collection unit determines the user's sleep stage using AI. For example, the collection unit preferentially collects EEG data when the user is in REM sleep. The collection unit can also filter and collect EEG data when the user is in non-REM sleep. The collection unit can also select the type of EEG data to collect depending on the user's sleep stage. This allows for more accurate EEG data to be collected by filtering based on the user's sleep stage. Some or all of the above-described processing in the collection unit can be performed using AI, for example, or without AI. For example, the collection unit can input the user's sleep stage data to a generation AI and have the generation AI perform filtering.

[0039] When collecting EEG data, the collection unit can prioritize collecting highly relevant data by taking into account the user's physical activity data. For example, when collecting EEG data, the collection unit prioritizes collecting highly relevant data by taking into account the user's physical activity data. The collection unit analyzes the user's physical activity data using AI. For example, the collection unit collects EEG data while the user is exercising. The collection unit can also collect EEG data when the user is relaxing. The collection unit can also collect EEG data while the user is sleeping. In this way, by taking into account the user's physical activity data, highly relevant EEG data can be preferentially collected. Some or all of the above-mentioned processing in the collection unit may be performed using AI, for example, or may be performed without using AI. For example, the collection unit can input the user's physical activity data to the generation AI and cause the generation AI to collect highly relevant data.

[0040] The collection unit can analyze the user's environmental sound data and remove noise when collecting EEG data. For example, the collection unit can analyze the user's environmental sound data and remove noise when collecting EEG data. The collection unit analyzes the environmental sound data using AI. For example, the collection unit can analyze environmental sounds around the user and remove noise. The collection unit can also minimize noise removal when the user is in a quiet environment. The collection unit can also strengthen noise removal when the user is in a noisy environment. This allows for more accurate EEG data to be collected by analyzing the user's environmental sound data and removing noise. Some or all of the above-described processing in the collection unit can be performed using AI, for example, or without AI. For example, the collection unit can input the environmental sound data to a generation AI and have the generation AI perform noise removal.

[0041] The analysis unit can detect specific patterns in the EEG data during analysis and identify the dream content corresponding to the pattern. For example, the analysis unit can detect specific patterns in the EEG data during analysis and identify the dream content corresponding to the pattern. The analysis unit analyzes the EEG data using AI. For example, when a specific EEG pattern appears, the analysis unit can identify the dream content corresponding to that pattern. The analysis unit can also analyze the pattern of the EEG data and identify the dream content. The analysis unit can also detect specific patterns in the EEG data and identify the dream content based on the pattern. In this way, the dream content can be identified by detecting specific patterns in the EEG data. Some or all of the above-mentioned processing in the analysis unit may be performed using AI, for example, or may be performed without using AI. For example, the analysis unit can input the EEG data to a generation AI and have the generation AI identify the dream content.

[0042] The analysis unit can improve the accuracy of the analysis by referring to the user's past dream data during analysis. For example, the analysis unit can improve the accuracy of the analysis by referring to the user's past dream data during analysis. The analysis unit uses AI to refer to the past dream data. For example, the analysis unit improves the analysis accuracy based on the user's past dream data. The analysis unit can also adjust the analysis algorithm based on the user's past dream data. The analysis unit can also analyze the user's past dream data and improve the analysis accuracy. In this way, the analysis accuracy is improved by referring to the user's past dream data. Some or all of the above-mentioned processing in the analysis unit may be performed using AI, for example, or may be performed without using AI. For example, the analysis unit can input the past dream data into the generation AI and cause the generation AI to improve the analysis accuracy.

[0043] The analysis unit can improve the analysis accuracy by taking into account the user's sleep environment data during analysis. For example, the analysis unit improves the analysis accuracy by taking into account the user's sleep environment data during analysis. The analysis unit analyzes the sleep environment data using AI. For example, the analysis unit improves the analysis accuracy based on the user's sleep environment data. The analysis unit can also adjust the analysis algorithm by referring to the user's sleep environment data. The analysis unit can also analyze the user's sleep environment data and improve the analysis accuracy. In this way, the analysis accuracy is improved by taking into account the user's sleep environment data. Some or all of the above-mentioned processing in the analysis unit may be performed using AI, for example, or may be performed without using AI. For example, the analysis unit can input the sleep environment data to the generation AI and cause the generation AI to improve the analysis accuracy.

[0044] The analysis unit can perform analysis by combining the user's brainwave data and heartbeat data during analysis. For example, the analysis unit performs analysis by combining the user's brainwave data and heartbeat data during analysis. The analysis unit analyzes the brainwave data and heartbeat data using AI. For example, the analysis unit performs analysis by combining the user's brainwave data and heartbeat data. The analysis unit can also improve the accuracy of the analysis based on the user's brainwave data and heartbeat data. The analysis unit can also adjust the analysis algorithm by referring to the user's brainwave data and heartbeat data. In this way, the analysis accuracy is improved by combining the user's brainwave data and heartbeat data. Some or all of the above-described processing in the analysis unit may be performed using AI, for example, or may be performed without using AI. For example, the analysis unit can input the brainwave data and heartbeat data to a generation AI and have the generation AI perform the analysis.

[0045] The generation unit can generate VR video based on the identified dream content at the time of generation. The generation unit generates VR video based on the identified dream content at the time of generation, for example. The generation unit generates VR video using AI. For example, if the user dreamed of flying in the sky, the generation unit can generate VR video to recreate that dream. Furthermore, if the user dreamed of swimming in the ocean, the generation unit can generate VR video to recreate that dream. Furthermore, if the user dreamed of an adventure, the generation unit can generate VR video to recreate that dream. In this way, the dream the user had can be recreated by generating VR video based on the identified dream content. Some or all of the above-described processing in the generation unit may be performed using AI, for example, or may be performed without using AI. For example, the generation unit can input the identified dream content into a generation AI and cause the generation AI to generate VR video.

[0046] The generation unit can improve the accuracy of the VR video by referring to the user's past dream data during generation. For example, the generation unit can improve the accuracy of the VR video by referring to the user's past dream data during generation. The generation unit references the past dream data using AI. For example, the generation unit improves the accuracy of the VR video based on the user's past dream data. The generation unit can also adjust the method for generating the VR video by referring to the user's past dream data. The generation unit can also analyze the user's past dream data and improve the accuracy of the VR video. In this way, the accuracy of the VR video is improved by referring to the user's past dream data. Some or all of the above-mentioned processing in the generation unit may be performed using AI, for example, or may be performed without using AI. For example, the generation unit can input past dream data into the generation AI and cause the generation AI to improve the accuracy of the VR video.

[0047] The generation unit can customize the color tones and scenes of the VR video based on the user's preferences at the time of generation. For example, the generation unit customizes the color tones and scenes of the VR video based on the user's preferences at the time of generation. The generation unit customizes the VR video using AI. For example, the generation unit customizes the color tones of the VR video based on the user's preferred color tones. The generation unit can also customize scenes of the VR video based on the user's preferred scenes. The generation unit can also customize the effects of the VR video based on the user's preferences. This allows for customizing the color tones and scenes of the VR video based on the user's preferences, thereby providing a more personalized experience. Some or all of the above-described processing in the generation unit may be performed using AI, for example, or may be performed without using AI. For example, the generation unit can input user preference data into the generation AI and have the generation AI perform the customization.

[0048] The generation unit can improve the interactivity of the VR video by taking into account the user's body movement data during generation. For example, the generation unit improves the interactivity of the VR video by taking into account the user's body movement data during generation. The generation unit analyzes the body movement data using AI. For example, the generation unit improves the interactivity of the VR video based on the user's body movement data. The generation unit can also adjust the method of generating the VR video by referring to the user's body movement data. The generation unit can also analyze the user's body movement data and improve the interactivity of the VR video. In this way, the interactivity of the VR video is improved by taking into account the user's body movement data. Some or all of the above-mentioned processing in the generation unit may be performed using AI, for example, or may be performed without using AI. For example, the generation unit can input body movement data to a generation AI and cause the generation AI to improve the interactivity.

[0049] The consent unit can select the optimal consent acquisition method by referring to the user's past consent history when obtaining consent. For example, the consent unit selects the optimal consent acquisition method by referring to the user's past consent history when obtaining consent. The consent unit refers to the past consent history using AI. For example, the consent unit selects the optimal consent acquisition method based on the user's past consent history. The consent unit can also adjust the consent acquisition method by referring to the user's past consent history. The consent unit can also analyze the user's past consent history and suggest the optimal consent acquisition method. In this way, the optimal consent acquisition method can be selected by referring to the user's past consent history. Some or all of the above-mentioned processing in the consent unit may be performed using AI, for example, or may be performed without using AI. For example, the consent unit can input the past consent history into the generation AI and cause the generation AI to select the optimal consent acquisition method.

[0050] The consent unit can select the optimal consent acquisition method by taking into account the user's device information when obtaining consent. For example, the consent unit selects the optimal consent acquisition method by taking into account the user's device information when obtaining consent. The consent unit analyzes the device information using AI. For example, if the user is using a smartphone, the consent unit can provide a consent acquisition method that matches the screen size. Furthermore, if the user is using a tablet, the consent unit can also provide a consent acquisition method that is optimized for a large screen. Furthermore, if the user is using a smartwatch, the consent unit can also provide a simple and highly visible consent acquisition method. This makes it possible to select the optimal consent acquisition method by taking into account the user's device information. Some or all of the above-described processing in the consent unit may be performed using AI, for example, or may be performed without using AI. For example, the consent unit can input device information into a generation AI and cause the generation AI to select the optimal consent acquisition method.

[0051] The sharing unit can select the optimal sharing method by referring to the user's past sharing history when sharing. For example, the sharing unit selects the optimal sharing method by referring to the user's past sharing history when sharing. The sharing unit refers to the past sharing history using AI. For example, the sharing unit selects the optimal sharing method based on the user's past sharing history. The sharing unit can also adjust the sharing method by referring to the user's past sharing history. The sharing unit can also analyze the user's past sharing history and suggest the optimal sharing method. In this way, the optimal sharing method can be selected by referring to the user's past sharing history. Some or all of the above-mentioned processing in the sharing unit may be performed using AI, for example, or may be performed without using AI. For example, the sharing unit can input the past sharing history into the generation AI and cause the generation AI to select the optimal sharing method.

[0052] The sharing unit can customize the shared content based on the user's privacy settings when sharing. For example, the sharing unit customizes the shared content based on the user's privacy settings when sharing. The sharing unit analyzes the privacy settings using AI. For example, the sharing unit customizes the shared content based on the user's privacy settings. The sharing unit can also refer to the user's privacy settings and adjust the shared content. The sharing unit can also analyze the user's privacy settings and suggest optimal shared content. In this way, the shared content can be customized based on the user's privacy settings, allowing sharing while protecting privacy. Some or all of the above-mentioned processing in the sharing unit may be performed using AI, for example, or may be performed without using AI. For example, the sharing unit can input privacy settings to a generation AI and cause the generation AI to customize the shared content.

[0053] The sharing unit can analyze the user's social media activity at the time of sharing and suggest a relevant sharing method. For example, the sharing unit can analyze the user's social media activity at the time of sharing and suggest a relevant sharing method. The sharing unit analyzes the social media activity using AI. For example, the sharing unit can suggest an optimal sharing method based on the user's social media activity. The sharing unit can also refer to the user's social media activity and adjust the sharing method. The sharing unit can also analyze the user's social media activity and select an optimal sharing method. In this way, a relevant sharing method can be suggested by analyzing the user's social media activity. Some or all of the above-mentioned processing in the sharing unit may be performed using AI, for example, or may be performed without using AI. For example, the sharing unit can input social media activity data to a generation AI and have the generation AI suggest a sharing method.

[0054] The sharing unit can select the optimal sharing method by taking into account the user's geographical location information when sharing. For example, the sharing unit selects the optimal sharing method by taking into account the user's geographical location information when sharing. The sharing unit analyzes the geographical location information using AI. For example, the sharing unit selects the optimal sharing method based on the user's geographical location information. The sharing unit can also adjust the sharing method by referring to the user's geographical location information. The sharing unit can also analyze the user's geographical location information and suggest the optimal sharing method. In this way, the optimal sharing method can be selected by taking into account the user's geographical location information. Some or all of the above-mentioned processing in the sharing unit may be performed using AI, for example, or may be performed without using AI. For example, the sharing unit can input geographical location information to a generation AI and cause the generation AI to select the optimal sharing method.

[0055] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0056] The collection unit can analyze the user's past brain wave data and select the optimal collection method. For example, the most stable collection method can be selected based on the user's past brain wave data. Collection can also be performed during a specific time period. Furthermore, optimal device settings can be selected. In this way, the optimal collection method can be selected by analyzing the user's past brain wave data.

[0057] The collection unit can filter the EEG data based on the user's sleep stage when collecting the data. For example, the collection unit can prioritize collection of EEG data during the user's REM sleep. The collection unit can also filter and collect EEG data during non-REM sleep. Furthermore, the collection unit can select the type of EEG data to collect depending on the user's sleep stage. By filtering based on the user's sleep stage, more accurate EEG data can be collected.

[0058] During analysis, the analysis unit can improve the accuracy of the analysis by referring to the user's past dream data. For example, the analysis accuracy can be improved based on the user's past dream data. The analysis algorithm can also be adjusted based on the past dream data. Furthermore, the analysis accuracy can be improved by analyzing the past dream data. In this way, the analysis accuracy can be improved by referring to the user's past dream data.

[0059] The generation unit can customize the color tones and scenes of the VR video based on the user's preferences during generation. For example, the generation unit can customize the color tones of the VR video based on the user's preferred color tones. The generation unit can also customize the scenes of the VR video based on the user's preferred scenes. Furthermore, the generation unit can customize the effects of the VR video based on the user's preferences. This allows the generation unit to provide a more personalized experience by customizing the color tones and scenes of the VR video based on the user's preferences.

[0060] The sharing unit can customize the shared content based on the user's privacy settings when sharing. For example, the shared content is customized based on the user's privacy settings. The sharing unit can also refer to the privacy settings and adjust the shared content. Furthermore, the sharing unit can analyze the privacy settings and suggest the optimal shared content. This allows the shared content to be customized based on the user's privacy settings, allowing sharing while protecting privacy.

[0061] The sharing unit can analyze the user's social media activity at the time of sharing and suggest relevant sharing methods. For example, it can suggest the optimal sharing method based on the user's social media activity. It can also refer to the social media activity and adjust the sharing method. It can also analyze the social media activity and select the optimal sharing method. In this way, it can suggest relevant sharing methods by analyzing the user's social media activity.

[0062] The processing flow of the first embodiment will be briefly explained below.

[0063] Step 1: The collection unit collects EEG data. The collection unit collects EEG data using, for example, a headband-type device. The headband-type device is designed so that the user can wear it comfortably even while sleeping. The collection unit collects EEG data in real time and transmits it to the AI. Step 2: The analysis unit analyzes the EEG data collected by the collection unit. The analysis unit detects specific EEG patterns and identifies the dream content corresponding to those patterns. Specific EEG patterns include alpha waves, beta waves, theta waves, etc. The analysis unit uses AI to analyze the EEG data and uses an algorithm to identify the dream content. Step 3: The generation unit generates VR video based on the content of the dream identified by the analysis unit. The generation unit generates VR video to recreate the dream the user had based on the content of the dream identified. The generation unit generates VR video using AI. For example, if the user had a dream about flying in the sky, the generation unit generates VR video to recreate that dream. Step 4: The consent unit obtains the user's consent. The consent unit allows the user to choose whether to share the content of their dream with others after waking up. The consent unit obtains consent using AI. For example, the consent unit provides an interface that asks the user whether to share the content of their dream with others. Step 5: The sharing unit shares the content of the dream with others based on the consent obtained by the consent unit. The sharing unit shares the content of the dream with others based on the user's consent. The sharing unit shares the content of the dream using AI. For example, the sharing unit provides an interface for sharing the content of the user's dream with family and friends.

[0064] (Example 2) A dream replay system according to an embodiment of the present invention reads brainwaves, analyzes them with AI, and replays the dreams of that day in VR. In this dream replay system, a user wears a device to read brainwaves while sleeping, collects brainwave data in real time, and transmits it to AI. Next, AI analyzes the collected brainwave data to identify the content of the dream. Based on the analyzed dream content, VR video is generated. The generated VR video is used to replay the dream after the user wakes up. Furthermore, with the user's consent, the content can be shared with others or re-edited into a video of their choice. This allows users to replay their dreams in detail and share them with others. Furthermore, the ability to customize and enjoy the content of dreams provides a richer experience. For example, a user wears a device to read brainwaves while sleeping. This device collects brainwave data in real time and transmits it to AI. For example, a headband-type device can be used, allowing the user to wear it comfortably while sleeping. Next, AI analyzes the collected brainwave data. The AI ​​analyzes the brainwave data to identify the content of the dream. For example, when a specific brainwave pattern appears, it is possible to identify the dream content corresponding to that pattern. This allows for a detailed understanding of the user's dream. VR video is generated based on the analyzed dream content. Based on the identified dream content, AI generates VR video to recreate the user's dream. For example, if a user dreams of flying, VR video is generated to recreate that dream. This allows the user to relive the dream after waking up. Furthermore, with the user's consent, the content can be shared with others. For example, a user can show the content of their dream to family and friends. They can also re-edit the video to their liking. For example, specific scenes can be added or the color tone of the video can be changed based on the content of the dream. This allows users to share their dreams with others and customize them for enjoyment. This service allows users to recreate their dreams in detail and share them with others.Furthermore, the user can customize and enjoy the content of their dreams, providing a richer experience. This allows the dream reproduction system to reproduce the user's dreams in detail and share them with others. Furthermore, the user can customize and enjoy the content of their dreams, providing a richer experience.

[0065] A dream reproduction system according to an embodiment includes a collection unit, an analysis unit, a generation unit, a consent unit, and a sharing unit. The collection unit collects EEG data. The collection unit collects EEG data using, for example, a headband-type device. The headband-type device is designed to be comfortably worn by a user while sleeping. For example, the collection unit collects EEG data in real time and transmits it to an AI. The analysis unit analyzes the EEG data collected by the collection unit. For example, the analysis unit detects a specific EEG pattern and identifies the dream content corresponding to the pattern. The specific EEG patterns include alpha waves, beta waves, theta waves, etc. The analysis unit analyzes the EEG data using AI to identify the dream content. For example, the analysis unit uses an algorithm to analyze the pattern of EEG data and identify the dream content. The generation unit generates VR video based on the dream content identified by the analysis unit. For example, the generation unit generates VR video to recreate the dream the user had based on the identified dream content. The generation unit generates VR video using AI. For example, if the user had a dream about flying in the sky, the generation unit generates VR video to recreate that dream. The consent unit obtains the user's consent. For example, the consent unit allows the user to choose whether to share the content of the dream with others after waking up. The consent unit obtains consent using AI. For example, the consent unit provides an interface that asks the user whether to share the content of the dream with others. The sharing unit shares the content of the dream with others based on the consent obtained by the consent unit. For example, the sharing unit shares the content of the dream with others based on the user's consent. The sharing unit shares the content of the dream using AI. For example, the sharing unit provides an interface for sharing the content of the user's dream with family and friends. This allows the dream reproduction system according to the embodiment to recreate the user's dream in detail and share it with others. Furthermore, the content of the dream can be customized and enjoyed, providing a richer experience.

[0066] The collection unit can collect EEG data using a headband-type device. The headband-type device is designed, for example, to be worn comfortably by the user while sleeping. The collection unit, for example, collects EEG data in real time using the headband-type device and transmits the data to an AI. For example, the collection unit collects EEG data using the headband-type device and transmits the data to an AI. As a result, by using the headband-type device, EEG data can be collected comfortably by the user while sleeping. Some or all of the above-described processing in the collection unit may be performed, for example, using an AI, or may be performed without using an AI. For example, the collection unit can input EEG data collected using the headband-type device to a generation AI and cause the generation AI to analyze the EEG data.

[0067] The analysis unit can detect specific brainwave patterns and identify the dream content corresponding to those patterns. For example, the analysis unit can detect specific brainwave patterns and identify the dream content corresponding to those patterns. Specific brainwave patterns include, for example, alpha waves, beta waves, and theta waves. The analysis unit uses AI to analyze the brainwave data and identify the dream content. For example, the analysis unit uses an algorithm that analyzes the patterns of brainwave data and identifies the dream content. By detecting specific brainwave patterns, the dream content can be understood in detail. Some or all of the above-mentioned processing in the analysis unit may be performed using AI, for example, or may be performed without using AI. For example, the analysis unit can input the brainwave data to a generation AI and have the generation AI identify the dream content.

[0068] The generation unit can generate VR video based on the content of the identified dream. For example, the generation unit generates VR video for recreating the dream the user had based on the content of the identified dream. The generation unit generates VR video using AI. For example, if the user had a dream of flying in the sky, the generation unit generates VR video for recreating that dream. In this way, the dream the user had can be reproduced by generating VR video based on the content of the identified dream. Some or all of the above-described processing in the generation unit may be performed using AI, for example, or may be performed without using AI. For example, the generation unit can input the content of the identified dream into a generation AI and cause the generation AI to generate VR video.

[0069] The consent unit can enable the user to choose whether to share the content of their dreams with a third party after waking up. The consent unit, for example, enables the user to choose whether to share the content of their dreams with other people after waking up. The consent unit obtains consent using AI. For example, the consent unit provides the user with an interface that asks the user whether to share the content of their dreams with other people. This allows the user to choose whether to share the content of their dreams with other people after waking up. Some or all of the above-mentioned processing in the consent unit may be performed using AI, for example, or may be performed without using AI. For example, the consent unit can input the user's consent into the generation AI and cause the generation AI to obtain consent.

[0070] The sharing unit can share the content of the dream with other people based on the user's consent. The sharing unit, for example, shares the content of the dream with other people based on the user's consent. The sharing unit shares the content of the dream using AI. For example, the sharing unit provides an interface for sharing the content of the user's dream with family and friends. This allows the content of the dream to be shared with other people based on the user's consent. Some or all of the above-mentioned processing in the sharing unit may be performed using AI, for example, or may be performed without using AI. For example, the sharing unit can input the content of the user's dream into a generation AI and have the generation AI execute the sharing.

[0071] The sharing unit can prevent personal identifying information from being included when sharing, in order to protect privacy. For example, the sharing unit prevents personal identifying information from being included when sharing, in order to protect privacy. The sharing unit removes personal identifying information using AI. For example, the sharing unit uses an algorithm that automatically removes personal identifying information when sharing the content of the user's dream. This prevents personal information from being included when sharing, in order to protect privacy. Some or all of the above-mentioned processing in the sharing unit may be performed using AI, for example, or may be performed without using AI. For example, the sharing unit may input the content of the user's dream into a generation AI and have the generation AI remove personal identifying information.

[0072] The generation unit can add specific scenes or change the color tone of the image based on the content of the dream. For example, the generation unit can add specific scenes or change the color tone of the image based on the content of the dream. The generation unit customizes the VR image using AI. For example, the generation unit uses an algorithm that adds specific scenes or changes the color tone of the image based on the content of the dream the user had. This allows the user's experience to be customized by adding specific scenes or changing the color tone of the image based on the content of the dream. Some or all of the above-mentioned processing in the generation unit may be performed using AI, for example, or may be performed without using AI. For example, the generation unit can input the content of the dream into the generation AI and have the generation AI add specific scenes or change the color tone of the image.

[0073] The collection unit can estimate the user's emotion and adjust the timing of collecting EEG data based on the determined user's emotion. The collection unit, for example, estimates the user's emotion and adjusts the timing of collecting EEG data based on the estimated user's emotion. The collection unit estimates the user's emotion using AI. For example, the collection unit starts collecting EEG data when the user is relaxed. Furthermore, when the user is feeling stressed, the collection unit can delay the collection timing and wait until the user is relaxed. Furthermore, the collection unit can collect EEG data when the user enters deep sleep. In this way, by adjusting the timing of collecting EEG data based on the user's emotion, data can be collected at a more appropriate timing. Some or all of the above-described processing in the collection unit may be performed using AI, for example, or may be performed without using AI. For example, the collection unit can input the user's emotion data to a generation AI and cause the generation AI to adjust the collection timing.

[0074] The collection unit can analyze the user's past EEG data and select the optimal collection method. For example, the collection unit analyzes the user's past EEG data and selects the optimal collection method. The collection unit analyzes the past EEG data using AI. For example, the collection unit selects the most stable collection method based on the user's past EEG data. The collection unit can also perform collection at a specific time period from the user's past EEG data. The collection unit can also analyze the user's past EEG data and select optimal device settings. In this way, the optimal collection method can be selected by analyzing the user's past EEG data. Some or all of the above-mentioned processing in the collection unit may be performed using AI, for example, or may be performed without using AI. For example, the collection unit can input the past EEG data into a generation AI and cause the generation AI to select the optimal collection method.

[0075] The collection unit can filter the EEG data based on the user's sleep stage when collecting the EEG data. For example, the collection unit can filter the EEG data based on the user's sleep stage when collecting the EEG data. The collection unit determines the user's sleep stage using AI. For example, the collection unit preferentially collects EEG data when the user is in REM sleep. The collection unit can also filter and collect EEG data when the user is in non-REM sleep. The collection unit can also select the type of EEG data to collect depending on the user's sleep stage. This allows for more accurate EEG data to be collected by filtering based on the user's sleep stage. Some or all of the above-described processing in the collection unit can be performed using AI, for example, or without AI. For example, the collection unit can input the user's sleep stage data to a generation AI and have the generation AI perform filtering.

[0076] The collection unit can estimate the user's emotion and determine the priority of the brain wave data to be collected based on the determined user's emotion. The collection unit, for example, estimates the user's emotion and determines the priority of the brain wave data to be collected based on the estimated user's emotion. The collection unit estimates the user's emotion using AI. For example, if the user is relaxed, the collection unit can prioritize collecting relaxed brain wave data. Also, if the user is stressed, the collection unit can prioritize collecting stressed brain wave data. Also, if the user is excited, the collection unit can prioritize collecting excited brain wave data. In this way, by determining the priority of the brain wave data to be collected based on the user's emotion, important data can be collected preferentially. Some or all of the above-mentioned processing in the collection unit may be performed using AI, for example, or may be performed without using AI. For example, the collection unit can input the user's emotion data to a generation AI and have the generation AI determine the priority.

[0077] When collecting EEG data, the collection unit can prioritize collecting highly relevant data by taking into account the user's physical activity data. For example, when collecting EEG data, the collection unit prioritizes collecting highly relevant data by taking into account the user's physical activity data. The collection unit analyzes the user's physical activity data using AI. For example, the collection unit collects EEG data while the user is exercising. The collection unit can also collect EEG data when the user is relaxing. The collection unit can also collect EEG data while the user is sleeping. In this way, by taking into account the user's physical activity data, highly relevant EEG data can be preferentially collected. Some or all of the above-mentioned processing in the collection unit may be performed using AI, for example, or may be performed without using AI. For example, the collection unit can input the user's physical activity data to the generation AI and cause the generation AI to collect highly relevant data.

[0078] The collection unit can analyze the user's environmental sound data and remove noise when collecting EEG data. For example, the collection unit can analyze the user's environmental sound data and remove noise when collecting EEG data. The collection unit analyzes the environmental sound data using AI. For example, the collection unit can analyze environmental sounds around the user and remove noise. The collection unit can also minimize noise removal when the user is in a quiet environment. The collection unit can also strengthen noise removal when the user is in a noisy environment. This allows for more accurate EEG data to be collected by analyzing the user's environmental sound data and removing noise. Some or all of the above-described processing in the collection unit can be performed using AI, for example, or without AI. For example, the collection unit can input the environmental sound data to a generation AI and have the generation AI perform noise removal.

[0079] The analysis unit can estimate the user's emotions and adjust the analysis algorithm based on the determined user's emotions. The analysis unit, for example, estimates the user's emotions and adjusts the analysis algorithm based on the estimated user's emotions. The analysis unit estimates the user's emotions using AI. For example, if the user is relaxed, the analysis unit can emphasize the relaxed brain wave pattern in the analysis. Also, if the user is stressed, the analysis unit can emphasize the stressed brain wave pattern in the analysis. Also, if the user is excited, the analysis unit can emphasize the excited brain wave pattern in the analysis. This improves the analysis accuracy by adjusting the analysis algorithm based on the user's emotions. Some or all of the above-mentioned processing in the analysis unit may be performed using AI, for example, or may be performed without using AI. For example, the analysis unit can input the user's emotion data into the generation AI and cause the generation AI to adjust the analysis algorithm.

[0080] The analysis unit can detect specific patterns in the EEG data during analysis and identify the dream content corresponding to the pattern. For example, the analysis unit can detect specific patterns in the EEG data during analysis and identify the dream content corresponding to the pattern. The analysis unit analyzes the EEG data using AI. For example, when a specific EEG pattern appears, the analysis unit can identify the dream content corresponding to that pattern. The analysis unit can also analyze the pattern of the EEG data and identify the dream content. The analysis unit can also detect specific patterns in the EEG data and identify the dream content based on the pattern. In this way, the dream content can be identified by detecting specific patterns in the EEG data. Some or all of the above-mentioned processing in the analysis unit may be performed using AI, for example, or may be performed without using AI. For example, the analysis unit can input the EEG data to a generation AI and have the generation AI identify the dream content.

[0081] The analysis unit can improve the accuracy of the analysis by referring to the user's past dream data during analysis. For example, the analysis unit can improve the accuracy of the analysis by referring to the user's past dream data during analysis. The analysis unit uses AI to refer to the past dream data. For example, the analysis unit improves the analysis accuracy based on the user's past dream data. The analysis unit can also adjust the analysis algorithm based on the user's past dream data. The analysis unit can also analyze the user's past dream data and improve the analysis accuracy. In this way, the analysis accuracy is improved by referring to the user's past dream data. Some or all of the above-mentioned processing in the analysis unit may be performed using AI, for example, or may be performed without using AI. For example, the analysis unit can input the past dream data into the generation AI and cause the generation AI to improve the analysis accuracy.

[0082] The analysis unit can estimate the user's emotions and adjust the display method of the analysis results based on the determined user's emotions. The analysis unit, for example, estimates the user's emotions and adjusts the display method of the analysis results based on the estimated user's emotions. The analysis unit estimates the user's emotions using AI. For example, the analysis unit can display detailed analysis results when the user is relaxed. The analysis unit can also display concise analysis results when the user is stressed. The analysis unit can also display visually stimulating analysis results when the user is excited. In this way, by adjusting the display method of the analysis results based on the user's emotions, it is possible to provide an optimal display method for the user. Some or all of the above-described processing in the analysis unit may be performed using AI, for example, or may be performed without using AI. For example, the analysis unit can input the user's emotion data into a generation AI and have the generation AI adjust the display method.

[0083] The analysis unit can improve the analysis accuracy by taking into account the user's sleep environment data during analysis. For example, the analysis unit improves the analysis accuracy by taking into account the user's sleep environment data during analysis. The analysis unit analyzes the sleep environment data using AI. For example, the analysis unit improves the analysis accuracy based on the user's sleep environment data. The analysis unit can also adjust the analysis algorithm by referring to the user's sleep environment data. The analysis unit can also analyze the user's sleep environment data and improve the analysis accuracy. In this way, the analysis accuracy is improved by taking into account the user's sleep environment data. Some or all of the above-mentioned processing in the analysis unit may be performed using AI, for example, or may be performed without using AI. For example, the analysis unit can input the sleep environment data to the generation AI and cause the generation AI to improve the analysis accuracy.

[0084] The analysis unit can perform analysis by combining the user's brainwave data and heartbeat data during analysis. For example, the analysis unit performs analysis by combining the user's brainwave data and heartbeat data during analysis. The analysis unit analyzes the brainwave data and heartbeat data using AI. For example, the analysis unit performs analysis by combining the user's brainwave data and heartbeat data. The analysis unit can also improve the accuracy of the analysis based on the user's brainwave data and heartbeat data. The analysis unit can also adjust the analysis algorithm by referring to the user's brainwave data and heartbeat data. In this way, the analysis accuracy is improved by combining the user's brainwave data and heartbeat data. Some or all of the above-described processing in the analysis unit may be performed using AI, for example, or may be performed without using AI. For example, the analysis unit can input the brainwave data and heartbeat data to a generation AI and have the generation AI perform the analysis.

[0085] The generation unit can estimate the user's emotions and adjust the generation method of the VR images based on the determined user's emotions. For example, the generation unit estimates the user's emotions and adjusts the generation method of the VR images based on the estimated user's emotions. The generation unit estimates the user's emotions using AI. For example, if the user is relaxed, the generation unit can generate VR images that progress at a leisurely pace. If the user is excited, the generation unit can also generate VR images with visually stimulating effects. If the user is stressed, the generation unit can also generate VR images with calm colors. In this way, by adjusting the generation method of the VR images based on the user's emotions, more appropriate VR images can be generated. Some or all of the above-described processing in the generation unit may be performed using AI, for example, or may be performed without using AI. For example, the generation unit can input user's emotion data into the generation AI and cause the generation AI to adjust the generation method.

[0086] The generation unit can generate VR video based on the identified dream content at the time of generation. The generation unit generates VR video based on the identified dream content at the time of generation, for example. The generation unit generates VR video using AI. For example, if the user dreamed of flying in the sky, the generation unit can generate VR video to recreate that dream. Furthermore, if the user dreamed of swimming in the ocean, the generation unit can generate VR video to recreate that dream. Furthermore, if the user dreamed of an adventure, the generation unit can generate VR video to recreate that dream. In this way, the dream the user had can be recreated by generating VR video based on the identified dream content. Some or all of the above-described processing in the generation unit may be performed using AI, for example, or may be performed without using AI. For example, the generation unit can input the identified dream content into a generation AI and cause the generation AI to generate VR video.

[0087] The generation unit can improve the accuracy of the VR video by referring to the user's past dream data during generation. For example, the generation unit can improve the accuracy of the VR video by referring to the user's past dream data during generation. The generation unit references the past dream data using AI. For example, the generation unit improves the accuracy of the VR video based on the user's past dream data. The generation unit can also adjust the method for generating the VR video by referring to the user's past dream data. The generation unit can also analyze the user's past dream data and improve the accuracy of the VR video. In this way, the accuracy of the VR video is improved by referring to the user's past dream data. Some or all of the above-mentioned processing in the generation unit may be performed using AI, for example, or may be performed without using AI. For example, the generation unit can input past dream data into the generation AI and cause the generation AI to improve the accuracy of the VR video.

[0088] The generation unit can estimate the user's emotions and adjust the display method of the VR video based on the determined user's emotions. For example, the generation unit estimates the user's emotions and adjusts the display method of the VR video based on the estimated user's emotions. The generation unit estimates the user's emotions using AI. For example, if the user is relaxed, the generation unit can display VR video progressing at a leisurely pace. If the user is excited, the generation unit can also display VR video with visually stimulating effects. If the user is stressed, the generation unit can also display VR video with calming colors. This allows for adjusting the display method of the VR video based on the user's emotions, thereby providing a more appropriate display method. Some or all of the above-described processing in the generation unit may be performed using AI, for example, or may be performed without using AI. For example, the generation unit can input user's emotion data into the generation AI and cause the generation AI to adjust the display method.

[0089] The generation unit can customize the color tones and scenes of the VR video based on the user's preferences at the time of generation. For example, the generation unit customizes the color tones and scenes of the VR video based on the user's preferences at the time of generation. The generation unit customizes the VR video using AI. For example, the generation unit customizes the color tones of the VR video based on the user's preferred color tones. The generation unit can also customize scenes of the VR video based on the user's preferred scenes. The generation unit can also customize the effects of the VR video based on the user's preferences. This allows for customizing the color tones and scenes of the VR video based on the user's preferences, thereby providing a more personalized experience. Some or all of the above-described processing in the generation unit may be performed using AI, for example, or may be performed without using AI. For example, the generation unit can input user preference data into the generation AI and have the generation AI perform the customization.

[0090] The generation unit can improve the interactivity of the VR video by taking into account the user's body movement data during generation. For example, the generation unit improves the interactivity of the VR video by taking into account the user's body movement data during generation. The generation unit analyzes the body movement data using AI. For example, the generation unit improves the interactivity of the VR video based on the user's body movement data. The generation unit can also adjust the method of generating the VR video by referring to the user's body movement data. The generation unit can also analyze the user's body movement data and improve the interactivity of the VR video. In this way, the interactivity of the VR video is improved by taking into account the user's body movement data. Some or all of the above-mentioned processing in the generation unit may be performed using AI, for example, or may be performed without using AI. For example, the generation unit can input body movement data to a generation AI and cause the generation AI to improve the interactivity.

[0091] The consent unit can estimate the user's emotions and adjust the consent acquisition method based on the determined user's emotions. The consent unit, for example, estimates the user's emotions and adjusts the consent acquisition method based on the estimated user's emotions. The consent unit estimates the user's emotions using AI. For example, if the user is relaxed, the consent unit can provide a consent acquisition method that includes detailed explanations. Also, if the user is feeling stressed, the consent unit can provide a concise consent acquisition method. Also, if the user is excited, the consent unit can provide a visually stimulating consent acquisition method. In this way, by adjusting the consent acquisition method based on the user's emotions, a more appropriate consent acquisition method can be provided. Some or all of the above-mentioned processing in the consent unit may be performed using AI, for example, or may be performed without using AI. For example, the consent unit can input the user's emotion data into the generation AI and cause the generation AI to adjust the consent acquisition method.

[0092] The consent unit can select the optimal consent acquisition method by referring to the user's past consent history when obtaining consent. For example, the consent unit selects the optimal consent acquisition method by referring to the user's past consent history when obtaining consent. The consent unit refers to the past consent history using AI. For example, the consent unit selects the optimal consent acquisition method based on the user's past consent history. The consent unit can also adjust the consent acquisition method by referring to the user's past consent history. The consent unit can also analyze the user's past consent history and suggest the optimal consent acquisition method. In this way, the optimal consent acquisition method can be selected by referring to the user's past consent history. Some or all of the above-mentioned processing in the consent unit may be performed using AI, for example, or may be performed without using AI. For example, the consent unit can input the past consent history into the generation AI and cause the generation AI to select the optimal consent acquisition method.

[0093] The consent unit can estimate the user's emotions and adjust the timing of consent acquisition based on the determined user's emotions. The consent unit, for example, estimates the user's emotions and adjusts the timing of consent acquisition based on the estimated user's emotions. The consent unit estimates the user's emotions using AI. For example, the consent unit can advance the timing of consent acquisition when the user is relaxed. The consent unit can also delay the timing of consent acquisition when the user is feeling stressed. The consent unit can also adjust the timing of consent acquisition when the user is excited. In this way, by adjusting the timing of consent acquisition based on the user's emotions, consent can be acquired at a more appropriate time. Some or all of the above-mentioned processing in the consent unit may be performed using AI, for example, or may be performed without using AI. For example, the consent unit can input the user's emotion data into the generation AI and cause the generation AI to adjust the timing of consent acquisition.

[0094] The consent unit can select the optimal consent acquisition method by taking into account the user's device information when obtaining consent. For example, the consent unit selects the optimal consent acquisition method by taking into account the user's device information when obtaining consent. The consent unit analyzes the device information using AI. For example, if the user is using a smartphone, the consent unit can provide a consent acquisition method that matches the screen size. Furthermore, if the user is using a tablet, the consent unit can also provide a consent acquisition method that is optimized for a large screen. Furthermore, if the user is using a smartwatch, the consent unit can also provide a simple and highly visible consent acquisition method. This makes it possible to select the optimal consent acquisition method by taking into account the user's device information. Some or all of the above-described processing in the consent unit may be performed using AI, for example, or may be performed without using AI. For example, the consent unit can input device information into a generation AI and cause the generation AI to select the optimal consent acquisition method.

[0095] The sharing unit can estimate the user's emotions and adjust the sharing method based on the determined user's emotions. For example, the sharing unit estimates the user's emotions and adjusts the sharing method based on the estimated user's emotions. The sharing unit estimates the user's emotions using AI. For example, the sharing unit can provide a detailed sharing method when the user is relaxed. Furthermore, the sharing unit can provide a concise sharing method when the user is stressed. Furthermore, the sharing unit can provide a visually stimulating sharing method when the user is excited. In this way, by adjusting the sharing method based on the user's emotions, a more appropriate sharing method can be provided. Some or all of the above-described processing in the sharing unit may be performed using AI, for example, or may be performed without using AI. For example, the sharing unit can input the user's emotion data into the generation AI and cause the generation AI to adjust the sharing method.

[0096] The sharing unit can select the optimal sharing method by referring to the user's past sharing history when sharing. For example, the sharing unit selects the optimal sharing method by referring to the user's past sharing history when sharing. The sharing unit refers to the past sharing history using AI. For example, the sharing unit selects the optimal sharing method based on the user's past sharing history. The sharing unit can also adjust the sharing method by referring to the user's past sharing history. The sharing unit can also analyze the user's past sharing history and suggest the optimal sharing method. In this way, the optimal sharing method can be selected by referring to the user's past sharing history. Some or all of the above-mentioned processing in the sharing unit may be performed using AI, for example, or may be performed without using AI. For example, the sharing unit can input the past sharing history into the generation AI and cause the generation AI to select the optimal sharing method.

[0097] The sharing unit can customize the shared content based on the user's privacy settings when sharing. For example, the sharing unit customizes the shared content based on the user's privacy settings when sharing. The sharing unit analyzes the privacy settings using AI. For example, the sharing unit customizes the shared content based on the user's privacy settings. The sharing unit can also refer to the user's privacy settings and adjust the shared content. The sharing unit can also analyze the user's privacy settings and suggest optimal shared content. In this way, the shared content can be customized based on the user's privacy settings, allowing sharing while protecting privacy. Some or all of the above-mentioned processing in the sharing unit may be performed using AI, for example, or may be performed without using AI. For example, the sharing unit can input privacy settings to a generation AI and cause the generation AI to customize the shared content.

[0098] The sharing unit can estimate the user's emotions and determine the priority of sharing based on the determined user's emotions. The sharing unit, for example, estimates the user's emotions and determines the priority of sharing based on the estimated user's emotions. The sharing unit estimates the user's emotions using AI. For example, if the user is relaxed, the sharing unit can prioritize detailed shared content. Also, if the user is stressed, the sharing unit can prioritize concise shared content. Also, if the user is excited, the sharing unit can prioritize visually stimulating shared content. In this way, by determining the priority of sharing based on the user's emotions, more appropriate shared content can be provided. Some or all of the above-described processing in the sharing unit may be performed using AI, for example, or may be performed without using AI. For example, the sharing unit can input the user's emotion data into a generation AI and have the generation AI determine the priority of sharing.

[0099] The sharing unit can analyze the user's social media activity at the time of sharing and suggest a relevant sharing method. For example, the sharing unit can analyze the user's social media activity at the time of sharing and suggest a relevant sharing method. The sharing unit analyzes the social media activity using AI. For example, the sharing unit can suggest an optimal sharing method based on the user's social media activity. The sharing unit can also refer to the user's social media activity and adjust the sharing method. The sharing unit can also analyze the user's social media activity and select an optimal sharing method. In this way, a relevant sharing method can be suggested by analyzing the user's social media activity. Some or all of the above-mentioned processing in the sharing unit may be performed using AI, for example, or may be performed without using AI. For example, the sharing unit can input social media activity data to a generation AI and have the generation AI suggest a sharing method.

[0100] The sharing unit can select the optimal sharing method by taking into account the user's geographical location information when sharing. For example, the sharing unit selects the optimal sharing method by taking into account the user's geographical location information when sharing. The sharing unit analyzes the geographical location information using AI. For example, the sharing unit selects the optimal sharing method based on the user's geographical location information. The sharing unit can also adjust the sharing method by referring to the user's geographical location information. The sharing unit can also analyze the user's geographical location information and suggest the optimal sharing method. In this way, the optimal sharing method can be selected by taking into account the user's geographical location information. Some or all of the above-mentioned processing in the sharing unit may be performed using AI, for example, or may be performed without using AI. For example, the sharing unit can input geographical location information to a generation AI and cause the generation AI to select the optimal sharing method. === Hard Collateral 1-1 === Each of the multiple elements, including the collection unit, analysis unit, generation unit, consent unit, and sharing unit, described above, is realized, for example, by at least one of the smart device 14 and the data processing device 12. For example, the collection unit collects EEG data in real time using a headband-type device of the smart device 14 and transmits it to the data processing device 12. The analysis unit analyzes the EEG data using the specific processing unit 290 of the data processing device 12 to identify the content of the dream. The generation unit generates VR video based on the content of the dream identified by the specific processing unit 290 of the data processing device 12. The consent unit obtains the user's consent using the control unit 46A of the smart device 14, and the sharing unit shares the content of the dream with others using the specific processing unit 290 of the data processing device 12. The collection unit can estimate the user's emotion and adjust the timing of collecting EEG data based on the determined emotion of the user. === Hard Collateral 1-2 === Each of the multiple elements, including the collection unit, analysis unit, generation unit, consent unit, and sharing unit, described above, is realized, for example, by at least one of the smart glasses 214 and the data processing device 12. For example, the collection unit collects EEG data in real time using a headband-type device of the smart glasses 214 and transmits it to the data processing device 12. The analysis unit analyzes the EEG data using the specific processing unit 290 of the data processing device 12 to identify the content of the dream. The generation unit generates VR video based on the dream content identified by the specific processing unit 290 of the data processing device 12. The consent unit obtains the user's consent using the control unit 46A of the smart glasses 214, and the sharing unit shares the content of the dream with others using the specific processing unit 290 of the data processing device 12. The collection unit can estimate the user's emotion and adjust the timing of collecting EEG data based on the determined user's emotion. === Hard Collateral 1-3 === Each of the multiple elements including the collection unit, analysis unit, generation unit, consent unit, and sharing unit described above is realized, for example, by at least one of the headset-type terminal 314 and the data processing device 12. For example, the collection unit collects EEG data in real time using a headband-type device of the headset-type terminal 314 and transmits it to the data processing device 12. The analysis unit analyzes the EEG data using the specific processing unit 290 of the data processing device 12 to identify the content of the dream. The generation unit generates VR video based on the content of the dream identified by the specific processing unit 290 of the data processing device 12. The consent unit obtains the user's consent using the control unit 46A of the headset-type terminal 314, and the sharing unit shares the content of the dream with others using the specific processing unit 290 of the data processing device 12. The collection unit can estimate the user's emotion and adjust the timing of collecting EEG data based on the determined emotion of the user. === Hard Collateral 1-4 === Each of the multiple elements including the collection unit, analysis unit, generation unit, consent unit, and sharing unit described above is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the collection unit collects EEG data in real time using a headband-type device of the robot 414 and transmits it to the data processing device 12. The analysis unit analyzes the EEG data using the specific processing unit 290 of the data processing device 12 to identify the content of the dream. The generation unit generates VR video based on the content of the dream identified by the specific processing unit 290 of the data processing device 12. The consent unit obtains the user's consent using the control unit 46A of the robot 414, and the sharing unit shares the content of the dream with others using the specific processing unit 290 of the data processing device 12. The collection unit can estimate the user's emotions and adjust the timing of collecting EEG data based on the determined user's emotions.

[0101] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0102] The analysis unit can estimate the user's emotions and adjust the display method of the analysis results based on the estimated user emotions. For example, if the user is relaxed, detailed analysis results can be displayed. If the user is stressed, concise analysis results can be displayed. Furthermore, if the user is excited, visually stimulating analysis results can be displayed. In this way, by adjusting the display method of the analysis results based on the user's emotions, it is possible to provide the optimal display method for the user.

[0103] The collection unit can analyze the user's past brain wave data and select the optimal collection method. For example, the most stable collection method can be selected based on the user's past brain wave data. Collection can also be performed during a specific time period. Furthermore, optimal device settings can be selected. In this way, the optimal collection method can be selected by analyzing the user's past brain wave data.

[0104] The generation unit can estimate the user's emotions and adjust the method for generating VR images based on the estimated user emotions. For example, if the user is relaxed, the generation unit can generate VR images that progress at a leisurely pace. If the user is excited, the generation unit can generate VR images with visually stimulating effects. Furthermore, if the user is stressed, the generation unit can generate VR images with calming colors. In this way, by adjusting the method for generating VR images based on the user's emotions, more appropriate VR images can be generated.

[0105] The sharing unit can estimate the user's emotions and adjust the sharing method based on the estimated user's emotions. For example, if the user is relaxed, a detailed sharing method can be provided. If the user is stressed, a simple sharing method can be provided. Furthermore, if the user is excited, a visually stimulating sharing method can be provided. In this way, by adjusting the sharing method based on the user's emotions, a more appropriate sharing method can be provided.

[0106] The consent unit can estimate the user's emotions and adjust the consent acquisition method based on the estimated user's emotions. For example, if the user is relaxed, a consent acquisition method including detailed explanations can be provided. If the user is stressed, a simple consent acquisition method can be provided. Furthermore, if the user is excited, a visually stimulating consent acquisition method can be provided. In this way, by adjusting the consent acquisition method based on the user's emotions, a more appropriate consent acquisition method can be provided.

[0107] The collection unit can filter the EEG data based on the user's sleep stage when collecting the data. For example, the collection unit can prioritize collection of EEG data during the user's REM sleep. The collection unit can also filter and collect EEG data during non-REM sleep. Furthermore, the collection unit can select the type of EEG data to collect depending on the user's sleep stage. By filtering based on the user's sleep stage, more accurate EEG data can be collected.

[0108] During analysis, the analysis unit can improve the accuracy of the analysis by referring to the user's past dream data. For example, the analysis accuracy can be improved based on the user's past dream data. The analysis algorithm can also be adjusted based on the past dream data. Furthermore, the analysis accuracy can be improved by analyzing the past dream data. In this way, the analysis accuracy can be improved by referring to the user's past dream data.

[0109] The generation unit can customize the color tones and scenes of the VR video based on the user's preferences during generation. For example, the generation unit can customize the color tones of the VR video based on the user's preferred color tones. The generation unit can also customize the scenes of the VR video based on the user's preferred scenes. Furthermore, the generation unit can customize the effects of the VR video based on the user's preferences. This allows the generation unit to provide a more personalized experience by customizing the color tones and scenes of the VR video based on the user's preferences.

[0110] The sharing unit can customize the shared content based on the user's privacy settings when sharing. For example, the shared content is customized based on the user's privacy settings. The sharing unit can also refer to the privacy settings and adjust the shared content. Furthermore, the sharing unit can analyze the privacy settings and suggest the optimal shared content. This allows the shared content to be customized based on the user's privacy settings, allowing sharing while protecting privacy.

[0111] The sharing unit can analyze the user's social media activity at the time of sharing and suggest relevant sharing methods. For example, it can suggest the optimal sharing method based on the user's social media activity. It can also refer to the social media activity and adjust the sharing method. It can also analyze the social media activity and select the optimal sharing method. In this way, it can suggest relevant sharing methods by analyzing the user's social media activity.

[0112] The processing flow of the second embodiment will be briefly explained below.

[0113] Step 1: The collection unit collects EEG data. The collection unit collects EEG data using, for example, a headband-type device. The headband-type device is designed so that the user can wear it comfortably even while sleeping. The collection unit collects EEG data in real time and transmits it to the AI. Step 2: The analysis unit analyzes the EEG data collected by the collection unit. The analysis unit detects specific EEG patterns and identifies the dream content corresponding to those patterns. Specific EEG patterns include alpha waves, beta waves, theta waves, etc. The analysis unit uses AI to analyze the EEG data and uses an algorithm to identify the dream content. Step 3: The generation unit generates VR video based on the content of the dream identified by the analysis unit. The generation unit generates VR video to recreate the dream the user had based on the content of the dream identified. The generation unit generates VR video using AI. For example, if the user had a dream about flying in the sky, the generation unit generates VR video to recreate that dream. Step 4: The consent unit obtains the user's consent. The consent unit allows the user to choose whether to share the content of their dream with others after waking up. The consent unit obtains consent using AI. For example, the consent unit provides an interface that asks the user whether to share the content of their dream with others. Step 5: The sharing unit shares the content of the dream with others based on the consent obtained by the consent unit. The sharing unit shares the content of the dream with others based on the user's consent. The sharing unit shares the content of the dream using AI. For example, the sharing unit provides an interface for sharing the content of the user's dream with family and friends.

[0114] 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.

[0115] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search engine).<URL: https: / / openai.com / blog / chatgpt> Examples of the generative AI include a neural network (NN) and a neural network (NN). The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image (e.g., still image data or video data). The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in one or more data formats of voice data, text data, image data, etc. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and may perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-mentioned parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. The processing performed by an AI including the generative AI may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI including the generative AI.

[0116] 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.

[0117] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0118] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0119] 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0120] 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.

[0121] 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.

[0122] 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.

[0123] 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).

[0124] 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.

[0125] 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.

[0126] 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.

[0127] 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.

[0128] 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.

[0129] 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.

[0130] 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.

[0131] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in one or more data formats, such as audio data, text data, and image data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[0132] 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.

[0133] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0134] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0135] 5, the data processing system 310 includes the data processing device 12 and a headset type terminal 314. An example of the data processing device 12 is a server.

[0136] 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.

[0137] 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.

[0138] 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.

[0139] 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).

[0140] 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.

[0141] 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.

[0142] 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.

[0143] 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.

[0144] In the headset type terminal 314, the identification process is performed by the processor 46. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. Note that the headset type terminal 314 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the identification processing unit 290 using these models.

[0145] 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.

[0146] 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.

[0147] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in one or more data formats, such as audio data, text data, and image data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[0148] 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.

[0149] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0150] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0151] 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.

[0152] 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.

[0153] 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.

[0154] 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.

[0155] 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).

[0156] 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.

[0157] 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.

[0158] 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.

[0159] 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.

[0160] 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.

[0161] In the robot 414, the processor 46 performs the identification process. The storage 50 stores the identification program 60. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as the control unit 46A in accordance with the identification program 60 executed on the RAM 48. The robot 414 also has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform the same process as the identification processing unit 290 using these models.

[0162] 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.

[0163] 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.

[0164] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in one or more data formats, such as audio data, text data, and image data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[0165] 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.

[0166] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0167] 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.

[0168] 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.

[0169] 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.

[0170] 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).

[0171] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. Emotions can also be created for robots, cars, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on speech emotion recognition and brain physiological signal analysis systems for emotions, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the area called "reaction," where sensation is dominant. The right half of the emotion map lists emotions belonging to the area called "situation," where situational awareness is dominant.

[0172] 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."

[0173] 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.

[0174] 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.

[0175] 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.

[0176] 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.

[0177] 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.

[0178] 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.

[0179] 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.

[0180] 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.

[0181] 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.

[0182] 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.

[0183] 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.

[0184] 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.

[0185] [Explanation of symbols]

[0186] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. a collection unit that collects electroencephalogram data; an analysis unit that analyzes the electroencephalogram data collected by the collection unit; a generation unit that generates a VR video based on the content of the dream identified by the analysis unit; a consent unit for obtaining consent from a user; a sharing unit that shares the content of the dream with a third party based on the consent obtained by the consent unit; Equipped with A system characterized by:

2. The collecting unit Collecting EEG data using a headband-type device 2. The system of claim 1.

3. The analysis unit Detecting specific brainwave patterns and identifying the dream content that corresponds to those patterns 2. The system of claim 1.

4. The generation unit Generate VR images based on the content of identified dreams 2. The system of claim 1.

5. The consent unit Allow users to choose whether to share their dream content with third parties after waking up 2. The system of claim 1.

6. The common part is Share your dream content with others with your consent 2. The system of claim 1.

7. The common part is To protect privacy, please avoid including any personally identifiable information when sharing.

2. The system of claim 1.

8. The generation unit Add specific scenes based on the content of the dream, or change the color tone of the footage.

2. The system of claim 1.

Images