system

Abstract

Provide a system. A device for acquiring a user's brain signal; means for analyzing the user's brain signal data to identify the user's intentions, emotional state, and visual needs; means for collecting video data of a real-world environment surrounding a user; A means for analyzing the collected video data and identifying surrounding environmental information; means for generating virtual elements to customize the user's experience based on the analysis results of the brain signal data and the video data of the real environment; means for providing the generated virtual elements to a user; A system including:

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] Visually impaired people, the elderly, and people with memory loss or dementia have difficulty accurately grasping the real world and past memories, which causes anxiety and confusion in their daily lives. Furthermore, there is a lack of means to provide information about the real world in an easily understandable format, resulting in problems of reduced safety and quality in travel and communication. Conventional technologies have difficulty providing personalized experiences suited to these users, and an effective solution is needed. [Means for solving the problem]

[0005] The present invention provides a means for acquiring and analyzing a user's brain signals to identify the user's intentions, emotional state, and visual needs, and a means for collecting and analyzing video data of the real-world environment surrounding the user to accurately identify surrounding environmental information. This allows for the creation of a system that generates virtual elements to customize the user's experience and provides them to the user, thereby enabling a safe and rich mixed reality experience for the visually impaired, the elderly, and those suffering from memory loss or dementia. Furthermore, by including a means for identifying the position and movement of surrounding obstacles and objects based on the video data of the real environment, and a means for analyzing the user's brain signal data and the video data of the real environment to identify objects of the user's interest, the provision of a more personalized experience is realized.

[0006] A "user" is a person who provides brain signal data and video data of the real environment in order to use this system.

[0007] "Brain signals" are electrical signals emitted by the user's brain that indicate their intentions, emotional state, and visual needs.

[0008] "Brain signal data" refers to digital data of brain signals obtained from the user's brain, and is used for analysis.

[0009] "Analysis" is the process of analyzing existing data and extracting specific information.

[0010] The "real environment" refers to the physical environment in which the user actually exists, and is the subject of video data collected by a camera or the like.

[0011] "Video data" refers to data of moving or still images of a real environment collected by a sensor such as a camera.

[0012] The "surrounding environment" refers to the spatial situation in which the user is interested, in addition to the location where the user is present, and includes objects, obstacles, and the like.

[0013] "Virtual elements" are virtual information generated by the system and are elements of the visual, audio, and tactile experience provided to the user.

[0014] "Customization" is the process of tailoring the experience and information provided to a user according to their individual needs and preferences.

[0015] An "obstacle" is an object that may impede the user's movement or field of view, and should be identified to be avoided.

[0016] "Means for identifying" refers to a method or device for discovering or recognizing specific information or objects through the analysis of data.

[0017] "Real-time" refers to the processing and provision of information without actual time or delay.

[0018] "Mixed reality" is a technology that integrates virtual elements into real-world situations to provide users with an integrated experience. [Brief explanation of the drawings]

[0019] [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

[0020] 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.

[0021] First, the terms used in the following description will be explained.

[0022] 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).

[0023] 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.

[0024] 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.

[0025] 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.

[0026] 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."

[0027] [First embodiment]

[0028] FIG. 1 shows an example of the configuration of a data processing system 10 according to the first embodiment.

[0029] 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.

[0030] 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).

[0031] 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.

[0032] 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.

[0033] 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.

[0034] 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.

[0035] FIG. 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0036] 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.

[0037] 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.

[0038] 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.

[0039] 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."

[0040] This invention is a mixed reality (MR) system that adds virtual elements to a real environment to provide a richer sensory experience for the visually impaired, elderly people, and people with memory loss or dementia. Below, we will explain in detail the program-based processing of this system.

[0041] Acquiring and analyzing user's brain signals

[0042] 1. Acquiring Brain Signals

[0043] The user wears a wearable device for measuring EEG, which measures and acquires electrical signals generated by the brain in real time.

[0044] 2. Transmission and analysis of brain signal data

[0045] The terminal transmits the acquired brain signal data to a server.

[0046] The server analyzes the received brain signal data to identify the user's intentions, emotional state, visual needs, etc. This analysis is performed using machine learning algorithms and data processing techniques.

[0047] Real-world data collection

[0048] 1. Video data collection

[0049] The device (specifically, smart glasses or a mobile device worn by the user) collects images of the real-world environment around the user in real time via a camera.

[0050] 2. Video data transmission and analysis

[0051] The terminal transmits the collected video data to the server.

[0052] The server analyzes the received video data and obtains information about the surrounding environment, including object detection and movement analysis.

[0053] Creating and providing virtual elements

[0054] 1. Creating Virtual Elements

[0055] The server generates appropriate virtual elements based on the analysis of brain signal data and video data of the real environment. For example, if a visually impaired person senses an obstacle ahead, it generates an audio warning to indicate the presence of an obstacle.

[0056] 2. Customizing Virtual Elements

[0057] The server customizes the generated virtual elements to the user's individual requirements, for example adjusting the tone and volume of audio alerts depending on the user's audio feedback preferences.

[0058] 3. Providing mixed reality experiences

[0059] The server transmits the generated customized virtual element to the terminal.

[0060] The device integrates the transmitted virtual elements with the image of the real environment, providing the user with a real-time MR experience.

[0061] Specific examples

[0062] Specific examples for the visually impaired

[0063] 1. Brain signal acquisition and analysis

[0064] The user wears a headset for measuring electroencephalograms.

[0065] The device collects brain signal data from the headset in real time and transmits it to a server.

[0066] The server determines through analysis that the user intends to "pay attention to what is ahead."

[0067] 2. Real-world data collection

[0068] The terminal acquires image data of the area in front of the user through a camera.

[0069] The terminal transmits the collected video data to the server.

[0070] Through video analysis, the server recognizes that there is an obstacle (e.g., a bicycle) ahead.

[0071] 3. Creating and providing virtual elements

[0072] The server generates a warning sound based on the results of the brain signal analysis and video analysis.

[0073] The server customizes the generated alert audio according to the user's audio feedback preferences.

[0074] The server sends a customized alert sound to the terminal.

[0075] The terminal plays the received warning audio to the user, providing audio guidance such as "There is a bicycle ahead. Be careful."

[0076] As described above, this system is designed to enable people with visual impairments, the elderly, and dementia patients to enjoy safe and rich sensory experiences in real-world environments, thereby supporting safer and better lives.

[0077] The processing flow will be explained below.

[0078] Step 1:

[0079] The user puts on a wearable device (e.g., an EEG headset) to measure brain signals, which allows for real-time measurements of the brain's electrical activity.

[0080] Step 2:

[0081] The device receives brain signal data acquired from the headset in real time, and performs preprocessing such as noise filtering on the received data to improve analysis accuracy.

[0082] Step 3:

[0083] The device encrypts the pre-processed brain signal data before transmitting it to the server, ensuring data security.

[0084] Step 4:

[0085] The server analyzes the received brain signal data to identify the user's intentions, emotional state, visual needs, etc. It uses machine learning algorithms to quickly extract this information.

[0086] Step 5:

[0087] The device collects images of the real-world environment around the user in real time through a camera, and the collection point of this image data can be changed according to the user's visual needs.

[0088] Step 6:

[0089] The terminals send the collected video data to the server, where it is appropriately compressed and encrypted for efficient and secure transmission.

[0090] Step 7:

[0091] The server analyzes the received video data and identifies the surrounding environment (obstacles, moving objects, points of interest, etc.) using an image recognition algorithm.

[0092] Step 8:

[0093] The server integrates the results of the brain signal analysis with environmental information to generate appropriate virtual elements (audio guidance, warnings, visual aids, etc.), which are customized to suit the user's preferences and needs.

[0094] Step 9:

[0095] The server then sends the generated customized virtual elements to the device, and this data is also encrypted to ensure security.

[0096] Step 10:

[0097] The device then combines the received virtual elements with the video of the real environment in real time, and displays or outputs audio to the user. Through this MR experience, the user can receive information about the real environment in an easy-to-understand format.

[0098] Examples:

[0099] Step 1:

[0100] The user wears a headset for measuring electroencephalograms.

[0101] Step 2:

[0102] The device receives brain signal data from the headset in real time.

[0103] Step 3:

[0104] The device encrypts the received brain signal data and transmits it to a server.

[0105] Step 4:

[0106] The server analyzes the brain signal data and determines that the user intends to "focus their attention on what is ahead."

[0107] Step 5:

[0108] The terminal uses a camera to collect video data of the area in front of the user.

[0109] Step 6:

[0110] The terminal transmits the collected video data to the server.

[0111] Step 7:

[0112] Through video analysis, the server recognizes that there is an obstacle (bicycle) ahead.

[0113] Step 8:

[0114] The server generates and customizes the warning sound based on the analysis results.

[0115] Step 9:

[0116] The server transmits the generated warning sound to the terminal.

[0117] Step 10:

[0118] The terminal plays the received warning audio to the user, providing audio guidance such as "There is a bicycle ahead. Please be careful."

[0119] Example 1

[0120] 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."

[0121] It is difficult for visually impaired people, the elderly, and people with memory loss or dementia to enjoy safe and rich sensory experiences in real-world environments. Such users are unable to recognize or interpret information about their surroundings, making it difficult for them to move around or live safely. Conventional technologies have not adequately resolved these issues, and effective means are needed to improve users' safety and quality of life. Therefore, the present invention aims to solve these problems.

[0122] 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.

[0123] In this invention, the server includes means for analyzing the user's brain signal data to identify the user's intention, emotional state, and visual needs, means for analyzing surrounding environmental information, and means for generating virtual elements to customize the user's experience, thereby fusing the user's brain signal data with data from the real environment to provide virtual elements tailored to individual needs, enabling a safe and rich sensory experience.

[0124] "User" refers to a person whose brain signals are acquired using the system.

[0125] "Brain signals" refer to electrical signals generated by the user's brain.

[0126] "Data" refers to the collection of information used by the system, including information collected and analyzed as signals or images.

[0127] "Virtual Elements" refers to virtual content that is generated based on the analysis of brain signal data and video data to supplement or enhance the user's experience, including sounds, visual effects, and other multimedia elements.

[0128] "Environmental information" refers to information such as the position and movement of objects in the real world around the user, which is obtained through the analysis of video data.

[0129] "Analysis" refers to the act of processing data to derive meaningful results, specifically using machine learning and object detection algorithms.

[0130] "Customization" refers to the act of tailoring or modifying generated virtual elements to suit the specific needs and desires of a user.

[0131] "Means" refers to an apparatus, method, or process for accomplishing a particular function or action.

[0132] "Transmit" refers to the act of transferring data from one device to another.

[0133] "Collection" refers to the act of gathering specific information. This can be done using devices such as sensors or cameras.

[0134] "Providing" refers to the act of allowing a user to use the generated virtual element.

[0135] The above provides definitions of important terms that may be included in the claims.

[0136] The present invention is a mixed reality (MR) system that adds virtual elements to a real environment to provide a richer sensory experience for the visually impaired, the elderly, and those with memory loss or dementia. Specific embodiments for implementing the present invention are described below.

[0137] Acquiring and analyzing user's brain signals

[0138] A user wears a wearable device for measuring EEG (e.g., a general-purpose EEG headset). This device acquires EEG signals from the scalp in real time using multiple electrodes. The acquired brain signal data is collected while the user is performing daily activities.

[0139] The terminal (smartphone or dedicated device) transmits the brain signal data acquired from this device to a server via wireless communication (Bluetooth or Wi-Fi).

[0140] The server analyzes the received brain signal data using machine learning algorithms, specifically TENSORFLOW®, which uses Python, to identify the user's intent (e.g., "I want to focus my attention on what's ahead"), emotional state (e.g., tension, relaxation), and visual needs.

[0141] Real-world data collection

[0142] A user wears smart glasses or a head-mounted display (e.g., Microsoft® HoloLens®) and observes the real-world environment around them. The camera in the smart glasses captures images of the user's field of vision in real time. This image data is temporarily stored in the device's internal memory.

[0143] The terminal transmits the temporarily stored video data to the server at regular intervals (for example, one frame per second).

[0144] The server analyzes the received video data using an object detection algorithm based on OpenCV to identify information about the surrounding environment (obstacles and dynamic environmental changes).

[0145] Creating and providing virtual elements

[0146] The server uses a generative AI model to generate appropriate virtual elements based on the results of analyzing the brain signal data and the video data. For example, if it detects an obstacle in front of the user, it generates a warning sound or visual effect.

[0147] The server customizes the generated virtual elements to the user's individual needs, for example adjusting the tone and volume of audio alerts to suit the user's preferences.

[0148] The server sends customized virtual elements to the device, which then integrates them into the image of the real world, providing the user with a real-time MR experience. Specifically, a warning sound is played from the smartglasses' speaker, informing the user, "There is an obstacle ahead. Please be careful."

[0149] This will enable people with visual impairments, the elderly, and dementia patients to enjoy safe and rich sensory experiences in real-world environments. For example, visually impaired people walking around town will be able to see obstacles ahead in advance, improving safety.

[0150] Prompt Sentence Examples

[0151] "Please explain how a mixed reality system works, where the user wears a headset that measures EEG and uses a device that collects data on the surrounding environment to support safe mobility in the real world."

[0152] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0153] Step 1:

[0154] Acquiring brain signals

[0155] The user wears a wearable device for measuring brain waves, which acquires brain waves in real time through electrodes on the scalp.

[0156] Input: EEG electrical signals

[0157] Output: Digital brain signal data

[0158] How it works: The user puts on the headset, and the device uses sensors to capture electrical brainwave signals and convert them into digital data.

[0159] Step 2:

[0160] Transmission of brain signal data

[0161] The device transmits the acquired brain signal data to a server via Bluetooth or Wi-Fi.

[0162] Input: Brain signal data from a wearable device

[0163] Output: Digital brain signal data sent to a server

[0164] Specific operation: The terminal communicates with the wearable device, collects data, and sends it to the server.

[0165] Step 3:

[0166] Analysis of brain signal data

[0167] The server analyzes the received brain signal data using a machine learning model in TensorFlow with Python.

[0168] Input: Digital brain signal data sent to the server

[0169] Output: Data about the user's intent, emotional state, and visual needs

[0170] Specific operation: The server receives the data, runs it through a machine learning model, and derives analytical results.

[0171] Step 4:

[0172] Video data collection

[0173] Users wear smart glasses or a head-mounted display and observe the real-world environment around them, with the device's camera capturing images in real time.

[0174] Input: Surrounding real-world environment

[0175] Output: Video data collected by the camera

[0176] Specific operation: The user wears the device and the camera captures real-time video.

[0177] Step 5:

[0178] Video data transmission

[0179] The device temporarily stores the video data collected by the camera in its internal memory and transmits it to the server at regular intervals.

[0180] Input: Video data from the camera

[0181] Output: Video data sent to the server

[0182] Specific operation: The device stores the video data in its internal memory and sends it to the server.

[0183] Step 6:

[0184] Video data analysis

[0185] The server analyzes the received video data using an object detection algorithm based on OpenCV.

[0186] Input: Video data sent to the server

[0187] Output: Surrounding environment information (position and movement of obstacles, etc.)

[0188] Specific operation: The server receives the video data, analyzes it using an object detection algorithm, and identifies environmental information.

[0189] Step 7:

[0190] Creating Virtual Elements

[0191] The server uses a generative AI model to generate appropriate virtual elements based on the results of analyzing the brain signal data and the video data.

[0192] Input: Brain signal analysis results and video data analysis results

[0193] Output: Generated virtual elements (sound, visual effects, etc.)

[0194] Specific operation: The server runs a generative AI model based on the analysis results to generate virtual elements.

[0195] Step 8:

[0196] Customizing Virtual Elements

[0197] The server customizes the generated virtual elements to the user's individual needs.

[0198] Input: Generated virtual elements, individual user needs

[0199] Output: Customized Virtual Elements

[0200] What it does: The server adjusts, modifies, and optimizes the virtual elements.

[0201] Step 9:

[0202] Sending customized virtual elements

[0203] The server transmits the customized virtual element to the terminal.

[0204] Input: Customized Virtual Elements

[0205] Output: customized virtual elements sent to the device

[0206] Specific operation: The server sends virtual elements to the terminal and reflects them in real time.

[0207] Step 10:

[0208] Providing MR experiences

[0209] The device then integrates the received virtual elements into the image of the real environment, providing the user with an MR experience.

[0210] Input: Customized virtual elements, video data of the real environment

[0211] Output: The integrated mixed reality experience (audio alerts and visual effects provided to the user)

[0212] Specific operation: The device combines virtual elements and video data to play them, providing the user with an MR experience.

[0213] (Application example 1)

[0214] 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."

[0215] Visually impaired people and the elderly have a reduced ability to detect suspicious people and dangerous situations in the real world, making it difficult for them to ensure safety in their daily lives. Currently available technologies are often insufficient to recognize the environment and respond appropriately without relying on sight or hearing. To solve this problem, a system is needed that can detect surrounding dangers in real time and provide warnings to users.

[0216] 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.

[0217] In this invention, the server includes means for analyzing the user's electroencephalogram data and video data to recognize suspicious individuals and dangerous situations, means for generating and providing warning audio and visual signals for the recognized suspicious individuals and dangerous situations, and means for generating virtual elements to customize the user's experience, thereby enabling support for visually impaired people and the elderly to recognize surrounding dangers in real time and respond appropriately.

[0218] "Brain signals" refer to electrical signals generated by the user's brain.

[0219] "Brain signal data" refers to digital data obtained from acquired brain signals.

[0220] "Intention" refers to the sense that a user is trying to act with a specific action or purpose.

[0221] "Emotional state" refers to the user's psychological state of mind and emotions.

[0222] "Visual requirements" refer to information or objects that a user visually desires.

[0223] "Real environment" refers to the physical environment in which the user actually resides.

[0224] "Video data" refers to image or video data captured through a camera or other imaging device.

[0225] "Virtual elements" refer to digitally generated virtual information or objects that are integrated into a real environment.

[0226] A "suspicious person" refers to a person in the surrounding environment who may potentially pose a danger or cause anxiety to the user.

[0227] "Dangerous situation" refers to a situation or environment that may pose a threat to the safety of the user.

[0228] "Warning voice" refers to voice generated to warn the user of danger and caution.

[0229] "Visual signal" refers to an image or signal displayed to visually alert a user to danger or caution.

[0230] This invention is a system that supports safety for visually impaired people and the elderly by detecting surrounding dangers in real time. Specifically, smart glasses worn by the user work in cooperation with a server and a terminal connected to the glasses.

[0231] System Configuration

[0232] Hardware

[0233] Smart glasses: Wearable devices equipped with a camera and audio output.

[0234] EEG measurement device: A headset for capturing the user's brain signals in real time.

[0235] Server: A central processing unit that analyzes brain signals and video data.

[0236] software

[0237] Data analysis platform: Software containing machine learning algorithms for analyzing EEG and video data.

[0238] Warning generation algorithm: A program that detects suspicious individuals or dangerous situations and generates appropriate warnings.

[0239] Program processing flow

[0240] 1. Acquisition and transmission of brain signal data

[0241] The user wears an EEG headset, which captures the user's brain signals in real time.

[0242] The acquired brain signal data is sent to a server via the terminal.

[0243] 2. Collecting and transmitting data from the real world

[0244] The camera in the smart glasses collects video data of the real-world environment around the user.

[0245] The collected video data is sent to the server in real time via the terminal.

[0246] 3. Data Analysis

[0247] The server analyzes the brain signal data to identify the user's intentions, emotional state, and visual needs.

[0248] At the same time, the video data is analyzed to identify the surrounding environment, suspicious individuals, and the location and movement of obstacles, using machine learning algorithms and data processing technology.

[0249] 4. Creating and Providing Virtual Elements

[0250] Based on the analysis, the server generates virtual elements that customize the user's experience, such as audio and visual warnings if a suspicious person is detected.

[0251] The generated virtual elements are transmitted to the smart glasses in real time and presented to the user.

[0252] Specific examples

[0253] As a user walks around town, an EEG headset captures the user's brain signals. At the same time, the camera in the smart glasses collects video data of the surrounding area. This data is sent to a server, which analyzes the EEG data to determine the user's alert state and analyzes the video data to recognize suspicious individuals and dangerous situations. If a suspicious individual is detected, the server generates a warning voice saying, "A suspicious individual is present. Please be careful," and sends it to the user through the smart glasses.

[0254] Example prompts for generative AI models

[0255] Please implement an app that will issue an audio warning if EEG data indicates a certain level of alertness and detects a suspicious person ahead.

[0256] The data used is 128-point EEG data and real-time video data.

[0257] We use a peak detection algorithm to analyze the EEG data, and the face detection function of OpenCV to analyze the video data.

[0258] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0259] Step 1:

[0260] The user wears an EEG headset, which captures electrical signals generated by the brain in real time. The captured brain signal data reflects the user's brainwave activity at that moment.

[0261] Input: User's brain signals

[0262] Data processing: The headset converts electrical signals generated by the brain into digital data

[0263] Output: Brain signal data

[0264] Step 2:

[0265] The device transmits the brain signal data acquired from the headset to a server in real time, so it is necessary to minimize delays.

[0266] Input: Brain signal data

[0267] Data processing: Digital signal processing and data compression are performed

[0268] Output: Brain signal data sent to the server

[0269] Step 3:

[0270] The device uses the camera in the smart glasses to collect video data of the real-world environment around the user, which contains information about the real world in the user's field of view.

[0271] Input: Video of the user's surroundings

[0272] Data processing: Converting analog video data into digital video data

[0273] Output: Collected video data

[0274] Step 4:

[0275] The device transmits the collected video data to a server, also in real time and designed to minimize latency.

[0276] Input: Collected video data

[0277] Data processing: Compression and packetization of video data

[0278] Output: Video data sent to the server

[0279] Step 5:

[0280] The server analyzes the received brain signal data using machine learning algorithms to identify the user's intent, emotional state, and visual needs.

[0281] Input: Brain signal data sent to the server

[0282] Data processing: Analysis using machine learning algorithms

[0283] Output: User intent, emotional state, visual requirements

[0284] Step 6:

[0285] The server analyzes the received video data using an object detection algorithm to identify the location and movement of suspicious individuals and obstacles.

[0286] Input: Video data sent to the server

[0287] Data processing: object detection and motion analysis

[0288] Output: Location and movement information of suspicious persons and obstacles

[0289] Step 7:

[0290] Based on the results of the brain signal analysis and the video data analysis, the server generates virtual elements to customize the user's experience, such as generating audio and visual warnings if a suspicious person is detected.

[0291] Input: User's intentions, emotional state, visual needs, location and movement information of suspicious people and obstacles

[0292] Data processing: Creation of customized virtual elements

[0293] Output: Generated virtual elements (audio and visual warning signals)

[0294] Step 8:

[0295] The server transmits the generated virtual elements to the terminal, and the terminal provides the received virtual elements to the user through the smart glasses.

[0296] Input: The generated virtual element

[0297] Data processing: decoding and playback of audio data and visual signals

[0298] Output: Audio and visual warning signals provided to the user

[0299] 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.

[0300] This invention is a mixed reality (MR) system that adds virtual elements to a real environment and provides a richer sensory experience based on emotional states for the visually impaired, elderly people, and people with memory loss or dementia. The details of the program-based processing of this system are described below.

[0301] Acquiring and analyzing user's brain signals

[0302] 1. Acquiring Brain Signals

[0303] The user puts on a wearable device for measuring brain waves (e.g., an EEG headset), which prepares the device to measure the brain's electrical activity in real time.

[0304] 2. Transmission and analysis of brain signal data

[0305] The device receives brain signal data acquired from the headset in real time, and performs preprocessing such as noise filtering on the received data to improve analysis accuracy.

[0306] The device encrypts and transmits the preprocessed brain signal data to a server.

[0307] The server analyzes the received brain signal data to identify the user's intentions, emotional state, visual needs, etc. It uses machine learning algorithms to quickly extract this information.

[0308] Real-world data collection

[0309] 1. Video data collection

[0310] The device (e.g., smart glasses worn by the user or a mobile device) collects images of the real-world environment around the user in real time via a camera.

[0311] 2. Video data transmission and analysis

[0312] The terminals send the collected video data to the server, where it is appropriately compressed, encrypted, and transmitted safely and efficiently.

[0313] The server analyzes the received video data and identifies the surrounding environment (obstacles, moving objects, points of interest, etc.) using image recognition algorithms.

[0314] Sentiment Engine and Sentiment Analysis

[0315] 1. How the Emotion Engine Works

[0316] The server uses an emotion engine to identify the user's emotional state based on the analysis of the brain signal data. For example, the emotion engine analyzes information such as whether the user is relaxed or tense.

[0317] 2. Real-time emotion monitoring

[0318] The server monitors the user's emotional state in real time and adjusts the MR experience according to emotional changes, for example, adding relaxing virtual elements if the user feels anxious.

[0319] Creating and providing virtual elements

[0320] 1. Creating Virtual Elements

[0321] The server generates appropriate virtual elements based on the analysis of brain signal data, emotional state, and video data of the real environment. For example, if a visually impaired person senses an obstacle ahead, it generates a warning sound.

[0322] The generated virtual elements are customized to the user's individual requirements and emotional state, for example using a calmer voice tone for a user feeling anxious.

[0323] 3. Providing mixed reality experiences

[0324] The server transmits the generated virtual elements to the terminal.

[0325] The terminal integrates the transmitted virtual elements with the image of the real environment and provides it to the user in real time.

[0326] Specific examples

[0327] Specific examples for the visually impaired

[0328] 1. Brain signal acquisition and analysis

[0329] The user wears a headset for measuring electroencephalograms.

[0330] The device collects brain signal data from the headset in real time and transmits it to a server.

[0331] The server determines through analysis that the user intends to "pay attention to what is ahead."

[0332] 2. Real-world data collection

[0333] The terminal acquires image data of the area in front of the user through a camera.

[0334] The terminal transmits the collected video data to the server.

[0335] Through video analysis, the server recognizes that there is an obstacle (bicycle) ahead.

[0336] 3. Emotion Engine and Emotion Analysis

[0337] The server uses an emotion engine to analyze the user's emotional state and determine that the user is "feeling a little anxious."

[0338] 4. Creating and Providing Virtual Elements

[0339] The server generates a warning sound based on the analysis results and customizes the tone of the sound to be gentle to ease the user's anxiety.

[0340] The server transmits the generated warning sound to the terminal.

[0341] The terminal plays the received warning audio to the user, providing voice guidance in a gentle tone saying, "There is a bicycle ahead. Please be careful."

[0342] This system is designed to enable people with visual impairments, the elderly, and dementia patients to enjoy a safe and rich sensory experience in a real environment. In particular, the use of an emotion engine enables appropriate support according to the user's emotional state, supporting a safe and high-quality life.

[0343] The processing flow will be explained below.

[0344] Step 1:

[0345] The user puts on a wearable device for measuring brain waves (e.g., an EEG headset), which prepares the device to measure the electrical activity of the user's brain in real time.

[0346] Step 2:

[0347] The terminal receives brain signal data acquired from the wearable device in real time, and performs preprocessing such as noise filtering on the received data to improve the accuracy of the analysis.

[0348] Step 3:

[0349] The device encrypts and transmits the pre-processed brain signal data to a server, ensuring data security at this stage.

[0350] Step 4:

[0351] The server analyzes the received brain signal data to identify the user's intentions, emotional state, visual needs, etc. It uses machine learning algorithms to quickly extract this information.

[0352] Step 5:

[0353] The device collects images of the real-world environment around the user in real time through a camera, and the camera's collection points may be adjusted based on specific visual requirements.

[0354] Step 6:

[0355] The terminals send the collected video data to the server, where it is appropriately compressed, encrypted, and transmitted safely and efficiently.

[0356] Step 7:

[0357] The server analyzes the received video data and uses image recognition algorithms to identify the surrounding environment (obstacles, moving objects, points of interest, etc.).

[0358] Step 8:

[0359] The server operates an emotion engine based on the analysis of the brain signal data to identify the user's emotional state. The emotion engine analyzes whether the user is relaxed, tense, or anxious.

[0360] Step 9:

[0361] The server monitors the emotion engine in real time and generates virtual elements according to changes in emotions, for example, generating relaxing music and images if the user is feeling anxious.

[0362] Step 10:

[0363] The server then transmits the generated virtual elements to the device, including audio alerts and visual aids tailored to the user's emotional state.

[0364] Step 11:

[0365] The device then combines the received virtual elements with the image of the real environment in real time, and displays or outputs audio to the user. Through this MR experience, the user can enjoy a rich sensory experience linked to their emotions.

[0366] Examples:

[0367] Specific examples for the visually impaired

[0368] Step 1:

[0369] The user wears a headset for measuring electroencephalograms.

[0370] Step 2:

[0371] The device receives brain signal data from the headset in real time.

[0372] Step 3:

[0373] The device encrypts the received brain signal data and transmits it to a server.

[0374] Step 4:

[0375] The server analyzes the brain signal data and determines that the user intends to "focus their attention on what is ahead."

[0376] Step 5:

[0377] The terminal uses a camera to collect video data of the area in front of the user.

[0378] Step 6:

[0379] The terminal transmits the collected video data to the server.

[0380] Step 7:

[0381] The server analyzes the video data and recognizes that there is an obstacle (bicycle) ahead.

[0382] Step 8:

[0383] The server uses the results of the brain signal analysis and an emotion engine to determine that the user is "feeling a little anxious."

[0384] Step 9:

[0385] The server generates a gentle tone of warning audio based on the user's emotional state.

[0386] Step 10:

[0387] The server transmits the generated warning sound to the terminal.

[0388] Step 11:

[0389] The terminal plays the received warning audio to the user, providing voice guidance in a gentle tone saying, "There is a bicycle ahead. Please be careful."

[0390] Example 2

[0391] 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."

[0392] In order for visually impaired people, elderly people, and people with memory loss or dementia to enjoy safe and rich sensory experiences in real-world environments, a system that can accurately grasp information about the surrounding environment and provide appropriate support is necessary. However, conventional technologies have difficulty responding to the user's emotional state and individual needs, and safety and convenience have not been sufficiently ensured. Therefore, in order for users to live their lives with peace of mind, a system that can obtain environmental information in real time and provide customized support according to the user's condition is required.

[0393] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[0394] In this invention, the server includes means for analyzing the user's brain signal data and identifying the user's intention, emotional state, and visual requirements, means for analyzing the video data of the real environment and identifying surrounding environmental information, and means for generating virtual elements for customizing the user's experience based on the results of the analysis of the brain signal data and the video data of the real environment, and adjusting the virtual elements according to the user's emotional state. This makes it possible to comprehensively analyze the user's brain signals and information about the real environment, and provide appropriate support according to the user's emotional state and a customized experience in real time.

[0395] "User" refers to individuals who use the system, such as the visually impaired, elderly, or those with memory loss or dementia.

[0396] "Brain signals" refers to data about a user's brain's electrical activity, such as electroencephalograms, acquired using a wearable device such as an EEG headset.

[0397] A "wearable device" refers to a device worn by a user that can measure and collect data such as brain signals in real time.

[0398] A "terminal" refers to a device used by a user that has the function of collecting brain signal data and video data and transmitting them to a server.

[0399] "Preprocessing" refers to processing performed to improve the quality of acquired brain signal data, and includes noise filtering and the like.

[0400] "Encryption" is a process performed to ensure data security, and refers to a technology that prevents transmitted data from being deciphered by third parties.

[0401] "Server" refers to a central processing unit that receives and analyzes brain signal data and video data.

[0402] "Analysis" refers to the data processing performed to identify the user's intentions, emotional state, and environmental information based on the acquired data.

[0403] "Video data of the real environment" refers to video information of the surroundings collected through smart glasses worn by the user or the camera on a mobile device.

[0404] "Customization" refers to adjusting the virtual elements provided by the system according to the user's individual requirements and emotional state.

[0405] "Virtual elements" refer to virtual information or objects that are added to a real environment to enrich the user's experience.

[0406] "Providing" refers to the process of presenting the generated virtual elements to the user in a manner that is integrated with the real environment.

[0407] This invention is a mixed reality (MR) system that adds virtual elements to a real environment and provides a richer sensory experience based on emotional status for the visually impaired, elderly people, and people with memory loss or dementia. This system has the following configuration, centered around the user, terminals, and server.

[0408] Acquiring and analyzing user's brain signals

[0409] The user wears a wearable device for measuring brain waves. This device is an EEG headset (for example, the "Emotiv Epoc+" manufactured by Emotiv). By wearing the device, the brain's electrical signals are ready to be measured in real time.

[0410] The device receives real-time brain signal data from the headset and performs pre-processing, including noise filtering, such as using a band-pass filter to filter out specific frequency bands, before transmitting the pre-processed data to a server using AES encryption.

[0411] The server analyzes the received brain signal data using machine learning algorithms (e.g., neural networks using Keras) to identify the user's intentions and emotional state. This analysis allows for a quick understanding of the user's real-time state.

[0412] Real-world data collection

[0413] The device collects real-time images of the surrounding real-world environment through the camera of smart glasses or mobile devices worn by the user, such as Google® Glass®.

[0414] The collected video data is compressed using the H.264 codec, encrypted via SSL / TLS, and then sent to a server, where it is analyzed using the YOLO (You Only Look Once) algorithm to identify surrounding environmental information (e.g., obstacles and moving objects).

[0415] Sentiment Engine and Sentiment Analysis

[0416] The server runs an emotion engine based on the analysis of brain signal data. The emotion engine uses a support vector machine (SVM) to determine the user's emotional state (e.g., whether they are relaxed or nervous) in real time.

[0417] Creating and providing virtual elements

[0418] The server generates appropriate virtual elements based on the analysis of brain signal data and video data of the real environment. The generated virtual elements are customized according to the user's individual requirements and emotional state. For example, if a visually impaired person senses an obstacle ahead, an audio warning will be generated, and if it is determined that the user is feeling anxious, the tone of the audio will be customized to be gentler.

[0419] The generated virtual elements are sent from the server to the device, which then integrates them with images of the real environment and provides them to the user in real time. For example, a warning message can be displayed on the smart glasses display while an audio warning is played.

[0420] Examples of concrete examples and prompts

[0421] Specific examples for the visually impaired

[0422] 1. The user wears an EEG headset to collect brain signal data.

[0423] 2. The device preprocesses the brain signal data and sends it to the server.

[0424] 3. The server determines the user's intent to "pay attention to what's ahead."

[0425] 4. The device acquires image data of the area in front of it through the Google Glass camera and sends it to the server.

[0426] 5. The server uses the YOLO algorithm to recognize that there is an obstacle (bicycle) ahead.

[0427] 6. The server uses an emotion engine to analyze that the user is "feeling a little anxious."

[0428] 7. The server generates a warning voice and sends a gentle voice tone message to the terminal saying, "There is a bicycle ahead. Be careful."

[0429] 8. The device plays the received warning audio to the user, integrating it with the real-world environment in real time.

[0430] Prompt Sentence Examples

[0431] 1. Please explain in detail the processing flow of a system that uses EEG and video data to generate audio warnings to help visually impaired people avoid obstacles ahead.

[0432] 2. Please explain in detail the processing flow of a system that analyzes the emotional state of an elderly person based on EEG data and generates and provides virtual elements to help them relax when they are feeling anxious.

[0433] In this way, this system can support a safe and high-quality life by integrating and analyzing the user's emotional state and real-world environment.

[0434] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0435] Step 1:

[0436] The user wears a wearable device for measuring brain waves (e.g., an EEG headset), which allows the brain's electrical activity to be measured in real time. The input is the user's brain's electrical signals, and the output is the brain wave data measured by the EEG headset.

[0437] Step 2:

[0438] The terminal receives brain signal data collected from the headset in real time. The received EEG data undergoes preprocessing such as noise filtering. Preprocessing involves using a bandpass filter to filter out specific frequency bands. The input is brain signal data from the EEG headset, and the output is preprocessed, high-quality brain signal data.

[0439] Step 3:

[0440] The device encrypts the preprocessed brain signal data and sends it to the server. The AES encryption method is used for encryption, ensuring secure data transmission. The input is preprocessed EEG data, and the output is encrypted EEG data.

[0441] Step 4:

[0442] The server receives the encrypted brain signal data, decrypts it, and analyzes it. It uses machine learning algorithms (e.g., neural networks) to identify the user's intentions and emotional state. The input is the encrypted and transmitted brain wave data, and the output is the user's intentions and emotional state.

[0443] Step 5:

[0444] The device collects video data of the real-world environment around the user in real time through a camera (e.g., a camera in smart glasses). This video data includes surrounding objects and moving objects. The input is the video of the real-world environment captured by the camera, and the output is the collected video data.

[0445] Step 6:

[0446] The terminal compresses the collected video data using the H.264 codec, encrypts it using SSL / TLS, and then sends it to the server. The input is the collected video data, and the output is the encrypted video data.

[0447] Step 7:

[0448] The server receives the encrypted video data, decrypts it, and analyzes it. It uses the YOLO algorithm to identify the surrounding environment (e.g., obstacles and moving objects). The input is the encrypted video data, and the output is the identified environment information.

[0449] Step 8:

[0450] The server runs an emotion engine based on preprocessed EEG data and environmental information. The emotion engine uses a support vector machine (SVM) to determine the user's emotional state. The inputs are the user's intention, emotional state, and environmental information, and the output is the user's emotional state.

[0451] Step 9:

[0452] The server generates virtual elements based on the analysis results and customizes them according to the user's emotional state. For example, it generates a warning sound for obstacles and sets it to a gentle tone if necessary. The input is the user's emotional state, environmental information, and brainwave data, and the output is a customized virtual element (e.g., a warning sound).

[0453] Step 10:

[0454] The server sends the generated virtual elements to the device. The device integrates the virtual elements with the image of the real environment and provides it to the user in real time. For example, a warning message can be displayed on the smart glasses display while a warning sound is played. The input is the customized virtual elements, and the output is the virtual elements that are displayed and played to the user.

[0455] (Application example 2)

[0456] 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."

[0457] It is necessary to provide an environment in which users, such as the visually impaired, elderly people, dementia patients, and factory workers, can work safely and efficiently in real-world environments. However, it is currently difficult for these users to receive appropriate advice and warnings based on their emotional state. In particular, when working in a factory, there is a lack of systems that provide support based on emotional state, so it is necessary to improve user safety and work efficiency.

[0458] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 2 is realized by the following means.

[0459] In this invention, the server includes means for acquiring a user's brain signal, means for analyzing the user's brain signal data and identifying the user's intention, emotional state, and visual requirements, means for collecting video data of the real environment around the user, means for analyzing the collected video data and identifying surrounding environmental information, means for generating virtual elements to customize the user's experience based on the analysis results of the brain signal data and the video data of the real environment, means for providing the generated virtual elements to the user, means for monitoring the emotional state of workers and generating and providing feedback based on the specific emotional state, and means for identifying obstacles and dangerous areas in the factory and providing warnings to the user. This makes it possible to provide appropriate feedback according to the user's emotional state and improve the safety and efficiency of work in the factory.

[0460] "Brain signals" are electrical signals emitted from the user's brain, and are data that reflect the state of thoughts, emotions, intentions, etc.

[0461] "Brain signal data" refers to information that is measured and recorded in digital form and is used to analyze the user's brain activity.

[0462] "User intent" refers to information about a user's behavior or interests, such as what the user is planning to do or what the user is paying attention to.

[0463] "Emotional state" refers to the user's current emotion (e.g., joy, sadness, anger, anxiety, etc.) and is determined by analyzing brain signal data.

[0464] A "visual need" is a user's visual need for information or assistance, and is identified based on the user's intentions and emotional state.

[0465] The "surrounding real environment" refers to the location where the user is currently physically present and the environment around it, and is collected as video data using devices such as cameras.

[0466] "Video data" is digital data that contains visual information of the real environment, captured by a camera or the like.

[0467] "Environmental information" refers to information about objects and obstacles around the user, as well as their positions and movements, and is determined by analyzing video data.

[0468] "Virtual elements" are digital information or content that is added to a real-world environment to customize a user's experience.

[0469] "Feedback" refers to information or advice that a system provides to a user, and is provided using audio or visual means.

[0470] "Obstacles" or "dangerous areas" are objects that are obstacles or dangerous places for the user, and are identified by analyzing video data of the real environment.

[0471] To implement this invention, hardware such as a wearable device for measuring electroencephalograms (e.g., an EEG headset), smart glasses, and a server is required. Furthermore, software such as a machine learning algorithm (e.g., TensorFlow, PyTorch) and an image recognition algorithm (e.g., OpenCV) is required. A specific embodiment of this system is described below.

[0472] Acquiring and analyzing user's brain signals

[0473] The user wears an EEG headset to measure brain waves, which measures the brain's electrical activity in real time. This data is collected through smart glasses and received in real time by a device. The device performs preprocessing to improve the accuracy of the data analysis, such as noise filtering, and then encrypts and transmits the data to a server. The server uses machine learning algorithms (TensorFlow, PyTorch) to analyze the brain signal data and identify the user's intentions and emotional state.

[0474] Real-world data collection and analysis

[0475] The camera in the smart glasses worn by the user collects video data of the real-world environment around the user in real time. The device preprocesses this data, appropriately compresses and encrypts it, and sends it to the server. The server analyzes the received video data and identifies obstacles, points of interest, dangerous areas, etc. This processing uses an image recognition algorithm (OpenCV).

[0476] Emotion Engine and Feedback Generation

[0477] The server uses an emotion engine to identify the user's emotional state based on the analysis of brain signal data. If the user is feeling anxious or stressed, it generates virtual elements (e.g., calming music or visual messages) to promote relaxation. It also generates warning messages about obstacles and dangerous areas based on the analysis of data from the real environment. These virtual elements are provided to the user through the smart glasses.

[0478] Specific examples

[0479] For example, if a factory worker is feeling stressed, their state is recognized in real time by the server through brainwave data analysis. As a result, relaxing music or calming visual messages are generated and displayed in the worker's smart glasses. Furthermore, if the worker approaches a dangerous area, their movements are identified through video data analysis, and a warning message is displayed on the smart glasses. This system improves worker safety and work efficiency.

[0480] Prompt Sentence Examples

[0481] "Generate optimal visual and audio feedback based on EEG data and on-site video data to bring workers back to a relaxed state."

[0482] "Devour a method to provide relaxing music when a worker's emotional state is judged to be stressed, and simultaneously display a warning message when the worker approaches a dangerous area on the job site."

[0483] As described above, the present invention is a system for providing real-time feedback according to a user's emotional state, and is particularly intended to improve safety and efficiency for factory workers.

[0484] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0485] Step 1:

[0486] The user wears an EEG headset to measure brain waves and acquire brain signals.

[0487] Input: User's EEG signal.

[0488] How it works: The EEG headset detects electrical signals emitted from the user's brain in real time and collects them as digital data.

[0489] Output: Acquired brain signal data.

[0490] Step 2:

[0491] The device receives brain signal data acquired from an EEG headset, and performs pre-processing such as noise filtering on the received data.

[0492] Input: Brain signal data transmitted from an EEG headset.

[0493] Specific operation: The device receives brain signal data in real time and passes it through a noise filter to improve data accuracy.

[0494] Output: Preprocessed brain signal data.

[0495] Step 3:

[0496] The device encrypts and transmits the preprocessed brain signal data to a server.

[0497] Input: Preprocessed brain signal data.

[0498] What it does: The device encrypts the data and sends it to the server using a secure communication protocol.

[0499] Output: Encrypted brain signal data sent to server.

[0500] Step 4:

[0501] The server analyzes the brain signal data to identify the user's intentions and emotional state.

[0502] Input: Encrypted brain signal data.

[0503] What it does: The server decrypts the data and uses machine learning algorithms (TensorFlow, PyTorch) to identify the user's intent and emotional state.

[0504] Output: Analysis of the user's intent and emotional state.

[0505] Step 5:

[0506] The camera in the user's smart glasses collects video data of the surrounding real-world environment.

[0507] Input: Real-world environment.

[0508] How it works: The camera in the smart glasses captures images of the surroundings in real time.

[0509] Output: Video data.

[0510] Step 6:

[0511] The terminal transmits the collected video data to the server.

[0512] Input: Video data.

[0513] Specific operation: The device compresses and encrypts the video data before sending it to the server.

[0514] Output: Video data sent to the server.

[0515] Step 7:

[0516] The server analyzes the video data and identifies obstacles and dangerous areas.

[0517] Input: Video data.

[0518] Specific operation: Using image recognition algorithms (OpenCV), data analysis is performed to identify obstacles and dangerous areas in the video.

[0519] Output: Analysis results of obstacles and dangerous areas.

[0520] Step 8:

[0521] The server generates virtual elements according to the user's emotional state based on the results of analyzing the brain signal data and video data.

[0522] Input: Analysis results of the user's intent and emotional state, analysis results of obstacles and dangerous areas.

[0523] Specific behavior: The server uses the generative AI model to generate appropriate feedback and warning messages for the user.

[0524] Output: Virtual elements (e.g. audio messages, visual messages).

[0525] Step 9:

[0526] The server transmits the generated virtual elements to the user's smart glasses.

[0527] Input: Virtual element.

[0528] What happens: The server formats the virtual elements appropriately and sends them to the smart glasses.

[0529] Output: Virtual elements sent to the user's smart glasses.

[0530] Step 10:

[0531] The user's smart glasses display or play the received virtual elements and provide feedback to the user.

[0532] Input: Virtual element.

[0533] What it does: The smart glasses display visual messages and play audio messages.

[0534] Output: Feedback provided to the user.

[0535] 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.

[0536] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search engine).<URL: https: / / openai.com / blog / chatgpt> ), 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.

[0537] 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.

[0538] [Second embodiment]

[0539] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0540] 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.

[0541] 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).

[0542] 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.

[0543] 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.

[0544] 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).

[0545] 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.

[0546] 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.

[0547] 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.

[0548] 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.

[0549] 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.

[0550] 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."

[0551] This invention is a mixed reality (MR) system that adds virtual elements to a real environment to provide a richer sensory experience for the visually impaired, elderly people, and people with memory loss or dementia. Below, we will explain in detail the program-based processing of this system.

[0552] Acquiring and analyzing user's brain signals

[0553] 1. Acquiring Brain Signals

[0554] The user wears a wearable device for measuring EEG, which measures and acquires electrical signals generated by the brain in real time.

[0555] 2. Transmission and analysis of brain signal data

[0556] The terminal transmits the acquired brain signal data to a server.

[0557] The server analyzes the received brain signal data to identify the user's intentions, emotional state, visual needs, etc. This analysis is performed using machine learning algorithms and data processing techniques.

[0558] Real-world data collection

[0559] 1. Video data collection

[0560] The device (specifically, smart glasses or a mobile device worn by the user) collects images of the real-world environment around the user in real time via a camera.

[0561] 2. Video data transmission and analysis

[0562] The terminal transmits the collected video data to the server.

[0563] The server analyzes the received video data and obtains information about the surrounding environment, including object detection and movement analysis.

[0564] Creating and providing virtual elements

[0565] 1. Creating Virtual Elements

[0566] The server generates appropriate virtual elements based on the analysis of brain signal data and video data of the real environment. For example, if a visually impaired person senses an obstacle ahead, it generates an audio warning to indicate the presence of an obstacle.

[0567] 2. Customizing Virtual Elements

[0568] The server customizes the generated virtual elements to the user's individual requirements, for example adjusting the tone and volume of audio alerts depending on the user's audio feedback preferences.

[0569] 3. Providing mixed reality experiences

[0570] The server transmits the generated customized virtual element to the terminal.

[0571] The device integrates the transmitted virtual elements with the image of the real environment, providing the user with a real-time MR experience.

[0572] Specific examples

[0573] Specific examples for the visually impaired

[0574] 1. Brain signal acquisition and analysis

[0575] The user wears a headset for measuring electroencephalograms.

[0576] The device collects brain signal data from the headset in real time and transmits it to a server.

[0577] The server determines through analysis that the user intends to "pay attention to what is ahead."

[0578] 2. Real-world data collection

[0579] The terminal acquires image data of the area in front of the user through a camera.

[0580] The terminal transmits the collected video data to the server.

[0581] Through video analysis, the server recognizes that there is an obstacle (e.g., a bicycle) ahead.

[0582] 3. Creating and providing virtual elements

[0583] The server generates a warning sound based on the results of the brain signal analysis and video analysis.

[0584] The server customizes the generated alert audio according to the user's audio feedback preferences.

[0585] The server sends a customized alert sound to the terminal.

[0586] The terminal plays the received warning audio to the user, providing audio guidance such as "There is a bicycle ahead. Be careful."

[0587] As described above, this system is designed to enable people with visual impairments, the elderly, and dementia patients to enjoy safe and rich sensory experiences in real-world environments, thereby supporting safer and better lives.

[0588] The processing flow will be explained below.

[0589] Step 1:

[0590] The user puts on a wearable device (e.g., an EEG headset) to measure brain signals, which allows for real-time measurements of the brain's electrical activity.

[0591] Step 2:

[0592] The device receives brain signal data acquired from the headset in real time, and performs preprocessing such as noise filtering on the received data to improve analysis accuracy.

[0593] Step 3:

[0594] The device encrypts the pre-processed brain signal data before transmitting it to the server, ensuring data security.

[0595] Step 4:

[0596] The server analyzes the received brain signal data to identify the user's intentions, emotional state, visual needs, etc. It uses machine learning algorithms to quickly extract this information.

[0597] Step 5:

[0598] The device collects images of the real-world environment around the user in real time through a camera, and the collection point of this image data can be changed according to the user's visual needs.

[0599] Step 6:

[0600] The terminals send the collected video data to the server, where it is appropriately compressed and encrypted for efficient and secure transmission.

[0601] Step 7:

[0602] The server analyzes the received video data and identifies the surrounding environment (obstacles, moving objects, points of interest, etc.) using an image recognition algorithm.

[0603] Step 8:

[0604] The server integrates the results of the brain signal analysis with environmental information to generate appropriate virtual elements (audio guidance, warnings, visual aids, etc.), which are customized to suit the user's preferences and needs.

[0605] Step 9:

[0606] The server then sends the generated customized virtual elements to the device, and this data is also encrypted to ensure security.

[0607] Step 10:

[0608] The device then combines the received virtual elements with the video of the real environment in real time, and displays or outputs audio to the user. Through this MR experience, the user can receive information about the real environment in an easy-to-understand format.

[0609] Examples:

[0610] Step 1:

[0611] The user wears a headset for measuring electroencephalograms.

[0612] Step 2:

[0613] The device receives brain signal data from the headset in real time.

[0614] Step 3:

[0615] The device encrypts the received brain signal data and transmits it to a server.

[0616] Step 4:

[0617] The server analyzes the brain signal data and determines that the user intends to "focus their attention on what is ahead."

[0618] Step 5:

[0619] The terminal uses a camera to collect video data of the area in front of the user.

[0620] Step 6:

[0621] The terminal transmits the collected video data to the server.

[0622] Step 7:

[0623] Through video analysis, the server recognizes that there is an obstacle (bicycle) ahead.

[0624] Step 8:

[0625] The server generates and customizes the warning sound based on the analysis results.

[0626] Step 9:

[0627] The server transmits the generated warning sound to the terminal.

[0628] Step 10:

[0629] The terminal plays the received warning audio to the user, providing audio guidance such as "There is a bicycle ahead. Please be careful."

[0630] Example 1

[0631] 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."

[0632] It is difficult for visually impaired people, the elderly, and people with memory loss or dementia to enjoy safe and rich sensory experiences in real-world environments. Such users are unable to recognize or interpret information about their surroundings, making it difficult for them to move around or live safely. Conventional technologies have not adequately resolved these issues, and effective means are needed to improve users' safety and quality of life. Therefore, the present invention aims to solve these problems.

[0633] 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.

[0634] In this invention, the server includes means for analyzing the user's brain signal data to identify the user's intention, emotional state, and visual needs, means for analyzing surrounding environmental information, and means for generating virtual elements to customize the user's experience, thereby fusing the user's brain signal data with data from the real environment to provide virtual elements tailored to individual needs, enabling a safe and rich sensory experience.

[0635] "User" refers to a person whose brain signals are acquired using the system.

[0636] "Brain signals" refer to electrical signals generated by the user's brain.

[0637] "Data" refers to the collection of information used by the system, including information collected and analyzed as signals or images.

[0638] "Virtual Elements" refers to virtual content that is generated based on the analysis of brain signal data and video data to supplement or enhance the user's experience, including sounds, visual effects, and other multimedia elements.

[0639] "Environmental information" refers to information such as the position and movement of objects in the real world around the user, which is obtained through the analysis of video data.

[0640] "Analysis" refers to the act of processing data to derive meaningful results, specifically using machine learning and object detection algorithms.

[0641] "Customization" refers to the act of tailoring or modifying generated virtual elements to suit the specific needs and desires of a user.

[0642] "Means" refers to an apparatus, method, or process for accomplishing a particular function or action.

[0643] "Transmit" refers to the act of transferring data from one device to another.

[0644] "Collection" refers to the act of gathering specific information. This can be done using devices such as sensors or cameras.

[0645] "Providing" refers to the act of allowing a user to use the generated virtual element.

[0646] The above provides definitions of important terms that may be included in the claims.

[0647] The present invention is a mixed reality (MR) system that adds virtual elements to a real environment to provide a richer sensory experience for the visually impaired, the elderly, and those with memory loss or dementia. Specific embodiments for implementing the present invention are described below.

[0648] Acquiring and analyzing user's brain signals

[0649] A user wears a wearable device for measuring EEG (e.g., a general-purpose EEG headset). This device acquires EEG signals from the scalp in real time using multiple electrodes. The acquired brain signal data is collected while the user is performing daily activities.

[0650] The terminal (smartphone or dedicated device) transmits the brain signal data acquired from this device to a server via wireless communication (Bluetooth or Wi-Fi).

[0651] The server analyzes the received brain signal data using machine learning algorithms, specifically TensorFlow with Python, to identify the user's intent (e.g., "I want to pay attention to what's ahead"), emotional state (e.g., tension, relaxation), and visual needs.

[0652] Real-world data collection

[0653] Users wear smart glasses or a head-mounted display (e.g., Microsoft HoloLens) and observe the real-world environment around them. The camera in the smart glasses captures images of the user's field of vision in real time. This image data is temporarily stored in the device's internal memory.

[0654] The terminal transmits the temporarily stored video data to the server at regular intervals (for example, one frame per second).

[0655] The server analyzes the received video data using an object detection algorithm based on OpenCV to identify information about the surrounding environment (obstacles and dynamic environmental changes).

[0656] Creating and providing virtual elements

[0657] The server uses a generative AI model to generate appropriate virtual elements based on the results of analyzing the brain signal data and the video data. For example, if it detects an obstacle in front of the user, it generates a warning sound or visual effect.

[0658] The server customizes the generated virtual elements to the user's individual needs, for example adjusting the tone and volume of audio alerts to suit the user's preferences.

[0659] The server sends customized virtual elements to the device, which then integrates them into the image of the real world, providing the user with a real-time MR experience. Specifically, a warning sound is played from the smartglasses' speaker, informing the user, "There is an obstacle ahead. Please be careful."

[0660] This will enable people with visual impairments, the elderly, and dementia patients to enjoy safe and rich sensory experiences in real-world environments. For example, visually impaired people walking around town will be able to see obstacles ahead in advance, improving safety.

[0661] Prompt Sentence Examples

[0662] "Please explain how a mixed reality system works, where the user wears a headset that measures EEG and uses a device that collects data on the surrounding environment to support safe mobility in the real world."

[0663] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0664] Step 1:

[0665] Acquiring brain signals

[0666] The user wears a wearable device for measuring brain waves, which acquires brain waves in real time through electrodes on the scalp.

[0667] Input: EEG electrical signals

[0668] Output: Digital brain signal data

[0669] How it works: The user puts on the headset, and the device uses sensors to capture electrical brainwave signals and convert them into digital data.

[0670] Step 2:

[0671] Transmission of brain signal data

[0672] The device transmits the acquired brain signal data to a server via Bluetooth or Wi-Fi.

[0673] Input: Brain signal data from a wearable device

[0674] Output: Digital brain signal data sent to a server

[0675] Specific operation: The terminal communicates with the wearable device, collects data, and sends it to the server.

[0676] Step 3:

[0677] Analysis of brain signal data

[0678] The server analyzes the received brain signal data using a machine learning model in TensorFlow with Python.

[0679] Input: Digital brain signal data sent to the server

[0680] Output: Data about the user's intent, emotional state, and visual needs

[0681] Specific operation: The server receives the data, runs it through a machine learning model, and derives analytical results.

[0682] Step 4:

[0683] Video data collection

[0684] Users wear smart glasses or a head-mounted display and observe the real-world environment around them, with the device's camera capturing images in real time.

[0685] Input: Surrounding real-world environment

[0686] Output: Video data collected by the camera

[0687] Specific operation: The user wears the device and the camera captures real-time video.

[0688] Step 5:

[0689] Video data transmission

[0690] The device temporarily stores the video data collected by the camera in its internal memory and transmits it to the server at regular intervals.

[0691] Input: Video data from the camera

[0692] Output: Video data sent to the server

[0693] Specific operation: The device stores the video data in its internal memory and sends it to the server.

[0694] Step 6:

[0695] Video data analysis

[0696] The server analyzes the received video data using an object detection algorithm based on OpenCV.

[0697] Input: Video data sent to the server

[0698] Output: Surrounding environment information (position and movement of obstacles, etc.)

[0699] Specific operation: The server receives the video data, analyzes it using an object detection algorithm, and identifies environmental information.

[0700] Step 7:

[0701] Creating Virtual Elements

[0702] The server uses a generative AI model to generate appropriate virtual elements based on the results of analyzing the brain signal data and the video data.

[0703] Input: Brain signal analysis results and video data analysis results

[0704] Output: Generated virtual elements (sound, visual effects, etc.)

[0705] Specific operation: The server runs a generative AI model based on the analysis results to generate virtual elements.

[0706] Step 8:

[0707] Customizing Virtual Elements

[0708] The server customizes the generated virtual elements to the user's individual needs.

[0709] Input: Generated virtual elements, individual user needs

[0710] Output: Customized Virtual Elements

[0711] What it does: The server adjusts, modifies, and optimizes the virtual elements.

[0712] Step 9:

[0713] Sending customized virtual elements

[0714] The server transmits the customized virtual element to the terminal.

[0715] Input: Customized Virtual Elements

[0716] Output: customized virtual elements sent to the device

[0717] Specific operation: The server sends virtual elements to the terminal and reflects them in real time.

[0718] Step 10:

[0719] Providing MR experiences

[0720] The device then integrates the received virtual elements into the image of the real environment, providing the user with an MR experience.

[0721] Input: Customized virtual elements, video data of the real environment

[0722] Output: The integrated mixed reality experience (audio alerts and visual effects provided to the user)

[0723] Specific operation: The device combines virtual elements and video data to play them, providing the user with an MR experience.

[0724] (Application example 1)

[0725] 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."

[0726] Visually impaired people and the elderly have a reduced ability to detect suspicious people and dangerous situations in the real world, making it difficult for them to ensure safety in their daily lives. Currently available technologies are often insufficient to recognize the environment and respond appropriately without relying on sight or hearing. To solve this problem, a system is needed that can detect surrounding dangers in real time and provide warnings to users.

[0727] 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.

[0728] In this invention, the server includes means for analyzing the user's electroencephalogram data and video data to recognize suspicious individuals and dangerous situations, means for generating and providing warning audio and visual signals for the recognized suspicious individuals and dangerous situations, and means for generating virtual elements to customize the user's experience, thereby enabling support for visually impaired people and the elderly to recognize surrounding dangers in real time and respond appropriately.

[0729] "Brain signals" refer to electrical signals generated by the user's brain.

[0730] "Brain signal data" refers to digital data obtained from acquired brain signals.

[0731] "Intention" refers to the sense that a user is trying to act with a specific action or purpose.

[0732] "Emotional state" refers to the user's psychological state of mind and emotions.

[0733] "Visual requirements" refer to information or objects that a user visually desires.

[0734] "Real environment" refers to the physical environment in which the user actually resides.

[0735] "Video data" refers to image or video data captured through a camera or other imaging device.

[0736] "Virtual elements" refer to digitally generated virtual information or objects that are integrated into a real environment.

[0737] A "suspicious person" refers to a person in the surrounding environment who may potentially pose a danger or cause anxiety to the user.

[0738] "Dangerous situation" refers to a situation or environment that may pose a threat to the safety of the user.

[0739] "Warning voice" refers to voice generated to warn the user of danger and caution.

[0740] "Visual signal" refers to an image or signal displayed to visually alert a user to danger or caution.

[0741] This invention is a system that supports safety for visually impaired people and the elderly by detecting surrounding dangers in real time. Specifically, smart glasses worn by the user work in cooperation with a server and a terminal connected to the glasses.

[0742] System Configuration

[0743] Hardware

[0744] Smart glasses: Wearable devices equipped with a camera and audio output.

[0745] EEG measurement device: A headset for capturing the user's brain signals in real time.

[0746] Server: A central processing unit that analyzes brain signals and video data.

[0747] software

[0748] Data analysis platform: Software containing machine learning algorithms for analyzing EEG and video data.

[0749] Warning generation algorithm: A program that detects suspicious individuals or dangerous situations and generates appropriate warnings.

[0750] Program processing flow

[0751] 1. Acquisition and transmission of brain signal data

[0752] The user wears an EEG headset, which captures the user's brain signals in real time.

[0753] The acquired brain signal data is sent to a server via the terminal.

[0754] 2. Collecting and transmitting data from the real world

[0755] The camera in the smart glasses collects video data of the real-world environment around the user.

[0756] The collected video data is sent to the server in real time via the terminal.

[0757] 3. Data Analysis

[0758] The server analyzes the brain signal data to identify the user's intentions, emotional state, and visual needs.

[0759] At the same time, the video data is analyzed to identify the surrounding environment, suspicious individuals, and the location and movement of obstacles, using machine learning algorithms and data processing technology.

[0760] 4. Creating and Providing Virtual Elements

[0761] Based on the analysis, the server generates virtual elements that customize the user's experience, such as audio and visual warnings if a suspicious person is detected.

[0762] The generated virtual elements are transmitted to the smart glasses in real time and presented to the user.

[0763] Specific examples

[0764] As a user walks around town, an EEG headset captures the user's brain signals. At the same time, the camera in the smart glasses collects video data of the surrounding area. This data is sent to a server, which analyzes the EEG data to determine the user's alert state and analyzes the video data to recognize suspicious individuals and dangerous situations. If a suspicious individual is detected, the server generates a warning voice saying, "A suspicious individual is present. Please be careful," and sends it to the user through the smart glasses.

[0765] Example prompts for generative AI models

[0766] Please implement an app that will issue an audio warning if EEG data indicates a certain level of alertness and detects a suspicious person ahead.

[0767] The data used is 128-point EEG data and real-time video data.

[0768] We use a peak detection algorithm to analyze the EEG data, and the face detection function of OpenCV to analyze the video data.

[0769] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0770] Step 1:

[0771] The user wears an EEG headset, which captures electrical signals generated by the brain in real time. The captured brain signal data reflects the user's brainwave activity at that moment.

[0772] Input: User's brain signals

[0773] Data processing: The headset converts electrical signals generated by the brain into digital data

[0774] Output: Brain signal data

[0775] Step 2:

[0776] The device transmits the brain signal data acquired from the headset to a server in real time, so it is necessary to minimize delays.

[0777] Input: Brain signal data

[0778] Data processing: Digital signal processing and data compression are performed

[0779] Output: Brain signal data sent to the server

[0780] Step 3:

[0781] The device uses the camera in the smart glasses to collect video data of the real-world environment around the user, which contains information about the real world in the user's field of view.

[0782] Input: Video of the user's surroundings

[0783] Data processing: Converting analog video data into digital video data

[0784] Output: Collected video data

[0785] Step 4:

[0786] The device transmits the collected video data to a server, also in real time and designed to minimize latency.

[0787] Input: Collected video data

[0788] Data processing: Compression and packetization of video data

[0789] Output: Video data sent to the server

[0790] Step 5:

[0791] The server analyzes the received brain signal data using machine learning algorithms to identify the user's intent, emotional state, and visual needs.

[0792] Input: Brain signal data sent to the server

[0793] Data processing: Analysis using machine learning algorithms

[0794] Output: User intent, emotional state, visual requirements

[0795] Step 6:

[0796] The server analyzes the received video data using an object detection algorithm to identify the location and movement of suspicious individuals and obstacles.

[0797] Input: Video data sent to the server

[0798] Data processing: object detection and motion analysis

[0799] Output: Location and movement information of suspicious persons and obstacles

[0800] Step 7:

[0801] Based on the results of the brain signal analysis and the video data analysis, the server generates virtual elements to customize the user's experience, such as generating audio and visual warnings if a suspicious person is detected.

[0802] Input: User's intentions, emotional state, visual needs, location and movement information of suspicious people and obstacles

[0803] Data processing: Creation of customized virtual elements

[0804] Output: Generated virtual elements (audio and visual warning signals)

[0805] Step 8:

[0806] The server transmits the generated virtual elements to the terminal, and the terminal provides the received virtual elements to the user through the smart glasses.

[0807] Input: The generated virtual element

[0808] Data processing: decoding and playback of audio data and visual signals

[0809] Output: Audio and visual warning signals provided to the user

[0810] 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.

[0811] This invention is a mixed reality (MR) system that adds virtual elements to a real environment and provides a richer sensory experience based on emotional states for the visually impaired, elderly people, and people with memory loss or dementia. The details of the program-based processing of this system are described below.

[0812] Acquiring and analyzing user's brain signals

[0813] 1. Acquiring Brain Signals

[0814] The user puts on a wearable device for measuring brain waves (e.g., an EEG headset), which prepares the device to measure the brain's electrical activity in real time.

[0815] 2. Transmission and analysis of brain signal data

[0816] The device receives brain signal data acquired from the headset in real time, and performs preprocessing such as noise filtering on the received data to improve analysis accuracy.

[0817] The device encrypts and transmits the preprocessed brain signal data to a server.

[0818] The server analyzes the received brain signal data to identify the user's intentions, emotional state, visual needs, etc. It uses machine learning algorithms to quickly extract this information.

[0819] Real-world data collection

[0820] 1. Video data collection

[0821] The device (e.g., smart glasses worn by the user or a mobile device) collects images of the real-world environment around the user in real time via a camera.

[0822] 2. Video data transmission and analysis

[0823] The terminals send the collected video data to the server, where it is appropriately compressed, encrypted, and transmitted safely and efficiently.

[0824] The server analyzes the received video data and identifies the surrounding environment (obstacles, moving objects, points of interest, etc.) using image recognition algorithms.

[0825] Sentiment Engine and Sentiment Analysis

[0826] 1. How the Emotion Engine Works

[0827] The server uses an emotion engine to identify the user's emotional state based on the analysis of the brain signal data. For example, the emotion engine analyzes information such as whether the user is relaxed or tense.

[0828] 2. Real-time emotion monitoring

[0829] The server monitors the user's emotional state in real time and adjusts the MR experience according to emotional changes, for example, adding relaxing virtual elements if the user feels anxious.

[0830] Creating and providing virtual elements

[0831] 1. Creating Virtual Elements

[0832] The server generates appropriate virtual elements based on the analysis of brain signal data, emotional state, and video data of the real environment. For example, if a visually impaired person senses an obstacle ahead, it generates a warning sound.

[0833] The generated virtual elements are customized to the user's individual requirements and emotional state, for example using a calmer voice tone for a user feeling anxious.

[0834] 3. Providing mixed reality experiences

[0835] The server transmits the generated virtual elements to the terminal.

[0836] The terminal integrates the transmitted virtual elements with the image of the real environment and provides it to the user in real time.

[0837] Specific examples

[0838] Specific examples for the visually impaired

[0839] 1. Brain signal acquisition and analysis

[0840] The user wears a headset for measuring electroencephalograms.

[0841] The device collects brain signal data from the headset in real time and transmits it to a server.

[0842] The server determines through analysis that the user intends to "pay attention to what is ahead."

[0843] 2. Real-world data collection

[0844] The terminal acquires image data of the area in front of the user through a camera.

[0845] The terminal transmits the collected video data to the server.

[0846] Through video analysis, the server recognizes that there is an obstacle (bicycle) ahead.

[0847] 3. Emotion Engine and Emotion Analysis

[0848] The server uses an emotion engine to analyze the user's emotional state and determine that the user is "feeling a little anxious."

[0849] 4. Creating and Providing Virtual Elements

[0850] The server generates a warning sound based on the analysis results and customizes the tone of the sound to be gentle to ease the user's anxiety.

[0851] The server transmits the generated warning sound to the terminal.

[0852] The terminal plays the received warning audio to the user, providing voice guidance in a gentle tone saying, "There is a bicycle ahead. Please be careful."

[0853] This system is designed to enable people with visual impairments, the elderly, and dementia patients to enjoy a safe and rich sensory experience in a real environment. In particular, the use of an emotion engine enables appropriate support according to the user's emotional state, supporting a safe and high-quality life.

[0854] The processing flow will be explained below.

[0855] Step 1:

[0856] The user puts on a wearable device for measuring brain waves (e.g., an EEG headset), which prepares the device to measure the electrical activity of the user's brain in real time.

[0857] Step 2:

[0858] The terminal receives brain signal data acquired from the wearable device in real time, and performs preprocessing such as noise filtering on the received data to improve the accuracy of the analysis.

[0859] Step 3:

[0860] The device encrypts and transmits the pre-processed brain signal data to a server, ensuring data security at this stage.

[0861] Step 4:

[0862] The server analyzes the received brain signal data to identify the user's intentions, emotional state, visual needs, etc. It uses machine learning algorithms to quickly extract this information.

[0863] Step 5:

[0864] The device collects images of the real-world environment around the user in real time through a camera, and the camera's collection points may be adjusted based on specific visual requirements.

[0865] Step 6:

[0866] The terminals send the collected video data to the server, where it is appropriately compressed, encrypted, and transmitted safely and efficiently.

[0867] Step 7:

[0868] The server analyzes the received video data and uses image recognition algorithms to identify the surrounding environment (obstacles, moving objects, points of interest, etc.).

[0869] Step 8:

[0870] The server operates an emotion engine based on the analysis of the brain signal data to identify the user's emotional state. The emotion engine analyzes whether the user is relaxed, tense, or anxious.

[0871] Step 9:

[0872] The server monitors the emotion engine in real time and generates virtual elements according to changes in emotions, for example, generating relaxing music and images if the user is feeling anxious.

[0873] Step 10:

[0874] The server then transmits the generated virtual elements to the device, including audio alerts and visual aids tailored to the user's emotional state.

[0875] Step 11:

[0876] The device then combines the received virtual elements with the image of the real environment in real time, and displays or outputs audio to the user. Through this MR experience, the user can enjoy a rich sensory experience linked to their emotions.

[0877] Examples:

[0878] Specific examples for the visually impaired

[0879] Step 1:

[0880] The user wears a headset for measuring electroencephalograms.

[0881] Step 2:

[0882] The device receives brain signal data from the headset in real time.

[0883] Step 3:

[0884] The device encrypts the received brain signal data and transmits it to a server.

[0885] Step 4:

[0886] The server analyzes the brain signal data and determines that the user intends to "focus their attention on what is ahead."

[0887] Step 5:

[0888] The terminal uses a camera to collect video data of the area in front of the user.

[0889] Step 6:

[0890] The terminal transmits the collected video data to the server.

[0891] Step 7:

[0892] The server analyzes the video data and recognizes that there is an obstacle (bicycle) ahead.

[0893] Step 8:

[0894] The server uses the results of the brain signal analysis and an emotion engine to determine that the user is "feeling a little anxious."

[0895] Step 9:

[0896] The server generates a gentle tone of warning audio based on the user's emotional state.

[0897] Step 10:

[0898] The server transmits the generated warning sound to the terminal.

[0899] Step 11:

[0900] The terminal plays the received warning audio to the user, providing voice guidance in a gentle tone saying, "There is a bicycle ahead. Please be careful."

[0901] Example 2

[0902] 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."

[0903] In order for visually impaired people, elderly people, and people with memory loss or dementia to enjoy safe and rich sensory experiences in real-world environments, a system that can accurately grasp information about the surrounding environment and provide appropriate support is necessary. However, conventional technologies have difficulty responding to the user's emotional state and individual needs, and safety and convenience have not been sufficiently ensured. Therefore, in order for users to live their lives with peace of mind, a system that can obtain environmental information in real time and provide customized support according to the user's condition is required.

[0904] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[0905] In this invention, the server includes means for analyzing the user's brain signal data and identifying the user's intention, emotional state, and visual requirements, means for analyzing the video data of the real environment and identifying surrounding environmental information, and means for generating virtual elements for customizing the user's experience based on the results of the analysis of the brain signal data and the video data of the real environment, and adjusting the virtual elements according to the user's emotional state. This makes it possible to comprehensively analyze the user's brain signals and information about the real environment, and provide appropriate support according to the user's emotional state and a customized experience in real time.

[0906] "User" refers to individuals who use the system, such as the visually impaired, elderly, or those with memory loss or dementia.

[0907] "Brain signals" refers to data about a user's brain's electrical activity, such as electroencephalograms, acquired using a wearable device such as an EEG headset.

[0908] A "wearable device" refers to a device worn by a user that can measure and collect data such as brain signals in real time.

[0909] A "terminal" refers to a device used by a user that has the function of collecting brain signal data and video data and transmitting them to a server.

[0910] "Preprocessing" refers to processing performed to improve the quality of acquired brain signal data, and includes noise filtering and the like.

[0911] "Encryption" is a process performed to ensure data security, and refers to a technology that prevents transmitted data from being deciphered by third parties.

[0912] "Server" refers to a central processing unit that receives and analyzes brain signal data and video data.

[0913] "Analysis" refers to the data processing performed to identify the user's intentions, emotional state, and environmental information based on the acquired data.

[0914] "Video data of the real environment" refers to video information of the surroundings collected through smart glasses worn by the user or the camera on a mobile device.

[0915] "Customization" refers to adjusting the virtual elements provided by the system according to the user's individual requirements and emotional state.

[0916] "Virtual elements" refer to virtual information or objects that are added to a real environment to enrich the user's experience.

[0917] "Providing" refers to the process of presenting the generated virtual elements to the user in a manner that is integrated with the real environment.

[0918] This invention is a mixed reality (MR) system that adds virtual elements to a real environment and provides a richer sensory experience based on emotional status for the visually impaired, elderly people, and people with memory loss or dementia. This system has the following configuration, centered around the user, terminals, and server.

[0919] Acquiring and analyzing user's brain signals

[0920] The user wears a wearable device for measuring brain waves. This device is an EEG headset (for example, the "Emotiv Epoc+" manufactured by Emotiv). By wearing the device, the brain's electrical signals are ready to be measured in real time.

[0921] The device receives real-time brain signal data from the headset and performs pre-processing, including noise filtering, such as using a band-pass filter to filter out specific frequency bands, before transmitting the pre-processed data to a server using AES encryption.

[0922] The server analyzes the received brain signal data using machine learning algorithms (e.g., neural networks using Keras) to identify the user's intentions and emotional state. This analysis allows for a quick understanding of the user's real-time state.

[0923] Real-world data collection

[0924] The device collects real-time images of the surrounding real-world environment through the camera on the smart glasses or mobile device worn by the user, such as Google Glass.

[0925] The collected video data is compressed using the H.264 codec, encrypted via SSL / TLS, and then sent to a server, where it is analyzed using the YOLO (You Only Look Once) algorithm to identify surrounding environmental information (e.g., obstacles and moving objects).

[0926] Sentiment Engine and Sentiment Analysis

[0927] The server runs an emotion engine based on the analysis of brain signal data. The emotion engine uses a support vector machine (SVM) to determine the user's emotional state (e.g., whether they are relaxed or nervous) in real time.

[0928] Creating and providing virtual elements

[0929] The server generates appropriate virtual elements based on the analysis of brain signal data and video data of the real environment. The generated virtual elements are customized according to the user's individual requirements and emotional state. For example, if a visually impaired person senses an obstacle ahead, an audio warning will be generated, and if it is determined that the user is feeling anxious, the tone of the audio will be customized to be gentler.

[0930] The generated virtual elements are sent from the server to the device, which then integrates them with images of the real environment and provides them to the user in real time. For example, a warning message can be displayed on the smart glasses display while an audio warning is played.

[0931] Examples of concrete examples and prompts

[0932] Specific examples for the visually impaired

[0933] 1. The user wears an EEG headset to collect brain signal data.

[0934] 2. The device preprocesses the brain signal data and sends it to the server.

[0935] 3. The server determines the user's intent to "pay attention to what's ahead."

[0936] 4. The device acquires image data of the area in front of it through the Google Glass camera and sends it to the server.

[0937] 5. The server uses the YOLO algorithm to recognize that there is an obstacle (bicycle) ahead.

[0938] 6. The server uses an emotion engine to analyze that the user is "feeling a little anxious."

[0939] 7. The server generates a warning voice and sends a gentle voice tone message to the terminal saying, "There is a bicycle ahead. Be careful."

[0940] 8. The device plays the received warning audio to the user, integrating it with the real-world environment in real time.

[0941] Prompt Sentence Examples

[0942] 1. Please explain in detail the processing flow of a system that uses EEG and video data to generate audio warnings to help visually impaired people avoid obstacles ahead.

[0943] 2. Please explain in detail the processing flow of a system that analyzes the emotional state of an elderly person based on EEG data and generates and provides virtual elements to help them relax when they are feeling anxious.

[0944] In this way, this system can support a safe and high-quality life by integrating and analyzing the user's emotional state and real-world environment.

[0945] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0946] Step 1:

[0947] The user wears a wearable device for measuring brain waves (e.g., an EEG headset), which allows the brain's electrical activity to be measured in real time. The input is the user's brain's electrical signals, and the output is the brain wave data measured by the EEG headset.

[0948] Step 2:

[0949] The terminal receives brain signal data collected from the headset in real time. The received EEG data undergoes preprocessing such as noise filtering. Preprocessing involves using a bandpass filter to filter out specific frequency bands. The input is brain signal data from the EEG headset, and the output is preprocessed, high-quality brain signal data.

[0950] Step 3:

[0951] The device encrypts the preprocessed brain signal data and sends it to the server. The AES encryption method is used for encryption, ensuring secure data transmission. The input is preprocessed EEG data, and the output is encrypted EEG data.

[0952] Step 4:

[0953] The server receives the encrypted brain signal data, decrypts it, and analyzes it. It uses machine learning algorithms (e.g., neural networks) to identify the user's intentions and emotional state. The input is the encrypted and transmitted brain wave data, and the output is the user's intentions and emotional state.

[0954] Step 5:

[0955] The device collects video data of the real-world environment around the user in real time through a camera (e.g., a camera in smart glasses). This video data includes surrounding objects and moving objects. The input is the video of the real-world environment captured by the camera, and the output is the collected video data.

[0956] Step 6:

[0957] The terminal compresses the collected video data using the H.264 codec, encrypts it using SSL / TLS, and then sends it to the server. The input is the collected video data, and the output is the encrypted video data.

[0958] Step 7:

[0959] The server receives the encrypted video data, decrypts it, and analyzes it. It uses the YOLO algorithm to identify the surrounding environment (e.g., obstacles and moving objects). The input is the encrypted video data, and the output is the identified environment information.

[0960] Step 8:

[0961] The server runs an emotion engine based on preprocessed EEG data and environmental information. The emotion engine uses a support vector machine (SVM) to determine the user's emotional state. The inputs are the user's intention, emotional state, and environmental information, and the output is the user's emotional state.

[0962] Step 9:

[0963] The server generates virtual elements based on the analysis results and customizes them according to the user's emotional state. For example, it generates a warning sound for obstacles and sets it to a gentle tone if necessary. The input is the user's emotional state, environmental information, and brainwave data, and the output is a customized virtual element (e.g., a warning sound).

[0964] Step 10:

[0965] The server sends the generated virtual elements to the device. The device integrates the virtual elements with the image of the real environment and provides it to the user in real time. For example, a warning message can be displayed on the smart glasses display while a warning sound is played. The input is the customized virtual elements, and the output is the virtual elements that are displayed and played to the user.

[0966] (Application example 2)

[0967] 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."

[0968] It is necessary to provide an environment in which users, such as the visually impaired, elderly people, dementia patients, and factory workers, can work safely and efficiently in real-world environments. However, it is currently difficult for these users to receive appropriate advice and warnings based on their emotional state. In particular, when working in a factory, there is a lack of systems that provide support based on emotional state, so it is necessary to improve user safety and work efficiency.

[0969] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 2 is realized by the following means.

[0970] In this invention, the server includes means for acquiring a user's brain signal, means for analyzing the user's brain signal data and identifying the user's intention, emotional state, and visual requirements, means for collecting video data of the real environment around the user, means for analyzing the collected video data and identifying surrounding environmental information, means for generating virtual elements to customize the user's experience based on the analysis results of the brain signal data and the video data of the real environment, means for providing the generated virtual elements to the user, means for monitoring the emotional state of workers and generating and providing feedback based on the specific emotional state, and means for identifying obstacles and dangerous areas in the factory and providing warnings to the user. This makes it possible to provide appropriate feedback according to the user's emotional state and improve the safety and efficiency of work in the factory.

[0971] "Brain signals" are electrical signals emitted from the user's brain, and are data that reflect the state of thoughts, emotions, intentions, etc.

[0972] "Brain signal data" refers to information that is measured and recorded in digital form and is used to analyze the user's brain activity.

[0973] "User intent" refers to information about a user's behavior or interests, such as what the user is planning to do or what the user is paying attention to.

[0974] "Emotional state" refers to the user's current emotion (e.g., joy, sadness, anger, anxiety, etc.) and is determined by analyzing brain signal data.

[0975] A "visual need" is a user's visual need for information or assistance, and is identified based on the user's intentions and emotional state.

[0976] The "surrounding real environment" refers to the location where the user is currently physically present and the environment around it, and is collected as video data using devices such as cameras.

[0977] "Video data" is digital data that contains visual information of the real environment, captured by a camera or the like.

[0978] "Environmental information" refers to information about objects and obstacles around the user, as well as their positions and movements, and is determined by analyzing video data.

[0979] "Virtual elements" are digital information or content that is added to a real-world environment to customize a user's experience.

[0980] "Feedback" refers to information or advice that a system provides to a user, and is provided using audio or visual means.

[0981] "Obstacles" or "dangerous areas" are objects that are obstacles or dangerous places for the user, and are identified by analyzing video data of the real environment.

[0982] To implement this invention, hardware such as a wearable device for measuring electroencephalograms (e.g., an EEG headset), smart glasses, and a server is required. Furthermore, software such as a machine learning algorithm (e.g., TensorFlow, PyTorch) and an image recognition algorithm (e.g., OpenCV) is required. A specific embodiment of this system is described below.

[0983] Acquiring and analyzing user's brain signals

[0984] The user wears an EEG headset to measure brain waves, which measures the brain's electrical activity in real time. This data is collected through smart glasses and received in real time by a device. The device performs preprocessing to improve the accuracy of the data analysis, such as noise filtering, and then encrypts and transmits the data to a server. The server uses machine learning algorithms (TensorFlow, PyTorch) to analyze the brain signal data and identify the user's intentions and emotional state.

[0985] Real-world data collection and analysis

[0986] The camera in the smart glasses worn by the user collects video data of the real-world environment around the user in real time. The device preprocesses this data, appropriately compresses and encrypts it, and sends it to the server. The server analyzes the received video data and identifies obstacles, points of interest, dangerous areas, etc. This processing uses an image recognition algorithm (OpenCV).

[0987] Emotion Engine and Feedback Generation

[0988] The server uses an emotion engine to identify the user's emotional state based on the analysis of brain signal data. If the user is feeling anxious or stressed, it generates virtual elements (e.g., calming music or visual messages) to promote relaxation. It also generates warning messages about obstacles and dangerous areas based on the analysis of data from the real environment. These virtual elements are provided to the user through the smart glasses.

[0989] Specific examples

[0990] For example, if a factory worker is feeling stressed, their state is recognized in real time by the server through brainwave data analysis. As a result, relaxing music or calming visual messages are generated and displayed in the worker's smart glasses. Furthermore, if the worker approaches a dangerous area, their movements are identified through video data analysis, and a warning message is displayed on the smart glasses. This system improves worker safety and work efficiency.

[0991] Prompt Sentence Examples

[0992] "Generate optimal visual and audio feedback based on EEG data and on-site video data to bring workers back to a relaxed state."

[0993] "Devour a method to provide relaxing music when a worker's emotional state is judged to be stressed, and simultaneously display a warning message when the worker approaches a dangerous area on the job site."

[0994] As described above, the present invention is a system for providing real-time feedback according to a user's emotional state, and is particularly intended to improve safety and efficiency for factory workers.

[0995] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0996] Step 1:

[0997] The user wears an EEG headset to measure brain waves and acquire brain signals.

[0998] Input: User's EEG signal.

[0999] How it works: The EEG headset detects electrical signals emitted from the user's brain in real time and collects them as digital data.

[1000] Output: Acquired brain signal data.

[1001] Step 2:

[1002] The device receives brain signal data acquired from an EEG headset, and performs pre-processing such as noise filtering on the received data.

[1003] Input: Brain signal data transmitted from an EEG headset.

[1004] Specific operation: The device receives brain signal data in real time and passes it through a noise filter to improve data accuracy.

[1005] Output: Preprocessed brain signal data.

[1006] Step 3:

[1007] The device encrypts and transmits the preprocessed brain signal data to a server.

[1008] Input: Preprocessed brain signal data.

[1009] What it does: The device encrypts the data and sends it to the server using a secure communication protocol.

[1010] Output: Encrypted brain signal data sent to server.

[1011] Step 4:

[1012] The server analyzes the brain signal data to identify the user's intentions and emotional state.

[1013] Input: Encrypted brain signal data.

[1014] What it does: The server decrypts the data and uses machine learning algorithms (TensorFlow, PyTorch) to identify the user's intent and emotional state.

[1015] Output: Analysis of the user's intent and emotional state.

[1016] Step 5:

[1017] The camera in the user's smart glasses collects video data of the surrounding real-world environment.

[1018] Input: Real-world environment.

[1019] How it works: The camera in the smart glasses captures images of the surroundings in real time.

[1020] Output: Video data.

[1021] Step 6:

[1022] The terminal transmits the collected video data to the server.

[1023] Input: Video data.

[1024] Specific operation: The device compresses and encrypts the video data before sending it to the server.

[1025] Output: Video data sent to the server.

[1026] Step 7:

[1027] The server analyzes the video data and identifies obstacles and dangerous areas.

[1028] Input: Video data.

[1029] Specific operation: Using image recognition algorithms (OpenCV), data analysis is performed to identify obstacles and dangerous areas in the video.

[1030] Output: Analysis results of obstacles and dangerous areas.

[1031] Step 8:

[1032] The server generates virtual elements according to the user's emotional state based on the results of analyzing the brain signal data and video data.

[1033] Input: Analysis results of the user's intent and emotional state, analysis results of obstacles and dangerous areas.

[1034] Specific behavior: The server uses the generative AI model to generate appropriate feedback and warning messages for the user.

[1035] Output: Virtual elements (e.g. audio messages, visual messages).

[1036] Step 9:

[1037] The server transmits the generated virtual elements to the user's smart glasses.

[1038] Input: Virtual element.

[1039] What happens: The server formats the virtual elements appropriately and sends them to the smart glasses.

[1040] Output: Virtual elements sent to the user's smart glasses.

[1041] Step 10:

[1042] The user's smart glasses display or play the received virtual elements and provide feedback to the user.

[1043] Input: Virtual element.

[1044] What it does: The smart glasses display visual messages and play audio messages.

[1045] Output: Feedback provided to the user.

[1046] 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.

[1047] 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.

[1048] 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.

[1049] [Third embodiment]

[1050] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[1051] 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.

[1052] 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).

[1053] 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.

[1054] 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.

[1055] 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).

[1056] 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.

[1057] 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.

[1058] 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.

[1059] 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.

[1060] 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.

[1061] 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."

[1062] This invention is a mixed reality (MR) system that adds virtual elements to a real environment to provide a richer sensory experience for the visually impaired, elderly people, and people with memory loss or dementia. Below, we will explain in detail the program-based processing of this system.

[1063] Acquiring and analyzing user's brain signals

[1064] 1. Acquiring Brain Signals

[1065] The user wears a wearable device for measuring EEG, which measures and acquires electrical signals generated by the brain in real time.

[1066] 2. Transmission and analysis of brain signal data

[1067] The terminal transmits the acquired brain signal data to a server.

[1068] The server analyzes the received brain signal data to identify the user's intentions, emotional state, visual needs, etc. This analysis is performed using machine learning algorithms and data processing techniques.

[1069] Real-world data collection

[1070] 1. Video data collection

[1071] The device (specifically, smart glasses or a mobile device worn by the user) collects images of the real-world environment around the user in real time via a camera.

[1072] 2. Video data transmission and analysis

[1073] The terminal transmits the collected video data to the server.

[1074] The server analyzes the received video data and obtains information about the surrounding environment, including object detection and movement analysis.

[1075] Creating and providing virtual elements

[1076] 1. Creating Virtual Elements

[1077] The server generates appropriate virtual elements based on the analysis of brain signal data and video data of the real environment. For example, if a visually impaired person senses an obstacle ahead, it generates an audio warning to indicate the presence of an obstacle.

[1078] 2. Customizing Virtual Elements

[1079] The server customizes the generated virtual elements to the user's individual requirements, for example adjusting the tone and volume of audio alerts depending on the user's audio feedback preferences.

[1080] 3. Providing mixed reality experiences

[1081] The server transmits the generated customized virtual element to the terminal.

[1082] The device integrates the transmitted virtual elements with the image of the real environment, providing the user with a real-time MR experience.

[1083] Specific examples

[1084] Specific examples for the visually impaired

[1085] 1. Brain signal acquisition and analysis

[1086] The user wears a headset for measuring electroencephalograms.

[1087] The device collects brain signal data from the headset in real time and transmits it to a server.

[1088] The server determines through analysis that the user intends to "pay attention to what is ahead."

[1089] 2. Real-world data collection

[1090] The terminal acquires image data of the area in front of the user through a camera.

[1091] The terminal transmits the collected video data to the server.

[1092] Through video analysis, the server recognizes that there is an obstacle (e.g., a bicycle) ahead.

[1093] 3. Creating and providing virtual elements

[1094] The server generates a warning sound based on the results of the brain signal analysis and video analysis.

[1095] The server customizes the generated alert audio according to the user's audio feedback preferences.

[1096] The server sends a customized alert sound to the terminal.

[1097] The terminal plays the received warning audio to the user, providing audio guidance such as "There is a bicycle ahead. Be careful."

[1098] As described above, this system is designed to enable people with visual impairments, the elderly, and dementia patients to enjoy safe and rich sensory experiences in real-world environments, thereby supporting safer and better lives.

[1099] The processing flow will be explained below.

[1100] Step 1:

[1101] The user puts on a wearable device (e.g., an EEG headset) to measure brain signals, which allows for real-time measurements of the brain's electrical activity.

[1102] Step 2:

[1103] The device receives brain signal data acquired from the headset in real time, and performs preprocessing such as noise filtering on the received data to improve analysis accuracy.

[1104] Step 3:

[1105] The device encrypts the pre-processed brain signal data before transmitting it to the server, ensuring data security.

[1106] Step 4:

[1107] The server analyzes the received brain signal data to identify the user's intentions, emotional state, visual needs, etc. It uses machine learning algorithms to quickly extract this information.

[1108] Step 5:

[1109] The device collects images of the real-world environment around the user in real time through a camera, and the collection point of this image data can be changed according to the user's visual needs.

[1110] Step 6:

[1111] The terminals send the collected video data to the server, where it is appropriately compressed and encrypted for efficient and secure transmission.

[1112] Step 7:

[1113] The server analyzes the received video data and identifies the surrounding environment (obstacles, moving objects, points of interest, etc.) using an image recognition algorithm.

[1114] Step 8:

[1115] The server integrates the results of the brain signal analysis with environmental information to generate appropriate virtual elements (audio guidance, warnings, visual aids, etc.), which are customized to suit the user's preferences and needs.

[1116] Step 9:

[1117] The server then sends the generated customized virtual elements to the device, and this data is also encrypted to ensure security.

[1118] Step 10:

[1119] The device then combines the received virtual elements with the video of the real environment in real time, and displays or outputs audio to the user. Through this MR experience, the user can receive information about the real environment in an easy-to-understand format.

[1120] Examples:

[1121] Step 1:

[1122] The user wears a headset for measuring electroencephalograms.

[1123] Step 2:

[1124] The device receives brain signal data from the headset in real time.

[1125] Step 3:

[1126] The device encrypts the received brain signal data and transmits it to a server.

[1127] Step 4:

[1128] The server analyzes the brain signal data and determines that the user intends to "focus their attention on what is ahead."

[1129] Step 5:

[1130] The terminal uses a camera to collect video data of the area in front of the user.

[1131] Step 6:

[1132] The terminal transmits the collected video data to the server.

[1133] Step 7:

[1134] Through video analysis, the server recognizes that there is an obstacle (bicycle) ahead.

[1135] Step 8:

[1136] The server generates and customizes the warning sound based on the analysis results.

[1137] Step 9:

[1138] The server transmits the generated warning sound to the terminal.

[1139] Step 10:

[1140] The terminal plays the received warning audio to the user, providing audio guidance such as "There is a bicycle ahead. Please be careful."

[1141] Example 1

[1142] 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."

[1143] It is difficult for visually impaired people, the elderly, and people with memory loss or dementia to enjoy safe and rich sensory experiences in real-world environments. Such users are unable to recognize or interpret information about their surroundings, making it difficult for them to move around or live safely. Conventional technologies have not adequately resolved these issues, and effective means are needed to improve users' safety and quality of life. Therefore, the present invention aims to solve these problems.

[1144] 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.

[1145] In this invention, the server includes means for analyzing the user's brain signal data to identify the user's intention, emotional state, and visual needs, means for analyzing surrounding environmental information, and means for generating virtual elements to customize the user's experience, thereby fusing the user's brain signal data with data from the real environment to provide virtual elements tailored to individual needs, enabling a safe and rich sensory experience.

[1146] "User" refers to a person whose brain signals are acquired using the system.

[1147] "Brain signals" refer to electrical signals generated by the user's brain.

[1148] "Data" refers to the collection of information used by the system, including information collected and analyzed as signals or images.

[1149] "Virtual Elements" refers to virtual content that is generated based on the analysis of brain signal data and video data to supplement or enhance the user's experience, including sounds, visual effects, and other multimedia elements.

[1150] "Environmental information" refers to information such as the position and movement of objects in the real world around the user, which is obtained through the analysis of video data.

[1151] "Analysis" refers to the act of processing data to derive meaningful results, specifically using machine learning and object detection algorithms.

[1152] "Customization" refers to the act of tailoring or modifying generated virtual elements to suit the specific needs and desires of a user.

[1153] "Means" refers to an apparatus, method, or process for accomplishing a particular function or action.

[1154] "Transmit" refers to the act of transferring data from one device to another.

[1155] "Collection" refers to the act of gathering specific information. This can be done using devices such as sensors or cameras.

[1156] "Providing" refers to the act of allowing a user to use the generated virtual element.

[1157] The above provides definitions of important terms that may be included in the claims.

[1158] The present invention is a mixed reality (MR) system that adds virtual elements to a real environment to provide a richer sensory experience for the visually impaired, the elderly, and those with memory loss or dementia. Specific embodiments for implementing the present invention are described below.

[1159] Acquiring and analyzing user's brain signals

[1160] A user wears a wearable device for measuring EEG (e.g., a general-purpose EEG headset). This device acquires EEG signals from the scalp in real time using multiple electrodes. The acquired brain signal data is collected while the user is performing daily activities.

[1161] The terminal (smartphone or dedicated device) transmits the brain signal data acquired from this device to a server via wireless communication (Bluetooth or Wi-Fi).

[1162] The server analyzes the received brain signal data using machine learning algorithms, specifically TensorFlow with Python, to identify the user's intent (e.g., "I want to pay attention to what's ahead"), emotional state (e.g., tension, relaxation), and visual needs.

[1163] Real-world data collection

[1164] Users wear smart glasses or a head-mounted display (e.g., Microsoft HoloLens) and observe the real-world environment around them. The camera in the smart glasses captures images of the user's field of vision in real time. This image data is temporarily stored in the device's internal memory.

[1165] The terminal transmits the temporarily stored video data to the server at regular intervals (for example, one frame per second).

[1166] The server analyzes the received video data using an object detection algorithm based on OpenCV to identify information about the surrounding environment (obstacles and dynamic environmental changes).

[1167] Creating and providing virtual elements

[1168] The server uses a generative AI model to generate appropriate virtual elements based on the results of analyzing the brain signal data and the video data. For example, if it detects an obstacle in front of the user, it generates a warning sound or visual effect.

[1169] The server customizes the generated virtual elements to the user's individual needs, for example adjusting the tone and volume of audio alerts to suit the user's preferences.

[1170] The server sends customized virtual elements to the device, which then integrates them into the image of the real world, providing the user with a real-time MR experience. Specifically, a warning sound is played from the smartglasses' speaker, informing the user, "There is an obstacle ahead. Please be careful."

[1171] This will enable people with visual impairments, the elderly, and dementia patients to enjoy safe and rich sensory experiences in real-world environments. For example, visually impaired people walking around town will be able to see obstacles ahead in advance, improving safety.

[1172] Prompt Sentence Examples

[1173] "Please explain how a mixed reality system works, where the user wears a headset that measures EEG and uses a device that collects data on the surrounding environment to support safe mobility in the real world."

[1174] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1175] Step 1:

[1176] Acquiring brain signals

[1177] The user wears a wearable device for measuring brain waves, which acquires brain waves in real time through electrodes on the scalp.

[1178] Input: EEG electrical signals

[1179] Output: Digital brain signal data

[1180] How it works: The user puts on the headset, and the device uses sensors to capture electrical brainwave signals and convert them into digital data.

[1181] Step 2:

[1182] Transmission of brain signal data

[1183] The device transmits the acquired brain signal data to a server via Bluetooth or Wi-Fi.

[1184] Input: Brain signal data from a wearable device

[1185] Output: Digital brain signal data sent to a server

[1186] Specific operation: The terminal communicates with the wearable device, collects data, and sends it to the server.

[1187] Step 3:

[1188] Analysis of brain signal data

[1189] The server analyzes the received brain signal data using a machine learning model in TensorFlow with Python.

[1190] Input: Digital brain signal data sent to the server

[1191] Output: Data about the user's intent, emotional state, and visual needs

[1192] Specific operation: The server receives the data, runs it through a machine learning model, and derives analytical results.

[1193] Step 4:

[1194] Video data collection

[1195] Users wear smart glasses or a head-mounted display and observe the real-world environment around them, with the device's camera capturing images in real time.

[1196] Input: Surrounding real-world environment

[1197] Output: Video data collected by the camera

[1198] Specific operation: The user wears the device and the camera captures real-time video.

[1199] Step 5:

[1200] Video data transmission

[1201] The device temporarily stores the video data collected by the camera in its internal memory and transmits it to the server at regular intervals.

[1202] Input: Video data from the camera

[1203] Output: Video data sent to the server

[1204] Specific operation: The device stores the video data in its internal memory and sends it to the server.

[1205] Step 6:

[1206] Video data analysis

[1207] The server analyzes the received video data using an object detection algorithm based on OpenCV.

[1208] Input: Video data sent to the server

[1209] Output: Surrounding environment information (position and movement of obstacles, etc.)

[1210] Specific operation: The server receives the video data, analyzes it using an object detection algorithm, and identifies environmental information.

[1211] Step 7:

[1212] Creating Virtual Elements

[1213] The server uses a generative AI model to generate appropriate virtual elements based on the results of analyzing the brain signal data and the video data.

[1214] Input: Brain signal analysis results and video data analysis results

[1215] Output: Generated virtual elements (sound, visual effects, etc.)

[1216] Specific operation: The server runs a generative AI model based on the analysis results to generate virtual elements.

[1217] Step 8:

[1218] Customizing Virtual Elements

[1219] The server customizes the generated virtual elements to the user's individual needs.

[1220] Input: Generated virtual elements, individual user needs

[1221] Output: Customized Virtual Elements

[1222] What it does: The server adjusts, modifies, and optimizes the virtual elements.

[1223] Step 9:

[1224] Sending customized virtual elements

[1225] The server transmits the customized virtual element to the terminal.

[1226] Input: Customized Virtual Elements

[1227] Output: customized virtual elements sent to the device

[1228] Specific operation: The server sends virtual elements to the terminal and reflects them in real time.

[1229] Step 10:

[1230] Providing MR experiences

[1231] The device then integrates the received virtual elements into the image of the real environment, providing the user with an MR experience.

[1232] Input: Customized virtual elements, video data of the real environment

[1233] Output: The integrated mixed reality experience (audio alerts and visual effects provided to the user)

[1234] Specific operation: The device combines virtual elements and video data to play them, providing the user with an MR experience.

[1235] (Application example 1)

[1236] 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."

[1237] Visually impaired people and the elderly have a reduced ability to detect suspicious people and dangerous situations in the real world, making it difficult for them to ensure safety in their daily lives. Currently available technologies are often insufficient to recognize the environment and respond appropriately without relying on sight or hearing. To solve this problem, a system is needed that can detect surrounding dangers in real time and provide warnings to users.

[1238] 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.

[1239] In this invention, the server includes means for analyzing the user's electroencephalogram data and video data to recognize suspicious individuals and dangerous situations, means for generating and providing warning audio and visual signals for the recognized suspicious individuals and dangerous situations, and means for generating virtual elements to customize the user's experience, thereby enabling support for visually impaired people and the elderly to recognize surrounding dangers in real time and respond appropriately.

[1240] "Brain signals" refer to electrical signals generated by the user's brain.

[1241] "Brain signal data" refers to digital data obtained from acquired brain signals.

[1242] "Intention" refers to the sense that a user is trying to act with a specific action or purpose.

[1243] "Emotional state" refers to the user's psychological state of mind and emotions.

[1244] "Visual requirements" refer to information or objects that a user visually desires.

[1245] "Real environment" refers to the physical environment in which the user actually resides.

[1246] "Video data" refers to image or video data captured through a camera or other imaging device.

[1247] "Virtual elements" refer to digitally generated virtual information or objects that are integrated into a real environment.

[1248] A "suspicious person" refers to a person in the surrounding environment who may potentially pose a danger or cause anxiety to the user.

[1249] "Dangerous situation" refers to a situation or environment that may pose a threat to the safety of the user.

[1250] "Warning voice" refers to voice generated to warn the user of danger and caution.

[1251] "Visual signal" refers to an image or signal displayed to visually alert a user to danger or caution.

[1252] This invention is a system that supports safety for visually impaired people and the elderly by detecting surrounding dangers in real time. Specifically, smart glasses worn by the user work in cooperation with a server and a terminal connected to the glasses.

[1253] System Configuration

[1254] Hardware

[1255] Smart glasses: Wearable devices equipped with a camera and audio output.

[1256] EEG measurement device: A headset for capturing the user's brain signals in real time.

[1257] Server: A central processing unit that analyzes brain signals and video data.

[1258] software

[1259] Data analysis platform: Software containing machine learning algorithms for analyzing EEG and video data.

[1260] Warning generation algorithm: A program that detects suspicious individuals or dangerous situations and generates appropriate warnings.

[1261] Program processing flow

[1262] 1. Acquisition and transmission of brain signal data

[1263] The user wears an EEG headset, which captures the user's brain signals in real time.

[1264] The acquired brain signal data is sent to a server via the terminal.

[1265] 2. Collecting and transmitting data from the real world

[1266] The camera in the smart glasses collects video data of the real-world environment around the user.

[1267] The collected video data is sent to the server in real time via the terminal.

[1268] 3. Data Analysis

[1269] The server analyzes the brain signal data to identify the user's intentions, emotional state, and visual needs.

[1270] At the same time, the video data is analyzed to identify the surrounding environment, suspicious individuals, and the location and movement of obstacles, using machine learning algorithms and data processing technology.

[1271] 4. Creating and Providing Virtual Elements

[1272] Based on the analysis, the server generates virtual elements that customize the user's experience, such as audio and visual warnings if a suspicious person is detected.

[1273] The generated virtual elements are transmitted to the smart glasses in real time and presented to the user.

[1274] Specific examples

[1275] As a user walks around town, an EEG headset captures the user's brain signals. At the same time, the camera in the smart glasses collects video data of the surrounding area. This data is sent to a server, which analyzes the EEG data to determine the user's alert state and analyzes the video data to recognize suspicious individuals and dangerous situations. If a suspicious individual is detected, the server generates a warning voice saying, "A suspicious individual is present. Please be careful," and sends it to the user through the smart glasses.

[1276] Example prompts for generative AI models

[1277] Please implement an app that will issue an audio warning if EEG data indicates a certain level of alertness and detects a suspicious person ahead.

[1278] The data used is 128-point EEG data and real-time video data.

[1279] We use a peak detection algorithm to analyze the EEG data, and the face detection function of OpenCV to analyze the video data.

[1280] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1281] Step 1:

[1282] The user wears an EEG headset, which captures electrical signals generated by the brain in real time. The captured brain signal data reflects the user's brainwave activity at that moment.

[1283] Input: User's brain signals

[1284] Data processing: The headset converts electrical signals generated by the brain into digital data

[1285] Output: Brain signal data

[1286] Step 2:

[1287] The device transmits the brain signal data acquired from the headset to a server in real time, so it is necessary to minimize delays.

[1288] Input: Brain signal data

[1289] Data processing: Digital signal processing and data compression are performed

[1290] Output: Brain signal data sent to the server

[1291] Step 3:

[1292] The device uses the camera in the smart glasses to collect video data of the real-world environment around the user, which contains information about the real world in the user's field of view.

[1293] Input: Video of the user's surroundings

[1294] Data processing: Converting analog video data into digital video data

[1295] Output: Collected video data

[1296] Step 4:

[1297] The device transmits the collected video data to a server, also in real time and designed to minimize latency.

[1298] Input: Collected video data

[1299] Data processing: Compression and packetization of video data

[1300] Output: Video data sent to the server

[1301] Step 5:

[1302] The server analyzes the received brain signal data using machine learning algorithms to identify the user's intent, emotional state, and visual needs.

[1303] Input: Brain signal data sent to the server

[1304] Data processing: Analysis using machine learning algorithms

[1305] Output: User intent, emotional state, visual requirements

[1306] Step 6:

[1307] The server analyzes the received video data using an object detection algorithm to identify the location and movement of suspicious individuals and obstacles.

[1308] Input: Video data sent to the server

[1309] Data processing: object detection and motion analysis

[1310] Output: Location and movement information of suspicious persons and obstacles

[1311] Step 7:

[1312] Based on the results of the brain signal analysis and the video data analysis, the server generates virtual elements to customize the user's experience, such as generating audio and visual warnings if a suspicious person is detected.

[1313] Input: User's intentions, emotional state, visual needs, location and movement information of suspicious people and obstacles

[1314] Data processing: Creation of customized virtual elements

[1315] Output: Generated virtual elements (audio and visual warning signals)

[1316] Step 8:

[1317] The server transmits the generated virtual elements to the terminal, and the terminal provides the received virtual elements to the user through the smart glasses.

[1318] Input: The generated virtual element

[1319] Data processing: decoding and playback of audio data and visual signals

[1320] Output: Audio and visual warning signals provided to the user

[1321] 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.

[1322] This invention is a mixed reality (MR) system that adds virtual elements to a real environment and provides a richer sensory experience based on emotional states for the visually impaired, elderly people, and people with memory loss or dementia. The details of the program-based processing of this system are described below.

[1323] Acquiring and analyzing user's brain signals

[1324] 1. Acquiring Brain Signals

[1325] The user puts on a wearable device for measuring brain waves (e.g., an EEG headset), which prepares the device to measure the brain's electrical activity in real time.

[1326] 2. Transmission and analysis of brain signal data

[1327] The device receives brain signal data acquired from the headset in real time, and performs preprocessing such as noise filtering on the received data to improve analysis accuracy.

[1328] The device encrypts and transmits the preprocessed brain signal data to a server.

[1329] The server analyzes the received brain signal data to identify the user's intentions, emotional state, visual needs, etc. It uses machine learning algorithms to quickly extract this information.

[1330] Real-world data collection

[1331] 1. Video data collection

[1332] The device (e.g., smart glasses worn by the user or a mobile device) collects images of the real-world environment around the user in real time via a camera.

[1333] 2. Video data transmission and analysis

[1334] The terminals send the collected video data to the server, where it is appropriately compressed, encrypted, and transmitted safely and efficiently.

[1335] The server analyzes the received video data and identifies the surrounding environment (obstacles, moving objects, points of interest, etc.) using image recognition algorithms.

[1336] Sentiment Engine and Sentiment Analysis

[1337] 1. How the Emotion Engine Works

[1338] The server uses an emotion engine to identify the user's emotional state based on the analysis of the brain signal data. For example, the emotion engine analyzes information such as whether the user is relaxed or tense.

[1339] 2. Real-time emotion monitoring

[1340] The server monitors the user's emotional state in real time and adjusts the MR experience according to emotional changes, for example, adding relaxing virtual elements if the user feels anxious.

[1341] Creating and providing virtual elements

[1342] 1. Creating Virtual Elements

[1343] The server generates appropriate virtual elements based on the analysis of brain signal data, emotional state, and video data of the real environment. For example, if a visually impaired person senses an obstacle ahead, it generates a warning sound.

[1344] The generated virtual elements are customized to the user's individual requirements and emotional state, for example using a calmer voice tone for a user feeling anxious.

[1345] 3. Providing mixed reality experiences

[1346] The server transmits the generated virtual elements to the terminal.

[1347] The terminal integrates the transmitted virtual elements with the image of the real environment and provides it to the user in real time.

[1348] Specific examples

[1349] Specific examples for the visually impaired

[1350] 1. Brain signal acquisition and analysis

[1351] The user wears a headset for measuring electroencephalograms.

[1352] The device collects brain signal data from the headset in real time and transmits it to a server.

[1353] The server determines through analysis that the user intends to "pay attention to what is ahead."

[1354] 2. Real-world data collection

[1355] The terminal acquires image data of the area in front of the user through a camera.

[1356] The terminal transmits the collected video data to the server.

[1357] Through video analysis, the server recognizes that there is an obstacle (bicycle) ahead.

[1358] 3. Emotion Engine and Emotion Analysis

[1359] The server uses an emotion engine to analyze the user's emotional state and determine that the user is "feeling a little anxious."

[1360] 4. Creating and Providing Virtual Elements

[1361] The server generates a warning sound based on the analysis results and customizes the tone of the sound to be gentle to ease the user's anxiety.

[1362] The server transmits the generated warning sound to the terminal.

[1363] The terminal plays the received warning audio to the user, providing voice guidance in a gentle tone saying, "There is a bicycle ahead. Please be careful."

[1364] This system is designed to enable people with visual impairments, the elderly, and dementia patients to enjoy a safe and rich sensory experience in a real environment. In particular, the use of an emotion engine enables appropriate support according to the user's emotional state, supporting a safe and high-quality life.

[1365] The processing flow will be explained below.

[1366] Step 1:

[1367] The user puts on a wearable device for measuring brain waves (e.g., an EEG headset), which prepares the device to measure the electrical activity of the user's brain in real time.

[1368] Step 2:

[1369] The terminal receives brain signal data acquired from the wearable device in real time, and performs preprocessing such as noise filtering on the received data to improve the accuracy of the analysis.

[1370] Step 3:

[1371] The device encrypts and transmits the pre-processed brain signal data to a server, ensuring data security at this stage.

[1372] Step 4:

[1373] The server analyzes the received brain signal data to identify the user's intentions, emotional state, visual needs, etc. It uses machine learning algorithms to quickly extract this information.

[1374] Step 5:

[1375] The device collects images of the real-world environment around the user in real time through a camera, and the camera's collection points may be adjusted based on specific visual requirements.

[1376] Step 6:

[1377] The terminals send the collected video data to the server, where it is appropriately compressed, encrypted, and transmitted safely and efficiently.

[1378] Step 7:

[1379] The server analyzes the received video data and uses image recognition algorithms to identify the surrounding environment (obstacles, moving objects, points of interest, etc.).

[1380] Step 8:

[1381] The server operates an emotion engine based on the analysis of the brain signal data to identify the user's emotional state. The emotion engine analyzes whether the user is relaxed, tense, or anxious.

[1382] Step 9:

[1383] The server monitors the emotion engine in real time and generates virtual elements according to changes in emotions, for example, generating relaxing music and images if the user is feeling anxious.

[1384] Step 10:

[1385] The server then transmits the generated virtual elements to the device, including audio alerts and visual aids tailored to the user's emotional state.

[1386] Step 11:

[1387] The device then combines the received virtual elements with the image of the real environment in real time, and displays or outputs audio to the user. Through this MR experience, the user can enjoy a rich sensory experience linked to their emotions.

[1388] Examples:

[1389] Specific examples for the visually impaired

[1390] Step 1:

[1391] The user wears a headset for measuring electroencephalograms.

[1392] Step 2:

[1393] The device receives brain signal data from the headset in real time.

[1394] Step 3:

[1395] The device encrypts the received brain signal data and transmits it to a server.

[1396] Step 4:

[1397] The server analyzes the brain signal data and determines that the user intends to "focus their attention on what is ahead."

[1398] Step 5:

[1399] The terminal uses a camera to collect video data of the area in front of the user.

[1400] Step 6:

[1401] The terminal transmits the collected video data to the server.

[1402] Step 7:

[1403] The server analyzes the video data and recognizes that there is an obstacle (bicycle) ahead.

[1404] Step 8:

[1405] The server uses the results of the brain signal analysis and an emotion engine to determine that the user is "feeling a little anxious."

[1406] Step 9:

[1407] The server generates a gentle tone of warning audio based on the user's emotional state.

[1408] Step 10:

[1409] The server transmits the generated warning sound to the terminal.

[1410] Step 11:

[1411] The terminal plays the received warning audio to the user, providing voice guidance in a gentle tone saying, "There is a bicycle ahead. Please be careful."

[1412] Example 2

[1413] 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."

[1414] In order for visually impaired people, elderly people, and people with memory loss or dementia to enjoy safe and rich sensory experiences in real-world environments, a system that can accurately grasp information about the surrounding environment and provide appropriate support is necessary. However, conventional technologies have difficulty responding to the user's emotional state and individual needs, and safety and convenience have not been sufficiently ensured. Therefore, in order for users to live their lives with peace of mind, a system that can obtain environmental information in real time and provide customized support according to the user's condition is required.

[1415] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[1416] In this invention, the server includes means for analyzing the user's brain signal data and identifying the user's intention, emotional state, and visual requirements, means for analyzing the video data of the real environment and identifying surrounding environmental information, and means for generating virtual elements for customizing the user's experience based on the results of the analysis of the brain signal data and the video data of the real environment, and adjusting the virtual elements according to the user's emotional state. This makes it possible to comprehensively analyze the user's brain signals and information about the real environment, and provide appropriate support according to the user's emotional state and a customized experience in real time.

[1417] "User" refers to individuals who use the system, such as the visually impaired, elderly, or those with memory loss or dementia.

[1418] "Brain signals" refers to data about a user's brain's electrical activity, such as electroencephalograms, acquired using a wearable device such as an EEG headset.

[1419] A "wearable device" refers to a device worn by a user that can measure and collect data such as brain signals in real time.

[1420] A "terminal" refers to a device used by a user that has the function of collecting brain signal data and video data and transmitting them to a server.

[1421] "Preprocessing" refers to processing performed to improve the quality of acquired brain signal data, and includes noise filtering and the like.

[1422] "Encryption" is a process performed to ensure data security, and refers to a technology that prevents transmitted data from being deciphered by third parties.

[1423] "Server" refers to a central processing unit that receives and analyzes brain signal data and video data.

[1424] "Analysis" refers to the data processing performed to identify the user's intentions, emotional state, and environmental information based on the acquired data.

[1425] "Video data of the real environment" refers to video information of the surroundings collected through smart glasses worn by the user or the camera on a mobile device.

[1426] "Customization" refers to adjusting the virtual elements provided by the system according to the user's individual requirements and emotional state.

[1427] "Virtual elements" refer to virtual information or objects that are added to a real environment to enrich the user's experience.

[1428] "Providing" refers to the process of presenting the generated virtual elements to the user in a manner that is integrated with the real environment.

[1429] This invention is a mixed reality (MR) system that adds virtual elements to a real environment and provides a richer sensory experience based on emotional status for the visually impaired, elderly people, and people with memory loss or dementia. This system has the following configuration, centered around the user, terminals, and server.

[1430] Acquiring and analyzing user's brain signals

[1431] The user wears a wearable device for measuring brain waves. This device is an EEG headset (for example, the "Emotiv Epoc+" manufactured by Emotiv). By wearing the device, the brain's electrical signals are ready to be measured in real time.

[1432] The device receives real-time brain signal data from the headset and performs pre-processing, including noise filtering, such as using a band-pass filter to filter out specific frequency bands, before transmitting the pre-processed data to a server using AES encryption.

[1433] The server analyzes the received brain signal data using machine learning algorithms (e.g., neural networks using Keras) to identify the user's intentions and emotional state. This analysis allows for a quick understanding of the user's real-time state.

[1434] Real-world data collection

[1435] The device collects real-time images of the surrounding real-world environment through the camera on the smart glasses or mobile device worn by the user, such as Google Glass.

[1436] The collected video data is compressed using the H.264 codec, encrypted via SSL / TLS, and then sent to a server, where it is analyzed using the YOLO (You Only Look Once) algorithm to identify surrounding environmental information (e.g., obstacles and moving objects).

[1437] Sentiment Engine and Sentiment Analysis

[1438] The server runs an emotion engine based on the analysis of brain signal data. The emotion engine uses a support vector machine (SVM) to determine the user's emotional state (e.g., whether they are relaxed or nervous) in real time.

[1439] Creating and providing virtual elements

[1440] The server generates appropriate virtual elements based on the analysis of brain signal data and video data of the real environment. The generated virtual elements are customized according to the user's individual requirements and emotional state. For example, if a visually impaired person senses an obstacle ahead, an audio warning will be generated, and if it is determined that the user is feeling anxious, the tone of the audio will be customized to be gentler.

[1441] The generated virtual elements are sent from the server to the device, which then integrates them with images of the real environment and provides them to the user in real time. For example, a warning message can be displayed on the smart glasses display while an audio warning is played.

[1442] Examples of concrete examples and prompts

[1443] Specific examples for the visually impaired

[1444] 1. The user wears an EEG headset to collect brain signal data.

[1445] 2. The device preprocesses the brain signal data and sends it to the server.

[1446] 3. The server determines the user's intent to "pay attention to what's ahead."

[1447] 4. The device acquires image data of the area in front of it through the Google Glass camera and sends it to the server.

[1448] 5. The server uses the YOLO algorithm to recognize that there is an obstacle (bicycle) ahead.

[1449] 6. The server uses an emotion engine to analyze that the user is "feeling a little anxious."

[1450] 7. The server generates a warning voice and sends a gentle voice tone message to the terminal saying, "There is a bicycle ahead. Be careful."

[1451] 8. The device plays the received warning audio to the user, integrating it with the real-world environment in real time.

[1452] Prompt Sentence Examples

[1453] 1. Please explain in detail the processing flow of a system that uses EEG and video data to generate audio warnings to help visually impaired people avoid obstacles ahead.

[1454] 2. Please explain in detail the processing flow of a system that analyzes the emotional state of an elderly person based on EEG data and generates and provides virtual elements to help them relax when they are feeling anxious.

[1455] In this way, this system can support a safe and high-quality life by integrating and analyzing the user's emotional state and real-world environment.

[1456] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1457] Step 1:

[1458] The user wears a wearable device for measuring brain waves (e.g., an EEG headset), which allows the brain's electrical activity to be measured in real time. The input is the user's brain's electrical signals, and the output is the brain wave data measured by the EEG headset.

[1459] Step 2:

[1460] The terminal receives brain signal data collected from the headset in real time. The received EEG data undergoes preprocessing such as noise filtering. Preprocessing involves using a bandpass filter to filter out specific frequency bands. The input is brain signal data from the EEG headset, and the output is preprocessed, high-quality brain signal data.

[1461] Step 3:

[1462] The device encrypts the preprocessed brain signal data and sends it to the server. The AES encryption method is used for encryption, ensuring secure data transmission. The input is preprocessed EEG data, and the output is encrypted EEG data.

[1463] Step 4:

[1464] The server receives the encrypted brain signal data, decrypts it, and analyzes it. It uses machine learning algorithms (e.g., neural networks) to identify the user's intentions and emotional state. The input is the encrypted and transmitted brain wave data, and the output is the user's intentions and emotional state.

[1465] Step 5:

[1466] The device collects video data of the real-world environment around the user in real time through a camera (e.g., a camera in smart glasses). This video data includes surrounding objects and moving objects. The input is the video of the real-world environment captured by the camera, and the output is the collected video data.

[1467] Step 6:

[1468] The terminal compresses the collected video data using the H.264 codec, encrypts it using SSL / TLS, and then sends it to the server. The input is the collected video data, and the output is the encrypted video data.

[1469] Step 7:

[1470] The server receives the encrypted video data, decrypts it, and analyzes it. It uses the YOLO algorithm to identify the surrounding environment (e.g., obstacles and moving objects). The input is the encrypted video data, and the output is the identified environment information.

[1471] Step 8:

[1472] The server runs an emotion engine based on preprocessed EEG data and environmental information. The emotion engine uses a support vector machine (SVM) to determine the user's emotional state. The inputs are the user's intention, emotional state, and environmental information, and the output is the user's emotional state.

[1473] Step 9:

[1474] The server generates virtual elements based on the analysis results and customizes them according to the user's emotional state. For example, it generates a warning sound for obstacles and sets it to a gentle tone if necessary. The input is the user's emotional state, environmental information, and brainwave data, and the output is a customized virtual element (e.g., a warning sound).

[1475] Step 10:

[1476] The server sends the generated virtual elements to the device. The device integrates the virtual elements with the image of the real environment and provides it to the user in real time. For example, a warning message can be displayed on the smart glasses display while a warning sound is played. The input is the customized virtual elements, and the output is the virtual elements that are displayed and played to the user.

[1477] (Application example 2)

[1478] 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."

[1479] It is necessary to provide an environment in which users, such as the visually impaired, elderly people, dementia patients, and factory workers, can work safely and efficiently in real-world environments. However, it is currently difficult for these users to receive appropriate advice and warnings based on their emotional state. In particular, when working in a factory, there is a lack of systems that provide support based on emotional state, so it is necessary to improve user safety and work efficiency.

[1480] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 2 is realized by the following means.

[1481] In this invention, the server includes means for acquiring a user's brain signal, means for analyzing the user's brain signal data and identifying the user's intention, emotional state, and visual requirements, means for collecting video data of the real environment around the user, means for analyzing the collected video data and identifying surrounding environmental information, means for generating virtual elements to customize the user's experience based on the analysis results of the brain signal data and the video data of the real environment, means for providing the generated virtual elements to the user, means for monitoring the emotional state of workers and generating and providing feedback based on the specific emotional state, and means for identifying obstacles and dangerous areas in the factory and providing warnings to the user. This makes it possible to provide appropriate feedback according to the user's emotional state and improve the safety and efficiency of work in the factory.

[1482] "Brain signals" are electrical signals emitted from the user's brain, and are data that reflect the state of thoughts, emotions, intentions, etc.

[1483] "Brain signal data" refers to information that is measured and recorded in digital form and is used to analyze the user's brain activity.

[1484] "User intent" refers to information about a user's behavior or interests, such as what the user is planning to do or what the user is paying attention to.

[1485] "Emotional state" refers to the user's current emotion (e.g., joy, sadness, anger, anxiety, etc.) and is determined by analyzing brain signal data.

[1486] A "visual need" is a user's visual need for information or assistance, and is identified based on the user's intentions and emotional state.

[1487] The "surrounding real environment" refers to the location where the user is currently physically present and the environment around it, and is collected as video data using devices such as cameras.

[1488] "Video data" is digital data that contains visual information of the real environment, captured by a camera or the like.

[1489] "Environmental information" refers to information about objects and obstacles around the user, as well as their positions and movements, and is determined by analyzing video data.

[1490] "Virtual elements" are digital information or content that is added to a real-world environment to customize a user's experience.

[1491] "Feedback" refers to information or advice that a system provides to a user, and is provided using audio or visual means.

[1492] "Obstacles" or "dangerous areas" are objects that are obstacles or dangerous places for the user, and are identified by analyzing video data of the real environment.

[1493] To implement this invention, hardware such as a wearable device for measuring electroencephalograms (e.g., an EEG headset), smart glasses, and a server is required. Furthermore, software such as a machine learning algorithm (e.g., TensorFlow, PyTorch) and an image recognition algorithm (e.g., OpenCV) is required. A specific embodiment of this system is described below.

[1494] Acquiring and analyzing user's brain signals

[1495] The user wears an EEG headset to measure brain waves, which measures the brain's electrical activity in real time. This data is collected through smart glasses and received in real time by a device. The device performs preprocessing to improve the accuracy of the data analysis, such as noise filtering, and then encrypts and transmits the data to a server. The server uses machine learning algorithms (TensorFlow, PyTorch) to analyze the brain signal data and identify the user's intentions and emotional state.

[1496] Real-world data collection and analysis

[1497] The camera in the smart glasses worn by the user collects video data of the real-world environment around the user in real time. The device preprocesses this data, appropriately compresses and encrypts it, and sends it to the server. The server analyzes the received video data and identifies obstacles, points of interest, dangerous areas, etc. This processing uses an image recognition algorithm (OpenCV).

[1498] Emotion Engine and Feedback Generation

[1499] The server uses an emotion engine to identify the user's emotional state based on the analysis of brain signal data. If the user is feeling anxious or stressed, it generates virtual elements (e.g., calming music or visual messages) to promote relaxation. It also generates warning messages about obstacles and dangerous areas based on the analysis of data from the real environment. These virtual elements are provided to the user through the smart glasses.

[1500] Specific examples

[1501] For example, if a factory worker is feeling stressed, their state is recognized in real time by the server through brainwave data analysis. As a result, relaxing music or calming visual messages are generated and displayed in the worker's smart glasses. Furthermore, if the worker approaches a dangerous area, their movements are identified through video data analysis, and a warning message is displayed on the smart glasses. This system improves worker safety and work efficiency.

[1502] Prompt Sentence Examples

[1503] "Generate optimal visual and audio feedback based on EEG data and on-site video data to bring workers back to a relaxed state."

[1504] "Devour a method to provide relaxing music when a worker's emotional state is judged to be stressed, and simultaneously display a warning message when the worker approaches a dangerous area on the job site."

[1505] As described above, the present invention is a system for providing real-time feedback according to a user's emotional state, and is particularly intended to improve safety and efficiency for factory workers.

[1506] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1507] Step 1:

[1508] The user wears an EEG headset to measure brain waves and acquire brain signals.

[1509] Input: User's EEG signal.

[1510] How it works: The EEG headset detects electrical signals emitted from the user's brain in real time and collects them as digital data.

[1511] Output: Acquired brain signal data.

[1512] Step 2:

[1513] The device receives brain signal data acquired from an EEG headset, and performs pre-processing such as noise filtering on the received data.

[1514] Input: Brain signal data transmitted from an EEG headset.

[1515] Specific operation: The device receives brain signal data in real time and passes it through a noise filter to improve data accuracy.

[1516] Output: Preprocessed brain signal data.

[1517] Step 3:

[1518] The device encrypts and transmits the preprocessed brain signal data to a server.

[1519] Input: Preprocessed brain signal data.

[1520] What it does: The device encrypts the data and sends it to the server using a secure communication protocol.

[1521] Output: Encrypted brain signal data sent to server.

[1522] Step 4:

[1523] The server analyzes the brain signal data to identify the user's intentions and emotional state.

[1524] Input: Encrypted brain signal data.

[1525] What it does: The server decrypts the data and uses machine learning algorithms (TensorFlow, PyTorch) to identify the user's intent and emotional state.

[1526] Output: Analysis of the user's intent and emotional state.

[1527] Step 5:

[1528] The camera in the user's smart glasses collects video data of the surrounding real-world environment.

[1529] Input: Real-world environment.

[1530] How it works: The camera in the smart glasses captures images of the surroundings in real time.

[1531] Output: Video data.

[1532] Step 6:

[1533] The terminal transmits the collected video data to the server.

[1534] Input: Video data.

[1535] Specific operation: The device compresses and encrypts the video data before sending it to the server.

[1536] Output: Video data sent to the server.

[1537] Step 7:

[1538] The server analyzes the video data and identifies obstacles and dangerous areas.

[1539] Input: Video data.

[1540] Specific operation: Using image recognition algorithms (OpenCV), data analysis is performed to identify obstacles and dangerous areas in the video.

[1541] Output: Analysis results of obstacles and dangerous areas.

[1542] Step 8:

[1543] The server generates virtual elements according to the user's emotional state based on the results of analyzing the brain signal data and video data.

[1544] Input: Analysis results of the user's intent and emotional state, analysis results of obstacles and dangerous areas.

[1545] Specific behavior: The server uses the generative AI model to generate appropriate feedback and warning messages for the user.

[1546] Output: Virtual elements (e.g. audio messages, visual messages).

[1547] Step 9:

[1548] The server transmits the generated virtual elements to the user's smart glasses.

[1549] Input: Virtual element.

[1550] What happens: The server formats the virtual elements appropriately and sends them to the smart glasses.

[1551] Output: Virtual elements sent to the user's smart glasses.

[1552] Step 10:

[1553] The user's smart glasses display or play the received virtual elements and provide feedback to the user.

[1554] Input: Virtual element.

[1555] What it does: The smart glasses display visual messages and play audio messages.

[1556] Output: Feedback provided to the user.

[1557] 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.

[1558] 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.

[1559] 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.

[1560] [Fourth embodiment]

[1561] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[1562] 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.

[1563] 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).

[1564] 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.

[1565] 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.

[1566] 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).

[1567] 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.

[1568] 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.

[1569] 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.

[1570] 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.

[1571] 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.

[1572] 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.

[1573] 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."

[1574] This invention is a mixed reality (MR) system that adds virtual elements to a real environment to provide a richer sensory experience for the visually impaired, elderly people, and people with memory loss or dementia. Below, we will explain in detail the program-based processing of this system.

[1575] Acquiring and analyzing user's brain signals

[1576] 1. Acquiring Brain Signals

[1577] The user wears a wearable device for measuring EEG, which measures and acquires electrical signals generated by the brain in real time.

[1578] 2. Transmission and analysis of brain signal data

[1579] The terminal transmits the acquired brain signal data to a server.

[1580] The server analyzes the received brain signal data to identify the user's intentions, emotional state, visual needs, etc. This analysis is performed using machine learning algorithms and data processing techniques.

[1581] Real-world data collection

[1582] 1. Video data collection

[1583] The device (specifically, smart glasses or a mobile device worn by the user) collects images of the real-world environment around the user in real time via a camera.

[1584] 2. Video data transmission and analysis

[1585] The terminal transmits the collected video data to the server.

[1586] The server analyzes the received video data and obtains information about the surrounding environment, including object detection and movement analysis.

[1587] Creating and providing virtual elements

[1588] 1. Creating Virtual Elements

[1589] The server generates appropriate virtual elements based on the analysis of brain signal data and video data of the real environment. For example, if a visually impaired person senses an obstacle ahead, it generates an audio warning to indicate the presence of an obstacle.

[1590] 2. Customizing Virtual Elements

[1591] The server customizes the generated virtual elements to the user's individual requirements, for example adjusting the tone and volume of audio alerts depending on the user's audio feedback preferences.

[1592] 3. Providing mixed reality experiences

[1593] The server transmits the generated customized virtual element to the terminal.

[1594] The device integrates the transmitted virtual elements with the image of the real environment, providing the user with a real-time MR experience.

[1595] Specific examples

[1596] Specific examples for the visually impaired

[1597] 1. Brain signal acquisition and analysis

[1598] The user wears a headset for measuring electroencephalograms.

[1599] The device collects brain signal data from the headset in real time and transmits it to a server.

[1600] The server determines through analysis that the user intends to "pay attention to what is ahead."

[1601] 2. Real-world data collection

[1602] The terminal acquires image data of the area in front of the user through a camera.

[1603] The terminal transmits the collected video data to the server.

[1604] Through video analysis, the server recognizes that there is an obstacle (e.g., a bicycle) ahead.

[1605] 3. Creating and providing virtual elements

[1606] The server generates a warning sound based on the results of the brain signal analysis and video analysis.

[1607] The server customizes the generated alert audio according to the user's audio feedback preferences.

[1608] The server sends a customized alert sound to the terminal.

[1609] The terminal plays the received warning audio to the user, providing audio guidance such as "There is a bicycle ahead. Be careful."

[1610] As described above, this system is designed to enable people with visual impairments, the elderly, and dementia patients to enjoy safe and rich sensory experiences in real-world environments, thereby supporting safer and better lives.

[1611] The processing flow will be explained below.

[1612] Step 1:

[1613] The user puts on a wearable device (e.g., an EEG headset) to measure brain signals, which allows for real-time measurements of the brain's electrical activity.

[1614] Step 2:

[1615] The device receives brain signal data acquired from the headset in real time, and performs preprocessing such as noise filtering on the received data to improve analysis accuracy.

[1616] Step 3:

[1617] The device encrypts the pre-processed brain signal data before transmitting it to the server, ensuring data security.

[1618] Step 4:

[1619] The server analyzes the received brain signal data to identify the user's intentions, emotional state, visual needs, etc. It uses machine learning algorithms to quickly extract this information.

[1620] Step 5:

[1621] The device collects images of the real-world environment around the user in real time through a camera, and the collection point of this image data can be changed according to the user's visual needs.

[1622] Step 6:

[1623] The terminals send the collected video data to the server, where it is appropriately compressed and encrypted for efficient and secure transmission.

[1624] Step 7:

[1625] The server analyzes the received video data and identifies the surrounding environment (obstacles, moving objects, points of interest, etc.) using an image recognition algorithm.

[1626] Step 8:

[1627] The server integrates the results of the brain signal analysis with environmental information to generate appropriate virtual elements (audio guidance, warnings, visual aids, etc.), which are customized to suit the user's preferences and needs.

[1628] Step 9:

[1629] The server then sends the generated customized virtual elements to the device, and this data is also encrypted to ensure security.

[1630] Step 10:

[1631] The device then combines the received virtual elements with the video of the real environment in real time, and displays or outputs audio to the user. Through this MR experience, the user can receive information about the real environment in an easy-to-understand format.

[1632] Examples:

[1633] Step 1:

[1634] The user wears a headset for measuring electroencephalograms.

[1635] Step 2:

[1636] The device receives brain signal data from the headset in real time.

[1637] Step 3:

[1638] The device encrypts the received brain signal data and transmits it to a server.

[1639] Step 4:

[1640] The server analyzes the brain signal data and determines that the user intends to "focus their attention on what is ahead."

[1641] Step 5:

[1642] The terminal uses a camera to collect video data of the area in front of the user.

[1643] Step 6:

[1644] The terminal transmits the collected video data to the server.

[1645] Step 7:

[1646] Through video analysis, the server recognizes that there is an obstacle (bicycle) ahead.

[1647] Step 8:

[1648] The server generates and customizes the warning sound based on the analysis results.

[1649] Step 9:

[1650] The server transmits the generated warning sound to the terminal.

[1651] Step 10:

[1652] The terminal plays the received warning audio to the user, providing audio guidance such as "There is a bicycle ahead. Please be careful."

[1653] Example 1

[1654] 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."

[1655] It is difficult for visually impaired people, the elderly, and people with memory loss or dementia to enjoy safe and rich sensory experiences in real-world environments. Such users are unable to recognize or interpret information about their surroundings, making it difficult for them to move around or live safely. Conventional technologies have not adequately resolved these issues, and effective means are needed to improve users' safety and quality of life. Therefore, the present invention aims to solve these problems.

[1656] 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.

[1657] In this invention, the server includes means for analyzing the user's brain signal data to identify the user's intention, emotional state, and visual needs, means for analyzing surrounding environmental information, and means for generating virtual elements to customize the user's experience, thereby fusing the user's brain signal data with data from the real environment to provide virtual elements tailored to individual needs, enabling a safe and rich sensory experience.

[1658] "User" refers to a person whose brain signals are acquired using the system.

[1659] "Brain signals" refer to electrical signals generated by the user's brain.

[1660] "Data" refers to the collection of information used by the system, including information collected and analyzed as signals or images.

[1661] "Virtual Elements" refers to virtual content that is generated based on the analysis of brain signal data and video data to supplement or enhance the user's experience, including sounds, visual effects, and other multimedia elements.

[1662] "Environmental information" refers to information such as the position and movement of objects in the real world around the user, which is obtained through the analysis of video data.

[1663] "Analysis" refers to the act of processing data to derive meaningful results, specifically using machine learning and object detection algorithms.

[1664] "Customization" refers to the act of tailoring or modifying generated virtual elements to suit the specific needs and desires of a user.

[1665] "Means" refers to an apparatus, method, or process for accomplishing a particular function or action.

[1666] "Transmit" refers to the act of transferring data from one device to another.

[1667] "Collection" refers to the act of gathering specific information. This can be done using devices such as sensors or cameras.

[1668] "Providing" refers to the act of allowing a user to use the generated virtual element.

[1669] The above provides definitions of important terms that may be included in the claims.

[1670] The present invention is a mixed reality (MR) system that adds virtual elements to a real environment to provide a richer sensory experience for the visually impaired, the elderly, and those with memory loss or dementia. Specific embodiments for implementing the present invention are described below.

[1671] Acquiring and analyzing user's brain signals

[1672] A user wears a wearable device for measuring EEG (e.g., a general-purpose EEG headset). This device acquires EEG signals from the scalp in real time using multiple electrodes. The acquired brain signal data is collected while the user is performing daily activities.

[1673] The terminal (smartphone or dedicated device) transmits the brain signal data acquired from this device to a server via wireless communication (Bluetooth or Wi-Fi).

[1674] The server analyzes the received brain signal data using machine learning algorithms, specifically TensorFlow with Python, to identify the user's intent (e.g., "I want to pay attention to what's ahead"), emotional state (e.g., tension, relaxation), and visual needs.

[1675] Real-world data collection

[1676] Users wear smart glasses or a head-mounted display (e.g., Microsoft HoloLens) and observe the real-world environment around them. The camera in the smart glasses captures images of the user's field of vision in real time. This image data is temporarily stored in the device's internal memory.

[1677] The terminal transmits the temporarily stored video data to the server at regular intervals (for example, one frame per second).

[1678] The server analyzes the received video data using an object detection algorithm based on OpenCV to identify information about the surrounding environment (obstacles and dynamic environmental changes).

[1679] Creating and providing virtual elements

[1680] The server uses a generative AI model to generate appropriate virtual elements based on the results of analyzing the brain signal data and the video data. For example, if it detects an obstacle in front of the user, it generates a warning sound or visual effect.

[1681] The server customizes the generated virtual elements to the user's individual needs, for example adjusting the tone and volume of audio alerts to suit the user's preferences.

[1682] The server sends customized virtual elements to the device, which then integrates them into the image of the real world, providing the user with a real-time MR experience. Specifically, a warning sound is played from the smartglasses' speaker, informing the user, "There is an obstacle ahead. Please be careful."

[1683] This will enable people with visual impairments, the elderly, and dementia patients to enjoy safe and rich sensory experiences in real-world environments. For example, visually impaired people walking around town will be able to see obstacles ahead in advance, improving safety.

[1684] Prompt Sentence Examples

[1685] "Please explain how a mixed reality system works, where the user wears a headset that measures EEG and uses a device that collects data on the surrounding environment to support safe mobility in the real world."

[1686] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1687] Step 1:

[1688] Acquiring brain signals

[1689] The user wears a wearable device for measuring brain waves, which acquires brain waves in real time through electrodes on the scalp.

[1690] Input: EEG electrical signals

[1691] Output: Digital brain signal data

[1692] How it works: The user puts on the headset, and the device uses sensors to capture electrical brainwave signals and convert them into digital data.

[1693] Step 2:

[1694] Transmission of brain signal data

[1695] The device transmits the acquired brain signal data to a server via Bluetooth or Wi-Fi.

[1696] Input: Brain signal data from a wearable device

[1697] Output: Digital brain signal data sent to a server

[1698] Specific operation: The terminal communicates with the wearable device, collects data, and sends it to the server.

[1699] Step 3:

[1700] Analysis of brain signal data

[1701] The server analyzes the received brain signal data using a machine learning model in TensorFlow with Python.

[1702] Input: Digital brain signal data sent to the server

[1703] Output: Data about the user's intent, emotional state, and visual needs

[1704] Specific operation: The server receives the data, runs it through a machine learning model, and derives analytical results.

[1705] Step 4:

[1706] Video data collection

[1707] Users wear smart glasses or a head-mounted display and observe the real-world environment around them, with the device's camera capturing images in real time.

[1708] Input: Surrounding real-world environment

[1709] Output: Video data collected by the camera

[1710] Specific operation: The user wears the device and the camera captures real-time video.

[1711] Step 5:

[1712] Video data transmission

[1713] The device temporarily stores the video data collected by the camera in its internal memory and transmits it to the server at regular intervals.

[1714] Input: Video data from the camera

[1715] Output: Video data sent to the server

[1716] Specific operation: The device stores the video data in its internal memory and sends it to the server.

[1717] Step 6:

[1718] Video data analysis

[1719] The server analyzes the received video data using an object detection algorithm based on OpenCV.

[1720] Input: Video data sent to the server

[1721] Output: Surrounding environment information (position and movement of obstacles, etc.)

[1722] Specific operation: The server receives the video data, analyzes it using an object detection algorithm, and identifies environmental information.

[1723] Step 7:

[1724] Creating Virtual Elements

[1725] The server uses a generative AI model to generate appropriate virtual elements based on the results of analyzing the brain signal data and the video data.

[1726] Input: Brain signal analysis results and video data analysis results

[1727] Output: Generated virtual elements (sound, visual effects, etc.)

[1728] Specific operation: The server runs a generative AI model based on the analysis results to generate virtual elements.

[1729] Step 8:

[1730] Customizing Virtual Elements

[1731] The server customizes the generated virtual elements to the user's individual needs.

[1732] Input: Generated virtual elements, individual user needs

[1733] Output: Customized Virtual Elements

[1734] What it does: The server adjusts, modifies, and optimizes the virtual elements.

[1735] Step 9:

[1736] Sending customized virtual elements

[1737] The server transmits the customized virtual element to the terminal.

[1738] Input: Customized Virtual Elements

[1739] Output: customized virtual elements sent to the device

[1740] Specific operation: The server sends virtual elements to the terminal and reflects them in real time.

[1741] Step 10:

[1742] Providing MR experiences

[1743] The device then integrates the received virtual elements into the image of the real environment, providing the user with an MR experience.

[1744] Input: Customized virtual elements, video data of the real environment

[1745] Output: The integrated mixed reality experience (audio alerts and visual effects provided to the user)

[1746] Specific operation: The device combines virtual elements and video data to play them, providing the user with an MR experience.

[1747] (Application example 1)

[1748] 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."

[1749] Visually impaired people and the elderly have a reduced ability to detect suspicious people and dangerous situations in the real world, making it difficult for them to ensure safety in their daily lives. Currently available technologies are often insufficient to recognize the environment and respond appropriately without relying on sight or hearing. To solve this problem, a system is needed that can detect surrounding dangers in real time and provide warnings to users.

[1750] 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.

[1751] In this invention, the server includes means for analyzing the user's electroencephalogram data and video data to recognize suspicious individuals and dangerous situations, means for generating and providing warning audio and visual signals for the recognized suspicious individuals and dangerous situations, and means for generating virtual elements to customize the user's experience, thereby enabling support for visually impaired people and the elderly to recognize surrounding dangers in real time and respond appropriately.

[1752] "Brain signals" refer to electrical signals generated by the user's brain.

[1753] "Brain signal data" refers to digital data obtained from acquired brain signals.

[1754] "Intention" refers to the sense that a user is trying to act with a specific action or purpose.

[1755] "Emotional state" refers to the user's psychological state of mind and emotions.

[1756] "Visual requirements" refer to information or objects that a user visually desires.

[1757] "Real environment" refers to the physical environment in which the user actually resides.

[1758] "Video data" refers to image or video data captured through a camera or other imaging device.

[1759] "Virtual elements" refer to digitally generated virtual information or objects that are integrated into a real environment.

[1760] A "suspicious person" refers to a person in the surrounding environment who may potentially pose a danger or cause anxiety to the user.

[1761] "Dangerous situation" refers to a situation or environment that may pose a threat to the safety of the user.

[1762] "Warning voice" refers to voice generated to warn the user of danger and caution.

[1763] "Visual signal" refers to an image or signal displayed to visually alert a user to danger or caution.

[1764] This invention is a system that supports safety for visually impaired people and the elderly by detecting surrounding dangers in real time. Specifically, smart glasses worn by the user work in cooperation with a server and a terminal connected to the glasses.

[1765] System Configuration

[1766] Hardware

[1767] Smart glasses: Wearable devices equipped with a camera and audio output.

[1768] EEG measurement device: A headset for capturing the user's brain signals in real time.

[1769] Server: A central processing unit that analyzes brain signals and video data.

[1770] software

[1771] Data analysis platform: Software containing machine learning algorithms for analyzing EEG and video data.

[1772] Warning generation algorithm: A program that detects suspicious individuals or dangerous situations and generates appropriate warnings.

[1773] Program processing flow

[1774] 1. Acquisition and transmission of brain signal data

[1775] The user wears an EEG headset, which captures the user's brain signals in real time.

[1776] The acquired brain signal data is sent to a server via the terminal.

[1777] 2. Collecting and transmitting data from the real world

[1778] The camera in the smart glasses collects video data of the real-world environment around the user.

[1779] The collected video data is sent to the server in real time via the terminal.

[1780] 3. Data Analysis

[1781] The server analyzes the brain signal data to identify the user's intentions, emotional state, and visual needs.

[1782] At the same time, the video data is analyzed to identify the surrounding environment, suspicious individuals, and the location and movement of obstacles, using machine learning algorithms and data processing technology.

[1783] 4. Creating and Providing Virtual Elements

[1784] Based on the analysis, the server generates virtual elements that customize the user's experience, such as audio and visual warnings if a suspicious person is detected.

[1785] The generated virtual elements are transmitted to the smart glasses in real time and presented to the user.

[1786] Specific examples

[1787] As a user walks around town, an EEG headset captures the user's brain signals. At the same time, the camera in the smart glasses collects video data of the surrounding area. This data is sent to a server, which analyzes the EEG data to determine the user's alert state and analyzes the video data to recognize suspicious individuals and dangerous situations. If a suspicious individual is detected, the server generates a warning voice saying, "A suspicious individual is present. Please be careful," and sends it to the user through the smart glasses.

[1788] Example prompts for generative AI models

[1789] Please implement an app that will issue an audio warning if EEG data indicates a certain level of alertness and detects a suspicious person ahead.

[1790] The data used is 128-point EEG data and real-time video data.

[1791] We use a peak detection algorithm to analyze the EEG data, and the face detection function of OpenCV to analyze the video data.

[1792] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1793] Step 1:

[1794] The user wears an EEG headset, which captures electrical signals generated by the brain in real time. The captured brain signal data reflects the user's brainwave activity at that moment.

[1795] Input: User's brain signals

[1796] Data processing: The headset converts electrical signals generated by the brain into digital data

[1797] Output: Brain signal data

[1798] Step 2:

[1799] The device transmits the brain signal data acquired from the headset to a server in real time, so it is necessary to minimize delays.

[1800] Input: Brain signal data

[1801] Data processing: Digital signal processing and data compression are performed

[1802] Output: Brain signal data sent to the server

[1803] Step 3:

[1804] The device uses the camera in the smart glasses to collect video data of the real-world environment around the user, which contains information about the real world in the user's field of view.

[1805] Input: Video of the user's surroundings

[1806] Data processing: Converting analog video data into digital video data

[1807] Output: Collected video data

[1808] Step 4:

[1809] The device transmits the collected video data to a server, also in real time and designed to minimize latency.

[1810] Input: Collected video data

[1811] Data processing: Compression and packetization of video data

[1812] Output: Video data sent to the server

[1813] Step 5:

[1814] The server analyzes the received brain signal data using machine learning algorithms to identify the user's intent, emotional state, and visual needs.

[1815] Input: Brain signal data sent to the server

[1816] Data processing: Analysis using machine learning algorithms

[1817] Output: User intent, emotional state, visual requirements

[1818] Step 6:

[1819] The server analyzes the received video data using an object detection algorithm to identify the location and movement of suspicious individuals and obstacles.

[1820] Input: Video data sent to the server

[1821] Data processing: object detection and motion analysis

[1822] Output: Location and movement information of suspicious persons and obstacles

[1823] Step 7:

[1824] Based on the results of the brain signal analysis and the video data analysis, the server generates virtual elements to customize the user's experience, such as generating audio and visual warnings if a suspicious person is detected.

[1825] Input: User's intentions, emotional state, visual needs, location and movement information of suspicious people and obstacles

[1826] Data processing: Creation of customized virtual elements

[1827] Output: Generated virtual elements (audio and visual warning signals)

[1828] Step 8:

[1829] The server transmits the generated virtual elements to the terminal, and the terminal provides the received virtual elements to the user through the smart glasses.

[1830] Input: The generated virtual element

[1831] Data processing: decoding and playback of audio data and visual signals

[1832] Output: Audio and visual warning signals provided to the user

[1833] 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.

[1834] This invention is a mixed reality (MR) system that adds virtual elements to a real environment and provides a richer sensory experience based on emotional states for the visually impaired, elderly people, and people with memory loss or dementia. The details of the program-based processing of this system are described below.

[1835] Acquiring and analyzing user's brain signals

[1836] 1. Acquiring Brain Signals

[1837] The user puts on a wearable device for measuring brain waves (e.g., an EEG headset), which prepares the device to measure the brain's electrical activity in real time.

[1838] 2. Transmission and analysis of brain signal data

[1839] The device receives brain signal data acquired from the headset in real time, and performs preprocessing such as noise filtering on the received data to improve analysis accuracy.

[1840] The device encrypts and transmits the preprocessed brain signal data to a server.

[1841] The server analyzes the received brain signal data to identify the user's intentions, emotional state, visual needs, etc. It uses machine learning algorithms to quickly extract this information.

[1842] Real-world data collection

[1843] 1. Video data collection

[1844] The device (e.g., smart glasses worn by the user or a mobile device) collects images of the real-world environment around the user in real time via a camera.

[1845] 2. Video data transmission and analysis

[1846] The terminals send the collected video data to the server, where it is appropriately compressed, encrypted, and transmitted safely and efficiently.

[1847] The server analyzes the received video data and identifies the surrounding environment (obstacles, moving objects, points of interest, etc.) using image recognition algorithms.

[1848] Sentiment Engine and Sentiment Analysis

[1849] 1. How the Emotion Engine Works

[1850] The server uses an emotion engine to identify the user's emotional state based on the analysis of the brain signal data. For example, the emotion engine analyzes information such as whether the user is relaxed or tense.

[1851] 2. Real-time emotion monitoring

[1852] The server monitors the user's emotional state in real time and adjusts the MR experience according to emotional changes, for example, adding relaxing virtual elements if the user feels anxious.

[1853] Creating and providing virtual elements

[1854] 1. Creating Virtual Elements

[1855] The server generates appropriate virtual elements based on the analysis of brain signal data, emotional state, and video data of the real environment. For example, if a visually impaired person senses an obstacle ahead, it generates a warning sound.

[1856] The generated virtual elements are customized to the user's individual requirements and emotional state, for example using a calmer voice tone for a user feeling anxious.

[1857] 3. Providing mixed reality experiences

[1858] The server transmits the generated virtual elements to the terminal.

[1859] The terminal integrates the transmitted virtual elements with the image of the real environment and provides it to the user in real time.

[1860] Specific examples

[1861] Specific examples for the visually impaired

[1862] 1. Brain signal acquisition and analysis

[1863] The user wears a headset for measuring electroencephalograms.

[1864] The device collects brain signal data from the headset in real time and transmits it to a server.

[1865] The server determines through analysis that the user intends to "pay attention to what is ahead."

[1866] 2. Real-world data collection

[1867] The terminal acquires image data of the area in front of the user through a camera.

[1868] The terminal transmits the collected video data to the server.

[1869] Through video analysis, the server recognizes that there is an obstacle (bicycle) ahead.

[1870] 3. Emotion Engine and Emotion Analysis

[1871] The server uses an emotion engine to analyze the user's emotional state and determine that the user is "feeling a little anxious."

[1872] 4. Creating and Providing Virtual Elements

[1873] The server generates a warning sound based on the analysis results and customizes the tone of the sound to be gentle to ease the user's anxiety.

[1874] The server transmits the generated warning sound to the terminal.

[1875] The terminal plays the received warning audio to the user, providing voice guidance in a gentle tone saying, "There is a bicycle ahead. Please be careful."

[1876] This system is designed to enable people with visual impairments, the elderly, and dementia patients to enjoy a safe and rich sensory experience in a real environment. In particular, the use of an emotion engine enables appropriate support according to the user's emotional state, supporting a safe and high-quality life.

[1877] The processing flow will be explained below.

[1878] Step 1:

[1879] The user puts on a wearable device for measuring brain waves (e.g., an EEG headset), which prepares the device to measure the electrical activity of the user's brain in real time.

[1880] Step 2:

[1881] The terminal receives brain signal data acquired from the wearable device in real time, and performs preprocessing such as noise filtering on the received data to improve the accuracy of the analysis.

[1882] Step 3:

[1883] The device encrypts and transmits the pre-processed brain signal data to a server, ensuring data security at this stage.

[1884] Step 4:

[1885] The server analyzes the received brain signal data to identify the user's intentions, emotional state, visual needs, etc. It uses machine learning algorithms to quickly extract this information.

[1886] Step 5:

[1887] The device collects images of the real-world environment around the user in real time through a camera, and the camera's collection points may be adjusted based on specific visual requirements.

[1888] Step 6:

[1889] The terminals send the collected video data to the server, where it is appropriately compressed, encrypted, and transmitted safely and efficiently.

[1890] Step 7:

[1891] The server analyzes the received video data and uses image recognition algorithms to identify the surrounding environment (obstacles, moving objects, points of interest, etc.).

[1892] Step 8:

[1893] The server operates an emotion engine based on the analysis of the brain signal data to identify the user's emotional state. The emotion engine analyzes whether the user is relaxed, tense, or anxious.

[1894] Step 9:

[1895] The server monitors the emotion engine in real time and generates virtual elements according to changes in emotions, for example, generating relaxing music and images if the user is feeling anxious.

[1896] Step 10:

[1897] The server then transmits the generated virtual elements to the device, including audio alerts and visual aids tailored to the user's emotional state.

[1898] Step 11:

[1899] The device then combines the received virtual elements with the image of the real environment in real time, and displays or outputs audio to the user. Through this MR experience, the user can enjoy a rich sensory experience linked to their emotions.

[1900] Examples:

[1901] Specific examples for the visually impaired

[1902] Step 1:

[1903] The user wears a headset for measuring electroencephalograms.

[1904] Step 2:

[1905] The device receives brain signal data from the headset in real time.

[1906] Step 3:

[1907] The device encrypts the received brain signal data and transmits it to a server.

[1908] Step 4:

[1909] The server analyzes the brain signal data and determines that the user intends to "focus their attention on what is ahead."

[1910] Step 5:

[1911] The terminal uses a camera to collect video data of the area in front of the user.

[1912] Step 6:

[1913] The terminal transmits the collected video data to the server.

[1914] Step 7:

[1915] The server analyzes the video data and recognizes that there is an obstacle (bicycle) ahead.

[1916] Step 8:

[1917] The server uses the results of the brain signal analysis and an emotion engine to determine that the user is "feeling a little anxious."

[1918] Step 9:

[1919] The server generates a gentle tone of warning audio based on the user's emotional state.

[1920] Step 10:

[1921] The server transmits the generated warning sound to the terminal.

[1922] Step 11:

[1923] The terminal plays the received warning audio to the user, providing voice guidance in a gentle tone saying, "There is a bicycle ahead. Please be careful."

[1924] Example 2

[1925] 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."

[1926] In order for visually impaired people, elderly people, and people with memory loss or dementia to enjoy safe and rich sensory experiences in real-world environments, a system that can accurately grasp information about the surrounding environment and provide appropriate support is necessary. However, conventional technologies have difficulty responding to the user's emotional state and individual needs, and safety and convenience have not been sufficiently ensured. Therefore, in order for users to live their lives with peace of mind, a system that can obtain environmental information in real time and provide customized support according to the user's condition is required.

[1927] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[1928] In this invention, the server includes means for analyzing the user's brain signal data and identifying the user's intention, emotional state, and visual requirements, means for analyzing the video data of the real environment and identifying surrounding environmental information, and means for generating virtual elements for customizing the user's experience based on the results of the analysis of the brain signal data and the video data of the real environment, and adjusting the virtual elements according to the user's emotional state. This makes it possible to comprehensively analyze the user's brain signals and information about the real environment, and provide appropriate support according to the user's emotional state and a customized experience in real time.

[1929] "User" refers to individuals who use the system, such as the visually impaired, elderly, or those with memory loss or dementia.

[1930] "Brain signals" refers to data about a user's brain's electrical activity, such as electroencephalograms, acquired using a wearable device such as an EEG headset.

[1931] A "wearable device" refers to a device worn by a user that can measure and collect data such as brain signals in real time.

[1932] A "terminal" refers to a device used by a user that has the function of collecting brain signal data and video data and transmitting them to a server.

[1933] "Preprocessing" refers to processing performed to improve the quality of acquired brain signal data, and includes noise filtering and the like.

[1934] "Encryption" is a process performed to ensure data security, and refers to a technology that prevents transmitted data from being deciphered by third parties.

[1935] "Server" refers to a central processing unit that receives and analyzes brain signal data and video data.

[1936] "Analysis" refers to the data processing performed to identify the user's intentions, emotional state, and environmental information based on the acquired data.

[1937] "Video data of the real environment" refers to video information of the surroundings collected through smart glasses worn by the user or the camera on a mobile device.

[1938] "Customization" refers to adjusting the virtual elements provided by the system according to the user's individual requirements and emotional state.

[1939] "Virtual elements" refer to virtual information or objects that are added to a real environment to enrich the user's experience.

[1940] "Providing" refers to the process of presenting the generated virtual elements to the user in a manner that is integrated with the real environment.

[1941] This invention is a mixed reality (MR) system that adds virtual elements to a real environment and provides a richer sensory experience based on emotional status for the visually impaired, elderly people, and people with memory loss or dementia. This system has the following configuration, centered around the user, terminals, and server.

[1942] Acquiring and analyzing user's brain signals

[1943] The user wears a wearable device for measuring brain waves. This device is an EEG headset (for example, the "Emotiv Epoc+" manufactured by Emotiv). By wearing the device, the brain's electrical signals are ready to be measured in real time.

[1944] The device receives real-time brain signal data from the headset and performs pre-processing, including noise filtering, such as using a band-pass filter to filter out specific frequency bands, before transmitting the pre-processed data to a server using AES encryption.

[1945] The server analyzes the received brain signal data using machine learning algorithms (e.g., neural networks using Keras) to identify the user's intentions and emotional state. This analysis allows for a quick understanding of the user's real-time state.

[1946] Real-world data collection

[1947] The device collects real-time images of the surrounding real-world environment through the camera on the smart glasses or mobile device worn by the user, such as Google Glass.

[1948] The collected video data is compressed using the H.264 codec, encrypted via SSL / TLS, and then sent to a server, where it is analyzed using the YOLO (You Only Look Once) algorithm to identify surrounding environmental information (e.g., obstacles and moving objects).

[1949] Sentiment Engine and Sentiment Analysis

[1950] The server runs an emotion engine based on the analysis of brain signal data. The emotion engine uses a support vector machine (SVM) to determine the user's emotional state (e.g., whether they are relaxed or nervous) in real time.

[1951] Creating and providing virtual elements

[1952] The server generates appropriate virtual elements based on the analysis of brain signal data and video data of the real environment. The generated virtual elements are customized according to the user's individual requirements and emotional state. For example, if a visually impaired person senses an obstacle ahead, an audio warning will be generated, and if it is determined that the user is feeling anxious, the tone of the audio will be customized to be gentler.

[1953] The generated virtual elements are sent from the server to the device, which then integrates them with images of the real environment and provides them to the user in real time. For example, a warning message can be displayed on the smart glasses display while an audio warning is played.

[1954] Examples of concrete examples and prompts

[1955] Specific examples for the visually impaired

[1956] 1. The user wears an EEG headset to collect brain signal data.

[1957] 2. The device preprocesses the brain signal data and sends it to the server.

[1958] 3. The server determines the user's intent to "pay attention to what's ahead."

[1959] 4. The device acquires image data of the area in front of it through the Google Glass camera and sends it to the server.

[1960] 5. The server uses the YOLO algorithm to recognize that there is an obstacle (bicycle) ahead.

[1961] 6. The server uses an emotion engine to analyze that the user is "feeling a little anxious."

[1962] 7. The server generates a warning voice and sends a gentle voice tone message to the terminal saying, "There is a bicycle ahead. Be careful."

[1963] 8. The device plays the received warning audio to the user, integrating it with the real-world environment in real time.

[1964] Prompt Sentence Examples

[1965] 1. Please explain in detail the processing flow of a system that uses EEG and video data to generate audio warnings to help visually impaired people avoid obstacles ahead.

[1966] 2. Please explain in detail the processing flow of a system that analyzes the emotional state of an elderly person based on EEG data and generates and provides virtual elements to help them relax when they are feeling anxious.

[1967] In this way, this system can support a safe and high-quality life by integrating and analyzing the user's emotional state and real-world environment.

[1968] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1969] Step 1:

[1970] The user wears a wearable device for measuring brain waves (e.g., an EEG headset), which allows the brain's electrical activity to be measured in real time. The input is the user's brain's electrical signals, and the output is the brain wave data measured by the EEG headset.

[1971] Step 2:

[1972] The terminal receives brain signal data collected from the headset in real time. The received EEG data undergoes preprocessing such as noise filtering. Preprocessing involves using a bandpass filter to filter out specific frequency bands. The input is brain signal data from the EEG headset, and the output is preprocessed, high-quality brain signal data.

[1973] Step 3:

[1974] The device encrypts the preprocessed brain signal data and sends it to the server. The AES encryption method is used for encryption, ensuring secure data transmission. The input is preprocessed EEG data, and the output is encrypted EEG data.

[1975] Step 4:

[1976] The server receives the encrypted brain signal data, decrypts it, and analyzes it. It uses machine learning algorithms (e.g., neural networks) to identify the user's intentions and emotional state. The input is the encrypted and transmitted brain wave data, and the output is the user's intentions and emotional state.

[1977] Step 5:

[1978] The device collects video data of the real-world environment around the user in real time through a camera (e.g., a camera in smart glasses). This video data includes surrounding objects and moving objects. The input is the video of the real-world environment captured by the camera, and the output is the collected video data.

[1979] Step 6:

[1980] The terminal compresses the collected video data using the H.264 codec, encrypts it using SSL / TLS, and then sends it to the server. The input is the collected video data, and the output is the encrypted video data.

[1981] Step 7:

[1982] The server receives the encrypted video data, decrypts it, and analyzes it. It uses the YOLO algorithm to identify the surrounding environment (e.g., obstacles and moving objects). The input is the encrypted video data, and the output is the identified environment information.

[1983] Step 8:

[1984] The server runs an emotion engine based on preprocessed EEG data and environmental information. The emotion engine uses a support vector machine (SVM) to determine the user's emotional state. The inputs are the user's intention, emotional state, and environmental information, and the output is the user's emotional state.

[1985] Step 9:

[1986] The server generates virtual elements based on the analysis results and customizes them according to the user's emotional state. For example, it generates a warning sound for obstacles and sets it to a gentle tone if necessary. The input is the user's emotional state, environmental information, and brainwave data, and the output is a customized virtual element (e.g., a warning sound).

[1987] Step 10:

[1988] The server sends the generated virtual elements to the device. The device integrates the virtual elements with the image of the real environment and provides it to the user in real time. For example, a warning message can be displayed on the smart glasses display while a warning sound is played. The input is the customized virtual elements, and the output is the virtual elements that are displayed and played to the user.

[1989] (Application example 2)

[1990] 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."

[1991] It is necessary to provide an environment in which users, such as the visually impaired, elderly people, dementia patients, and factory workers, can work safely and efficiently in real-world environments. However, it is currently difficult for these users to receive appropriate advice and warnings based on their emotional state. In particular, when working in a factory, there is a lack of systems that provide support based on emotional state, so it is necessary to improve user safety and work efficiency.

[1992] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 2 is realized by the following means.

[1993] In this invention, the server includes means for acquiring a user's brain signal, means for analyzing the user's brain signal data and identifying the user's intention, emotional state, and visual requirements, means for collecting video data of the real environment around the user, means for analyzing the collected video data and identifying surrounding environmental information, means for generating virtual elements to customize the user's experience based on the analysis results of the brain signal data and the video data of the real environment, means for providing the generated virtual elements to the user, means for monitoring the emotional state of workers and generating and providing feedback based on the specific emotional state, and means for identifying obstacles and dangerous areas in the factory and providing warnings to the user. This makes it possible to provide appropriate feedback according to the user's emotional state and improve the safety and efficiency of work in the factory.

[1994] "Brain signals" are electrical signals emitted from the user's brain, and are data that reflect the state of thoughts, emotions, intentions, etc.

[1995] "Brain signal data" refers to information that is measured and recorded in digital form and is used to analyze the user's brain activity.

[1996] "User intent" refers to information about a user's behavior or interests, such as what the user is planning to do or what the user is paying attention to.

[1997] "Emotional state" refers to the user's current emotion (e.g., joy, sadness, anger, anxiety, etc.) and is determined by analyzing brain signal data.

[1998] A "visual need" is a user's visual need for information or assistance, and is identified based on the user's intentions and emotional state.

[1999] The "surrounding real environment" refers to the location where the user is currently physically present and the environment around it, and is collected as video data using devices such as cameras.

[2000] "Video data" is digital data that contains visual information of the real environment, captured by a camera or the like.

[2001] "Environmental information" refers to information about objects and obstacles around the user, as well as their positions and movements, and is determined by analyzing video data.

[2002] "Virtual elements" are digital information or content that is added to a real-world environment to customize a user's experience.

[2003] "Feedback" refers to information or advice that a system provides to a user, and is provided using audio or visual means.

[2004] "Obstacles" or "dangerous areas" are objects that are obstacles or dangerous places for the user, and are identified by analyzing video data of the real environment.

[2005] To implement this invention, hardware such as a wearable device for measuring electroencephalograms (e.g., an EEG headset), smart glasses, and a server is required. Furthermore, software such as a machine learning algorithm (e.g., TensorFlow, PyTorch) and an image recognition algorithm (e.g., OpenCV) is required. A specific embodiment of this system is described below.

[2006] Acquiring and analyzing user's brain signals

[2007] The user wears an EEG headset to measure brain waves, which measures the brain's electrical activity in real time. This data is collected through smart glasses and received in real time by a device. The device performs preprocessing to improve the accuracy of the data analysis, such as noise filtering, and then encrypts and transmits the data to a server. The server uses machine learning algorithms (TensorFlow, PyTorch) to analyze the brain signal data and identify the user's intentions and emotional state.

[2008] Real-world data collection and analysis

[2009] The camera in the smart glasses worn by the user collects video data of the real-world environment around the user in real time. The device preprocesses this data, appropriately compresses and encrypts it, and sends it to the server. The server analyzes the received video data and identifies obstacles, points of interest, dangerous areas, etc. This processing uses an image recognition algorithm (OpenCV).

[2010] Emotion Engine and Feedback Generation

[2011] The server uses an emotion engine to identify the user's emotional state based on the analysis of brain signal data. If the user is feeling anxious or stressed, it generates virtual elements (e.g., calming music or visual messages) to promote relaxation. It also generates warning messages about obstacles and dangerous areas based on the analysis of data from the real environment. These virtual elements are provided to the user through the smart glasses.

[2012] Specific examples

[2013] For example, if a factory worker is feeling stressed, their state is recognized in real time by the server through brainwave data analysis. As a result, relaxing music or calming visual messages are generated and displayed in the worker's smart glasses. Furthermore, if the worker approaches a dangerous area, their movements are identified through video data analysis, and a warning message is displayed on the smart glasses. This system improves worker safety and work efficiency.

[2014] Prompt Sentence Examples

[2015] "Generate optimal visual and audio feedback based on EEG data and on-site video data to bring workers back to a relaxed state."

[2016] "Devour a method to provide relaxing music when a worker's emotional state is judged to be stressed, and simultaneously display a warning message when the worker approaches a dangerous area on the job site."

[2017] As described above, the present invention is a system for providing real-time feedback according to a user's emotional state, and is particularly intended to improve safety and efficiency for factory workers.

[2018] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[2019] Step 1:

[2020] The user wears an EEG headset to measure brain waves and acquire brain signals.

[2021] Input: User's EEG signal.

[2022] How it works: The EEG headset detects electrical signals emitted from the user's brain in real time and collects them as digital data.

[2023] Output: Acquired brain signal data.

[2024] Step 2:

[2025] The device receives brain signal data acquired from an EEG headset, and performs pre-processing such as noise filtering on the received data.

[2026] Input: Brain signal data transmitted from an EEG headset.

[2027] Specific operation: The device receives brain signal data in real time and passes it through a noise filter to improve data accuracy.

[2028] Output: Preprocessed brain signal data.

[2029] Step 3:

[2030] The device encrypts and transmits the preprocessed brain signal data to a server.

[2031] Input: Preprocessed brain signal data.

[2032] What it does: The device encrypts the data and sends it to the server using a secure communication protocol.

[2033] Output: Encrypted brain signal data sent to server.

[2034] Step 4:

[2035] The server analyzes the brain signal data to identify the user's intentions and emotional state.

[2036] Input: Encrypted brain signal data.

[2037] What it does: The server decrypts the data and uses machine learning algorithms (TensorFlow, PyTorch) to identify the user's intent and emotional state.

[2038] Output: Analysis of the user's intent and emotional state.

[2039] Step 5:

[2040] The camera in the user's smart glasses collects video data of the surrounding real-world environment.

[2041] Input: Real-world environment.

[2042] How it works: The camera in the smart glasses captures images of the surroundings in real time.

[2043] Output: Video data.

[2044] Step 6:

[2045] The terminal transmits the collected video data to the server.

[2046] Input: Video data.

[2047] Specific operation: The device compresses and encrypts the video data before sending it to the server.

[2048] Output: Video data sent to the server.

[2049] Step 7:

[2050] The server analyzes the video data and identifies obstacles and dangerous areas.

[2051] Input: Video data.

[2052] Specific operation: Using image recognition algorithms (OpenCV), data analysis is performed to identify obstacles and dangerous areas in the video.

[2053] Output: Analysis results of obstacles and dangerous areas.

[2054] Step 8:

[2055] The server generates virtual elements according to the user's emotional state based on the results of analyzing the brain signal data and video data.

[2056] Input: Analysis results of the user's intent and emotional state, analysis results of obstacles and dangerous areas.

[2057] Specific behavior: The server uses the generative AI model to generate appropriate feedback and warning messages for the user.

[2058] Output: Virtual elements (e.g. audio messages, visual messages).

[2059] Step 9:

[2060] The server transmits the generated virtual elements to the user's smart glasses.

[2061] Input: Virtual element.

[2062] What happens: The server formats the virtual elements appropriately and sends them to the smart glasses.

[2063] Output: Virtual elements sent to the user's smart glasses.

[2064] Step 10:

[2065] The user's smart glasses display or play the received virtual elements and provide feedback to the user.

[2066] Input: Virtual element.

[2067] What it does: The smart glasses display visual messages and play audio messages.

[2068] Output: Feedback provided to the user.

[2069] 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.

[2070] 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.

[2071] 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.

[2072] 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.

[2073] 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.

[2074] 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.

[2075] 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).

[2076] 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.

[2077] 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."

[2078] 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.

[2079] 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).

[2080] 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.

[2081] 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.

[2082] 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.

[2083] 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.

[2084] 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.

[2085] 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.

[2086] 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.

[2087] 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.

[2088] 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.

[2089] 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.

[2090] The following is further disclosed regarding the above embodiment.

[2091] (Claim 1)

[2092] means for acquiring a user's brain signals;

[2093] means for analyzing the user's brain signal data to identify the user's intentions, emotional state, and visual needs;

[2094] means for collecting video data of a real-world environment surrounding a user;

[2095] A means for analyzing the collected video data and identifying surrounding environmental information;

[2096] means for generating virtual elements to customize the user's experience based on the analysis results of the brain signal data and the video data of the real environment;

[2097] means for providing the generated virtual elements to a user;

[2098] A system including:

[2099] (Claim 2)

[2100] 10. The system of claim 1, further comprising means for identifying the positions and movements of surrounding obstacles and objects based on video data of the real environment.

[2101] (Claim 3)

[2102] 2. The system according to claim 1, further comprising means for analyzing the user's brain signal data and the video data of the real environment to identify an object in which the user is interested.

[2103] "Example 1"

[2104] (Claim 1)

[2105] means for acquiring a user's brain signals;

[2106] means for transmitting brain signal data of a user;

[2107] means for analyzing the user's brain signal data to identify the user's intentions, emotional state, and visual needs;

[2108] means for collecting video data of a real-world environment surrounding a user;

[2109] means for transmitting the collected video data;

[2110] A means for analyzing the transmitted video data and identifying surrounding environmental information;

[2111] means for generating virtual elements to customize the user's experience based on the analysis results of the brain signal data and the video data of the real environment;

[2112] means for providing the generated virtual elements to a user;

[2113] A system including:

[2114] (Claim 2)

[2115] 10. The system of claim 1, further comprising means for identifying the positions and movements of surrounding obstacles and objects based on video data of the real environment.

[2116] (Claim 3)

[2117] 2. The system according to claim 1, further comprising means for analyzing the user's brain signal data and the video data of the real environment to identify an object in which the user is interested.

[2118] "Application Example 1"

[2119] (Claim 1)

[2120] means for acquiring a user's brain signals;

[2121] means for analyzing the user's brain signal data to identify the user's intentions, emotional state, and visual needs;

[2122] means for collecting video data of a real-world environment surrounding a user;

[2123] A means for analyzing the collected video data and identifying surrounding environmental information;

[2124] means for generating virtual elements to customize the user's experience based on the analysis results of the brain signal data and the video data of the real environment;

[2125] means for providing the generated virtual elements to a user;

[2126] A means for analyzing the user's brain wave data and video data to recognize suspicious individuals and dangerous situations;

[2127] means for generating and providing audio and visual warning signals in response to a recognized suspicious person or dangerous situation;

[2128] A system including:

[2129] (Claim 2)

[2130] 10. The system of claim 1, further comprising means for identifying the positions and movements of surrounding obstacles and objects based on video data of the real environment.

[2131] (Claim 3)

[2132] 2. The system according to claim 1, further comprising means for analyzing the user's brain signal data and the video data of the real environment to identify an object in which the user is interested.

[2133] "Example 2: Combining Emotion Engines"

[2134] (Claim 1)

[2135] a means for a user to wear a wearable device for acquiring brain signals;

[2136] A means for the terminal to receive and preprocess the user's brain signal data in real time;

[2137] a means for the terminal to encrypt the preprocessed brain signal data and transmit the encrypted data to a server;

[2138] A means for the server to analyze the user's brain signal data and identify the user's intention, emotional state, and visual needs;

[2139] A means for the terminal to collect video data of the real environment around the user through a camera;

[2140] A means for transmitting the video data collected by the terminal to a server;

[2141] A means for analyzing the video data collected by the server and identifying information about the surrounding environment;

[2142] a means for the server to generate virtual elements that customize the user's experience based on the analysis results of the brain signal data and the video data of the real environment, and to adjust the virtual elements according to the user's emotional state;

[2143] a means for transmitting the generated virtual elements from the server to a terminal to provide the virtual elements to a user, and integrating the virtual elements with an image of the real environment;

[2144] A system including:

[2145] (Claim 2)

[2146] 10. The system of claim 1, further comprising means for identifying the positions and movements of surrounding obstacles and objects based on video data of the real environment.

[2147] (Claim 3)

[2148] 2. The system according to claim 1, further comprising means for analyzing the user's brain signal data and the video data of the real environment to identify an object in which the user is interested.

[2149] "Application example 2 when combining emotion engines"

[2150] (Claim 1)

[2151] means for acquiring a user's brain signals;

[2152] means for analyzing the user's brain signal data to identify the user's intentions, emotional state, and visual needs;

[2153] means for collecting video data of a real-world environment surrounding a user;

[2154] A means for analyzing the collected video data and identifying surrounding environmental information;

[2155] means for generating virtual elements to customize the user's experience based on the analysis results of the brain signal data and the video data of the real environment;

[2156] means for providing the generated virtual elements to a user;

[2157] means for monitoring the worker's emotional state and generating and providing feedback based on the worker's particular emotional state;

[2158] A means of identifying obstacles and dangerous areas within the factory and providing warnings to users;

[2159] A system including:

[2160] (Claim 2)

[2161] 10. The system of claim 1, further comprising means for identifying the positions and movements of surrounding obstacles and objects based on video data of the real environment.

[2162] (Claim 3)

[2163] 2. The system according to claim 1, further comprising means for analyzing the user's brain signal data and the video data of the real environment to identify an object in which the user is interested. [Explanation of symbols]

[2164] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot< / url:> < / url:> < / url:> < / url:>

Claims

1. means for acquiring a user's brain signals; means for analyzing the user's brain signal data to identify the user's intentions, emotional state, and visual needs; means for collecting video data of a real-world environment surrounding a user; A means for analyzing the collected video data and identifying surrounding environmental information; means for generating virtual elements to customize the user's experience based on the analysis of the brain signal data and the analysis of the video data of the real environment; means for providing the generated virtual elements to a user; A system including:

2. The system of claim 1 further comprising means for identifying the positions and movements of surrounding obstacles and objects based on video data of the real environment.

3. The system according to claim 1 , further comprising means for analyzing the user's brain signal data and the image data of the real environment to identify an object in which the user is interested.

Images