system

Abstract

We provide the system. [Solution] A means of acquiring biosignals obtained during the user's sleep, A means for processing the aforementioned biological signal to remove noise and normalize it, A means for transmitting the aforementioned biological signals to a server using high-speed communication technology, The server includes means for generating an image using the biological signal as input, A means for performing psychological analysis based on the aforementioned image, Means for displaying the image and analysis results to the user, A system that includes this.

Description

Technical Field

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

Background Art

[0002] Patent Document 1 discloses a persona chatbot control method performed by at least one processor, including steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to an explanation of a 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

Summary of the Invention

Problems to be Solved by the Invention

[0004] So far, means for understanding the content of dreams in detail and grasping one's own psychological state and potential problems have been limited. Currently, even if the content of a dream is recorded, it is difficult to obtain feedback based on its psychological interpretation, and there is a lack of support for users to deepen self-understanding. Therefore, it is an issue to provide means for comprehensively analyzing the dreams that users see and visualizing psychological elements to assist in finding one's own inner feelings, traumas, and growth potential.

Means for Solving the Problems

[0005] This invention acquires the user's brainwaves, which are biosignals, denoise and normalize them, and then transmits them to a server. The server then generates images that visualize the received brainwaves using a machine learning model. As a result, the user can visually recognize the visuals of their dreams and gain insights based on psychological analysis. This allows the user to gain a deeper understanding of their own potential emotions and problems.

[0006] A "user" is a person who uses this system to analyze their dreams and gain psychological insights.

[0007] "Biosignals" primarily refer to brainwaves and are a general term for action potentials or electrical signals obtained from the user's body.

[0008] "Noise reduction" refers to the process of removing unwanted external interference and device errors from biological signals.

[0009] "Normalization" is a data processing technique that standardizes biological signals into a format that is easier to analyze.

[0010] "High-speed communication technology" refers to a means of communication for rapidly transferring large amounts of data, and in this system, it specifically refers to 5G communication technology.

[0011] A "server" is a computer system that receives biological signals, processes and stores them, and then generates and analyzes images.

[0012] "Image generation" is the process of converting the characteristics of biological signals into visual information using machine learning models.

[0013] A "machine learning model" is a collection of algorithms and statistical models used to analyze data, and it is a technology that learns data patterns to make predictions and classifications.

[0014] "Psychological analysis" is an evaluation process that uses the content of a user's dreams and the generated images to identify the user's underlying emotions and psychological issues.

Brief Description of the Drawings

[0015] [Figure 1] It is a conceptual diagram showing an example of the configuration of a data processing system according to the first embodiment. [Figure 2] It is a conceptual diagram showing an example of the main functions of a data processing device and a smart device according to the first embodiment. [Figure 3] It is a conceptual diagram showing an example of the configuration of a data processing system according to the second embodiment. [Figure 4] It is a conceptual diagram showing an example of the main functions of a data processing device and smart glasses according to the second embodiment. [Figure 5] It is a conceptual diagram showing an example of the configuration of a data processing system according to the third embodiment. [Figure 6] It is a conceptual diagram showing an example of the main functions of a data processing device and a headset-type terminal according to the third embodiment. [Figure 7] It is a conceptual diagram showing an example of the configuration of a data processing system according to the fourth embodiment. [Figure 8] It is a conceptual diagram showing an example of the main functions of a data processing device and a robot according to the fourth embodiment. [Figure 9] It shows an emotion map to which a plurality of emotions are mapped. [Figure 10] It shows an emotion map to which a plurality of emotions are mapped. [Figure 11] It is a sequence diagram showing the processing flow of the data processing system in Example 1. [Figure 12] It is a sequence diagram showing the processing flow of the data processing system in Application Example 1. [Figure 13] It is a sequence diagram showing the processing flow of the data processing system in Example 2 when an emotion engine is combined. [Figure 14] It is a sequence diagram showing the processing flow of the data processing system in Application Example 2 when an emotion engine is combined.

Modes for Carrying Out the Invention

[0016] Hereinafter, an example of an embodiment of a system according to the technology of the present disclosure will be described with reference to the accompanying drawings.

[0017] First, the terms used in the following description will be explained.

[0018] In the following embodiments, the numbered processor (hereinafter simply referred to as "processor") may be a single arithmetic unit or a combination of multiple arithmetic units. Also, the processor may be a single type of arithmetic unit or a combination of multiple types of arithmetic units. Examples of arithmetic units include a CPU (Central Processing Unit), a GPU (Graphics Processing Unit), a GPGPU (General-Purpose computing on Graphics Processing Units), an APU (Accelerated Processing Unit), and the like.

[0019] In the following embodiments, the numbered RAM (Random Access Memory) is a memory in which information is temporarily stored and is used as a work memory by the processor.

[0020] In the following embodiments, the numbered storage is one or more non-volatile storage devices that store various programs and various parameters, etc. Examples of non-volatile storage devices include flash memory (SSD (Solid State Drive)), magnetic disks (e.g., hard disks), or magnetic tapes, and the like.

[0021] In the following embodiments, the signed communication interface (I / F) is an interface that includes a communication processor and an antenna, etc. The communication interface manages communication between multiple computers. Examples of communication standards applicable to the communication interface include wireless communication standards such as 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), or Bluetooth (registered trademark).

[0022] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B." That is, "A and / or B" means that it may be A alone, or B alone, or a combination of A and B. Furthermore, in this specification, the same concept as "A and / or B" applies when expressing three or more things linked by "and / or."

[0023] [First Embodiment]

[0024] Figure 1 shows an example of the configuration of the data processing system 10 according to the first embodiment.

[0025] As shown in Figure 1, the 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.

[0026] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0027] The smart device 14 comprises a computer 36, a reception device 38, an output device 40, a camera 42, and a communication interface 44. The computer 36 comprises a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The reception device 38, output device 40, and camera 42 are also connected to the bus 52.

[0028] The reception device 38 is equipped with a touch panel 38A and a microphone 38B, etc., and receives user input. The touch panel 38A receives user input by detecting contact with an object (e.g., a pen or finger). The microphone 38B receives user input by detecting the user's voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and 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.

[0029] 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 perceptible to the user 20 (e.g., audio and / or text). The display 40A displays visible information such as text and images according to instructions from the processor 46. The speaker 40B outputs audio according to instructions from the processor 46. The camera 42 is a small digital camera equipped with an optical system such as a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0030] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various types of information between processor 46 and processor 28 via network 54.

[0031] Figure 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0032] As shown in Figure 2, in the data processing device 12, a specific processing 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" related to the technology of this 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 according to the specific processing program 56 executed on the RAM 30.

[0033] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0034] In the smart device 14, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The reception output program 60 is used in conjunction with a 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 processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0035] Next, the specific processing performed by the specific processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the smart device 14 as the "terminal".

[0036] This invention is a system for analyzing and visualizing a user's dreams to gain psychological insights. The system mainly consists of a process of acquiring biosignals using the user's brainwaves and visualizing them, and a process of presenting the results of the psychological analysis based on those signs.

[0037] The device acquires brainwaves in real time through an electroencephalogram (EEG) reader worn by the user while they sleep. This data is then denoised and normalized within the device. Noise reduction is performed to filter out interference from the external environment and improve the purity of the biosignals. The normalized data is then sent to a server according to a pre-configured transmission schedule. High-speed communication technology is used for this transmission, ensuring that the data is transferred quickly and securely.

[0038] The server receives brainwave data transmitted from the terminal. After receiving the data, it analyzes it using a machine learning model. The machine learning model uses deep learning techniques, particularly generative adversarial networks (GANs), to visualize the information obtained from the brainwaves as an image. This image generation makes it possible to abstractly depict the user's dream scenes.

[0039] Next, the server performs a psychological analysis using the generated images and raw data. The analysis is primarily aimed at interpreting the user's potential emotions and mental state that the dream content may suggest. This analysis applies standard psychological theories and models and is evaluated in conjunction with the user's personal past data.

[0040] Ultimately, the device provides the user with the analysis results obtained from the server. The user can then view the generated dream images and the psychological analysis results through the app's interface. This allows the user to gain a deeper understanding of their inner self and obtain insights to address potential problems and emotions.

[0041] For example, if a user is suffering from anxiety, the images generated from their brainwave data visually represent the origins of their anxiety and related emotions, divided into multiple scenes. Based on these images, psychological analysis suggests the basis of the anxiety and elements that can be improved, providing the user with specific advice. This allows the user to gain insights into resolving their emotional issues through the content of their dreams.

[0042] The following describes the processing flow.

[0043] Step 1:

[0044] The device acquires brainwave data in real time using an electroencephalogram (EEG) reader worn by the user. The data is continuously stored in the device's memory.

[0045] Step 2:

[0046] The device uses the acquired electroencephalogram (EEG) data to perform noise reduction. Specifically, it applies filtering technology to improve data accuracy by filtering out abnormal values ​​and external interference.

[0047] Step 3:

[0048] The device normalizes the noise-removed data and converts it into a unified format. This makes the data easier to analyze.

[0049] Step 4:

[0050] The terminal sends pre-processed data to the server using 5G communication technology. The transmission includes error checking to ensure data integrity.

[0051] Step 5:

[0052] The server receives the EEG data from the terminal and prepares it for analysis. The received data is then passed to the analysis platform.

[0053] Step 6:

[0054] The server uses the received data as input to run a machine learning model. The model uses deep learning techniques to generate images that represent the content of the dream from the data.

[0055] Step 7:

[0056] The server performs a psychological analysis based on the generated images. The analysis uses algorithms that identify the user's psychological state and potential problems as reflected in the dream content.

[0057] Step 8:

[0058] The server compiles and visualizes the analysis results and sends them to the terminal. The results include the generated images and explanations of the psychological insights.

[0059] Step 9:

[0060] The device presents the user with data received from the server. Through the app, the user can view visualized dream images and the corresponding psychological analysis results. This allows the user to gain a deeper understanding of their own psychological state.

[0061] (Example 1)

[0062] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server," and the smart device 14 will be referred to as the "terminal."

[0063] In modern society, it is important for individuals to understand their own inner emotions and psychological states, but many people lack the means to grasp and resolve them on their own. In particular, there is a need for a system that analyzes a person's psychological state through the unconscious phenomenon of dreams and helps visualize and understand it using appropriate methods.

[0064] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0065] In this invention, the server includes means for acquiring the user's biological signals, means for converting visual representations into specific dream scenes using a generative adversarial network, and means for performing psychological analysis based on the visual representations. This makes it possible to analyze and visualize an individual's dreams and provide psychological insights.

[0066] A "user" refers to an individual who uses the system to analyze their own psychological state and the content of their dreams.

[0067] "Rest" refers to a state in which the user is sleeping, or a state similar to sleep.

[0068] "Biological signals" refer to electrical activity detected from the user's body, including but not limited to brain waves.

[0069] "Interference signals" refer to external electromagnetic waves and environmental noise that interfere with the user's biological signals.

[0070] "High-speed communication technology" refers to technologies that use advanced communication protocols such as 5G and Wi-Fi 6 to quickly transfer large amounts of data.

[0071] A "processing unit" refers to a computer device, such as a server, that receives, processes, and analyzes data.

[0072] "Visual representation" refers to images and graphics that can be visually displayed by extracting biological signals such as the user's brainwaves.

[0073] A "generative adversarial network" is a deep learning technique that uses training data to generate new data or images.

[0074] "Psychological analysis" refers to the analytical process of interpreting a user's psychological state and emotions based on visual representations and biological signals.

[0075] This invention is a system that gains psychological insights by analyzing and visualizing the user's dreams. A specific embodiment of this system is shown below.

[0076] The user wears a special sensor device while resting. This device is worn on the head and acquires biological signals, including brain waves, in real time.

[0077] The terminal receives biological signals transmitted from sensors. After reception, the signals are denoised and normalized using built-in software. Digital filtering technology is used for denoising to improve signal purity. In the normalization process, the signal amplitude is standardized to a certain range to facilitate analysis. The processed data is transmitted to a computing device using high-speed communication technology (e.g., 5G or Wi-Fi 6).

[0078] The server acts as a computing device, analyzing the received biological signals. The analysis utilizes a generative adversarial network (GAN), a type of generative AI model. This model generates visual representations from the input signals, visually depicting the user's dream scenes. Next, a psychological analysis is performed based on the generated visual representations. This analysis uses standard psychological theories to evaluate the user's emotions and subconscious. The evaluation also includes comparisons with past data.

[0079] Ultimately, the terminal displays the visual representation of the dream and the results of the psychological analysis transmitted from the server on the user interface. The user can review these results through the application and gain insights into their inner self. For example, if the user sees an animal in their dream, the system analyzes the symbolism of that animal in relation to their psychological state and provides advice regarding specific emotions and situations. An example of a prompt used in this case would be: "Generate a dream scene from the user's brainwave data and perform a psychological analysis. Provide specific advice regarding the emotions and states that may be suggested by the generated image."

[0080] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0081] Step 1:

[0082] The user rests while wearing a sensor device. The device acquires the user's brainwave signals in real time. The input is the user's brainwaves, and the output is raw brainwave signal data transmitted to the terminal.

[0083] Step 2:

[0084] The terminal performs noise reduction processing on the received electroencephalogram (EEG) signal data. The input is raw EEG signal data, and the output is EEG signal data with noise removed. Specifically, it uses digital filtering technology to eliminate high-frequency interference components.

[0085] Step 3:

[0086] The terminal normalizes the signal after noise reduction. The input is noise-reduced EEG signal data, and the output is normalized EEG data. Normalization adjusts the data amplitude to a certain range, improving data consistency.

[0087] Step 4:

[0088] The terminal transmits normalized EEG data to the server. The input is normalized EEG data, and the output is data transferred to the server. Specifically, high-speed communication technology is used to encrypt and securely transmit the data.

[0089] Step 5:

[0090] The server generates visual representations using a generative adversarial network (GAN), a generative AI model, based on the received brainwave data. The input is normalized brainwave data, and the output is image data representing scenes from the user's dream.

[0091] Step 6:

[0092] The server performs psychological analysis based on the generated images. The input is the generated dream images, and the output is the results of the psychological analysis. Specifically, it applies standard psychological theories to evaluate the user's emotions and mental state.

[0093] Step 7:

[0094] The terminal provides the user with dream images and psychological analysis results transmitted from the server. Inputs include image data and analysis results from the server, and these are displayed on the interface as output. Through this, the user can deepen their self-understanding.

[0095] (Application Example 1)

[0096] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server," and the smart device 14 will be referred to as the "terminal."

[0097] There is a need to provide a means to improve mental health by subtly analyzing users' stress and anxiety-related psychological issues during sleep, thereby offering individually tailored coping strategies. Currently, there is a lack of systems that can acquire and visualize these emotional states in real time, perform psychological analysis, and provide specific advice.

[0098] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0099] In this invention, the server includes means for acquiring biosignals obtained during the user's sleep, means for generating images using the biosignals and performing psychological analysis, and means for identifying stress and anxiety and advising the user on coping strategies based on the analysis results. This allows the user to unconsciously understand their own emotional state and obtain specific coping strategies for stress and anxiety.

[0100] A "user" refers to an individual who uses this system, specifically the entity that provides biosignals such as brain waves during sleep.

[0101] "Biosignals" refer to data representing the electrical activity of biological origin, such as a user's brainwaves, and are used to analyze their psychological state.

[0102] "Noise removal and normalization methods" refer to processes that remove external interference from acquired biosignals, thereby improving the consistency and reliability of the data.

[0103] "Means of transmitting to an information processing device using high-speed communication technology" refers to technical means for rapidly and safely transferring biological signals to an information processing device, and is a technology designed for high-speed and highly reliable communication.

[0104] An "information processing device" is a computer system necessary for analyzing received biological signals, visualizing them as images, and performing psychological analysis.

[0105] "Means of generating images" refers to the process of creating images that visually represent a user's psychological state or dreams based on biosignal data.

[0106] "Methods for conducting psychological analysis" refers to the process of interpreting and analyzing the user's potential emotional state and psychological issues based on the generated images, using specialized theories.

[0107] "Means of identifying and providing advice for stress and anxiety states" refers to a process of evaluating a user's psychological state and suggesting appropriate coping strategies for individual stressors and anxieties.

[0108] This invention embodies a system for analyzing a user's psychological state during sleep and providing practical advice. This system utilizes an electroencephalogram (EEG) reader worn by the user while sleeping, which acquires biosignals in real time. The acquired EEG data is denoised and normalized on a terminal, ensuring highly reliable data.

[0109] The terminal transmits this normalized biosignal to an information processing device (server) quickly and securely using high-speed communication technology. The server uses the received biosignal data and a generative AI model to generate visual information from brainwaves. Specifically, it employs a generative adversarial network (GAN) to create an abstract image representing the user's psychological state based on brainwave patterns.

[0110] Next, the server performs a psychological analysis based on the generated images, using standard psychological theories and models. This identifies the user's stress and anxiety levels, and develops coping strategies. The results of this analysis are displayed through an application on the user's smartphone or tablet. The user can review the visualized dreams and psychological state and receive recommended coping strategies.

[0111] For example, if a user is experiencing anxiety due to work-related stress, the generated image will reflect that focus, and the server can offer relaxation techniques and specific action suggestions to alleviate the anxiety. Advice such as, "After work, take a walk in nature and practice deep breathing to calm your mind," might be provided.

[0112] Example of a prompt:

[0113] "We analyze the user's brainwave data to visualize underlying stressors."

[0114] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0115] Step 1:

[0116] The user wears an electroencephalogram (EEG) reader while sleeping, and the device acquires biosignals in real time. The input is the user's EEG data, and the output is the initial biosignal data. At this stage, the device aims to accurately collect the data and transmit it to the terminal in an appropriate format.

[0117] Step 2:

[0118] The terminal processes the received biosignal data and performs noise reduction. The input is the initial biosignal data, and the output is clean biosignal data with noise removed. In this step, a signal processing algorithm is used to filter out interference caused by the external environment and equipment, improving the purity of the data.

[0119] Step 3:

[0120] The terminal normalizes clean biosignal data. The input is denoised biosignal data, and the output is normalized biosignal data. This process ensures data consistency and standardization, and performs conversions to accommodate different measurement conditions and data ranges.

[0121] Step 4:

[0122] The terminal transmits normalized biosignal data to the server using high-speed communication technology. The input is the normalized biosignal data, and the output is the status indicating that the data transfer to the server is complete. This process is performed using a communication protocol with the aim of delivering data to the server quickly and securely.

[0123] Step 5:

[0124] The server uses the received biosignal data to generate an image using a generative AI model, specifically a generative adversarial network (GAN). The input is normalized biosignal data transferred to the server, and the output is image data. In this step, an abstract dream scene is visualized based on a machine learning algorithm.

[0125] Step 6:

[0126] The server performs psychological analysis based on the generated image data. The input is the generated image data, and the output is psychological state information as a result of the analysis. The server applies standard psychological theories and models to interpret the user's potential emotions and mental state.

[0127] Step 7:

[0128] The server sends the analysis results to the terminal, which then displays them to the user. The input is psychological state information, and the output is the notification status to the user. In this final step, specific advice and solutions are displayed, allowing the user to take action based on the visualized information.

[0129] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0130] This invention is a system that uses the user's brainwaves to analyze dreams that occur during sleep, and further evaluates the user's emotions in detail using an emotion engine. The aim of this system is to provide deep psychological insights by acquiring and analyzing the user's biosignals.

[0131] In this system, the terminal acquires biosignals in real time from an electroencephalogram (EEG) reader while the user is asleep. The acquired signals are then processed on the terminal to remove noise and normalize the data. The data is transmitted to the server in real time or at specified intervals. High-speed communication technology is used for communication with the server, enabling efficient transmission of big data.

[0132] The server uses the received biometric signals as input to visualize the dream content based on a machine learning model. Deep learning techniques such as generative adversarial networks (GANs) are employed in this visualization process. The generated images provide a visual abstract representation of the user's dream.

[0133] The key here is the use of an emotion engine. This engine analyzes the generated images and combines them with the user's past data and real-time biosignals to estimate the user's emotional state. This engine takes psychological factors seriously and can identify the user's underlying emotions and psychological challenges.

[0134] For example, suppose a user has been feeling anxious in their recent life. This system analyzes the user's brainwaves during sleep, and the emotion engine detects the emotion of anxiety along with the generated dream images. This allows the emotion engine to interpret how the user's anxiety is influencing their dreams, and based on the results, it provides triggers and mitigation strategies for the resulting emotions.

[0135] Ultimately, the server sends the analysis results to the terminal, and the user can view the dream images and emotion-based analysis results through the application interface. This allows the user to understand their emotional state more objectively and in detail, helping them to take appropriate action. This entire process helps support the improvement of the user's psychological health.

[0136] The following describes the processing flow.

[0137] Step 1:

[0138] The device uses an electroencephalogram (EEG) reader worn by the user to acquire brainwaves, which are biological signals during sleep, in real time. The acquired data is temporarily stored in the device's memory.

[0139] Step 2:

[0140] The device removes noise and normalizes the acquired electroencephalogram (EEG) data. Noise reduction applies an algorithm to filter out unnecessary data, while normalization converts the data into a consistent format to facilitate analysis.

[0141] Step 3:

[0142] The device transmits pre-processed EEG data to the server using 5G communication technology. This transmission incorporates an error checking function to verify the integrity of the data.

[0143] Step 4:

[0144] The server stores the brainwave data received from the terminal and prepares it for transmission to a machine learning model. During this preparation stage, the data is made ready for analysis.

[0145] Step 5:

[0146] The server inputs the received data into a machine learning model to generate images that visually represent the content of the dream. A generative adversarial network (GAN) is used for image generation to depict characteristic dream scenes.

[0147] Step 6:

[0148] The server performs psychological analysis based on the generated images. At the heart of this analysis is an emotion engine that evaluates the user's emotional state using historical data and real-time biosignals.

[0149] Step 7:

[0150] The server compiles the evaluation results from the emotion engine and derives insights into the user's potential emotions and psychological issues. This identifies specific influences and trends linked to emotional states.

[0151] Step 8:

[0152] The server sends the analysis results to the terminal. The user can then view the data received via communication through the application's interface.

[0153] Step 9:

[0154] The device presents the user with images of their dreams and the results of an emotional analysis. This allows the user to visually understand their own psychological state and gain insights to take appropriate action as needed.

[0155] (Example 2)

[0156] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server" and the smart device 14 as the "terminal".

[0157] Conventional biometric data analysis systems have found it difficult to grasp a user's psychological state in detail and accurately, and in particular, they have been unable to comprehensively evaluate dreams experienced during sleep and the emotions associated with them. Furthermore, there has been a lack of technology to visually display the content of dreams, and users have not been provided with sufficient information to understand their own psychological state.

[0158] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0159] In this invention, the server includes means for acquiring biometric data obtained during the user's rest period, means for processing the biometric data to remove noise and normalize it, and means for transmitting the biometric data to a processing device using high-speed transmission technology. This makes it possible to analyze the user's psychological state in detail and visually display the content of dreams.

[0160] "Biometric data" refers to data that shows electrical signals and biological indicators obtained from an individual, and specifically includes brain waves and heart rate.

[0161] "Removing noise and normalizing" is a process that improves the accuracy of analysis by removing unwanted noise from biological data and standardizing the data format and criteria.

[0162] "High-speed transmission technology" refers to technologies for rapidly moving data, and typically means the ability to transmit large amounts of data in a short time using communication methods with high bandwidth.

[0163] A "processing device" is a computer system used for analyzing and transforming data, and in particular includes a central processing unit responsible for data aggregation and analysis.

[0164] "Visual data" refers to information in image or video format generated through data analysis, and is particularly used to visually represent abstract concepts or states.

[0165] "Psychological analysis" refers to analytical methods used to understand an individual's psychological state and emotions, and includes the evaluation of emotions and behaviors based on biological and visual data.

[0166] An "emotion engine" is a software component that automatically evaluates an individual's emotional state based on information extracted from data.

[0167] A "mathematical model" is a computational model that expresses real-world phenomena using mathematical formulas and algorithms based on data, and is used for prediction and analysis.

[0168] This invention is a system that acquires biometric data during a user's rest period and analyzes it to evaluate their psychological state. The following describes a specific configuration for implementing this system.

[0169] Hardware configuration

[0170] The user wears a biosignal sensor to monitor their brainwaves. The sensor has the capability to acquire biometric data such as brainwaves and heart rate in real time and is connected to a terminal. The terminal is a computing device for data collection and initial processing, and is typically a smartphone or tablet. High-speed communication technologies such as Wi-Fi 6 or 5G are used to transmit data to the server.

[0171] Software Configuration

[0172] On the device, software runs to perform noise reduction and data normalization on the acquired biometric data. This process enables efficient transmission of low-noise data to the server. The server uses a generative AI model based on the received data to generate visual data of the user's dreams. This model includes generative adversarial network (GAN) technology. The generated visual data is further analyzed by an emotion engine and used to evaluate the user's psychological state.

[0173] Specific example

[0174] For example, if a user has been experiencing stress over the past few weeks, the system analyzes the user's brainwaves and visualizes their dreams. The emotion engine then extracts the emotions indicating stress from the visual data. This information provides the user with the resources to understand the cause of their stress and take steps to mitigate it.

[0175] Example of a prompt

[0176] "Use a generative AI model based on the user's biometric data to visualize their dreams. Furthermore, utilize an emotion engine to analyze the user's emotions from the visual data and provide a detailed emotional assessment."

[0177] This invention aims to improve psychological health by analyzing users' biometric data in detail.

[0178] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0179] Step 1:

[0180] The device acquires brainwave data in real time through a biosignal sensor while the user is sleeping. Biosignals are taken in as input and converted into digital data. Specifically, when the sensor is attached to the head, it detects weak electrical signals and transmits the data to the device. The output is the digitized biosignal data.

[0181] Step 2:

[0182] The device removes noise from acquired biometric data and normalizes the data. It receives digitized biometric data as input and uses filtering techniques to remove unwanted noise. In addition, it performs normalization to maintain data consistency. Specifically, the algorithm detects peaks and outliers in the data and processes them to smooth them out. The output is clean, analyzable data.

[0183] Step 3:

[0184] The terminal sends pre-processed biometric data to a server where a generating AI model is located. It receives denoised data as input using high-speed communication technology (e.g., Wi-Fi 6) and forwards it to the server. Specifically, the terminal compresses the data and efficiently transmits it via the communication protocol. The output is the data correctly transmitted to the server.

[0185] Step 4:

[0186] The server uses a generative AI model to generate visual dream data from received biometric data. It receives pre-processed biometric data as input and uses a generative adversarial network (GAN) to visualize the dream. Specifically, a mathematical model within the server processes the data and executes an algorithm to generate abstract visual data. The output is the generated visual dream data.

[0187] Step 5:

[0188] The server analyzes the generated visual data using an emotion engine to evaluate the user's emotional state. It receives the generated visual data as input and estimates emotions by comparing it with past data and current biosignals. Specifically, the emotion engine performs image analysis and calculates psychological evaluations. The output is data representing the analyzed emotional state.

[0189] Step 6:

[0190] The server sends the analysis results to the terminal, and the user views the dream's visual data and emotional evaluation results through the application. The server uses the processed analysis data as input and sends it to the terminal. Specifically, the server converts the data format to a format that can be visualized on the user's terminal and transmits it via an appropriate communication channel. The output consists of data and evaluation results provided in a format that the user can view.

[0191] (Application Example 2)

[0192] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as a "server" and the smart device 14 as a "terminal".

[0193] In modern brick-and-mortar stores, there is a demand for improved service tailored to individual customer needs. However, traditional services are primarily based on visual and behavioral data, making it difficult to provide personalized experiences that reflect customers' inner emotions and psychological states in real time. This results in a lack of effective means to improve customer satisfaction.

[0194] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0195] In this invention, the server includes means for generating visual information from biological signals as input, means for performing psychological analysis based on the visual information and evaluating the current emotional state, and means for presenting information based on the customer's emotional state and providing a personalized experience in real time. This makes it possible to provide services that are tailored to the customer's emotions and psychological state.

[0196] "Users" refer to individuals who use the system, and are the subjects of acquisition and analysis of their biometric signals.

[0197] "Biosignals" refer to the basic data for decision analysis acquired during the user's sleep, and include electroencephalograms (EEGs).

[0198] "Visual information" refers to image information generated by machine learning models based on biosignals, and it visualizes the user's dreams and psychological state.

[0199] "Psychological analysis" is the process of analyzing and evaluating mental processes and emotional states based on visual information.

[0200] "Emotional state" refers to the user's mental reactions and psychological health status, as determined through psychological analysis.

[0201] An "information processing device" is a mechanical central device used to receive and analyze biological signals, and refers to a server.

[0202] A "personalized experience" means providing services and information that are individually optimized according to the user's specific psychological state.

[0203] To implement this invention, a program is first required to manage the entire system's operation. This program uses the Python programming language, utilizing TENSORFLOW® and PyTorch as its primary libraries. The terminal is equipped with a function to acquire biosignals in real time from an electroencephalogram (EEG) reader while the user is asleep. The acquired biosignals are de-noised and normalized before being transmitted to a server using high-speed communication technology. The WebSocket protocol is used for this communication.

[0204] On the server side, visual information is generated using a generative AI model based on the received biosignals. This generation utilizes a deep learning technique called GAN (Generative Adversarial Network). The generated visual information undergoes psychological analysis, and the user's emotional state is estimated using an emotion engine. This engine is implemented using TensorFlow and performs sophisticated emotion analysis by combining real-time biosignal data with historical data.

[0205] The results of this analysis are visually presented on the smart glasses display or mobile device to provide a personalized experience based on the user's psychological state. For example, imagine a customer at a cafe receiving suggestions for products that promote relaxation. In this case, recommended product information based on the analysis results would be displayed on the screen.

[0206] An example of a prompt to input into a generative AI model is the instruction, "Evaluate the customer's current psychological state and suggest stress-reducing products based on that." This establishes an effective means of improving the customer experience in physical stores.

[0207] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0208] Step 1:

[0209] The device acquires biosignals from an electroencephalogram (EEG) reader while the user is asleep. This signal is then used as input for noise reduction and normalization, generating clean, easily analyzable data. This enables highly accurate data analysis.

[0210] Step 2:

[0211] The device transmits clean biosignals to the server using high-speed communication technology. The protocol used is WebSocket, which allows large amounts of data to be efficiently transferred to the server in real time.

[0212] Step 3:

[0213] The server uses a generative AI model to generate visual information based on the received biometric signals. This process utilizes the TensorFlow library and employs GANs, a deep learning technique, to output image data that visually represents the content of the dream.

[0214] Step 4:

[0215] The server inputs the generated visual information into the emotion engine. Here, a machine learning model performs a psychological analysis and evaluates the user's emotional state. This uses historical data and real-time biometric data to generate metadata for the emotion evaluation.

[0216] Step 5:

[0217] The server generates information to provide a personalized experience to the user based on the results of an assessment of their emotional state. This information is transmitted to the smart glasses display or mobile device and presented visually to the user. Specifically, product information and service details optimized according to the user's psychological state are output.

[0218] Step 6:

[0219] Users can receive visual information and emotional analysis results through smart glasses or mobile devices, and use this information to optimize their choices and actions in daily life. This can lead to an improvement in their psychological well-being.

[0220] 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 user input for the result of the specific processing. The control unit 46A transmits the audio data indicating 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.

[0221] Data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of data generation model 58 is ChatGPT (registered trademark) (Internet search).<URL: https: / / openai.com / blog / chatgpt> ), Gemini (registered trademark) (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0222] In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and the specific processing may also be performed by the smart device 14.

[0223] [Second Embodiment]

[0224] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0225] As shown in Figure 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0226] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0227] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication interface 44. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, and camera 42 are also connected to the bus 52.

[0228] The microphone 238 receives voice signals from the user 20 and receives instructions from the user 20. The microphone 238 captures the voice signals from the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0229] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the area around the user 20 (for example, an imaging range defined by a field of view equivalent to the width of a typical healthy person's field of vision).

[0230] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0231] Figure 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Figure 4, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0232] The specific processing program 56 is an example of a "program" relating to the technology of this 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.

[0233] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0234] In the smart glasses 214, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. 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 processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0235] Next, the identification processing performed by the identification processing unit 290 of the data processing device 12 will be described. 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".

[0236] This invention is a system for analyzing and visualizing a user's dreams to gain psychological insights. The system mainly consists of a process of acquiring biosignals using the user's brainwaves and visualizing them, and a process of presenting the results of the psychological analysis based on those signs.

[0237] The device acquires brainwaves in real time through an electroencephalogram (EEG) reader worn by the user while they sleep. This data is then denoised and normalized within the device. Noise reduction is performed to filter out interference from the external environment and improve the purity of the biosignals. The normalized data is then sent to a server according to a pre-configured transmission schedule. High-speed communication technology is used for this transmission, ensuring that the data is transferred quickly and securely.

[0238] The server receives brainwave data transmitted from the terminal. After receiving the data, it analyzes it using a machine learning model. The machine learning model uses deep learning techniques, particularly generative adversarial networks (GANs), to visualize the information obtained from the brainwaves as an image. This image generation makes it possible to abstractly depict the user's dream scenes.

[0239] Next, the server performs a psychological analysis using the generated images and raw data. The analysis is primarily aimed at interpreting the user's potential emotions and mental state that the dream content may suggest. This analysis applies standard psychological theories and models and is evaluated in conjunction with the user's personal past data.

[0240] Ultimately, the device provides the user with the analysis results obtained from the server. The user can then view the generated dream images and the psychological analysis results through the app's interface. This allows the user to gain a deeper understanding of their inner self and obtain insights to address potential problems and emotions.

[0241] For example, if a user is suffering from anxiety, the images generated from their brainwave data visually represent the origins of their anxiety and related emotions, divided into multiple scenes. Based on these images, psychological analysis suggests the basis of the anxiety and elements that can be improved, providing the user with specific advice. This allows the user to gain insights into resolving their emotional issues through the content of their dreams.

[0242] The following describes the processing flow.

[0243] Step 1:

[0244] The device acquires brainwave data in real time using an electroencephalogram (EEG) reader worn by the user. The data is continuously stored in the device's memory.

[0245] Step 2:

[0246] The device uses the acquired electroencephalogram (EEG) data to perform noise reduction. Specifically, it applies filtering technology to improve data accuracy by filtering out abnormal values ​​and external interference.

[0247] Step 3:

[0248] The device normalizes the noise-removed data and converts it into a unified format. This makes the data easier to analyze.

[0249] Step 4:

[0250] The terminal sends pre-processed data to the server using 5G communication technology. The transmission includes error checking to ensure data integrity.

[0251] Step 5:

[0252] The server receives the EEG data from the terminal and prepares it for analysis. The received data is then passed to the analysis platform.

[0253] Step 6:

[0254] The server uses the received data as input to run a machine learning model. The model uses deep learning techniques to generate images that represent the content of the dream from the data.

[0255] Step 7:

[0256] The server performs a psychological analysis based on the generated images. The analysis uses algorithms that identify the user's psychological state and potential problems as reflected in the dream content.

[0257] Step 8:

[0258] The server compiles and visualizes the analysis results and sends them to the terminal. The results include the generated images and explanations of the psychological insights.

[0259] Step 9:

[0260] The device presents the user with data received from the server. Through the app, the user can view visualized dream images and the corresponding psychological analysis results. This allows the user to gain a deeper understanding of their own psychological state.

[0261] (Example 1)

[0262] Next, we will describe Example 1. 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."

[0263] In modern society, it is important for individuals to understand their own inner emotions and psychological states, but many people lack the means to grasp and resolve them on their own. In particular, there is a need for a system that analyzes a person's psychological state through the unconscious phenomenon of dreams and helps visualize and understand it using appropriate methods.

[0264] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0265] In this invention, the server includes means for acquiring the user's biological signals, means for converting visual representations into specific dream scenes using a generative adversarial network, and means for performing psychological analysis based on the visual representations. This makes it possible to analyze and visualize an individual's dreams and provide psychological insights.

[0266] A "user" refers to an individual who uses the system to analyze their own psychological state and the content of their dreams.

[0267] "Rest" refers to a state in which the user is sleeping, or a state similar to sleep.

[0268] "Biological signals" refer to electrical activity detected from the user's body, including but not limited to brain waves.

[0269] "Interference signals" refer to external electromagnetic waves and environmental noise that interfere with the user's biological signals.

[0270] "High-speed communication technology" refers to technologies that use advanced communication protocols such as 5G and Wi-Fi 6 to quickly transfer large amounts of data.

[0271] A "processing unit" refers to a computer device, such as a server, that receives, processes, and analyzes data.

[0272] "Visual representation" refers to images and graphics that can be visually displayed by extracting biological signals such as the user's brainwaves.

[0273] A "generative adversarial network" is a deep learning technique that uses training data to generate new data or images.

[0274] "Psychological analysis" refers to the analytical process of interpreting a user's psychological state and emotions based on visual representations and biological signals.

[0275] This invention is a system that gains psychological insights by analyzing and visualizing the user's dreams. A specific embodiment of this system is shown below.

[0276] The user wears a special sensor device while resting. This device is worn on the head and acquires biological signals, including brain waves, in real time.

[0277] The terminal receives biological signals transmitted from sensors. After reception, the signals are denoised and normalized using built-in software. Digital filtering technology is used for denoising to improve signal purity. In the normalization process, the signal amplitude is standardized to a certain range to facilitate analysis. The processed data is transmitted to a computing device using high-speed communication technology (e.g., 5G or Wi-Fi 6).

[0278] The server acts as a computing device, analyzing the received biological signals. The analysis utilizes a generative adversarial network (GAN), a type of generative AI model. This model generates visual representations from the input signals, visually depicting the user's dream scenes. Next, a psychological analysis is performed based on the generated visual representations. This analysis uses standard psychological theories to evaluate the user's emotions and subconscious. The evaluation also includes comparisons with past data.

[0279] Ultimately, the terminal displays the visual representation of the dream and the results of the psychological analysis transmitted from the server on the user interface. The user can review these results through the application and gain insights into their inner self. For example, if the user sees an animal in their dream, the system analyzes the symbolism of that animal in relation to their psychological state and provides advice regarding specific emotions and situations. An example of a prompt used in this case would be: "Generate a dream scene from the user's brainwave data and perform a psychological analysis. Provide specific advice regarding the emotions and states that may be suggested by the generated image."

[0280] The flow of the specific process in Example 1 will be described using FIG. 11.

[0281] Step 1:

[0282] The user wears the sensor device and takes a rest. The device acquires the user's brain wave signal in real time. The input is the user's brain waves, and the output is the raw brain wave signal data transmitted to the terminal.

[0283] Step 2:

[0284] The terminal performs noise removal processing on the received brain wave signal data. The input is the raw brain wave signal data, and the output is the brain wave signal data with noise removed. Specifically, digital filtering technology is used to eliminate high-frequency interference components.

[0285] Step 3:

[0286] The terminal normalizes the signal after noise removal. The input is the brain wave signal data with noise removed, and the output is the normalized brain wave data. In normalization, the amplitude of the data is adjusted to a certain range to enhance the consistency of the data.

[0287] Step 4:

[0288] The terminal transmits the normalized brain wave data to the server. The input is the normalized brain wave data, and the output is the data transferred to the server. Specifically, high-speed communication technology is utilized to encrypt the data and transmit it securely.

[0289] Step 5:

[0290] Based on the received brain wave data, the server generates visual representations using the generative adversarial network (GAN), which is a generative AI model. The input is the normalized brain wave data, and the output is the image data representing the user's dream scene.

[0291] Step 6:

[0292] The server performs psychological analysis based on the generated images. The input is the generated dream images, and the output is the results of the psychological analysis. Specifically, it applies standard psychological theories to evaluate the user's emotions and mental state.

[0293] Step 7:

[0294] The terminal provides the user with dream images and psychological analysis results transmitted from the server. Inputs include image data and analysis results from the server, and these are displayed on the interface as output. Through this, the user can deepen their self-understanding.

[0295] (Application Example 1)

[0296] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server," and the smart glasses 214 will be referred to as the "terminal."

[0297] There is a need to provide a means to improve mental health by subtly analyzing users' stress and anxiety-related psychological issues during sleep, thereby offering individually tailored coping strategies. Currently, there is a lack of systems that can acquire and visualize these emotional states in real time, perform psychological analysis, and provide specific advice.

[0298] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0299] In this invention, the server includes means for acquiring biosignals obtained during the user's sleep, means for generating images using the biosignals and performing psychological analysis, and means for identifying stress and anxiety and advising the user on coping strategies based on the analysis results. This allows the user to unconsciously understand their own emotional state and obtain specific coping strategies for stress and anxiety.

[0300] "User" refers to an individual who uses this system, specifically the entity that provides biological signals such as brain waves during sleep.

[0301] "Biological signal" refers to data representing electrical activities derived from organisms such as the user's brain waves, which is used for the analysis of mental states.

[0302] "Means for removing noise and normalizing" is a process for removing external interference from the acquired biological signals and improving the consistency and reliability as data.

[0303] "Means for transmitting to an information processing device using high-speed communication technology" refers to technical means for quickly and securely transferring biological signals to an information processing device, which is a technology designed for high-speed and highly reliable communication.

[0304] "Information processing device" is a computer system necessary for analyzing the received biological signals, visualizing them as images, and performing psychological analysis.

[0305] "Means for generating an image" refers to a process of creating an image that visually represents the user's mental state and dreams based on biological signal data.

[0306] "Means for performing psychological analysis" is a process of interpreting and analyzing the user's potential emotional state and psychological issues based on specialized theories based on the generated image.

[0307] "Means for identifying stress and anxiety states and providing advice for coping" refers to a process of evaluating the user's mental state and proposing appropriate coping methods for individual stress sources and anxieties.

[0308] This invention embodies a system for analyzing a user's psychological state during sleep and providing practical advice. This system utilizes an electroencephalogram (EEG) reader worn by the user while sleeping, which acquires biosignals in real time. The acquired EEG data is denoised and normalized on a terminal, ensuring highly reliable data.

[0309] The terminal transmits this normalized biosignal to an information processing device (server) quickly and securely using high-speed communication technology. The server uses the received biosignal data and a generative AI model to generate visual information from brainwaves. Specifically, it employs a generative adversarial network (GAN) to create an abstract image representing the user's psychological state based on brainwave patterns.

[0310] Next, the server performs a psychological analysis based on the generated images, using standard psychological theories and models. This identifies the user's stress and anxiety levels, and develops coping strategies. The results of this analysis are displayed through an application on the user's smartphone or tablet. The user can review the visualized dreams and psychological state and receive recommended coping strategies.

[0311] For example, if a user is experiencing anxiety due to work-related stress, the generated image will reflect that focus, and the server can offer relaxation techniques and specific action suggestions to alleviate the anxiety. Advice such as, "After work, take a walk in nature and practice deep breathing to calm your mind," might be provided.

[0312] Example of a prompt:

[0313] "We analyze the user's brainwave data to visualize underlying stressors."

[0314] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0315] Step 1:

[0316] The user wears an electroencephalogram (EEG) reader while sleeping, and the device acquires biosignals in real time. The input is the user's EEG data, and the output is the initial biosignal data. At this stage, the device aims to accurately collect the data and transmit it to the terminal in an appropriate format.

[0317] Step 2:

[0318] The terminal processes the received biosignal data and performs noise reduction. The input is the initial biosignal data, and the output is clean biosignal data with noise removed. In this step, a signal processing algorithm is used to filter out interference caused by the external environment and equipment, improving the purity of the data.

[0319] Step 3:

[0320] The terminal normalizes clean biosignal data. The input is denoised biosignal data, and the output is normalized biosignal data. This process ensures data consistency and standardization, and performs conversions to accommodate different measurement conditions and data ranges.

[0321] Step 4:

[0322] The terminal transmits normalized biosignal data to the server using high-speed communication technology. The input is the normalized biosignal data, and the output is the status indicating that the data transfer to the server is complete. This process is performed using a communication protocol with the aim of delivering data to the server quickly and securely.

[0323] Step 5:

[0324] The server uses the received biosignal data to generate an image using a generative AI model, specifically a generative adversarial network (GAN). The input is normalized biosignal data transferred to the server, and the output is image data. In this step, an abstract dream scene is visualized based on a machine learning algorithm.

[0325] Step 6:

[0326] The server performs psychological analysis based on the generated image data. The input is the generated image data, and the output is psychological state information as a result of the analysis. The server applies standard psychological theories and models to interpret the user's potential emotions and mental state.

[0327] Step 7:

[0328] The server sends the analysis results to the terminal, which then displays them to the user. The input is psychological state information, and the output is the notification status to the user. In this final step, specific advice and solutions are displayed, allowing the user to take action based on the visualized information.

[0329] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0330] This invention is a system that uses the user's brainwaves to analyze dreams that occur during sleep, and further evaluates the user's emotions in detail using an emotion engine. The aim of this system is to provide deep psychological insights by acquiring and analyzing the user's biosignals.

[0331] In this system, the terminal acquires biosignals in real time from an electroencephalogram (EEG) reader while the user is asleep. The acquired signals are then processed on the terminal to remove noise and normalize the data. The data is transmitted to the server in real time or at specified intervals. High-speed communication technology is used for communication with the server, enabling efficient transmission of big data.

[0332] The server uses the received biometric signals as input to visualize the dream content based on a machine learning model. Deep learning techniques such as generative adversarial networks (GANs) are employed in this visualization process. The generated images provide a visual abstract representation of the user's dream.

[0333] The key here is the use of an emotion engine. This engine analyzes the generated images and combines them with the user's past data and real-time biosignals to estimate the user's emotional state. This engine takes psychological factors seriously and can identify the user's underlying emotions and psychological challenges.

[0334] For example, suppose a user has been feeling anxious in their recent life. This system analyzes the user's brainwaves during sleep, and the emotion engine detects the emotion of anxiety along with the generated dream images. This allows the emotion engine to interpret how the user's anxiety is influencing their dreams, and based on the results, it provides triggers and mitigation strategies for the resulting emotions.

[0335] Ultimately, the server sends the analysis results to the terminal, and the user can view the dream images and emotion-based analysis results through the application interface. This allows the user to understand their emotional state more objectively and in detail, helping them to take appropriate action. This entire process helps support the improvement of the user's psychological health.

[0336] The following describes the processing flow.

[0337] Step 1:

[0338] The device uses an electroencephalogram (EEG) reader worn by the user to acquire brainwaves, which are biological signals during sleep, in real time. The acquired data is temporarily stored in the device's memory.

[0339] Step 2:

[0340] The device removes noise and normalizes the acquired electroencephalogram (EEG) data. Noise reduction applies an algorithm to filter out unnecessary data, while normalization converts the data into a consistent format to facilitate analysis.

[0341] Step 3:

[0342] The device transmits pre-processed EEG data to the server using 5G communication technology. This transmission incorporates an error checking function to verify the integrity of the data.

[0343] Step 4:

[0344] The server stores the brainwave data received from the terminal and prepares it for transmission to a machine learning model. During this preparation stage, the data is made ready for analysis.

[0345] Step 5:

[0346] The server inputs the received data into a machine learning model to generate images that visually represent the content of the dream. A generative adversarial network (GAN) is used for image generation to depict characteristic dream scenes.

[0347] Step 6:

[0348] The server performs psychological analysis based on the generated images. At the heart of this analysis is an emotion engine that evaluates the user's emotional state using historical data and real-time biosignals.

[0349] Step 7:

[0350] The server compiles the evaluation results from the emotion engine and derives insights into the user's potential emotions and psychological issues. This identifies specific influences and trends linked to emotional states.

[0351] Step 8:

[0352] The server sends the analysis results to the terminal. The user can then view the data received via communication through the application's interface.

[0353] Step 9:

[0354] The device presents the user with images of their dreams and the results of an emotional analysis. This allows the user to visually understand their own psychological state and gain insights to take appropriate action as needed.

[0355] (Example 2)

[0356] Next, we will describe Example 2. 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".

[0357] Conventional biometric data analysis systems have found it difficult to grasp a user's psychological state in detail and accurately, and in particular, they have been unable to comprehensively evaluate dreams experienced during sleep and the emotions associated with them. Furthermore, there has been a lack of technology to visually display the content of dreams, and users have not been provided with sufficient information to understand their own psychological state.

[0358] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0359] In this invention, the server includes means for acquiring biometric data obtained during the user's rest period, means for processing the biometric data to remove noise and normalize it, and means for transmitting the biometric data to a processing device using high-speed transmission technology. This makes it possible to analyze the user's psychological state in detail and visually display the content of dreams.

[0360] "Biometric data" refers to data that shows electrical signals and biological indicators obtained from an individual, and specifically includes brain waves and heart rate.

[0361] "Removing noise and normalizing" is a process that improves the accuracy of analysis by removing unwanted noise from biological data and standardizing the data format and criteria.

[0362] "High-speed transmission technology" refers to technologies for rapidly moving data, and typically means the ability to transmit large amounts of data in a short time using communication methods with high bandwidth.

[0363] A "processing device" is a computer system used for analyzing and transforming data, and in particular includes a central processing unit responsible for data aggregation and analysis.

[0364] "Visual data" refers to information in image or video format generated through data analysis, and is particularly used to visually represent abstract concepts or states.

[0365] "Psychological analysis" refers to analytical methods used to understand an individual's psychological state and emotions, and includes the evaluation of emotions and behaviors based on biological and visual data.

[0366] An "emotion engine" is a software component that automatically evaluates an individual's emotional state based on information extracted from data.

[0367] A "mathematical model" is a computational model that expresses real-world phenomena using mathematical formulas and algorithms based on data, and is used for prediction and analysis.

[0368] This invention is a system that acquires biometric data during a user's rest period and analyzes it to evaluate their psychological state. The following describes a specific configuration for implementing this system.

[0369] Hardware configuration

[0370] The user wears a biosignal sensor to monitor their brainwaves. The sensor has the capability to acquire biometric data such as brainwaves and heart rate in real time and is connected to a terminal. The terminal is a computing device for data collection and initial processing, and is typically a smartphone or tablet. High-speed communication technologies such as Wi-Fi 6 or 5G are used to transmit data to the server.

[0371] Software Configuration

[0372] On the device, software runs to perform noise reduction and data normalization on the acquired biometric data. This process enables efficient transmission of low-noise data to the server. The server uses a generative AI model based on the received data to generate visual data of the user's dreams. This model includes generative adversarial network (GAN) technology. The generated visual data is further analyzed by an emotion engine and used to evaluate the user's psychological state.

[0373] Specific example

[0374] For example, if a user has been experiencing stress over the past few weeks, the system analyzes the user's brainwaves and visualizes their dreams. The emotion engine then extracts the emotions indicating stress from the visual data. This information provides the user with the resources to understand the cause of their stress and take steps to mitigate it.

[0375] Example of a prompt

[0376] "Use a generative AI model based on the user's biometric data to visualize their dreams. Furthermore, utilize an emotion engine to analyze the user's emotions from the visual data and provide a detailed emotional assessment."

[0377] This invention aims to improve psychological health by analyzing users' biometric data in detail.

[0378] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0379] Step 1:

[0380] The device acquires brainwave data in real time through a biosignal sensor while the user is sleeping. Biosignals are taken in as input and converted into digital data. Specifically, when the sensor is attached to the head, it detects weak electrical signals and transmits the data to the device. The output is the digitized biosignal data.

[0381] Step 2:

[0382] The device removes noise from acquired biometric data and normalizes the data. It receives digitized biometric data as input and uses filtering techniques to remove unwanted noise. In addition, it performs normalization to maintain data consistency. Specifically, the algorithm detects peaks and outliers in the data and processes them to smooth them out. The output is clean, analyzable data.

[0383] Step 3:

[0384] The terminal sends pre-processed biometric data to a server where a generating AI model is located. It receives denoised data as input using high-speed communication technology (e.g., Wi-Fi 6) and forwards it to the server. Specifically, the terminal compresses the data and efficiently transmits it via the communication protocol. The output is the data correctly transmitted to the server.

[0385] Step 4:

[0386] The server uses a generative AI model to generate visual dream data from received biometric data. It receives pre-processed biometric data as input and uses a generative adversarial network (GAN) to visualize the dream. Specifically, a mathematical model within the server processes the data and executes an algorithm to generate abstract visual data. The output is the generated visual dream data.

[0387] Step 5:

[0388] The server analyzes the generated visual data using an emotion engine to evaluate the user's emotional state. It receives the generated visual data as input and estimates emotions by comparing it with past data and current biosignals. Specifically, the emotion engine performs image analysis and calculates psychological evaluations. The output is data representing the analyzed emotional state.

[0389] Step 6:

[0390] The server sends the analysis results to the terminal, and the user views the dream's visual data and emotional evaluation results through the application. The server uses the processed analysis data as input and sends it to the terminal. Specifically, the server converts the data format to a format that can be visualized on the user's terminal and transmits it via an appropriate communication channel. The output consists of data and evaluation results provided in a format that the user can view.

[0391] (Application Example 2)

[0392] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as the "server," and the smart glasses 214 will be referred to as the "terminal."

[0393] In modern brick-and-mortar stores, there is a demand for improved service tailored to individual customer needs. However, traditional services are primarily based on visual and behavioral data, making it difficult to provide personalized experiences that reflect customers' inner emotions and psychological states in real time. This results in a lack of effective means to improve customer satisfaction.

[0394] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0395] In this invention, the server includes means for generating visual information from biological signals as input, means for performing psychological analysis based on the visual information and evaluating the current emotional state, and means for presenting information based on the customer's emotional state and providing a personalized experience in real time. This makes it possible to provide services that are tailored to the customer's emotions and psychological state.

[0396] "Users" refer to individuals who use the system, and are the subjects of acquisition and analysis of their biometric signals.

[0397] "Biosignals" refer to the basic data for decision analysis acquired during the user's sleep, and include electroencephalograms (EEGs).

[0398] "Visual information" refers to image information generated by machine learning models based on biosignals, and it visualizes the user's dreams and psychological state.

[0399] "Psychological analysis" is the process of analyzing and evaluating mental processes and emotional states based on visual information.

[0400] "Emotional state" refers to the user's mental reactions and psychological health status, as determined through psychological analysis.

[0401] An "information processing device" is a mechanical central device used to receive and analyze biological signals, and refers to a server.

[0402] A "personalized experience" means providing services and information that are individually optimized according to the user's specific psychological state.

[0403] To implement this invention, a program is first required to manage the entire system's operation. This program uses the Python programming language, leveraging TensorFlow and PyTorch as its primary libraries. The terminal is equipped with a function to acquire biosignals in real time from an electroencephalogram (EEG) reader while the user is asleep. The acquired biosignals are de-noised and normalized before being transmitted to a server using high-speed communication technology. The WebSocket protocol is used for this communication.

[0404] On the server side, visual information is generated using a generative AI model based on the received biosignals. This generation utilizes a deep learning technique called GAN (Generative Adversarial Network). The generated visual information undergoes psychological analysis, and the user's emotional state is estimated using an emotion engine. This engine is implemented using TensorFlow and performs sophisticated emotion analysis by combining real-time biosignal data with historical data.

[0405] The results of this analysis are visually presented on the smart glasses display or mobile device to provide a personalized experience based on the user's psychological state. For example, imagine a customer at a cafe receiving suggestions for products that promote relaxation. In this case, recommended product information based on the analysis results would be displayed on the screen.

[0406] An example of a prompt to input into a generative AI model is the instruction, "Evaluate the customer's current psychological state and suggest stress-reducing products based on that." This establishes an effective means of improving the customer experience in physical stores.

[0407] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0408] Step 1:

[0409] The device acquires biosignals from an electroencephalogram (EEG) reader while the user is asleep. This signal is then used as input for noise reduction and normalization, generating clean, easily analyzable data. This enables highly accurate data analysis.

[0410] Step 2:

[0411] The device transmits clean biosignals to the server using high-speed communication technology. The protocol used is WebSocket, which allows large amounts of data to be efficiently transferred to the server in real time.

[0412] Step 3:

[0413] The server uses a generative AI model to generate visual information based on the received biometric signals. This process utilizes the TensorFlow library and employs GANs, a deep learning technique, to output image data that visually represents the content of the dream.

[0414] Step 4:

[0415] The server inputs the generated visual information into the emotion engine. Here, a machine learning model performs a psychological analysis and evaluates the user's emotional state. This uses historical data and real-time biometric data to generate metadata for the emotion evaluation.

[0416] Step 5:

[0417] The server generates information to provide a personalized experience to the user based on the results of an assessment of their emotional state. This information is transmitted to the smart glasses display or mobile device and presented visually to the user. Specifically, product information and service details optimized according to the user's psychological state are output.

[0418] Step 6:

[0419] Users can receive visual information and emotional analysis results through smart glasses or mobile devices, and use this information to optimize their choices and actions in daily life. This can lead to an improvement in their psychological well-being.

[0420] 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 user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0421] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). One example of data generation model 58 is ChatGPT (Internet search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0422] In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and the specific processing may also be performed by the smart glasses 214.

[0423] [Third Embodiment]

[0424] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0425] As shown in Figure 5, the data processing system 310 includes a data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0426] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0427] The headset terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a display 343. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and display 343 are also connected to the bus 52.

[0428] The microphone 238 receives voice signals from the user 20 and receives instructions from the user 20. The microphone 238 captures the voice signals from the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0429] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the area around the user 20 (for example, an imaging range defined by a field of view equivalent to the width of a typical healthy person's field of vision).

[0430] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0431] Figure 6 shows an example of the main functions of the data processing device 12 and the headset terminal 314. As shown in Figure 6, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0432] The specific processing program 56 is an example of a "program" relating to the technology of this 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.

[0433] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0434] In the headset terminal 314, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. 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 processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0435] Next, the specific processing performed by the specific processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the headset terminal 314 will be referred to as the "terminal".

[0436] This invention is a system for analyzing and visualizing a user's dreams to gain psychological insights. The system mainly consists of a process of acquiring biosignals using the user's brainwaves and visualizing them, and a process of presenting the results of the psychological analysis based on those signs.

[0437] The device acquires brainwaves in real time through an electroencephalogram (EEG) reader worn by the user while they sleep. This data is then denoised and normalized within the device. Noise reduction is performed to filter out interference from the external environment and improve the purity of the biosignals. The normalized data is then sent to a server according to a pre-configured transmission schedule. High-speed communication technology is used for this transmission, ensuring that the data is transferred quickly and securely.

[0438] The server receives brainwave data transmitted from the terminal. After receiving the data, it analyzes it using a machine learning model. The machine learning model uses deep learning techniques, particularly generative adversarial networks (GANs), to visualize the information obtained from the brainwaves as an image. This image generation makes it possible to abstractly depict the user's dream scenes.

[0439] Next, the server performs a psychological analysis using the generated images and raw data. The analysis is primarily aimed at interpreting the user's potential emotions and mental state that the dream content may suggest. This analysis applies standard psychological theories and models and is evaluated in conjunction with the user's personal past data.

[0440] Ultimately, the device provides the user with the analysis results obtained from the server. The user can then view the generated dream images and the psychological analysis results through the app's interface. This allows the user to gain a deeper understanding of their inner self and obtain insights to address potential problems and emotions.

[0441] For example, if a user is suffering from anxiety, the images generated from their brainwave data visually represent the origins of their anxiety and related emotions, divided into multiple scenes. Based on these images, psychological analysis suggests the basis of the anxiety and elements that can be improved, providing the user with specific advice. This allows the user to gain insights into resolving their emotional issues through the content of their dreams.

[0442] The following describes the processing flow.

[0443] Step 1:

[0444] The device acquires brainwave data in real time using an electroencephalogram (EEG) reader worn by the user. The data is continuously stored in the device's memory.

[0445] Step 2:

[0446] The device uses the acquired electroencephalogram (EEG) data to perform noise reduction. Specifically, it applies filtering technology to improve data accuracy by filtering out abnormal values ​​and external interference.

[0447] Step 3:

[0448] The device normalizes the noise-removed data and converts it into a unified format. This makes the data easier to analyze.

[0449] Step 4:

[0450] The terminal sends pre-processed data to the server using 5G communication technology. The transmission includes error checking to ensure data integrity.

[0451] Step 5:

[0452] The server receives the EEG data from the terminal and prepares it for analysis. The received data is then passed to the analysis platform.

[0453] Step 6:

[0454] The server uses the received data as input to run a machine learning model. The model uses deep learning techniques to generate images that represent the content of the dream from the data.

[0455] Step 7:

[0456] The server performs a psychological analysis based on the generated images. The analysis uses algorithms that identify the user's psychological state and potential problems as reflected in the dream content.

[0457] Step 8:

[0458] The server compiles and visualizes the analysis results and sends them to the terminal. The results include the generated images and explanations of the psychological insights.

[0459] Step 9:

[0460] The device presents the user with data received from the server. Through the app, the user can view visualized dream images and the corresponding psychological analysis results. This allows the user to gain a deeper understanding of their own psychological state.

[0461] (Example 1)

[0462] Next, we will describe Example 1. 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."

[0463] In modern society, it is important for individuals to understand their own inner emotions and psychological states, but many people lack the means to grasp and resolve them on their own. In particular, there is a need for a system that analyzes a person's psychological state through the unconscious phenomenon of dreams and helps visualize and understand it using appropriate methods.

[0464] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0465] In this invention, the server includes means for acquiring the user's biological signals, means for converting visual representations into specific dream scenes using a generative adversarial network, and means for performing psychological analysis based on the visual representations. This makes it possible to analyze and visualize an individual's dreams and provide psychological insights.

[0466] A "user" refers to an individual who uses the system to analyze their own psychological state and the content of their dreams.

[0467] "Rest" refers to a state in which the user is sleeping, or a state similar to sleep.

[0468] "Biological signals" refer to electrical activity detected from the user's body, including but not limited to brain waves.

[0469] "Interference signals" refer to external electromagnetic waves and environmental noise that interfere with the user's biological signals.

[0470] "High-speed communication technology" refers to technologies that use advanced communication protocols such as 5G and Wi-Fi 6 to quickly transfer large amounts of data.

[0471] A "processing unit" refers to a computer device, such as a server, that receives, processes, and analyzes data.

[0472] "Visual representation" refers to images and graphics that can be visually displayed by extracting biological signals such as the user's brainwaves.

[0473] A "generative adversarial network" is a deep learning technique that uses training data to generate new data or images.

[0474] "Psychological analysis" refers to the analytical process of interpreting a user's psychological state and emotions based on visual representations and biological signals.

[0475] This invention is a system that gains psychological insights by analyzing and visualizing the user's dreams. A specific embodiment of this system is shown below.

[0476] The user wears a special sensor device while resting. This device is worn on the head and acquires biological signals, including brain waves, in real time.

[0477] The terminal receives biological signals transmitted from sensors. After reception, the signals are denoised and normalized using built-in software. Digital filtering technology is used for denoising to improve signal purity. In the normalization process, the signal amplitude is standardized to a certain range to facilitate analysis. The processed data is transmitted to a computing device using high-speed communication technology (e.g., 5G or Wi-Fi 6).

[0478] The server acts as a computing device, analyzing the received biological signals. The analysis utilizes a generative adversarial network (GAN), a type of generative AI model. This model generates visual representations from the input signals, visually depicting the user's dream scenes. Next, a psychological analysis is performed based on the generated visual representations. This analysis uses standard psychological theories to evaluate the user's emotions and subconscious. The evaluation also includes comparisons with past data.

[0479] Ultimately, the terminal displays the visual representation of the dream and the results of the psychological analysis transmitted from the server on the user interface. The user can review these results through the application and gain insights into their inner self. For example, if the user sees an animal in their dream, the system analyzes the symbolism of that animal in relation to their psychological state and provides advice regarding specific emotions and situations. An example of a prompt used in this case would be: "Generate a dream scene from the user's brainwave data and perform a psychological analysis. Provide specific advice regarding the emotions and states that may be suggested by the generated image."

[0480] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0481] Step 1:

[0482] The user rests while wearing a sensor device. The device acquires the user's brainwave signals in real time. The input is the user's brainwaves, and the output is raw brainwave signal data transmitted to the terminal.

[0483] Step 2:

[0484] The terminal performs noise reduction processing on the received electroencephalogram (EEG) signal data. The input is raw EEG signal data, and the output is EEG signal data with noise removed. Specifically, it uses digital filtering technology to eliminate high-frequency interference components.

[0485] Step 3:

[0486] The terminal normalizes the signal after noise reduction. The input is noise-reduced EEG signal data, and the output is normalized EEG data. Normalization adjusts the data amplitude to a certain range, improving data consistency.

[0487] Step 4:

[0488] The terminal transmits normalized EEG data to the server. The input is normalized EEG data, and the output is data transferred to the server. Specifically, high-speed communication technology is used to encrypt and securely transmit the data.

[0489] Step 5:

[0490] The server generates visual representations using a generative adversarial network (GAN), a generative AI model, based on the received brainwave data. The input is normalized brainwave data, and the output is image data representing scenes from the user's dream.

[0491] Step 6:

[0492] The server performs psychological analysis based on the generated images. The input is the generated dream images, and the output is the results of the psychological analysis. Specifically, it applies standard psychological theories to evaluate the user's emotions and mental state.

[0493] Step 7:

[0494] The terminal provides the user with dream images and psychological analysis results transmitted from the server. Inputs include image data and analysis results from the server, and these are displayed on the interface as output. Through this, the user can deepen their self-understanding.

[0495] (Application Example 1)

[0496] Next, we will explain Application Example 1. In the following explanation, 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."

[0497] There is a need to provide a means to improve mental health by subtly analyzing users' stress and anxiety-related psychological issues during sleep, thereby offering individually tailored coping strategies. Currently, there is a lack of systems that can acquire and visualize these emotional states in real time, perform psychological analysis, and provide specific advice.

[0498] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0499] In this invention, the server includes means for acquiring biosignals obtained during the user's sleep, means for generating images using the biosignals and performing psychological analysis, and means for identifying stress and anxiety and advising the user on coping strategies based on the analysis results. This allows the user to unconsciously understand their own emotional state and obtain specific coping strategies for stress and anxiety.

[0500] A "user" refers to an individual who uses this system, specifically the entity that provides biosignals such as brain waves during sleep.

[0501] "Biosignals" refer to data representing the electrical activity of biological origin, such as a user's brainwaves, and are used to analyze their psychological state.

[0502] "Noise removal and normalization methods" refer to processes that remove external interference from acquired biosignals, thereby improving the consistency and reliability of the data.

[0503] "Means of transmitting to an information processing device using high-speed communication technology" refers to technical means for rapidly and safely transferring biological signals to an information processing device, and is a technology designed for high-speed and highly reliable communication.

[0504] An "information processing device" is a computer system necessary for analyzing received biological signals, visualizing them as images, and performing psychological analysis.

[0505] "Means of generating images" refers to the process of creating images that visually represent a user's psychological state or dreams based on biosignal data.

[0506] "Methods for conducting psychological analysis" refers to the process of interpreting and analyzing the user's potential emotional state and psychological issues based on the generated images, using specialized theories.

[0507] "Means of identifying and providing advice for stress and anxiety states" refers to a process of evaluating a user's psychological state and suggesting appropriate coping strategies for individual stressors and anxieties.

[0508] This invention embodies a system for analyzing a user's psychological state during sleep and providing practical advice. This system utilizes an electroencephalogram (EEG) reader worn by the user while sleeping, which acquires biosignals in real time. The acquired EEG data is denoised and normalized on a terminal, ensuring highly reliable data.

[0509] The terminal transmits this normalized biosignal to an information processing device (server) quickly and securely using high-speed communication technology. The server uses the received biosignal data and a generative AI model to generate visual information from brainwaves. Specifically, it employs a generative adversarial network (GAN) to create an abstract image representing the user's psychological state based on brainwave patterns.

[0510] Next, the server performs a psychological analysis based on the generated images, using standard psychological theories and models. This identifies the user's stress and anxiety levels, and develops coping strategies. The results of this analysis are displayed through an application on the user's smartphone or tablet. The user can review the visualized dreams and psychological state and receive recommended coping strategies.

[0511] For example, if a user is experiencing anxiety due to work-related stress, the generated image will reflect that focus, and the server can offer relaxation techniques and specific action suggestions to alleviate the anxiety. Advice such as, "After work, take a walk in nature and practice deep breathing to calm your mind," might be provided.

[0512] Example of a prompt:

[0513] "We analyze the user's brainwave data to visualize underlying stressors."

[0514] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0515] Step 1:

[0516] The user wears an electroencephalogram (EEG) reader while sleeping, and the device acquires biosignals in real time. The input is the user's EEG data, and the output is the initial biosignal data. At this stage, the device aims to accurately collect the data and transmit it to the terminal in an appropriate format.

[0517] Step 2:

[0518] The terminal processes the received biosignal data and performs noise reduction. The input is the initial biosignal data, and the output is clean biosignal data with noise removed. In this step, a signal processing algorithm is used to filter out interference caused by the external environment and equipment, improving the purity of the data.

[0519] Step 3:

[0520] The terminal normalizes clean biosignal data. The input is denoised biosignal data, and the output is normalized biosignal data. This process ensures data consistency and standardization, and performs conversions to accommodate different measurement conditions and data ranges.

[0521] Step 4:

[0522] The terminal transmits normalized biosignal data to the server using high-speed communication technology. The input is the normalized biosignal data, and the output is the status indicating that the data transfer to the server is complete. This process is performed using a communication protocol with the aim of delivering data to the server quickly and securely.

[0523] Step 5:

[0524] The server uses the received biosignal data to generate an image using a generative AI model, specifically a generative adversarial network (GAN). The input is normalized biosignal data transferred to the server, and the output is image data. In this step, an abstract dream scene is visualized based on a machine learning algorithm.

[0525] Step 6:

[0526] The server performs psychological analysis based on the generated image data. The input is the generated image data, and the output is psychological state information as a result of the analysis. The server applies standard psychological theories and models to interpret the user's potential emotions and mental state.

[0527] Step 7:

[0528] The server sends the analysis results to the terminal, which then displays them to the user. The input is psychological state information, and the output is the notification status to the user. In this final step, specific advice and solutions are displayed, allowing the user to take action based on the visualized information.

[0529] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0530] This invention is a system that uses the user's brainwaves to analyze dreams that occur during sleep, and further evaluates the user's emotions in detail using an emotion engine. The aim of this system is to provide deep psychological insights by acquiring and analyzing the user's biosignals.

[0531] In this system, the terminal acquires biosignals in real time from an electroencephalogram (EEG) reader while the user is asleep. The acquired signals are then processed on the terminal to remove noise and normalize the data. The data is transmitted to the server in real time or at specified intervals. High-speed communication technology is used for communication with the server, enabling efficient transmission of big data.

[0532] The server uses the received biometric signals as input to visualize the dream content based on a machine learning model. Deep learning techniques such as generative adversarial networks (GANs) are employed in this visualization process. The generated images provide a visual abstract representation of the user's dream.

[0533] The key here is the use of an emotion engine. This engine analyzes the generated images and combines them with the user's past data and real-time biosignals to estimate the user's emotional state. This engine takes psychological factors seriously and can identify the user's underlying emotions and psychological challenges.

[0534] For example, suppose a user has been feeling anxious in their recent life. This system analyzes the user's brainwaves during sleep, and the emotion engine detects the emotion of anxiety along with the generated dream images. This allows the emotion engine to interpret how the user's anxiety is influencing their dreams, and based on the results, it provides triggers and mitigation strategies for the resulting emotions.

[0535] Ultimately, the server sends the analysis results to the terminal, and the user can view the dream images and emotion-based analysis results through the application interface. This allows the user to understand their emotional state more objectively and in detail, helping them to take appropriate action. This entire process helps support the improvement of the user's psychological health.

[0536] The following describes the processing flow.

[0537] Step 1:

[0538] The device uses an electroencephalogram (EEG) reader worn by the user to acquire brainwaves, which are biological signals during sleep, in real time. The acquired data is temporarily stored in the device's memory.

[0539] Step 2:

[0540] The device removes noise and normalizes the acquired electroencephalogram (EEG) data. Noise reduction applies an algorithm to filter out unnecessary data, while normalization converts the data into a consistent format to facilitate analysis.

[0541] Step 3:

[0542] The device transmits pre-processed EEG data to the server using 5G communication technology. This transmission incorporates an error checking function to verify the integrity of the data.

[0543] Step 4:

[0544] The server stores the brainwave data received from the terminal and prepares it for transmission to a machine learning model. During this preparation stage, the data is made ready for analysis.

[0545] Step 5:

[0546] The server inputs the received data into a machine learning model to generate images that visually represent the content of the dream. A generative adversarial network (GAN) is used for image generation to depict characteristic dream scenes.

[0547] Step 6:

[0548] The server performs psychological analysis based on the generated images. At the heart of this analysis is an emotion engine that evaluates the user's emotional state using historical data and real-time biosignals.

[0549] Step 7:

[0550] The server compiles the evaluation results from the emotion engine and derives insights into the user's potential emotions and psychological issues. This identifies specific influences and trends linked to emotional states.

[0551] Step 8:

[0552] The server sends the analysis results to the terminal. The user can then view the data received via communication through the application's interface.

[0553] Step 9:

[0554] The device presents the user with images of their dreams and the results of an emotional analysis. This allows the user to visually understand their own psychological state and gain insights to take appropriate action as needed.

[0555] (Example 2)

[0556] Next, we will describe Example 2. 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."

[0557] Conventional biometric data analysis systems have found it difficult to grasp a user's psychological state in detail and accurately, and in particular, they have been unable to comprehensively evaluate dreams experienced during sleep and the emotions associated with them. Furthermore, there has been a lack of technology to visually display the content of dreams, and users have not been provided with sufficient information to understand their own psychological state.

[0558] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0559] In this invention, the server includes means for acquiring biometric data obtained during the user's rest period, means for processing the biometric data to remove noise and normalize it, and means for transmitting the biometric data to a processing device using high-speed transmission technology. This makes it possible to analyze the user's psychological state in detail and visually display the content of dreams.

[0560] "Biometric data" refers to data that shows electrical signals and biological indicators obtained from an individual, and specifically includes brain waves and heart rate.

[0561] "Removing noise and normalizing" is a process that improves the accuracy of analysis by removing unwanted noise from biological data and standardizing the data format and criteria.

[0562] "High-speed transmission technology" refers to technologies for rapidly moving data, and typically means the ability to transmit large amounts of data in a short time using communication methods with high bandwidth.

[0563] A "processing device" is a computer system used for analyzing and transforming data, and in particular includes a central processing unit responsible for data aggregation and analysis.

[0564] "Visual data" refers to information in image or video format generated through data analysis, and is particularly used to visually represent abstract concepts or states.

[0565] "Psychological analysis" refers to analytical methods used to understand an individual's psychological state and emotions, and includes the evaluation of emotions and behaviors based on biological and visual data.

[0566] An "emotion engine" is a software component that automatically evaluates an individual's emotional state based on information extracted from data.

[0567] A "mathematical model" is a computational model that expresses real-world phenomena using mathematical formulas and algorithms based on data, and is used for prediction and analysis.

[0568] This invention is a system that acquires biometric data during a user's rest period and analyzes it to evaluate their psychological state. The following describes a specific configuration for implementing this system.

[0569] Hardware configuration

[0570] The user wears a biosignal sensor to monitor their brainwaves. The sensor has the capability to acquire biometric data such as brainwaves and heart rate in real time and is connected to a terminal. The terminal is a computing device for data collection and initial processing, and is typically a smartphone or tablet. High-speed communication technologies such as Wi-Fi 6 or 5G are used to transmit data to the server.

[0571] Software Configuration

[0572] On the device, software runs to perform noise reduction and data normalization on the acquired biometric data. This process enables efficient transmission of low-noise data to the server. The server uses a generative AI model based on the received data to generate visual data of the user's dreams. This model includes generative adversarial network (GAN) technology. The generated visual data is further analyzed by an emotion engine and used to evaluate the user's psychological state.

[0573] Specific example

[0574] For example, if a user has been experiencing stress over the past few weeks, the system analyzes the user's brainwaves and visualizes their dreams. The emotion engine then extracts the emotions indicating stress from the visual data. This information provides the user with the resources to understand the cause of their stress and take steps to mitigate it.

[0575] Example of a prompt

[0576] "Use a generative AI model based on the user's biometric data to visualize their dreams. Furthermore, utilize an emotion engine to analyze the user's emotions from the visual data and provide a detailed emotional assessment."

[0577] This invention aims to improve psychological health by analyzing users' biometric data in detail.

[0578] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0579] Step 1:

[0580] The device acquires brainwave data in real time through a biosignal sensor while the user is sleeping. Biosignals are taken in as input and converted into digital data. Specifically, when the sensor is attached to the head, it detects weak electrical signals and transmits the data to the device. The output is the digitized biosignal data.

[0581] Step 2:

[0582] The device removes noise from acquired biometric data and normalizes the data. It receives digitized biometric data as input and uses filtering techniques to remove unwanted noise. In addition, it performs normalization to maintain data consistency. Specifically, the algorithm detects peaks and outliers in the data and processes them to smooth them out. The output is clean, analyzable data.

[0583] Step 3:

[0584] The terminal sends pre-processed biometric data to a server where a generating AI model is located. It receives denoised data as input using high-speed communication technology (e.g., Wi-Fi 6) and forwards it to the server. Specifically, the terminal compresses the data and efficiently transmits it via the communication protocol. The output is the data correctly transmitted to the server.

[0585] Step 4:

[0586] The server uses a generative AI model to generate visual dream data from received biometric data. It receives pre-processed biometric data as input and uses a generative adversarial network (GAN) to visualize the dream. Specifically, a mathematical model within the server processes the data and executes an algorithm to generate abstract visual data. The output is the generated visual dream data.

[0587] Step 5:

[0588] The server analyzes the generated visual data using an emotion engine to evaluate the user's emotional state. It receives the generated visual data as input and estimates emotions by comparing it with past data and current biosignals. Specifically, the emotion engine performs image analysis and calculates psychological evaluations. The output is data representing the analyzed emotional state.

[0589] Step 6:

[0590] The server sends the analysis results to the terminal, and the user views the dream's visual data and emotional evaluation results through the application. The server uses the processed analysis data as input and sends it to the terminal. Specifically, the server converts the data format to a format that can be visualized on the user's terminal and transmits it via an appropriate communication channel. The output consists of data and evaluation results provided in a format that the user can view.

[0591] (Application Example 2)

[0592] Next, we will explain application example 2. In the following explanation, 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."

[0593] In modern brick-and-mortar stores, there is a demand for improved service tailored to individual customer needs. However, traditional services are primarily based on visual and behavioral data, making it difficult to provide personalized experiences that reflect customers' inner emotions and psychological states in real time. This results in a lack of effective means to improve customer satisfaction.

[0594] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0595] In this invention, the server includes means for generating visual information from biological signals as input, means for performing psychological analysis based on the visual information and evaluating the current emotional state, and means for presenting information based on the customer's emotional state and providing a personalized experience in real time. This makes it possible to provide services that are tailored to the customer's emotions and psychological state.

[0596] "Users" refer to individuals who use the system, and are the subjects of acquisition and analysis of their biometric signals.

[0597] "Biosignals" refer to the basic data for decision analysis acquired during the user's sleep, and include electroencephalograms (EEGs).

[0598] "Visual information" refers to image information generated by machine learning models based on biosignals, and it visualizes the user's dreams and psychological state.

[0599] "Psychological analysis" is the process of analyzing and evaluating mental processes and emotional states based on visual information.

[0600] "Emotional state" refers to the user's mental reactions and psychological health status, as determined through psychological analysis.

[0601] An "information processing device" is a mechanical central device used to receive and analyze biological signals, and refers to a server.

[0602] A "personalized experience" means providing services and information that are individually optimized according to the user's specific psychological state.

[0603] To implement this invention, a program is first required to manage the entire system's operation. This program uses the Python programming language, leveraging TensorFlow and PyTorch as its primary libraries. The terminal is equipped with a function to acquire biosignals in real time from an electroencephalogram (EEG) reader while the user is asleep. The acquired biosignals are de-noised and normalized before being transmitted to a server using high-speed communication technology. The WebSocket protocol is used for this communication.

[0604] On the server side, visual information is generated using a generative AI model based on the received biosignals. This generation utilizes a deep learning technique called GAN (Generative Adversarial Network). The generated visual information undergoes psychological analysis, and the user's emotional state is estimated using an emotion engine. This engine is implemented using TensorFlow and performs sophisticated emotion analysis by combining real-time biosignal data with historical data.

[0605] The results of this analysis are visually presented on the smart glasses display or mobile device to provide a personalized experience based on the user's psychological state. For example, imagine a customer at a cafe receiving suggestions for products that promote relaxation. In this case, recommended product information based on the analysis results would be displayed on the screen.

[0606] An example of a prompt to input into a generative AI model is the instruction, "Evaluate the customer's current psychological state and suggest stress-reducing products based on that." This establishes an effective means of improving the customer experience in physical stores.

[0607] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0608] Step 1:

[0609] The device acquires biosignals from an electroencephalogram (EEG) reader while the user is asleep. This signal is then used as input for noise reduction and normalization, generating clean, easily analyzable data. This enables highly accurate data analysis.

[0610] Step 2:

[0611] The device transmits clean biosignals to the server using high-speed communication technology. The protocol used is WebSocket, which allows large amounts of data to be efficiently transferred to the server in real time.

[0612] Step 3:

[0613] The server uses a generative AI model to generate visual information based on the received biometric signals. This process utilizes the TensorFlow library and employs GANs, a deep learning technique, to output image data that visually represents the content of the dream.

[0614] Step 4:

[0615] The server inputs the generated visual information into the emotion engine. Here, a machine learning model performs a psychological analysis and evaluates the user's emotional state. This uses historical data and real-time biometric data to generate metadata for the emotion evaluation.

[0616] Step 5:

[0617] The server generates information to provide a personalized experience to the user based on the results of an assessment of their emotional state. This information is transmitted to the smart glasses display or mobile device and presented visually to the user. Specifically, product information and service details optimized according to the user's psychological state are output.

[0618] Step 6:

[0619] Users can receive visual information and emotional analysis results through smart glasses or mobile devices, and use this information to optimize their choices and actions in daily life. This can lead to an improvement in their psychological well-being.

[0620] The specific processing unit 290 transmits the result of the specific processing to the headset terminal 314. In the headset terminal 314, the control unit 46A causes the speaker 240 and display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0621] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). One example of data generation model 58 is ChatGPT (Internet Search).<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0622] In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and specific processing may also be performed by the headset terminal 314.

[0623] [Fourth Embodiment]

[0624] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0625] As shown in Figure 7, the 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.

[0626] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0627] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a controlled object 443. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and controlled object 443 are also connected to the bus 52.

[0628] The microphone 238 receives voice signals from the user 20 and receives instructions from the user 20. The microphone 238 captures the voice signals from the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0629] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the area around the user 20 (for example, an imaging range defined by a field of view equivalent to the width of a typical healthy person's field of vision).

[0630] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0631] The controlled object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors in the arms, hands, and feet. Some of the robot 414's emotions can be expressed by controlling these motors. Furthermore, the robot 414's facial expressions can also be expressed by controlling the illumination state of the LEDs in its eyes.

[0632] Figure 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Figure 8, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0633] The specific processing program 56 is an example of a "program" relating to the technology of this 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.

[0634] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0635] In robot 414, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. 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 processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0636] Next, the specific processing performed by the specific processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0637] This invention is a system for analyzing and visualizing a user's dreams to gain psychological insights. The system mainly consists of a process of acquiring biosignals using the user's brainwaves and visualizing them, and a process of presenting the results of the psychological analysis based on those signs.

[0638] The device acquires brainwaves in real time through an electroencephalogram (EEG) reader worn by the user while they sleep. This data is then denoised and normalized within the device. Noise reduction is performed to filter out interference from the external environment and improve the purity of the biosignals. The normalized data is then sent to a server according to a pre-configured transmission schedule. High-speed communication technology is used for this transmission, ensuring that the data is transferred quickly and securely.

[0639] The server receives brainwave data transmitted from the terminal. After receiving the data, it analyzes it using a machine learning model. The machine learning model uses deep learning techniques, particularly generative adversarial networks (GANs), to visualize the information obtained from the brainwaves as an image. This image generation makes it possible to abstractly depict the user's dream scenes.

[0640] Next, the server performs a psychological analysis using the generated images and raw data. The analysis is primarily aimed at interpreting the user's potential emotions and mental state that the dream content may suggest. This analysis applies standard psychological theories and models and is evaluated in conjunction with the user's personal past data.

[0641] Ultimately, the device provides the user with the analysis results obtained from the server. The user can then view the generated dream images and the psychological analysis results through the app's interface. This allows the user to gain a deeper understanding of their inner self and obtain insights to address potential problems and emotions.

[0642] For example, if a user is suffering from anxiety, the images generated from their brainwave data visually represent the origins of their anxiety and related emotions, divided into multiple scenes. Based on these images, psychological analysis suggests the basis of the anxiety and elements that can be improved, providing the user with specific advice. This allows the user to gain insights into resolving their emotional issues through the content of their dreams.

[0643] The following describes the processing flow.

[0644] Step 1:

[0645] The device acquires brainwave data in real time using an electroencephalogram (EEG) reader worn by the user. The data is continuously stored in the device's memory.

[0646] Step 2:

[0647] The device uses the acquired electroencephalogram (EEG) data to perform noise reduction. Specifically, it applies filtering technology to improve data accuracy by filtering out abnormal values ​​and external interference.

[0648] Step 3:

[0649] The device normalizes the noise-removed data and converts it into a unified format. This makes the data easier to analyze.

[0650] Step 4:

[0651] The terminal sends pre-processed data to the server using 5G communication technology. The transmission includes error checking to ensure data integrity.

[0652] Step 5:

[0653] The server receives the EEG data from the terminal and prepares it for analysis. The received data is then passed to the analysis platform.

[0654] Step 6:

[0655] The server uses the received data as input to run a machine learning model. The model uses deep learning techniques to generate images that represent the content of the dream from the data.

[0656] Step 7:

[0657] The server performs a psychological analysis based on the generated images. The analysis uses algorithms that identify the user's psychological state and potential problems as reflected in the dream content.

[0658] Step 8:

[0659] The server compiles and visualizes the analysis results and sends them to the terminal. The results include the generated images and explanations of the psychological insights.

[0660] Step 9:

[0661] The device presents the user with data received from the server. Through the app, the user can view visualized dream images and the corresponding psychological analysis results. This allows the user to gain a deeper understanding of their own psychological state.

[0662] (Example 1)

[0663] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0664] In modern society, it is important for individuals to understand their own inner emotions and psychological states, but many people lack the means to grasp and resolve them on their own. In particular, there is a need for a system that analyzes a person's psychological state through the unconscious phenomenon of dreams and helps visualize and understand it using appropriate methods.

[0665] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0666] In this invention, the server includes means for acquiring the user's biological signals, means for converting visual representations into specific dream scenes using a generative adversarial network, and means for performing psychological analysis based on the visual representations. This makes it possible to analyze and visualize an individual's dreams and provide psychological insights.

[0667] A "user" refers to an individual who uses the system to analyze their own psychological state and the content of their dreams.

[0668] "Rest" refers to a state in which the user is sleeping, or a state similar to sleep.

[0669] "Biological signals" refer to electrical activity detected from the user's body, including but not limited to brain waves.

[0670] "Interference signals" refer to external electromagnetic waves and environmental noise that interfere with the user's biological signals.

[0671] "High-speed communication technology" refers to technologies that use advanced communication protocols such as 5G and Wi-Fi 6 to quickly transfer large amounts of data.

[0672] A "processing unit" refers to a computer device, such as a server, that receives, processes, and analyzes data.

[0673] "Visual representation" refers to images and graphics that can be visually displayed by extracting biological signals such as the user's brainwaves.

[0674] A "generative adversarial network" is a deep learning technique that uses training data to generate new data or images.

[0675] "Psychological analysis" refers to the analytical process of interpreting a user's psychological state and emotions based on visual representations and biological signals.

[0676] This invention is a system that gains psychological insights by analyzing and visualizing the user's dreams. A specific embodiment of this system is shown below.

[0677] The user wears a special sensor device while resting. This device is worn on the head and acquires biological signals, including brain waves, in real time.

[0678] The terminal receives biological signals transmitted from sensors. After reception, the signals are denoised and normalized using built-in software. Digital filtering technology is used for denoising to improve signal purity. In the normalization process, the signal amplitude is standardized to a certain range to facilitate analysis. The processed data is transmitted to a computing device using high-speed communication technology (e.g., 5G or Wi-Fi 6).

[0679] The server acts as a computing device, analyzing the received biological signals. The analysis utilizes a generative adversarial network (GAN), a type of generative AI model. This model generates visual representations from the input signals, visually depicting the user's dream scenes. Next, a psychological analysis is performed based on the generated visual representations. This analysis uses standard psychological theories to evaluate the user's emotions and subconscious. The evaluation also includes comparisons with past data.

[0680] Ultimately, the terminal displays the visual representation of the dream and the results of the psychological analysis transmitted from the server on the user interface. The user can review these results through the application and gain insights into their inner self. For example, if the user sees an animal in their dream, the system analyzes the symbolism of that animal in relation to their psychological state and provides advice regarding specific emotions and situations. An example of a prompt used in this case would be: "Generate a dream scene from the user's brainwave data and perform a psychological analysis. Provide specific advice regarding the emotions and states that may be suggested by the generated image."

[0681] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0682] Step 1:

[0683] The user rests while wearing a sensor device. The device acquires the user's brainwave signals in real time. The input is the user's brainwaves, and the output is raw brainwave signal data transmitted to the terminal.

[0684] Step 2:

[0685] The terminal performs noise reduction processing on the received electroencephalogram (EEG) signal data. The input is raw EEG signal data, and the output is EEG signal data with noise removed. Specifically, it uses digital filtering technology to eliminate high-frequency interference components.

[0686] Step 3:

[0687] The terminal normalizes the signal after noise reduction. The input is noise-reduced EEG signal data, and the output is normalized EEG data. Normalization adjusts the data amplitude to a certain range, improving data consistency.

[0688] Step 4:

[0689] The terminal transmits normalized EEG data to the server. The input is normalized EEG data, and the output is data transferred to the server. Specifically, high-speed communication technology is used to encrypt and securely transmit the data.

[0690] Step 5:

[0691] The server generates visual representations using a generative adversarial network (GAN), a generative AI model, based on the received brainwave data. The input is normalized brainwave data, and the output is image data representing scenes from the user's dream.

[0692] Step 6:

[0693] The server performs psychological analysis based on the generated images. The input is the generated dream images, and the output is the results of the psychological analysis. Specifically, it applies standard psychological theories to evaluate the user's emotions and mental state.

[0694] Step 7:

[0695] The terminal provides the user with dream images and psychological analysis results transmitted from the server. Inputs include image data and analysis results from the server, and these are displayed on the interface as output. Through this, the user can deepen their self-understanding.

[0696] (Application Example 1)

[0697] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0698] There is a need to provide a means to improve mental health by subtly analyzing users' stress and anxiety-related psychological issues during sleep, thereby offering individually tailored coping strategies. Currently, there is a lack of systems that can acquire and visualize these emotional states in real time, perform psychological analysis, and provide specific advice.

[0699] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0700] In this invention, the server includes means for acquiring biosignals obtained during the user's sleep, means for generating images using the biosignals and performing psychological analysis, and means for identifying stress and anxiety and advising the user on coping strategies based on the analysis results. This allows the user to unconsciously understand their own emotional state and obtain specific coping strategies for stress and anxiety.

[0701] A "user" refers to an individual who uses this system, specifically the entity that provides biosignals such as brain waves during sleep.

[0702] "Biosignals" refer to data representing the electrical activity of biological origin, such as a user's brainwaves, and are used to analyze their psychological state.

[0703] "Noise removal and normalization methods" refer to processes that remove external interference from acquired biosignals, thereby improving the consistency and reliability of the data.

[0704] "Means of transmitting to an information processing device using high-speed communication technology" refers to technical means for rapidly and safely transferring biological signals to an information processing device, and is a technology designed for high-speed and highly reliable communication.

[0705] An "information processing device" is a computer system necessary for analyzing received biological signals, visualizing them as images, and performing psychological analysis.

[0706] "Means of generating images" refers to the process of creating images that visually represent a user's psychological state or dreams based on biosignal data.

[0707] "Methods for conducting psychological analysis" refers to the process of interpreting and analyzing the user's potential emotional state and psychological issues based on the generated images, using specialized theories.

[0708] "Means of identifying and providing advice for stress and anxiety states" refers to a process of evaluating a user's psychological state and suggesting appropriate coping strategies for individual stressors and anxieties.

[0709] This invention embodies a system for analyzing a user's psychological state during sleep and providing practical advice. This system utilizes an electroencephalogram (EEG) reader worn by the user while sleeping, which acquires biosignals in real time. The acquired EEG data is denoised and normalized on a terminal, ensuring highly reliable data.

[0710] The terminal transmits this normalized biosignal to an information processing device (server) quickly and securely using high-speed communication technology. The server uses the received biosignal data and a generative AI model to generate visual information from brainwaves. Specifically, it employs a generative adversarial network (GAN) to create an abstract image representing the user's psychological state based on brainwave patterns.

[0711] Next, the server performs a psychological analysis based on the generated images, using standard psychological theories and models. This identifies the user's stress and anxiety levels, and develops coping strategies. The results of this analysis are displayed through an application on the user's smartphone or tablet. The user can review the visualized dreams and psychological state and receive recommended coping strategies.

[0712] For example, if a user is experiencing anxiety due to work-related stress, the generated image will reflect that focus, and the server can offer relaxation techniques and specific action suggestions to alleviate the anxiety. Advice such as, "After work, take a walk in nature and practice deep breathing to calm your mind," might be provided.

[0713] Example of a prompt:

[0714] "We analyze the user's brainwave data to visualize underlying stressors."

[0715] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0716] Step 1:

[0717] The user wears an electroencephalogram (EEG) reader while sleeping, and the device acquires biosignals in real time. The input is the user's EEG data, and the output is the initial biosignal data. At this stage, the device aims to accurately collect the data and transmit it to the terminal in an appropriate format.

[0718] Step 2:

[0719] The terminal processes the received biosignal data and performs noise reduction. The input is the initial biosignal data, and the output is clean biosignal data with noise removed. In this step, a signal processing algorithm is used to filter out interference caused by the external environment and equipment, improving the purity of the data.

[0720] Step 3:

[0721] The terminal normalizes clean biosignal data. The input is denoised biosignal data, and the output is normalized biosignal data. This process ensures data consistency and standardization, and performs conversions to accommodate different measurement conditions and data ranges.

[0722] Step 4:

[0723] The terminal transmits normalized biosignal data to the server using high-speed communication technology. The input is the normalized biosignal data, and the output is the status indicating that the data transfer to the server is complete. This process is performed using a communication protocol with the aim of delivering data to the server quickly and securely.

[0724] Step 5:

[0725] The server uses the received biosignal data to generate an image using a generative AI model, specifically a generative adversarial network (GAN). The input is normalized biosignal data transferred to the server, and the output is image data. In this step, an abstract dream scene is visualized based on a machine learning algorithm.

[0726] Step 6:

[0727] The server performs psychological analysis based on the generated image data. The input is the generated image data, and the output is psychological state information as a result of the analysis. The server applies standard psychological theories and models to interpret the user's potential emotions and mental state.

[0728] Step 7:

[0729] The server sends the analysis results to the terminal, which then displays them to the user. The input is psychological state information, and the output is the notification status to the user. In this final step, specific advice and solutions are displayed, allowing the user to take action based on the visualized information.

[0730] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0731] This invention is a system that uses the user's brainwaves to analyze dreams that occur during sleep, and further evaluates the user's emotions in detail using an emotion engine. The aim of this system is to provide deep psychological insights by acquiring and analyzing the user's biosignals.

[0732] In this system, the terminal acquires biosignals in real time from an electroencephalogram (EEG) reader while the user is asleep. The acquired signals are then processed on the terminal to remove noise and normalize the data. The data is transmitted to the server in real time or at specified intervals. High-speed communication technology is used for communication with the server, enabling efficient transmission of big data.

[0733] The server uses the received biometric signals as input to visualize the dream content based on a machine learning model. Deep learning techniques such as generative adversarial networks (GANs) are employed in this visualization process. The generated images provide a visual abstract representation of the user's dream.

[0734] The key here is the use of an emotion engine. This engine analyzes the generated images and combines them with the user's past data and real-time biosignals to estimate the user's emotional state. This engine takes psychological factors seriously and can identify the user's underlying emotions and psychological challenges.

[0735] For example, suppose a user has been feeling anxious in their recent life. This system analyzes the user's brainwaves during sleep, and the emotion engine detects the emotion of anxiety along with the generated dream images. This allows the emotion engine to interpret how the user's anxiety is influencing their dreams, and based on the results, it provides triggers and mitigation strategies for the resulting emotions.

[0736] Ultimately, the server sends the analysis results to the terminal, and the user can view the dream images and emotion-based analysis results through the application interface. This allows the user to understand their emotional state more objectively and in detail, helping them to take appropriate action. This entire process helps support the improvement of the user's psychological health.

[0737] The following describes the processing flow.

[0738] Step 1:

[0739] The device uses an electroencephalogram (EEG) reader worn by the user to acquire brainwaves, which are biological signals during sleep, in real time. The acquired data is temporarily stored in the device's memory.

[0740] Step 2:

[0741] The device removes noise and normalizes the acquired electroencephalogram (EEG) data. Noise reduction applies an algorithm to filter out unnecessary data, while normalization converts the data into a consistent format to facilitate analysis.

[0742] Step 3:

[0743] The device transmits pre-processed EEG data to the server using 5G communication technology. This transmission incorporates an error checking function to verify the integrity of the data.

[0744] Step 4:

[0745] The server stores the brainwave data received from the terminal and prepares it for transmission to a machine learning model. During this preparation stage, the data is made ready for analysis.

[0746] Step 5:

[0747] The server inputs the received data into a machine learning model to generate images that visually represent the content of the dream. A generative adversarial network (GAN) is used for image generation to depict characteristic dream scenes.

[0748] Step 6:

[0749] The server performs psychological analysis based on the generated images. At the heart of this analysis is an emotion engine that evaluates the user's emotional state using historical data and real-time biosignals.

[0750] Step 7:

[0751] The server compiles the evaluation results from the emotion engine and derives insights into the user's potential emotions and psychological issues. This identifies specific influences and trends linked to emotional states.

[0752] Step 8:

[0753] The server sends the analysis results to the terminal. The user can then view the data received via communication through the application's interface.

[0754] Step 9:

[0755] The device presents the user with images of their dreams and the results of an emotional analysis. This allows the user to visually understand their own psychological state and gain insights to take appropriate action as needed.

[0756] (Example 2)

[0757] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0758] Conventional biometric data analysis systems have found it difficult to grasp a user's psychological state in detail and accurately, and in particular, they have been unable to comprehensively evaluate dreams experienced during sleep and the emotions associated with them. Furthermore, there has been a lack of technology to visually display the content of dreams, and users have not been provided with sufficient information to understand their own psychological state.

[0759] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0760] In this invention, the server includes means for acquiring biometric data obtained during the user's rest period, means for processing the biometric data to remove noise and normalize it, and means for transmitting the biometric data to a processing device using high-speed transmission technology. This makes it possible to analyze the user's psychological state in detail and visually display the content of dreams.

[0761] "Biometric data" refers to data that shows electrical signals and biological indicators obtained from an individual, and specifically includes brain waves and heart rate.

[0762] "Removing noise and normalizing" is a process that improves the accuracy of analysis by removing unwanted noise from biological data and standardizing the data format and criteria.

[0763] "High-speed transmission technology" refers to technologies for rapidly moving data, and typically means the ability to transmit large amounts of data in a short time using communication methods with high bandwidth.

[0764] A "processing device" is a computer system used for analyzing and transforming data, and in particular includes a central processing unit responsible for data aggregation and analysis.

[0765] "Visual data" refers to information in image or video format generated through data analysis, and is particularly used to visually represent abstract concepts or states.

[0766] "Psychological analysis" refers to analytical methods used to understand an individual's psychological state and emotions, and includes the evaluation of emotions and behaviors based on biological and visual data.

[0767] An "emotion engine" is a software component that automatically evaluates an individual's emotional state based on information extracted from data.

[0768] A "mathematical model" is a computational model that expresses real-world phenomena using mathematical formulas and algorithms based on data, and is used for prediction and analysis.

[0769] This invention is a system that acquires biometric data during a user's rest period and analyzes it to evaluate their psychological state. The following describes a specific configuration for implementing this system.

[0770] Hardware configuration

[0771] The user wears a biosignal sensor to monitor their brainwaves. The sensor has the capability to acquire biometric data such as brainwaves and heart rate in real time and is connected to a terminal. The terminal is a computing device for data collection and initial processing, and is typically a smartphone or tablet. High-speed communication technologies such as Wi-Fi 6 or 5G are used to transmit data to the server.

[0772] Software Configuration

[0773] On the device, software runs to perform noise reduction and data normalization on the acquired biometric data. This process enables efficient transmission of low-noise data to the server. The server uses a generative AI model based on the received data to generate visual data of the user's dreams. This model includes generative adversarial network (GAN) technology. The generated visual data is further analyzed by an emotion engine and used to evaluate the user's psychological state.

[0774] Specific example

[0775] For example, if a user has been experiencing stress over the past few weeks, the system analyzes the user's brainwaves and visualizes their dreams. The emotion engine then extracts the emotions indicating stress from the visual data. This information provides the user with the resources to understand the cause of their stress and take steps to mitigate it.

[0776] Example of a prompt

[0777] "Use a generative AI model based on the user's biometric data to visualize their dreams. Furthermore, utilize an emotion engine to analyze the user's emotions from the visual data and provide a detailed emotional assessment."

[0778] This invention aims to improve psychological health by analyzing users' biometric data in detail.

[0779] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0780] Step 1:

[0781] The device acquires brainwave data in real time through a biosignal sensor while the user is sleeping. Biosignals are taken in as input and converted into digital data. Specifically, when the sensor is attached to the head, it detects weak electrical signals and transmits the data to the device. The output is the digitized biosignal data.

[0782] Step 2:

[0783] The device removes noise from acquired biometric data and normalizes the data. It receives digitized biometric data as input and uses filtering techniques to remove unwanted noise. In addition, it performs normalization to maintain data consistency. Specifically, the algorithm detects peaks and outliers in the data and processes them to smooth them out. The output is clean, analyzable data.

[0784] Step 3:

[0785] The terminal sends pre-processed biometric data to a server where a generating AI model is located. It receives denoised data as input using high-speed communication technology (e.g., Wi-Fi 6) and forwards it to the server. Specifically, the terminal compresses the data and efficiently transmits it via the communication protocol. The output is the data correctly transmitted to the server.

[0786] Step 4:

[0787] The server uses a generative AI model to generate visual dream data from received biometric data. It receives pre-processed biometric data as input and uses a generative adversarial network (GAN) to visualize the dream. Specifically, a mathematical model within the server processes the data and executes an algorithm to generate abstract visual data. The output is the generated visual dream data.

[0788] Step 5:

[0789] The server analyzes the generated visual data using an emotion engine to evaluate the user's emotional state. It receives the generated visual data as input and estimates emotions by comparing it with past data and current biosignals. Specifically, the emotion engine performs image analysis and calculates psychological evaluations. The output is data representing the analyzed emotional state.

[0790] Step 6:

[0791] The server sends the analysis results to the terminal, and the user views the dream's visual data and emotional evaluation results through the application. The server uses the processed analysis data as input and sends it to the terminal. Specifically, the server converts the data format to a format that can be visualized on the user's terminal and transmits it via an appropriate communication channel. The output consists of data and evaluation results provided in a format that the user can view.

[0792] (Application Example 2)

[0793] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0794] In modern brick-and-mortar stores, there is a demand for improved service tailored to individual customer needs. However, traditional services are primarily based on visual and behavioral data, making it difficult to provide personalized experiences that reflect customers' inner emotions and psychological states in real time. This results in a lack of effective means to improve customer satisfaction.

[0795] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0796] In this invention, the server includes means for generating visual information from biological signals as input, means for performing psychological analysis based on the visual information and evaluating the current emotional state, and means for presenting information based on the customer's emotional state and providing a personalized experience in real time. This makes it possible to provide services that are tailored to the customer's emotions and psychological state.

[0797] "Users" refer to individuals who use the system, and are the subjects of acquisition and analysis of their biometric signals.

[0798] "Biosignals" refer to the basic data for decision analysis acquired during the user's sleep, and include electroencephalograms (EEGs).

[0799] "Visual information" refers to image information generated by machine learning models based on biosignals, and it visualizes the user's dreams and psychological state.

[0800] "Psychological analysis" is the process of analyzing and evaluating mental processes and emotional states based on visual information.

[0801] "Emotional state" refers to the user's mental reactions and psychological health status, as determined through psychological analysis.

[0802] An "information processing device" is a mechanical central device used to receive and analyze biological signals, and refers to a server.

[0803] A "personalized experience" means providing services and information that are individually optimized according to the user's specific psychological state.

[0804] To implement this invention, a program is first required to manage the entire system's operation. This program uses the Python programming language, leveraging TensorFlow and PyTorch as its primary libraries. The terminal is equipped with a function to acquire biosignals in real time from an electroencephalogram (EEG) reader while the user is asleep. The acquired biosignals are de-noised and normalized before being transmitted to a server using high-speed communication technology. The WebSocket protocol is used for this communication.

[0805] On the server side, visual information is generated using a generative AI model based on the received biosignals. This generation utilizes a deep learning technique called GAN (Generative Adversarial Network). The generated visual information undergoes psychological analysis, and the user's emotional state is estimated using an emotion engine. This engine is implemented using TensorFlow and performs sophisticated emotion analysis by combining real-time biosignal data with historical data.

[0806] The results of this analysis are visually presented on the smart glasses display or mobile device to provide a personalized experience based on the user's psychological state. For example, imagine a customer at a cafe receiving suggestions for products that promote relaxation. In this case, recommended product information based on the analysis results would be displayed on the screen.

[0807] An example of a prompt to input into a generative AI model is the instruction, "Evaluate the customer's current psychological state and suggest stress-reducing products based on that." This establishes an effective means of improving the customer experience in physical stores.

[0808] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0809] Step 1:

[0810] The device acquires biosignals from an electroencephalogram (EEG) reader while the user is asleep. This signal is then used as input for noise reduction and normalization, generating clean, easily analyzable data. This enables highly accurate data analysis.

[0811] Step 2:

[0812] The device transmits clean biosignals to the server using high-speed communication technology. The protocol used is WebSocket, which allows large amounts of data to be efficiently transferred to the server in real time.

[0813] Step 3:

[0814] The server uses a generative AI model to generate visual information based on the received biometric signals. This process utilizes the TensorFlow library and employs GANs, a deep learning technique, to output image data that visually represents the content of the dream.

[0815] Step 4:

[0816] The server inputs the generated visual information into the emotion engine. Here, a machine learning model performs a psychological analysis and evaluates the user's emotional state. This uses historical data and real-time biometric data to generate metadata for the emotion evaluation.

[0817] Step 5:

[0818] The server generates information to provide a personalized experience to the user based on the results of an assessment of their emotional state. This information is transmitted to the smart glasses display or mobile device and presented visually to the user. Specifically, product information and service details optimized according to the user's psychological state are output.

[0819] Step 6:

[0820] Users can receive visual information and emotional analysis results through smart glasses or mobile devices, and use this information to optimize their choices and actions in daily life. This can lead to an improvement in their psychological well-being.

[0821] 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 controlled object 443 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0822] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). One example of data generation model 58 is ChatGPT (Internet Search).<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0823] 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 this disclosure is not limited thereto, and the specific processing may also be performed by the robot 414.

[0824] Furthermore, the emotion identification model 59, acting 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 a specific mapping, which is an emotion map (see Figure 9). Similarly, the emotion identification model 59 may also determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0825] Figure 9 shows an emotion map 400 in which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. The closer to the center of the concentric circles, the more primitive the emotions are located. Further out of the concentric circles, emotions representing states and actions arising from mental states are located. Emotion is a concept that includes feelings and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions occurring in the brain are located. On the right side of the concentric circles, emotions that are generally induced by situational judgment are located. Above and below the concentric circles, emotions that are generally generated from reactions occurring in the brain and induced by situational judgment are located. In addition, the emotion of "pleasure" is located on the upper side of the concentric circles, and the emotion of "displeasure" is located on the lower side. Thus, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions arise, and emotions that are likely to occur simultaneously are mapped close together.

[0826] These emotions are distributed at the 3 o'clock position on the Emotion Map 400, and usually fluctuate between feelings of security and anxiety. In the right half of the Emotion Map 400, situational awareness takes precedence over internal feelings, resulting in a calm impression.

[0827] The inside of the Emotion Map 400 represents inner thoughts, while the outside represents actions. Therefore, the further you go from the outside of the Emotion Map 400, the more visible (expressed in actions) your emotions become.

[0828] Here, human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. Similarly, in robots, cars, motorcycles, etc., emotions can be created based on various balances, such as posture and battery level. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. The emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on a system for analyzing brain physiological signals of speech emotion recognition and emotion, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map contains emotions belonging to a region called "response," where sensation is dominant. The right half of the emotion map contains emotions belonging to a region called "situation," where situational awareness is dominant.

[0829] The emotion map defines two emotions that promote learning. One is the emotion around the middle of the negative "repentance" and "reflection" on the situation side. In other words, it is 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 the emotion around the positive "desire" on the reaction side. In other words, it is when the robot has positive feelings such as "I want more" or "I want to know more."

[0830] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​representing each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple training data sets, which are combinations of user input and emotion values ​​representing each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions located close together have similar values, as shown in the emotion map 900 in Figure 10. Figure 10 shows an example where multiple emotions such as "reassured," "calm," and "confident" have similar emotion values.

[0831] The above description primarily focuses on the functions of the data processing device 12 in relation to this disclosure. However, the system related to this disclosure is not necessarily implemented on a server. The system related to this disclosure may be implemented as a general information processing system. This disclosure may be implemented, for example, as a software program that runs on a personal computer or as an application that runs on a smartphone. The method related to this disclosure may be provided to users in SaaS (Software as a Service) format.

[0832] In the above embodiment, an example was given in which a specific process is performed by a single computer 22. However, the technology of this disclosure is not limited thereto, and a distributed processing of the specific process may be performed by multiple computers, including computer 22. For example, a data generation model 58 may be provided in an external device of the data processing device 12, and the external device may generate data according to the input data.

[0833] In the above embodiment, an example was given in which the specific processing program 56 is stored in the storage 32, but the technology of this disclosure is not limited thereto. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-temporary storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-temporary storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes specific processing according to the specific processing program 56.

[0834] 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.

[0835] Furthermore, it is not necessary to store the entirety 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 the entirety of the specific processing program 56 in the storage 32; it is acceptable to store only a portion of the specific processing program 56.

[0836] The following types of processors can be used as hardware resources to perform specific processing. Examples of processors include a CPU, a general-purpose processor that functions as a hardware resource to perform specific processing by executing software, i.e., a program. Other examples of processors include dedicated electrical circuits, such as FPGAs (Field-Programmable Gate Arrays), PLDs (Programmable Logic Devices), or ASICs (Application Specific Integrated Circuits), which have circuit configurations specifically designed to perform specific processing. All of these processors have built-in or connected memory, and all of them perform specific processing by using memory.

[0837] The hardware resource that performs a specific process may consist of one of these various processors, or it may consist of 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). Alternatively, the hardware resource that performs a specific process may consist of a single processor.

[0838] Examples of configurations using a single processor include, firstly, a configuration in which one or more CPUs and software are combined to form a single processor, and this processor functions as a hardware resource that performs a specific process. Secondly, there is a configuration using a processor that realizes the functions of the entire system, including multiple hardware resources that perform a specific process, on a single IC chip, as exemplified by SoCs (System-on-a-chip). In this way, a specific process is realized using one or more of the above types of processors as hardware resources.

[0839] Furthermore, the hardware structure of these various processors can more specifically utilize electrical circuits that combine circuit elements such as semiconductor devices. Also, the specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps can be deleted, new steps added, or the processing order rearranged, as long as it does not deviate from the main purpose.

[0840] The descriptions and illustrations presented above are detailed explanations of the technical aspects of this disclosure and are merely examples of the technical aspects. For example, the above descriptions of the structure, function, operation, and effect are examples of the structure, function, operation, and effect of the technical aspects of this disclosure. Therefore, it goes without saying that you may delete unnecessary parts, add new elements, or replace elements in the descriptions and illustrations presented above, as long as you do not deviate from the essence of the technical aspects of this disclosure. Furthermore, in order to avoid confusion and facilitate understanding of the technical aspects of this disclosure, explanations of common technical knowledge and the like that do not require special explanation to enable the implementation of the technical aspects of this disclosure have been omitted from the descriptions and illustrations presented above.

[0841] All documents, patent applications, and technical standards described herein are incorporated by reference to the same extent as if each individual document, patent application, and technical standard were specifically and individually noted to be incorporated by reference.

[0842] The following is further disclosed regarding the embodiments described above.

[0843] (Claim 1)

[0844] A means of acquiring biosignals obtained during the user's sleep,

[0845] A means for processing the aforementioned biological signal to remove noise and normalize it,

[0846] A means for transmitting the aforementioned biological signals to a server using high-speed communication technology,

[0847] The server includes means for generating an image using the biological signal as input,

[0848] A means for performing psychological analysis based on the aforementioned image,

[0849] Means for displaying the image and analysis results to the user,

[0850] A system that includes this.

[0851] (Claim 2)

[0852] The system according to claim 1, characterized in that the aforementioned biological signal is an electroencephalogram.

[0853] (Claim 3)

[0854] The system according to claim 1, characterized in that the image generation is performed using a machine learning model.

[0855] "Example 1"

[0856] (Claim 1)

[0857] A means of acquiring biological signals obtained during the user's rest period,

[0858] The means for processing the aforementioned biological signals to remove interfering signals and normalize them,

[0859] Means for transmitting the aforementioned biological signals to a computing device using high-speed communication technology,

[0860] The computing device includes means for generating a visual representation using the biological signal as input,

[0861] A method for transforming visual representations into concrete dream scenes using generative adversarial networks,

[0862] A means for performing psychological analysis based on the aforementioned visual representation,

[0863] Means for displaying the visual representation and analysis results to the user,

[0864] A system that includes this.

[0865] (Claim 2)

[0866] The system according to claim 1, characterized in that the aforementioned biological signal is an electroencephalogram.

[0867] (Claim 3)

[0868] The system according to claim 1, characterized in that the visual representation generation is performed using a machine learning model.

[0869] "Application Example 1"

[0870] (Claim 1)

[0871] A means of acquiring biosignals obtained during the user's sleep,

[0872] A means for processing the aforementioned biological signal to remove noise and normalize it,

[0873] Means for transmitting the aforementioned biological signals to an information processing device using high-speed communication technology,

[0874] The information processing device includes means for generating an image using the biological signal as input,

[0875] A means for performing psychological analysis based on the aforementioned image,

[0876] Means for displaying the image and analysis results to the user,

[0877] A means of identifying stress and anxiety states and providing advice on how to cope with them,

[0878] A system that includes this.

[0879] (Claim 2)

[0880] The system according to claim 1, characterized in that the aforementioned biological signal is an electroencephalogram.

[0881] (Claim 3)

[0882] The system according to claim 1, characterized in that the image generation is performed using a machine learning model.

[0883] "Example 2 of combining an emotion engine"

[0884] (Claim 1)

[0885] A means of acquiring biometric data obtained while the user is resting,

[0886] A means for processing the aforementioned biological data to remove noise and normalize it,

[0887] Means for transmitting the aforementioned biological data to a processing device using high-speed transmission technology,

[0888] The processing device includes means for generating visual data using the biological data as input,

[0889] A means for performing psychological analysis based on the aforementioned visual data,

[0890] A means for analyzing the visual data and past data using an emotion engine to evaluate the user's emotional state,

[0891] Means for displaying the aforementioned visual data and analysis results to the user,

[0892] A system that includes this.

[0893] (Claim 2)

[0894] The system according to claim 1, characterized in that the aforementioned biological data is electroencephalogram (EEG).

[0895] (Claim 3)

[0896] The system according to claim 1, characterized in that the aforementioned visual data generation is performed using a mathematical model.

[0897] "Application example 2 when combining with an emotional engine"

[0898] (Claim 1)

[0899] A means of acquiring biosignals obtained during the user's sleep,

[0900] A means for processing the aforementioned biological signal to remove noise and normalize it,

[0901] Means for transmitting the aforementioned biological signals to an information processing device using high-speed communication technology,

[0902] The information processing device includes means for generating visual information using the biological signal as input,

[0903] A means of performing a psychological analysis based on the aforementioned visual information and evaluating the current emotional state,

[0904] A means of presenting information based on the customer's emotional state and providing a personalized experience in real time,

[0905] Means for displaying the aforementioned visual information and analysis results to the customer,

[0906] A system that includes this.

[0907] (Claim 2)

[0908] The system according to claim 1, characterized in that the aforementioned biological signal is an electroencephalogram.

[0909] (Claim 3)

[0910] The system according to claim 1, characterized in that the visual information generation is performed using a machine learning model. [Explanation of Symbols]

[0911] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots< / url:> < / url:> < / url:> < / url:>

Claims

1. A means of acquiring biosignals obtained during the user's sleep, A means for processing the aforementioned biological signal to remove noise and normalize it, A means for transmitting the aforementioned biological signals to a server using high-speed communication technology, The server includes means for generating an image using the biological signal as input, A means for performing psychological analysis based on the aforementioned image, Means for displaying the image and analysis results to the user, A system that includes this.

2. The system according to claim 1, characterized in that the aforementioned biological signal is an electroencephalogram.

3. The system according to claim 1, characterized in that the image generation is performed using a machine learning model.

Images