System

Abstract

A system is provided.SOLUTION: A system including means for authenticating a user using a biometric recognition technology, means for scanning a brain wave of the user and acquiring the brain wave data, means for analyzing the acquired brain wave data and converting consciousness and memory information into digital data, means for storing the converted digital data in a cyber space, means for converting the digital data stored in the cyber space into a format that can be accessed and downloaded according to a user's request, and means for reproducing the downloaded data using a generative AI model.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] This technology addresses the fear of losing consciousness and memories when facing physical aging and death. Losing one's memories and experiences is a major source of anxiety, especially for elderly people and patients undergoing end-of-life care. Furthermore, with the evolution of digital technology in modern society, individuals are increasingly seeking a permanent existence, but no technology has been established to date to achieve this. [Means for solving the problem]

[0005] This system includes a means for authenticating a user using biometric recognition technology, a means for scanning the user's brainwaves and acquiring the brainwave data, a means for analyzing the acquired brainwave data and converting consciousness and memory information into digital data, a means for storing the converted digital data in cyberspace, a means for converting the digital data stored in cyberspace into an accessible and downloadable format upon request by the user, and a means for reproducing the downloaded data using a generative AI model. This system allows users to reflect on their memories and experiences and to exist in a new form in cyberspace even after death. As a result, it significantly reduces people's anxiety about memory loss and provides a new means for perpetuating the value of an individual's existence.

[0006] "Biometric recognition technology" is a technology that identifies individuals using biological characteristics, such as fingerprint authentication or facial recognition.

[0007] "User" refers to an individual who uses this system and wishes to digitize and preserve their memories and consciousness.

[0008] An "EEG scan" is the process of measuring electrical activity (brain waves) generated by the brain and obtaining that data.

[0009] "Electroencephalogram data" refers to electrical signal data obtained by an electroencephalogram scan, and includes information about the user's thoughts and memories.

[0010] "Analysis" is the process of processing acquired data and extracting and understanding the necessary information.

[0011] "Consciousness" is a general term for the sensations, thoughts, and perceptions that a user is currently experiencing.

[0012] "Memory" is the ability of a user to store and recall events and information that they have experienced in the past.

[0013] "Digital data" is a general term for data in a format that can be processed and stored electronically.

[0014] "Cyberspace" is a virtual realm that exists on the Internet where data can be stored, managed, and accessed.

[0015] "Storage" is the act of keeping data in a fixed location so that it can be retrieved as needed.

[0016] "Access" is the act of having the authority to read, retrieve, and use stored data.

[0017] "Downloading" is the process of transferring data stored in cyberspace to a user's device and making it available for use.

[0018] A "generative AI model" is an algorithm that uses artificial intelligence techniques to generate specific formats and content based on data.

[0019] "Playback" is the act of providing information to a user in the form of visual or auditory information based on stored data. [Brief explanation of the drawings]

[0020] [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

[0021] 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.

[0022] First, the terms used in the following description will be explained.

[0023] 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).

[0024] 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.

[0025] 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.

[0026] 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.

[0027] 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."

[0028] [First embodiment]

[0029] FIG. 1 shows an example of the configuration of a data processing system 10 according to the first embodiment.

[0030] 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.

[0031] 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).

[0032] 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.

[0033] 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.

[0034] 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.

[0035] 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.

[0036] FIG. 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0037] 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.

[0038] 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.

[0039] 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.

[0040] 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."

[0041] A system for carrying out the present invention is a system that digitizes and stores a user's consciousness and memories, and makes them permanently accessible in cyberspace. Specific embodiments will be described below.

[0042] System configuration overview

[0043] The system consists of the following main components:

[0044] 1. User authentication module

[0045] 2. EEG Scanning Device

[0046] 3. Data Analysis Module

[0047] 4. Data storage module

[0048] 5. Data Access / Download Module

[0049] 6. Memory Replay Module Using Generative AI

[0050] These components work together to digitize the user's consciousness and memories, and store and play them back in cyberspace.

[0051] User Authentication Module

[0052] The terminal uses biometric recognition technology to authenticate the user when the system starts up. The user can log in to the system using fingerprint or facial recognition. The server verifies this and, if authentication is successful, allows further processing.

[0053] Brain wave scanning device

[0054] The device asks the user to wear an EEG scanner, which collects real-time brainwave data. The user concentrates on a specific memory or thought, which is then reflected in the scanner as brainwave data.

[0055] Data Analysis Module

[0056] The server receives and analyzes the EEG data sent from the device. Using a specific algorithm, the server extracts information about the user's consciousness and memory from the EEG data. This information is then converted and stored as digital data.

[0057] Data Storage Module

[0058] The server stores the analyzed digital data in cyberspace, and during the storage process, the data is distributed across multiple backup servers to ensure data redundancy.

[0059] Data Access / Download Module

[0060] When a user wants to review the stored data, they access cyberspace via their device and request the specified data. The server processes the request, searches for the relevant data, converts it into a downloadable format, and sends it to the device.

[0061] Memory regeneration module using generative AI

[0062] The device then uses a generative AI model to play back the downloaded memory data, allowing the user to experience the memory visually and audibly. The real-time information displayed is monitored by a server, which makes corrections as needed.

[0063] Specific examples

[0064] A specific usage scenario of the system is described below.

[0065] User Authentication

[0066] Device: "Starting recognition mode"

[0067] The user places their fingerprint on the scanner.

[0068] The device sends the fingerprint data to the server.

[0069] The server compares the fingerprint data with the database and returns the authentication result.

[0070] Terminal: "Authentication complete. Welcome, user."

[0071] EEG scan begins

[0072] Terminal: "Starting memory scan"

[0073] The user wears an EEG scanner and imagines their college graduation ceremony.

[0074] The device receives the EEG data from the scanner and transmits it to a server.

[0075] The server analyzes the data and extracts the stored information.

[0076] Data conversion and storage

[0077] The server converts the analyzed data into a digital format.

[0078] The server stores the converted data on a backup server.

[0079] Data Access

[0080] User: "Replay my graduation memory."

[0081] The terminal sends this request to the server.

[0082] The server searches for the corresponding stored data and transmits it to the terminal.

[0083] The terminal converts the data into a playable format.

[0084] memory replay

[0085] Terminal: "Memories will be recreated using generative AI."

[0086] The device activates the generative AI model and plays a hologram of the graduation scene.

[0087] The user experiences the replayed memory.

[0088] In this way, this system digitizes the user's consciousness and memories, making them available for storage, access, and playback in cyberspace, thereby guaranteeing the perpetual existence of individuals.

[0089] The processing flow will be explained below.

[0090] Step 1:

[0091] The device boots up and presents the user with a login screen, where the user attempts to access the system using biometric authentication (e.g., fingerprint or facial recognition).

[0092] Step 2:

[0093] The user enters their fingerprint into a biometric device or faces a face recognition camera, which captures the authentication data.

[0094] Step 3:

[0095] The device sends the acquired biometric authentication data to the server, which compares the received data with a database and, if authentication is successful, grants the user permission to log in.

[0096] Step 4:

[0097] The terminal notifies the user of the successful authentication result. After the user has logged in, they can proceed to the next step.

[0098] Step 5:

[0099] The device instructs the user to wear an EEG scanner, which the user then wears and concentrates on a specific memory or thought.

[0100] Step 6:

[0101] The device receives data in real time from the EEG scanner, acquires EEG data, and immediately transmits the acquired EEG data to a server.

[0102] Step 7:

[0103] The server analyzes the received brainwave data and extracts the user's consciousness and memory information, which is then converted into digital data.

[0104] Step 8:

[0105] The server organizes the converted digital data and prepares it for storage. The data is stored across multiple backup servers to ensure redundancy.

[0106] Step 9:

[0107] The server notifies the terminal that the save is complete, and the user confirms that their digital data has been safely saved.

[0108] Step 10:

[0109] When a user attempts to access the stored data, the user requests access to cyberspace from the terminal.

[0110] Step 11:

[0111] The server receives an access request from the user and searches for the specified stored data. If the corresponding data is found, the server sends it to the terminal.

[0112] Step 12:

[0113] The terminal converts the received data into a playable format and prepares it for presentation to the user.

[0114] Step 13:

[0115] The device runs a generative AI model and processes the playback data, which is then used as visuals and audio and presented to the user.

[0116] Step 14:

[0117] The user experiences memories reproduced by the generative AI. The device works in conjunction with the server to monitor and correct the data in real time during playback to maintain its integrity and quality.

[0118] Step 15:

[0119] The device will notify the user that the storage recovery is complete and the process will end, allowing the user to start a new storage scan or save process.

[0120] Example 1

[0121] 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."

[0122] In recent years, there has been a demand for technology that can store an individual's consciousness and memories as digital data and access and replay them in cyberspace. However, achieving this requires complex processes, such as user authentication, scanning and analyzing EEG data, storing and accessing the data, and replaying it using a generative AI model. Previous technology lacked a system that could handle these processes in an integrated manner, leaving users lacking the means to properly manage and replay their consciousness and memories.

[0123] 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.

[0124] In this invention, the server includes means for authenticating a user using biometric authentication technology, means for scanning the user's brainwaves and acquiring the brainwave data, means for analyzing the acquired brainwave data and converting consciousness and memory information into digital data, means for storing the converted digital data in cyberspace, means for converting the digital data stored in cyberspace into a format that can be accessed and downloaded upon request by the user, means for replaying the downloaded data using a generative AI model, and means for monitoring the replayed memory data in real time and making corrections as necessary, thereby enabling the user's consciousness and memories to be digitized and permanently stored, accessed, and replayed in cyberspace.

[0125] "Biometric authentication technology" is a technology that identifies individuals using specific physical characteristics of the user, such as fingerprint authentication or facial authentication.

[0126] "Scanning brainwaves" is the process of acquiring a user's brainwave signals using sensors.

[0127] "Electroencephalogram data" refers to data that is a digital record of scanned electroencephalogram signals.

[0128] "Conversion to digital data" refers to the process of analyzing EEG data, which is an analog signal, and converting it into digital information.

[0129] "Cyberspace storage" is the process of storing converted digital data in data storage on the Internet.

[0130] "Converting to an accessible and downloadable format" is the process of converting stored digital data into a format that can be played or viewed by a user.

[0131] A "generative AI model" is a technology that uses a pre-trained artificial intelligence model to reproduce a user's memories and consciousness as visual or audio data.

[0132] "Monitoring stored data reproduced in real time" refers to the process of monitoring stored data being reproduced in real time and correcting its contents as necessary.

[0133] "Correction" is the process of correcting errors or inconsistencies that occur during playback of stored data.

[0134] "Means" is a term that refers to a device or method used to achieve a particular purpose.

[0135] MODE FOR CARRYING OUT THE INVENTION

[0136] A system for carrying out this invention digitizes and stores a user's consciousness and memories, making them permanently accessible in cyberspace. A specific embodiment of this system will be described below.

[0137] System Configuration

[0138] The system consists of the following main components:

[0139] 1. User authentication module

[0140] 2. EEG Scanning Device

[0141] 3. Data Analysis Module

[0142] 4. Data storage module

[0143] 5. Data Access / Download Module

[0144] 6. Memory Replay Module Using Generative AI

[0145] User Authentication Module

[0146] The terminal uses biometric authentication technology to authenticate the user when the system starts up. Common biometric authentication technologies include fingerprint authentication and facial authentication. The user performs these biometric authentications to log in to the system. The server receives the authentication data (e.g., fingerprint data or facial image data), compares it with a registered database, and returns the authentication result to the terminal.

[0147] Brain wave scanning device

[0148] The device prompts the user to wear an EEG scanner (e.g., a specific headset device) and collects real-time brainwave data. The user concentrates on a specific memory or thought, which is reflected in the scanner as brainwave data. The device then transmits this data to a server.

[0149] Data Analysis Module

[0150] The server analyzes the EEG data sent from the device using specific algorithms (e.g., FFT analysis or machine learning algorithms). This analysis extracts the user's consciousness and memory information, which is then converted into digital data. Frameworks such as TENSORFLOW (registered trademark) and PyTorch are often used for data analysis.

[0151] Data Storage Module

[0152] The server stores the analyzed digital data in cyberspace. The stored data is distributed across multiple backup servers to ensure redundancy. For example, by using a cloud storage service, the reliability and availability of the data is ensured.

[0153] Data Access / Download Module

[0154] When a user wants to look back at the stored data, they can access cyberspace via their device and request the specified data. The server receives the request, searches for the relevant data, converts it into a downloadable format, and sends it to the device.

[0155] Memory regeneration module using generative AI

[0156] The device uses a generative AI model to play back memory data downloaded from the server, allowing the user to experience memories through visuals and audio. The generative AI model uses pre-trained large-scale language models (e.g., the GPT series) and image generation models. The information played back in real time is monitored by the server, and corrections are made as necessary.

[0157] Specific examples

[0158] Below are some specific scenarios in which the system can be used:

[0159] 1. User Authentication

[0160] Device: "Starting recognition mode" is displayed.

[0161] The user places their fingerprint on the scanner.

[0162] The device sends the fingerprint data to the server.

[0163] The server checks the database and displays the message "Authentication complete. Welcome, user."

[0164] 2. Starting the EEG scan

[0165] Terminal: "Starting memory scan" is displayed.

[0166] The user wears an EEG scanner and recalls a specific memory (e.g., a college graduation ceremony).

[0167] The device receives the brainwave data from the scanner and transmits it to the server.

[0168] 3. Data Analysis and Storage

[0169] The server analyzes the received brainwave data and converts it into digital data.

[0170] The server stores the converted data in a distributed manner across backup servers.

[0171] 4. Data Access and Memory Replay

[0172] A user requests, "Play my graduation memory."

[0173] The terminal sends a request to the server.

[0174] The server searches for the corresponding stored data and transmits it to the terminal.

[0175] The device uses a generative AI model to play a graduation scene.

[0176] The user experiences the replayed memory.

[0177] Prompt Sentence Examples

[0178] "Replay your college graduation memories."

[0179] "Show us a visual memory of your family trip."

[0180] In this way, this system digitizes the user's consciousness and memories, making them available for storage, access, and playback in cyberspace.

[0181] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0182] Step 1: User authentication

[0183] The terminal starts user authentication when the system is started.

[0184] Input: The user performs fingerprint or facial recognition.

[0185] Operation:

[0186] 1. The device captures biometric data using a fingerprint scanner or camera.

[0187] 2. The device sends the acquired biometric data to the server.

[0188] 3. The server checks the authentication data stored in a database.

[0189] Output: The server returns the authentication result to the terminal, and if authentication is successful, access to the system is permitted.

[0190] Specific operation: For example, the server displays "Authentication completed. Welcome, user" on the terminal.

[0191] Step 2: Start the EEG scan

[0192] The device prompts the user to wear an EEG scanner and collects EEG data in real time.

[0193] Input: User wears an EEG scanner and focuses on a specific memory or thought.

[0194] Operation:

[0195] 1. The device collects EEG data through a scanner.

[0196] 2. The device transmits the collected brainwave data to the server in real time.

[0197] Output: The device sends the collected EEG data to the server.

[0198] Specific actions: For example, the device will display "Starting memory scan."

[0199] Step 3: Data analysis

[0200] The server analyzes the brain wave data sent from the terminal.

[0201] Input: Real-time EEG data sent from the device.

[0202] Operation:

[0203] 1. The server analyzes the EEG data using a specific algorithm (e.g., FFT analysis).

[0204] 2. The server extracts consciousness and memory information from the analysis results and converts it into digital data.

[0205] Output: Consciousness and memory information converted into digital form.

[0206] Specific behavior: For example, the server displays a spinning icon during the analysis process.

[0207] Step 4: Save Data

[0208] The server stores the analyzed digital data in cyberspace.

[0209] Input: Analyzed digital data.

[0210] Operation:

[0211] 1. The server stores digital data across multiple backup servers.

[0212] 2. The server takes steps to ensure data redundancy (e.g., set up a RAID configuration).

[0213] Output: Digital data stored in cyberspace.

[0214] Specific action: For example, the server logs "Data stored securely."

[0215] Step 5: Data Access Request

[0216] When a user wishes to review the stored data, the user makes a request via the terminal.

[0217] Input: The user enters a prompt statement requesting specific stored data.

[0218] Operation:

[0219] 1. The device sends the user's request to the server.

[0220] 2. The server searches for the corresponding stored data.

[0221] Output: Obtaining the relevant stored data.

[0222] Specific operation: For example, the user inputs "Please play the memory of the graduation ceremony."

[0223] Step 6: Download the stored data

[0224] The server receives the request and transmits the specified stored data to the terminal.

[0225] Input: A request for specific stored data.

[0226] Operation:

[0227] 1. The server searches and retrieves the relevant stored data from the database.

[0228] 2. The server converts the stored data into a downloadable format and sends it to the device.

[0229] Output: Stored data converted into a downloadable format.

[0230] Specific behavior: For example, the server displays "Sending data...".

[0231] Step 7: Memory Replay

[0232] The device reproduces the transmitted stored data using the generative AI model.

[0233] Input: Downloaded memory data.

[0234] Operation:

[0235] 1. The device inputs stored data into a generative AI model (e.g., GPT-4 (registered trademark)).

[0236] 2. A generative AI model generates visual and audio data to replay memories.

[0237] 3. The server monitors the playback in real time and makes corrections as needed.

[0238] Output: Visual and audio data of the replayed memory.

[0239] Specific operation: For example, the device displays "Memories will be reproduced using generative AI," and the user experiences the memories reproduced in a hologram.

[0240] Following this step, the system will digitize the user's consciousness and memories, allowing them to be stored, accessed, and replayed in cyberspace.

[0241] (Application example 1)

[0242] 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."

[0243] In the current shopping experience, it is difficult for users to easily recall their past purchasing history and experiences, making it difficult to receive personalized advice and suggestions. Shopping in brick-and-mortar stores, in particular, is limited in the information that can be obtained at one time, resulting in a lack of effective means to improve user satisfaction. Given this situation, a personal shopping assistant that utilizes a user's past purchasing experiences could improve the quality of the shopping experience.

[0244] 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.

[0245] In this invention, the server includes means for authenticating a user using biometric recognition technology, means for scanning the user's brainwaves and acquiring the brainwave data, means for analyzing the acquired brainwave data and converting consciousness and memory information into digital data, means for storing the converted digital data in cyberspace, means for converting the digital data stored in cyberspace into an accessible and downloadable format upon request from the user, and means for reproducing the downloaded data using a generative AI model and reproducing the stored data visually and audibly based on the user's purchasing history and preferences, thereby enabling personalized suggestions and advice based on the user's individual preferences and past purchasing history.

[0246] "Biometric recognition technology" refers to technology that uses biological information to identify individuals, such as fingerprint authentication or facial authentication.

[0247] "User authentication" is the process of verifying a user's identity when accessing a system.

[0248] An "EEG scan" is the process of measuring a user's brain waves and acquiring that data.

[0249] "EEG data" is digital data indicating brain activity obtained by an EEG scan.

[0250] "Data analysis" is a means of processing acquired EEG data and extracting consciousness and memory information.

[0251] "Digital data" refers to data that is an electronic representation of analyzed consciousness and memory information.

[0252] "Cyberspace" refers to a virtual digital environment such as the Internet.

[0253] A "backup server" is a redundant server for duplicating and storing digital data.

[0254] "Access" refers to having the authority to read, write, and manipulate stored digital data.

[0255] "Downloading" means taking data stored on a server into a user's terminal.

[0256] A "generative AI model" is an artificial intelligence technology that generates new data (images, text, etc.) based on input data.

[0257] "Visual" is a means of visually presenting information.

[0258] "Speech" is a means of conveying information auditorily.

[0259] A "personal shopping assistant" is a support system that provides suggestions and advice based on a user's individual preferences and past purchasing history.

[0260] The system for implementing this invention is a system that digitizes and stores a user's consciousness and memories, making them permanently accessible in cyberspace. This system is composed of the following main components:

[0261] System Configuration

[0262] 1. User authentication module

[0263] 2. EEG Scanning Device

[0264] 3. Data Analysis Module

[0265] 4. Data storage module

[0266] 5. Data Access / Download Module

[0267] 6. Memory Replay Module Using Generative AI

[0268] These components work together to digitize the user's consciousness and memories, and store and play them back in cyberspace.

[0269] User Authentication Module

[0270] The device uses biometric technology to authenticate users when they start up the system. Users can log in to the system using facial recognition. Once authentication is complete, access is granted.

[0271] Brain wave scanning device

[0272] Using an EEG scanner (e.g., Neurosky, EMOTIV), the user acquires brainwave data, which contains memory information related to a particular shopping experience.

[0273] Data Analysis Module

[0274] The server receives the EEG data sent from the device and analyzes it using an EEG analysis library such as EEGLab, which extracts the user's memory information and converts it into digital data.

[0275] Data Storage Module

[0276] The server stores the converted digital data in cyberspace. To ensure data redundancy, the data is stored across multiple backup servers.

[0277] Data Access / Download Module

[0278] When a user wants to play back the stored data, they access cyberspace via their device and request the data they need. The server processes the request, retrieves the data, converts it into a downloadable format, and sends it to the device.

[0279] Memory regeneration module using generative AI

[0280] The device then uses a generative AI model (e.g., ChatGPT (registered trademark), DALL·E) to play back the downloaded memory data, allowing the user to experience the memory visually and audibly. The generative AI model used has the ability to generate new data based on input data, and accurate memory playback is achieved by using prompt sentences.

[0281] Specific examples

[0282] Example prompt sentence:

[0283] "Please replay User A's Christmas shopping experience in December 2021, focusing specifically on the clothing section."

[0284] The prompts allow the generative AI model to recreate the user's memories visually and audibly, bringing past shopping experiences to life, allowing the user to receive more personalized advice and suggestions as they shop.

[0285] In this way, the system leverages the user's memory to enrich the in-store shopping experience and provide assistance based on individual preferences and past purchasing history.

[0286] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0287] Step 1:

[0288] User Authentication

[0289] Input: User's facial recognition data

[0290] Specific operation: The device's camera is used to scan the user's face and the data is sent to the server.

[0291] Data processing: The server uses biometric recognition technology to match the facial recognition data with the registered data in the database.

[0292] Output: The authentication result is returned to the terminal. If authentication is successful, access to the system is granted.

[0293] Step 2:

[0294] EEG scan

[0295] Input: User's brainwave data

[0296] How it works: The user wears an EEG scanner and recalls a specific memory. The device collects real-time EEG data from the scanner.

[0297] Data processing: The acquired EEG data is sent to the server.

[0298] Output: EEG data is sent to the server.

[0299] Step 3:

[0300] Data analysis

[0301] Input: EEG data sent to the server

[0302] Specific operation: The server uses an EEG analysis library such as EEGLab to analyze the transmitted EEG data.

[0303] Data processing: Extract consciousness and memory information from EEG data and convert it into digital data.

[0304] Output: Digital data is produced.

[0305] Step 4:

[0306] Data storage

[0307] Input: Converted digital data

[0308] Specific operation: The server stores the converted digital data in cyberspace.

[0309] Data processing: To ensure data redundancy, data is stored across multiple backup servers.

[0310] Output: The saving process is complete and the data is safely stored.

[0311] Step 5:

[0312] Data Request

[0313] Input: User's request to play back stored data

[0314] Specific operation: The user requests playback of stored data via the terminal.

[0315] Data Processing: The server retrieves the stored data based on the request and converts it into a downloadable format.

[0316] Output: Data is sent to the terminal.

[0317] Step 6:

[0318] memory replay

[0319] Input: Downloaded memory data

[0320] Specific operation: The device uses a generative AI model (e.g., ChatGPT or DALL·E) to regenerate stored data.

[0321] Data processing: Based on the prompt, the generative AI model recreates the user's memory visually and audibly.

[0322] Output: A specific memory playback such as "Play back User A's Christmas shopping experience in December 2021, focusing specifically on the clothing section."

[0323] Through the above steps, the user can access the system and experience his or her past purchasing experiences in a personalized manner.

[0324] 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.

[0325] A system for implementing this invention digitizes and stores a user's consciousness and memories, making them permanently accessible in cyberspace. Furthermore, this system incorporates an emotion engine that recognizes and reflects the user's emotions. Specific embodiments are described in detail below.

[0326] System configuration overview

[0327] The system consists of the following main components:

[0328] 1. User authentication module

[0329] 2. EEG Scanning Device

[0330] 3. Data Analysis Module

[0331] 4. Data storage module

[0332] 5. Data Access / Download Module

[0333] 6. Memory Replay Module Using Generative AI

[0334] 7. Emotion Engine

[0335] These components work together to digitize the user's consciousness, memories, and emotions, and store and reproduce them in cyberspace.

[0336] User Authentication Module

[0337] The terminal uses biometric recognition technology to authenticate the user when the system starts up. The user can log in to the system using fingerprint or facial recognition. The server verifies this and, if authentication is successful, allows further processing.

[0338] Brain wave scanning device

[0339] The device asks the user to wear an EEG scanner, which collects real-time brainwave data. The user concentrates on a specific memory or thought, which is then reflected in the scanner as brainwave data.

[0340] Emotion Engine

[0341] The device monitors and acquires information about the user's emotions in real time using an emotion engine, which analyzes emotions from the user's facial expressions, voice, and other biometric data.

[0342] Data Analysis Module

[0343] The server receives and analyzes the EEG and emotional data sent from the device. Using a specific algorithm, the server extracts the user's consciousness and memory information from the EEG data and integrates it with emotional information. This information is then converted and stored as digital data.

[0344] Data Storage Module

[0345] The server stores the analyzed digital data in cyberspace, and during the storage process, the data is distributed across multiple backup servers to ensure data redundancy.

[0346] Data Access / Download Module

[0347] When a user wants to review the stored data, they access cyberspace via their device and request the specified data. The server processes the request, searches for the relevant data, converts it into a downloadable format, and sends it to the device.

[0348] Memory regeneration module using generative AI

[0349] The device then uses a generative AI model to play back the downloaded memory data, allowing the user to experience the memory visually and audibly. The real-time information displayed is monitored by a server, which makes corrections as needed.

[0350] Specific examples

[0351] A specific usage scenario of the system is described below.

[0352] User Authentication

[0353] Device: "Starting recognition mode"

[0354] The user places their fingerprint on the scanner.

[0355] The device sends the fingerprint data to the server.

[0356] The server compares the fingerprint data with the database and returns the authentication result.

[0357] Terminal: "Authentication complete. Welcome, user."

[0358] EEG scanning and emotion recognition begins

[0359] Terminal: "Starting memory scan"

[0360] The user wears an EEG scanner and imagines their college graduation ceremony.

[0361] The device receives the EEG data from the scanner and transmits it to a server.

[0362] At the same time, the emotion engine acquires emotion data in real time from the user's facial expressions and voice.

[0363] The server analyzes the brainwave data and emotional data, and integrates and extracts memory information and emotional information.

[0364] Data conversion and storage

[0365] The server converts the analyzed data into a digital format.

[0366] The server stores the converted data on a backup server.

[0367] Data Access

[0368] User: "Replay my graduation memory."

[0369] The terminal sends this request to the server.

[0370] The server searches for the corresponding stored data and transmits it to the terminal.

[0371] The terminal converts the data into a playable format.

[0372] memory replay

[0373] Terminal: "Memories will be recreated using generative AI."

[0374] The device activates the generative AI model and plays a hologram of the graduation scene.

[0375] The user experiences the replayed memory.

[0376] The data being played back reflects the user's emotional information, enhancing the sense of realism.

[0377] In this way, this system digitizes the user's consciousness, memories, and even emotions, and makes them available for storage, access, and playback in cyberspace, thereby guaranteeing the permanent existence of individuals.

[0378] The processing flow will be explained below.

[0379] Step 1:

[0380] The device boots up and presents the user with a login screen, where the user attempts to access the system using biometric authentication (e.g., fingerprint or facial recognition).

[0381] Step 2:

[0382] The user enters their fingerprint into a biometric device or faces a face recognition camera, which captures the authentication data.

[0383] Step 3:

[0384] The device sends the acquired biometric authentication data to the server, which compares the received data with a database and, if authentication is successful, grants the user permission to log in.

[0385] Step 4:

[0386] The terminal notifies the user of the successful authentication result. After the user has logged in, they can proceed to the next step.

[0387] Step 5:

[0388] The device instructs the user to wear an EEG scanner, which the user then wears and concentrates on a specific memory or thought.

[0389] Step 6:

[0390] The device receives data in real time from the EEG scanner, acquires EEG data, and immediately transmits the acquired EEG data to a server.

[0391] Step 7:

[0392] The device uses an emotion engine to obtain emotional data in real time from the user's facial expressions, voice, and biometric signals.

[0393] Step 8:

[0394] The server analyzes the received brainwave and emotional data, extracts the user's consciousness and memory information, and integrates the emotional information. This integrated information is then converted into digital data.

[0395] Step 9:

[0396] The server organizes the converted digital data and prepares it for storage. The data is stored across multiple backup servers to ensure redundancy.

[0397] Step 10:

[0398] The server notifies the terminal that the save is complete, and the user confirms that their digital data has been safely saved.

[0399] Step 11:

[0400] When a user attempts to access the stored data, the user requests access to cyberspace from the terminal.

[0401] Step 12:

[0402] The server receives an access request from the user and searches for the specified stored data and emotion information. If the server finds the corresponding data, it sends it to the terminal.

[0403] Step 13:

[0404] The terminal converts the received data into a playable format and prepares it for presentation to the user.

[0405] Step 14:

[0406] The device runs a generative AI model and processes the playback data, which is then used as visuals and audio and presented to the user.

[0407] Step 15:

[0408] The user experiences memories reproduced by the generative AI. The reproduced data also reflects the user's emotional information, providing a more immersive experience. The device works in conjunction with the server to monitor and correct the data in real time to maintain the integrity and quality of the data being reproduced.

[0409] Step 16:

[0410] The device will notify the user that the storage recovery is complete and the process will end, allowing the user to start a new storage scan or save process.

[0411] Example 2

[0412] 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."

[0413] Despite advances in modern information technology, there are currently no systems that can accurately digitize a user's consciousness, memories, and even emotions, permanently store them, and then replay them as needed. Conventional systems acquire and store a user's consciousness and memory information in a fragmented manner, without integrating it with emotional information, making it impossible to fully reproduce the user's actual experience. Furthermore, methods for storing digitized data often lack redundancy and security. Therefore, there is a need for a system that can comprehensively digitize a user's consciousness, memories, and emotions, and store and replay them securely and permanently.

[0414] The specific processing by the specific 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 authenticating a user using biometric recognition technology, means for scanning the user's brain waves and acquiring the brain wave data, means for analyzing the acquired brain wave data and converting consciousness and memory information into digital data, means for acquiring the user's emotions through facial expressions and voice and generating emotional data, means for integrating the converted consciousness and memory information with the emotional data and storing it in cyberspace as digital data, means for converting the digital data stored in cyberspace into a format that can be accessed and downloaded upon request by the user, and means for reproducing the downloaded data using a generative AI model. This makes it possible to digitize the user's consciousness, memories, and emotions in an integrated manner and store and reproduce them securely and permanently.

[0415] "Biometric recognition technology" is a technology that authenticates individuals using biometric information such as a user's fingerprint, face, or iris.

[0416] "User authentication" is the process of using biometric technology to verify a user's identity and grant them access to a system.

[0417] "EEG scanning" refers to devices and technologies that record and capture a user's brain waves in real time.

[0418] "Electroencephalogram data" is digital information representing electrical activity acquired by an electroencephalogram scanning device.

[0419] "Consciousness and memory information" refers to data about the user's thoughts and memories that is analyzed and extracted from the user's brain wave data.

[0420] "Converting to digital data" refers to the process of converting electroencephalogram data and emotion data acquired as analog signals into a digital format.

[0421] "Emotion data" is digital information that indicates the emotional state of the user analyzed from facial expressions, voice, etc.

[0422] "Synthesis" is the process of combining different types of data (e.g., consciousness and memory information with emotional data) into a single dataset.

[0423] "Cyberspace" refers to virtual spaces where digital data is stored, such as the Internet and cloud storage.

[0424] "Accessible and downloadable format" refers to the conversion of digital data into a format that is easily accessible and retrievable by users.

[0425] A "generative AI model" is a type of artificial intelligence that refers to an algorithm that generates and reproduces new information and content from input data.

[0426] MODE FOR CARRYING OUT THE INVENTION

[0427] This invention is a system that digitizes a user's consciousness, memories, and emotions and stores, accesses, and reproduces them in cyberspace. Using the hardware and software shown below, the system acquires the user's consciousness, memories, and emotions and processes them as digital data.

[0428] System Configuration

[0429] The system consists of the following main components:

[0430] 1. User authentication module

[0431] 2. EEG Scanning Device

[0432] 3. Data Analysis Module

[0433] 4. Data storage module

[0434] 5. Data Access / Download Module

[0435] 6. Memory Replay Module Using Generative AI

[0436] 7. Emotion Engine

[0437] User Authentication Module

[0438] The device performs user authentication using, for example, fingerprint or facial recognition. The user places their fingerprint on a scanner or stands in front of a facial recognition camera. The device then sends this biometric data to a server, which compares it with a database and returns the authentication result.

[0439] Brain wave scanning device

[0440] The device asks the user to wear an EEG scanner, which collects real-time brainwave data. The user concentrates on a specific memory or thought, which is then collected as brainwave data. EEG scanning can be done using, for example, electroencephalography (EEG).

[0441] Emotion Engine

[0442] The device uses an emotion engine to acquire emotion data from the user's facial expressions and voice. This is done using input devices such as a camera and microphone. The acquired emotion data is also sent to the server in real time.

[0443] Data Analysis Module

[0444] The server receives the EEG and emotional data sent from the device and analyzes it using a specific algorithm. The analyzed data is converted into the user's consciousness and memory information, and emotional information is integrated. For example, analyzing the data using a machine learning model makes it possible to extract highly accurate consciousness and memory information.

[0445] Data Storage Module

[0446] The server stores the analyzed digital data in cyberspace. To ensure data redundancy, the data is stored across multiple backup servers. For example, a distributed database (such as Hadoop or Cassandra) can be used here.

[0447] Data Access / Download Module

[0448] When a user wants to look back at the stored memory data, they access cyberspace via their device and request the specified memory data. The server receives the request, searches for the corresponding data, converts it into a downloadable format, and sends it to the device.

[0449] Memory regeneration module using generative AI

[0450] The device then uses a generative AI model to play back the downloaded memory data, allowing the user to experience the memory visually and aurally. For example, playback can be performed using a VR device or a holographic display. Emotional information is reflected in the data being played, enhancing the sense of realism.

[0451] Specific examples

[0452] The following are specific usage scenarios for the system:

[0453] User Authentication

[0454] Device: "Starting recognition mode"

[0455] The user places their fingerprint on the scanner.

[0456] The device sends the fingerprint data to the server.

[0457] The server compares the fingerprint data with the database and returns the authentication result.

[0458] Terminal: "Authentication complete. Welcome, user."

[0459] EEG scanning and emotion recognition begins

[0460] Terminal: "Starting memory scan"

[0461] The user wears an EEG scanner and imagines their college graduation ceremony.

[0462] The device receives EEG data from the scanner and transmits it to the server, where an emotion engine simultaneously captures emotion data from the user's facial expressions and voice in real time.

[0463] The server analyzes the brainwave data and emotional data, and integrates and extracts memory information and emotional information.

[0464] Data conversion and storage

[0465] The server converts the analyzed data into a digital format.

[0466] The server stores the converted data on a backup server.

[0467] Data Access

[0468] User: "Replay my graduation memory."

[0469] The terminal sends this request to the server.

[0470] The server searches for the corresponding stored data and transmits it to the terminal.

[0471] The terminal converts the data into a playable format.

[0472] memory replay

[0473] Terminal: "Memories will be recreated using generative AI."

[0474] The device activates the generative AI model and plays a hologram of the graduation scene.

[0475] The user experiences the replayed memory. The data being replayed reflects the user's emotional information, enhancing the sense of realism.

[0476] This system makes it possible to digitize the user's consciousness, memories, and even emotions, and store, access, and replay them in cyberspace, thereby ensuring the perpetual existence of the individual.

[0477] Examples of prompt statements

[0478] "Replay your college graduation memories."

[0479] This prompt triggers the process of data access and storage recovery.

[0480] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0481] Step 1:

[0482] User Authentication

[0483] Input: The device captures fingerprint or facial recognition information. The user places their fingerprint on the scanner or stands in front of the facial recognition camera.

[0484] Specific operation: The device uses biometric recognition technology to acquire the user's fingerprint and facial data, and then transmits the acquired data to the server using a secure communication protocol (e.g., HTTPS).

[0485] Output: The server checks the database and returns the authentication result. The terminal displays the message "Authentication completed."

[0486] Step 2:

[0487] EEG scan begins

[0488] Input: Pre-authentication information obtained by the device from the user. The user wears an EEG scanner.

[0489] Specific operation: The device prompts the user to wear an EEG scanner, and acquires EEG data in real time through the scanner.

[0490] Output: The acquired EEG data is stored on the terminal, and when a certain amount is accumulated, it is sent to the server.

[0491] Step 3:

[0492] Emotion data acquisition

[0493] Input: The device collects real-time biometric data (facial expressions, voice) and brainwave data. The user focuses on a specific memory or thought.

[0494] Specific operation: The device activates the emotion engine and analyzes emotion data from the user's facial expressions and voice. Data is collected using the camera and microphone.

[0495] Output: Emotion data is sent to the server along with EEG data.

[0496] Step 4:

[0497] Data analysis and integration

[0498] Input: EEG data and emotion data received by the server.

[0499] Specific operation: The server analyzes EEG data and emotion data using specific algorithms. It uses machine learning models to extract consciousness and memory information and integrate it with emotion information.

[0500] Output: Convert the analysis results into digital data.

[0501] Step 5:

[0502] Data storage

[0503] Input: Consciousness, memory information, and emotion data analyzed on the server.

[0504] Specific operation: The server stores this data in a distributed database. To increase data redundancy, the data is stored in multiple backup servers.

[0505] Output: Digital data is stored securely and backed up.

[0506] Step 6:

[0507] Data Access and Download

[0508] Input: A data access request from the device (e.g., a prompt "Please play my graduation memory").

[0509] Specific operation: The server receives the request, searches the database for the corresponding stored data, converts the found data into a downloadable format, and sends it to the device.

[0510] Output: The terminal obtains the stored data specified by the user and converts it into a playable format.

[0511] Step 7:

[0512] memory replay

[0513] Input: Stored data downloaded to the terminal.

[0514] Specific operation: The device activates the generative AI model and reproduces the memory data through sight and sound. The visual and audio of the memory are reproduced using a VR device or holographic display.

[0515] Output: The user experiences the replayed memory. Emotional information is reflected in the replayed data, enhancing the sense of realism.

[0516] (Application example 2)

[0517] 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."

[0518] With conventional technology, it is difficult to store a user's memories and consciousness as digital data, and the technology for integrating information, including emotions, is insufficient. As a result, there has been no system that allows users to realistically replay past memories and experiences and reflect on them, including their emotions. As a result, memory replay is limited to a simple visual and auditory experience, making it difficult to reproduce a sense of presence or emotions. To solve this problem, a system is needed that can integrate and store memories and emotions and replay them using advanced technology such as holograms.

[0519] The specific processing by the specific 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 authenticating a user using biometric recognition technology, means for scanning the user's brain waves and acquiring the brain wave data, means for analyzing the acquired brain wave data and converting consciousness and memory information into digital data, means for analyzing the acquired emotional data and integrating the information as digital data, means for storing the converted digital data in cyberspace, means for converting the digital data stored in cyberspace into an accessible and downloadable format upon request from the user, means for reproducing the downloaded data using a generative AI model and presenting it to the user in holographic form or on a visual display, and means including an emotion engine that reflects the user's emotional state in real time. This makes it possible to store the user's memories and consciousness as digital data, integrate emotional information, and then reproduce the data with a sense of realism using a hologram or a visual display.

[0520] "Biometric recognition technology" is a technology that identifies individuals using biometric information such as a user's fingerprint or facial recognition.

[0521] An "EEG scanner" is a device that reads a user's brain waves in real time and acquires brain wave data.

[0522] "Electroencephalogram data" refers to data obtained by converting electrical signals obtained from the user's electroencephalogram into digital format.

[0523] The "emotion engine" analyzes emotions from biometric data such as the user's facial expressions and voice, and reflects the user's emotional state in real time.

[0524] "Consciousness and memory information" is information about the state of consciousness and memory contents extracted from the user's electroencephalogram data.

[0525] "Means for converting into digital data" refers to a system or algorithm that analyzes the acquired brainwave data and emotional data and converts it into digital form.

[0526] "Cyberspace" refers to virtual spaces such as the Internet and the cloud, where digital data can be stored, accessed, and shared.

[0527] A "generative AI model" is an algorithm or program that uses artificial intelligence to generate stored memory data into a reproducible form.

[0528] "Hologram format" is a technology for displaying three-dimensional images and videos, which are displayed with a visually three-dimensional appearance.

[0529] "Visual display" refers to a physical screen or device for presenting visual information to a user.

[0530] A "backup server" is a server that stores replicated data to ensure data redundancy.

[0531] "Emotion information" is digital data that analyzes the user's emotional state and is acquired by the emotion engine.

[0532] MODE FOR CARRYING OUT THE INVENTION

[0533] To implement this invention, it is necessary to build a system that digitizes and stores a user's consciousness and memories and makes them permanently accessible in cyberspace. This system is composed of the following main components: a user authentication module, an EEG scanning device, a data analysis module, a data storage module, a data access / download module, a memory playback module using generative AI, and an emotion engine. These components work together to digitize a user's consciousness, memories, and emotions, and store and play them back in cyberspace.

[0534] User Authentication Module

[0535] The server authenticates the user using biometric recognition technology, such as fingerprint or facial recognition, and allows them to log in to the system. The user enters their authentication information into the terminal, and the server compares it with a database and returns the authentication result.

[0536] Brain wave scanning device

[0537] The device has the user wear an EEG scanner, which collects real-time brainwave data. When the user concentrates on a specific memory or thought, that information is reflected in the scanner as brainwave data. The device then transmits this brainwave data to a server.

[0538] Emotion Engine

[0539] The device uses an emotion engine to acquire the user's emotional data in real time. The emotion engine analyzes the user's emotions from facial expressions, voice, and other biometric data. The acquired emotional data is sent to the server along with brainwave data.

[0540] Data Analysis Module

[0541] The server receives and analyzes the EEG and emotional data sent from the device. Using a specific algorithm, the server extracts the user's consciousness and memory information from the EEG data and integrates it with emotional information. This information is then converted and stored as digital data.

[0542] Data Storage Module

[0543] The server stores the analyzed digital data in cyberspace, and during the storage process, the data is distributed across multiple backup servers to ensure data redundancy.

[0544] Data Access / Download Module

[0545] When a user wants to review the stored data, they access cyberspace via their device and request the specified data. The server processes the request, searches for the relevant data, converts it into a downloadable format, and sends it to the device.

[0546] Memory regeneration module using generative AI

[0547] The device uses a generative AI model to play back the downloaded memory data, allowing the user to experience the memory visually and aurally. The played back information is monitored by a server, and corrections are made as necessary. The generative AI model provides a sense of realism, as if the user is actually experiencing past memories.

[0548] Specific examples

[0549] User Authentication

[0550] Device: "Starting recognition mode"

[0551] The user places their fingerprint on the scanner.

[0552] The device sends the fingerprint data to the server.

[0553] The server compares the fingerprint data with the database and returns the authentication result.

[0554] Terminal: "Authentication complete. Welcome, user."

[0555] EEG scanning and emotion recognition begins

[0556] Terminal: "Starting memory scan"

[0557] The user wears an EEG scanner and imagines their college graduation ceremony.

[0558] The device receives the EEG data from the scanner and transmits it to a server.

[0559] At the same time, the emotion engine acquires emotion data in real time from the user's facial expressions and voice.

[0560] The server analyzes the brainwave data and emotional data, and integrates and extracts memory information and emotional information.

[0561] Data conversion and storage

[0562] The server converts the analyzed data into a digital format.

[0563] The server stores the converted data on a backup server.

[0564] Data Access

[0565] User: "Replay my graduation memory."

[0566] The terminal sends this request to the server.

[0567] The server searches for the corresponding stored data and transmits it to the terminal.

[0568] The terminal converts the data into a playable format.

[0569] memory replay

[0570] Terminal: "Memories will be recreated using generative AI."

[0571] The device activates the generative AI model and plays a hologram of the graduation scene.

[0572] The user experiences the replayed memory.

[0573] The data being played back reflects the user's emotional information, enhancing the sense of realism.

[0574] Prompt Sentence Examples

[0575] "Please replay user X's memory of their college graduation. The user's emotions are filled with joy and excitement. Please replay it in a holographic format to create a realistic experience."

[0576] This particular embodiment of the invention allows users to store their memories and emotions as digital data and have a realistic experience when playing it back.

[0577] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0578] Step 1: User authentication

[0579] How it works: The device prompts the user to enter authentication information (fingerprint or face).

[0580] Input: Fingerprint data or face data

[0581] Data processing / calculation: The device collects authentication information and sends it to the server, which checks it against a database and performs authentication.

[0582] Output: Authentication result (success or failure)

[0583] Step 2: Start the EEG scan

[0584] How it works: The device prompts the user to wear an EEG scanner. The user puts on the scanner and concentrates on a specific memory.

[0585] Input: User's brainwaves

[0586] Data processing / calculation: The EEG scanner acquires EEG data in real time and transmits it to the terminal.

[0587] Output: Acquired EEG data

[0588] Step 3: Collecting emotion data

[0589] Operation: The device activates the emotion engine and acquires emotion data in real time from the user's facial expressions and voice.

[0590] Input: User's facial expression data, voice data

[0591] Data processing / calculation: The emotion engine analyzes facial expression data and voice data to detect emotional states.

[0592] Output: User emotion data

[0593] Step 4: Analyze and synthesize data

[0594] Operation: The server analyzes the brain wave data and emotion data sent from the device.

[0595] Input: EEG data, emotion data

[0596] Data processing / computation: Using specific algorithms, consciousness and memory information is extracted from EEG data and emotional information is integrated.

[0597] Output: Integrated memory and emotion data

[0598] Step 5: Save Data

[0599] How it works: The server converts the analyzed data into digital form and stores it in cyberspace.

[0600] Input: Integrated memory and emotion data

[0601] Data processing / calculation: Data is digitized and stored in multiple backup servers.

[0602] Output: Stored digital data

[0603] Step 6: Data Access

[0604] Operation: A user requests playback of stored stored data, and the terminal sends the request to the server.

[0605] Input: User's memory recall request

[0606] Data processing / computation: The server processes the request, retrieves the relevant stored data, and converts it into a downloadable format.

[0607] Output: Downloaded memory data

[0608] Step 7: Memory Replay

[0609] How it works: The device uses generative AI to recreate stored data in holographic form or on a visual display.

[0610] Input: Downloaded memory data

[0611] Data processing / computation: A generative AI model analyzes stored data and generates it in a format that can be reproduced as a hologram or visual display.

[0612] Output: Reproducible stored data

[0613] Step 8: Reflect and Replay Emotions

[0614] Operation: The device acquires the user's real-time emotional information during playback and reflects it in the playback.

[0615] Input: Current user emotion data

[0616] Data processing / calculation: The emotion engine analyzes the emotional data acquired in real time and reflects it in the memory data being played back.

[0617] Output: Memory replay data reflecting emotional information

[0618] These processing steps allow users to digitize their memories and emotions in detail, allowing them to later re-experience them in a realistic way.

[0619] 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.

[0620] 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 (registered trademark) (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.

[0621] 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.

[0622] [Second embodiment]

[0623] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0624] 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.

[0625] 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).

[0626] 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.

[0627] 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.

[0628] 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).

[0629] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0630] 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.

[0631] 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.

[0632] 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.

[0633] In the smart glasses 214, the 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.

[0634] 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."

[0635] A system for carrying out the present invention is a system that digitizes and stores a user's consciousness and memories, and makes them permanently accessible in cyberspace. Specific embodiments will be described below.

[0636] System configuration overview

[0637] The system consists of the following main components:

[0638] 1. User authentication module

[0639] 2. EEG Scanning Device

[0640] 3. Data Analysis Module

[0641] 4. Data storage module

[0642] 5. Data Access / Download Module

[0643] 6. Memory Replay Module Using Generative AI

[0644] These components work together to digitize the user's consciousness and memories, and store and play them back in cyberspace.

[0645] User Authentication Module

[0646] The terminal uses biometric recognition technology to authenticate the user when the system starts up. The user can log in to the system using fingerprint or facial recognition. The server verifies this and, if authentication is successful, allows further processing.

[0647] Brain wave scanning device

[0648] The device asks the user to wear an EEG scanner, which collects real-time brainwave data. The user concentrates on a specific memory or thought, which is then reflected in the scanner as brainwave data.

[0649] Data Analysis Module

[0650] The server receives and analyzes the EEG data sent from the device. Using a specific algorithm, the server extracts information about the user's consciousness and memory from the EEG data. This information is then converted and stored as digital data.

[0651] Data Storage Module

[0652] The server stores the analyzed digital data in cyberspace, and during the storage process, the data is distributed across multiple backup servers to ensure data redundancy.

[0653] Data Access / Download Module

[0654] When a user wants to review the stored data, they access cyberspace via their device and request the specified data. The server processes the request, searches for the relevant data, converts it into a downloadable format, and sends it to the device.

[0655] Memory regeneration module using generative AI

[0656] The device then uses a generative AI model to play back the downloaded memory data, allowing the user to experience the memory visually and audibly. The real-time information displayed is monitored by a server, which makes corrections as needed.

[0657] Specific examples

[0658] A specific usage scenario of the system is described below.

[0659] User Authentication

[0660] Device: "Starting recognition mode"

[0661] The user places their fingerprint on the scanner.

[0662] The device sends the fingerprint data to the server.

[0663] The server compares the fingerprint data with the database and returns the authentication result.

[0664] Terminal: "Authentication complete. Welcome, user."

[0665] EEG scan begins

[0666] Terminal: "Starting memory scan"

[0667] The user wears an EEG scanner and imagines their college graduation ceremony.

[0668] The device receives the EEG data from the scanner and transmits it to a server.

[0669] The server analyzes the data and extracts the stored information.

[0670] Data conversion and storage

[0671] The server converts the analyzed data into a digital format.

[0672] The server stores the converted data on a backup server.

[0673] Data Access

[0674] User: "Replay my graduation memory."

[0675] The terminal sends this request to the server.

[0676] The server searches for the corresponding stored data and transmits it to the terminal.

[0677] The terminal converts the data into a playable format.

[0678] memory replay

[0679] Terminal: "Memories will be recreated using generative AI."

[0680] The device activates the generative AI model and plays a hologram of the graduation scene.

[0681] The user experiences the replayed memory.

[0682] In this way, this system digitizes the user's consciousness and memories, making them available for storage, access, and playback in cyberspace, thereby guaranteeing the perpetual existence of individuals.

[0683] The processing flow will be explained below.

[0684] Step 1:

[0685] The device boots up and presents the user with a login screen, where the user attempts to access the system using biometric authentication (e.g., fingerprint or facial recognition).

[0686] Step 2:

[0687] The user enters their fingerprint into a biometric device or faces a face recognition camera, which captures the authentication data.

[0688] Step 3:

[0689] The device sends the acquired biometric authentication data to the server, which compares the received data with a database and, if authentication is successful, grants the user permission to log in.

[0690] Step 4:

[0691] The terminal notifies the user of the successful authentication result. After the user has logged in, they can proceed to the next step.

[0692] Step 5:

[0693] The device instructs the user to wear an EEG scanner, which the user then wears and concentrates on a specific memory or thought.

[0694] Step 6:

[0695] The device receives data in real time from the EEG scanner, acquires EEG data, and immediately transmits the acquired EEG data to a server.

[0696] Step 7:

[0697] The server analyzes the received brainwave data and extracts the user's consciousness and memory information, which is then converted into digital data.

[0698] Step 8:

[0699] The server organizes the converted digital data and prepares it for storage. The data is stored across multiple backup servers to ensure redundancy.

[0700] Step 9:

[0701] The server notifies the terminal that the save is complete, and the user confirms that their digital data has been safely saved.

[0702] Step 10:

[0703] When a user attempts to access the stored data, the user requests access to cyberspace from the terminal.

[0704] Step 11:

[0705] The server receives an access request from the user and searches for the specified stored data. If the corresponding data is found, the server sends it to the terminal.

[0706] Step 12:

[0707] The terminal converts the received data into a playable format and prepares it for presentation to the user.

[0708] Step 13:

[0709] The device runs a generative AI model and processes the playback data, which is then used as visuals and audio and presented to the user.

[0710] Step 14:

[0711] The user experiences memories reproduced by the generative AI. The device works in conjunction with the server to monitor and correct the data in real time during playback to maintain its integrity and quality.

[0712] Step 15:

[0713] The device will notify the user that the storage recovery is complete and the process will end, allowing the user to start a new storage scan or save process.

[0714] Example 1

[0715] 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."

[0716] In recent years, there has been a demand for technology that can store an individual's consciousness and memories as digital data and access and replay them in cyberspace. However, achieving this requires complex processes, such as user authentication, scanning and analyzing EEG data, storing and accessing the data, and replaying it using a generative AI model. Previous technology lacked a system that could handle these processes in an integrated manner, leaving users lacking the means to properly manage and replay their consciousness and memories.

[0717] 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.

[0718] In this invention, the server includes means for authenticating a user using biometric authentication technology, means for scanning the user's brainwaves and acquiring the brainwave data, means for analyzing the acquired brainwave data and converting consciousness and memory information into digital data, means for storing the converted digital data in cyberspace, means for converting the digital data stored in cyberspace into a format that can be accessed and downloaded upon request by the user, means for replaying the downloaded data using a generative AI model, and means for monitoring the replayed memory data in real time and making corrections as necessary, thereby enabling the user's consciousness and memories to be digitized and permanently stored, accessed, and replayed in cyberspace.

[0719] "Biometric authentication technology" is a technology that identifies individuals using specific physical characteristics of the user, such as fingerprint authentication or facial authentication.

[0720] "Scanning brainwaves" is the process of acquiring a user's brainwave signals using sensors.

[0721] "Electroencephalogram data" refers to data that is a digital record of scanned electroencephalogram signals.

[0722] "Conversion to digital data" refers to the process of analyzing EEG data, which is an analog signal, and converting it into digital information.

[0723] "Cyberspace storage" is the process of storing converted digital data in data storage on the Internet.

[0724] "Converting to an accessible and downloadable format" is the process of converting stored digital data into a format that can be played or viewed by a user.

[0725] A "generative AI model" is a technology that uses a pre-trained artificial intelligence model to reproduce a user's memories and consciousness as visual or audio data.

[0726] "Monitoring stored data reproduced in real time" refers to the process of monitoring stored data being reproduced in real time and correcting its contents as necessary.

[0727] "Correction" is the process of correcting errors or inconsistencies that occur during playback of stored data.

[0728] "Means" is a term that refers to a device or method used to achieve a particular purpose.

[0729] MODE FOR CARRYING OUT THE INVENTION

[0730] A system for carrying out this invention digitizes and stores a user's consciousness and memories, making them permanently accessible in cyberspace. A specific embodiment of this system will be described below.

[0731] System Configuration

[0732] The system consists of the following main components:

[0733] 1. User authentication module

[0734] 2. EEG Scanning Device

[0735] 3. Data Analysis Module

[0736] 4. Data storage module

[0737] 5. Data Access / Download Module

[0738] 6. Memory Replay Module Using Generative AI

[0739] User Authentication Module

[0740] The terminal uses biometric authentication technology to authenticate the user when the system starts up. Common biometric authentication technologies include fingerprint authentication and facial authentication. The user performs these biometric authentications to log in to the system. The server receives the authentication data (e.g., fingerprint data or facial image data), compares it with a registered database, and returns the authentication result to the terminal.

[0741] Brain wave scanning device

[0742] The device prompts the user to wear an EEG scanner (e.g., a specific headset device) and collects real-time brainwave data. The user concentrates on a specific memory or thought, which is reflected in the scanner as brainwave data. The device then transmits this data to a server.

[0743] Data Analysis Module

[0744] The server analyzes the EEG data sent from the device using specific algorithms (e.g., FFT analysis or machine learning algorithms). This analysis extracts the user's consciousness and memory information, which is then converted into digital data. Frameworks such as TensorFlow and PyTorch are often used for data analysis.

[0745] Data Storage Module

[0746] The server stores the analyzed digital data in cyberspace. The stored data is distributed across multiple backup servers to ensure redundancy. For example, by using a cloud storage service, the reliability and availability of the data is ensured.

[0747] Data Access / Download Module

[0748] When a user wants to look back at the stored data, they can access cyberspace via their device and request the specified data. The server receives the request, searches for the relevant data, converts it into a downloadable format, and sends it to the device.

[0749] Memory regeneration module using generative AI

[0750] The device uses a generative AI model to play back memory data downloaded from the server, allowing the user to experience memories through visuals and audio. The generative AI model uses pre-trained large-scale language models (e.g., the GPT series) and image generation models. The information played back in real time is monitored by the server, and corrections are made as necessary.

[0751] Specific examples

[0752] Below are some specific scenarios in which the system can be used:

[0753] 1. User Authentication

[0754] Device: "Starting recognition mode" is displayed.

[0755] The user places their fingerprint on the scanner.

[0756] The device sends the fingerprint data to the server.

[0757] The server checks the database and displays the message "Authentication complete. Welcome, user."

[0758] 2. Starting the EEG scan

[0759] Terminal: "Starting memory scan" is displayed.

[0760] The user wears an EEG scanner and recalls a specific memory (e.g., a college graduation ceremony).

[0761] The device receives the brainwave data from the scanner and transmits it to the server.

[0762] 3. Data Analysis and Storage

[0763] The server analyzes the received brainwave data and converts it into digital data.

[0764] The server stores the converted data in a distributed manner across backup servers.

[0765] 4. Data Access and Memory Replay

[0766] A user requests, "Play my graduation memory."

[0767] The terminal sends a request to the server.

[0768] The server searches for the corresponding stored data and transmits it to the terminal.

[0769] The device uses a generative AI model to play a graduation scene.

[0770] The user experiences the replayed memory.

[0771] Prompt Sentence Examples

[0772] "Replay your college graduation memories."

[0773] "Show us a visual memory of your family trip."

[0774] In this way, this system digitizes the user's consciousness and memories, making them available for storage, access, and playback in cyberspace.

[0775] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0776] Step 1: User authentication

[0777] The terminal starts user authentication when the system is started.

[0778] Input: The user performs fingerprint or facial recognition.

[0779] Operation:

[0780] 1. The device captures biometric data using a fingerprint scanner or camera.

[0781] 2. The device sends the acquired biometric data to the server.

[0782] 3. The server checks the authentication data stored in a database.

[0783] Output: The server returns the authentication result to the terminal, and if authentication is successful, access to the system is permitted.

[0784] Specific operation: For example, the server displays "Authentication completed. Welcome, user" on the terminal.

[0785] Step 2: Start the EEG scan

[0786] The device prompts the user to wear an EEG scanner and collects EEG data in real time.

[0787] Input: User wears an EEG scanner and focuses on a specific memory or thought.

[0788] Operation:

[0789] 1. The device collects EEG data through a scanner.

[0790] 2. The device transmits the collected brainwave data to the server in real time.

[0791] Output: The device sends the collected EEG data to the server.

[0792] Specific actions: For example, the device will display "Starting memory scan."

[0793] Step 3: Data analysis

[0794] The server analyzes the brain wave data sent from the terminal.

[0795] Input: Real-time EEG data sent from the device.

[0796] Operation:

[0797] 1. The server analyzes the EEG data using a specific algorithm (e.g., FFT analysis).

[0798] 2. The server extracts consciousness and memory information from the analysis results and converts it into digital data.

[0799] Output: Consciousness and memory information converted into digital form.

[0800] Specific behavior: For example, the server displays a spinning icon during the analysis process.

[0801] Step 4: Save Data

[0802] The server stores the analyzed digital data in cyberspace.

[0803] Input: Analyzed digital data.

[0804] Operation:

[0805] 1. The server stores digital data across multiple backup servers.

[0806] 2. The server takes steps to ensure data redundancy (e.g., set up a RAID configuration).

[0807] Output: Digital data stored in cyberspace.

[0808] Specific action: For example, the server logs "Data stored securely."

[0809] Step 5: Data Access Request

[0810] When a user wishes to review the stored data, the user makes a request via the terminal.

[0811] Input: The user enters a prompt statement requesting specific stored data.

[0812] Operation:

[0813] 1. The device sends the user's request to the server.

[0814] 2. The server searches for the corresponding stored data.

[0815] Output: Obtaining the relevant stored data.

[0816] Specific operation: For example, the user inputs "Please play the memory of the graduation ceremony."

[0817] Step 6: Download the stored data

[0818] The server receives the request and transmits the specified stored data to the terminal.

[0819] Input: A request for specific stored data.

[0820] Operation:

[0821] 1. The server searches and retrieves the relevant stored data from the database.

[0822] 2. The server converts the stored data into a downloadable format and sends it to the device.

[0823] Output: Stored data converted into a downloadable format.

[0824] Specific behavior: For example, the server displays "Sending data...".

[0825] Step 7: Memory Replay

[0826] The device reproduces the transmitted stored data using the generative AI model.

[0827] Input: Downloaded memory data.

[0828] Operation:

[0829] 1. The device inputs stored data into a generative AI model (e.g., GPT-4).

[0830] 2. A generative AI model generates visual and audio data to replay memories.

[0831] 3. The server monitors the playback in real time and makes corrections as needed.

[0832] Output: Visual and audio data of the replayed memory.

[0833] Specific operation: For example, the device displays "Memories will be reproduced using generative AI," and the user experiences the memories reproduced in a hologram.

[0834] Following this step, the system will digitize the user's consciousness and memories, allowing them to be stored, accessed, and replayed in cyberspace.

[0835] (Application example 1)

[0836] 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."

[0837] In the current shopping experience, it is difficult for users to easily recall their past purchasing history and experiences, making it difficult to receive personalized advice and suggestions. Shopping in brick-and-mortar stores, in particular, is limited in the information that can be obtained at one time, resulting in a lack of effective means to improve user satisfaction. Given this situation, a personal shopping assistant that utilizes a user's past purchasing experiences could improve the quality of the shopping experience.

[0838] 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.

[0839] In this invention, the server includes means for authenticating a user using biometric recognition technology, means for scanning the user's brainwaves and acquiring the brainwave data, means for analyzing the acquired brainwave data and converting consciousness and memory information into digital data, means for storing the converted digital data in cyberspace, means for converting the digital data stored in cyberspace into an accessible and downloadable format upon request from the user, and means for reproducing the downloaded data using a generative AI model and reproducing the stored data visually and audibly based on the user's purchasing history and preferences, thereby enabling personalized suggestions and advice based on the user's individual preferences and past purchasing history.

[0840] "Biometric recognition technology" refers to technology that uses biological information to identify individuals, such as fingerprint authentication or facial authentication.

[0841] "User authentication" is the process of verifying a user's identity when accessing a system.

[0842] An "EEG scan" is the process of measuring a user's brain waves and acquiring that data.

[0843] "EEG data" is digital data indicating brain activity obtained by an EEG scan.

[0844] "Data analysis" is a means of processing acquired EEG data and extracting consciousness and memory information.

[0845] "Digital data" refers to data that is an electronic representation of analyzed consciousness and memory information.

[0846] "Cyberspace" refers to a virtual digital environment such as the Internet.

[0847] A "backup server" is a redundant server for duplicating and storing digital data.

[0848] "Access" refers to having the authority to read, write, and manipulate stored digital data.

[0849] "Downloading" means taking data stored on a server into a user's terminal.

[0850] A "generative AI model" is an artificial intelligence technology that generates new data (images, text, etc.) based on input data.

[0851] "Visual" is a means of visually presenting information.

[0852] "Speech" is a means of conveying information auditorily.

[0853] A "personal shopping assistant" is a support system that provides suggestions and advice based on a user's individual preferences and past purchasing history.

[0854] The system for implementing this invention is a system that digitizes and stores a user's consciousness and memories, making them permanently accessible in cyberspace. This system is composed of the following main components:

[0855] System Configuration

[0856] 1. User authentication module

[0857] 2. EEG Scanning Device

[0858] 3. Data Analysis Module

[0859] 4. Data storage module

[0860] 5. Data Access / Download Module

[0861] 6. Memory Replay Module Using Generative AI

[0862] These components work together to digitize the user's consciousness and memories, and store and play them back in cyberspace.

[0863] User Authentication Module

[0864] The device uses biometric technology to authenticate users when they start up the system. Users can log in to the system using facial recognition. Once authentication is complete, access is granted.

[0865] Brain wave scanning device

[0866] Using an EEG scanner (e.g., Neurosky, EMOTIV), the user acquires brainwave data, which contains memory information related to a particular shopping experience.

[0867] Data Analysis Module

[0868] The server receives the EEG data sent from the device and analyzes it using an EEG analysis library such as EEGLab, which extracts the user's memory information and converts it into digital data.

[0869] Data Storage Module

[0870] The server stores the converted digital data in cyberspace. To ensure data redundancy, the data is stored across multiple backup servers.

[0871] Data Access / Download Module

[0872] When a user wants to play back the stored data, they access cyberspace via their device and request the data they need. The server processes the request, retrieves the data, converts it into a downloadable format, and sends it to the device.

[0873] Memory regeneration module using generative AI

[0874] The device then uses a generative AI model (e.g., ChatGPT, DALL·E) to play back the downloaded memory data, allowing the user to experience the memory visually and audibly. The generative AI model used has the ability to generate new data based on input data, and accurate memory playback is achieved by using prompt sentences.

[0875] Specific examples

[0876] Example prompt sentence:

[0877] "Please replay User A's Christmas shopping experience in December 2021, focusing specifically on the clothing section."

[0878] The prompts allow the generative AI model to recreate the user's memories visually and audibly, bringing past shopping experiences to life, allowing the user to receive more personalized advice and suggestions as they shop.

[0879] In this way, the system leverages the user's memory to enrich the in-store shopping experience and provide assistance based on individual preferences and past purchasing history.

[0880] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0881] Step 1:

[0882] User Authentication

[0883] Input: User's facial recognition data

[0884] Specific operation: The device's camera is used to scan the user's face and the data is sent to the server.

[0885] Data processing: The server uses biometric recognition technology to match the facial recognition data with the registered data in the database.

[0886] Output: The authentication result is returned to the terminal. If authentication is successful, access to the system is granted.

[0887] Step 2:

[0888] EEG scan

[0889] Input: User's brainwave data

[0890] How it works: The user wears an EEG scanner and recalls a specific memory. The device collects real-time EEG data from the scanner.

[0891] Data processing: The acquired EEG data is sent to the server.

[0892] Output: EEG data is sent to the server.

[0893] Step 3:

[0894] Data analysis

[0895] Input: EEG data sent to the server

[0896] Specific operation: The server uses an EEG analysis library such as EEGLab to analyze the transmitted EEG data.

[0897] Data processing: Extract consciousness and memory information from EEG data and convert it into digital data.

[0898] Output: Digital data is produced.

[0899] Step 4:

[0900] Data storage

[0901] Input: Converted digital data

[0902] Specific operation: The server stores the converted digital data in cyberspace.

[0903] Data processing: To ensure data redundancy, data is stored across multiple backup servers.

[0904] Output: The saving process is complete and the data is safely stored.

[0905] Step 5:

[0906] Data Request

[0907] Input: User's request to play back stored data

[0908] Specific operation: The user requests playback of stored data via the terminal.

[0909] Data Processing: The server retrieves the stored data based on the request and converts it into a downloadable format.

[0910] Output: Data is sent to the terminal.

[0911] Step 6:

[0912] memory replay

[0913] Input: Downloaded memory data

[0914] Specific operation: The device uses a generative AI model (e.g., ChatGPT or DALL·E) to regenerate stored data.

[0915] Data processing: Based on the prompt, the generative AI model recreates the user's memory visually and audibly.

[0916] Output: A specific memory playback such as "Play back User A's Christmas shopping experience in December 2021, focusing specifically on the clothing section."

[0917] Through the above steps, the user can access the system and experience his or her past purchasing experiences in a personalized manner.

[0918] 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.

[0919] A system for implementing this invention digitizes and stores a user's consciousness and memories, making them permanently accessible in cyberspace. Furthermore, this system incorporates an emotion engine that recognizes and reflects the user's emotions. Specific embodiments are described in detail below.

[0920] System configuration overview

[0921] The system consists of the following main components:

[0922] 1. User authentication module

[0923] 2. EEG Scanning Device

[0924] 3. Data Analysis Module

[0925] 4. Data storage module

[0926] 5. Data Access / Download Module

[0927] 6. Memory Replay Module Using Generative AI

[0928] 7. Emotion Engine

[0929] These components work together to digitize the user's consciousness, memories, and emotions, and store and reproduce them in cyberspace.

[0930] User Authentication Module

[0931] The terminal uses biometric recognition technology to authenticate the user when the system starts up. The user can log in to the system using fingerprint or facial recognition. The server verifies this and, if authentication is successful, allows further processing.

[0932] Brain wave scanning device

[0933] The device asks the user to wear an EEG scanner, which collects real-time brainwave data. The user concentrates on a specific memory or thought, which is then reflected in the scanner as brainwave data.

[0934] Emotion Engine

[0935] The device monitors and acquires information about the user's emotions in real time using an emotion engine, which analyzes emotions from the user's facial expressions, voice, and other biometric data.

[0936] Data Analysis Module

[0937] The server receives and analyzes the EEG and emotional data sent from the device. Using a specific algorithm, the server extracts the user's consciousness and memory information from the EEG data and integrates it with emotional information. This information is then converted and stored as digital data.

[0938] Data Storage Module

[0939] The server stores the analyzed digital data in cyberspace, and during the storage process, the data is distributed across multiple backup servers to ensure data redundancy.

[0940] Data Access / Download Module

[0941] When a user wants to review the stored data, they access cyberspace via their device and request the specified data. The server processes the request, searches for the relevant data, converts it into a downloadable format, and sends it to the device.

[0942] Memory regeneration module using generative AI

[0943] The device then uses a generative AI model to play back the downloaded memory data, allowing the user to experience the memory visually and audibly. The real-time information displayed is monitored by a server, which makes corrections as needed.

[0944] Specific examples

[0945] A specific usage scenario of the system is described below.

[0946] User Authentication

[0947] Device: "Starting recognition mode"

[0948] The user places their fingerprint on the scanner.

[0949] The device sends the fingerprint data to the server.

[0950] The server compares the fingerprint data with the database and returns the authentication result.

[0951] Terminal: "Authentication complete. Welcome, user."

[0952] EEG scanning and emotion recognition begins

[0953] Terminal: "Starting memory scan"

[0954] The user wears an EEG scanner and imagines their college graduation ceremony.

[0955] The device receives the EEG data from the scanner and transmits it to a server.

[0956] At the same time, the emotion engine acquires emotion data in real time from the user's facial expressions and voice.

[0957] The server analyzes the brainwave data and emotional data, and integrates and extracts memory information and emotional information.

[0958] Data conversion and storage

[0959] The server converts the analyzed data into a digital format.

[0960] The server stores the converted data on a backup server.

[0961] Data Access

[0962] User: "Replay my graduation memory."

[0963] The terminal sends this request to the server.

[0964] The server searches for the corresponding stored data and transmits it to the terminal.

[0965] The terminal converts the data into a playable format.

[0966] memory replay

[0967] Terminal: "Memories will be recreated using generative AI."

[0968] The device activates the generative AI model and plays a hologram of the graduation scene.

[0969] The user experiences the replayed memory.

[0970] The data being played back reflects the user's emotional information, enhancing the sense of realism.

[0971] In this way, this system digitizes the user's consciousness, memories, and even emotions, and makes them available for storage, access, and playback in cyberspace, thereby guaranteeing the permanent existence of individuals.

[0972] The processing flow will be explained below.

[0973] Step 1:

[0974] The device boots up and presents the user with a login screen, where the user attempts to access the system using biometric authentication (e.g., fingerprint or facial recognition).

[0975] Step 2:

[0976] The user enters their fingerprint into a biometric device or faces a face recognition camera, which captures the authentication data.

[0977] Step 3:

[0978] The device sends the acquired biometric authentication data to the server, which compares the received data with a database and, if authentication is successful, grants the user permission to log in.

[0979] Step 4:

[0980] The terminal notifies the user of the successful authentication result. After the user has logged in, they can proceed to the next step.

[0981] Step 5:

[0982] The device instructs the user to wear an EEG scanner, which the user then wears and concentrates on a specific memory or thought.

[0983] Step 6:

[0984] The device receives data in real time from the EEG scanner, acquires EEG data, and immediately transmits the acquired EEG data to a server.

[0985] Step 7:

[0986] The device uses an emotion engine to obtain emotional data in real time from the user's facial expressions, voice, and biometric signals.

[0987] Step 8:

[0988] The server analyzes the received brainwave and emotional data, extracts the user's consciousness and memory information, and integrates the emotional information. This integrated information is then converted into digital data.

[0989] Step 9:

[0990] The server organizes the converted digital data and prepares it for storage. The data is stored across multiple backup servers to ensure redundancy.

[0991] Step 10:

[0992] The server notifies the terminal that the save is complete, and the user confirms that their digital data has been safely saved.

[0993] Step 11:

[0994] When a user attempts to access the stored data, the user requests access to cyberspace from the terminal.

[0995] Step 12:

[0996] The server receives an access request from the user and searches for the specified stored data and emotion information. If the server finds the corresponding data, it sends it to the terminal.

[0997] Step 13:

[0998] The terminal converts the received data into a playable format and prepares it for presentation to the user.

[0999] Step 14:

[1000] The device runs a generative AI model and processes the playback data, which is then used as visuals and audio and presented to the user.

[1001] Step 15:

[1002] The user experiences memories reproduced by the generative AI. The reproduced data also reflects the user's emotional information, providing a more immersive experience. The device works in conjunction with the server to monitor and correct the data in real time to maintain the integrity and quality of the data being reproduced.

[1003] Step 16:

[1004] The device will notify the user that the storage recovery is complete and the process will end, allowing the user to start a new storage scan or save process.

[1005] Example 2

[1006] 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."

[1007] Despite advances in modern information technology, there are currently no systems that can accurately digitize a user's consciousness, memories, and even emotions, permanently store them, and then replay them as needed. Conventional systems acquire and store a user's consciousness and memory information in a fragmented manner, without integrating it with emotional information, making it impossible to fully reproduce the user's actual experience. Furthermore, methods for storing digitized data often lack redundancy and security. Therefore, there is a need for a system that can comprehensively digitize a user's consciousness, memories, and emotions, and store and replay them securely and permanently.

[1008] The specific processing by the specific 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 authenticating a user using biometric recognition technology, means for scanning the user's brain waves and acquiring the brain wave data, means for analyzing the acquired brain wave data and converting consciousness and memory information into digital data, means for acquiring the user's emotions through facial expressions and voice and generating emotional data, means for integrating the converted consciousness and memory information with the emotional data and storing it in cyberspace as digital data, means for converting the digital data stored in cyberspace into a format that can be accessed and downloaded upon request by the user, and means for reproducing the downloaded data using a generative AI model. This makes it possible to digitize the user's consciousness, memories, and emotions in an integrated manner and store and reproduce them securely and permanently.

[1009] "Biometric recognition technology" is a technology that authenticates individuals using biometric information such as a user's fingerprint, face, or iris.

[1010] "User authentication" is the process of using biometric technology to verify a user's identity and grant them access to a system.

[1011] "EEG scanning" refers to devices and technologies that record and capture a user's brain waves in real time.

[1012] "Electroencephalogram data" is digital information representing electrical activity acquired by an electroencephalogram scanning device.

[1013] "Consciousness and memory information" refers to data about the user's thoughts and memories that is analyzed and extracted from the user's brain wave data.

[1014] "Converting to digital data" refers to the process of converting electroencephalogram data and emotion data acquired as analog signals into a digital format.

[1015] "Emotion data" is digital information that indicates the emotional state of the user analyzed from facial expressions, voice, etc.

[1016] "Synthesis" is the process of combining different types of data (e.g., consciousness and memory information with emotional data) into a single dataset.

[1017] "Cyberspace" refers to virtual spaces where digital data is stored, such as the Internet and cloud storage.

[1018] "Accessible and downloadable format" refers to the conversion of digital data into a format that is easily accessible and retrievable by users.

[1019] A "generative AI model" is a type of artificial intelligence that refers to an algorithm that generates and reproduces new information and content from input data.

[1020] MODE FOR CARRYING OUT THE INVENTION

[1021] This invention is a system that digitizes a user's consciousness, memories, and emotions and stores, accesses, and reproduces them in cyberspace. Using the hardware and software shown below, the system acquires the user's consciousness, memories, and emotions and processes them as digital data.

[1022] System Configuration

[1023] The system consists of the following main components:

[1024] 1. User authentication module

[1025] 2. EEG Scanning Device

[1026] 3. Data Analysis Module

[1027] 4. Data storage module

[1028] 5. Data Access / Download Module

[1029] 6. Memory Replay Module Using Generative AI

[1030] 7. Emotion Engine

[1031] User Authentication Module

[1032] The device performs user authentication using, for example, fingerprint or facial recognition. The user places their fingerprint on a scanner or stands in front of a facial recognition camera. The device then sends this biometric data to a server, which compares it with a database and returns the authentication result.

[1033] Brain wave scanning device

[1034] The device asks the user to wear an EEG scanner, which collects real-time brainwave data. The user concentrates on a specific memory or thought, which is then collected as brainwave data. EEG scanning can be done using, for example, electroencephalography (EEG).

[1035] Emotion Engine

[1036] The device uses an emotion engine to acquire emotion data from the user's facial expressions and voice. This is done using input devices such as a camera and microphone. The acquired emotion data is also sent to the server in real time.

[1037] Data Analysis Module

[1038] The server receives the EEG and emotional data sent from the device and analyzes it using a specific algorithm. The analyzed data is converted into the user's consciousness and memory information, and emotional information is integrated. For example, analyzing the data using a machine learning model makes it possible to extract highly accurate consciousness and memory information.

[1039] Data Storage Module

[1040] The server stores the analyzed digital data in cyberspace. To ensure data redundancy, the data is stored across multiple backup servers. For example, a distributed database (such as Hadoop or Cassandra) can be used here.

[1041] Data Access / Download Module

[1042] When a user wants to look back at the stored memory data, they access cyberspace via their device and request the specified memory data. The server receives the request, searches for the corresponding data, converts it into a downloadable format, and sends it to the device.

[1043] Memory regeneration module using generative AI

[1044] The device then uses a generative AI model to play back the downloaded memory data, allowing the user to experience the memory visually and aurally. For example, playback can be performed using a VR device or a holographic display. Emotional information is reflected in the data being played, enhancing the sense of realism.

[1045] Specific examples

[1046] The following are specific usage scenarios for the system:

[1047] User Authentication

[1048] Device: "Starting recognition mode"

[1049] The user places their fingerprint on the scanner.

[1050] The device sends the fingerprint data to the server.

[1051] The server compares the fingerprint data with the database and returns the authentication result.

[1052] Terminal: "Authentication complete. Welcome, user."

[1053] EEG scanning and emotion recognition begins

[1054] Terminal: "Starting memory scan"

[1055] The user wears an EEG scanner and imagines their college graduation ceremony.

[1056] The device receives EEG data from the scanner and transmits it to the server, where an emotion engine simultaneously captures emotion data from the user's facial expressions and voice in real time.

[1057] The server analyzes the brainwave data and emotional data, and integrates and extracts memory information and emotional information.

[1058] Data conversion and storage

[1059] The server converts the analyzed data into a digital format.

[1060] The server stores the converted data on a backup server.

[1061] Data Access

[1062] User: "Replay my graduation memory."

[1063] The terminal sends this request to the server.

[1064] The server searches for the corresponding stored data and transmits it to the terminal.

[1065] The terminal converts the data into a playable format.

[1066] memory replay

[1067] Terminal: "Memories will be recreated using generative AI."

[1068] The device activates the generative AI model and plays a hologram of the graduation scene.

[1069] The user experiences the replayed memory. The data being replayed reflects the user's emotional information, enhancing the sense of realism.

[1070] This system makes it possible to digitize the user's consciousness, memories, and even emotions, and store, access, and replay them in cyberspace, thereby ensuring the perpetual existence of the individual.

[1071] Examples of prompt statements

[1072] "Replay your college graduation memories."

[1073] This prompt triggers the process of data access and storage recovery.

[1074] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1075] Step 1:

[1076] User Authentication

[1077] Input: The device captures fingerprint or facial recognition information. The user places their fingerprint on the scanner or stands in front of the facial recognition camera.

[1078] Specific operation: The device uses biometric recognition technology to acquire the user's fingerprint and facial data, and then transmits the acquired data to the server using a secure communication protocol (e.g., HTTPS).

[1079] Output: The server checks the database and returns the authentication result. The terminal displays the message "Authentication completed."

[1080] Step 2:

[1081] EEG scan begins

[1082] Input: Pre-authentication information obtained by the device from the user. The user wears an EEG scanner.

[1083] Specific operation: The device prompts the user to wear an EEG scanner, and acquires EEG data in real time through the scanner.

[1084] Output: The acquired EEG data is stored on the terminal, and when a certain amount is accumulated, it is sent to the server.

[1085] Step 3:

[1086] Emotion data acquisition

[1087] Input: The device collects real-time biometric data (facial expressions, voice) and brainwave data. The user focuses on a specific memory or thought.

[1088] Specific operation: The device activates the emotion engine and analyzes emotion data from the user's facial expressions and voice. Data is collected using the camera and microphone.

[1089] Output: Emotion data is sent to the server along with EEG data.

[1090] Step 4:

[1091] Data analysis and integration

[1092] Input: EEG data and emotion data received by the server.

[1093] Specific operation: The server analyzes EEG data and emotion data using specific algorithms. It uses machine learning models to extract consciousness and memory information and integrate it with emotion information.

[1094] Output: Convert the analysis results into digital data.

[1095] Step 5:

[1096] Data storage

[1097] Input: Consciousness, memory information, and emotion data analyzed on the server.

[1098] Specific operation: The server stores this data in a distributed database. To increase data redundancy, the data is stored in multiple backup servers.

[1099] Output: Digital data is stored securely and backed up.

[1100] Step 6:

[1101] Data Access and Download

[1102] Input: A data access request from the device (e.g., a prompt "Please play my graduation memory").

[1103] Specific operation: The server receives the request, searches the database for the corresponding stored data, converts the found data into a downloadable format, and sends it to the device.

[1104] Output: The terminal obtains the stored data specified by the user and converts it into a playable format.

[1105] Step 7:

[1106] memory replay

[1107] Input: Stored data downloaded to the terminal.

[1108] Specific operation: The device activates the generative AI model and reproduces the memory data through sight and sound. The visual and audio of the memory are reproduced using a VR device or holographic display.

[1109] Output: The user experiences the replayed memory. Emotional information is reflected in the replayed data, enhancing the sense of realism.

[1110] (Application example 2)

[1111] 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."

[1112] With conventional technology, it is difficult to store a user's memories and consciousness as digital data, and the technology for integrating information, including emotions, is insufficient. As a result, there has been no system that allows users to realistically replay past memories and experiences and reflect on them, including their emotions. As a result, memory replay is limited to a simple visual and auditory experience, making it difficult to reproduce a sense of presence or emotions. To solve this problem, a system is needed that can integrate and store memories and emotions and replay them using advanced technology such as holograms.

[1113] The specific processing by the specific 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 authenticating a user using biometric recognition technology, means for scanning the user's brain waves and acquiring the brain wave data, means for analyzing the acquired brain wave data and converting consciousness and memory information into digital data, means for analyzing the acquired emotional data and integrating the information as digital data, means for storing the converted digital data in cyberspace, means for converting the digital data stored in cyberspace into an accessible and downloadable format upon request from the user, means for reproducing the downloaded data using a generative AI model and presenting it to the user in holographic form or on a visual display, and means including an emotion engine that reflects the user's emotional state in real time. This makes it possible to store the user's memories and consciousness as digital data, integrate emotional information, and then reproduce the data with a sense of realism using a hologram or a visual display.

[1114] "Biometric recognition technology" is a technology that identifies individuals using biometric information such as a user's fingerprint or facial recognition.

[1115] An "EEG scanner" is a device that reads a user's brain waves in real time and acquires brain wave data.

[1116] "Electroencephalogram data" refers to data obtained by converting electrical signals obtained from the user's electroencephalogram into digital format.

[1117] The "emotion engine" analyzes emotions from biometric data such as the user's facial expressions and voice, and reflects the user's emotional state in real time.

[1118] "Consciousness and memory information" is information about the state of consciousness and memory contents extracted from the user's electroencephalogram data.

[1119] "Means for converting into digital data" refers to a system or algorithm that analyzes the acquired brainwave data and emotional data and converts it into digital form.

[1120] "Cyberspace" refers to virtual spaces such as the Internet and the cloud, where digital data can be stored, accessed, and shared.

[1121] A "generative AI model" is an algorithm or program that uses artificial intelligence to generate stored memory data into a reproducible form.

[1122] "Hologram format" is a technology for displaying three-dimensional images and videos, which are displayed with a visually three-dimensional appearance.

[1123] "Visual display" refers to a physical screen or device for presenting visual information to a user.

[1124] A "backup server" is a server that stores replicated data to ensure data redundancy.

[1125] "Emotion information" is digital data that analyzes the user's emotional state and is acquired by the emotion engine.

[1126] MODE FOR CARRYING OUT THE INVENTION

[1127] To implement this invention, it is necessary to build a system that digitizes and stores a user's consciousness and memories and makes them permanently accessible in cyberspace. This system is composed of the following main components: a user authentication module, an EEG scanning device, a data analysis module, a data storage module, a data access / download module, a memory playback module using generative AI, and an emotion engine. These components work together to digitize a user's consciousness, memories, and emotions, and store and play them back in cyberspace.

[1128] User Authentication Module

[1129] The server authenticates the user using biometric recognition technology, such as fingerprint or facial recognition, and allows them to log in to the system. The user enters their authentication information into the terminal, and the server compares it with a database and returns the authentication result.

[1130] Brain wave scanning device

[1131] The device has the user wear an EEG scanner, which collects real-time brainwave data. When the user concentrates on a specific memory or thought, that information is reflected in the scanner as brainwave data. The device then transmits this brainwave data to a server.

[1132] Emotion Engine

[1133] The device uses an emotion engine to acquire the user's emotional data in real time. The emotion engine analyzes the user's emotions from facial expressions, voice, and other biometric data. The acquired emotional data is sent to the server along with brainwave data.

[1134] Data Analysis Module

[1135] The server receives and analyzes the EEG and emotional data sent from the device. Using a specific algorithm, the server extracts the user's consciousness and memory information from the EEG data and integrates it with emotional information. This information is then converted and stored as digital data.

[1136] Data Storage Module

[1137] The server stores the analyzed digital data in cyberspace, and during the storage process, the data is distributed across multiple backup servers to ensure data redundancy.

[1138] Data Access / Download Module

[1139] When a user wants to review the stored data, they access cyberspace via their device and request the specified data. The server processes the request, searches for the relevant data, converts it into a downloadable format, and sends it to the device.

[1140] Memory regeneration module using generative AI

[1141] The device uses a generative AI model to play back the downloaded memory data, allowing the user to experience the memory visually and aurally. The played back information is monitored by a server, and corrections are made as necessary. The generative AI model provides a sense of realism, as if the user is actually experiencing past memories.

[1142] Specific examples

[1143] User Authentication

[1144] Device: "Starting recognition mode"

[1145] The user places their fingerprint on the scanner.

[1146] The device sends the fingerprint data to the server.

[1147] The server compares the fingerprint data with the database and returns the authentication result.

[1148] Terminal: "Authentication complete. Welcome, user."

[1149] EEG scanning and emotion recognition begins

[1150] Terminal: "Starting memory scan"

[1151] The user wears an EEG scanner and imagines their college graduation ceremony.

[1152] The device receives the EEG data from the scanner and transmits it to a server.

[1153] At the same time, the emotion engine acquires emotion data in real time from the user's facial expressions and voice.

[1154] The server analyzes the brainwave data and emotional data, and integrates and extracts memory information and emotional information.

[1155] Data conversion and storage

[1156] The server converts the analyzed data into a digital format.

[1157] The server stores the converted data on a backup server.

[1158] Data Access

[1159] User: "Replay my graduation memory."

[1160] The terminal sends this request to the server.

[1161] The server searches for the corresponding stored data and transmits it to the terminal.

[1162] The terminal converts the data into a playable format.

[1163] memory replay

[1164] Terminal: "Memories will be recreated using generative AI."

[1165] The device activates the generative AI model and plays a hologram of the graduation scene.

[1166] The user experiences the replayed memory.

[1167] The data being played back reflects the user's emotional information, enhancing the sense of realism.

[1168] Prompt Sentence Examples

[1169] "Please replay user X's memory of their college graduation. The user's emotions are filled with joy and excitement. Please replay it in a holographic format to create a realistic experience."

[1170] This particular embodiment of the invention allows users to store their memories and emotions as digital data and have a realistic experience when playing it back.

[1171] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1172] Step 1: User authentication

[1173] How it works: The device prompts the user to enter authentication information (fingerprint or face).

[1174] Input: Fingerprint data or face data

[1175] Data processing / calculation: The device collects authentication information and sends it to the server, which checks it against a database and performs authentication.

[1176] Output: Authentication result (success or failure)

[1177] Step 2: Start the EEG scan

[1178] How it works: The device prompts the user to wear an EEG scanner. The user puts on the scanner and concentrates on a specific memory.

[1179] Input: User's brainwaves

[1180] Data processing / calculation: The EEG scanner acquires EEG data in real time and transmits it to the terminal.

[1181] Output: Acquired EEG data

[1182] Step 3: Collecting emotion data

[1183] Operation: The device activates the emotion engine and acquires emotion data in real time from the user's facial expressions and voice.

[1184] Input: User's facial expression data, voice data

[1185] Data processing / calculation: The emotion engine analyzes facial expression data and voice data to detect emotional states.

[1186] Output: User emotion data

[1187] Step 4: Analyze and synthesize data

[1188] Operation: The server analyzes the brain wave data and emotion data sent from the device.

[1189] Input: EEG data, emotion data

[1190] Data processing / computation: Using specific algorithms, consciousness and memory information is extracted from EEG data and emotional information is integrated.

[1191] Output: Integrated memory and emotion data

[1192] Step 5: Save Data

[1193] How it works: The server converts the analyzed data into digital form and stores it in cyberspace.

[1194] Input: Integrated memory and emotion data

[1195] Data processing / calculation: Data is digitized and stored in multiple backup servers.

[1196] Output: Stored digital data

[1197] Step 6: Data Access

[1198] Operation: A user requests playback of stored stored data, and the terminal sends the request to the server.

[1199] Input: User's memory recall request

[1200] Data processing / computation: The server processes the request, retrieves the relevant stored data, and converts it into a downloadable format.

[1201] Output: Downloaded memory data

[1202] Step 7: Memory Replay

[1203] How it works: The device uses generative AI to recreate stored data in holographic form or on a visual display.

[1204] Input: Downloaded memory data

[1205] Data processing / computation: A generative AI model analyzes stored data and generates it in a format that can be reproduced as a hologram or visual display.

[1206] Output: Reproducible stored data

[1207] Step 8: Reflect and Replay Emotions

[1208] Operation: The device acquires the user's real-time emotional information during playback and reflects it in the playback.

[1209] Input: Current user emotion data

[1210] Data processing / calculation: The emotion engine analyzes the emotional data acquired in real time and reflects it in the memory data being played back.

[1211] Output: Memory replay data reflecting emotional information

[1212] These processing steps allow users to digitize their memories and emotions in detail, allowing them to later re-experience them in a realistic way.

[1213] 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.

[1214] 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.

[1215] 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.

[1216] [Third embodiment]

[1217] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[1218] 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.

[1219] 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).

[1220] 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.

[1221] 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.

[1222] 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).

[1223] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[1224] 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.

[1225] 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.

[1226] 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.

[1227] 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.

[1228] 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."

[1229] A system for carrying out the present invention is a system that digitizes and stores a user's consciousness and memories, and makes them permanently accessible in cyberspace. Specific embodiments will be described below.

[1230] System configuration overview

[1231] The system consists of the following main components:

[1232] 1. User authentication module

[1233] 2. EEG Scanning Device

[1234] 3. Data Analysis Module

[1235] 4. Data storage module

[1236] 5. Data Access / Download Module

[1237] 6. Memory Replay Module Using Generative AI

[1238] These components work together to digitize the user's consciousness and memories, and store and play them back in cyberspace.

[1239] User Authentication Module

[1240] The terminal uses biometric recognition technology to authenticate the user when the system starts up. The user can log in to the system using fingerprint or facial recognition. The server verifies this and, if authentication is successful, allows further processing.

[1241] Brain wave scanning device

[1242] The device asks the user to wear an EEG scanner, which collects real-time brainwave data. The user concentrates on a specific memory or thought, which is then reflected in the scanner as brainwave data.

[1243] Data Analysis Module

[1244] The server receives and analyzes the EEG data sent from the device. Using a specific algorithm, the server extracts information about the user's consciousness and memory from the EEG data. This information is then converted and stored as digital data.

[1245] Data Storage Module

[1246] The server stores the analyzed digital data in cyberspace, and during the storage process, the data is distributed across multiple backup servers to ensure data redundancy.

[1247] Data Access / Download Module

[1248] When a user wants to review the stored data, they access cyberspace via their device and request the specified data. The server processes the request, searches for the relevant data, converts it into a downloadable format, and sends it to the device.

[1249] Memory regeneration module using generative AI

[1250] The device then uses a generative AI model to play back the downloaded memory data, allowing the user to experience the memory visually and audibly. The real-time information displayed is monitored by a server, which makes corrections as needed.

[1251] Specific examples

[1252] A specific usage scenario of the system is described below.

[1253] User Authentication

[1254] Device: "Starting recognition mode"

[1255] The user places their fingerprint on the scanner.

[1256] The device sends the fingerprint data to the server.

[1257] The server compares the fingerprint data with the database and returns the authentication result.

[1258] Terminal: "Authentication complete. Welcome, user."

[1259] EEG scan begins

[1260] Terminal: "Starting memory scan"

[1261] The user wears an EEG scanner and imagines their college graduation ceremony.

[1262] The device receives the EEG data from the scanner and transmits it to a server.

[1263] The server analyzes the data and extracts the stored information.

[1264] Data conversion and storage

[1265] The server converts the analyzed data into a digital format.

[1266] The server stores the converted data on a backup server.

[1267] Data Access

[1268] User: "Replay my graduation memory."

[1269] The terminal sends this request to the server.

[1270] The server searches for the corresponding stored data and transmits it to the terminal.

[1271] The terminal converts the data into a playable format.

[1272] memory replay

[1273] Terminal: "Memories will be recreated using generative AI."

[1274] The device activates the generative AI model and plays a hologram of the graduation scene.

[1275] The user experiences the replayed memory.

[1276] In this way, this system digitizes the user's consciousness and memories, making them available for storage, access, and playback in cyberspace, thereby guaranteeing the perpetual existence of individuals.

[1277] The processing flow will be explained below.

[1278] Step 1:

[1279] The device boots up and presents the user with a login screen, where the user attempts to access the system using biometric authentication (e.g., fingerprint or facial recognition).

[1280] Step 2:

[1281] The user enters their fingerprint into a biometric device or faces a face recognition camera, which captures the authentication data.

[1282] Step 3:

[1283] The device sends the acquired biometric authentication data to the server, which compares the received data with a database and, if authentication is successful, grants the user permission to log in.

[1284] Step 4:

[1285] The terminal notifies the user of the successful authentication result. After the user has logged in, they can proceed to the next step.

[1286] Step 5:

[1287] The device instructs the user to wear an EEG scanner, which the user then wears and concentrates on a specific memory or thought.

[1288] Step 6:

[1289] The device receives data in real time from the EEG scanner, acquires EEG data, and immediately transmits the acquired EEG data to a server.

[1290] Step 7:

[1291] The server analyzes the received brainwave data and extracts the user's consciousness and memory information, which is then converted into digital data.

[1292] Step 8:

[1293] The server organizes the converted digital data and prepares it for storage. The data is stored across multiple backup servers to ensure redundancy.

[1294] Step 9:

[1295] The server notifies the terminal that the save is complete, and the user confirms that their digital data has been safely saved.

[1296] Step 10:

[1297] When a user attempts to access the stored data, the user requests access to cyberspace from the terminal.

[1298] Step 11:

[1299] The server receives an access request from the user and searches for the specified stored data. If the corresponding data is found, the server sends it to the terminal.

[1300] Step 12:

[1301] The terminal converts the received data into a playable format and prepares it for presentation to the user.

[1302] Step 13:

[1303] The device runs a generative AI model and processes the playback data, which is then used as visuals and audio and presented to the user.

[1304] Step 14:

[1305] The user experiences memories reproduced by the generative AI. The device works in conjunction with the server to monitor and correct the data in real time during playback to maintain its integrity and quality.

[1306] Step 15:

[1307] The device will notify the user that the storage recovery is complete and the process will end, allowing the user to start a new storage scan or save process.

[1308] Example 1

[1309] 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."

[1310] In recent years, there has been a demand for technology that can store an individual's consciousness and memories as digital data and access and replay them in cyberspace. However, achieving this requires complex processes, such as user authentication, scanning and analyzing EEG data, storing and accessing the data, and replaying it using a generative AI model. Previous technology lacked a system that could handle these processes in an integrated manner, leaving users lacking the means to properly manage and replay their consciousness and memories.

[1311] 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.

[1312] In this invention, the server includes means for authenticating a user using biometric authentication technology, means for scanning the user's brainwaves and acquiring the brainwave data, means for analyzing the acquired brainwave data and converting consciousness and memory information into digital data, means for storing the converted digital data in cyberspace, means for converting the digital data stored in cyberspace into a format that can be accessed and downloaded upon request by the user, means for replaying the downloaded data using a generative AI model, and means for monitoring the replayed memory data in real time and making corrections as necessary, thereby enabling the user's consciousness and memories to be digitized and permanently stored, accessed, and replayed in cyberspace.

[1313] "Biometric authentication technology" is a technology that identifies individuals using specific physical characteristics of the user, such as fingerprint authentication or facial authentication.

[1314] "Scanning brainwaves" is the process of acquiring a user's brainwave signals using sensors.

[1315] "Electroencephalogram data" refers to data that is a digital record of scanned electroencephalogram signals.

[1316] "Conversion to digital data" refers to the process of analyzing EEG data, which is an analog signal, and converting it into digital information.

[1317] "Cyberspace storage" is the process of storing converted digital data in data storage on the Internet.

[1318] "Converting to an accessible and downloadable format" is the process of converting stored digital data into a format that can be played or viewed by a user.

[1319] A "generative AI model" is a technology that uses a pre-trained artificial intelligence model to reproduce a user's memories and consciousness as visual or audio data.

[1320] "Monitoring stored data reproduced in real time" refers to the process of monitoring stored data being reproduced in real time and correcting its contents as necessary.

[1321] "Correction" is the process of correcting errors or inconsistencies that occur during playback of stored data.

[1322] "Means" is a term that refers to a device or method used to achieve a particular purpose.

[1323] MODE FOR CARRYING OUT THE INVENTION

[1324] A system for carrying out this invention digitizes and stores a user's consciousness and memories, making them permanently accessible in cyberspace. A specific embodiment of this system will be described below.

[1325] System Configuration

[1326] The system consists of the following main components:

[1327] 1. User authentication module

[1328] 2. EEG Scanning Device

[1329] 3. Data Analysis Module

[1330] 4. Data storage module

[1331] 5. Data Access / Download Module

[1332] 6. Memory Replay Module Using Generative AI

[1333] User Authentication Module

[1334] The terminal uses biometric authentication technology to authenticate the user when the system starts up. Common biometric authentication technologies include fingerprint authentication and facial authentication. The user performs these biometric authentications to log in to the system. The server receives the authentication data (e.g., fingerprint data or facial image data), compares it with a registered database, and returns the authentication result to the terminal.

[1335] Brain wave scanning device

[1336] The device prompts the user to wear an EEG scanner (e.g., a specific headset device) and collects real-time brainwave data. The user concentrates on a specific memory or thought, which is reflected in the scanner as brainwave data. The device then transmits this data to a server.

[1337] Data Analysis Module

[1338] The server analyzes the EEG data sent from the device using specific algorithms (e.g., FFT analysis or machine learning algorithms). This analysis extracts the user's consciousness and memory information, which is then converted into digital data. Frameworks such as TensorFlow and PyTorch are often used for data analysis.

[1339] Data Storage Module

[1340] The server stores the analyzed digital data in cyberspace. The stored data is distributed across multiple backup servers to ensure redundancy. For example, by using a cloud storage service, the reliability and availability of the data is ensured.

[1341] Data Access / Download Module

[1342] When a user wants to look back at the stored data, they can access cyberspace via their device and request the specified data. The server receives the request, searches for the relevant data, converts it into a downloadable format, and sends it to the device.

[1343] Memory regeneration module using generative AI

[1344] The device uses a generative AI model to play back memory data downloaded from the server, allowing the user to experience memories through visuals and audio. The generative AI model uses pre-trained large-scale language models (e.g., the GPT series) and image generation models. The information played back in real time is monitored by the server, and corrections are made as necessary.

[1345] Specific examples

[1346] Below are some specific scenarios in which the system can be used:

[1347] 1. User Authentication

[1348] Device: "Starting recognition mode" is displayed.

[1349] The user places their fingerprint on the scanner.

[1350] The device sends the fingerprint data to the server.

[1351] The server checks the database and displays the message "Authentication complete. Welcome, user."

[1352] 2. Starting the EEG scan

[1353] Terminal: "Starting memory scan" is displayed.

[1354] The user wears an EEG scanner and recalls a specific memory (e.g., a college graduation ceremony).

[1355] The device receives the brainwave data from the scanner and transmits it to the server.

[1356] 3. Data Analysis and Storage

[1357] The server analyzes the received brainwave data and converts it into digital data.

[1358] The server stores the converted data in a distributed manner across backup servers.

[1359] 4. Data Access and Memory Replay

[1360] A user requests, "Play my graduation memory."

[1361] The terminal sends a request to the server.

[1362] The server searches for the corresponding stored data and transmits it to the terminal.

[1363] The device uses a generative AI model to play a graduation scene.

[1364] The user experiences the replayed memory.

[1365] Prompt Sentence Examples

[1366] "Replay your college graduation memories."

[1367] "Show us a visual memory of your family trip."

[1368] In this way, this system digitizes the user's consciousness and memories, making them available for storage, access, and playback in cyberspace.

[1369] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1370] Step 1: User authentication

[1371] The terminal starts user authentication when the system is started.

[1372] Input: The user performs fingerprint or facial recognition.

[1373] Operation:

[1374] 1. The device captures biometric data using a fingerprint scanner or camera.

[1375] 2. The device sends the acquired biometric data to the server.

[1376] 3. The server checks the authentication data stored in a database.

[1377] Output: The server returns the authentication result to the terminal, and if authentication is successful, access to the system is permitted.

[1378] Specific operation: For example, the server displays "Authentication completed. Welcome, user" on the terminal.

[1379] Step 2: Start the EEG scan

[1380] The device prompts the user to wear an EEG scanner and collects EEG data in real time.

[1381] Input: User wears an EEG scanner and focuses on a specific memory or thought.

[1382] Operation:

[1383] 1. The device collects EEG data through a scanner.

[1384] 2. The device transmits the collected brainwave data to the server in real time.

[1385] Output: The device sends the collected EEG data to the server.

[1386] Specific actions: For example, the device will display "Starting memory scan."

[1387] Step 3: Data analysis

[1388] The server analyzes the brain wave data sent from the terminal.

[1389] Input: Real-time EEG data sent from the device.

[1390] Operation:

[1391] 1. The server analyzes the EEG data using a specific algorithm (e.g., FFT analysis).

[1392] 2. The server extracts consciousness and memory information from the analysis results and converts it into digital data.

[1393] Output: Consciousness and memory information converted into digital form.

[1394] Specific behavior: For example, the server displays a spinning icon during the analysis process.

[1395] Step 4: Save Data

[1396] The server stores the analyzed digital data in cyberspace.

[1397] Input: Analyzed digital data.

[1398] Operation:

[1399] 1. The server stores digital data across multiple backup servers.

[1400] 2. The server takes steps to ensure data redundancy (e.g., set up a RAID configuration).

[1401] Output: Digital data stored in cyberspace.

[1402] Specific action: For example, the server logs "Data stored securely."

[1403] Step 5: Data Access Request

[1404] When a user wishes to review the stored data, the user makes a request via the terminal.

[1405] Input: The user enters a prompt statement requesting specific stored data.

[1406] Operation:

[1407] 1. The device sends the user's request to the server.

[1408] 2. The server searches for the corresponding stored data.

[1409] Output: Obtaining the relevant stored data.

[1410] Specific operation: For example, the user inputs "Please play the memory of the graduation ceremony."

[1411] Step 6: Download the stored data

[1412] The server receives the request and transmits the specified stored data to the terminal.

[1413] Input: A request for specific stored data.

[1414] Operation:

[1415] 1. The server searches and retrieves the relevant stored data from the database.

[1416] 2. The server converts the stored data into a downloadable format and sends it to the device.

[1417] Output: Stored data converted into a downloadable format.

[1418] Specific behavior: For example, the server displays "Sending data...".

[1419] Step 7: Memory Replay

[1420] The device reproduces the transmitted stored data using the generative AI model.

[1421] Input: Downloaded memory data.

[1422] Operation:

[1423] 1. The device inputs stored data into a generative AI model (e.g., GPT-4).

[1424] 2. A generative AI model generates visual and audio data to replay memories.

[1425] 3. The server monitors the playback in real time and makes corrections as needed.

[1426] Output: Visual and audio data of the replayed memory.

[1427] Specific operation: For example, the device displays "Memories will be reproduced using generative AI," and the user experiences the memories reproduced in a hologram.

[1428] Following this step, the system will digitize the user's consciousness and memories, allowing them to be stored, accessed, and replayed in cyberspace.

[1429] (Application example 1)

[1430] 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."

[1431] In the current shopping experience, it is difficult for users to easily recall their past purchasing history and experiences, making it difficult to receive personalized advice and suggestions. Shopping in brick-and-mortar stores, in particular, is limited in the information that can be obtained at one time, resulting in a lack of effective means to improve user satisfaction. Given this situation, a personal shopping assistant that utilizes a user's past purchasing experiences could improve the quality of the shopping experience.

[1432] 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.

[1433] In this invention, the server includes means for authenticating a user using biometric recognition technology, means for scanning the user's brainwaves and acquiring the brainwave data, means for analyzing the acquired brainwave data and converting consciousness and memory information into digital data, means for storing the converted digital data in cyberspace, means for converting the digital data stored in cyberspace into an accessible and downloadable format upon request from the user, and means for reproducing the downloaded data using a generative AI model and reproducing the stored data visually and audibly based on the user's purchasing history and preferences, thereby enabling personalized suggestions and advice based on the user's individual preferences and past purchasing history.

[1434] "Biometric recognition technology" refers to technology that uses biological information to identify individuals, such as fingerprint authentication or facial authentication.

[1435] "User authentication" is the process of verifying a user's identity when accessing a system.

[1436] An "EEG scan" is the process of measuring a user's brain waves and acquiring that data.

[1437] "EEG data" is digital data indicating brain activity obtained by an EEG scan.

[1438] "Data analysis" is a means of processing acquired EEG data and extracting consciousness and memory information.

[1439] "Digital data" refers to data that is an electronic representation of analyzed consciousness and memory information.

[1440] "Cyberspace" refers to a virtual digital environment such as the Internet.

[1441] A "backup server" is a redundant server for duplicating and storing digital data.

[1442] "Access" refers to having the authority to read, write, and manipulate stored digital data.

[1443] "Downloading" means taking data stored on a server into a user's terminal.

[1444] A "generative AI model" is an artificial intelligence technology that generates new data (images, text, etc.) based on input data.

[1445] "Visual" is a means of visually presenting information.

[1446] "Speech" is a means of conveying information auditorily.

[1447] A "personal shopping assistant" is a support system that provides suggestions and advice based on a user's individual preferences and past purchasing history.

[1448] The system for implementing this invention is a system that digitizes and stores a user's consciousness and memories, making them permanently accessible in cyberspace. This system is composed of the following main components:

[1449] System Configuration

[1450] 1. User authentication module

[1451] 2. EEG Scanning Device

[1452] 3. Data Analysis Module

[1453] 4. Data storage module

[1454] 5. Data Access / Download Module

[1455] 6. Memory Replay Module Using Generative AI

[1456] These components work together to digitize the user's consciousness and memories, and store and play them back in cyberspace.

[1457] User Authentication Module

[1458] The device uses biometric technology to authenticate users when they start up the system. Users can log in to the system using facial recognition. Once authentication is complete, access is granted.

[1459] Brain wave scanning device

[1460] Using an EEG scanner (e.g., Neurosky, EMOTIV), the user acquires brainwave data, which contains memory information related to a particular shopping experience.

[1461] Data Analysis Module

[1462] The server receives the EEG data sent from the device and analyzes it using an EEG analysis library such as EEGLab, which extracts the user's memory information and converts it into digital data.

[1463] Data Storage Module

[1464] The server stores the converted digital data in cyberspace. To ensure data redundancy, the data is stored across multiple backup servers.

[1465] Data Access / Download Module

[1466] When a user wants to play back the stored data, they access cyberspace via their device and request the data they need. The server processes the request, retrieves the data, converts it into a downloadable format, and sends it to the device.

[1467] Memory regeneration module using generative AI

[1468] The device then uses a generative AI model (e.g., ChatGPT, DALL·E) to play back the downloaded memory data, allowing the user to experience the memory visually and audibly. The generative AI model used has the ability to generate new data based on input data, and accurate memory playback is achieved by using prompt sentences.

[1469] Specific examples

[1470] Example prompt sentence:

[1471] "Please replay User A's Christmas shopping experience in December 2021, focusing specifically on the clothing section."

[1472] The prompts allow the generative AI model to recreate the user's memories visually and audibly, bringing past shopping experiences to life, allowing the user to receive more personalized advice and suggestions as they shop.

[1473] In this way, the system leverages the user's memory to enrich the in-store shopping experience and provide assistance based on individual preferences and past purchasing history.

[1474] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1475] Step 1:

[1476] User Authentication

[1477] Input: User's facial recognition data

[1478] Specific operation: The device's camera is used to scan the user's face and the data is sent to the server.

[1479] Data processing: The server uses biometric recognition technology to match the facial recognition data with the registered data in the database.

[1480] Output: The authentication result is returned to the terminal. If authentication is successful, access to the system is granted.

[1481] Step 2:

[1482] EEG scan

[1483] Input: User's brainwave data

[1484] How it works: The user wears an EEG scanner and recalls a specific memory. The device collects real-time EEG data from the scanner.

[1485] Data processing: The acquired EEG data is sent to the server.

[1486] Output: EEG data is sent to the server.

[1487] Step 3:

[1488] Data analysis

[1489] Input: EEG data sent to the server

[1490] Specific operation: The server uses an EEG analysis library such as EEGLab to analyze the transmitted EEG data.

[1491] Data processing: Extract consciousness and memory information from EEG data and convert it into digital data.

[1492] Output: Digital data is produced.

[1493] Step 4:

[1494] Data storage

[1495] Input: Converted digital data

[1496] Specific operation: The server stores the converted digital data in cyberspace.

[1497] Data processing: To ensure data redundancy, data is stored across multiple backup servers.

[1498] Output: The saving process is complete and the data is safely stored.

[1499] Step 5:

[1500] Data Request

[1501] Input: User's request to play back stored data

[1502] Specific operation: The user requests playback of stored data via the terminal.

[1503] Data Processing: The server retrieves the stored data based on the request and converts it into a downloadable format.

[1504] Output: Data is sent to the terminal.

[1505] Step 6:

[1506] memory replay

[1507] Input: Downloaded memory data

[1508] Specific operation: The device uses a generative AI model (e.g., ChatGPT or DALL·E) to regenerate stored data.

[1509] Data processing: Based on the prompt, the generative AI model recreates the user's memory visually and audibly.

[1510] Output: A specific memory playback such as "Play back User A's Christmas shopping experience in December 2021, focusing specifically on the clothing section."

[1511] Through the above steps, the user can access the system and experience his or her past purchasing experiences in a personalized manner.

[1512] 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.

[1513] A system for implementing this invention digitizes and stores a user's consciousness and memories, making them permanently accessible in cyberspace. Furthermore, this system incorporates an emotion engine that recognizes and reflects the user's emotions. Specific embodiments are described in detail below.

[1514] System configuration overview

[1515] The system consists of the following main components:

[1516] 1. User authentication module

[1517] 2. EEG Scanning Device

[1518] 3. Data Analysis Module

[1519] 4. Data storage module

[1520] 5. Data Access / Download Module

[1521] 6. Memory Replay Module Using Generative AI

[1522] 7. Emotion Engine

[1523] These components work together to digitize the user's consciousness, memories, and emotions, and store and reproduce them in cyberspace.

[1524] User Authentication Module

[1525] The terminal uses biometric recognition technology to authenticate the user when the system starts up. The user can log in to the system using fingerprint or facial recognition. The server verifies this and, if authentication is successful, allows further processing.

[1526] Brain wave scanning device

[1527] The device asks the user to wear an EEG scanner, which collects real-time brainwave data. The user concentrates on a specific memory or thought, which is then reflected in the scanner as brainwave data.

[1528] Emotion Engine

[1529] The device monitors and acquires information about the user's emotions in real time using an emotion engine, which analyzes emotions from the user's facial expressions, voice, and other biometric data.

[1530] Data Analysis Module

[1531] The server receives and analyzes the EEG and emotional data sent from the device. Using a specific algorithm, the server extracts the user's consciousness and memory information from the EEG data and integrates it with emotional information. This information is then converted and stored as digital data.

[1532] Data Storage Module

[1533] The server stores the analyzed digital data in cyberspace, and during the storage process, the data is distributed across multiple backup servers to ensure data redundancy.

[1534] Data Access / Download Module

[1535] When a user wants to review the stored data, they access cyberspace via their device and request the specified data. The server processes the request, searches for the relevant data, converts it into a downloadable format, and sends it to the device.

[1536] Memory regeneration module using generative AI

[1537] The device then uses a generative AI model to play back the downloaded memory data, allowing the user to experience the memory visually and audibly. The real-time information displayed is monitored by a server, which makes corrections as needed.

[1538] Specific examples

[1539] A specific usage scenario of the system is described below.

[1540] User Authentication

[1541] Device: "Starting recognition mode"

[1542] The user places their fingerprint on the scanner.

[1543] The device sends the fingerprint data to the server.

[1544] The server compares the fingerprint data with the database and returns the authentication result.

[1545] Terminal: "Authentication complete. Welcome, user."

[1546] EEG scanning and emotion recognition begins

[1547] Terminal: "Starting memory scan"

[1548] The user wears an EEG scanner and imagines their college graduation ceremony.

[1549] The device receives the EEG data from the scanner and transmits it to a server.

[1550] At the same time, the emotion engine acquires emotion data in real time from the user's facial expressions and voice.

[1551] The server analyzes the brainwave data and emotional data, and integrates and extracts memory information and emotional information.

[1552] Data conversion and storage

[1553] The server converts the analyzed data into a digital format.

[1554] The server stores the converted data on a backup server.

[1555] Data Access

[1556] User: "Replay my graduation memory."

[1557] The terminal sends this request to the server.

[1558] The server searches for the corresponding stored data and transmits it to the terminal.

[1559] The terminal converts the data into a playable format.

[1560] memory replay

[1561] Terminal: "Memories will be recreated using generative AI."

[1562] The device activates the generative AI model and plays a hologram of the graduation scene.

[1563] The user experiences the replayed memory.

[1564] The data being played back reflects the user's emotional information, enhancing the sense of realism.

[1565] In this way, this system digitizes the user's consciousness, memories, and even emotions, and makes them available for storage, access, and playback in cyberspace, thereby guaranteeing the permanent existence of individuals.

[1566] The processing flow will be explained below.

[1567] Step 1:

[1568] The device boots up and presents the user with a login screen, where the user attempts to access the system using biometric authentication (e.g., fingerprint or facial recognition).

[1569] Step 2:

[1570] The user enters their fingerprint into a biometric device or faces a face recognition camera, which captures the authentication data.

[1571] Step 3:

[1572] The device sends the acquired biometric authentication data to the server, which compares the received data with a database and, if authentication is successful, grants the user permission to log in.

[1573] Step 4:

[1574] The terminal notifies the user of the successful authentication result. After the user has logged in, they can proceed to the next step.

[1575] Step 5:

[1576] The device instructs the user to wear an EEG scanner, which the user then wears and concentrates on a specific memory or thought.

[1577] Step 6:

[1578] The device receives data in real time from the EEG scanner, acquires EEG data, and immediately transmits the acquired EEG data to a server.

[1579] Step 7:

[1580] The device uses an emotion engine to obtain emotional data in real time from the user's facial expressions, voice, and biometric signals.

[1581] Step 8:

[1582] The server analyzes the received brainwave and emotional data, extracts the user's consciousness and memory information, and integrates the emotional information. This integrated information is then converted into digital data.

[1583] Step 9:

[1584] The server organizes the converted digital data and prepares it for storage. The data is stored across multiple backup servers to ensure redundancy.

[1585] Step 10:

[1586] The server notifies the terminal that the save is complete, and the user confirms that their digital data has been safely saved.

[1587] Step 11:

[1588] When a user attempts to access the stored data, the user requests access to cyberspace from the terminal.

[1589] Step 12:

[1590] The server receives an access request from the user and searches for the specified stored data and emotion information. If the server finds the corresponding data, it sends it to the terminal.

[1591] Step 13:

[1592] The terminal converts the received data into a playable format and prepares it for presentation to the user.

[1593] Step 14:

[1594] The device runs a generative AI model and processes the playback data, which is then used as visuals and audio and presented to the user.

[1595] Step 15:

[1596] The user experiences memories reproduced by the generative AI. The reproduced data also reflects the user's emotional information, providing a more immersive experience. The device works in conjunction with the server to monitor and correct the data in real time to maintain the integrity and quality of the data being reproduced.

[1597] Step 16:

[1598] The device will notify the user that the storage recovery is complete and the process will end, allowing the user to start a new storage scan or save process.

[1599] Example 2

[1600] 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."

[1601] Despite advances in modern information technology, there are currently no systems that can accurately digitize a user's consciousness, memories, and even emotions, permanently store them, and then replay them as needed. Conventional systems acquire and store a user's consciousness and memory information in a fragmented manner, without integrating it with emotional information, making it impossible to fully reproduce the user's actual experience. Furthermore, methods for storing digitized data often lack redundancy and security. Therefore, there is a need for a system that can comprehensively digitize a user's consciousness, memories, and emotions, and store and replay them securely and permanently.

[1602] The specific processing by the specific 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 authenticating a user using biometric recognition technology, means for scanning the user's brain waves and acquiring the brain wave data, means for analyzing the acquired brain wave data and converting consciousness and memory information into digital data, means for acquiring the user's emotions through facial expressions and voice and generating emotional data, means for integrating the converted consciousness and memory information with the emotional data and storing it in cyberspace as digital data, means for converting the digital data stored in cyberspace into a format that can be accessed and downloaded upon request by the user, and means for reproducing the downloaded data using a generative AI model. This makes it possible to digitize the user's consciousness, memories, and emotions in an integrated manner and store and reproduce them securely and permanently.

[1603] "Biometric recognition technology" is a technology that authenticates individuals using biometric information such as a user's fingerprint, face, or iris.

[1604] "User authentication" is the process of using biometric technology to verify a user's identity and grant them access to a system.

[1605] "EEG scanning" refers to devices and technologies that record and capture a user's brain waves in real time.

[1606] "Electroencephalogram data" is digital information representing electrical activity acquired by an electroencephalogram scanning device.

[1607] "Consciousness and memory information" refers to data about the user's thoughts and memories that is analyzed and extracted from the user's brain wave data.

[1608] "Converting to digital data" refers to the process of converting electroencephalogram data and emotion data acquired as analog signals into a digital format.

[1609] "Emotion data" is digital information that indicates the emotional state of the user analyzed from facial expressions, voice, etc.

[1610] "Synthesis" is the process of combining different types of data (e.g., consciousness and memory information with emotional data) into a single dataset.

[1611] "Cyberspace" refers to virtual spaces where digital data is stored, such as the Internet and cloud storage.

[1612] "Accessible and downloadable format" refers to the conversion of digital data into a format that is easily accessible and retrievable by users.

[1613] A "generative AI model" is a type of artificial intelligence that refers to an algorithm that generates and reproduces new information and content from input data.

[1614] MODE FOR CARRYING OUT THE INVENTION

[1615] This invention is a system that digitizes a user's consciousness, memories, and emotions and stores, accesses, and reproduces them in cyberspace. Using the hardware and software shown below, the system acquires the user's consciousness, memories, and emotions and processes them as digital data.

[1616] System Configuration

[1617] The system consists of the following main components:

[1618] 1. User authentication module

[1619] 2. EEG Scanning Device

[1620] 3. Data Analysis Module

[1621] 4. Data storage module

[1622] 5. Data Access / Download Module

[1623] 6. Memory Replay Module Using Generative AI

[1624] 7. Emotion Engine

[1625] User Authentication Module

[1626] The device performs user authentication using, for example, fingerprint or facial recognition. The user places their fingerprint on a scanner or stands in front of a facial recognition camera. The device then sends this biometric data to a server, which compares it with a database and returns the authentication result.

[1627] Brain wave scanning device

[1628] The device asks the user to wear an EEG scanner, which collects real-time brainwave data. The user concentrates on a specific memory or thought, which is then collected as brainwave data. EEG scanning can be done using, for example, electroencephalography (EEG).

[1629] Emotion Engine

[1630] The device uses an emotion engine to acquire emotion data from the user's facial expressions and voice. This is done using input devices such as a camera and microphone. The acquired emotion data is also sent to the server in real time.

[1631] Data Analysis Module

[1632] The server receives the EEG and emotional data sent from the device and analyzes it using a specific algorithm. The analyzed data is converted into the user's consciousness and memory information, and emotional information is integrated. For example, analyzing the data using a machine learning model makes it possible to extract highly accurate consciousness and memory information.

[1633] Data Storage Module

[1634] The server stores the analyzed digital data in cyberspace. To ensure data redundancy, the data is stored across multiple backup servers. For example, a distributed database (such as Hadoop or Cassandra) can be used here.

[1635] Data Access / Download Module

[1636] When a user wants to look back at the stored memory data, they access cyberspace via their device and request the specified memory data. The server receives the request, searches for the corresponding data, converts it into a downloadable format, and sends it to the device.

[1637] Memory regeneration module using generative AI

[1638] The device then uses a generative AI model to play back the downloaded memory data, allowing the user to experience the memory visually and aurally. For example, playback can be performed using a VR device or a holographic display. Emotional information is reflected in the data being played, enhancing the sense of realism.

[1639] Specific examples

[1640] The following are specific usage scenarios for the system:

[1641] User Authentication

[1642] Device: "Starting recognition mode"

[1643] The user places their fingerprint on the scanner.

[1644] The device sends the fingerprint data to the server.

[1645] The server compares the fingerprint data with the database and returns the authentication result.

[1646] Terminal: "Authentication complete. Welcome, user."

[1647] EEG scanning and emotion recognition begins

[1648] Terminal: "Starting memory scan"

[1649] The user wears an EEG scanner and imagines their college graduation ceremony.

[1650] The device receives EEG data from the scanner and transmits it to the server, where an emotion engine simultaneously captures emotion data from the user's facial expressions and voice in real time.

[1651] The server analyzes the brainwave data and emotional data, and integrates and extracts memory information and emotional information.

[1652] Data conversion and storage

[1653] The server converts the analyzed data into a digital format.

[1654] The server stores the converted data on a backup server.

[1655] Data Access

[1656] User: "Replay my graduation memory."

[1657] The terminal sends this request to the server.

[1658] The server searches for the corresponding stored data and transmits it to the terminal.

[1659] The terminal converts the data into a playable format.

[1660] memory replay

[1661] Terminal: "Memories will be recreated using generative AI."

[1662] The device activates the generative AI model and plays a hologram of the graduation scene.

[1663] The user experiences the replayed memory. The data being replayed reflects the user's emotional information, enhancing the sense of realism.

[1664] This system makes it possible to digitize the user's consciousness, memories, and even emotions, and store, access, and replay them in cyberspace, thereby ensuring the perpetual existence of the individual.

[1665] Examples of prompt statements

[1666] "Replay your college graduation memories."

[1667] This prompt triggers the process of data access and storage recovery.

[1668] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1669] Step 1:

[1670] User Authentication

[1671] Input: The device captures fingerprint or facial recognition information. The user places their fingerprint on the scanner or stands in front of the facial recognition camera.

[1672] Specific operation: The device uses biometric recognition technology to acquire the user's fingerprint and facial data, and then transmits the acquired data to the server using a secure communication protocol (e.g., HTTPS).

[1673] Output: The server checks the database and returns the authentication result. The terminal displays the message "Authentication completed."

[1674] Step 2:

[1675] EEG scan begins

[1676] Input: Pre-authentication information obtained by the device from the user. The user wears an EEG scanner.

[1677] Specific operation: The device prompts the user to wear an EEG scanner, and acquires EEG data in real time through the scanner.

[1678] Output: The acquired EEG data is stored on the terminal, and when a certain amount is accumulated, it is sent to the server.

[1679] Step 3:

[1680] Emotion data acquisition

[1681] Input: The device collects real-time biometric data (facial expressions, voice) and brainwave data. The user focuses on a specific memory or thought.

[1682] Specific operation: The device activates the emotion engine and analyzes emotion data from the user's facial expressions and voice. Data is collected using the camera and microphone.

[1683] Output: Emotion data is sent to the server along with EEG data.

[1684] Step 4:

[1685] Data analysis and integration

[1686] Input: EEG data and emotion data received by the server.

[1687] Specific operation: The server analyzes EEG data and emotion data using specific algorithms. It uses machine learning models to extract consciousness and memory information and integrate it with emotion information.

[1688] Output: Convert the analysis results into digital data.

[1689] Step 5:

[1690] Data storage

[1691] Input: Consciousness, memory information, and emotion data analyzed on the server.

[1692] Specific operation: The server stores this data in a distributed database. To increase data redundancy, the data is stored in multiple backup servers.

[1693] Output: Digital data is stored securely and backed up.

[1694] Step 6:

[1695] Data Access and Download

[1696] Input: A data access request from the device (e.g., a prompt "Please play my graduation memory").

[1697] Specific operation: The server receives the request, searches the database for the corresponding stored data, converts the found data into a downloadable format, and sends it to the device.

[1698] Output: The terminal obtains the stored data specified by the user and converts it into a playable format.

[1699] Step 7:

[1700] memory replay

[1701] Input: Stored data downloaded to the terminal.

[1702] Specific operation: The device activates the generative AI model and reproduces the memory data through sight and sound. The visual and audio of the memory are reproduced using a VR device or holographic display.

[1703] Output: The user experiences the replayed memory. Emotional information is reflected in the replayed data, enhancing the sense of realism.

[1704] (Application example 2)

[1705] 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."

[1706] With conventional technology, it is difficult to store a user's memories and consciousness as digital data, and the technology for integrating information, including emotions, is insufficient. As a result, there has been no system that allows users to realistically replay past memories and experiences and reflect on them, including their emotions. As a result, memory replay is limited to a simple visual and auditory experience, making it difficult to reproduce a sense of presence or emotions. To solve this problem, a system is needed that can integrate and store memories and emotions and replay them using advanced technology such as holograms.

[1707] The specific processing by the specific 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 authenticating a user using biometric recognition technology, means for scanning the user's brain waves and acquiring the brain wave data, means for analyzing the acquired brain wave data and converting consciousness and memory information into digital data, means for analyzing the acquired emotional data and integrating the information as digital data, means for storing the converted digital data in cyberspace, means for converting the digital data stored in cyberspace into an accessible and downloadable format upon request from the user, means for reproducing the downloaded data using a generative AI model and presenting it to the user in holographic form or on a visual display, and means including an emotion engine that reflects the user's emotional state in real time. This makes it possible to store the user's memories and consciousness as digital data, integrate emotional information, and then reproduce the data with a sense of realism using a hologram or a visual display.

[1708] "Biometric recognition technology" is a technology that identifies individuals using biometric information such as a user's fingerprint or facial recognition.

[1709] An "EEG scanner" is a device that reads a user's brain waves in real time and acquires brain wave data.

[1710] "Electroencephalogram data" refers to data obtained by converting electrical signals obtained from the user's electroencephalogram into digital format.

[1711] The "emotion engine" analyzes emotions from biometric data such as the user's facial expressions and voice, and reflects the user's emotional state in real time.

[1712] "Consciousness and memory information" is information about the state of consciousness and memory contents extracted from the user's electroencephalogram data.

[1713] "Means for converting into digital data" refers to a system or algorithm that analyzes the acquired brainwave data and emotional data and converts it into digital form.

[1714] "Cyberspace" refers to virtual spaces such as the Internet and the cloud, where digital data can be stored, accessed, and shared.

[1715] A "generative AI model" is an algorithm or program that uses artificial intelligence to generate stored memory data into a reproducible form.

[1716] "Hologram format" is a technology for displaying three-dimensional images and videos, which are displayed with a visually three-dimensional appearance.

[1717] "Visual display" refers to a physical screen or device for presenting visual information to a user.

[1718] A "backup server" is a server that stores replicated data to ensure data redundancy.

[1719] "Emotion information" is digital data that analyzes the user's emotional state and is acquired by the emotion engine.

[1720] MODE FOR CARRYING OUT THE INVENTION

[1721] To implement this invention, it is necessary to build a system that digitizes and stores a user's consciousness and memories and makes them permanently accessible in cyberspace. This system is composed of the following main components: a user authentication module, an EEG scanning device, a data analysis module, a data storage module, a data access / download module, a memory playback module using generative AI, and an emotion engine. These components work together to digitize a user's consciousness, memories, and emotions, and store and play them back in cyberspace.

[1722] User Authentication Module

[1723] The server authenticates the user using biometric recognition technology, such as fingerprint or facial recognition, and allows them to log in to the system. The user enters their authentication information into the terminal, and the server compares it with a database and returns the authentication result.

[1724] Brain wave scanning device

[1725] The device has the user wear an EEG scanner, which collects real-time brainwave data. When the user concentrates on a specific memory or thought, that information is reflected in the scanner as brainwave data. The device then transmits this brainwave data to a server.

[1726] Emotion Engine

[1727] The device uses an emotion engine to acquire the user's emotional data in real time. The emotion engine analyzes the user's emotions from facial expressions, voice, and other biometric data. The acquired emotional data is sent to the server along with brainwave data.

[1728] Data Analysis Module

[1729] The server receives and analyzes the EEG and emotional data sent from the device. Using a specific algorithm, the server extracts the user's consciousness and memory information from the EEG data and integrates it with emotional information. This information is then converted and stored as digital data.

[1730] Data Storage Module

[1731] The server stores the analyzed digital data in cyberspace, and during the storage process, the data is distributed across multiple backup servers to ensure data redundancy.

[1732] Data Access / Download Module

[1733] When a user wants to review the stored data, they access cyberspace via their device and request the specified data. The server processes the request, searches for the relevant data, converts it into a downloadable format, and sends it to the device.

[1734] Memory regeneration module using generative AI

[1735] The device uses a generative AI model to play back the downloaded memory data, allowing the user to experience the memory visually and aurally. The played back information is monitored by a server, and corrections are made as necessary. The generative AI model provides a sense of realism, as if the user is actually experiencing past memories.

[1736] Specific examples

[1737] User Authentication

[1738] Device: "Starting recognition mode"

[1739] The user places their fingerprint on the scanner.

[1740] The device sends the fingerprint data to the server.

[1741] The server compares the fingerprint data with the database and returns the authentication result.

[1742] Terminal: "Authentication complete. Welcome, user."

[1743] EEG scanning and emotion recognition begins

[1744] Terminal: "Starting memory scan"

[1745] The user wears an EEG scanner and imagines their college graduation ceremony.

[1746] The device receives the EEG data from the scanner and transmits it to a server.

[1747] At the same time, the emotion engine acquires emotion data in real time from the user's facial expressions and voice.

[1748] The server analyzes the brainwave data and emotional data, and integrates and extracts memory information and emotional information.

[1749] Data conversion and storage

[1750] The server converts the analyzed data into a digital format.

[1751] The server stores the converted data on a backup server.

[1752] Data Access

[1753] User: "Replay my graduation memory."

[1754] The terminal sends this request to the server.

[1755] The server searches for the corresponding stored data and transmits it to the terminal.

[1756] The terminal converts the data into a playable format.

[1757] memory replay

[1758] Terminal: "Memories will be recreated using generative AI."

[1759] The device activates the generative AI model and plays a hologram of the graduation scene.

[1760] The user experiences the replayed memory.

[1761] The data being played back reflects the user's emotional information, enhancing the sense of realism.

[1762] Prompt Sentence Examples

[1763] "Please replay user X's memory of their college graduation. The user's emotions are filled with joy and excitement. Please replay it in a holographic format to create a realistic experience."

[1764] This particular embodiment of the invention allows users to store their memories and emotions as digital data and have a realistic experience when playing it back.

[1765] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1766] Step 1: User authentication

[1767] How it works: The device prompts the user to enter authentication information (fingerprint or face).

[1768] Input: Fingerprint data or face data

[1769] Data processing / calculation: The device collects authentication information and sends it to the server, which checks it against a database and performs authentication.

[1770] Output: Authentication result (success or failure)

[1771] Step 2: Start the EEG scan

[1772] How it works: The device prompts the user to wear an EEG scanner. The user puts on the scanner and concentrates on a specific memory.

[1773] Input: User's brainwaves

[1774] Data processing / calculation: The EEG scanner acquires EEG data in real time and transmits it to the terminal.

[1775] Output: Acquired EEG data

[1776] Step 3: Collecting emotion data

[1777] Operation: The device activates the emotion engine and acquires emotion data in real time from the user's facial expressions and voice.

[1778] Input: User's facial expression data, voice data

[1779] Data processing / calculation: The emotion engine analyzes facial expression data and voice data to detect emotional states.

[1780] Output: User emotion data

[1781] Step 4: Analyze and synthesize data

[1782] Operation: The server analyzes the brain wave data and emotion data sent from the device.

[1783] Input: EEG data, emotion data

[1784] Data processing / computation: Using specific algorithms, consciousness and memory information is extracted from EEG data and emotional information is integrated.

[1785] Output: Integrated memory and emotion data

[1786] Step 5: Save Data

[1787] How it works: The server converts the analyzed data into digital form and stores it in cyberspace.

[1788] Input: Integrated memory and emotion data

[1789] Data processing / calculation: Data is digitized and stored in multiple backup servers.

[1790] Output: Stored digital data

[1791] Step 6: Data Access

[1792] Operation: A user requests playback of stored stored data, and the terminal sends the request to the server.

[1793] Input: User's memory recall request

[1794] Data processing / computation: The server processes the request, retrieves the relevant stored data, and converts it into a downloadable format.

[1795] Output: Downloaded memory data

[1796] Step 7: Memory Replay

[1797] How it works: The device uses generative AI to recreate stored data in holographic form or on a visual display.

[1798] Input: Downloaded memory data

[1799] Data processing / computation: A generative AI model analyzes stored data and generates it in a format that can be reproduced as a hologram or visual display.

[1800] Output: Reproducible stored data

[1801] Step 8: Reflect and Replay Emotions

[1802] Operation: The device acquires the user's real-time emotional information during playback and reflects it in the playback.

[1803] Input: Current user emotion data

[1804] Data processing / calculation: The emotion engine analyzes the emotional data acquired in real time and reflects it in the memory data being played back.

[1805] Output: Memory replay data reflecting emotional information

[1806] These processing steps allow users to digitize their memories and emotions in detail, allowing them to later re-experience them in a realistic way.

[1807] 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.

[1808] 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.

[1809] 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.

[1810] [Fourth embodiment]

[1811] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[1812] 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.

[1813] 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).

[1814] 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.

[1815] 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.

[1816] 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).

[1817] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[1818] 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.

[1819] 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.

[1820] 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.

[1821] 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.

[1822] 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.

[1823] 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."

[1824] A system for carrying out the present invention is a system that digitizes and stores a user's consciousness and memories, and makes them permanently accessible in cyberspace. Specific embodiments will be described below.

[1825] System configuration overview

[1826] The system consists of the following main components:

[1827] 1. User authentication module

[1828] 2. EEG Scanning Device

[1829] 3. Data Analysis Module

[1830] 4. Data storage module

[1831] 5. Data Access / Download Module

[1832] 6. Memory Replay Module Using Generative AI

[1833] These components work together to digitize the user's consciousness and memories, and store and play them back in cyberspace.

[1834] User Authentication Module

[1835] The terminal uses biometric recognition technology to authenticate the user when the system starts up. The user can log in to the system using fingerprint or facial recognition. The server verifies this and, if authentication is successful, allows further processing.

[1836] Brain wave scanning device

[1837] The device asks the user to wear an EEG scanner, which collects real-time brainwave data. The user concentrates on a specific memory or thought, which is then reflected in the scanner as brainwave data.

[1838] Data Analysis Module

[1839] The server receives and analyzes the EEG data sent from the device. Using a specific algorithm, the server extracts information about the user's consciousness and memory from the EEG data. This information is then converted and stored as digital data.

[1840] Data Storage Module

[1841] The server stores the analyzed digital data in cyberspace, and during the storage process, the data is distributed across multiple backup servers to ensure data redundancy.

[1842] Data Access / Download Module

[1843] When a user wants to review the stored data, they access cyberspace via their device and request the specified data. The server processes the request, searches for the relevant data, converts it into a downloadable format, and sends it to the device.

[1844] Memory regeneration module using generative AI

[1845] The device then uses a generative AI model to play back the downloaded memory data, allowing the user to experience the memory visually and audibly. The real-time information displayed is monitored by a server, which makes corrections as needed.

[1846] Specific examples

[1847] A specific usage scenario of the system is described below.

[1848] User Authentication

[1849] Device: "Starting recognition mode"

[1850] The user places their fingerprint on the scanner.

[1851] The device sends the fingerprint data to the server.

[1852] The server compares the fingerprint data with the database and returns the authentication result.

[1853] Terminal: "Authentication complete. Welcome, user."

[1854] EEG scan begins

[1855] Terminal: "Starting memory scan"

[1856] The user wears an EEG scanner and imagines their college graduation ceremony.

[1857] The device receives the EEG data from the scanner and transmits it to a server.

[1858] The server analyzes the data and extracts the stored information.

[1859] Data conversion and storage

[1860] The server converts the analyzed data into a digital format.

[1861] The server stores the converted data on a backup server.

[1862] Data Access

[1863] User: "Replay my graduation memory."

[1864] The terminal sends this request to the server.

[1865] The server searches for the corresponding stored data and transmits it to the terminal.

[1866] The terminal converts the data into a playable format.

[1867] memory replay

[1868] Terminal: "Memories will be recreated using generative AI."

[1869] The device activates the generative AI model and plays a hologram of the graduation scene.

[1870] The user experiences the replayed memory.

[1871] In this way, this system digitizes the user's consciousness and memories, making them available for storage, access, and playback in cyberspace, thereby guaranteeing the perpetual existence of individuals.

[1872] The processing flow will be explained below.

[1873] Step 1:

[1874] The device boots up and presents the user with a login screen, where the user attempts to access the system using biometric authentication (e.g., fingerprint or facial recognition).

[1875] Step 2:

[1876] The user enters their fingerprint into a biometric device or faces a face recognition camera, which captures the authentication data.

[1877] Step 3:

[1878] The device sends the acquired biometric authentication data to the server, which compares the received data with a database and, if authentication is successful, grants the user permission to log in.

[1879] Step 4:

[1880] The terminal notifies the user of the successful authentication result. After the user has logged in, they can proceed to the next step.

[1881] Step 5:

[1882] The device instructs the user to wear an EEG scanner, which the user then wears and concentrates on a specific memory or thought.

[1883] Step 6:

[1884] The device receives data in real time from the EEG scanner, acquires EEG data, and immediately transmits the acquired EEG data to a server.

[1885] Step 7:

[1886] The server analyzes the received brainwave data and extracts the user's consciousness and memory information, which is then converted into digital data.

[1887] Step 8:

[1888] The server organizes the converted digital data and prepares it for storage. The data is stored across multiple backup servers to ensure redundancy.

[1889] Step 9:

[1890] The server notifies the terminal that the save is complete, and the user confirms that their digital data has been safely saved.

[1891] Step 10:

[1892] When a user attempts to access the stored data, the user requests access to cyberspace from the terminal.

[1893] Step 11:

[1894] The server receives an access request from the user and searches for the specified stored data. If the corresponding data is found, the server sends it to the terminal.

[1895] Step 12:

[1896] The terminal converts the received data into a playable format and prepares it for presentation to the user.

[1897] Step 13:

[1898] The device runs a generative AI model and processes the playback data, which is then used as visuals and audio and presented to the user.

[1899] Step 14:

[1900] The user experiences memories reproduced by the generative AI. The device works in conjunction with the server to monitor and correct the data in real time during playback to maintain its integrity and quality.

[1901] Step 15:

[1902] The device will notify the user that the storage recovery is complete and the process will end, allowing the user to start a new storage scan or save process.

[1903] Example 1

[1904] 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."

[1905] In recent years, there has been a demand for technology that can store an individual's consciousness and memories as digital data and access and replay them in cyberspace. However, achieving this requires complex processes, such as user authentication, scanning and analyzing EEG data, storing and accessing the data, and replaying it using a generative AI model. Previous technology lacked a system that could handle these processes in an integrated manner, leaving users lacking the means to properly manage and replay their consciousness and memories.

[1906] 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.

[1907] In this invention, the server includes means for authenticating a user using biometric authentication technology, means for scanning the user's brainwaves and acquiring the brainwave data, means for analyzing the acquired brainwave data and converting consciousness and memory information into digital data, means for storing the converted digital data in cyberspace, means for converting the digital data stored in cyberspace into a format that can be accessed and downloaded upon request by the user, means for replaying the downloaded data using a generative AI model, and means for monitoring the replayed memory data in real time and making corrections as necessary, thereby enabling the user's consciousness and memories to be digitized and permanently stored, accessed, and replayed in cyberspace.

[1908] "Biometric authentication technology" is a technology that identifies individuals using specific physical characteristics of the user, such as fingerprint authentication or facial authentication.

[1909] "Scanning brainwaves" is the process of acquiring a user's brainwave signals using sensors.

[1910] "Electroencephalogram data" refers to data that is a digital record of scanned electroencephalogram signals.

[1911] "Conversion to digital data" refers to the process of analyzing EEG data, which is an analog signal, and converting it into digital information.

[1912] "Cyberspace storage" is the process of storing converted digital data in data storage on the Internet.

[1913] "Converting to an accessible and downloadable format" is the process of converting stored digital data into a format that can be played or viewed by a user.

[1914] A "generative AI model" is a technology that uses a pre-trained artificial intelligence model to reproduce a user's memories and consciousness as visual or audio data.

[1915] "Monitoring stored data reproduced in real time" refers to the process of monitoring stored data being reproduced in real time and correcting its contents as necessary.

[1916] "Correction" is the process of correcting errors or inconsistencies that occur during playback of stored data.

[1917] "Means" is a term that refers to a device or method used to achieve a particular purpose.

[1918] MODE FOR CARRYING OUT THE INVENTION

[1919] A system for carrying out this invention digitizes and stores a user's consciousness and memories, making them permanently accessible in cyberspace. A specific embodiment of this system will be described below.

[1920] System Configuration

[1921] The system consists of the following main components:

[1922] 1. User authentication module

[1923] 2. EEG Scanning Device

[1924] 3. Data Analysis Module

[1925] 4. Data storage module

[1926] 5. Data Access / Download Module

[1927] 6. Memory Replay Module Using Generative AI

[1928] User Authentication Module

[1929] The terminal uses biometric authentication technology to authenticate the user when the system starts up. Common biometric authentication technologies include fingerprint authentication and facial authentication. The user performs these biometric authentications to log in to the system. The server receives the authentication data (e.g., fingerprint data or facial image data), compares it with a registered database, and returns the authentication result to the terminal.

[1930] Brain wave scanning device

[1931] The device prompts the user to wear an EEG scanner (e.g., a specific headset device) and collects real-time brainwave data. The user concentrates on a specific memory or thought, which is reflected in the scanner as brainwave data. The device then transmits this data to a server.

[1932] Data Analysis Module

[1933] The server analyzes the EEG data sent from the device using specific algorithms (e.g., FFT analysis or machine learning algorithms). This analysis extracts the user's consciousness and memory information, which is then converted into digital data. Frameworks such as TensorFlow and PyTorch are often used for data analysis.

[1934] Data Storage Module

[1935] The server stores the analyzed digital data in cyberspace. The stored data is distributed across multiple backup servers to ensure redundancy. For example, by using a cloud storage service, the reliability and availability of the data is ensured.

[1936] Data Access / Download Module

[1937] When a user wants to look back at the stored data, they can access cyberspace via their device and request the specified data. The server receives the request, searches for the relevant data, converts it into a downloadable format, and sends it to the device.

[1938] Memory regeneration module using generative AI

[1939] The device uses a generative AI model to play back memory data downloaded from the server, allowing the user to experience memories through visuals and audio. The generative AI model uses pre-trained large-scale language models (e.g., the GPT series) and image generation models. The information played back in real time is monitored by the server, and corrections are made as necessary.

[1940] Specific examples

[1941] Below are some specific scenarios in which the system can be used:

[1942] 1. User Authentication

[1943] Device: "Starting recognition mode" is displayed.

[1944] The user places their fingerprint on the scanner.

[1945] The device sends the fingerprint data to the server.

[1946] The server checks the database and displays the message "Authentication complete. Welcome, user."

[1947] 2. Starting the EEG scan

[1948] Terminal: "Starting memory scan" is displayed.

[1949] The user wears an EEG scanner and recalls a specific memory (e.g., a college graduation ceremony).

[1950] The device receives the brainwave data from the scanner and transmits it to the server.

[1951] 3. Data Analysis and Storage

[1952] The server analyzes the received brainwave data and converts it into digital data.

[1953] The server stores the converted data in a distributed manner across backup servers.

[1954] 4. Data Access and Memory Replay

[1955] A user requests, "Play my graduation memory."

[1956] The terminal sends a request to the server.

[1957] The server searches for the corresponding stored data and transmits it to the terminal.

[1958] The device uses a generative AI model to play a graduation scene.

[1959] The user experiences the replayed memory.

[1960] Prompt Sentence Examples

[1961] "Replay your college graduation memories."

[1962] "Show us a visual memory of your family trip."

[1963] In this way, this system digitizes the user's consciousness and memories, making them available for storage, access, and playback in cyberspace.

[1964] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1965] Step 1: User authentication

[1966] The terminal starts user authentication when the system is started.

[1967] Input: The user performs fingerprint or facial recognition.

[1968] Operation:

[1969] 1. The device captures biometric data using a fingerprint scanner or camera.

[1970] 2. The device sends the acquired biometric data to the server.

[1971] 3. The server checks the authentication data stored in a database.

[1972] Output: The server returns the authentication result to the terminal, and if authentication is successful, access to the system is permitted.

[1973] Specific operation: For example, the server displays "Authentication completed. Welcome, user" on the terminal.

[1974] Step 2: Start the EEG scan

[1975] The device prompts the user to wear an EEG scanner and collects EEG data in real time.

[1976] Input: User wears an EEG scanner and focuses on a specific memory or thought.

[1977] Operation:

[1978] 1. The device collects EEG data through a scanner.

[1979] 2. The device transmits the collected brainwave data to the server in real time.

[1980] Output: The device sends the collected EEG data to the server.

[1981] Specific actions: For example, the device will display "Starting memory scan."

[1982] Step 3: Data analysis

[1983] The server analyzes the brain wave data sent from the terminal.

[1984] Input: Real-time EEG data sent from the device.

[1985] Operation:

[1986] 1. The server analyzes the EEG data using a specific algorithm (e.g., FFT analysis).

[1987] 2. The server extracts consciousness and memory information from the analysis results and converts it into digital data.

[1988] Output: Consciousness and memory information converted into digital form.

[1989] Specific behavior: For example, the server displays a spinning icon during the analysis process.

[1990] Step 4: Save Data

[1991] The server stores the analyzed digital data in cyberspace.

[1992] Input: Analyzed digital data.

[1993] Operation:

[1994] 1. The server stores digital data across multiple backup servers.

[1995] 2. The server takes steps to ensure data redundancy (e.g., set up a RAID configuration).

[1996] Output: Digital data stored in cyberspace.

[1997] Specific action: For example, the server logs "Data stored securely."

[1998] Step 5: Data Access Request

[1999] When a user wishes to review the stored data, the user makes a request via the terminal.

[2000] Input: The user enters a prompt statement requesting specific stored data.

[2001] Operation:

[2002] 1. The device sends the user's request to the server.

[2003] 2. The server searches for the corresponding stored data.

[2004] Output: Obtaining the relevant stored data.

[2005] Specific operation: For example, the user inputs "Please play the memory of the graduation ceremony."

[2006] Step 6: Download the stored data

[2007] The server receives the request and transmits the specified stored data to the terminal.

[2008] Input: A request for specific stored data.

[2009] Operation:

[2010] 1. The server searches and retrieves the relevant stored data from the database.

[2011] 2. The server converts the stored data into a downloadable format and sends it to the device.

[2012] Output: Stored data converted into a downloadable format.

[2013] Specific behavior: For example, the server displays "Sending data...".

[2014] Step 7: Memory Replay

[2015] The device reproduces the transmitted stored data using the generative AI model.

[2016] Input: Downloaded memory data.

[2017] Operation:

[2018] 1. The device inputs stored data into a generative AI model (e.g., GPT-4).

[2019] 2. A generative AI model generates visual and audio data to replay memories.

[2020] 3. The server monitors the playback in real time and makes corrections as needed.

[2021] Output: Visual and audio data of the replayed memory.

[2022] Specific operation: For example, the device displays "Memories will be reproduced using generative AI," and the user experiences the memories reproduced in a hologram.

[2023] Following this step, the system will digitize the user's consciousness and memories, allowing them to be stored, accessed, and replayed in cyberspace.

[2024] (Application example 1)

[2025] 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."

[2026] In the current shopping experience, it is difficult for users to easily recall their past purchasing history and experiences, making it difficult to receive personalized advice and suggestions. Shopping in brick-and-mortar stores, in particular, is limited in the information that can be obtained at one time, resulting in a lack of effective means to improve user satisfaction. Given this situation, a personal shopping assistant that utilizes a user's past purchasing experiences could improve the quality of the shopping experience.

[2027] 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.

[2028] In this invention, the server includes means for authenticating a user using biometric recognition technology, means for scanning the user's brainwaves and acquiring the brainwave data, means for analyzing the acquired brainwave data and converting consciousness and memory information into digital data, means for storing the converted digital data in cyberspace, means for converting the digital data stored in cyberspace into an accessible and downloadable format upon request from the user, and means for reproducing the downloaded data using a generative AI model and reproducing the stored data visually and audibly based on the user's purchasing history and preferences, thereby enabling personalized suggestions and advice based on the user's individual preferences and past purchasing history.

[2029] "Biometric recognition technology" refers to technology that uses biological information to identify individuals, such as fingerprint authentication or facial authentication.

[2030] "User authentication" is the process of verifying a user's identity when accessing a system.

[2031] An "EEG scan" is the process of measuring a user's brain waves and acquiring that data.

[2032] "EEG data" is digital data indicating brain activity obtained by an EEG scan.

[2033] "Data analysis" is a means of processing acquired EEG data and extracting consciousness and memory information.

[2034] "Digital data" refers to data that is an electronic representation of analyzed consciousness and memory information.

[2035] "Cyberspace" refers to a virtual digital environment such as the Internet.

[2036] A "backup server" is a redundant server for duplicating and storing digital data.

[2037] "Access" refers to having the authority to read, write, and manipulate stored digital data.

[2038] "Downloading" means taking data stored on a server into a user's terminal.

[2039] A "generative AI model" is an artificial intelligence technology that generates new data (images, text, etc.) based on input data.

[2040] "Visual" is a means of visually presenting information.

[2041] "Speech" is a means of conveying information auditorily.

[2042] A "personal shopping assistant" is a support system that provides suggestions and advice based on a user's individual preferences and past purchasing history.

[2043] The system for implementing this invention is a system that digitizes and stores a user's consciousness and memories, making them permanently accessible in cyberspace. This system is composed of the following main components:

[2044] System Configuration

[2045] 1. User authentication module

[2046] 2. EEG Scanning Device

[2047] 3. Data Analysis Module

[2048] 4. Data storage module

[2049] 5. Data Access / Download Module

[2050] 6. Memory Replay Module Using Generative AI

[2051] These components work together to digitize the user's consciousness and memories, and store and play them back in cyberspace.

[2052] User Authentication Module

[2053] The device uses biometric technology to authenticate users when they start up the system. Users can log in to the system using facial recognition. Once authentication is complete, access is granted.

[2054] Brain wave scanning device

[2055] Using an EEG scanner (e.g., Neurosky, EMOTIV), the user acquires brainwave data, which contains memory information related to a particular shopping experience.

[2056] Data Analysis Module

[2057] The server receives the EEG data sent from the device and analyzes it using an EEG analysis library such as EEGLab, which extracts the user's memory information and converts it into digital data.

[2058] Data Storage Module

[2059] The server stores the converted digital data in cyberspace. To ensure data redundancy, the data is stored across multiple backup servers.

[2060] Data Access / Download Module

[2061] When a user wants to play back the stored data, they access cyberspace via their device and request the data they need. The server processes the request, retrieves the data, converts it into a downloadable format, and sends it to the device.

[2062] Memory regeneration module using generative AI

[2063] The device then uses a generative AI model (e.g., ChatGPT, DALL·E) to play back the downloaded memory data, allowing the user to experience the memory visually and audibly. The generative AI model used has the ability to generate new data based on input data, and accurate memory playback is achieved by using prompt sentences.

[2064] Specific examples

[2065] Example prompt sentence:

[2066] "Please replay User A's Christmas shopping experience in December 2021, focusing specifically on the clothing section."

[2067] The prompts allow the generative AI model to recreate the user's memories visually and audibly, bringing past shopping experiences to life, allowing the user to receive more personalized advice and suggestions as they shop.

[2068] In this way, the system leverages the user's memory to enrich the in-store shopping experience and provide assistance based on individual preferences and past purchasing history.

[2069] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[2070] Step 1:

[2071] User Authentication

[2072] Input: User's facial recognition data

[2073] Specific operation: The device's camera is used to scan the user's face and the data is sent to the server.

[2074] Data processing: The server uses biometric recognition technology to match the facial recognition data with the registered data in the database.

[2075] Output: The authentication result is returned to the terminal. If authentication is successful, access to the system is granted.

[2076] Step 2:

[2077] EEG scan

[2078] Input: User's brainwave data

[2079] How it works: The user wears an EEG scanner and recalls a specific memory. The device collects real-time EEG data from the scanner.

[2080] Data processing: The acquired EEG data is sent to the server.

[2081] Output: EEG data is sent to the server.

[2082] Step 3:

[2083] Data analysis

[2084] Input: EEG data sent to the server

[2085] Specific operation: The server uses an EEG analysis library such as EEGLab to analyze the transmitted EEG data.

[2086] Data processing: Extract consciousness and memory information from EEG data and convert it into digital data.

[2087] Output: Digital data is produced.

[2088] Step 4:

[2089] Data storage

[2090] Input: Converted digital data

[2091] Specific operation: The server stores the converted digital data in cyberspace.

[2092] Data processing: To ensure data redundancy, data is stored across multiple backup servers.

[2093] Output: The saving process is complete and the data is safely stored.

[2094] Step 5:

[2095] Data Request

[2096] Input: User's request to play back stored data

[2097] Specific operation: The user requests playback of stored data via the terminal.

[2098] Data Processing: The server retrieves the stored data based on the request and converts it into a downloadable format.

[2099] Output: Data is sent to the terminal.

[2100] Step 6:

[2101] memory replay

[2102] Input: Downloaded memory data

[2103] Specific operation: The device uses a generative AI model (e.g., ChatGPT or DALL·E) to regenerate stored data.

[2104] Data processing: Based on the prompt, the generative AI model recreates the user's memory visually and audibly.

[2105] Output: A specific memory playback such as "Play back User A's Christmas shopping experience in December 2021, focusing specifically on the clothing section."

[2106] Through the above steps, the user can access the system and experience his or her past purchasing experiences in a personalized manner.

[2107] 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.

[2108] A system for implementing this invention digitizes and stores a user's consciousness and memories, making them permanently accessible in cyberspace. Furthermore, this system incorporates an emotion engine that recognizes and reflects the user's emotions. Specific embodiments are described in detail below.

[2109] System configuration overview

[2110] The system consists of the following main components:

[2111] 1. User authentication module

[2112] 2. EEG Scanning Device

[2113] 3. Data Analysis Module

[2114] 4. Data storage module

[2115] 5. Data Access / Download Module

[2116] 6. Memory Replay Module Using Generative AI

[2117] 7. Emotion Engine

[2118] These components work together to digitize the user's consciousness, memories, and emotions, and store and reproduce them in cyberspace.

[2119] User Authentication Module

[2120] The terminal uses biometric recognition technology to authenticate the user when the system starts up. The user can log in to the system using fingerprint or facial recognition. The server verifies this and, if authentication is successful, allows further processing.

[2121] Brain wave scanning device

[2122] The device asks the user to wear an EEG scanner, which collects real-time brainwave data. The user concentrates on a specific memory or thought, which is then reflected in the scanner as brainwave data.

[2123] Emotion Engine

[2124] The device monitors and acquires information about the user's emotions in real time using an emotion engine, which analyzes emotions from the user's facial expressions, voice, and other biometric data.

[2125] Data Analysis Module

[2126] The server receives and analyzes the EEG and emotional data sent from the device. Using a specific algorithm, the server extracts the user's consciousness and memory information from the EEG data and integrates it with emotional information. This information is then converted and stored as digital data.

[2127] Data Storage Module

[2128] The server stores the analyzed digital data in cyberspace, and during the storage process, the data is distributed across multiple backup servers to ensure data redundancy.

[2129] Data Access / Download Module

[2130] When a user wants to review the stored data, they access cyberspace via their device and request the specified data. The server processes the request, searches for the relevant data, converts it into a downloadable format, and sends it to the device.

[2131] Memory regeneration module using generative AI

[2132] The device then uses a generative AI model to play back the downloaded memory data, allowing the user to experience the memory visually and audibly. The real-time information displayed is monitored by a server, which makes corrections as needed.

[2133] Specific examples

[2134] A specific usage scenario of the system is described below.

[2135] User Authentication

[2136] Device: "Starting recognition mode"

[2137] The user places their fingerprint on the scanner.

[2138] The device sends the fingerprint data to the server.

[2139] The server compares the fingerprint data with the database and returns the authentication result.

[2140] Terminal: "Authentication complete. Welcome, user."

[2141] EEG scanning and emotion recognition begins

[2142] Terminal: "Starting memory scan"

[2143] The user wears an EEG scanner and imagines their college graduation ceremony.

[2144] The device receives the EEG data from the scanner and transmits it to a server.

[2145] At the same time, the emotion engine acquires emotion data in real time from the user's facial expressions and voice.

[2146] The server analyzes the brainwave data and emotional data, and integrates and extracts memory information and emotional information.

[2147] Data conversion and storage

[2148] The server converts the analyzed data into a digital format.

[2149] The server stores the converted data on a backup server.

[2150] Data Access

[2151] User: "Replay my graduation memory."

[2152] The terminal sends this request to the server.

[2153] The server searches for the corresponding stored data and transmits it to the terminal.

[2154] The terminal converts the data into a playable format.

[2155] memory replay

[2156] Terminal: "Memories will be recreated using generative AI."

[2157] The device activates the generative AI model and plays a hologram of the graduation scene.

[2158] The user experiences the replayed memory.

[2159] The data being played back reflects the user's emotional information, enhancing the sense of realism.

[2160] In this way, this system digitizes the user's consciousness, memories, and even emotions, and makes them available for storage, access, and playback in cyberspace, thereby guaranteeing the permanent existence of individuals.

[2161] The processing flow will be explained below.

[2162] Step 1:

[2163] The device boots up and presents the user with a login screen, where the user attempts to access the system using biometric authentication (e.g., fingerprint or facial recognition).

[2164] Step 2:

[2165] The user enters their fingerprint into a biometric device or faces a face recognition camera, which captures the authentication data.

[2166] Step 3:

[2167] The device sends the acquired biometric authentication data to the server, which compares the received data with a database and, if authentication is successful, grants the user permission to log in.

[2168] Step 4:

[2169] The terminal notifies the user of the successful authentication result. After the user has logged in, they can proceed to the next step.

[2170] Step 5:

[2171] The device instructs the user to wear an EEG scanner, which the user then wears and concentrates on a specific memory or thought.

[2172] Step 6:

[2173] The device receives data in real time from the EEG scanner, acquires EEG data, and immediately transmits the acquired EEG data to a server.

[2174] Step 7:

[2175] The device uses an emotion engine to obtain emotional data in real time from the user's facial expressions, voice, and biometric signals.

[2176] Step 8:

[2177] The server analyzes the received brainwave and emotional data, extracts the user's consciousness and memory information, and integrates the emotional information. This integrated information is then converted into digital data.

[2178] Step 9:

[2179] The server organizes the converted digital data and prepares it for storage. The data is stored across multiple backup servers to ensure redundancy.

[2180] Step 10:

[2181] The server notifies the terminal that the save is complete, and the user confirms that their digital data has been safely saved.

[2182] Step 11:

[2183] When a user attempts to access the stored data, the user requests access to cyberspace from the terminal.

[2184] Step 12:

[2185] The server receives an access request from the user and searches for the specified stored data and emotion information. If the server finds the corresponding data, it sends it to the terminal.

[2186] Step 13:

[2187] The terminal converts the received data into a playable format and prepares it for presentation to the user.

[2188] Step 14:

[2189] The device runs a generative AI model and processes the playback data, which is then used as visuals and audio and presented to the user.

[2190] Step 15:

[2191] The user experiences memories reproduced by the generative AI. The reproduced data also reflects the user's emotional information, providing a more immersive experience. The device works in conjunction with the server to monitor and correct the data in real time to maintain the integrity and quality of the data being reproduced.

[2192] Step 16:

[2193] The device will notify the user that the storage recovery is complete and the process will end, allowing the user to start a new storage scan or save process.

[2194] Example 2

[2195] 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."

[2196] Despite advances in modern information technology, there are currently no systems that can accurately digitize a user's consciousness, memories, and even emotions, permanently store them, and then replay them as needed. Conventional systems acquire and store a user's consciousness and memory information in a fragmented manner, without integrating it with emotional information, making it impossible to fully reproduce the user's actual experience. Furthermore, methods for storing digitized data often lack redundancy and security. Therefore, there is a need for a system that can comprehensively digitize a user's consciousness, memories, and emotions, and store and replay them securely and permanently.

[2197] The specific processing by the specific 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 authenticating a user using biometric recognition technology, means for scanning the user's brain waves and acquiring the brain wave data, means for analyzing the acquired brain wave data and converting consciousness and memory information into digital data, means for acquiring the user's emotions through facial expressions and voice and generating emotional data, means for integrating the converted consciousness and memory information with the emotional data and storing it in cyberspace as digital data, means for converting the digital data stored in cyberspace into a format that can be accessed and downloaded upon request by the user, and means for reproducing the downloaded data using a generative AI model. This makes it possible to digitize the user's consciousness, memories, and emotions in an integrated manner and store and reproduce them securely and permanently.

[2198] "Biometric recognition technology" is a technology that authenticates individuals using biometric information such as a user's fingerprint, face, or iris.

[2199] "User authentication" is the process of using biometric technology to verify a user's identity and grant them access to a system.

[2200] "EEG scanning" refers to devices and technologies that record and capture a user's brain waves in real time.

[2201] "Electroencephalogram data" is digital information representing electrical activity acquired by an electroencephalogram scanning device.

[2202] "Consciousness and memory information" refers to data about the user's thoughts and memories that is analyzed and extracted from the user's brain wave data.

[2203] "Converting to digital data" refers to the process of converting electroencephalogram data and emotion data acquired as analog signals into a digital format.

[2204] "Emotion data" is digital information that indicates the emotional state of the user analyzed from facial expressions, voice, etc.

[2205] "Synthesis" is the process of combining different types of data (e.g., consciousness and memory information with emotional data) into a single dataset.

[2206] "Cyberspace" refers to virtual spaces where digital data is stored, such as the Internet and cloud storage.

[2207] "Accessible and downloadable format" refers to the conversion of digital data into a format that is easily accessible and retrievable by users.

[2208] A "generative AI model" is a type of artificial intelligence that refers to an algorithm that generates and reproduces new information and content from input data.

[2209] MODE FOR CARRYING OUT THE INVENTION

[2210] This invention is a system that digitizes a user's consciousness, memories, and emotions and stores, accesses, and reproduces them in cyberspace. Using the hardware and software shown below, the system acquires the user's consciousness, memories, and emotions and processes them as digital data.

[2211] System Configuration

[2212] The system consists of the following main components:

[2213] 1. User authentication module

[2214] 2. EEG Scanning Device

[2215] 3. Data Analysis Module

[2216] 4. Data storage module

[2217] 5. Data Access / Download Module

[2218] 6. Memory Replay Module Using Generative AI

[2219] 7. Emotion Engine

[2220] User Authentication Module

[2221] The device performs user authentication using, for example, fingerprint or facial recognition. The user places their fingerprint on a scanner or stands in front of a facial recognition camera. The device then sends this biometric data to a server, which compares it with a database and returns the authentication result.

[2222] Brain wave scanning device

[2223] The device asks the user to wear an EEG scanner, which collects real-time brainwave data. The user concentrates on a specific memory or thought, which is then collected as brainwave data. EEG scanning can be done using, for example, electroencephalography (EEG).

[2224] Emotion Engine

[2225] The device uses an emotion engine to acquire emotion data from the user's facial expressions and voice. This is done using input devices such as a camera and microphone. The acquired emotion data is also sent to the server in real time.

[2226] Data Analysis Module

[2227] The server receives the EEG and emotional data sent from the device and analyzes it using a specific algorithm. The analyzed data is converted into the user's consciousness and memory information, and emotional information is integrated. For example, analyzing the data using a machine learning model makes it possible to extract highly accurate consciousness and memory information.

[2228] Data Storage Module

[2229] The server stores the analyzed digital data in cyberspace. To ensure data redundancy, the data is stored across multiple backup servers. For example, a distributed database (such as Hadoop or Cassandra) can be used here.

[2230] Data Access / Download Module

[2231] When a user wants to look back at the stored memory data, they access cyberspace via their device and request the specified memory data. The server receives the request, searches for the corresponding data, converts it into a downloadable format, and sends it to the device.

[2232] Memory regeneration module using generative AI

[2233] The device then uses a generative AI model to play back the downloaded memory data, allowing the user to experience the memory visually and aurally. For example, playback can be performed using a VR device or a holographic display. Emotional information is reflected in the data being played, enhancing the sense of realism.

[2234] Specific examples

[2235] The following are specific usage scenarios for the system:

[2236] User Authentication

[2237] Device: "Starting recognition mode"

[2238] The user places their fingerprint on the scanner.

[2239] The device sends the fingerprint data to the server.

[2240] The server compares the fingerprint data with the database and returns the authentication result.

[2241] Terminal: "Authentication complete. Welcome, user."

[2242] EEG scanning and emotion recognition begins

[2243] Terminal: "Starting memory scan"

[2244] The user wears an EEG scanner and imagines their college graduation ceremony.

[2245] The device receives EEG data from the scanner and transmits it to the server, where an emotion engine simultaneously captures emotion data from the user's facial expressions and voice in real time.

[2246] The server analyzes the brainwave data and emotional data, and integrates and extracts memory information and emotional information.

[2247] Data conversion and storage

[2248] The server converts the analyzed data into a digital format.

[2249] The server stores the converted data on a backup server.

[2250] Data Access

[2251] User: "Replay my graduation memory."

[2252] The terminal sends this request to the server.

[2253] The server searches for the corresponding stored data and transmits it to the terminal.

[2254] The terminal converts the data into a playable format.

[2255] memory replay

[2256] Terminal: "Memories will be recreated using generative AI."

[2257] The device activates the generative AI model and plays a hologram of the graduation scene.

[2258] The user experiences the replayed memory. The data being replayed reflects the user's emotional information, enhancing the sense of realism.

[2259] This system makes it possible to digitize the user's consciousness, memories, and even emotions, and store, access, and replay them in cyberspace, thereby ensuring the perpetual existence of the individual.

[2260] Examples of prompt statements

[2261] "Replay your college graduation memories."

[2262] This prompt triggers the process of data access and storage recovery.

[2263] The flow of the identification process in the second embodiment will be described with reference to FIG.

[2264] Step 1:

[2265] User Authentication

[2266] Input: The device captures fingerprint or facial recognition information. The user places their fingerprint on the scanner or stands in front of the facial recognition camera.

[2267] Specific operation: The device uses biometric recognition technology to acquire the user's fingerprint and facial data, and then transmits the acquired data to the server using a secure communication protocol (e.g., HTTPS).

[2268] Output: The server checks the database and returns the authentication result. The terminal displays the message "Authentication completed."

[2269] Step 2:

[2270] EEG scan begins

[2271] Input: Pre-authentication information obtained by the device from the user. The user wears an EEG scanner.

[2272] Specific operation: The device prompts the user to wear an EEG scanner, and acquires EEG data in real time through the scanner.

[2273] Output: The acquired EEG data is stored on the terminal, and when a certain amount is accumulated, it is sent to the server.

[2274] Step 3:

[2275] Emotion data acquisition

[2276] Input: The device collects real-time biometric data (facial expressions, voice) and brainwave data. The user focuses on a specific memory or thought.

[2277] Specific operation: The device activates the emotion engine and analyzes emotion data from the user's facial expressions and voice. Data is collected using the camera and microphone.

[2278] Output: Emotion data is sent to the server along with EEG data.

[2279] Step 4:

[2280] Data analysis and integration

[2281] Input: EEG data and emotion data received by the server.

[2282] Specific operation: The server analyzes EEG data and emotion data using specific algorithms. It uses machine learning models to extract consciousness and memory information and integrate it with emotion information.

[2283] Output: Convert the analysis results into digital data.

[2284] Step 5:

[2285] Data storage

[2286] Input: Consciousness, memory information, and emotion data analyzed on the server.

[2287] Specific operation: The server stores this data in a distributed database. To increase data redundancy, the data is stored in multiple backup servers.

[2288] Output: Digital data is stored securely and backed up.

[2289] Step 6:

[2290] Data Access and Download

[2291] Input: A data access request from the device (e.g., a prompt "Please play my graduation memory").

[2292] Specific operation: The server receives the request, searches the database for the corresponding stored data, converts the found data into a downloadable format, and sends it to the device.

[2293] Output: The terminal obtains the stored data specified by the user and converts it into a playable format.

[2294] Step 7:

[2295] memory replay

[2296] Input: Stored data downloaded to the terminal.

[2297] Specific operation: The device activates the generative AI model and reproduces the memory data through sight and sound. The visual and audio of the memory are reproduced using a VR device or holographic display.

[2298] Output: The user experiences the replayed memory. Emotional information is reflected in the replayed data, enhancing the sense of realism.

[2299] (Application example 2)

[2300] 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."

[2301] With conventional technology, it is difficult to store a user's memories and consciousness as digital data, and the technology for integrating information, including emotions, is insufficient. As a result, there has been no system that allows users to realistically replay past memories and experiences and reflect on them, including their emotions. As a result, memory replay is limited to a simple visual and auditory experience, making it difficult to reproduce a sense of presence or emotions. To solve this problem, a system is needed that can integrate and store memories and emotions and replay them using advanced technology such as holograms.

[2302] The specific processing by the specific 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 authenticating a user using biometric recognition technology, means for scanning the user's brain waves and acquiring the brain wave data, means for analyzing the acquired brain wave data and converting consciousness and memory information into digital data, means for analyzing the acquired emotional data and integrating the information as digital data, means for storing the converted digital data in cyberspace, means for converting the digital data stored in cyberspace into an accessible and downloadable format upon request from the user, means for reproducing the downloaded data using a generative AI model and presenting it to the user in holographic form or on a visual display, and means including an emotion engine that reflects the user's emotional state in real time. This makes it possible to store the user's memories and consciousness as digital data, integrate emotional information, and then reproduce the data with a sense of realism using a hologram or a visual display.

[2303] "Biometric recognition technology" is a technology that identifies individuals using biometric information such as a user's fingerprint or facial recognition.

[2304] An "EEG scanner" is a device that reads a user's brain waves in real time and acquires brain wave data.

[2305] "Electroencephalogram data" refers to data obtained by converting electrical signals obtained from the user's electroencephalogram into digital format.

[2306] The "emotion engine" analyzes emotions from biometric data such as the user's facial expressions and voice, and reflects the user's emotional state in real time.

[2307] "Consciousness and memory information" is information about the state of consciousness and memory contents extracted from the user's electroencephalogram data.

[2308] "Means for converting into digital data" refers to a system or algorithm that analyzes the acquired brainwave data and emotional data and converts it into digital form.

[2309] "Cyberspace" refers to virtual spaces such as the Internet and the cloud, where digital data can be stored, accessed, and shared.

[2310] A "generative AI model" is an algorithm or program that uses artificial intelligence to generate stored memory data into a reproducible form.

[2311] "Hologram format" is a technology for displaying three-dimensional images and videos, which are displayed with a visually three-dimensional appearance.

[2312] "Visual display" refers to a physical screen or device for presenting visual information to a user.

[2313] A "backup server" is a server that stores replicated data to ensure data redundancy.

[2314] "Emotion information" is digital data that analyzes the user's emotional state and is acquired by the emotion engine.

[2315] MODE FOR CARRYING OUT THE INVENTION

[2316] To implement this invention, it is necessary to build a system that digitizes and stores a user's consciousness and memories and makes them permanently accessible in cyberspace. This system is composed of the following main components: a user authentication module, an EEG scanning device, a data analysis module, a data storage module, a data access / download module, a memory playback module using generative AI, and an emotion engine. These components work together to digitize a user's consciousness, memories, and emotions, and store and play them back in cyberspace.

[2317] User Authentication Module

[2318] The server authenticates the user using biometric recognition technology, such as fingerprint or facial recognition, and allows them to log in to the system. The user enters their authentication information into the terminal, and the server compares it with a database and returns the authentication result.

[2319] Brain wave scanning device

[2320] The device has the user wear an EEG scanner, which collects real-time brainwave data. When the user concentrates on a specific memory or thought, that information is reflected in the scanner as brainwave data. The device then transmits this brainwave data to a server.

[2321] Emotion Engine

[2322] The device uses an emotion engine to acquire the user's emotional data in real time. The emotion engine analyzes the user's emotions from facial expressions, voice, and other biometric data. The acquired emotional data is sent to the server along with brainwave data.

[2323] Data Analysis Module

[2324] The server receives and analyzes the EEG and emotional data sent from the device. Using a specific algorithm, the server extracts the user's consciousness and memory information from the EEG data and integrates it with emotional information. This information is then converted and stored as digital data.

[2325] Data Storage Module

[2326] The server stores the analyzed digital data in cyberspace, and during the storage process, the data is distributed across multiple backup servers to ensure data redundancy.

[2327] Data Access / Download Module

[2328] When a user wants to review the stored data, they access cyberspace via their device and request the specified data. The server processes the request, searches for the relevant data, converts it into a downloadable format, and sends it to the device.

[2329] Memory regeneration module using generative AI

[2330] The device uses a generative AI model to play back the downloaded memory data, allowing the user to experience the memory visually and aurally. The played back information is monitored by a server, and corrections are made as necessary. The generative AI model provides a sense of realism, as if the user is actually experiencing past memories.

[2331] Specific examples

[2332] User Authentication

[2333] Device: "Starting recognition mode"

[2334] The user places their fingerprint on the scanner.

[2335] The device sends the fingerprint data to the server.

[2336] The server compares the fingerprint data with the database and returns the authentication result.

[2337] Terminal: "Authentication complete. Welcome, user."

[2338] EEG scanning and emotion recognition begins

[2339] Terminal: "Starting memory scan"

[2340] The user wears an EEG scanner and imagines their college graduation ceremony.

[2341] The device receives the EEG data from the scanner and transmits it to a server.

[2342] At the same time, the emotion engine acquires emotion data in real time from the user's facial expressions and voice.

[2343] The server analyzes the brainwave data and emotional data, and integrates and extracts memory information and emotional information.

[2344] Data conversion and storage

[2345] The server converts the analyzed data into a digital format.

[2346] The server stores the converted data on a backup server.

[2347] Data Access

[2348] User: "Replay my graduation memory."

[2349] The terminal sends this request to the server.

[2350] The server searches for the corresponding stored data and transmits it to the terminal.

[2351] The terminal converts the data into a playable format.

[2352] memory replay

[2353] Terminal: "Memories will be recreated using generative AI."

[2354] The device activates the generative AI model and plays a hologram of the graduation scene.

[2355] The user experiences the replayed memory.

[2356] The data being played back reflects the user's emotional information, enhancing the sense of realism.

[2357] Prompt Sentence Examples

[2358] "Please replay user X's memory of their college graduation. The user's emotions are filled with joy and excitement. Please replay it in a holographic format to create a realistic experience."

[2359] This particular embodiment of the invention allows users to store their memories and emotions as digital data and have a realistic experience when playing it back.

[2360] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[2361] Step 1: User authentication

[2362] How it works: The device prompts the user to enter authentication information (fingerprint or face).

[2363] Input: Fingerprint data or face data

[2364] Data processing / calculation: The device collects authentication information and sends it to the server, which checks it against a database and performs authentication.

[2365] Output: Authentication result (success or failure)

[2366] Step 2: Start the EEG scan

[2367] How it works: The device prompts the user to wear an EEG scanner. The user puts on the scanner and concentrates on a specific memory.

[2368] Input: User's brainwaves

[2369] Data processing / calculation: The EEG scanner acquires EEG data in real time and transmits it to the terminal.

[2370] Output: Acquired EEG data

[2371] Step 3: Collecting emotion data

[2372] Operation: The device activates the emotion engine and acquires emotion data in real time from the user's facial expressions and voice.

[2373] Input: User's facial expression data, voice data

[2374] Data processing / calculation: The emotion engine analyzes facial expression data and voice data to detect emotional states.

[2375] Output: User emotion data

[2376] Step 4: Analyze and synthesize data

[2377] Operation: The server analyzes the brain wave data and emotion data sent from the device.

[2378] Input: EEG data, emotion data

[2379] Data processing / computation: Using specific algorithms, consciousness and memory information is extracted from EEG data and emotional information is integrated.

[2380] Output: Integrated memory and emotion data

[2381] Step 5: Save Data

[2382] How it works: The server converts the analyzed data into digital form and stores it in cyberspace.

[2383] Input: Integrated memory and emotion data

[2384] Data processing / calculation: Data is digitized and stored in multiple backup servers.

[2385] Output: Stored digital data

[2386] Step 6: Data Access

[2387] Operation: A user requests playback of stored stored data, and the terminal sends the request to the server.

[2388] Input: User's memory recall request

[2389] Data processing / computation: The server processes the request, retrieves the relevant stored data, and converts it into a downloadable format.

[2390] Output: Downloaded memory data

[2391] Step 7: Memory Replay

[2392] How it works: The device uses generative AI to recreate stored data in holographic form or on a visual display.

[2393] Input: Downloaded memory data

[2394] Data processing / computation: A generative AI model analyzes stored data and generates it in a format that can be reproduced as a hologram or visual display.

[2395] Output: Reproducible stored data

[2396] Step 8: Reflect and Replay Emotions

[2397] Operation: The device acquires the user's real-time emotional information during playback and reflects it in the playback.

[2398] Input: Current user emotion data

[2399] Data processing / calculation: The emotion engine analyzes the emotional data acquired in real time and reflects it in the memory data being played back.

[2400] Output: Memory replay data reflecting emotional information

[2401] These processing steps allow users to digitize their memories and emotions in detail, allowing them to later re-experience them in a realistic way.

[2402] 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.

[2403] 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.

[2404] 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.

[2405] 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.

[2406] 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.

[2407] 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.

[2408] 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).

[2409] 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.

[2410] 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."

[2411] 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.

[2412] 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).

[2413] 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.

[2414] 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.

[2415] 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.

[2416] 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.

[2417] 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.

[2418] 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.

[2419] 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.

[2420] 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.

[2421] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, to avoid confusion and facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[2422] 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.

[2423] The following is further disclosed regarding the above embodiment.

[2424] (Claim 1)

[2425] means for authenticating a user using biometric technology;

[2426] means for scanning the user's brain waves and acquiring said brain wave data;

[2427] A means for analyzing the acquired electroencephalogram data and converting consciousness and memory information into digital data;

[2428] A means for storing the converted digital data in cyberspace;

[2429] means for converting digital data stored in cyberspace into a format that can be accessed and downloaded upon request by a user;

[2430] a means for playing the downloaded data using a generative AI model;

[2431] A system including:

[2432] (Claim 2)

[2433] 10. The system of claim 1, wherein the user wears an electroencephalogram scanning device to acquire electroencephalogram data in real time.

[2434] (Claim 3)

[2435] 2. The system according to claim 1, wherein the stored digital data is distributed and stored across a plurality of backup servers.

[2436] "Example 1"

[2437] (Claim 1)

[2438] means for authenticating a user using biometric authentication technology;

[2439] means for scanning the user's brain waves and acquiring said brain wave data;

[2440] A means for analyzing the acquired electroencephalogram data and converting consciousness and memory information into digital data;

[2441] A means for storing the converted digital data in cyberspace;

[2442] means for converting digital data stored in cyberspace into a format that can be accessed and downloaded upon request by a user;

[2443] a means for playing the downloaded data using a generative AI model;

[2444] means for monitoring the stored data reproduced in real time and making corrections as necessary;

[2445] A system including:

[2446] (Claim 2)

[2447] 10. The system of claim 1, wherein the user wears an electroencephalogram scanning device to acquire electroencephalogram data in real time.

[2448] (Claim 3)

[2449] 2. The system according to claim 1, wherein the stored digital data is distributed and stored across a plurality of backup servers.

[2450] "Application Example 1"

[2451] (Claim 1)

[2452] means for authenticating a user using biometric technology;

[2453] means for scanning the user's brain waves and acquiring said brain wave data;

[2454] A means for analyzing the acquired electroencephalogram data and converting consciousness and memory information into digital data;

[2455] A means for storing the converted digital data in cyberspace;

[2456] means for converting digital data stored in cyberspace into a format that can be accessed and downloaded upon request by a user;

[2457] A means for playing back the downloaded data using a generative AI model to play back memorized data based on the user's purchasing history and preferences in visual and audio formats; and

[2458] A system including:

[2459] (Claim 2)

[2460] 10. The system of claim 1, wherein the user wears an electroencephalogram scanning device to acquire electroencephalogram data in real time.

[2461] (Claim 3)

[2462] 2. The system according to claim 1, wherein the stored digi...

Claims

1. means for authenticating a user using biometric technology; means for scanning the user's brain waves and acquiring said brain wave data; A means for analyzing the acquired electroencephalogram data and converting consciousness and memory information into digital data; A means for storing the converted digital data in cyberspace; means for converting digital data stored in cyberspace into a format that can be accessed and downloaded upon request by a user; a means for playing the downloaded data using a generative AI model; A system including:

2. 10. The system of claim 1, wherein the electroencephalogram scanning device is worn by a user and electroencephalogram data is acquired in real time.

3. 2. The system according to claim 1, wherein the stored digital data is distributed and stored in a plurality of backup servers.

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