System

Abstract

A system is provided.SOLUTION: A system comprising: means for acquiring brain waves and physical response data of a user; means for analyzing the acquired brain waves and physical response data to identify dream content; means for generating a video based on the identified dream content; and means for providing the generated video to the user.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] With current technology, it is difficult for users to record and re-experience their dreams in detail. As a result, many users forget the contents of their dreams or have vague memories that make it impossible to re-create them in detail. Furthermore, it is not practical to share dreams with others. The purpose of this invention is to provide a system that analyzes the dreams a user has, generates and provides them as video, and allows users to re-create and share their dream experiences with others. [Means for solving the problem]

[0005] The present invention is a system including means for acquiring a user's electroencephalogram and physical response data, means for identifying the content of a dream by analyzing the acquired electroencephalogram and physical response data, means for generating a video based on the identified dream content, and means for providing the generated video to the user. Specifically, the system acquires electroencephalogram and physical response data using a headband worn by the user while sleeping and transmits the acquired data to a server. The server analyzes the data and identifies the content of the dream. Next, a machine learning model is used to generate a video from the identified dream content, and the video is provided to the user. This allows the user to re-experience their dream in a realistic and detailed manner. The generated video can also be shared with others.

[0006] "Electroencephalogram data" refers to data obtained by measuring electrical activity generated from the user's brain, and includes specific waveforms such as alpha waves, beta waves, theta waves, and delta waves.

[0007] "Physical response data" refers to physiological response data obtained from the user's body, and includes heart rate, muscle tension, skin potential, and the like.

[0008] A "headband" is a device that a user wears while sleeping to acquire brain wave data and physical response data.

[0009] "Analysis" is the process of identifying the content of a user's dream based on the acquired brainwave data and physical response data using specific algorithms and machine learning models.

[0010] "Dream content" refers to specific elements of the dream the user had while sleeping, including places, characters, and events.

[0011] "Image generation" is the process of using a computer to create visual images based on the analyzed content of dreams.

[0012] "Providing" refers to making the generated video accessible to users, including making it viewable through cloud storage or an application. [Brief explanation of the drawings]

[0013] [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. [Figure 11] FIG. 3 is a sequence diagram showing a processing flow of the data processing system according to the first embodiment. [Figure 12] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system in Application Example 1. [Figure 13] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system according to the second embodiment when an emotion engine is combined. [Figure 14]FIG. 10 is a sequence diagram showing the flow of processing in the data processing system in Application Example 2 when an emotion engine is combined. DETAILED DESCRIPTION OF THE INVENTION

[0014] 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.

[0015] First, the terms used in the following description will be explained.

[0016] 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, a 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), and an APU (Accelerated Processing Unit).

[0017] 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.

[0018] 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.

[0019] 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), Bluetooth (registered trademark), etc.

[0020] 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."

[0021] [First embodiment]

[0022] FIG. 1 shows an example of the configuration of a data processing system 10 according to the first embodiment.

[0023] 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.

[0024] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the 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).

[0025] 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.

[0026] The reception device 38 includes a touch panel 38A, a microphone 38B, and the like, and receives user input. The touch panel 38A detects contact with an indicator (for example, a pen or a finger) to receive user input by the touch of the indicator. 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 acquires the data indicating the user input.

[0027] The output device 40 includes a display 40A and a speaker 40B, and presents data to the user 20 by outputting the data in a form of expression that the user 20 can perceive (for example, audio and / or text). The display 40A displays visible information such as text and images in accordance with instructions from the processor 46. The speaker 40B outputs audio in accordance with instructions from the processor 46. The 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.

[0028] 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.

[0029] FIG. 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0030] 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.

[0031] 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.

[0032] In the smart device 14, the processor 46 performs the reception output process. The storage 50 stores a reception output program 60. The reception output program 60 is used in conjunction with the specific processing program 56 by the data processing system 10. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[0033] Next, a description will be given of the specific processing performed by the specific processing unit 290 of the data processing device 12. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0034] The present invention relates to a system for recording the dreams of a user in detail and reproducing them as a video. An embodiment of this system will be described as follows.

[0035] Data Acquisition

[0036] Before going to sleep, the user puts on a headband to collect brainwave and physical response data. This headband is equipped with brainwave and physical response sensors, and monitors and records the user's brainwaves (alpha waves, beta waves, theta waves, delta waves, etc.) and physical responses (heart rate, muscle tension, skin potential, etc.) in real time. This data is continuously collected while the user sleeps and stored on the device.

[0037] Data analysis

[0038] The collected brainwave and physical response data is sent from the device to a server, which analyzes the data to identify the content of the user's dream. The analysis uses machine learning models and deep learning algorithms to identify each scene in the dream based on past data patterns.

[0039] Image Generation

[0040] The server generates a dream image based on the analysis results. Using AI technology, it visualizes the content of the identified dream and creates a video with specific scenes. This video faithfully reproduces the events, places, and movements of people that the user experienced in the dream.

[0041] Providing results

[0042] The generated dream footage is stored on a server and made accessible to the user. After waking up, the user can view the footage through a dedicated application. The footage can also be shared with others via cloud storage.

[0043] Specific examples

[0044] 1. A user wears a headband and goes to sleep. The headband collects EEG data ['delta', 'theta', 'alpha', 'beta'] and physical response data ['heart_rate', 'muscle_tension'].

[0045] 2. The device sends the collected data to a server, which receives and analyzes the data to generate a "dream analysis result."

[0046] 3. The server generates a video based on the analysis results, specifically recreating the places, characters, and events seen in the user's dream as a "dream video."

[0047] 4. The generated video is stored in cloud storage, and the user can view it through a dedicated application after waking up. The video can also be shared with others.

[0048] In this way, the present invention provides a system that allows users to re-create their dreams as images and experience them in detail, making it easier for them to re-experience the content of their dreams without forgetting them and to share them with others.

[0049] The processing flow will be explained below.

[0050] Step 1:

[0051] Before going to sleep, the user wears a special headband that contains brainwave and body response sensors, which enable the collection of brainwave and body response data.

[0052] Step 2:

[0053] While the user is sleeping, the headband monitors brain wave data (alpha waves, beta waves, theta waves, delta waves, etc.) and physical response data (heart rate, muscle tension, skin potential, etc.) in real time and stores the data on the device.

[0054] Step 3:

[0055] The device transmits the collected brainwave and physical response data to a server using a secure communication protocol.

[0056] Step 4:

[0057] The server receives the transmitted EEG and physical response data and runs it through an analysis algorithm, which uses machine learning models and deep learning algorithms to identify the content of the dream the user had.

[0058] Step 5:

[0059] The server identifies the details of the dream based on the analysis results, and then uses this data to activate a visual generation model, which includes past dream data and visual information, to recreate the identified dream scene.

[0060] Step 6:

[0061] The server generates a video with a sequence of scenes based on the content of the dream, including the places, people, and events the user experienced in the dream, providing a realistic visual experience.

[0062] Step 7:

[0063] The server saves the generated video in cloud storage for user access. A generated URL link is sent to the user's account.

[0064] Step 8:

[0065] After waking up, the user logs in to their account through a dedicated application and watches the generated dream video from the provided URL link.

[0066] Step 9:

[0067] Users can share the generated dream images with others as needed, while taking security and privacy into consideration when sharing.

[0068] This will create a system that allows users to re-experience their dreams in detail and share them with others if necessary.

[0069] Example 1

[0070] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0071] Conventional dream recording methods have difficulty accurately reproducing the content and specific scenes of a dream that a user has had. Furthermore, the means for sharing the content of a dream with others are limited, making it difficult to easily replay or share recorded dreams. This leads to problems such as users forgetting the content of their dreams or being unable to communicate in detail when talking about their dreams with others.

[0072] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[0073] In this invention, the server includes means for acquiring the user's electroencephalogram and physical response data, means for storing the acquired electroencephalogram and physical response data in a terminal, means for transmitting the data stored in the terminal to the server, means for analyzing the data received by the server to identify the content of the dream, means for generating video using a generative model based on the identified content of the dream, means for storing the generated video in cloud storage, and means for the user to access and view the stored video through a dedicated application. This allows the user to re-experience the dream in detail and easily share the content of the dream with others.

[0074] "User" refers to a person who uses the system to record and play back dreams.

[0075] "Electroencephalograms" refer to fluctuations in electrical potential generated by electrical activity in the brain, and include alpha waves, beta waves, theta waves, and delta waves.

[0076] "Physiological response data" refers to data related to the physiological responses of the body, such as the user's heart rate, muscle tension, and skin potential.

[0077] "Terminal" refers to a headband worn by the user or a device for temporarily storing data.

[0078] "Server" refers to a computer system that receives and analyzes data sent from the terminal, identifies the content of the dream, and generates an image.

[0079] "Analysis" refers to the process by which the server processes the data it receives and identifies the content of the dream the user had.

[0080] "Generative model" refers to a machine learning model or deep learning algorithm that generates images based on identified dream content.

[0081] "Video" is a visual reproduction of events or scenes experienced by the user in a dream.

[0082] "Cloud storage" refers to an online storage service that allows you to store data via the Internet.

[0083] "Purpose-built application" refers to software that allows users to access and view the generated dream images.

[0084] The present invention relates to a system for recording a user's dreams in detail and reproducing them as video. This system involves a series of processes for acquiring and analyzing the user's brainwave and physical response data, generating a video based on the analysis results, and providing the video to the user.

[0085] Data Acquisition Hardware and Software

[0086] The user wears a dedicated headband before going to bed. This headband has built-in brainwave and body response sensors that collect brainwave data (e.g., alpha waves, beta waves, theta waves, delta waves) and body response data (e.g., heart rate, muscle tension, skin potential) in real time. This data is stored on the device and sent to a server via Wi-Fi or Bluetooth.

[0087] Hardware and software for data analysis

[0088] The data collected by the device is sent to a server, which then analyzes it using scripts written in programming languages ​​such as Python, using machine learning libraries like TensorFlow and PyTorch to identify the content of the user's dreams based on past data patterns.

[0089] Hardware and software for image generation

[0090] Based on the identified dream content, the server uses a generative model to generate images. This uses image generation AI such as Stable Diffusion and Generative Adversarial Networks (GANs). This generative model visualizes the events, places, and movements of people experienced in the user's dream as concrete scenes.

[0091] Providing results

[0092] The generated dream images are stored on a server and made accessible to users through cloud storage. Users can view the generated images through a dedicated application. They can also share the images with others through cloud storage.

[0093] Specific examples

[0094] Data collection

[0095] 1. The user wears the headband and goes to sleep. The headband collects EEG data ['delta', 'theta', 'alpha', 'beta'] and physical response data ['heart_rate', 'muscle_tension'].

[0096] Data transmission and analysis

[0097] 2. The device sends the collected data to a server, which receives it, analyzes it, and generates a "dream analysis result."

[0098] Image Generation

[0099] 3. The server generates an image based on the analysis results. The image generation AI creates a "dream image" based on the content of the dream.

[0100] Video provision

[0101] 4. The generated video is stored in cloud storage, and after the user wakes up, they can watch the video through a dedicated application and share it with others via the cloud.

[0102] Prompt Sentence Examples

[0103] "Analyze the EEG data (['delta', 'theta', 'alpha', 'beta']) and physical response data (['heart_rate', 'muscle_tension']) to generate a visual representation of the user's dream."

[0104] Through this system, users can re-experience their dreams in detail and easily share them with others.

[0105] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0106] Step 1:

[0107] The user puts on the headband and activates the brainwave sensor and body response sensor, which then collects brainwave data (alpha waves, beta waves, theta waves, delta waves) and body response data (heart rate, muscle tension, skin potential) in real time.

[0108] Input: User's biometric signals

[0109] Output: Collected EEG and physical response data

[0110] How it works: The user turns on the headband and puts it firmly on their head. The sensors automatically collect data and send it to the device.

[0111] Step 2:

[0112] The data collected by the device is sent to the server in batches at regular intervals, using Wi-Fi or Bluetooth.

[0113] Input: EEG and physical response data stored on the device

[0114] Output: Biometric data sent to the server

[0115] Specific operation: The device creates a batch file of collected data every minute and sends it to the server via Wi-Fi or Bluetooth.

[0116] Step 3:

[0117] The server analyzes the received data. Using machine learning models and deep learning algorithms, the content of the user's dreams is identified based on past data patterns. The analysis is carried out using Python programs and libraries such as TensorFlow and PyTorch.

[0118] Input: Biometric data sent to the server

[0119] Output: Dream analysis results

[0120] What it does: A Python script is run on the server, and data analysis is performed using TensorFlow and PyTorch. The results of the analysis identify the content of the dream.

[0121] Step 4:

[0122] The server generates images based on the identified dream content, using image generation AI such as Stable Diffusion and GANs (Generative Adversarial Networks).

[0123] Input: Dream analysis results

[0124] Output: Generated dream video

[0125] Specific operation: Based on the identified dream scene, the image generation AI automatically generates a video containing specific scenes and characters.

[0126] Step 5:

[0127] The server stores the generated video in cloud storage and allows users to access it using a dedicated application, through which they can view the video and share it with others as needed.

[0128] Input: Generated dream footage

[0129] Output: Video stored in cloud storage and accessible to users

[0130] Specific operation: Video files are uploaded to cloud storage (e.g., AWS S3) and made accessible through a dedicated application. Users log in to the application to play and share the videos.

[0131] (Application example 1)

[0132] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0133] Conventional methods for recording dreams mainly involve recording them in text or pictures, making it difficult to visually recreate the content of a dream. This makes it difficult for users to accurately re-experience the details of their dreams or share them with others. Furthermore, there is no technology that can analyze and visualize the content of dreams, leaving users with a lack of concrete ways to recreate their dreams.

[0134] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[0135] In this invention, the server includes means for acquiring the user's electroencephalogram and physical response data, means for identifying the content of the dream by analyzing the acquired electroencephalogram and physical response data, means for generating a video based on the identified dream content, means for providing the generated video to the user, and means for sharing the generated video with others, thereby enabling the user to visually re-experience the details of the dream and share it with others via cloud storage or a dedicated application.

[0136] "User's brain wave and physical response data" refers to data on brain waves (alpha waves, beta waves, theta waves, delta waves, etc.) and physical responses (heart rate, muscle tension, skin potential, etc.) measured while the user is asleep.

[0137] The "acquisition means" refers to equipment and software for collecting the user's brainwave and physical response data using a device such as a headband.

[0138] The "means of analyzing and identifying the content of dreams" refers to a processing method for analyzing acquired brainwave and physical response data using machine learning models and deep learning algorithms to identify the content of dreams.

[0139] The "means for generating images" refers to the process and system for creating images using AI technology in order to visually reproduce the content of dreams using the analysis results.

[0140] The "means for providing" refers to a platform and application for providing the generated video in a form that is accessible to users.

[0141] "Means for sharing" refers to the functions and systems for sharing the generated video with other users through cloud storage or dedicated applications.

[0142] A "dedicated application" is software that a user uses to view and manage dream images.

[0143] A "generative AI model" is an artificial intelligence algorithm and learning model used to generate dream images based on brainwave and physical response data.

[0144] A "prompt" is a formalized instruction input to a generative AI model, and is a statement that serves as a guideline for generating images.

[0145] System Overview

[0146] The system for implementing this invention records the dreams of a user in detail and replays them as a video. The system includes the following main components:

[0147] Data acquisition module: Acquires the user's brainwave and physical response data.

[0148] Data analysis module: Analyzes the acquired data to identify the content of the dream.

[0149] Image Generation Module: Generates images based on the identified dream content.

[0150] Data provision module: Provides the generated video to the user and shares it with others.

[0151] Hardware and software used

[0152] Hardware:

[0153] Brainwave sensor headband

[0154] Heart rate and muscle tension sensors

[0155] Smartphone

[0156] software:

[0157] Data Collection Platform

[0158] Server analysis platform (e.g. Amazon Web Services, Google Cloud)

[0159] Video generation AI (e.g. Unity, Unreal Engine)

[0160] Mobile app (iOS / Android)

[0161] Data Acquisition Details

[0162] Before going to sleep, the user puts on a headband to collect brainwave and physical response data. The headband is equipped with brainwave and physical response sensors that monitor and record the user's brainwaves (alpha waves, beta waves, theta waves, delta waves, etc.) and physical responses (heart rate, muscle tension, skin potential, etc.) in real time. This data is continuously collected while the user sleeps and stored on a device such as a smartphone.

[0163] Data analysis

[0164] The collected brainwave and physical response data is sent from the smartphone to a server, which analyzes the data to identify the content of the user's dream. The analysis involves the use of machine learning models and deep learning algorithms to identify each scene in the dream based on past data patterns.

[0165] Video generation

[0166] The server generates a dream image based on the analysis results. The generative AI model visualizes the identified dream content and creates a video with specific scenes. This video faithfully reproduces the events, places, and movements of people that the user experienced in the dream.

[0167] Provision to users

[0168] The generated dream footage is stored on a server and made accessible to users through a cloud platform. Users can watch the footage after waking up through a dedicated mobile application. The footage can also be shared with others.

[0169] Specific examples

[0170] For example, if a user's brainwave data is collected as "alpha," "beta," "theta," and "delta" waves, and their physical response data is acquired as a heart rate of 65 and muscle tension of 12, these data are immediately recorded on a smartphone. The smartphone then sends the data to a server, which analyzes it. Based on the analysis results, the generative AI model receives a prompt, "Please specify the content of the dream the user had," and generates a video.

[0171] Prompt Sentence Examples

[0172] The prompt contains textual instructions such as:

[0173] "Concretely express the content of the user's dream"

[0174] This invention allows users to re-experience and visually share their dreams in detail.

[0175] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0176] Step 1:

[0177] The user goes to sleep wearing a headband equipped with brainwave and body response sensors. The headband collects real-time data on brainwaves (alpha, beta, theta, delta, etc.) and body responses (heart rate, muscle tension, skin potential, etc.). The input is biodata obtained by the sensors, and the output is a continuous record of this data.

[0178] Step 2:

[0179] The terminal records the data collected from the headband and transmits it to the server at specified intervals. Here, the input is brainwave data and physical response data, and the output is sending this data as data packets to the server.

[0180] Step 3:

[0181] The server analyzes the received data using machine learning models and deep learning algorithms to identify each scene in the user's dream based on past data patterns. The input is the transmitted data packet, and the output is the analysis results, including the content of the dream.

[0182] Step 4:

[0183] Based on the analysis results, the server generates a dream image by providing a prompt to the generative AI model. For example, the generative AI model creates an image based on a prompt such as "Specify the content of the dream the user had." The input is the analysis results and the prompt, and the output is the generated dream image.

[0184] Step 5:

[0185] The generated dream video is stored on a server and made accessible to users through a cloud platform. The input is the generated dream video, and the output is the saved video data.

[0186] Step 6:

[0187] Users download and view the dream video generated from cloud storage through a dedicated mobile application. The input is the video data on cloud storage, and the output is playable on the user's device.

[0188] Step 7:

[0189] The user shares the dream images they have created with others. The images are shared via cloud storage in the form of a link or other format. The input is the user's instructions and the image data, and the output is accessible to others.

[0190] Through these steps, a system can be realized that recreates the dreams a user has had in detail as video, allowing them to re-experience and share them.

[0191] Furthermore, an emotion engine that estimates the user's emotion may be combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0192] The present invention relates to a system that records the dreams of a user in detail and reproduces them as video. Furthermore, by combining it with an emotion engine that recognizes the user's emotions, it becomes possible to customize the content of dreams in more detail and individually. An embodiment of this system will be described as follows.

[0193] Data Acquisition

[0194] Before going to sleep, the user wears a headband to collect brainwave and physical response data. The headband is equipped with brainwave and physical response sensors, and monitors the user's brainwaves (alpha waves, beta waves, theta waves, delta waves, etc.) and physical responses (heart rate, muscle tension, skin potential, etc.) in real time and records them on a terminal.

[0195] Emotion recognition

[0196] The collected brainwave and physical response data is sent from the device to a server. The server then analyzes the user's emotions using an emotion engine. The emotion engine uses machine learning models to identify the user's emotional state in real time. This analysis identifies emotions such as happiness, fear, and surprise that the user felt during the dream.

[0197] Data analysis

[0198] The server analyzes the brainwave and physical response data transmitted along with the user's emotional state to identify the content of the dream the user had. This analysis identifies each scene in the dream based on past data patterns and analyzes the identified emotional state, making it possible to recreate the dream in more detail.

[0199] Image Generation

[0200] Based on the analysis results, the server generates a dream video. Taking into account the identified dream content and the user's emotional state, AI technology is used to visualize specific scenes and events. The video is further customized according to the user's emotions identified by the emotion engine, faithfully recreating the user's experience in the dream.

[0201] Providing results

[0202] The generated dream footage is stored on a server and made accessible to the user. After waking up, the user can log in to their account through a dedicated application and view the generated dream footage from the provided URL link. The generated footage can also be shared with others via cloud storage.

[0203] Specific examples

[0204] 1. The user wears the headband and goes to sleep. The headband collects EEG data ['delta', 'theta', 'alpha', 'beta'] and physical response data ['heart_rate', 'muscle_tension'].

[0205] 2. The device sends the collected data to the server, which receives the data and uses an emotion engine to analyze the user's emotions.

[0206] 3. The server analyzes the dream, taking into account the emotional state, and generates the "dream analysis results."

[0207] 4. The server generates a video based on the analysis results and emotional data. The video recreates the places, characters, and events the user experienced in their dream, according to their emotions.

[0208] 5. The generated video is stored in cloud storage, and the user can view it through a dedicated application after waking up. The video can also be shared with others if desired.

[0209] In this way, the present invention allows users to record their dreams in detail and recreate them as images that reflect the user's emotions, allowing users to relive their dreams without forgetting them and to easily share them with others.

[0210] The processing flow will be explained below.

[0211] Step 1:

[0212] Before going to sleep, the user wears a special headband that contains brainwave and body response sensors, which enable the collection of brainwave and body response data.

[0213] Step 2:

[0214] While the user is sleeping, the headband monitors brain wave data (alpha waves, beta waves, theta waves, delta waves, etc.) and physical response data (heart rate, muscle tension, skin potential, etc.) in real time and stores the data on the device.

[0215] Step 3:

[0216] The device transmits the collected brainwave and physical response data to a server using a secure communication protocol.

[0217] Step 4:

[0218] The server receives the transmitted brainwave and physical response data and analyzes the user's emotional state using an emotion engine, which uses machine learning models to analyze the data in real time and identify emotions such as happiness, fear, and surprise.

[0219] Step 5:

[0220] The server runs the brainwave and physical response data, along with the emotion engine's analysis results, through a dream analysis algorithm. This analysis identifies the content of the user's dream. The analysis algorithm learns data patterns to identify dream scenes.

[0221] Step 6:

[0222] The server activates an image generation model based on the identified dream content, which uses AI technology to generate visual images based on the identified dream content and emotional data.

[0223] Step 7:

[0224] The server then further customizes the generated video based on the emotional data, resulting in a video that more faithfully reproduces the emotions the user felt in their dream.

[0225] Step 8:

[0226] The server saves the completed video to cloud storage and generates a link for the user to access, which is associated with the user's account information.

[0227] Step 9:

[0228] After waking up, users log in to their account through a dedicated application and watch the generated dream footage, which faithfully recreates the scenes and emotions they experienced in their dreams.

[0229] Step 10:

[0230] Users can also share the generated footage with others via cloud storage, protecting security and privacy.

[0231] This will create a system that allows users to re-experience their dreams in detail and share them with others if necessary.

[0232] Example 2

[0233] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0234] Conventional dream recording systems have had difficulty recording the details of dreams or generating images that accurately reflect the user's emotions. Furthermore, they lacked a means for users to relive their dreams without forgetting them or easily share them with others.

[0235] The identification process by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means. In this invention, the server includes means for acquiring the user's electroencephalogram and physical reaction data, means for transmitting the acquired electroencephalogram and physical reaction data to the server, means for analyzing the user's emotional state using the electroencephalogram data and physical reaction data received by the server, means for identifying the content of the dream based on the analyzed emotional state and data, means for generating a video based on the identified dream content and emotional state, and means for providing the generated video to the user. This makes it possible to accurately record the content of the dream and generate a detailed dream video based on the user's emotions. Furthermore, it becomes easy for the user to re-experience the dream and share it with others.

[0236] "User" refers to an individual who uses the system to record their own brainwave and physical response data, analyze their dreams, and generate images.

[0237] "Electroencephalogram data" refers to recordings of the brain's electrical activity, such as alpha, beta, theta, and delta waves, collected by a user's headband.

[0238] "Physiological response data" refers to recordings of the body's physiological responses, including the user's heart rate, muscle tension, skin potential, etc.

[0239] "Headband" refers to a device worn by a user to record brainwave and physical response data in real time.

[0240] "Terminal" refers to an electronic device that temporarily stores the brain wave data and physical response data transmitted from the headband and transmits them to a server.

[0241] "Server" refers to a central control device that receives data sent from the terminals, analyzes and processes the data, and manages and provides the generated video.

[0242] "Emotional state" refers to an emotional state such as happiness, fear, surprise, etc., analyzed based on the user's electroencephalogram data and physical response data.

[0243] "Emotion engine" refers to a program or software that uses machine learning models to analyze a user's emotional state from brainwave data and physical response data.

[0244] "Dream content" refers to specific scenes, events, characters, etc., that the user experiences in a dream while sleeping.

[0245] "Image generation means" refers to a means for generating visual images using AI technology based on the analyzed dream content and emotional state.

[0246] "Cloud storage" refers to a remote storage device where generated videos are stored and made available for users to access and share over the Internet.

[0247] "Dedicated application" refers to software that allows users to access the server and view and manage the generated dream images.

[0248] "Machine learning model" refers to artificial intelligence techniques used to automatically perform tasks such as data analysis and identifying emotional states.

[0249] MODE FOR CARRYING OUT THE INVENTION

[0250] The present invention relates to a system that records the dreams of a user in detail and reproduces the content of the dream as a video. Furthermore, by combining it with an emotion engine that recognizes the user's emotions, it is possible to customize the content of the dream in more detail and individually. An embodiment of this system is as follows.

[0251] Data Acquisition

[0252] Before going to sleep, the user wears a headband to collect brainwave and physical response data. This headband is equipped with a brainwave sensor and a physical response sensor. These sensors record the user's brainwaves (e.g., alpha waves, beta waves, theta waves, delta waves, etc.) and physical responses (e.g., heart rate, muscle tension, skin potential, etc.) in real time and transmit the data to a terminal.

[0253] Data transmission and storage

[0254] The device transmits the recorded EEG and physical response data to a server via a secure protocol (e.g., HTTPS).

[0255] Emotion recognition

[0256] The server analyzes the received EEG and physical response data with an emotion engine, which uses machine learning models to identify the user's emotional state (e.g., happiness, fear, surprise, etc.).

[0257] Dream Analysis

[0258] The server then comprehensively analyzes the brainwave and physical response data based on the results of the emotion analysis to identify the content of the dream the user had. The analysis is based on past data patterns, and each scene in the dream is linked to the user's emotional state.

[0259] Image Generation

[0260] The server uses AI technology to generate a video based on the identified dream content and emotional state. This video visualizes the identified scenes and events, faithfully recreating the places and characters the user experienced in their dream.

[0261] Providing results

[0262] The generated dream footage is stored on a server and made accessible to the user. After waking up, the user can log in to their account through a dedicated application and view the generated dream footage from the provided URL link. The generated footage can also be shared with others via cloud storage.

[0263] Specific examples

[0264] 1. User puts on headband and goes to sleep:

[0265] The headband collects brainwave data ['delta', 'theta', 'alpha', 'beta'] and physical response data ['heart_rate', 'muscle_tension'].

[0266] 2. The device sends the collected data to the server:

[0267] The terminal packetizes the data and sends it to the server.

[0268] 3. The server analyzes the dream, taking into account the emotional state:

[0269] The server performs the analysis and generates the "dream analysis results."

[0270] 4. The server generates a video based on the analysis results and emotion data:

[0271] The AI ​​model generates images from the user's dreams and recreates them as "dream images."

[0272] 5. The generated video is saved in cloud storage:

[0273] After waking up, the user can watch the video through a dedicated application and share it with others if necessary.

[0274] Prompt Sentence Examples

[0275] What are the steps to record EEG and physical response data from a user wearing the headband?

[0276] Explain what types of emotional states the Emotion Engine analyzes.

[0277] Please tell me how to analyze the content of dreams that take place on the server.

[0278] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0279] Step 1: User wears the headband and collects data

[0280] Input: User's brain waves (alpha waves, beta waves, theta waves, delta waves, etc.) and physical responses (heart rate, muscle tension, skin potential, etc.)

[0281] Output: EEG data and physical response data

[0282] How it works: The user puts on the headband. The brainwave and body response sensors in the headband measure the user's data in real time and send it to the device.

[0283] Step 2: The device sends the collected data to the server

[0284] Input: EEG and physical response data obtained from a headband

[0285] Output: Data sent to the server

[0286] Specific operation: The device packetizes the data received from the headband and sends it to the server using a secure protocol (such as HTTPS).

[0287] Step 3: The server receives the data and performs sentiment analysis

[0288] Input: Brain wave data and physical response data sent from the terminal

[0289] Output: User's emotional state (e.g., happiness, fear, surprise, etc.)

[0290] How it works: The server stores the received data in a database. The emotion engine analyzes the data using machine learning models to identify the user's emotional state.

[0291] Step 4: The server identifies the content of the dream based on the analysis results

[0292] Input: EEG data, physical response data, and emotional state

[0293] Output: Identified dream content

[0294] How it works: The server uses an algorithm to analyze the content of dreams from past data patterns and identify each scene in the dream based on brainwave data and emotional state.

[0295] Step 5: The server generates a video based on the dream content and emotional state.

[0296] Input: Identified dream content and emotional state

[0297] Output: Generated dream image

[0298] How it works: The server inputs data into a generative AI model for video generation, which then generates a video visualization of the identified dream scenes or events.

[0299] Step 6: The server stores the generated video and provides it to the user.

[0300] Input: Generated dream image

[0301] Output: Video stored in cloud storage and provided to users

[0302] Specific operation: The server stores the generated video data in cloud storage. Users can view the video via a dedicated application, access the URL link, and share it with others as needed.

[0303] (Application example 2)

[0304] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0305] Conventional dream recording systems have difficulty recording the details of a user's dreams, and the video generation technology required to recreate the content of those dreams is insufficient. Furthermore, they lack the functionality to share the generated dream videos with other users or to comment or rate them. This has resulted in issues such as users being unable to fully re-experience their dreams and making it difficult to share them with other users.

[0306] The identification process by the identification processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes means for acquiring the user's electroencephalogram and physical response data, means for identifying the content of the dream by analyzing the acquired electroencephalogram and physical response data, means for generating a video based on the identified content of the dream, means for providing the generated video to the user, means for sharing the generated video with other users, and means for adding comments and ratings to the video. This allows the user to not only record the dream they had in detail, analyze it, and replay it as a video, but also share it with other users and make comments and ratings.

[0307] "User" refers to an individual who uses the dream recording system.

[0308] "Electroencephalograms" refers to a collection of electrical signals generated by a user's brain.

[0309] "Physical response data" refers to data that quantifies the physiological responses of the body, such as the user's heart rate, muscle tension, and skin potential.

[0310] "Means for acquiring" refers to devices and technologies for collecting the user's brainwave and physical response data.

[0311] "Means for analyzing" refers to a method or device for analyzing the acquired data and identifying the content of the user's dream.

[0312] "Identified dream content" refers to information that the user is said to have seen in their dream, derived from the analyzed data.

[0313] "Means for generating images" refers to a method or device for producing visual images based on identified dream content.

[0314] The "means for providing to the user" refers to a method or device that allows the user to view the generated video.

[0315] "Means for sharing with other users" refers to a method or device for allowing other users to view the generated video.

[0316] The term "means for adding comments and ratings" refers to a method or device that allows other users to express their opinions or rate a video.

[0317] Overview of Program Generation and Processing

[0318] The following system configuration is proposed as an embodiment of the present invention.

[0319] Data Acquisition

[0320] The user wears a headband equipped with an EEG sensor and a physical response sensor to acquire EEG and physical response data. The headband monitors the user's EEG (alpha waves, beta waves, theta waves, delta waves) and physical responses (heart rate, muscle tension, skin potential, etc.) in real time and records this data on a terminal.

[0321] Data transmission and analysis

[0322] The device sends the collected brainwave and physical response data to a server, which then runs an emotion engine that uses machine learning models like TensorFlow to identify the user's emotional state. Based on the analysis, emotions experienced during the dream, such as happiness, fear, or surprise, are identified.

[0323] Identifying dream content and generating images

[0324] The server analyzes the brainwave and physical response data sent along with the identified emotional state to identify the content of the user's dream. Based on the results of this data analysis, the server then generates a dream video. This video generation uses a generative AI model.

[0325] Providing and sharing video

[0326] The generated dream video is stored on a server and made accessible to users through cloud storage. Users can view the generated dream video by logging into their own account through a dedicated application. Furthermore, the generated video can be shared with other users, and they can also add comments and ratings to the video.

[0327] Hardware and software used

[0328] Hardware: Headband equipped with brainwave and body response sensors, data collection device (smartphone, tablet, etc.)

[0329] Software: TensorFlow (machine learning model execution), Flask (server-side application), OpenCV (image generation)

[0330] Specific examples

[0331] For example, while a user is wearing a headband and sleeping, brain wave data ['delta', 'theta', 'alpha', 'beta'] and physical response data ['heart_rate', 'muscle_tension'] are collected and sent from the device to a server. The server analyzes this data and identifies the user's emotional state as "happiness." Based on the analysis results, a generative AI model generates a dream video and saves it in cloud storage. After waking up, the user opens a dedicated application to view their dream video, share it with other users, and add comments and ratings.

[0332] Prompt Sentence Examples

[0333] An example prompt might be:

[0334] "Please explain a specific example of how a user's dreams are recorded, analyzed using an emotion engine, and then video is generated. For example, data collected from a headband worn by the user includes brain waves (alpha waves, beta waves, theta waves, delta waves) and physical responses (heart rate, muscle tension, skin potential, etc.). This data is sent to a server, and the server uses a machine learning model to identify the user's emotional state. Based on this, a video of the dream is generated, which the user can view through a dedicated application."

[0335] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0336] Step 1:

[0337] The user sleeps wearing a headband to collect brainwave and physical response data. This headband monitors and records the user's brainwaves (alpha waves, beta waves, theta waves, delta waves) and physical responses (heart rate, muscle tension, skin potential, etc.) in real time. The input is the user's physiological data, and the brainwave data and physical response data are sent to a terminal as output data from the headband.

[0338] Step 2:

[0339] The device collects the collected brainwave and physical response data and periodically transmits it to a server. The input is data from the headband, and the output is brainwave and physical response data transmitted to the server. This data is stored for later analysis.

[0340] Step 3:

[0341] The server receives the transmitted EEG and physical response data and analyzes the data. The analysis uses an emotion engine, which runs a machine learning model to identify the user's emotional state. The input is the transmitted physiological data, and the output is the identified emotional state (happiness, fear, surprise, etc.). This analysis clusters the data by emotion.

[0342] Step 4:

[0343] The server then uses the identified emotional state and the results of the data analysis to identify the details of the user's dream. This involves referencing past data patterns to extract and analyze similar dream content. The input is the analyzed emotional and physiological data, and the output is the identified dream content.

[0344] Step 5:

[0345] The server generates images based on the identified dream content. To do this, it uses a generative AI model to create images based on emotional data. The input is the identified dream content and emotional data, and the output is the generated dream image. AI-based image synthesis technology is used to generate the images.

[0346] Step 6:

[0347] The generated dream video is stored in cloud storage by the server. The input is the generated dream video, and the output is a video file stored in the cloud. The video stored in this storage can be accessed by the user later.

[0348] Step 7:

[0349] Users log in to their account using a dedicated application and watch the dream footage stored in the cloud storage. The input is the URL link of the cloud storage, and the output is the dream footage played through the application.

[0350] Step 8:

[0351] The generated dream video can be shared with other users through the sharing function. Furthermore, other users can add comments and ratings to the video. The input is other users' comments and rating data, and the output is the updated metadata of the video. This promotes communication.

[0352] 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.

[0353] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0354] In the above embodiment, an example in which the specific process is performed by the data processing device 12 has been given, but the technology of the present disclosure is not limited to this, and the specific process may be performed by the smart device 14.

[0355] [Second embodiment]

[0356] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0357] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0358] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the 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).

[0359] 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.

[0360] The microphone 238 receives instructions and the like from the user 20 by receiving voice uttered by the user 20. The microphone 238 captures the voice uttered by the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio in accordance with instructions from the processor 46.

[0361] 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 surroundings of user 20 (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0362] 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. 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.

[0363] 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.

[0364] 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 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.

[0365] 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.

[0366] In the smart glasses 214, the processor 46 performs the reception output process. A reception output program 60 is stored in the storage 50. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[0367] Next, a description will be given of the identification process performed by the identification processing unit 290 of the data processing device 12. In the following description, the data processing device 12 will be referred to as the "server" and the smart glasses 214 will be referred to as the "terminal."

[0368] The present invention relates to a system for recording the dreams of a user in detail and reproducing them as a video. An embodiment of this system will be described as follows.

[0369] Data Acquisition

[0370] Before going to sleep, the user puts on a headband to collect brainwave and physical response data. This headband is equipped with brainwave and physical response sensors, and monitors and records the user's brainwaves (alpha waves, beta waves, theta waves, delta waves, etc.) and physical responses (heart rate, muscle tension, skin potential, etc.) in real time. This data is continuously collected while the user sleeps and stored on the device.

[0371] Data analysis

[0372] The collected brainwave and physical response data is sent from the device to a server, which analyzes the data to identify the content of the user's dream. The analysis uses machine learning models and deep learning algorithms to identify each scene in the dream based on past data patterns.

[0373] Image Generation

[0374] The server generates a dream image based on the analysis results. Using AI technology, it visualizes the content of the identified dream and creates a video with specific scenes. This video faithfully reproduces the events, places, and movements of people that the user experienced in the dream.

[0375] Providing results

[0376] The generated dream footage is stored on a server and made accessible to the user. After waking up, the user can view the footage through a dedicated application. The footage can also be shared with others via cloud storage.

[0377] Specific examples

[0378] 1. A user wears a headband and goes to sleep. The headband collects EEG data ['delta', 'theta', 'alpha', 'beta'] and physical response data ['heart_rate', 'muscle_tension'].

[0379] 2. The device sends the collected data to a server, which receives and analyzes the data to generate a "dream analysis result."

[0380] 3. The server generates a video based on the analysis results, specifically recreating the places, characters, and events seen in the user's dream as a "dream video."

[0381] 4. The generated video is stored in cloud storage, and the user can view it through a dedicated application after waking up. The video can also be shared with others.

[0382] In this way, the present invention provides a system that allows users to re-create their dreams as images and experience them in detail, making it easier for them to re-experience the content of their dreams without forgetting them and to share them with others.

[0383] The processing flow will be explained below.

[0384] Step 1:

[0385] Before going to sleep, the user wears a special headband that contains brainwave and body response sensors, which enable the collection of brainwave and body response data.

[0386] Step 2:

[0387] While the user is sleeping, the headband monitors brain wave data (alpha waves, beta waves, theta waves, delta waves, etc.) and physical response data (heart rate, muscle tension, skin potential, etc.) in real time and stores the data on the device.

[0388] Step 3:

[0389] The device transmits the collected brainwave and physical response data to a server using a secure communication protocol.

[0390] Step 4:

[0391] The server receives the transmitted EEG and physical response data and runs it through an analysis algorithm, which uses machine learning models and deep learning algorithms to identify the content of the dream the user had.

[0392] Step 5:

[0393] The server identifies the details of the dream based on the analysis results, and then uses this data to activate a visual generation model, which includes past dream data and visual information, to recreate the identified dream scene.

[0394] Step 6:

[0395] The server generates a video with a sequence of scenes based on the content of the dream, including the places, people, and events the user experienced in the dream, providing a realistic visual experience.

[0396] Step 7:

[0397] The server saves the generated video in cloud storage for user access. A generated URL link is sent to the user's account.

[0398] Step 8:

[0399] After waking up, the user logs in to their account through a dedicated application and watches the generated dream video from the provided URL link.

[0400] Step 9:

[0401] Users can share the generated dream images with others as needed, while taking security and privacy into consideration when sharing.

[0402] This will create a system that allows users to re-experience their dreams in detail and share them with others if necessary.

[0403] Example 1

[0404] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0405] Conventional dream recording methods have difficulty accurately reproducing the content and specific scenes of a dream that a user has had. Furthermore, the means for sharing the content of a dream with others are limited, making it difficult to easily replay or share recorded dreams. This leads to problems such as users forgetting the content of their dreams or being unable to communicate in detail when talking about their dreams with others.

[0406] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[0407] In this invention, the server includes means for acquiring the user's electroencephalogram and physical response data, means for storing the acquired electroencephalogram and physical response data in a terminal, means for transmitting the data stored in the terminal to the server, means for analyzing the data received by the server to identify the content of the dream, means for generating video using a generative model based on the identified content of the dream, means for storing the generated video in cloud storage, and means for the user to access and view the stored video through a dedicated application. This allows the user to re-experience the dream in detail and easily share the content of the dream with others.

[0408] "User" refers to a person who uses the system to record and play back dreams.

[0409] "Electroencephalograms" refer to fluctuations in electrical potential generated by electrical activity in the brain, and include alpha waves, beta waves, theta waves, and delta waves.

[0410] "Physiological response data" refers to data related to the physiological responses of the body, such as the user's heart rate, muscle tension, and skin potential.

[0411] "Terminal" refers to a headband worn by the user or a device for temporarily storing data.

[0412] "Server" refers to a computer system that receives and analyzes data sent from the terminal, identifies the content of the dream, and generates an image.

[0413] "Analysis" refers to the process by which the server processes the data it receives and identifies the content of the dream the user had.

[0414] "Generative model" refers to a machine learning model or deep learning algorithm that generates images based on identified dream content.

[0415] "Video" is a visual reproduction of events or scenes experienced by the user in a dream.

[0416] "Cloud storage" refers to an online storage service that allows you to store data via the Internet.

[0417] "Purpose-built application" refers to software that allows users to access and view the generated dream images.

[0418] The present invention relates to a system for recording a user's dreams in detail and reproducing them as video. This system involves a series of processes for acquiring and analyzing the user's brainwave and physical response data, generating a video based on the analysis results, and providing the video to the user.

[0419] Data Acquisition Hardware and Software

[0420] The user wears a dedicated headband before going to bed. This headband has built-in brainwave and body response sensors that collect brainwave data (e.g., alpha waves, beta waves, theta waves, delta waves) and body response data (e.g., heart rate, muscle tension, skin potential) in real time. This data is stored on the device and sent to a server via Wi-Fi or Bluetooth.

[0421] Hardware and software for data analysis

[0422] The data collected by the device is sent to a server, which then analyzes it using scripts written in programming languages ​​such as Python, using machine learning libraries like TensorFlow and PyTorch to identify the content of the user's dreams based on past data patterns.

[0423] Hardware and software for image generation

[0424] Based on the identified dream content, the server uses a generative model to generate images. This uses image generation AI such as Stable Diffusion and Generative Adversarial Networks (GANs). This generative model visualizes the events, places, and movements of people experienced in the user's dream as concrete scenes.

[0425] Providing results

[0426] The generated dream images are stored on a server and made accessible to users through cloud storage. Users can view the generated images through a dedicated application. They can also share the images with others through cloud storage.

[0427] Specific examples

[0428] Data collection

[0429] 1. The user wears the headband and goes to sleep. The headband collects EEG data ['delta', 'theta', 'alpha', 'beta'] and physical response data ['heart_rate', 'muscle_tension'].

[0430] Data transmission and analysis

[0431] 2. The device sends the collected data to a server, which receives it, analyzes it, and generates a "dream analysis result."

[0432] Image Generation

[0433] 3. The server generates an image based on the analysis results. The image generation AI creates a "dream image" based on the content of the dream.

[0434] Video provision

[0435] 4. The generated video is stored in cloud storage, and after the user wakes up, they can watch the video through a dedicated application and share it with others via the cloud.

[0436] Prompt Sentence Examples

[0437] "Analyze the EEG data (['delta', 'theta', 'alpha', 'beta']) and physical response data (['heart_rate', 'muscle_tension']) to generate a visual representation of the user's dream."

[0438] Through this system, users can re-experience their dreams in detail and easily share them with others.

[0439] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0440] Step 1:

[0441] The user puts on the headband and activates the brainwave sensor and body response sensor, which then collects brainwave data (alpha waves, beta waves, theta waves, delta waves) and body response data (heart rate, muscle tension, skin potential) in real time.

[0442] Input: User's biometric signals

[0443] Output: Collected EEG and physical response data

[0444] How it works: The user turns on the headband and puts it firmly on their head. The sensors automatically collect data and send it to the device.

[0445] Step 2:

[0446] The data collected by the device is sent to the server in batches at regular intervals, using Wi-Fi or Bluetooth.

[0447] Input: EEG and physical response data stored on the device

[0448] Output: Biometric data sent to the server

[0449] Specific operation: The device creates a batch file of collected data every minute and sends it to the server via Wi-Fi or Bluetooth.

[0450] Step 3:

[0451] The server analyzes the received data. Using machine learning models and deep learning algorithms, the content of the user's dreams is identified based on past data patterns. The analysis is carried out using Python programs and libraries such as TensorFlow and PyTorch.

[0452] Input: Biometric data sent to the server

[0453] Output: Dream analysis results

[0454] What it does: A Python script is run on the server, and data analysis is performed using TensorFlow and PyTorch. The results of the analysis identify the content of the dream.

[0455] Step 4:

[0456] The server generates images based on the identified dream content, using image generation AI such as Stable Diffusion and GANs (Generative Adversarial Networks).

[0457] Input: Dream analysis results

[0458] Output: Generated dream video

[0459] Specific operation: Based on the identified dream scene, the image generation AI automatically generates a video containing specific scenes and characters.

[0460] Step 5:

[0461] The server stores the generated video in cloud storage and allows users to access it using a dedicated application, through which they can view the video and share it with others as needed.

[0462] Input: Generated dream footage

[0463] Output: Video stored in cloud storage and accessible to users

[0464] Specific operation: Video files are uploaded to cloud storage (e.g., AWS S3) and made accessible through a dedicated application. Users log in to the application to play and share the videos.

[0465] (Application example 1)

[0466] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0467] Conventional methods for recording dreams mainly involve recording them in text or pictures, making it difficult to visually recreate the content of a dream. This makes it difficult for users to accurately re-experience the details of their dreams or share them with others. Furthermore, there is no technology that can analyze and visualize the content of dreams, leaving users with a lack of concrete ways to recreate their dreams.

[0468] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[0469] In this invention, the server includes means for acquiring the user's electroencephalogram and physical response data, means for identifying the content of the dream by analyzing the acquired electroencephalogram and physical response data, means for generating a video based on the identified dream content, means for providing the generated video to the user, and means for sharing the generated video with others, thereby enabling the user to visually re-experience the details of the dream and share it with others via cloud storage or a dedicated application.

[0470] "User's brain wave and physical response data" refers to data on brain waves (alpha waves, beta waves, theta waves, delta waves, etc.) and physical responses (heart rate, muscle tension, skin potential, etc.) measured while the user is asleep.

[0471] The "acquisition means" refers to equipment and software for collecting the user's brainwave and physical response data using a device such as a headband.

[0472] The "means of analyzing and identifying the content of dreams" refers to a processing method for analyzing acquired brainwave and physical response data using machine learning models and deep learning algorithms to identify the content of dreams.

[0473] The "means for generating images" refers to the process and system for creating images using AI technology in order to visually reproduce the content of dreams using the analysis results.

[0474] The "means for providing" refers to a platform and application for providing the generated video in a form that is accessible to users.

[0475] "Means for sharing" refers to the functions and systems for sharing the generated video with other users through cloud storage or dedicated applications.

[0476] A "dedicated application" is software that a user uses to view and manage dream images.

[0477] A "generative AI model" is an artificial intelligence algorithm and learning model used to generate dream images based on brainwave and physical response data.

[0478] A "prompt" is a formalized instruction input to a generative AI model, and is a statement that serves as a guideline for generating images.

[0479] System Overview

[0480] The system for implementing this invention records the dreams of a user in detail and replays them as a video. The system includes the following main components:

[0481] Data acquisition module: Acquires the user's brainwave and physical response data.

[0482] Data analysis module: Analyzes the acquired data to identify the content of the dream.

[0483] Image Generation Module: Generates images based on the identified dream content.

[0484] Data provision module: Provides the generated video to the user and shares it with others.

[0485] Hardware and software used

[0486] Hardware:

[0487] Brainwave sensor headband

[0488] Heart rate and muscle tension sensors

[0489] Smartphone

[0490] software:

[0491] Data Collection Platform

[0492] Server analysis platform (e.g. Amazon Web Services, Google Cloud)

[0493] Video generation AI (e.g. Unity, Unreal Engine)

[0494] Mobile app (iOS / Android)

[0495] Data Acquisition Details

[0496] Before going to sleep, the user puts on a headband to collect brainwave and physical response data. The headband is equipped with brainwave and physical response sensors that monitor and record the user's brainwaves (alpha waves, beta waves, theta waves, delta waves, etc.) and physical responses (heart rate, muscle tension, skin potential, etc.) in real time. This data is continuously collected while the user sleeps and stored on a device such as a smartphone.

[0497] Data analysis

[0498] The collected brainwave and physical response data is sent from the smartphone to a server, which analyzes the data to identify the content of the user's dream. The analysis involves the use of machine learning models and deep learning algorithms to identify each scene in the dream based on past data patterns.

[0499] Video generation

[0500] The server generates a dream image based on the analysis results. The generative AI model visualizes the identified dream content and creates a video with specific scenes. This video faithfully reproduces the events, places, and movements of people that the user experienced in the dream.

[0501] Provision to users

[0502] The generated dream footage is stored on a server and made accessible to users through a cloud platform. Users can watch the footage after waking up through a dedicated mobile application. The footage can also be shared with others.

[0503] Specific examples

[0504] For example, if a user's brainwave data is collected as "alpha," "beta," "theta," and "delta" waves, and their physical response data is acquired as a heart rate of 65 and muscle tension of 12, these data are immediately recorded on a smartphone. The smartphone then sends the data to a server, which analyzes it. Based on the analysis results, the generative AI model receives a prompt, "Please specify the content of the dream the user had," and generates a video.

[0505] Prompt Sentence Examples

[0506] The prompt contains textual instructions such as:

[0507] "Concretely express the content of the user's dream"

[0508] This invention allows users to re-experience and visually share their dreams in detail.

[0509] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0510] Step 1:

[0511] The user goes to sleep wearing a headband equipped with brainwave and body response sensors. The headband collects real-time data on brainwaves (alpha, beta, theta, delta, etc.) and body responses (heart rate, muscle tension, skin potential, etc.). The input is biodata obtained by the sensors, and the output is a continuous record of this data.

[0512] Step 2:

[0513] The terminal records the data collected from the headband and transmits it to the server at specified intervals. Here, the input is brainwave data and physical response data, and the output is sending this data as data packets to the server.

[0514] Step 3:

[0515] The server analyzes the received data using machine learning models and deep learning algorithms to identify each scene in the user's dream based on past data patterns. The input is the transmitted data packet, and the output is the analysis results, including the content of the dream.

[0516] Step 4:

[0517] Based on the analysis results, the server generates a dream image by providing a prompt to the generative AI model. For example, the generative AI model creates an image based on a prompt such as "Specify the content of the dream the user had." The input is the analysis results and the prompt, and the output is the generated dream image.

[0518] Step 5:

[0519] The generated dream video is stored on a server and made accessible to users through a cloud platform. The input is the generated dream video, and the output is the saved video data.

[0520] Step 6:

[0521] Users download and view the dream video generated from cloud storage through a dedicated mobile application. The input is the video data on cloud storage, and the output is playable on the user's device.

[0522] Step 7:

[0523] The user shares the dream images they have created with others. The images are shared via cloud storage in the form of a link or other format. The input is the user's instructions and the image data, and the output is accessible to others.

[0524] Through these steps, a system can be realized that recreates the dreams a user has had in detail as video, allowing them to re-experience and share them.

[0525] Furthermore, an emotion engine that estimates the user's emotion may be further combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59, and perform identification processing using the user's emotion.

[0526] The present invention relates to a system that records the dreams of a user in detail and reproduces them as video. Furthermore, by combining it with an emotion engine that recognizes the user's emotions, it becomes possible to customize the content of dreams in more detail and individually. An embodiment of this system will be described as follows.

[0527] Data Acquisition

[0528] Before going to sleep, the user wears a headband to collect brainwave and physical response data. The headband is equipped with brainwave and physical response sensors, and monitors the user's brainwaves (alpha waves, beta waves, theta waves, delta waves, etc.) and physical responses (heart rate, muscle tension, skin potential, etc.) in real time and records them on a terminal.

[0529] Emotion recognition

[0530] The collected brainwave and physical response data is sent from the device to a server. The server then analyzes the user's emotions using an emotion engine. The emotion engine uses machine learning models to identify the user's emotional state in real time. This analysis identifies emotions such as happiness, fear, and surprise that the user felt during the dream.

[0531] Data analysis

[0532] The server analyzes the brainwave and physical response data transmitted along with the user's emotional state to identify the content of the dream the user had. This analysis identifies each scene in the dream based on past data patterns and analyzes the identified emotional state, making it possible to recreate the dream in more detail.

[0533] Image Generation

[0534] Based on the analysis results, the server generates a dream video. Taking into account the identified dream content and the user's emotional state, AI technology is used to visualize specific scenes and events. The video is further customized according to the user's emotions identified by the emotion engine, faithfully recreating the user's experience in the dream.

[0535] Providing results

[0536] The generated dream footage is stored on a server and made accessible to the user. After waking up, the user can log in to their account through a dedicated application and view the generated dream footage from the provided URL link. The generated footage can also be shared with others via cloud storage.

[0537] Specific examples

[0538] 1. The user wears the headband and goes to sleep. The headband collects EEG data ['delta', 'theta', 'alpha', 'beta'] and physical response data ['heart_rate', 'muscle_tension'].

[0539] 2. The device sends the collected data to the server, which receives the data and uses an emotion engine to analyze the user's emotions.

[0540] 3. The server analyzes the dream, taking into account the emotional state, and generates the "dream analysis results."

[0541] 4. The server generates a video based on the analysis results and emotional data. The video recreates the places, characters, and events the user experienced in their dream, according to their emotions.

[0542] 5. The generated video is stored in cloud storage, and the user can view it through a dedicated application after waking up. The video can also be shared with others if desired.

[0543] In this way, the present invention allows users to record their dreams in detail and recreate them as images that reflect the user's emotions, allowing users to relive their dreams without forgetting them and to easily share them with others.

[0544] The processing flow will be explained below.

[0545] Step 1:

[0546] Before going to sleep, the user wears a special headband that contains brainwave and body response sensors, which enable the collection of brainwave and body response data.

[0547] Step 2:

[0548] While the user is sleeping, the headband monitors brain wave data (alpha waves, beta waves, theta waves, delta waves, etc.) and physical response data (heart rate, muscle tension, skin potential, etc.) in real time and stores the data on the device.

[0549] Step 3:

[0550] The device transmits the collected brainwave and physical response data to a server using a secure communication protocol.

[0551] Step 4:

[0552] The server receives the transmitted brainwave and physical response data and analyzes the user's emotional state using an emotion engine, which uses machine learning models to analyze the data in real time and identify emotions such as happiness, fear, and surprise.

[0553] Step 5:

[0554] The server runs the brainwave and physical response data, along with the emotion engine's analysis results, through a dream analysis algorithm. This analysis identifies the content of the user's dream. The analysis algorithm learns data patterns to identify dream scenes.

[0555] Step 6:

[0556] The server activates an image generation model based on the identified dream content, which uses AI technology to generate visual images based on the identified dream content and emotional data.

[0557] Step 7:

[0558] The server then further customizes the generated video based on the emotional data, resulting in a video that more faithfully reproduces the emotions the user felt in their dream.

[0559] Step 8:

[0560] The server saves the completed video to cloud storage and generates a link for the user to access, which is associated with the user's account information.

[0561] Step 9:

[0562] After waking up, users log in to their account through a dedicated application and watch the generated dream footage, which faithfully recreates the scenes and emotions they experienced in their dreams.

[0563] Step 10:

[0564] Users can also share the generated footage with others via cloud storage, protecting security and privacy.

[0565] This will create a system that allows users to re-experience their dreams in detail and share them with others if necessary.

[0566] Example 2

[0567] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0568] Conventional dream recording systems have had difficulty recording the details of dreams or generating images that accurately reflect the user's emotions. Furthermore, they lacked a means for users to relive their dreams without forgetting them or easily share them with others.

[0569] The identification process by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means. In this invention, the server includes means for acquiring the user's electroencephalogram and physical reaction data, means for transmitting the acquired electroencephalogram and physical reaction data to the server, means for analyzing the user's emotional state using the electroencephalogram data and physical reaction data received by the server, means for identifying the content of the dream based on the analyzed emotional state and data, means for generating a video based on the identified dream content and emotional state, and means for providing the generated video to the user. This makes it possible to accurately record the content of the dream and generate a detailed dream video based on the user's emotions. Furthermore, it becomes easy for the user to re-experience the dream and share it with others.

[0570] "User" refers to an individual who uses the system to record their own brainwave and physical response data, analyze their dreams, and generate images.

[0571] "Electroencephalogram data" refers to recordings of the brain's electrical activity, such as alpha, beta, theta, and delta waves, collected by a user's headband.

[0572] "Physiological response data" refers to recordings of the body's physiological responses, including the user's heart rate, muscle tension, skin potential, etc.

[0573] "Headband" refers to a device worn by a user to record brainwave and physical response data in real time.

[0574] "Terminal" refers to an electronic device that temporarily stores the brain wave data and physical response data transmitted from the headband and transmits them to a server.

[0575] "Server" refers to a central control device that receives data sent from the terminals, analyzes and processes the data, and manages and provides the generated video.

[0576] "Emotional state" refers to an emotional state such as happiness, fear, surprise, etc., analyzed based on the user's electroencephalogram data and physical response data.

[0577] "Emotion engine" refers to a program or software that uses machine learning models to analyze a user's emotional state from brainwave data and physical response data.

[0578] "Dream content" refers to specific scenes, events, characters, etc., that the user experiences in a dream while sleeping.

[0579] "Image generation means" refers to a means for generating visual images using AI technology based on the analyzed dream content and emotional state.

[0580] "Cloud storage" refers to a remote storage device where generated videos are stored and made available for users to access and share over the Internet.

[0581] "Dedicated application" refers to software that allows users to access the server and view and manage the generated dream images.

[0582] "Machine learning model" refers to artificial intelligence techniques used to automatically perform tasks such as data analysis and identifying emotional states.

[0583] MODE FOR CARRYING OUT THE INVENTION

[0584] The present invention relates to a system that records the dreams of a user in detail and reproduces the content of the dream as a video. Furthermore, by combining it with an emotion engine that recognizes the user's emotions, it is possible to customize the content of the dream in more detail and individually. An embodiment of this system is as follows.

[0585] Data Acquisition

[0586] Before going to sleep, the user wears a headband to collect brainwave and physical response data. This headband is equipped with a brainwave sensor and a physical response sensor. These sensors record the user's brainwaves (e.g., alpha waves, beta waves, theta waves, delta waves, etc.) and physical responses (e.g., heart rate, muscle tension, skin potential, etc.) in real time and transmit the data to a terminal.

[0587] Data transmission and storage

[0588] The device transmits the recorded EEG and physical response data to a server via a secure protocol (e.g., HTTPS).

[0589] Emotion recognition

[0590] The server analyzes the received EEG and physical response data with an emotion engine, which uses machine learning models to identify the user's emotional state (e.g., happiness, fear, surprise, etc.).

[0591] Dream Analysis

[0592] The server then comprehensively analyzes the brainwave and physical response data based on the results of the emotion analysis to identify the content of the dream the user had. The analysis is based on past data patterns, and each scene in the dream is linked to the user's emotional state.

[0593] Image Generation

[0594] The server uses AI technology to generate a video based on the identified dream content and emotional state. This video visualizes the identified scenes and events, faithfully recreating the places and characters the user experienced in their dream.

[0595] Providing results

[0596] The generated dream footage is stored on a server and made accessible to the user. After waking up, the user can log in to their account through a dedicated application and view the generated dream footage from the provided URL link. The generated footage can also be shared with others via cloud storage.

[0597] Specific examples

[0598] 1. User puts on headband and goes to sleep:

[0599] The headband collects brainwave data ['delta', 'theta', 'alpha', 'beta'] and physical response data ['heart_rate', 'muscle_tension'].

[0600] 2. The device sends the collected data to the server:

[0601] The terminal packetizes the data and sends it to the server.

[0602] 3. The server analyzes the dream, taking into account the emotional state:

[0603] The server performs the analysis and generates the "dream analysis results."

[0604] 4. The server generates a video based on the analysis results and emotion data:

[0605] The AI ​​model generates images from the user's dreams and recreates them as "dream images."

[0606] 5. The generated video is saved in cloud storage:

[0607] After waking up, the user can watch the video through a dedicated application and share it with others if necessary.

[0608] Prompt Sentence Examples

[0609] What are the steps to record EEG and physical response data from a user wearing the headband?

[0610] Explain what types of emotional states the Emotion Engine analyzes.

[0611] Please tell me how to analyze the content of dreams that take place on the server.

[0612] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0613] Step 1: User wears the headband and collects data

[0614] Input: User's brain waves (alpha waves, beta waves, theta waves, delta waves, etc.) and physical responses (heart rate, muscle tension, skin potential, etc.)

[0615] Output: EEG data and physical response data

[0616] How it works: The user puts on the headband. The brainwave and body response sensors in the headband measure the user's data in real time and send it to the device.

[0617] Step 2: The device sends the collected data to the server

[0618] Input: EEG and physical response data obtained from a headband

[0619] Output: Data sent to the server

[0620] Specific operation: The device packetizes the data received from the headband and sends it to the server using a secure protocol (such as HTTPS).

[0621] Step 3: The server receives the data and performs sentiment analysis

[0622] Input: Brain wave data and physical response data sent from the terminal

[0623] Output: User's emotional state (e.g., happiness, fear, surprise, etc.)

[0624] How it works: The server stores the received data in a database. The emotion engine analyzes the data using machine learning models to identify the user's emotional state.

[0625] Step 4: The server identifies the content of the dream based on the analysis results

[0626] Input: EEG data, physical response data, and emotional state

[0627] Output: Identified dream content

[0628] How it works: The server uses an algorithm to analyze the content of dreams from past data patterns and identify each scene in the dream based on brainwave data and emotional state.

[0629] Step 5: The server generates a video based on the dream content and emotional state.

[0630] Input: Identified dream content and emotional state

[0631] Output: Generated dream image

[0632] How it works: The server inputs data into a generative AI model for video generation, which then generates a video visualization of the identified dream scenes or events.

[0633] Step 6: The server stores the generated video and provides it to the user.

[0634] Input: Generated dream image

[0635] Output: Video stored in cloud storage and provided to users

[0636] Specific operation: The server stores the generated video data in cloud storage. Users can view the video via a dedicated application, access the URL link, and share it with others as needed.

[0637] (Application example 2)

[0638] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0639] Conventional dream recording systems have difficulty recording the details of a user's dreams, and the video generation technology required to recreate the content of those dreams is insufficient. Furthermore, they lack the functionality to share the generated dream videos with other users or to comment or rate them. This has resulted in issues such as users being unable to fully re-experience their dreams and making it difficult to share them with other users.

[0640] The identification process by the identification processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes means for acquiring the user's electroencephalogram and physical response data, means for identifying the content of the dream by analyzing the acquired electroencephalogram and physical response data, means for generating a video based on the identified content of the dream, means for providing the generated video to the user, means for sharing the generated video with other users, and means for adding comments and ratings to the video. This allows the user to not only record the dream they had in detail, analyze it, and replay it as a video, but also share it with other users and make comments and ratings.

[0641] "User" refers to an individual who uses the dream recording system.

[0642] "Electroencephalograms" refers to a collection of electrical signals generated by a user's brain.

[0643] "Physical response data" refers to data that quantifies the physiological responses of the body, such as the user's heart rate, muscle tension, and skin potential.

[0644] "Means for acquiring" refers to devices and technologies for collecting the user's brainwave and physical response data.

[0645] "Means for analyzing" refers to a method or device for analyzing the acquired data and identifying the content of the user's dream.

[0646] "Identified dream content" refers to information that the user is said to have seen in their dream, derived from the analyzed data.

[0647] "Means for generating images" refers to a method or device for producing visual images based on identified dream content.

[0648] The "means for providing to the user" refers to a method or device that allows the user to view the generated video.

[0649] "Means for sharing with other users" refers to a method or device for allowing other users to view the generated video.

[0650] The term "means for adding comments and ratings" refers to a method or device that allows other users to express their opinions or rate a video.

[0651] Overview of Program Generation and Processing

[0652] The following system configuration is proposed as an embodiment of the present invention.

[0653] Data Acquisition

[0654] The user wears a headband equipped with an EEG sensor and a physical response sensor to acquire EEG and physical response data. The headband monitors the user's EEG (alpha waves, beta waves, theta waves, delta waves) and physical responses (heart rate, muscle tension, skin potential, etc.) in real time and records this data on a terminal.

[0655] Data transmission and analysis

[0656] The device sends the collected brainwave and physical response data to a server, which then runs an emotion engine that uses machine learning models like TensorFlow to identify the user's emotional state. Based on the analysis, emotions experienced during the dream, such as happiness, fear, or surprise, are identified.

[0657] Identifying dream content and generating images

[0658] The server analyzes the brainwave and physical response data sent along with the identified emotional state to identify the content of the user's dream. Based on the results of this data analysis, the server then generates a dream video. This video generation uses a generative AI model.

[0659] Providing and sharing video

[0660] The generated dream video is stored on a server and made accessible to users through cloud storage. Users can view the generated dream video by logging into their own account through a dedicated application. Furthermore, the generated video can be shared with other users, and they can also add comments and ratings to the video.

[0661] Hardware and software used

[0662] Hardware: Headband equipped with brainwave and body response sensors, data collection device (smartphone, tablet, etc.)

[0663] Software: TensorFlow (machine learning model execution), Flask (server-side application), OpenCV (image generation)

[0664] Specific examples

[0665] For example, while a user is wearing a headband and sleeping, brain wave data ['delta', 'theta', 'alpha', 'beta'] and physical response data ['heart_rate', 'muscle_tension'] are collected and sent from the device to a server. The server analyzes this data and identifies the user's emotional state as "happiness." Based on the analysis results, a generative AI model generates a dream video and saves it in cloud storage. After waking up, the user opens a dedicated application to view their dream video, share it with other users, and add comments and ratings.

[0666] Prompt Sentence Examples

[0667] An example prompt might be:

[0668] "Please explain a specific example of how a user's dreams are recorded, analyzed using an emotion engine, and then video is generated. For example, data collected from a headband worn by the user includes brain waves (alpha waves, beta waves, theta waves, delta waves) and physical responses (heart rate, muscle tension, skin potential, etc.). This data is sent to a server, and the server uses a machine learning model to identify the user's emotional state. Based on this, a video of the dream is generated, which the user can view through a dedicated application."

[0669] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0670] Step 1:

[0671] The user sleeps wearing a headband to collect brainwave and physical response data. This headband monitors and records the user's brainwaves (alpha waves, beta waves, theta waves, delta waves) and physical responses (heart rate, muscle tension, skin potential, etc.) in real time. The input is the user's physiological data, and the brainwave data and physical response data are sent to a terminal as output data from the headband.

[0672] Step 2:

[0673] The device collects the collected brainwave and physical response data and periodically transmits it to a server. The input is data from the headband, and the output is brainwave and physical response data transmitted to the server. This data is stored for later analysis.

[0674] Step 3:

[0675] The server receives the transmitted EEG and physical response data and analyzes the data. The analysis uses an emotion engine, which runs a machine learning model to identify the user's emotional state. The input is the transmitted physiological data, and the output is the identified emotional state (happiness, fear, surprise, etc.). This analysis clusters the data by emotion.

[0676] Step 4:

[0677] The server then uses the identified emotional state and the results of the data analysis to identify the details of the user's dream. This involves referencing past data patterns to extract and analyze similar dream content. The input is the analyzed emotional and physiological data, and the output is the identified dream content.

[0678] Step 5:

[0679] The server generates images based on the identified dream content. To do this, it uses a generative AI model to create images based on emotional data. The input is the identified dream content and emotional data, and the output is the generated dream image. AI-based image synthesis technology is used to generate the images.

[0680] Step 6:

[0681] The generated dream video is stored in cloud storage by the server. The input is the generated dream video, and the output is a video file stored in the cloud. The video stored in this storage can be accessed by the user later.

[0682] Step 7:

[0683] Users log in to their account using a dedicated application and watch the dream footage stored in the cloud storage. The input is the URL link of the cloud storage, and the output is the dream footage played through the application.

[0684] Step 8:

[0685] The generated dream video can be shared with other users through the sharing function. Furthermore, other users can add comments and ratings to the video. The input is other users' comments and rating data, and the output is the updated metadata of the video. This promotes communication.

[0686] 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.

[0687] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0688] In the above embodiment, an example in which the specific processing is performed by the data processing device 12 has been given, but the technology of the present disclosure is not limited to this, and the specific processing may be performed by the smart glasses 214.

[0689] [Third embodiment]

[0690] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0691] 5, the data processing system 310 includes the data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0692] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the 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).

[0693] 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.

[0694] The microphone 238 receives instructions and the like from the user 20 by receiving voice uttered by the user 20. The microphone 238 captures the voice uttered by the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio in accordance with instructions from the processor 46.

[0695] 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 surroundings of user 20 (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0696] 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. 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.

[0697] 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.

[0698] 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 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.

[0699] 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.

[0700] In the headset type terminal 314, a reception output process is performed by the processor 46. A reception output program 60 is stored in the storage 50. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[0701] Next, a description will be given of the identification process performed by the identification processing unit 290 of the data processing device 12. In the following description, the data processing device 12 will be referred to as the "server" and the headset type terminal 314 will be referred to as the "terminal."

[0702] The present invention relates to a system for recording the dreams of a user in detail and reproducing them as a video. An embodiment of this system will be described as follows.

[0703] Data Acquisition

[0704] Before going to sleep, the user puts on a headband to collect brainwave and physical response data. This headband is equipped with brainwave and physical response sensors, and monitors and records the user's brainwaves (alpha waves, beta waves, theta waves, delta waves, etc.) and physical responses (heart rate, muscle tension, skin potential, etc.) in real time. This data is continuously collected while the user sleeps and stored on the device.

[0705] Data analysis

[0706] The collected brainwave and physical response data is sent from the device to a server, which analyzes the data to identify the content of the user's dream. The analysis uses machine learning models and deep learning algorithms to identify each scene in the dream based on past data patterns.

[0707] Image Generation

[0708] The server generates a dream image based on the analysis results. Using AI technology, it visualizes the content of the identified dream and creates a video with specific scenes. This video faithfully reproduces the events, places, and movements of people that the user experienced in the dream.

[0709] Providing results

[0710] The generated dream footage is stored on a server and made accessible to the user. After waking up, the user can view the footage through a dedicated application. The footage can also be shared with others via cloud storage.

[0711] Specific examples

[0712] 1. A user wears a headband and goes to sleep. The headband collects EEG data ['delta', 'theta', 'alpha', 'beta'] and physical response data ['heart_rate', 'muscle_tension'].

[0713] 2. The device sends the collected data to a server, which receives and analyzes the data to generate a "dream analysis result."

[0714] 3. The server generates a video based on the analysis results, specifically recreating the places, characters, and events seen in the user's dream as a "dream video."

[0715] 4. The generated video is stored in cloud storage, and the user can view it through a dedicated application after waking up. The video can also be shared with others.

[0716] In this way, the present invention provides a system that allows users to re-create their dreams as images and experience them in detail, making it easier for them to re-experience the content of their dreams without forgetting them and to share them with others.

[0717] The processing flow will be explained below.

[0718] Step 1:

[0719] Before going to sleep, the user wears a special headband that contains brainwave and body response sensors, which enable the collection of brainwave and body response data.

[0720] Step 2:

[0721] While the user is sleeping, the headband monitors brain wave data (alpha waves, beta waves, theta waves, delta waves, etc.) and physical response data (heart rate, muscle tension, skin potential, etc.) in real time and stores the data on the device.

[0722] Step 3:

[0723] The device transmits the collected brainwave and physical response data to a server using a secure communication protocol.

[0724] Step 4:

[0725] The server receives the transmitted EEG and physical response data and runs it through an analysis algorithm, which uses machine learning models and deep learning algorithms to identify the content of the dream the user had.

[0726] Step 5:

[0727] The server identifies the details of the dream based on the analysis results, and then uses this data to activate a visual generation model, which includes past dream data and visual information, to recreate the identified dream scene.

[0728] Step 6:

[0729] The server generates a video with a sequence of scenes based on the content of the dream, including the places, people, and events the user experienced in the dream, providing a realistic visual experience.

[0730] Step 7:

[0731] The server saves the generated video in cloud storage for user access. A generated URL link is sent to the user's account.

[0732] Step 8:

[0733] After waking up, the user logs in to their account through a dedicated application and watches the generated dream video from the provided URL link.

[0734] Step 9:

[0735] Users can share the generated dream images with others as needed, while taking security and privacy into consideration when sharing.

[0736] This will create a system that allows users to re-experience their dreams in detail and share them with others if necessary.

[0737] Example 1

[0738] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[0739] Conventional dream recording methods have difficulty accurately reproducing the content and specific scenes of a dream that a user has had. Furthermore, the means for sharing the content of a dream with others are limited, making it difficult to easily replay or share recorded dreams. This leads to problems such as users forgetting the content of their dreams or being unable to communicate in detail when talking about their dreams with others.

[0740] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[0741] In this invention, the server includes means for acquiring the user's electroencephalogram and physical response data, means for storing the acquired electroencephalogram and physical response data in a terminal, means for transmitting the data stored in the terminal to the server, means for analyzing the data received by the server to identify the content of the dream, means for generating video using a generative model based on the identified content of the dream, means for storing the generated video in cloud storage, and means for the user to access and view the stored video through a dedicated application. This allows the user to re-experience the dream in detail and easily share the content of the dream with others.

[0742] "User" refers to a person who uses the system to record and play back dreams.

[0743] "Electroencephalograms" refer to fluctuations in electrical potential generated by electrical activity in the brain, and include alpha waves, beta waves, theta waves, and delta waves.

[0744] "Physiological response data" refers to data related to the physiological responses of the body, such as the user's heart rate, muscle tension, and skin potential.

[0745] "Terminal" refers to a headband worn by the user or a device for temporarily storing data.

[0746] "Server" refers to a computer system that receives and analyzes data sent from the terminal, identifies the content of the dream, and generates an image.

[0747] "Analysis" refers to the process by which the server processes the data it receives and identifies the content of the dream the user had.

[0748] "Generative model" refers to a machine learning model or deep learning algorithm that generates images based on identified dream content.

[0749] "Video" is a visual reproduction of events or scenes experienced by the user in a dream.

[0750] "Cloud storage" refers to an online storage service that allows you to store data via the Internet.

[0751] "Purpose-built application" refers to software that allows users to access and view the generated dream images.

[0752] The present invention relates to a system for recording a user's dreams in detail and reproducing them as video. This system involves a series of processes for acquiring and analyzing the user's brainwave and physical response data, generating a video based on the analysis results, and providing the video to the user.

[0753] Data Acquisition Hardware and Software

[0754] The user wears a dedicated headband before going to bed. This headband has built-in brainwave and body response sensors that collect brainwave data (e.g., alpha waves, beta waves, theta waves, delta waves) and body response data (e.g., heart rate, muscle tension, skin potential) in real time. This data is stored on the device and sent to a server via Wi-Fi or Bluetooth.

[0755] Hardware and software for data analysis

[0756] The data collected by the device is sent to a server, which then analyzes it using scripts written in programming languages ​​such as Python, using machine learning libraries like TensorFlow and PyTorch to identify the content of the user's dreams based on past data patterns.

[0757] Hardware and software for image generation

[0758] Based on the identified dream content, the server uses a generative model to generate images. This uses image generation AI such as Stable Diffusion and Generative Adversarial Networks (GANs). This generative model visualizes the events, places, and movements of people experienced in the user's dream as concrete scenes.

[0759] Providing results

[0760] The generated dream images are stored on a server and made accessible to users through cloud storage. Users can view the generated images through a dedicated application. They can also share the images with others through cloud storage.

[0761] Specific examples

[0762] Data collection

[0763] 1. The user wears the headband and goes to sleep. The headband collects EEG data ['delta', 'theta', 'alpha', 'beta'] and physical response data ['heart_rate', 'muscle_tension'].

[0764] Data transmission and analysis

[0765] 2. The device sends the collected data to a server, which receives it, analyzes it, and generates a "dream analysis result."

[0766] Image Generation

[0767] 3. The server generates an image based on the analysis results. The image generation AI creates a "dream image" based on the content of the dream.

[0768] Video provision

[0769] 4. The generated video is stored in cloud storage, and after the user wakes up, they can watch the video through a dedicated application and share it with others via the cloud.

[0770] Prompt Sentence Examples

[0771] "Analyze the EEG data (['delta', 'theta', 'alpha', 'beta']) and physical response data (['heart_rate', 'muscle_tension']) to generate a visual representation of the user's dream."

[0772] Through this system, users can re-experience their dreams in detail and easily share them with others.

[0773] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0774] Step 1:

[0775] The user puts on the headband and activates the brainwave sensor and body response sensor, which then collects brainwave data (alpha waves, beta waves, theta waves, delta waves) and body response data (heart rate, muscle tension, skin potential) in real time.

[0776] Input: User's biometric signals

[0777] Output: Collected EEG and physical response data

[0778] How it works: The user turns on the headband and puts it firmly on their head. The sensors automatically collect data and send it to the device.

[0779] Step 2:

[0780] The data collected by the device is sent to the server in batches at regular intervals, using Wi-Fi or Bluetooth.

[0781] Input: EEG and physical response data stored on the device

[0782] Output: Biometric data sent to the server

[0783] Specific operation: The device creates a batch file of collected data every minute and sends it to the server via Wi-Fi or Bluetooth.

[0784] Step 3:

[0785] The server analyzes the received data. Using machine learning models and deep learning algorithms, the content of the user's dreams is identified based on past data patterns. The analysis is carried out using Python programs and libraries such as TensorFlow and PyTorch.

[0786] Input: Biometric data sent to the server

[0787] Output: Dream analysis results

[0788] What it does: A Python script is run on the server, and data analysis is performed using TensorFlow and PyTorch. The results of the analysis identify the content of the dream.

[0789] Step 4:

[0790] The server generates images based on the identified dream content, using image generation AI such as Stable Diffusion and GANs (Generative Adversarial Networks).

[0791] Input: Dream analysis results

[0792] Output: Generated dream video

[0793] Specific operation: Based on the identified dream scene, the image generation AI automatically generates a video containing specific scenes and characters.

[0794] Step 5:

[0795] The server stores the generated video in cloud storage and allows users to access it using a dedicated application, through which they can view the video and share it with others as needed.

[0796] Input: Generated dream footage

[0797] Output: Video stored in cloud storage and accessible to users

[0798] Specific operation: Video files are uploaded to cloud storage (e.g., AWS S3) and made accessible through a dedicated application. Users log in to the application to play and share the videos.

[0799] (Application example 1)

[0800] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[0801] Conventional methods for recording dreams mainly involve recording them in text or pictures, making it difficult to visually recreate the content of a dream. This makes it difficult for users to accurately re-experience the details of their dreams or share them with others. Furthermore, there is no technology that can analyze and visualize the content of dreams, leaving users with a lack of concrete ways to recreate their dreams.

[0802] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[0803] In this invention, the server includes means for acquiring the user's electroencephalogram and physical response data, means for identifying the content of the dream by analyzing the acquired electroencephalogram and physical response data, means for generating a video based on the identified dream content, means for providing the generated video to the user, and means for sharing the generated video with others, thereby enabling the user to visually re-experience the details of the dream and share it with others via cloud storage or a dedicated application.

[0804] "User's brain wave and physical response data" refers to data on brain waves (alpha waves, beta waves, theta waves, delta waves, etc.) and physical responses (heart rate, muscle tension, skin potential, etc.) measured while the user is asleep.

[0805] The "acquisition means" refers to equipment and software for collecting the user's brainwave and physical response data using a device such as a headband.

[0806] The "means of analyzing and identifying the content of dreams" refers to a processing method for analyzing acquired brainwave and physical response data using machine learning models and deep learning algorithms to identify the content of dreams.

[0807] The "means for generating images" refers to the process and system for creating images using AI technology in order to visually reproduce the content of dreams using the analysis results.

[0808] The "means for providing" refers to a platform and application for providing the generated video in a form that is accessible to users.

[0809] "Means for sharing" refers to the functions and systems for sharing the generated video with other users through cloud storage or dedicated applications.

[0810] A "dedicated application" is software that a user uses to view and manage dream images.

[0811] A "generative AI model" is an artificial intelligence algorithm and learning model used to generate dream images based on brainwave and physical response data.

[0812] A "prompt" is a formalized instruction input to a generative AI model, and is a statement that serves as a guideline for generating images.

[0813] System Overview

[0814] The system for implementing this invention records the dreams of a user in detail and replays them as a video. The system includes the following main components:

[0815] Data acquisition module: Acquires the user's brainwave and physical response data.

[0816] Data analysis module: Analyzes the acquired data to identify the content of the dream.

[0817] Image Generation Module: Generates images based on the identified dream content.

[0818] Data provision module: Provides the generated video to the user and shares it with others.

[0819] Hardware and software used

[0820] Hardware:

[0821] Brainwave sensor headband

[0822] Heart rate and muscle tension sensors

[0823] Smartphone

[0824] software:

[0825] Data Collection Platform

[0826] Server analysis platform (e.g. Amazon Web Services, Google Cloud)

[0827] Video generation AI (e.g. Unity, Unreal Engine)

[0828] Mobile app (iOS / Android)

[0829] Data Acquisition Details

[0830] Before going to sleep, the user puts on a headband to collect brainwave and physical response data. The headband is equipped with brainwave and physical response sensors that monitor and record the user's brainwaves (alpha waves, beta waves, theta waves, delta waves, etc.) and physical responses (heart rate, muscle tension, skin potential, etc.) in real time. This data is continuously collected while the user sleeps and stored on a device such as a smartphone.

[0831] Data analysis

[0832] The collected brainwave and physical response data is sent from the smartphone to a server, which analyzes the data to identify the content of the user's dream. The analysis involves the use of machine learning models and deep learning algorithms to identify each scene in the dream based on past data patterns.

[0833] Video generation

[0834] The server generates a dream image based on the analysis results. The generative AI model visualizes the identified dream content and creates a video with specific scenes. This video faithfully reproduces the events, places, and movements of people that the user experienced in the dream.

[0835] Provision to users

[0836] The generated dream footage is stored on a server and made accessible to users through a cloud platform. Users can watch the footage after waking up through a dedicated mobile application. The footage can also be shared with others.

[0837] Specific examples

[0838] For example, if a user's brainwave data is collected as "alpha," "beta," "theta," and "delta" waves, and their physical response data is acquired as a heart rate of 65 and muscle tension of 12, these data are immediately recorded on a smartphone. The smartphone then sends the data to a server, which analyzes it. Based on the analysis results, the generative AI model receives a prompt, "Please specify the content of the dream the user had," and generates a video.

[0839] Prompt Sentence Examples

[0840] The prompt contains textual instructions such as:

[0841] "Concretely express the content of the user's dream"

[0842] This invention allows users to re-experience and visually share their dreams in detail.

[0843] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0844] Step 1:

[0845] The user goes to sleep wearing a headband equipped with brainwave and body response sensors. The headband collects real-time data on brainwaves (alpha, beta, theta, delta, etc.) and body responses (heart rate, muscle tension, skin potential, etc.). The input is biodata obtained by the sensors, and the output is a continuous record of this data.

[0846] Step 2:

[0847] The terminal records the data collected from the headband and transmits it to the server at specified intervals. Here, the input is brainwave data and physical response data, and the output is sending this data as data packets to the server.

[0848] Step 3:

[0849] The server analyzes the received data using machine learning models and deep learning algorithms to identify each scene in the user's dream based on past data patterns. The input is the transmitted data packet, and the output is the analysis results, including the content of the dream.

[0850] Step 4:

[0851] Based on the analysis results, the server generates a dream image by providing a prompt to the generative AI model. For example, the generative AI model creates an image based on a prompt such as "Specify the content of the dream the user had." The input is the analysis results and the prompt, and the output is the generated dream image.

[0852] Step 5:

[0853] The generated dream video is stored on a server and made accessible to users through a cloud platform. The input is the generated dream video, and the output is the saved video data.

[0854] Step 6:

[0855] Users download and view the dream video generated from cloud storage through a dedicated mobile application. The input is the video data on cloud storage, and the output is playable on the user's device.

[0856] Step 7:

[0857] The user shares the dream images they have created with others. The images are shared via cloud storage in the form of a link or other format. The input is the user's instructions and the image data, and the output is accessible to others.

[0858] Through these steps, a system can be realized that recreates the dreams a user has had in detail as video, allowing them to re-experience and share them.

[0859] Furthermore, an emotion engine that estimates the user's emotion may be further combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59, and perform identification processing using the user's emotion.

[0860] The present invention relates to a system that records the dreams of a user in detail and reproduces them as video. Furthermore, by combining it with an emotion engine that recognizes the user's emotions, it becomes possible to customize the content of dreams in more detail and individually. An embodiment of this system will be described as follows.

[0861] Data Acquisition

[0862] Before going to sleep, the user wears a headband to collect brainwave and physical response data. The headband is equipped with brainwave and physical response sensors, and monitors the user's brainwaves (alpha waves, beta waves, theta waves, delta waves, etc.) and physical responses (heart rate, muscle tension, skin potential, etc.) in real time and records them on a terminal.

[0863] Emotion recognition

[0864] The collected brainwave and physical response data is sent from the device to a server. The server then analyzes the user's emotions using an emotion engine. The emotion engine uses machine learning models to identify the user's emotional state in real time. This analysis identifies emotions such as happiness, fear, and surprise that the user felt during the dream.

[0865] Data analysis

[0866] The server analyzes the brainwave and physical response data transmitted along with the user's emotional state to identify the content of the dream the user had. This analysis identifies each scene in the dream based on past data patterns and analyzes the identified emotional state, making it possible to recreate the dream in more detail.

[0867] Image Generation

[0868] Based on the analysis results, the server generates a dream video. Taking into account the identified dream content and the user's emotional state, AI technology is used to visualize specific scenes and events. The video is further customized according to the user's emotions identified by the emotion engine, faithfully recreating the user's experience in the dream.

[0869] Providing results

[0870] The generated dream footage is stored on a server and made accessible to the user. After waking up, the user can log in to their account through a dedicated application and view the generated dream footage from the provided URL link. The generated footage can also be shared with others via cloud storage.

[0871] Specific examples

[0872] 1. The user wears the headband and goes to sleep. The headband collects EEG data ['delta', 'theta', 'alpha', 'beta'] and physical response data ['heart_rate', 'muscle_tension'].

[0873] 2. The device sends the collected data to the server, which receives the data and uses an emotion engine to analyze the user's emotions.

[0874] 3. The server analyzes the dream, taking into account the emotional state, and generates the "dream analysis results."

[0875] 4. The server generates a video based on the analysis results and emotional data. The video recreates the places, characters, and events the user experienced in their dream, according to their emotions.

[0876] 5. The generated video is stored in cloud storage, and the user can view it through a dedicated application after waking up. The video can also be shared with others if desired.

[0877] In this way, the present invention allows users to record their dreams in detail and recreate them as images that reflect the user's emotions, allowing users to relive their dreams without forgetting them and to easily share them with others.

[0878] The processing flow will be explained below.

[0879] Step 1:

[0880] Before going to sleep, the user wears a special headband that contains brainwave and body response sensors, which enable the collection of brainwave and body response data.

[0881] Step 2:

[0882] While the user is sleeping, the headband monitors brain wave data (alpha waves, beta waves, theta waves, delta waves, etc.) and physical response data (heart rate, muscle tension, skin potential, etc.) in real time and stores the data on the device.

[0883] Step 3:

[0884] The device transmits the collected brainwave and physical response data to a server using a secure communication protocol.

[0885] Step 4:

[0886] The server receives the transmitted brainwave and physical response data and analyzes the user's emotional state using an emotion engine, which uses machine learning models to analyze the data in real time and identify emotions such as happiness, fear, and surprise.

[0887] Step 5:

[0888] The server runs the brainwave and physical response data, along with the emotion engine's analysis results, through a dream analysis algorithm. This analysis identifies the content of the user's dream. The analysis algorithm learns data patterns to identify dream scenes.

[0889] Step 6:

[0890] The server activates an image generation model based on the identified dream content, which uses AI technology to generate visual images based on the identified dream content and emotional data.

[0891] Step 7:

[0892] The server then further customizes the generated video based on the emotional data, resulting in a video that more faithfully reproduces the emotions the user felt in their dream.

[0893] Step 8:

[0894] The server saves the completed video to cloud storage and generates a link for the user to access, which is associated with the user's account information.

[0895] Step 9:

[0896] After waking up, users log in to their account through a dedicated application and watch the generated dream footage, which faithfully recreates the scenes and emotions they experienced in their dreams.

[0897] Step 10:

[0898] Users can also share the generated footage with others via cloud storage, protecting security and privacy.

[0899] This will create a system that allows users to re-experience their dreams in detail and share them with others if necessary.

[0900] Example 2

[0901] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[0902] Conventional dream recording systems have had difficulty recording the details of dreams or generating images that accurately reflect the user's emotions. Furthermore, they lacked a means for users to relive their dreams without forgetting them or easily share them with others.

[0903] The identification process by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means. In this invention, the server includes means for acquiring the user's electroencephalogram and physical reaction data, means for transmitting the acquired electroencephalogram and physical reaction data to the server, means for analyzing the user's emotional state using the electroencephalogram data and physical reaction data received by the server, means for identifying the content of the dream based on the analyzed emotional state and data, means for generating a video based on the identified dream content and emotional state, and means for providing the generated video to the user. This makes it possible to accurately record the content of the dream and generate a detailed dream video based on the user's emotions. Furthermore, it becomes easy for the user to re-experience the dream and share it with others.

[0904] "User" refers to an individual who uses the system to record their own brainwave and physical response data, analyze their dreams, and generate images.

[0905] "Electroencephalogram data" refers to recordings of the brain's electrical activity, such as alpha, beta, theta, and delta waves, collected by a user's headband.

[0906] "Physiological response data" refers to recordings of the body's physiological responses, including the user's heart rate, muscle tension, skin potential, etc.

[0907] "Headband" refers to a device worn by a user to record brainwave and physical response data in real time.

[0908] "Terminal" refers to an electronic device that temporarily stores the brain wave data and physical response data transmitted from the headband and transmits them to a server.

[0909] "Server" refers to a central control device that receives data sent from the terminals, analyzes and processes the data, and manages and provides the generated video.

[0910] "Emotional state" refers to an emotional state such as happiness, fear, surprise, etc., analyzed based on the user's electroencephalogram data and physical response data.

[0911] "Emotion engine" refers to a program or software that uses machine learning models to analyze a user's emotional state from brainwave data and physical response data.

[0912] "Dream content" refers to specific scenes, events, characters, etc., that the user experiences in a dream while sleeping.

[0913] "Image generation means" refers to a means for generating visual images using AI technology based on the analyzed dream content and emotional state.

[0914] "Cloud storage" refers to a remote storage device where generated videos are stored and made available for users to access and share over the Internet.

[0915] "Dedicated application" refers to software that allows users to access the server and view and manage the generated dream images.

[0916] "Machine learning model" refers to artificial intelligence techniques used to automatically perform tasks such as data analysis and identifying emotional states.

[0917] MODE FOR CARRYING OUT THE INVENTION

[0918] The present invention relates to a system that records the dreams of a user in detail and reproduces the content of the dream as a video. Furthermore, by combining it with an emotion engine that recognizes the user's emotions, it is possible to customize the content of the dream in more detail and individually. An embodiment of this system is as follows.

[0919] Data Acquisition

[0920] Before going to sleep, the user wears a headband to collect brainwave and physical response data. This headband is equipped with a brainwave sensor and a physical response sensor. These sensors record the user's brainwaves (e.g., alpha waves, beta waves, theta waves, delta waves, etc.) and physical responses (e.g., heart rate, muscle tension, skin potential, etc.) in real time and transmit the data to a terminal.

[0921] Data transmission and storage

[0922] The device transmits the recorded EEG and physical response data to a server via a secure protocol (e.g., HTTPS).

[0923] Emotion recognition

[0924] The server analyzes the received EEG and physical response data with an emotion engine, which uses machine learning models to identify the user's emotional state (e.g., happiness, fear, surprise, etc.).

[0925] Dream Analysis

[0926] The server then comprehensively analyzes the brainwave and physical response data based on the results of the emotion analysis to identify the content of the dream the user had. The analysis is based on past data patterns, and each scene in the dream is linked to the user's emotional state.

[0927] Image Generation

[0928] The server uses AI technology to generate a video based on the identified dream content and emotional state. This video visualizes the identified scenes and events, faithfully recreating the places and characters the user experienced in their dream.

[0929] Providing results

[0930] The generated dream footage is stored on a server and made accessible to the user. After waking up, the user can log in to their account through a dedicated application and view the generated dream footage from the provided URL link. The generated footage can also be shared with others via cloud storage.

[0931] Specific examples

[0932] 1. User puts on headband and goes to sleep:

[0933] The headband collects brainwave data ['delta', 'theta', 'alpha', 'beta'] and physical response data ['heart_rate', 'muscle_tension'].

[0934] 2. The device sends the collected data to the server:

[0935] The terminal packetizes the data and sends it to the server.

[0936] 3. The server analyzes the dream, taking into account the emotional state:

[0937] The server performs the analysis and generates the "dream analysis results."

[0938] 4. The server generates a video based on the analysis results and emotion data:

[0939] The AI ​​model generates images from the user's dreams and recreates them as "dream images."

[0940] 5. The generated video is saved in cloud storage:

[0941] After waking up, the user can watch the video through a dedicated application and share it with others if necessary.

[0942] Prompt Sentence Examples

[0943] What are the steps to record EEG and physical response data from a user wearing the headband?

[0944] Explain what types of emotional states the Emotion Engine analyzes.

[0945] Please tell me how to analyze the content of dreams that take place on the server.

[0946] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0947] Step 1: User wears the headband and collects data

[0948] Input: User's brain waves (alpha waves, beta waves, theta waves, delta waves, etc.) and physical responses (heart rate, muscle tension, skin potential, etc.)

[0949] Output: EEG data and physical response data

[0950] How it works: The user puts on the headband. The brainwave and body response sensors in the headband measure the user's data in real time and send it to the device.

[0951] Step 2: The device sends the collected data to the server

[0952] Input: EEG and physical response data obtained from a headband

[0953] Output: Data sent to the server

[0954] Specific operation: The device packetizes the data received from the headband and sends it to the server using a secure protocol (such as HTTPS).

[0955] Step 3: The server receives the data and performs sentiment analysis

[0956] Input: Brain wave data and physical response data sent from the terminal

[0957] Output: User's emotional state (e.g., happiness, fear, surprise, etc.)

[0958] How it works: The server stores the received data in a database. The emotion engine analyzes the data using machine learning models to identify the user's emotional state.

[0959] Step 4: The server identifies the content of the dream based on the analysis results

[0960] Input: EEG data, physical response data, and emotional state

[0961] Output: Identified dream content

[0962] How it works: The server uses an algorithm to analyze the content of dreams from past data patterns and identify each scene in the dream based on brainwave data and emotional state.

[0963] Step 5: The server generates a video based on the dream content and emotional state.

[0964] Input: Identified dream content and emotional state

[0965] Output: Generated dream image

[0966] How it works: The server inputs data into a generative AI model for video generation, which then generates a video visualization of the identified dream scenes or events.

[0967] Step 6: The server stores the generated video and provides it to the user.

[0968] Input: Generated dream image

[0969] Output: Video stored in cloud storage and provided to users

[0970] Specific operation: The server stores the generated video data in cloud storage. Users can view the video via a dedicated application, access the URL link, and share it with others as needed.

[0971] (Application example 2)

[0972] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[0973] Conventional dream recording systems have difficulty recording the details of a user's dreams, and the video generation technology required to recreate the content of those dreams is insufficient. Furthermore, they lack the functionality to share the generated dream videos with other users or to comment or rate them. This has resulted in issues such as users being unable to fully re-experience their dreams and making it difficult to share them with other users.

[0974] The identification process by the identification processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes means for acquiring the user's electroencephalogram and physical response data, means for identifying the content of the dream by analyzing the acquired electroencephalogram and physical response data, means for generating a video based on the identified content of the dream, means for providing the generated video to the user, means for sharing the generated video with other users, and means for adding comments and ratings to the video. This allows the user to not only record the dream they had in detail, analyze it, and replay it as a video, but also share it with other users and make comments and ratings.

[0975] "User" refers to an individual who uses the dream recording system.

[0976] "Electroencephalograms" refers to a collection of electrical signals generated by a user's brain.

[0977] "Physical response data" refers to data that quantifies the physiological responses of the body, such as the user's heart rate, muscle tension, and skin potential.

[0978] "Means for acquiring" refers to devices and technologies for collecting the user's brainwave and physical response data.

[0979] "Means for analyzing" refers to a method or device for analyzing the acquired data and identifying the content of the user's dream.

[0980] "Identified dream content" refers to information that the user is said to have seen in their dream, derived from the analyzed data.

[0981] "Means for generating images" refers to a method or device for producing visual images based on identified dream content.

[0982] The "means for providing to the user" refers to a method or device that allows the user to view the generated video.

[0983] "Means for sharing with other users" refers to a method or device for allowing other users to view the generated video.

[0984] The term "means for adding comments and ratings" refers to a method or device that allows other users to express their opinions or rate a video.

[0985] Overview of Program Generation and Processing

[0986] The following system configuration is proposed as an embodiment of the present invention.

[0987] Data Acquisition

[0988] The user wears a headband equipped with an EEG sensor and a physical response sensor to acquire EEG and physical response data. The headband monitors the user's EEG (alpha waves, beta waves, theta waves, delta waves) and physical responses (heart rate, muscle tension, skin potential, etc.) in real time and records this data on a terminal.

[0989] Data transmission and analysis

[0990] The device sends the collected brainwave and physical response data to a server, which then runs an emotion engine that uses machine learning models like TensorFlow to identify the user's emotional state. Based on the analysis, emotions experienced during the dream, such as happiness, fear, or surprise, are identified.

[0991] Identifying dream content and generating images

[0992] The server analyzes the brainwave and physical response data sent along with the identified emotional state to identify the content of the user's dream. Based on the results of this data analysis, the server then generates a dream video. This video generation uses a generative AI model.

[0993] Providing and sharing video

[0994] The generated dream video is stored on a server and made accessible to users through cloud storage. Users can view the generated dream video by logging into their own account through a dedicated application. Furthermore, the generated video can be shared with other users, and they can also add comments and ratings to the video.

[0995] Hardware and software used

[0996] Hardware: Headband equipped with brainwave and body response sensors, data collection device (smartphone, tablet, etc.)

[0997] Software: TensorFlow (machine learning model execution), Flask (server-side application), OpenCV (image generation)

[0998] Specific examples

[0999] For example, while a user is wearing a headband and sleeping, brain wave data ['delta', 'theta', 'alpha', 'beta'] and physical response data ['heart_rate', 'muscle_tension'] are collected and sent from the device to a server. The server analyzes this data and identifies the user's emotional state as "happiness." Based on the analysis results, a generative AI model generates a dream video and saves it in cloud storage. After waking up, the user opens a dedicated application to view their dream video, share it with other users, and add comments and ratings.

[1000] Prompt Sentence Examples

[1001] An example prompt might be:

[1002] "Please explain a specific example of how a user's dreams are recorded, analyzed using an emotion engine, and then video is generated. For example, data collected from a headband worn by the user includes brain waves (alpha waves, beta waves, theta waves, delta waves) and physical responses (heart rate, muscle tension, skin potential, etc.). This data is sent to a server, and the server uses a machine learning model to identify the user's emotional state. Based on this, a video of the dream is generated, which the user can view through a dedicated application."

[1003] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1004] Step 1:

[1005] The user sleeps wearing a headband to collect brainwave and physical response data. This headband monitors and records the user's brainwaves (alpha waves, beta waves, theta waves, delta waves) and physical responses (heart rate, muscle tension, skin potential, etc.) in real time. The input is the user's physiological data, and the brainwave data and physical response data are sent to a terminal as output data from the headband.

[1006] Step 2:

[1007] The device collects the collected brainwave and physical response data and periodically transmits it to a server. The input is data from the headband, and the output is brainwave and physical response data transmitted to the server. This data is stored for later analysis.

[1008] Step 3:

[1009] The server receives the transmitted EEG and physical response data and analyzes the data. The analysis uses an emotion engine, which runs a machine learning model to identify the user's emotional state. The input is the transmitted physiological data, and the output is the identified emotional state (happiness, fear, surprise, etc.). This analysis clusters the data by emotion.

[1010] Step 4:

[1011] The server then uses the identified emotional state and the results of the data analysis to identify the details of the user's dream. This involves referencing past data patterns to extract and analyze similar dream content. The input is the analyzed emotional and physiological data, and the output is the identified dream content.

[1012] Step 5:

[1013] The server generates images based on the identified dream content. To do this, it uses a generative AI model to create images based on emotional data. The input is the identified dream content and emotional data, and the output is the generated dream image. AI-based image synthesis technology is used to generate the images.

[1014] Step 6:

[1015] The generated dream video is stored in cloud storage by the server. The input is the generated dream video, and the output is a video file stored in the cloud. The video stored in this storage can be accessed by the user later.

[1016] Step 7:

[1017] Users log in to their account using a dedicated application and watch the dream footage stored in the cloud storage. The input is the URL link of the cloud storage, and the output is the dream footage played through the application.

[1018] Step 8:

[1019] The generated dream video can be shared with other users through the sharing function. Furthermore, other users can add comments and ratings to the video. The input is other users' comments and rating data, and the output is the updated metadata of the video. This promotes communication.

[1020] 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.

[1021] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[1022] In the above embodiment, an example was given in which the specific processing is performed by the data processing device 12, but the technology of the present disclosure is not limited to this, and the specific processing may be performed by the headset type terminal 314.

[1023] [Fourth embodiment]

[1024] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[1025] 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.

[1026] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the 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).

[1027] 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.

[1028] The microphone 238 receives instructions and the like from the user 20 by receiving voice uttered by the user 20. The microphone 238 captures the voice uttered by the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio in accordance with instructions from the processor 46.

[1029] 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 surroundings of user 20 (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[1030] 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. 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.

[1031] The control object 443 includes a display device, LEDs in the eyes, and motors for driving 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.

[1032] 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.

[1033] 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 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.

[1034] 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.

[1035] In the robot 414, the processor 46 performs the reception output process. A reception output program 60 is stored in the storage 50. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[1036] Next, a description will be given of the specific processing performed by the specific processing unit 290 of the data processing device 12. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1037] The present invention relates to a system for recording the dreams of a user in detail and reproducing them as a video. An embodiment of this system will be described as follows.

[1038] Data Acquisition

[1039] Before going to sleep, the user puts on a headband to collect brainwave and physical response data. This headband is equipped with brainwave and physical response sensors, and monitors and records the user's brainwaves (alpha waves, beta waves, theta waves, delta waves, etc.) and physical responses (heart rate, muscle tension, skin potential, etc.) in real time. This data is continuously collected while the user sleeps and stored on the device.

[1040] Data analysis

[1041] The collected brainwave and physical response data is sent from the device to a server, which analyzes the data to identify the content of the user's dream. The analysis uses machine learning models and deep learning algorithms to identify each scene in the dream based on past data patterns.

[1042] Image Generation

[1043] The server generates a dream image based on the analysis results. Using AI technology, it visualizes the content of the identified dream and creates a video with specific scenes. This video faithfully reproduces the events, places, and movements of people that the user experienced in the dream.

[1044] Providing results

[1045] The generated dream footage is stored on a server and made accessible to the user. After waking up, the user can view the footage through a dedicated application. The footage can also be shared with others via cloud storage.

[1046] Specific examples

[1047] 1. A user wears a headband and goes to sleep. The headband collects EEG data ['delta', 'theta', 'alpha', 'beta'] and physical response data ['heart_rate', 'muscle_tension'].

[1048] 2. The device sends the collected data to a server, which receives and analyzes the data to generate a "dream analysis result."

[1049] 3. The server generates a video based on the analysis results, specifically recreating the places, characters, and events seen in the user's dream as a "dream video."

[1050] 4. The generated video is stored in cloud storage, and the user can view it through a dedicated application after waking up. The video can also be shared with others.

[1051] In this way, the present invention provides a system that allows users to re-create their dreams as images and experience them in detail, making it easier for them to re-experience the content of their dreams without forgetting them and to share them with others.

[1052] The processing flow will be explained below.

[1053] Step 1:

[1054] Before going to sleep, the user wears a special headband that contains brainwave and body response sensors, which enable the collection of brainwave and body response data.

[1055] Step 2:

[1056] While the user is sleeping, the headband monitors brain wave data (alpha waves, beta waves, theta waves, delta waves, etc.) and physical response data (heart rate, muscle tension, skin potential, etc.) in real time and stores the data on the device.

[1057] Step 3:

[1058] The device transmits the collected brainwave and physical response data to a server using a secure communication protocol.

[1059] Step 4:

[1060] The server receives the transmitted EEG and physical response data and runs it through an analysis algorithm, which uses machine learning models and deep learning algorithms to identify the content of the dream the user had.

[1061] Step 5:

[1062] The server identifies the details of the dream based on the analysis results, and then uses this data to activate a visual generation model, which includes past dream data and visual information, to recreate the identified dream scene.

[1063] Step 6:

[1064] The server generates a video with a sequence of scenes based on the content of the dream, including the places, people, and events the user experienced in the dream, providing a realistic visual experience.

[1065] Step 7:

[1066] The server saves the generated video in cloud storage for user access. A generated URL link is sent to the user's account.

[1067] Step 8:

[1068] After waking up, the user logs in to their account through a dedicated application and watches the generated dream video from the provided URL link.

[1069] Step 9:

[1070] Users can share the generated dream images with others as needed, while taking security and privacy into consideration when sharing.

[1071] This will create a system that allows users to re-experience their dreams in detail and share them with others if necessary.

[1072] Example 1

[1073] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1074] Conventional dream recording methods have difficulty accurately reproducing the content and specific scenes of a dream that a user has had. Furthermore, the means for sharing the content of a dream with others are limited, making it difficult to easily replay or share recorded dreams. This leads to problems such as users forgetting the content of their dreams or being unable to communicate in detail when talking about their dreams with others.

[1075] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[1076] In this invention, the server includes means for acquiring the user's electroencephalogram and physical response data, means for storing the acquired electroencephalogram and physical response data in a terminal, means for transmitting the data stored in the terminal to the server, means for analyzing the data received by the server to identify the content of the dream, means for generating video using a generative model based on the identified content of the dream, means for storing the generated video in cloud storage, and means for the user to access and view the stored video through a dedicated application. This allows the user to re-experience the dream in detail and easily share the content of the dream with others.

[1077] "User" refers to a person who uses the system to record and play back dreams.

[1078] "Electroencephalograms" refer to fluctuations in electrical potential generated by electrical activity in the brain, and include alpha waves, beta waves, theta waves, and delta waves.

[1079] "Physiological response data" refers to data related to the physiological responses of the body, such as the user's heart rate, muscle tension, and skin potential.

[1080] "Terminal" refers to a headband worn by the user or a device for temporarily storing data.

[1081] "Server" refers to a computer system that receives and analyzes data sent from the terminal, identifies the content of the dream, and generates an image.

[1082] "Analysis" refers to the process by which the server processes the data it receives and identifies the content of the dream the user had.

[1083] "Generative model" refers to a machine learning model or deep learning algorithm that generates images based on identified dream content.

[1084] "Video" is a visual reproduction of events or scenes experienced by the user in a dream.

[1085] "Cloud storage" refers to an online storage service that allows you to store data via the Internet.

[1086] "Purpose-built application" refers to software that allows users to access and view the generated dream images.

[1087] The present invention relates to a system for recording a user's dreams in detail and reproducing them as video. This system involves a series of processes for acquiring and analyzing the user's brainwave and physical response data, generating a video based on the analysis results, and providing the video to the user.

[1088] Data Acquisition Hardware and Software

[1089] The user wears a dedicated headband before going to bed. This headband has built-in brainwave and body response sensors that collect brainwave data (e.g., alpha waves, beta waves, theta waves, delta waves) and body response data (e.g., heart rate, muscle tension, skin potential) in real time. This data is stored on the device and sent to a server via Wi-Fi or Bluetooth.

[1090] Hardware and software for data analysis

[1091] The data collected by the device is sent to a server, which then analyzes it using scripts written in programming languages ​​such as Python, using machine learning libraries like TensorFlow and PyTorch to identify the content of the user's dreams based on past data patterns.

[1092] Hardware and software for image generation

[1093] Based on the identified dream content, the server uses a generative model to generate images. This uses image generation AI such as Stable Diffusion and Generative Adversarial Networks (GANs). This generative model visualizes the events, places, and movements of people experienced in the user's dream as concrete scenes.

[1094] Providing results

[1095] The generated dream images are stored on a server and made accessible to users through cloud storage. Users can view the generated images through a dedicated application. They can also share the images with others through cloud storage.

[1096] Specific examples

[1097] Data collection

[1098] 1. The user wears the headband and goes to sleep. The headband collects EEG data ['delta', 'theta', 'alpha', 'beta'] and physical response data ['heart_rate', 'muscle_tension'].

[1099] Data transmission and analysis

[1100] 2. The device sends the collected data to a server, which receives it, analyzes it, and generates a "dream analysis result."

[1101] Image Generation

[1102] 3. The server generates an image based on the analysis results. The image generation AI creates a "dream image" based on the content of the dream.

[1103] Video provision

[1104] 4. The generated video is stored in cloud storage, and after the user wakes up, they can watch the video through a dedicated application and share it with others via the cloud.

[1105] Prompt Sentence Examples

[1106] "Analyze the EEG data (['delta', 'theta', 'alpha', 'beta']) and physical response data (['heart_rate', 'muscle_tension']) to generate a visual representation of the user's dream."

[1107] Through this system, users can re-experience their dreams in detail and easily share them with others.

[1108] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1109] Step 1:

[1110] The user puts on the headband and activates the brainwave sensor and body response sensor, which then collects brainwave data (alpha waves, beta waves, theta waves, delta waves) and body response data (heart rate, muscle tension, skin potential) in real time.

[1111] Input: User's biometric signals

[1112] Output: Collected EEG and physical response data

[1113] How it works: The user turns on the headband and puts it firmly on their head. The sensors automatically collect data and send it to the device.

[1114] Step 2:

[1115] The data collected by the device is sent to the server in batches at regular intervals, using Wi-Fi or Bluetooth.

[1116] Input: EEG and physical response data stored on the device

[1117] Output: Biometric data sent to the server

[1118] Specific operation: The device creates a batch file of collected data every minute and sends it to the server via Wi-Fi or Bluetooth.

[1119] Step 3:

[1120] The server analyzes the received data. Using machine learning models and deep learning algorithms, the content of the user's dreams is identified based on past data patterns. The analysis is carried out using Python programs and libraries such as TensorFlow and PyTorch.

[1121] Input: Biometric data sent to the server

[1122] Output: Dream analysis results

[1123] What it does: A Python script is run on the server, and data analysis is performed using TensorFlow and PyTorch. The results of the analysis identify the content of the dream.

[1124] Step 4:

[1125] The server generates images based on the identified dream content, using image generation AI such as Stable Diffusion and GANs (Generative Adversarial Networks).

[1126] Input: Dream analysis results

[1127] Output: Generated dream video

[1128] Specific operation: Based on the identified dream scene, the image generation AI automatically generates a video containing specific scenes and characters.

[1129] Step 5:

[1130] The server stores the generated video in cloud storage and allows users to access it using a dedicated application, through which they can view the video and share it with others as needed.

[1131] Input: Generated dream footage

[1132] Output: Video stored in cloud storage and accessible to users

[1133] Specific operation: Video files are uploaded to cloud storage (e.g., AWS S3) and made accessible through a dedicated application. Users log in to the application to play and share the videos.

[1134] (Application example 1)

[1135] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1136] Conventional methods for recording dreams mainly involve recording them in text or pictures, making it difficult to visually recreate the content of a dream. This makes it difficult for users to accurately re-experience the details of their dreams or share them with others. Furthermore, there is no technology that can analyze and visualize the content of dreams, leaving users with a lack of concrete ways to recreate their dreams.

[1137] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[1138] In this invention, the server includes means for acquiring the user's electroencephalogram and physical response data, means for identifying the content of the dream by analyzing the acquired electroencephalogram and physical response data, means for generating a video based on the identified dream content, means for providing the generated video to the user, and means for sharing the generated video with others, thereby enabling the user to visually re-experience the details of the dream and share it with others via cloud storage or a dedicated application.

[1139] "User's brain wave and physical response data" refers to data on brain waves (alpha waves, beta waves, theta waves, delta waves, etc.) and physical responses (heart rate, muscle tension, skin potential, etc.) measured while the user is asleep.

[1140] The "acquisition means" refers to equipment and software for collecting the user's brainwave and physical response data using a device such as a headband.

[1141] The "means of analyzing and identifying the content of dreams" refers to a processing method for analyzing acquired brainwave and physical response data using machine learning models and deep learning algorithms to identify the content of dreams.

[1142] The "means for generating images" refers to the process and system for creating images using AI technology in order to visually reproduce the content of dreams using the analysis results.

[1143] The "means for providing" refers to a platform and application for providing the generated video in a form that is accessible to users.

[1144] "Means for sharing" refers to the functions and systems for sharing the generated video with other users through cloud storage or dedicated applications.

[1145] A "dedicated application" is software that a user uses to view and manage dream images.

[1146] A "generative AI model" is an artificial intelligence algorithm and learning model used to generate dream images based on brainwave and physical response data.

[1147] A "prompt" is a formalized instruction input to a generative AI model, and is a statement that serves as a guideline for generating images.

[1148] System Overview

[1149] The system for implementing this invention records the dreams of a user in detail and replays them as a video. The system includes the following main components:

[1150] Data acquisition module: Acquires the user's brainwave and physical response data.

[1151] Data analysis module: Analyzes the acquired data to identify the content of the dream.

[1152] Image Generation Module: Generates images based on the identified dream content.

[1153] Data provision module: Provides the generated video to the user and shares it with others.

[1154] Hardware and software used

[1155] Hardware:

[1156] Brainwave sensor headband

[1157] Heart rate and muscle tension sensors

[1158] Smartphone

[1159] software:

[1160] Data Collection Platform

[1161] Server analysis platform (e.g. Amazon Web Services, Google Cloud)

[1162] Video generation AI (e.g. Unity, Unreal Engine)

[1163] Mobile app (iOS / Android)

[1164] Data Acquisition Details

[1165] Before going to sleep, the user puts on a headband to collect brainwave and physical response data. The headband is equipped with brainwave and physical response sensors that monitor and record the user's brainwaves (alpha waves, beta waves, theta waves, delta waves, etc.) and physical responses (heart rate, muscle tension, skin potential, etc.) in real time. This data is continuously collected while the user sleeps and stored on a device such as a smartphone.

[1166] Data analysis

[1167] The collected brainwave and physical response data is sent from the smartphone to a server, which analyzes the data to identify the content of the user's dream. The analysis involves the use of machine learning models and deep learning algorithms to identify each scene in the dream based on past data patterns.

[1168] Video generation

[1169] The server generates a dream image based on the analysis results. The generative AI model visualizes the identified dream content and creates a video with specific scenes. This video faithfully reproduces the events, places, and movements of people that the user experienced in the dream.

[1170] Provision to users

[1171] The generated dream footage is stored on a server and made accessible to users through a cloud platform. Users can watch the footage after waking up through a dedicated mobile application. The footage can also be shared with others.

[1172] Specific examples

[1173] For example, if a user's brainwave data is collected as "alpha," "beta," "theta," and "delta" waves, and their physical response data is acquired as a heart rate of 65 and muscle tension of 12, these data are immediately recorded on a smartphone. The smartphone then sends the data to a server, which analyzes it. Based on the analysis results, the generative AI model receives a prompt, "Please specify the content of the dream the user had," and generates a video.

[1174] Prompt Sentence Examples

[1175] The prompt contains textual instructions such as:

[1176] "Concretely express the content of the user's dream"

[1177] This invention allows users to re-experience and visually share their dreams in detail.

[1178] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1179] Step 1:

[1180] The user goes to sleep wearing a headband equipped with brainwave and body response sensors. The headband collects real-time data on brainwaves (alpha, beta, theta, delta, etc.) and body responses (heart rate, muscle tension, skin potential, etc.). The input is biodata obtained by the sensors, and the output is a continuous record of this data.

[1181] Step 2:

[1182] The terminal records the data collected from the headband and transmits it to the server at specified intervals. Here, the input is brainwave data and physical response data, and the output is sending this data as data packets to the server.

[1183] Step 3:

[1184] The server analyzes the received data using machine learning models and deep learning algorithms to identify each scene in the user's dream based on past data patterns. The input is the transmitted data packet, and the output is the analysis results, including the content of the dream.

[1185] Step 4:

[1186] Based on the analysis results, the server generates a dream image by providing a prompt to the generative AI model. For example, the generative AI model creates an image based on a prompt such as "Specify the content of the dream the user had." The input is the analysis results and the prompt, and the output is the generated dream image.

[1187] Step 5:

[1188] The generated dream video is stored on a server and made accessible to users through a cloud platform. The input is the generated dream video, and the output is the saved video data.

[1189] Step 6:

[1190] Users download and view the dream video generated from cloud storage through a dedicated mobile application. The input is the video data on cloud storage, and the output is playable on the user's device.

[1191] Step 7:

[1192] The user shares the dream images they have created with others. The images are shared via cloud storage in the form of a link or other format. The input is the user's instructions and the image data, and the output is accessible to others.

[1193] Through these steps, a system can be realized that recreates the dreams a user has had in detail as video, allowing them to re-experience and share them.

[1194] Furthermore, an emotion engine that estimates the user's emotion may be further combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59, and perform identification processing using the user's emotion.

[1195] The present invention relates to a system that records the dreams of a user in detail and reproduces them as video. Furthermore, by combining it with an emotion engine that recognizes the user's emotions, it becomes possible to customize the content of dreams in more detail and individually. An embodiment of this system will be described as follows.

[1196] Data Acquisition

[1197] Before going to sleep, the user wears a headband to collect brainwave and physical response data. The headband is equipped with brainwave and physical response sensors, and monitors the user's brainwaves (alpha waves, beta waves, theta waves, delta waves, etc.) and physical responses (heart rate, muscle tension, skin potential, etc.) in real time and records them on a terminal.

[1198] Emotion recognition

[1199] The collected brainwave and physical response data is sent from the device to a server. The server then analyzes the user's emotions using an emotion engine. The emotion engine uses machine learning models to identify the user's emotional state in real time. This analysis identifies emotions such as happiness, fear, and surprise that the user felt during the dream.

[1200] Data analysis

[1201] The server analyzes the brainwave and physical response data transmitted along with the user's emotional state to identify the content of the dream the user had. This analysis identifies each scene in the dream based on past data patterns and analyzes the identified emotional state, making it possible to recreate the dream in more detail.

[1202] Image Generation

[1203] Based on the analysis results, the server generates a dream video. Taking into account the identified dream content and the user's emotional state, AI technology is used to visualize specific scenes and events. The video is further customized according to the user's emotions identified by the emotion engine, faithfully recreating the user's experience in the dream.

[1204] Providing results

[1205] The generated dream footage is stored on a server and made accessible to the user. After waking up, the user can log in to their account through a dedicated application and view the generated dream footage from the provided URL link. The generated footage can also be shared with others via cloud storage.

[1206] Specific examples

[1207] 1. The user wears the headband and goes to sleep. The headband collects EEG data ['delta', 'theta', 'alpha', 'beta'] and physical response data ['heart_rate', 'muscle_tension'].

[1208] 2. The device sends the collected data to the server, which receives the data and uses an emotion engine to analyze the user's emotions.

[1209] 3. The server analyzes the dream, taking into account the emotional state, and generates the "dream analysis results."

[1210] 4. The server generates a video based on the analysis results and emotional data. The video recreates the places, characters, and events the user experienced in their dream, according to their emotions.

[1211] 5. The generated video is stored in cloud storage, and the user can view it through a dedicated application after waking up. The video can also be shared with others if desired.

[1212] In this way, the present invention allows users to record their dreams in detail and recreate them as images that reflect the user's emotions, allowing users to relive their dreams without forgetting them and to easily share them with others.

[1213] The processing flow will be explained below.

[1214] Step 1:

[1215] Before going to sleep, the user wears a special headband that contains brainwave and body response sensors, which enable the collection of brainwave and body response data.

[1216] Step 2:

[1217] While the user is sleeping, the headband monitors brain wave data (alpha waves, beta waves, theta waves, delta waves, etc.) and physical response data (heart rate, muscle tension, skin potential, etc.) in real time and stores the data on the device.

[1218] Step 3:

[1219] The device transmits the collected brainwave and physical response data to a server using a secure communication protocol.

[1220] Step 4:

[1221] The server receives the transmitted brainwave and physical response data and analyzes the user's emotional state using an emotion engine, which uses machine learning models to analyze the data in real time and identify emotions such as happiness, fear, and surprise.

[1222] Step 5:

[1223] The server runs the brainwave and physical response data, along with the emotion engine's analysis results, through a dream analysis algorithm. This analysis identifies the content of the user's dream. The analysis algorithm learns data patterns to identify dream scenes.

[1224] Step 6:

[1225] The server activates an image generation model based on the identified dream content, which uses AI technology to generate visual images based on the identified dream content and emotional data.

[1226] Step 7:

[1227] The server then further customizes the generated video based on the emotional data, resulting in a video that more faithfully reproduces the emotions the user felt in their dream.

[1228] Step 8:

[1229] The server saves the completed video to cloud storage and generates a link for the user to access, which is associated with the user's account information.

[1230] Step 9:

[1231] After waking up, users log in to their account through a dedicated application and watch the generated dream footage, which faithfully recreates the scenes and emotions they experienced in their dreams.

[1232] Step 10:

[1233] Users can also share the generated footage with others via cloud storage, protecting security and privacy.

[1234] This will create a system that allows users to re-experience their dreams in detail and share them with others if necessary.

[1235] Example 2

[1236] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1237] Conventional dream recording systems have had difficulty recording the details of dreams or generating images that accurately reflect the user's emotions. Furthermore, they lacked a means for users to relive their dreams without forgetting them or easily share them with others.

[1238] The identification process by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means. In this invention, the server includes means for acquiring the user's electroencephalogram and physical reaction data, means for transmitting the acquired electroencephalogram and physical reaction data to the server, means for analyzing the user's emotional state using the electroencephalogram data and physical reaction data received by the server, means for identifying the content of the dream based on the analyzed emotional state and data, means for generating a video based on the identified dream content and emotional state, and means for providing the generated video to the user. This makes it possible to accurately record the content of the dream and generate a detailed dream video based on the user's emotions. Furthermore, it becomes easy for the user to re-experience the dream and share it with others.

[1239] "User" refers to an individual who uses the system to record their own brainwave and physical response data, analyze their dreams, and generate images.

[1240] "Electroencephalogram data" refers to recordings of the brain's electrical activity, such as alpha, beta, theta, and delta waves, collected by a user's headband.

[1241] "Physiological response data" refers to recordings of the body's physiological responses, including the user's heart rate, muscle tension, skin potential, etc.

[1242] "Headband" refers to a device worn by a user to record brainwave and physical response data in real time.

[1243] "Terminal" refers to an electronic device that temporarily stores the brain wave data and physical response data transmitted from the headband and transmits them to a server.

[1244] "Server" refers to a central control device that receives data sent from the terminals, analyzes and processes the data, and manages and provides the generated video.

[1245] "Emotional state" refers to an emotional state such as happiness, fear, surprise, etc., analyzed based on the user's electroencephalogram data and physical response data.

[1246] "Emotion engine" refers to a program or software that uses machine learning models to analyze a user's emotional state from brainwave data and physical response data.

[1247] "Dream content" refers to specific scenes, events, characters, etc., that the user experiences in a dream while sleeping.

[1248] "Image generation means" refers to a means for generating visual images using AI technology based on the analyzed dream content and emotional state.

[1249] "Cloud storage" refers to a remote storage device where generated videos are stored and made available for users to access and share over the Internet.

[1250] "Dedicated application" refers to software that allows users to access the server and view and manage the generated dream images.

[1251] "Machine learning model" refers to artificial intelligence techniques used to automatically perform tasks such as data analysis and identifying emotional states.

[1252] MODE FOR CARRYING OUT THE INVENTION

[1253] The present invention relates to a system that records the dreams of a user in detail and reproduces the content of the dream as a video. Furthermore, by combining it with an emotion engine that recognizes the user's emotions, it is possible to customize the content of the dream in more detail and individually. An embodiment of this system is as follows.

[1254] Data Acquisition

[1255] Before going to sleep, the user wears a headband to collect brainwave and physical response data. This headband is equipped with a brainwave sensor and a physical response sensor. These sensors record the user's brainwaves (e.g., alpha waves, beta waves, theta waves, delta waves, etc.) and physical responses (e.g., heart rate, muscle tension, skin potential, etc.) in real time and transmit the data to a terminal.

[1256] Data transmission and storage

[1257] The device transmits the recorded EEG and physical response data to a server via a secure protocol (e.g., HTTPS).

[1258] Emotion recognition

[1259] The server analyzes the received EEG and physical response data with an emotion engine, which uses machine learning models to identify the user's emotional state (e.g., happiness, fear, surprise, etc.).

[1260] Dream Analysis

[1261] The server then comprehensively analyzes the brainwave and physical response data based on the results of the emotion analysis to identify the content of the dream the user had. The analysis is based on past data patterns, and each scene in the dream is linked to the user's emotional state.

[1262] Image Generation

[1263] The server uses AI technology to generate a video based on the identified dream content and emotional state. This video visualizes the identified scenes and events, faithfully recreating the places and characters the user experienced in their dream.

[1264] Providing results

[1265] The generated dream footage is stored on a server and made accessible to the user. After waking up, the user can log in to their account through a dedicated application and view the generated dream footage from the provided URL link. The generated footage can also be shared with others via cloud storage.

[1266] Specific examples

[1267] 1. User puts on headband and goes to sleep:

[1268] The headband collects brainwave data ['delta', 'theta', 'alpha', 'beta'] and physical response data ['heart_rate', 'muscle_tension'].

[1269] 2. The device sends the collected data to the server:

[1270] The terminal packetizes the data and sends it to the server.

[1271] 3. The server analyzes the dream, taking into account the emotional state:

[1272] The server performs the analysis and generates the "dream analysis results."

[1273] 4. The server generates a video based on the analysis results and emotion data:

[1274] The AI ​​model generates images from the user's dreams and recreates them as "dream images."

[1275] 5. The generated video is saved in cloud storage:

[1276] After waking up, the user can watch the video through a dedicated application and share it with others if necessary.

[1277] Prompt Sentence Examples

[1278] What are the steps to record EEG and physical response data from a user wearing the headband?

[1279] Explain what types of emotional states the Emotion Engine analyzes.

[1280] Please tell me how to analyze the content of dreams that take place on the server.

[1281] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1282] Step 1: User wears the headband and collects data

[1283] Input: User's brain waves (alpha waves, beta waves, theta waves, delta waves, etc.) and physical responses (heart rate, muscle tension, skin potential, etc.)

[1284] Output: EEG data and physical response data

[1285] How it works: The user puts on the headband. The brainwave and body response sensors in the headband measure the user's data in real time and send it to the device.

[1286] Step 2: The device sends the collected data to the server

[1287] Input: EEG and physical response data obtained from a headband

[1288] Output: Data sent to the server

[1289] Specific operation: The device packetizes the data received from the headband and sends it to the server using a secure protocol (such as HTTPS).

[1290] Step 3: The server receives the data and performs sentiment analysis

[1291] Input: Brain wave data and physical response data sent from the terminal

[1292] Output: User's emotional state (e.g., happiness, fear, surprise, etc.)

[1293] How it works: The server stores the received data in a database. The emotion engine analyzes the data using machine learning models to identify the user's emotional state.

[1294] Step 4: The server identifies the content of the dream based on the analysis results

[1295] Input: EEG data, physical response data, and emotional state

[1296] Output: Identified dream content

[1297] How it works: The server uses an algorithm to analyze the content of dreams from past data patterns and identify each scene in the dream based on brainwave data and emotional state.

[1298] Step 5: The server generates a video based on the dream content and emotional state.

[1299] Input: Identified dream content and emotional state

[1300] Output: Generated dream image

[1301] How it works: The server inputs data into a generative AI model for video generation, which then generates a video visualization of the identified dream scenes or events.

[1302] Step 6: The server stores the generated video and provides it to the user.

[1303] Input: Generated dream image

[1304] Output: Video stored in cloud storage and provided to users

[1305] Specific operation: The server stores the generated video data in cloud storage. Users can view the video via a dedicated application, access the URL link, and share it with others as needed.

[1306] (Application example 2)

[1307] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1308] Conventional dream recording systems have difficulty recording the details of a user's dreams, and the video generation technology required to recreate the content of those dreams is insufficient. Furthermore, they lack the functionality to share the generated dream videos with other users or to comment or rate them. This has resulted in issues such as users being unable to fully re-experience their dreams and making it difficult to share them with other users.

[1309] The identification process by the identification processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes means for acquiring the user's electroencephalogram and physical response data, means for identifying the content of the dream by analyzing the acquired electroencephalogram and physical response data, means for generating a video based on the identified content of the dream, means for providing the generated video to the user, means for sharing the generated video with other users, and means for adding comments and ratings to the video. This allows the user to not only record the dream they had in detail, analyze it, and replay it as a video, but also share it with other users and make comments and ratings.

[1310] "User" refers to an individual who uses the dream recording system.

[1311] "Electroencephalograms" refers to a collection of electrical signals generated by a user's brain.

[1312] "Physical response data" refers to data that quantifies the physiological responses of the body, such as the user's heart rate, muscle tension, and skin potential.

[1313] "Means for acquiring" refers to devices and technologies for collecting the user's brainwave and physical response data.

[1314] "Means for analyzing" refers to a method or device for analyzing the acquired data and identifying the content of the user's dream.

[1315] "Identified dream content" refers to information that the user is said to have seen in their dream, derived from the analyzed data.

[1316] "Means for generating images" refers to a method or device for producing visual images based on identified dream content.

[1317] The "means for providing to the user" refers to a method or device that allows the user to view the generated video.

[1318] "Means for sharing with other users" refers to a method or device for allowing other users to view the generated video.

[1319] The term "means for adding comments and ratings" refers to a method or device that allows other users to express their opinions or rate a video.

[1320] Overview of Program Generation and Processing

[1321] The following system configuration is proposed as an embodiment of the present invention.

[1322] Data Acquisition

[1323] The user wears a headband equipped with an EEG sensor and a physical response sensor to acquire EEG and physical response data. The headband monitors the user's EEG (alpha waves, beta waves, theta waves, delta waves) and physical responses (heart rate, muscle tension, skin potential, etc.) in real time and records this data on a terminal.

[1324] Data transmission and analysis

[1325] The device sends the collected brainwave and physical response data to a server, which then runs an emotion engine that uses machine learning models like TensorFlow to identify the user's emotional state. Based on the analysis, emotions experienced during the dream, such as happiness, fear, or surprise, are identified.

[1326] Identifying dream content and generating images

[1327] The server analyzes the brainwave and physical response data sent along with the identified emotional state to identify the content of the user's dream. Based on the results of this data analysis, the server then generates a dream video. This video generation uses a generative AI model.

[1328] Providing and sharing video

[1329] The generated dream video is stored on a server and made accessible to users through cloud storage. Users can view the generated dream video by logging into their own account through a dedicated application. Furthermore, the generated video can be shared with other users, and they can also add comments and ratings to the video.

[1330] Hardware and software used

[1331] Hardware: Headband equipped with brainwave and body response sensors, data collection device (smartphone, tablet, etc.)

[1332] Software: TensorFlow (machine learning model execution), Flask (server-side application), OpenCV (image generation)

[1333] Specific examples

[1334] For example, while a user is wearing a headband and sleeping, brain wave data ['delta', 'theta', 'alpha', 'beta'] and physical response data ['heart_rate', 'muscle_tension'] are collected and sent from the device to a server. The server analyzes this data and identifies the user's emotional state as "happiness." Based on the analysis results, a generative AI model generates a dream video and saves it in cloud storage. After waking up, the user opens a dedicated application to view their dream video, share it with other users, and add comments and ratings.

[1335] Prompt Sentence Examples

[1336] An example prompt might be:

[1337] "Please explain a specific example of how a user's dreams are recorded, analyzed using an emotion engine, and then video is generated. For example, data collected from a headband worn by the user includes brain waves (alpha waves, beta waves, theta waves, delta waves) and physical responses (heart rate, muscle tension, skin potential, etc.). This data is sent to a server, and the server uses a machine learning model to identify the user's emotional state. Based on this, a video of the dream is generated, which the user can view through a dedicated application."

[1338] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1339] Step 1:

[1340] The user sleeps wearing a headband to collect brainwave and physical response data. This headband monitors and records the user's brainwaves (alpha waves, beta waves, theta waves, delta waves) and physical responses (heart rate, muscle tension, skin potential, etc.) in real time. The input is the user's physiological data, and the brainwave data and physical response data are sent to a terminal as output data from the headband.

[1341] Step 2:

[1342] The device collects the collected brainwave and physical response data and periodically transmits it to a server. The input is data from the headband, and the output is brainwave and physical response data transmitted to the server. This data is stored for later analysis.

[1343] Step 3:

[1344] The server receives the transmitted EEG and physical response data and analyzes the data. The analysis uses an emotion engine, which runs a machine learning model to identify the user's emotional state. The input is the transmitted physiological data, and the output is the identified emotional state (happiness, fear, surprise, etc.). This analysis clusters the data by emotion.

[1345] Step 4:

[1346] The server then uses the identified emotional state and the results of the data analysis to identify the details of the user's dream. This involves referencing past data patterns to extract and analyze similar dream content. The input is the analyzed emotional and physiological data, and the output is the identified dream content.

[1347] Step 5:

[1348] The server generates images based on the identified dream content. To do this, it uses a generative AI model to create images based on emotional data. The input is the identified dream content and emotional data, and the output is the generated dream image. AI-based image synthesis technology is used to generate the images.

[1349] Step 6:

[1350] The generated dream video is stored in cloud storage by the server. The input is the generated dream video, and the output is a video file stored in the cloud. The video stored in this storage can be accessed by the user later.

[1351] Step 7:

[1352] Users log in to their account using a dedicated application and watch the dream footage stored in the cloud storage. The input is the URL link of the cloud storage, and the output is the dream footage played through the application.

[1353] Step 8:

[1354] The generated dream video can be shared with other users through the sharing function. Furthermore, other users can add comments and ratings to the video. The input is other users' comments and rating data, and the output is the updated metadata of the video. This promotes communication.

[1355] 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.

[1356] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[1357] In the above embodiment, an example was given in which the specific processing is performed by the data processing device 12, but the technology of the present disclosure is not limited to this, and the specific processing may be performed by the robot 414.

[1358] 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.

[1359] FIG. 9 is a diagram illustrating 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 actions arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion includes both affect 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.

[1360] 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.

[1361] 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).

[1362] 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 indicated, and when they approach the ideal, a state of pleasure is indicated. Emotions can also be created for robots, automobiles, 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 indicated, and when they approach the ideal, a state of pleasure is indicated. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on Voice Emotion Recognition and Emotional Brain Physiological Signal Analysis Systems, Tokushima University, Doctoral Dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the "reaction" domain, where sensation is dominant. The right half of the emotion map lists emotions belonging to the "situation" domain, where situational awareness is dominant.

[1363] 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."

[1364] 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.

[1365] The system according to the present disclosure has been described above mainly with respect to the functions of the data processing device 12, but the system according to the present disclosure is not necessarily implemented on a server. The system according to the present disclosure may be implemented as a general information processing system. The present disclosure may be implemented, for example, as a software program running on a personal computer or an application running on a smartphone, etc. The method according to the present disclosure may be provided to users in the form of SaaS (Software as a Service).

[1366] In the above embodiment, an example was given in which the specific processing is performed by one computer 22, but the technology of the present disclosure is not limited to this, and the specific processing may be distributed and performed by a plurality of computers including the computer 22. For example, the data generation model 58 may be provided in an external device of the data processing device 12, and data may be generated in the external device in accordance with input data.

[1367] 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.

[1368] 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.

[1369] 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.

[1370] The hardware resource for executing a specific process can be any of the following processors: An example of a processor 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. Another example of a processor is 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.

[1371] The hardware resource that executes the specific processing 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 processing may be a single processor.

[1372] 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.

[1373] 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.

[1374] 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, in order to avoid confusion and to 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.

[1375] 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.

[1376] The following is further disclosed regarding the above embodiment.

[1377] (Claim 1)

[1378] means for acquiring electroencephalogram and physical response data of a user;

[1379] A means for identifying the content of dreams by analyzing the acquired brain wave and physical response data;

[1380] a means for generating an image based on the identified dream content;

[1381] means for providing the generated video to a user;

[1382] A system including:

[1383] (Claim 2)

[1384] The system of claim 1, wherein the video is generated using a machine learning model based on the analyzed dream content.

[1385] (Claim 3)

[1386] The system of claim 1, wherein, during analysis, each scene in the dream is identified from the EEG data.

[1387] "Example 1"

[1388] (Claim 1)

[1389] means for acquiring electroencephalogram and physical response data of a user;

[1390] A means for storing the acquired brain wave and physical response data in a terminal;

[1391] means for transmitting data stored in the terminal to a server;

[1392] A means for analyzing the data received by the server to identify the content of the dream;

[1393] A means for generating an image using a generative model based on the identified dream content;

[1394] A means for storing the generated video in cloud storage;

[1395] A means for users to access and view the stored video through a dedicated application;

[1396] A system including:

[1397] (Claim 2)

[1398] The system of claim 1, wherein the video is generated using a machine learning model based on the analyzed dream content.

[1399] (Claim 3)

[1400] The system of claim 1, wherein, during analysis, each scene in the dream is identified from the EEG data.

[1401] "Application Example 1"

[1402] (Claim 1)

[1403] means for acquiring electroencephalogram and physical response data of a user;

[1404] A means for identifying the content of dreams by analyzing the acquired brain wave and physical response data;

[1405] a means for generating an image based on the identified dream content;

[1406] means for providing the generated video to a user;

[1407] A means for sharing the generated video with others;

[1408] A system including:

[1409] (Claim 2)

[1410] The system of claim 1, wherein the video is generated using a machine learning model based on the analyzed dream content.

[1411] (Claim 3)

[1412] The system of claim 1, wherein, during analysis, each scene in the dream is identified from the EEG data.

[1413] (Claim 4)

[1414] 10. The system of claim 1, further comprising a dedicated application for a user to view the generated dream video.

[1415] (Claim 5)

[1416] The system of claim 2, wherein a prompt sentence is used when generating an image using a generative AI model.

[1417] "Example 2: Combining Emotion Engines"

[1418] (Claim 1)

[1419] means for acquiring electroencephalogram and physical response data of a user;

[1420] means for transmitting the acquired electroencephalogram and physical response data to a server;

[1421] means for analyzing the emotional state of the user using the electroencephalogram data and physical response data received by the server;

[1422] A method for identifying the content of dreams based on the analyzed emotional state and data,

[1423] means for generating an image based on the identified dream content and emotional state;

[1424] means for providing the generated video to a user;

[1425] A system including:

[1426] (Claim 2)

[1427] The system of claim 1, wherein the video is generated using a machine learning model based on the analyzed dream content and emotional state.

[1428] (Claim 3)

[1429] The system of claim 1, wherein, during analysis, each scene in the dream is identified from the EEG data.

[1430] "Application example 2 when combining emotion engines"

[1431] (Claim 1)

[1432] means for acquiring electroencephalogram and physical response data of a user;

[1433] A means for identifying the content of dreams by analyzing the acquired brain wave and physical response data;

[1434] a means for generating an image based on the identified dream content;

[1435] means for providing the generated video to a user;

[1436] a means for sharing the generated video with other users;

[1437] A way to add comments and ratings to videos,

[1438] A system including:

[1439] (Claim 2)

[1440] The system of claim 1, wherein the video is generated using a machine learning model based on the analyzed dream content.

[1441] (Claim 3)

[1442] The system of claim 1, wherein, during analysis, each scene in the dream is identified from the EEG data. [Explanation of symbols]

[1443] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot< / url:> < / url:> < / url:> < / url:>

Claims

1. means for acquiring electroencephalogram and physical response data of a user; A means for identifying the content of dreams by analyzing the acquired brain wave and physical response data; a means for generating an image based on the identified dream content; means for providing the generated video to a user; A system including:

2. The system of claim 1 , wherein the video is generated using a machine learning model based on the analyzed dream content.

3. 2. The system according to claim 1, wherein, during the analysis, each scene in the dream is identified from the electroencephalogram data.

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