System

Abstract

A system is provided.SOLUTION: A system comprising: means for acquiring brainwave data; means for storing the acquired brainwave data in a storage device; means for analyzing the brainwave data stored in the storage device by artificial intelligence; and means for outputting an analysis result.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] In modern society, many people suffer from psychological and mental challenges and sleep disorders. These conditions not only reduce the quality of life for individuals, but also have a significant impact on society as a whole. However, there is a lack of tools to accurately identify and predict unconscious mental conditions such as stress, anxiety, and depression. Conventional psychological assessment methods are usually based on self-reports or interviews, which are highly subjective and often make accurate diagnosis difficult. Therefore, there is a need for new systems that can more objectively and accurately identify and predict psychological conditions. [Means for solving the problem]

[0005] To solve the above-mentioned problems, the present invention proposes the following configuration. First, a means for acquiring EEG data is provided. Next, a means for storing the acquired EEG data in a storage device is provided. Next, a means for analyzing the EEG data stored in the storage device using artificial intelligence is provided. Finally, a means for outputting the analysis results is provided. This makes it possible to objectively and accurately identify and predict unconscious mental states such as stress, anxiety, and depression. Furthermore, by providing the analyzed EEG data as information for diagnosing the subject's mental health condition and providing treatment support, the quality of psychotherapy and counseling can be improved. Furthermore, by providing a communication means for sending and receiving EEG data via the Internet, data can be easily shared with medical professionals and psychotherapists in remote locations.

[0006] "Electroencephalogram data" refers to signals that electrically record the subject's brain activity, and is mainly measured by attaching electrodes to the scalp.

[0007] "Storage device" refers to a device or medium for temporarily or permanently storing acquired data, including hard disks, SSDs, flash memory, etc.

[0008] "Artificial intelligence" refers to automated computer programs and algorithms that analyze large amounts of data and develop specific patterns and predictions.

[0009] "Analysis" refers to the processing and analysis performed to examine data and understand its structure, meaning, and trends.

[0010] "Stress" refers to a psychological and physiological reaction state caused by external pressure or stress.

[0011] "Anxiety" refers to a psychological state of fear, worry, and restlessness about future events.

[0012] "Depression" refers to a psychological condition characterized by abnormally persistent sadness, loss of interest, and lethargy.

[0013] "Output" refers to the process of externally displaying, transmitting, or providing data or information such as analysis results to other devices or systems.

[0014] "Communication means" is a general term for technologies and devices for sending and receiving data from one point to another, including the Internet, Wi-Fi, Bluetooth, etc.

[0015] "Diagnosis" refers to the process of assessing and determining a subject's health or psychological state based on specific symptoms and data.

[0016] "Therapeutic support" refers to the provision of information and technology to assist medical professionals and psychotherapists in providing treatment and counseling.

[0017] "User" refers to any individual or organization that uses the System, including patients, medical professionals, and psychotherapists. [Brief explanation of the drawings]

[0018] [Figure 1] 1 is a conceptual diagram showing an example of the configuration of a data processing system according to a first embodiment. [Figure 2] 1 is a conceptual diagram showing an example of main functions of a data processing device and a smart device according to a first embodiment. [Figure 3] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a second embodiment. [Figure 4] FIG. 10 is a conceptual diagram showing an example of main functions of a data processing device and smart glasses according to a second embodiment. [Figure 5] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a third embodiment. [Figure 6] FIG. 11 is a conceptual diagram showing an example of main functions of a data processing device and a headset-type terminal according to a third embodiment. [Figure 7] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a fourth embodiment. [Figure 8] FIG. 10 is a conceptual diagram showing an example of main functions of a data processing device and a robot according to a fourth embodiment. [Figure 9] 1 shows an emotion map onto which multiple emotions are mapped. [Figure 10] 1 shows an emotion map onto which multiple emotions are mapped. [Figure 11] FIG. 3 is a sequence diagram showing a processing flow of the data processing system according to the first embodiment. [Figure 12] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system in Application Example 1. [Figure 13] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system according to the second embodiment when an emotion engine is combined. [Figure 14] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system in Application Example 2 when an emotion engine is combined. DETAILED DESCRIPTION OF THE INVENTION

[0019] An example of an embodiment of a system according to the technology of the present disclosure will be described below with reference to the accompanying drawings.

[0020] First, the terms used in the following description will be explained.

[0021] In the following embodiments, a coded processor (hereinafter simply referred to as a "processor") may be a single arithmetic device or a combination of multiple arithmetic devices. Furthermore, a processor may be a single type of arithmetic device or a combination of multiple types of arithmetic devices. Examples of arithmetic devices include a CPU (Central Processing Unit), a GPU (Graphics Processing Unit), a GPGPU (General-Purpose computing on Graphics Processing Units), and an APU (Accelerated Processing Unit).

[0022] In the following embodiments, a coded RAM (Random Access Memory) is a memory in which information is temporarily stored and is used as a working memory by a processor.

[0023] In the following embodiments, the coded storage is one or more non-volatile storage devices that store various programs, various parameters, etc. Examples of non-volatile storage devices include flash memory (SSD (Solid State Drive)), magnetic disks (e.g., hard disks), and magnetic tapes.

[0024] In the following embodiments, a communication I / F (Interface) with a symbol is an interface including a communication processor, an antenna, etc. The communication I / F controls communication between multiple computers. Examples of communication standards applied to the communication I / F include wireless communication standards including 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), Bluetooth (registered trademark), etc.

[0025] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B." In other words, "A and / or B" means that it may be only A, only B, or a combination of A and B. Furthermore, in this specification, the same concept as "A and / or B" is also applied when three or more things are expressed connected by "and / or."

[0026] [First embodiment]

[0027] FIG. 1 shows an example of the configuration of a data processing system 10 according to the first embodiment.

[0028] 1, a data processing system 10 includes a data processing device 12 and a smart device 14. An example of the data processing device 12 is a server.

[0029] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0030] The smart device 14 includes a computer 36, a reception device 38, an output device 40, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The reception device 38, the output device 40, and the camera 42 are also connected to the bus 52.

[0031] The reception device 38 includes a touch panel 38A, a microphone 38B, and the like, and receives user input. The touch panel 38A detects contact with an indicator (for example, a pen or a finger) to receive user input by the touch of the indicator. The microphone 38B detects the user's voice to receive user input by voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the data indicating the user input.

[0032] The output device 40 includes a display 40A and a speaker 40B, and presents data to the user 20 by outputting the data in a form of expression that the user 20 can perceive (for example, audio and / or text). The display 40A displays visible information such as text and images in accordance with instructions from the processor 46. The speaker 40B outputs audio in accordance with instructions from the processor 46. The camera 42 is a compact digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0033] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 control the exchange of various information between the processor 46 and the processor 28 via the network 54.

[0034] FIG. 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0035] 2, in the data processing device 12, a specific process is performed by the processor 28. A specific processing program 56 is stored in the storage 32. The specific processing program 56 is an example of a "program" according to the technology of the present disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific process is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0036] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0037] In the smart device 14, the processor 46 performs the reception output process. The storage 50 stores a reception output program 60. The reception output program 60 is used in conjunction with the specific processing program 56 by the data processing system 10. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[0038] Next, a description will be given of the specific processing performed by the specific processing unit 290 of the data processing device 12. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0039] The system of the present invention collects the user's brain wave data and analyzes it using artificial intelligence to identify and predict unconscious stress, anxiety, and depression, and provides the results to medical professionals and psychotherapists. This system consists of the following components:

[0040] First, a device is required to acquire EEG data. The user attaches electrodes to their scalp to measure EEG signals, which are then captured as digital data. This digital data is then sent to a server and stored in a storage device.

[0041] The server then stores the received EEG data in a storage device, where it is prepared for analysis. The server then analyzes the data using an artificial intelligence model, which uses pre-trained machine learning algorithms that have learned subconscious patterns of stress, anxiety, and depression from a large number of sample data.

[0042] Once the analysis is complete, the server outputs the results, which may include whether or not the patient is stressed, their level of anxiety, their level of depression, etc. The server has the means to provide these results to the user, and can also transmit the results over the Internet for use by medical professionals and psychotherapists.

[0043] Furthermore, by allowing users to check the results through an interface, it can promote self-insight and be used as reference information for necessary treatment or counseling. The analysis results are displayed in a concise and easy-to-understand format, and it can also suggest countermeasures depending on the state of stress, anxiety, and depression.

[0044] As a concrete example, consider a case where a user wears an EEG measuring device to capture EEG data while sleeping. This data is sent to a server and stored in a storage device. The server uses an artificial intelligence model to analyze this data and generate a result, such as "the user exhibits high stress levels." This result is provided to the user through a web interface and, if necessary, automatically sent to a medical professional. Medical professionals can use this information to recommend appropriate treatment methods for the user.

[0045] In this way, the system of the present invention enables highly accurate assessment of psychological states by analyzing electroencephalogram data, realizing a new approach to providing appropriate psychotherapy and treatment for individual users.

[0046] The processing flow will be explained below.

[0047] Step 1:

[0048] The user uses an electroencephalogram (EEG) measuring device to obtain brainwave data while sleeping. This device collects brainwave signals through electrodes attached to the scalp and converts them into digital data.

[0049] Step 2:

[0050] The user's device transmits this EEG data to a server, typically transferred over a network in real time or in batches.

[0051] Step 3:

[0052] The server stores the received EEG data in a storage device, and at this time, it also checks for errors to ensure data integrity and accurate storage.

[0053] Step 4:

[0054] The server prepares the EEG data stored in the storage device to be input into the artificial intelligence model (AI model), performing preprocessing such as data normalization and feature extraction.

[0055] Step 5:

[0056] The server analyzes the EEG data using a pre-trained AI model, which is trained with machine learning algorithms to identify unconscious patterns of stress, anxiety, and depression.

[0057] Step 6:

[0058] The server generates the analysis results output by the AI ​​model, specifically summarizing the results in the form of numerical values ​​and reports that indicate psychological states such as stress levels, depression levels, and anxiety levels.

[0059] Step 7:

[0060] The server outputs the analysis results to the user via a display device or a web interface, and the user can access a dedicated web page to check their own analysis results.

[0061] Step 8:

[0062] Users can use the analysis results to deepen their self-insight and share it with medical professionals or psychotherapists as needed. Medical professionals can then receive the analysis results and propose optimal treatment plans and counseling methods for the user.

[0063] Step 9:

[0064] The server continuously collects and stores data for further analysis and trend analysis, and this data is used to improve future models and analyze new psychological states.

[0065] Step 10:

[0066] The server provides analysis results in real time via the internet to those who need them, and is equipped with a function that allows data to be smoothly shared with medical professionals in remote locations, for example, through remote counseling or telemedicine.

[0067] In this way, this system realizes a series of processes that collect and analyze the user's EEG data and provide the results to the user and medical professionals, thereby supporting more effective psychotherapy and treatment.

[0068] Example 1

[0069] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0070] Conventional EEG data analysis systems have had difficulty accurately detecting and predicting unconscious stress, anxiety, and depression. Furthermore, there was a lack of a means to quickly and intuitively provide analysis results to users and medical professionals, hindering their linkage to appropriate psychotherapy and treatment. Furthermore, insufficient pre-processing, such as data normalization and noise removal, often limited the accuracy of the analysis.

[0071] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[0072] In this invention, the server

[0073] a means for acquiring electroencephalogram data;

[0074] means for storing the acquired electroencephalogram data in a storage device;

[0075] means for pre-processing the electroencephalogram data stored in the storage device;

[0076] A means for analyzing the pre-processed EEG data using a generative AI model;

[0077] A means for outputting the analysis results;

[0078] a display device for providing the analysis results to a user or a medical professional;

[0079] This makes it possible to detect unconscious psychological states with high accuracy and provide the results to relevant parties at the appropriate time.

[0080] "Electroencephalogram data" refers to information obtained by quantifying signals that represent the electrical activity of the user's brain, obtained by a measuring device.

[0081] A "storage device" is a physical or virtual storage that holds digital data and allows it to be accessed and manipulated as needed.

[0082] "Preprocessing" refers to the procedure of normalizing, removing noise, segmenting, and otherwise processing the acquired EEG data to convert it into a format suitable for analysis.

[0083] A "generative AI model" is a model that uses artificial intelligence algorithms that have been pre-trained with large amounts of data and are capable of detecting specific patterns and anomalies.

[0084] "Analysis" refers to computational processes performed to assess subconscious psychological states based on pre-processed EEG data.

[0085] The "analysis results" are judgment information regarding stress, anxiety, and depression obtained from the analysis of EEG data by the generative AI model.

[0086] A "display device" is a device or software interface that visually presents analysis results to a user or medical professional.

[0087] The present invention is a system that collects a user's electroencephalogram data, analyzes it using artificial intelligence, identifies and predicts unconscious stress, anxiety, and depression, and provides the results to medical professionals and psychotherapists. This system is composed of the following components:

[0088] First, the user needs a device to collect brainwave data. The user wears an EEG measuring device on their scalp. This device measures the electrical signals of the brainwaves and converts them into digital data. The EEG measuring device itself can be a commercially available electroencephalograph.

[0089] The acquired digital data is then sent over the Internet to a server. The server receives the data, first stores it temporarily in memory, and then saves it in a storage device such as a database. The stored data is then pre-processed for analysis. This pre-processing includes data normalization, noise removal, segmentation, etc.

[0090] Once pre-processed, the data is fed into a generative AI model, which has been pre-trained with a large amount of data and is capable of detecting specific patterns and anomalies, specifically neural networks that identify subconscious states of stress, anxiety, and depression.

[0091] The analysis results are generated on the server and provided to users and healthcare professionals. The results are displayed through a web interface, allowing users to check their own status and share it with healthcare professionals as needed. The analysis results are presented in a visually understandable format, such as graphs and charts.

[0092] As a concrete example, consider a case where a user wears an EEG device at night to capture EEG data while sleeping. This data is transmitted in real time to a server and stored in a storage device. The server immediately pre-processes the data and inputs it into a generative AI model. The analysis results in a judgment that "the user exhibits high stress levels." This result is provided to the user through a web interface and, if necessary, automatically sent to medical professionals.

[0093] An example prompt might be, "Implement a program that uses a user's EEG data to analyze their subconscious stress levels. The EEG data will be sent digitally to a server and stored on a storage device. An artificial intelligence model will be used to analyze the data to determine the presence or absence of stress, the level of anxiety, and the level of depression. The results of the analysis will be provided to the user through a web interface and automatically sent to a medical professional if necessary."

[0094] This system can detect unconscious psychological states with high accuracy and provide users with appropriate insights and medical information.

[0095] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0096] Overall processing flow

[0097] The processing flow of this system is shown below in specific steps. At each step, the specific operation, input, and output are also explained.

[0098] Step 1: Collect EEG data

[0099] The user wears the EEG measurement device and begins measuring. The device converts the brain's electrical signals into digital data.

[0100] Input: User's EEG signal

[0101] Processing: The EEG signal is converted into digital data by the EEG measuring device.

[0102] Output: Digital EEG data

[0103] Step 2: Sending EEG data

[0104] The electroencephalogram measuring device transmits the acquired digital data to a server via the Internet.

[0105] Input: Digital EEG data

[0106] Processing: Data encryption and secure transmission over the Internet

[0107] Output: EEG data sent to the server

[0108] Step 3: Save the EEG data

[0109] The server temporarily stores the received brain wave data in memory, and then saves it in a storage device such as a database.

[0110] Input: Received EEG data

[0111] Processing: Temporarily storing data and storing it in a database

[0112] Output: EEG data stored in a database

[0113] Step 4: Preprocessing the data

[0114] The server retrieves the EEG data stored in the storage device and performs pre-processing for analysis.

[0115] Input: Stored EEG data

[0116] Processing: Data normalization, denoising, segmentation (e.g. splitting data by time)

[0117] Output: Pre-processed EEG data

[0118] Step 5: Analyze the data

[0119] The server inputs the pre-processed EEG data into a generative AI model for analysis.

[0120] Input: Pre-processed EEG data

[0121] Processing: Generative AI models analyze data and detect patterns of stress, anxiety, and depression

[0122] Output: Analysis results (stress level, anxiety level, depression level)

[0123] Step 6: Generate analysis results

[0124] The server converts the analysis results obtained by the generative AI model into a format that is easy for humans to understand.

[0125] Input: Analysis results from a generative AI model

[0126] Processing: Converting analysis results into different formats, generating graphs and charts

[0127] Output: Visually easy-to-understand analysis results

[0128] Step 7: Provide analysis results

[0129] The server provides the generated analysis results to users and medical professionals via a web interface.

[0130] Input: Visualized analysis results

[0131] Processing: Displaying results via web interface

[0132] Output: User and healthcare professional review of results

[0133] Specific operation example

[0134] 1. The user wears the EEG measurement device and presses a button to start measurement.

[0135] 2. The EEG measuring device automatically converts the EEG into digital data and sends it to the server.

[0136] 3. The server temporarily stores the received data in memory and then records it in the database.

[0137] 4. The server denoises the data, splits it into time segments, and feeds it into a generative AI model.

[0138] 5. The server uses machine learning algorithms to analyze your stress and anxiety levels.

[0139] 6. The server converts the analysis results into graphs and charts and displays them in a web interface.

[0140] 7. Users and healthcare professionals can review the results through a web interface and take action as necessary.

[0141] The above are the processing steps of the program of this system and their specific operations.

[0142] (Application example 1)

[0143] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0144] Conventional EEG analysis systems were limited to analyzing a user's EEG data to evaluate their psychological state, but lacked the ability to immediately grasp fluctuations in the user's psychological state in real time and provide appropriate countermeasures. This meant that they were unable to quickly respond to the stress and psychological burden experienced by customers and employees, particularly in brick-and-mortar stores, posing challenges to improving customer experience and employee mental health care.

[0145] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[0146] In this invention, the server includes means for acquiring electroencephalogram data, means for storing the acquired electroencephalogram data in a storage device, means for analyzing the electroencephalogram data stored in the storage device using artificial intelligence, means for outputting the analysis results, and means for sending notifications in real time based on the analysis results. This enables real-time monitoring of the psychological states of customers and employees, allows appropriate countermeasures to be immediately provided, and enables an improved customer experience and mental health care for employees.

[0147] "EEG data" is digital data of electrical signals that measure the user's brain activity.

[0148] The "storage device" is a device for storing acquired electroencephalogram data.

[0149] "Artificial intelligence" is a technology that uses machine learning algorithms to analyze brainwave data and predict and identify subconscious psychological states.

[0150] The "analysis results" are information on stress levels and psychological state generated based on brain wave data analyzed by artificial intelligence.

[0151] "Notifications" are messages or alerts sent in real time via a display or other device with appropriate countermeasures based on the analysis results.

[0152] A "display device" is a device that visually presents analysis results and notifications to users.

[0153] "Real-time" means that the process from acquiring EEG data to notifying the analysis results is carried out instantly.

[0154] The system of this invention acquires a user's brainwave data in real time and analyzes it using artificial intelligence (AI) to identify stress, anxiety, and depression. Based on the analysis results, it also notifies appropriate countermeasures in real time, thereby improving the customer experience and mental health care for employees, primarily in brick-and-mortar stores.

[0155] Program Generation

[0156] In the system of the present invention, the program performs the following processing.

[0157] First, a user (customer or employee) wears an EEG measurement device, and EEG data is acquired from the device. EEG data is collected using hardware such as a wearable device or headset. This EEG data is sent to a server in real time and stored in a storage device.

[0158] The server analyzes the stored EEG data using a pre-trained AI model (e.g., a neural network model using Keras), which estimates the user's stress level and subconscious psychological state. The analysis uses a machine learning algorithm using a large amount of sample data.

[0159] The analysis results are generated in real time from the server, with specific output such as "The user's stress level is high." Based on this result, the system generates a notification suggesting appropriate countermeasures.

[0160] Processing Description

[0161] The server normalizes the acquired data, inputs it into an AI model, and outputs the analysis results. Specifically, a numerical calculation library such as Numpy is used for data normalization, and a deep learning framework such as Keras is used for machine learning.

[0162] The means for sending notifications to users based on the analysis results includes the ability to send messages in real time via a REST API, which are delivered to browsers and smartphone displays.

[0163] For example, if a store detects a customer's high stress level, it may play relaxing music or suggest a specific product for that customer. Similarly, if an employee is found to be under high stress, a notification will be sent to the manager, who will recommend appropriate breaks.

[0164] Specific examples

[0165] For example, suppose a customer wears a wearable device while selecting a product in a physical store, and brain wave data is collected at that time. This data is sent to a server in real time, and the stress level is analyzed. If the analysis result indicates that the customer's stress level is high, the system will notify the store staff to play relaxing music.

[0166] An example of a prompt sentence is, "Please suggest measures to take if customer M has a high stress level at a healthcare store. For example, this could include playing relaxing music, suggesting specific products, or guiding the customer through the store."

[0167] In this way, by using the system of the present invention, it is possible to grasp the psychological state of customers and respond appropriately in real time, thereby improving customer experience and providing mental health care for employees.

[0168] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0169] Step 1:

[0170] EEG data acquisition

[0171] The user wears an EEG measurement device, which captures brainwaves in real time. The device captures brainwaves as electrical signals, converts them into digital data, and transmits them to a server.

[0172] Input: User's EEG signal

[0173] Output: Digitized EEG data

[0174] Step 2:

[0175] Data storage

[0176] The server stores the received digitized EEG data in a storage device. In this step, the data is verified and stored accurately to ensure its reliability.

[0177] Input: Digitized EEG data

[0178] Output: EEG data stored in a memory device

[0179] Step 3:

[0180] Data Preprocessing

[0181] The server normalizes the EEG data stored in the storage device, a process that uses a numerical computing library such as Numpy to standardize the data and convert it into a format that can be input into an AI model.

[0182] Input: EEG data stored in a memory device

[0183] Output: Normalized EEG data

[0184] Step 4:

[0185] AI-based analysis

[0186] The server then feeds the normalized EEG data into a pre-trained AI model that analyzes stress levels and subconscious psychological states using deep learning frameworks such as Keras.

[0187] Input: Normalized EEG data

[0188] Output: Analysis results of stress level and psychological state

[0189] Step 5:

[0190] Saving and monitoring analysis results

[0191] The server stores the analysis results in a storage device and monitors them in real time. In this step, the server continuously monitors the user's psychological state based on the analysis results.

[0192] Input: Analysis results

[0193] Output: Analysis results and monitoring data stored in a storage device

[0194] Step 6:

[0195] Generate notifications

[0196] The server generates notifications based on the analysis, suggesting appropriate actions to take, which are provided to users and store staff in real time and sent via a REST API.

[0197] Input: Analysis results

[0198] Output: Notification message with workaround

[0199] Step 7:

[0200] Sending notifications

[0201] The server then sends the generated notification message to a display device or smartphone, allowing users and store staff to immediately understand the appropriate countermeasures based on the analysis results.

[0202] Input: Notification message

[0203] Output: Notification display to users and store staff

[0204] In this way, the system of the present invention collects and analyzes users' brainwave data and provides appropriate countermeasures in real time, thereby improving the customer experience in physical stores and providing mental health care for employees.

[0205] Furthermore, an emotion engine that estimates the user's emotion may be combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0206] The system of the present invention collects and analyzes the user's brain wave data, identifies and predicts the user's subconscious psychological state, and then uses an emotion engine to recognize the user's emotional state, providing the results to the user and medical professionals.

[0207] First, a device for acquiring EEG data is prepared. This device collects EEG signals through electrodes attached to the user's scalp and converts them into digital data. The user uses this device to acquire EEG data while sleeping or relaxing.

[0208] The user's device then transmits the acquired EEG data to a server, typically transferred in real time or in batches over the internet. The server stores the received EEG data in a storage device and performs error checking to ensure data integrity.

[0209] The server prepares the EEG data stored in the storage device for analysis. Specifically, it normalizes the data, extracts features, and performs preprocessing. The EEG data is then analyzed using a pre-trained artificial intelligence model. This allows it to identify and predict subconscious psychological states such as stress, anxiety, and depression.

[0210] Furthermore, the server uses an emotion engine to recognize the user's emotional state from the analysis of the brainwave data and psychological state. The emotion engine uses a machine learning algorithm and can analyze emotions from the user's brainwave patterns and other physiological data.

[0211] The analysis results include stress levels, depression levels, and anxiety levels, as well as emotional states (e.g., happiness, sadness, anger, etc.). The server outputs these results to the user via a display device or web interface. Users can access a dedicated web page or application to check their own analysis results and emotional states.

[0212] As a concrete example, consider the case where a user wears an EEG measuring device and acquires EEG data while relaxing at the end of the day. This data is sent to a server and stored in a storage device. The server analyzes the data using an artificial intelligence model and generates an analysis result such as, "The user has a high stress level and exhibits anxiety and mild depression." Furthermore, the emotion engine determines that "The user is currently experiencing strong feelings of anger." This result is provided to the user through a web interface and is also automatically sent to a medical professional. Based on this information, the medical professional can suggest the most appropriate treatment or counseling method for the user.

[0213] In this way, by analyzing EEG data and combining it with an emotion engine, this system can identify and predict the user's psychological and emotional state in detail and provide appropriate feedback and countermeasures, thereby improving the accuracy and effectiveness of psychotherapy and therapeutic support.

[0214] The processing flow will be explained below.

[0215] Step 1:

[0216] The user wears an EEG measuring device on their scalp to obtain brain wave data during sleep, relaxation, etc. This device electrically collects brain wave signals through electrodes and converts them into digital data.

[0217] Step 2:

[0218] The user's device transmits the acquired EEG data to a server. This data is transferred to the server via the Internet in real time or in batches. Security protocols and data compression techniques are used to ensure the data transmission is secure and efficient.

[0219] Step 3:

[0220] The server stores the received EEG data in a storage device. During the storage process, an error check is performed to confirm the integrity and accuracy of the data. The server notifies the user's device that the data has been successfully stored.

[0221] Step 4:

[0222] The server prepares the EEG data stored in the storage device for analysis. Specifically, it performs preprocessing such as data normalization, filtering, and feature extraction to prepare the data for appropriate analysis.

[0223] Step 5:

[0224] The server analyzes the pre-processed EEG data using a pre-trained artificial intelligence model that uses machine learning algorithms to identify unconscious patterns of stress, anxiety, and depression.

[0225] Step 6:

[0226] The server generates the analysis results from the AI ​​model. Specifically, the results are summarized in the form of numerical values ​​and reports that indicate psychological states such as stress levels, anxiety levels, and depression levels. These analysis results are also used as the base data for subsequent emotion analysis.

[0227] Step 7:

[0228] The server uses an emotion engine to recognize the user's emotional state from the EEG data and the results of the previous analysis. The emotion engine uses a machine learning algorithm to identify emotions from the user's EEG patterns. The results include the user's state of happiness, sadness, anger, etc.

[0229] Step 8:

[0230] The server integrates the analyzed psychological and emotional state results and outputs a single report that contains a complete picture of the user's psychological and emotional state in a format that is easy for users and medical professionals to understand.

[0231] Step 9:

[0232] The server displays the output analysis results via an interface for providing them to the user, who can then access a dedicated web page or application to check their own analysis results.

[0233] Step 10:

[0234] Users can deepen their self-insight based on the analysis results provided and share them with medical professionals or psychotherapists as needed, allowing medical professionals to propose optimal treatment plans and counseling methods for the user based on the analysis results.

[0235] In this way, this system collects and analyzes the user's brainwave data and combines it with an emotion engine to identify the user's subconscious psychological and emotional states in detail and provide appropriate feedback, enabling more accurate psychotherapy and treatment support.

[0236] Example 2

[0237] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0238] Conventional psychological and emotional state analysis systems have low accuracy, making it difficult to provide users with appropriate feedback or therapeutic support. Furthermore, obtaining highly accurate analysis results requires extensive specialized knowledge and expensive equipment, making them difficult for average users to use. The purpose of this invention is to solve these problems and provide a system that can obtain more accurate analysis results and is easy to use.

[0239] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[0240] In this invention, the server includes means for preprocessing EEG data, means for analyzing the preprocessed EEG data using an artificial intelligence model, and means for using an emotion engine that recognizes an emotional state from the EEG data and the analysis results. This enables accurate preprocessing of the EEG data, realizes highly accurate analysis of psychological and emotional states, and makes it possible to provide appropriate feedback and therapeutic support to the user.

[0241] "Electroencephalogram data" refers to electrical signals generated from the user's brain, acquired as digital data.

[0242] "Storage device" refers to a database or other digital storage medium for storing acquired EEG data.

[0243] "Preprocessing" refers to processes such as data normalization, feature extraction, and noise removal to prepare EEG data in a form that is easier to analyze.

[0244] An "artificial intelligence model" is a model trained using machine learning algorithms to analyze EEG data and identify psychological states.

[0245] The "emotion engine" is an algorithm that recognizes the user's emotional state based on the analysis results of brainwave data and artificial intelligence models.

[0246] "Analysis results" are data representing the user's psychological and emotional state obtained by the artificial intelligence model and emotion engine.

[0247] This invention is a system that collects and analyzes a user's electroencephalogram data to identify and predict their subconscious psychological and emotional states. This system includes a device for acquiring electroencephalogram data, a terminal for transmitting the data to a server, a server for storing and analyzing the data, and a function for outputting the analysis results.

[0248] First, the user uses a device to acquire brain waves. This device has the function of collecting brain wave signals through electrodes attached to the scalp and converting them into digital data. Specific devices include an electroencephalograph and scalp electrodes. The user uses this device to acquire brain wave data, usually while relaxed or asleep.

[0249] The user's device then transmits the acquired EEG data to a server via the internet. The device is equipped with Wi-Fi or mobile data capabilities to ensure stable communication. The data is sent in real time or in batches at the end of the day.

[0250] The server stores the received EEG data in a storage device (e.g., a database) and performs error checking. It then preprocesses the stored data. This preprocessing includes normalization, feature extraction, and noise removal. The server uses high-performance hardware (e.g., a CPU or GPU) to quickly perform these preprocessing steps.

[0251] The server then analyzes the pre-processed EEG data using a pre-trained artificial intelligence model that employs machine learning algorithms to identify and predict the user's subconscious psychological states, such as stress, anxiety, and depression.

[0252] After the analysis is complete, the server uses an emotion engine to recognize the user's emotional state from the analysis of the EEG data and psychological state. The emotion engine also uses machine learning algorithms to analyze emotions (e.g., happiness, sadness, anger, etc.) from the user's EEG patterns and other physiological data.

[0253] Finally, the psychological and emotional states obtained as analysis results are provided to the user via a dedicated web interface or mobile application. Users can check their own psychological and emotional states through this interface. The analysis results can also be automatically sent to medical professionals as needed to be used as reference information for treatment and counseling.

[0254] As a concrete example, consider a case where a user wears an EEG measuring device at the end of the day and captures EEG data while relaxing. This data is sent by the device to a server and stored in a storage device. The server analyzes the data using an artificial intelligence model and generates an analysis result such as "The user has a high stress level and exhibits anxiety and mild depression." The emotion engine then determines that "The user is currently experiencing strong emotions of anger." These results are notified to the user through a web interface and are also sent to medical professionals.

[0255] Example prompt sentence:

[0256] "The user wore an EEG measuring device and, while relaxed, brain wave data was collected. The data was sent to a server and stored. The server analyzed the data using an artificial intelligence model and produced results indicating the user's high stress levels, anxiety, and mild depression. Furthermore, the emotion engine determined that the user was experiencing strong feelings of anger. The analysis results were provided to the user through a web interface and also sent to medical professionals."

[0257] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0258] Step 1:

[0259] The user wears an EEG measurement device to acquire EEG data. The device collects EEG signals as digital data through electrodes attached to the scalp. For example, EEG data can be acquired by wearing the device while the user is relaxing at night. The input of this step is the user's EEG signals, and the output is digital EEG data.

[0260] Step 2:

[0261] The device transmits the acquired EEG data to a server via the internet. The device is equipped with Wi-Fi or mobile data capabilities to ensure stable communication. The data is transmitted in real time or in batches at the end of the day. The input of this step is digital EEG data, and the output is the EEG data transmitted to the server.

[0262] Step 3:

[0263] The server stores the received EEG data in a storage device. At this time, the server performs an error check to confirm the consistency of the data. For example, it maintains the integrity of the data by checking whether the data format is correct and whether there is any missing data. The input of this step is the EEG data sent from the terminal, and the output is the EEG data stored in the storage device.

[0264] Step 4:

[0265] The server preprocesses the EEG data stored in the storage device. Preprocessing includes data normalization, feature extraction, and noise removal. For example, applying a noise removal algorithm to improve the data quality. The input of this step is the EEG data stored in the storage device, and the output is the preprocessed EEG data.

[0266] Step 5:

[0267] The server analyzes the preprocessed EEG data using a pre-trained artificial intelligence model. This model uses machine learning algorithms to identify and predict the user's psychological states, such as stress, anxiety, and depression. For example, it uses a high-performance GPU to quickly analyze large amounts of data. The input of this step is the preprocessed EEG data, and the output is the psychological state analysis result.

[0268] Step 6:

[0269] The server uses an emotion engine to recognize the user's emotional state from the analysis results and EEG data. This emotion engine also uses a machine learning algorithm to analyze emotions such as happiness, sadness, and anger from EEG patterns. The input of this step is the psychological state analysis results and EEG data, and the output is the resulting emotional state.

[0270] Step 7:

[0271] The server notifies the user and healthcare professionals of the analysis results and the recognized emotional state. The results are provided through a dedicated web interface or mobile application. For example, users can check the results on their own devices, and healthcare professionals can use them as reference information for treatment. The input of this step is the emotional state result, and the output is the analysis results provided to the user and healthcare professionals.

[0272] (Application example 2)

[0273] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0274] Conventional autonomous vehicle systems have had the challenge of being unable to grasp the emotional and psychological states of drivers and passengers in real time and provide appropriate feedback accordingly. This has made it difficult to ensure maximum passenger safety and comfort. There has also been a lack of concrete measures to reduce psychological stress and anxiety.

[0275] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 2 is realized by the following means.

[0276] In this invention, the server includes means for acquiring electroencephalogram data, means for storing the acquired electroencephalogram data in a storage device, means for analyzing the electroencephalogram data stored in the storage device using artificial intelligence, and means for providing feedback to the vehicle control system based on the generated analysis results, thereby enabling real-time feedback based on the user's psychological and emotional states.

[0277] "Electroencephalogram data" refers to data that is generated by electronically collecting a user's electroencephalogram activity and converting that information into a digital format.

[0278] A "storage device" is a hardware or software component used to store acquired data.

[0279] "Artificial intelligence" refers to algorithms and systems that learn from large amounts of data and perform analysis and predictions.

[0280] "Analysis results" are the information obtained from the analysis of acquired EEG data by artificial intelligence.

[0281] An "automobile control system" is a system for controlling an autonomous vehicle, and is a device that has the function of automatically operating the vehicle.

[0282] "Feedback" refers to various responses and instructions given to the system or user based on the analysis results.

[0283] "Mental state" is information that indicates the user's mental state and emotional fluctuations.

[0284] This invention collects and analyzes the EEG data of passengers in an autonomous vehicle in real time, and provides feedback to the vehicle's control system based on the analysis results. To achieve this, an EEG data acquisition device, a server, a storage device, an artificial intelligence (AI) analysis system, and an automobile control system are required.

[0285] First, the user wears an EEG data acquisition device and EEG data is collected in real time. This data is transmitted to a server via wireless communication or the Internet. The server then stores the received EEG data in a storage device.

[0286] The server then transmits the stored EEG data to an AI analysis system for analysis. The AI ​​analysis system uses a pre-trained generative AI model to analyze the EEG data and identify the user's psychological and emotional state. For example, specific EEG patterns can be used to detect a user's stress level, anxiety, or relaxation state.

[0287] The analysis results are sent back to the server, which then provides feedback to the car's control system. For example, if the user is feeling highly stressed, the vehicle's control system can automatically select a relaxing route and adjust the in-car environment. It can also play ambient music or provide interesting information when the user is in a relaxed state.

[0288] This allows the driving experience of autonomous vehicles to be optimized based on the user's psychological and emotional state, improving safety and comfort.

[0289] For example, if a user feels high stress during a long drive, the server will automatically recommend a route change based on the analysis results, and the vehicle will switch to a route with better scenery.The system will also automatically adjust the in-car music selection and air conditioning settings to promote relaxation for the user.

[0290] Here are some examples of prompts to input to the generative AI model:

[0291] "Please suggest the best machine learning model to analyze the stress level and emotional state of the user from their EEG data. Also, I would like your advice on how to provide the best vehicle navigation action when high stress is detected."

[0292] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0293] Step 1:

[0294] The user wears an EEG data acquisition device. This device has the function of collecting the user's EEG in real time and converting it into a digital format. The input is the user's EEG signal, and the output is digitized EEG data.

[0295] Step 2:

[0296] The digitized EEG data collected by the EEG data acquisition device is sent to the user's device using wireless communication or Bluetooth. The input is the digitized EEG data, and the output is the data sent to the user's device.

[0297] Step 3:

[0298] The EEG data received by the user's device is sent to the server. The data is sent via the Internet. The input is the EEG data stored on the user's device, and the output is the EEG data sent to the server.

[0299] Step 4:

[0300] The EEG data received by the server is stored in a storage device. Specifically, a database management system is used. The input is the EEG data sent to the server, and the output is the data stored in the storage device.

[0301] Step 5:

[0302] The server uses artificial intelligence to analyze the EEG data stored in the storage device. A generative AI model is used for this analysis, and it performs feature extraction and data normalization. The input is the EEG data stored in the storage device, and the output is the analysis results.

[0303] Step 6:

[0304] The server receives the analysis results from the AI ​​and identifies the user's psychological and emotional state. Specifically, it determines the user's stress level, anxiety, relaxation state, etc. The input is the analysis results, and the output is the identified psychological and emotional state.

[0305] Step 7:

[0306] The server then provides feedback to the vehicle's control system based on the analysis results. For example, if high stress is detected, a relaxing route will be automatically selected. The input is the identified psychological and emotional state, and the output is vehicle control instructions as feedback.

[0307] Step 8:

[0308] The vehicle's control system receives feedback from the server and adjusts the vehicle's behavior and environmental settings, such as changing the navigation route, adjusting the air conditioning temperature, or selecting in-car music. The input is vehicle control instructions, and the output is the actual vehicle adjustment behavior.

[0309] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0310] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0311] In the above embodiment, an example in which the specific process is performed by the data processing device 12 has been given, but the technology of the present disclosure is not limited to this, and the specific process may be performed by the smart device 14.

[0312] [Second embodiment]

[0313] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0314] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0315] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0316] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, and the camera 42 are also connected to the bus 52.

[0317] The microphone 238 receives instructions and the like from the user 20 by receiving voice uttered by the user 20. The microphone 238 captures the voice uttered by the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio in accordance with instructions from the processor 46.

[0318] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the surroundings of user 20 (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0319] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0320] Fig. 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Fig. 4, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0321] The specific processing program 56 is an example of a "program" according to the technology of the present disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0322] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0323] In the smart glasses 214, the reception output process is performed by the processor 46. A reception output program 60 is stored in the storage 50. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[0324] Next, a description will be given of the identification process performed by the identification processing unit 290 of the data processing device 12. In the following description, the data processing device 12 will be referred to as the "server" and the smart glasses 214 will be referred to as the "terminal."

[0325] The system of the present invention collects the user's brain wave data and analyzes it using artificial intelligence to identify and predict unconscious stress, anxiety, and depression, and provides the results to medical professionals and psychotherapists. This system consists of the following components:

[0326] First, a device is required to acquire EEG data. The user attaches electrodes to their scalp to measure EEG signals, which are then captured as digital data. This digital data is then sent to a server and stored in a storage device.

[0327] The server then stores the received EEG data in a storage device, where it is prepared for analysis. The server then analyzes the data using an artificial intelligence model, which uses pre-trained machine learning algorithms that have learned subconscious patterns of stress, anxiety, and depression from a large number of sample data.

[0328] Once the analysis is complete, the server outputs the results, which may include whether or not the patient is stressed, their level of anxiety, their level of depression, etc. The server has the means to provide these results to the user, and can also transmit the results over the Internet for use by medical professionals and psychotherapists.

[0329] Furthermore, by allowing users to check the results through an interface, it can promote self-insight and be used as reference information for necessary treatment or counseling. The analysis results are displayed in a concise and easy-to-understand format, and it can also suggest countermeasures depending on the state of stress, anxiety, and depression.

[0330] As a concrete example, consider a case where a user wears an EEG measuring device to capture EEG data while sleeping. This data is sent to a server and stored in a storage device. The server uses an artificial intelligence model to analyze this data and generate a result, such as "the user exhibits high stress levels." This result is provided to the user through a web interface and, if necessary, automatically sent to a medical professional. Medical professionals can use this information to recommend appropriate treatment methods for the user.

[0331] In this way, the system of the present invention enables highly accurate assessment of psychological states by analyzing electroencephalogram data, realizing a new approach to providing appropriate psychotherapy and treatment for individual users.

[0332] The processing flow will be explained below.

[0333] Step 1:

[0334] The user uses an electroencephalogram (EEG) measuring device to obtain brainwave data while sleeping. This device collects brainwave signals through electrodes attached to the scalp and converts them into digital data.

[0335] Step 2:

[0336] The user's device transmits this EEG data to a server, typically transferred over a network in real time or in batches.

[0337] Step 3:

[0338] The server stores the received EEG data in a storage device, and at this time, it also checks for errors to ensure data integrity and accurate storage.

[0339] Step 4:

[0340] The server prepares the EEG data stored in the storage device to be input into the artificial intelligence model (AI model), performing preprocessing such as data normalization and feature extraction.

[0341] Step 5:

[0342] The server analyzes the EEG data using a pre-trained AI model, which is trained with machine learning algorithms to identify unconscious patterns of stress, anxiety, and depression.

[0343] Step 6:

[0344] The server generates the analysis results output by the AI ​​model, specifically summarizing the results in the form of numerical values ​​and reports that indicate psychological states such as stress levels, depression levels, and anxiety levels.

[0345] Step 7:

[0346] The server outputs the analysis results to the user via a display device or a web interface, and the user can access a dedicated web page to check their own analysis results.

[0347] Step 8:

[0348] Users can use the analysis results to deepen their self-insight and share it with medical professionals or psychotherapists as needed. Medical professionals can then receive the analysis results and propose optimal treatment plans and counseling methods for the user.

[0349] Step 9:

[0350] The server continuously collects and stores data for further analysis and trend analysis, and this data is used to improve future models and analyze new psychological states.

[0351] Step 10:

[0352] The server provides analysis results in real time via the internet to those who need them, and is equipped with a function that allows data to be smoothly shared with medical professionals in remote locations, for example, through remote counseling or telemedicine.

[0353] In this way, this system realizes a series of processes that collect and analyze the user's EEG data and provide the results to the user and medical professionals, thereby supporting more effective psychotherapy and treatment.

[0354] Example 1

[0355] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0356] Conventional EEG data analysis systems have had difficulty accurately detecting and predicting unconscious stress, anxiety, and depression. Furthermore, there was a lack of a means to quickly and intuitively provide analysis results to users and medical professionals, hindering their linkage to appropriate psychotherapy and treatment. Furthermore, insufficient pre-processing, such as data normalization and noise removal, often limited the accuracy of the analysis.

[0357] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[0358] In this invention, the server

[0359] a means for acquiring electroencephalogram data;

[0360] means for storing the acquired electroencephalogram data in a storage device;

[0361] means for pre-processing the electroencephalogram data stored in the storage device;

[0362] A means for analyzing the pre-processed EEG data using a generative AI model;

[0363] A means for outputting the analysis results;

[0364] a display device for providing the analysis results to a user or a medical professional;

[0365] This makes it possible to detect unconscious psychological states with high accuracy and provide the results to relevant parties at the appropriate time.

[0366] "Electroencephalogram data" refers to information obtained by quantifying signals that represent the electrical activity of the user's brain, obtained by a measuring device.

[0367] A "storage device" is a physical or virtual storage that holds digital data and allows it to be accessed and manipulated as needed.

[0368] "Preprocessing" refers to the procedure of normalizing, removing noise, segmenting, and otherwise processing the acquired EEG data to convert it into a format suitable for analysis.

[0369] A "generative AI model" is a model that uses artificial intelligence algorithms that have been pre-trained with large amounts of data and are capable of detecting specific patterns and anomalies.

[0370] "Analysis" refers to computational processes performed to assess subconscious psychological states based on pre-processed EEG data.

[0371] The "analysis results" are judgment information regarding stress, anxiety, and depression obtained from the analysis of EEG data by the generative AI model.

[0372] A "display device" is a device or software interface that visually presents analysis results to a user or medical professional.

[0373] The present invention is a system that collects a user's electroencephalogram data, analyzes it using artificial intelligence, identifies and predicts unconscious stress, anxiety, and depression, and provides the results to medical professionals and psychotherapists. This system is composed of the following components:

[0374] First, the user needs a device to collect brainwave data. The user wears an EEG measuring device on their scalp. This device measures the electrical signals of the brainwaves and converts them into digital data. The EEG measuring device itself can be a commercially available electroencephalograph.

[0375] The acquired digital data is then sent over the Internet to a server. The server receives the data, first stores it temporarily in memory, and then saves it in a storage device such as a database. The stored data is then pre-processed for analysis. This pre-processing includes data normalization, noise removal, segmentation, etc.

[0376] Once pre-processed, the data is fed into a generative AI model, which has been pre-trained with a large amount of data and is capable of detecting specific patterns and anomalies, specifically neural networks that identify subconscious states of stress, anxiety, and depression.

[0377] The analysis results are generated on the server and provided to users and healthcare professionals. The results are displayed through a web interface, allowing users to check their own status and share it with healthcare professionals as needed. The analysis results are presented in a visually understandable format, such as graphs and charts.

[0378] As a concrete example, consider a case where a user wears an EEG device at night to capture EEG data while sleeping. This data is transmitted in real time to a server and stored in a storage device. The server immediately pre-processes the data and inputs it into a generative AI model. The analysis results in a judgment that "the user exhibits high stress levels." This result is provided to the user through a web interface and, if necessary, automatically sent to medical professionals.

[0379] An example prompt might be, "Implement a program that uses a user's EEG data to analyze their subconscious stress levels. The EEG data will be sent digitally to a server and stored on a storage device. An artificial intelligence model will be used to analyze the data to determine the presence or absence of stress, the level of anxiety, and the level of depression. The results of the analysis will be provided to the user through a web interface and automatically sent to a medical professional if necessary."

[0380] This system can detect unconscious psychological states with high accuracy and provide users with appropriate insights and medical information.

[0381] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0382] Overall processing flow

[0383] The processing flow of this system is shown below in specific steps. At each step, the specific operation, input, and output are also explained.

[0384] Step 1: Collect EEG data

[0385] The user wears the EEG measurement device and begins measuring. The device converts the brain's electrical signals into digital data.

[0386] Input: User's EEG signal

[0387] Processing: The EEG signal is converted into digital data by the EEG measuring device.

[0388] Output: Digital EEG data

[0389] Step 2: Sending EEG data

[0390] The electroencephalogram measuring device transmits the acquired digital data to a server via the Internet.

[0391] Input: Digital EEG data

[0392] Processing: Data encryption and secure transmission over the Internet

[0393] Output: EEG data sent to the server

[0394] Step 3: Save the EEG data

[0395] The server temporarily stores the received brain wave data in memory, and then saves it in a storage device such as a database.

[0396] Input: Received EEG data

[0397] Processing: Temporarily storing data and storing it in a database

[0398] Output: EEG data stored in a database

[0399] Step 4: Preprocessing the data

[0400] The server retrieves the EEG data stored in the storage device and performs pre-processing for analysis.

[0401] Input: Stored EEG data

[0402] Processing: Data normalization, denoising, segmentation (e.g. splitting data by time)

[0403] Output: Pre-processed EEG data

[0404] Step 5: Analyze the data

[0405] The server inputs the pre-processed EEG data into a generative AI model for analysis.

[0406] Input: Pre-processed EEG data

[0407] Processing: Generative AI models analyze data and detect patterns of stress, anxiety, and depression

[0408] Output: Analysis results (stress level, anxiety level, depression level)

[0409] Step 6: Generate analysis results

[0410] The server converts the analysis results obtained by the generative AI model into a format that is easy for humans to understand.

[0411] Input: Analysis results from a generative AI model

[0412] Processing: Converting analysis results into different formats, generating graphs and charts

[0413] Output: Visually easy-to-understand analysis results

[0414] Step 7: Provide analysis results

[0415] The server provides the generated analysis results to users and medical professionals via a web interface.

[0416] Input: Visualized analysis results

[0417] Processing: Displaying results via web interface

[0418] Output: User and healthcare professional review of results

[0419] Specific operation example

[0420] 1. The user wears the EEG measurement device and presses a button to start measurement.

[0421] 2. The EEG measuring device automatically converts the EEG into digital data and sends it to the server.

[0422] 3. The server temporarily stores the received data in memory and then records it in the database.

[0423] 4. The server denoises the data, splits it into time segments, and feeds it into a generative AI model.

[0424] 5. The server uses machine learning algorithms to analyze your stress and anxiety levels.

[0425] 6. The server converts the analysis results into graphs and charts and displays them in a web interface.

[0426] 7. Users and healthcare professionals can review the results through a web interface and take action as necessary.

[0427] The above are the processing steps of the program of this system and their specific operations.

[0428] (Application example 1)

[0429] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0430] Conventional EEG analysis systems were limited to analyzing a user's EEG data to evaluate their psychological state, but lacked the ability to immediately grasp fluctuations in the user's psychological state in real time and provide appropriate countermeasures. This meant that they were unable to quickly respond to the stress and psychological burden experienced by customers and employees, particularly in brick-and-mortar stores, posing challenges to improving customer experience and employee mental health care.

[0431] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[0432] In this invention, the server includes means for acquiring electroencephalogram data, means for storing the acquired electroencephalogram data in a storage device, means for analyzing the electroencephalogram data stored in the storage device using artificial intelligence, means for outputting the analysis results, and means for sending notifications in real time based on the analysis results. This enables real-time monitoring of the psychological states of customers and employees, allows appropriate countermeasures to be immediately provided, and enables an improved customer experience and mental health care for employees.

[0433] "EEG data" is digital data of electrical signals that measure the user's brain activity.

[0434] The "storage device" is a device for storing acquired electroencephalogram data.

[0435] "Artificial intelligence" is a technology that uses machine learning algorithms to analyze brainwave data and predict and identify subconscious psychological states.

[0436] The "analysis results" are information on stress levels and psychological state generated based on brain wave data analyzed by artificial intelligence.

[0437] "Notifications" are messages or alerts sent in real time via a display or other device with appropriate countermeasures based on the analysis results.

[0438] A "display device" is a device that visually presents analysis results and notifications to users.

[0439] "Real-time" means that the process from acquiring EEG data to notifying the analysis results is carried out instantly.

[0440] The system of this invention acquires a user's brainwave data in real time and analyzes it using artificial intelligence (AI) to identify stress, anxiety, and depression. Based on the analysis results, it also notifies appropriate countermeasures in real time, thereby improving the customer experience and mental health care for employees, primarily in brick-and-mortar stores.

[0441] Program Generation

[0442] In the system of the present invention, the program performs the following processing.

[0443] First, a user (customer or employee) wears an EEG measurement device, and EEG data is acquired from the device. EEG data is collected using hardware such as a wearable device or headset. This EEG data is sent to a server in real time and stored in a storage device.

[0444] The server analyzes the stored EEG data using a pre-trained AI model (e.g., a neural network model using Keras), which estimates the user's stress level and subconscious psychological state. The analysis uses a machine learning algorithm using a large amount of sample data.

[0445] The analysis results are generated in real time from the server, with specific output such as "The user's stress level is high." Based on this result, the system generates a notification suggesting appropriate countermeasures.

[0446] Processing Description

[0447] The server normalizes the acquired data, inputs it into an AI model, and outputs the analysis results. Specifically, a numerical calculation library such as Numpy is used for data normalization, and a deep learning framework such as Keras is used for machine learning.

[0448] The means for sending notifications to users based on the analysis results includes the ability to send messages in real time via a REST API, which are delivered to browsers and smartphone displays.

[0449] For example, if a store detects a customer's high stress level, it may play relaxing music or suggest a specific product for that customer. Similarly, if an employee is found to be under high stress, a notification will be sent to the manager, who will recommend appropriate breaks.

[0450] Specific examples

[0451] For example, suppose a customer wears a wearable device while selecting a product in a physical store, and brain wave data is collected at that time. This data is sent to a server in real time, and the stress level is analyzed. If the analysis result indicates that the customer's stress level is high, the system will notify the store staff to play relaxing music.

[0452] An example of a prompt sentence is, "Please suggest measures to take if customer M has a high stress level at a healthcare store. For example, this could include playing relaxing music, suggesting specific products, or guiding the customer through the store."

[0453] In this way, by using the system of the present invention, it is possible to grasp the psychological state of customers and respond appropriately in real time, thereby improving customer experience and providing mental health care for employees.

[0454] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0455] Step 1:

[0456] EEG data acquisition

[0457] The user wears an EEG measurement device, which captures brainwaves in real time. The device captures brainwaves as electrical signals, converts them into digital data, and transmits them to a server.

[0458] Input: User's EEG signal

[0459] Output: Digitized EEG data

[0460] Step 2:

[0461] Data storage

[0462] The server stores the received digitized EEG data in a storage device. In this step, the data is verified and stored accurately to ensure its reliability.

[0463] Input: Digitized EEG data

[0464] Output: EEG data stored in a memory device

[0465] Step 3:

[0466] Data Preprocessing

[0467] The server normalizes the EEG data stored in the storage device, a process that uses a numerical computing library such as Numpy to standardize the data and convert it into a format that can be input into an AI model.

[0468] Input: EEG data stored in a memory device

[0469] Output: Normalized EEG data

[0470] Step 4:

[0471] AI-based analysis

[0472] The server then feeds the normalized EEG data into a pre-trained AI model that analyzes stress levels and subconscious psychological states using deep learning frameworks such as Keras.

[0473] Input: Normalized EEG data

[0474] Output: Analysis results of stress level and psychological state

[0475] Step 5:

[0476] Saving and monitoring analysis results

[0477] The server stores the analysis results in a storage device and monitors them in real time. In this step, the server continuously monitors the user's psychological state based on the analysis results.

[0478] Input: Analysis results

[0479] Output: Analysis results and monitoring data stored in a storage device

[0480] Step 6:

[0481] Generate notifications

[0482] The server generates notifications based on the analysis, suggesting appropriate actions to take, which are provided to users and store staff in real time and sent via a REST API.

[0483] Input: Analysis results

[0484] Output: Notification message with workaround

[0485] Step 7:

[0486] Sending notifications

[0487] The server then sends the generated notification message to a display device or smartphone, allowing users and store staff to immediately understand the appropriate countermeasures based on the analysis results.

[0488] Input: Notification message

[0489] Output: Notification display to users and store staff

[0490] In this way, the system of the present invention collects and analyzes users' brainwave data and provides appropriate countermeasures in real time, thereby improving the customer experience in physical stores and providing mental health care for employees.

[0491] Furthermore, an emotion engine that estimates the user's emotion may be further combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59, and perform identification processing using the user's emotion.

[0492] The system of the present invention collects and analyzes the user's brain wave data, identifies and predicts the user's subconscious psychological state, and then uses an emotion engine to recognize the user's emotional state, providing the results to the user and medical professionals.

[0493] First, a device for acquiring EEG data is prepared. This device collects EEG signals through electrodes attached to the user's scalp and converts them into digital data. The user uses this device to acquire EEG data while sleeping or relaxing.

[0494] The user's device then transmits the acquired EEG data to a server, typically transferred in real time or in batches over the internet. The server stores the received EEG data in a storage device and performs error checking to ensure data integrity.

[0495] The server prepares the EEG data stored in the storage device for analysis. Specifically, it normalizes the data, extracts features, and performs preprocessing. The EEG data is then analyzed using a pre-trained artificial intelligence model. This allows it to identify and predict subconscious psychological states such as stress, anxiety, and depression.

[0496] Furthermore, the server uses an emotion engine to recognize the user's emotional state from the analysis of the brainwave data and psychological state. The emotion engine uses a machine learning algorithm and can analyze emotions from the user's brainwave patterns and other physiological data.

[0497] The analysis results include stress levels, depression levels, and anxiety levels, as well as emotional states (e.g., happiness, sadness, anger, etc.). The server outputs these results to the user via a display device or web interface. Users can access a dedicated web page or application to check their own analysis results and emotional states.

[0498] As a concrete example, consider the case where a user wears an EEG measuring device and acquires EEG data while relaxing at the end of the day. This data is sent to a server and stored in a storage device. The server analyzes the data using an artificial intelligence model and generates an analysis result such as, "The user has a high stress level and exhibits anxiety and mild depression." Furthermore, the emotion engine determines that "The user is currently experiencing strong feelings of anger." This result is provided to the user through a web interface and is also automatically sent to a medical professional. Based on this information, the medical professional can suggest the most appropriate treatment or counseling method for the user.

[0499] In this way, by analyzing EEG data and combining it with an emotion engine, this system can identify and predict the user's psychological and emotional state in detail and provide appropriate feedback and countermeasures, thereby improving the accuracy and effectiveness of psychotherapy and therapeutic support.

[0500] The processing flow will be explained below.

[0501] Step 1:

[0502] The user wears an EEG measuring device on their scalp to obtain brain wave data during sleep, relaxation, etc. This device electrically collects brain wave signals through electrodes and converts them into digital data.

[0503] Step 2:

[0504] The user's device transmits the acquired EEG data to a server. This data is transferred to the server via the Internet in real time or in batches. Security protocols and data compression techniques are used to ensure the data transmission is secure and efficient.

[0505] Step 3:

[0506] The server stores the received EEG data in a storage device. During the storage process, an error check is performed to confirm the integrity and accuracy of the data. The server notifies the user's device that the data has been successfully stored.

[0507] Step 4:

[0508] The server prepares the EEG data stored in the storage device for analysis. Specifically, it performs preprocessing such as data normalization, filtering, and feature extraction to prepare the data for appropriate analysis.

[0509] Step 5:

[0510] The server analyzes the pre-processed EEG data using a pre-trained artificial intelligence model that uses machine learning algorithms to identify unconscious patterns of stress, anxiety, and depression.

[0511] Step 6:

[0512] The server generates the analysis results from the AI ​​model. Specifically, the results are summarized in the form of numerical values ​​and reports that indicate psychological states such as stress levels, anxiety levels, and depression levels. These analysis results are also used as the base data for subsequent emotion analysis.

[0513] Step 7:

[0514] The server uses an emotion engine to recognize the user's emotional state from the EEG data and the results of the previous analysis. The emotion engine uses a machine learning algorithm to identify emotions from the user's EEG patterns. The results include the user's state of happiness, sadness, anger, etc.

[0515] Step 8:

[0516] The server integrates the analyzed psychological and emotional state results and outputs a single report that contains a complete picture of the user's psychological and emotional state in a format that is easy for users and medical professionals to understand.

[0517] Step 9:

[0518] The server displays the output analysis results via an interface for providing them to the user, who can then access a dedicated web page or application to check their own analysis results.

[0519] Step 10:

[0520] Users can deepen their self-insight based on the analysis results provided and share them with medical professionals or psychotherapists as needed, allowing medical professionals to propose optimal treatment plans and counseling methods for the user based on the analysis results.

[0521] In this way, this system collects and analyzes the user's brainwave data and combines it with an emotion engine to identify the user's subconscious psychological and emotional states in detail and provide appropriate feedback, enabling more accurate psychotherapy and treatment support.

[0522] Example 2

[0523] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0524] Conventional psychological and emotional state analysis systems have low accuracy, making it difficult to provide users with appropriate feedback or therapeutic support. Furthermore, obtaining highly accurate analysis results requires extensive specialized knowledge and expensive equipment, making them difficult for average users to use. The purpose of this invention is to solve these problems and provide a system that can obtain more accurate analysis results and is easy to use.

[0525] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[0526] In this invention, the server includes means for preprocessing EEG data, means for analyzing the preprocessed EEG data using an artificial intelligence model, and means for using an emotion engine that recognizes an emotional state from the EEG data and the analysis results. This enables accurate preprocessing of the EEG data, realizes highly accurate analysis of psychological and emotional states, and makes it possible to provide appropriate feedback and therapeutic support to the user.

[0527] "Electroencephalogram data" refers to electrical signals generated from the user's brain, acquired as digital data.

[0528] "Storage device" refers to a database or other digital storage medium for storing acquired EEG data.

[0529] "Preprocessing" refers to processes such as data normalization, feature extraction, and noise removal to prepare EEG data in a form that is easier to analyze.

[0530] An "artificial intelligence model" is a model trained using machine learning algorithms to analyze EEG data and identify psychological states.

[0531] The "emotion engine" is an algorithm that recognizes the user's emotional state based on the analysis results of brainwave data and artificial intelligence models.

[0532] "Analysis results" are data representing the user's psychological and emotional state obtained by the artificial intelligence model and emotion engine.

[0533] This invention is a system that collects and analyzes a user's electroencephalogram data to identify and predict their subconscious psychological and emotional states. This system includes a device for acquiring electroencephalogram data, a terminal for transmitting the data to a server, a server for storing and analyzing the data, and a function for outputting the analysis results.

[0534] First, the user uses a device to acquire brain waves. This device has the function of collecting brain wave signals through electrodes attached to the scalp and converting them into digital data. Specific devices include an electroencephalograph and scalp electrodes. The user uses this device to acquire brain wave data, usually while relaxed or asleep.

[0535] The user's device then transmits the acquired EEG data to a server via the internet. The device is equipped with Wi-Fi or mobile data capabilities to ensure stable communication. The data is sent in real time or in batches at the end of the day.

[0536] The server stores the received EEG data in a storage device (e.g., a database) and performs error checking. It then preprocesses the stored data. This preprocessing includes normalization, feature extraction, and noise removal. The server uses high-performance hardware (e.g., a CPU or GPU) to quickly perform these preprocessing steps.

[0537] The server then analyzes the pre-processed EEG data using a pre-trained artificial intelligence model that employs machine learning algorithms to identify and predict the user's subconscious psychological states, such as stress, anxiety, and depression.

[0538] After the analysis is complete, the server uses an emotion engine to recognize the user's emotional state from the analysis of the EEG data and psychological state. The emotion engine also uses machine learning algorithms to analyze emotions (e.g., happiness, sadness, anger, etc.) from the user's EEG patterns and other physiological data.

[0539] Finally, the psychological and emotional states obtained as analysis results are provided to the user via a dedicated web interface or mobile application. Users can check their own psychological and emotional states through this interface. The analysis results can also be automatically sent to medical professionals as needed to be used as reference information for treatment and counseling.

[0540] As a concrete example, consider a case where a user wears an EEG measuring device at the end of the day and captures EEG data while relaxing. This data is sent by the device to a server and stored in a storage device. The server analyzes the data using an artificial intelligence model and generates an analysis result such as "The user has a high stress level and exhibits anxiety and mild depression." The emotion engine then determines that "The user is currently experiencing strong emotions of anger." These results are notified to the user through a web interface and are also sent to medical professionals.

[0541] Example prompt sentence:

[0542] "The user wore an EEG measuring device and, while relaxed, brain wave data was collected. The data was sent to a server and stored. The server analyzed the data using an artificial intelligence model and produced results indicating the user's high stress levels, anxiety, and mild depression. Furthermore, the emotion engine determined that the user was experiencing strong feelings of anger. The analysis results were provided to the user through a web interface and also sent to medical professionals."

[0543] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0544] Step 1:

[0545] The user wears an EEG measurement device to acquire EEG data. The device collects EEG signals as digital data through electrodes attached to the scalp. For example, EEG data can be acquired by wearing the device while the user is relaxing at night. The input of this step is the user's EEG signals, and the output is digital EEG data.

[0546] Step 2:

[0547] The device transmits the acquired EEG data to a server via the internet. The device is equipped with Wi-Fi or mobile data capabilities to ensure stable communication. The data is transmitted in real time or in batches at the end of the day. The input of this step is digital EEG data, and the output is the EEG data transmitted to the server.

[0548] Step 3:

[0549] The server stores the received EEG data in a storage device. At this time, the server performs an error check to confirm the consistency of the data. For example, it maintains the integrity of the data by checking whether the data format is correct and whether there is any missing data. The input of this step is the EEG data sent from the terminal, and the output is the EEG data stored in the storage device.

[0550] Step 4:

[0551] The server preprocesses the EEG data stored in the storage device. Preprocessing includes data normalization, feature extraction, and noise removal. For example, applying a noise removal algorithm to improve the data quality. The input of this step is the EEG data stored in the storage device, and the output is the preprocessed EEG data.

[0552] Step 5:

[0553] The server analyzes the preprocessed EEG data using a pre-trained artificial intelligence model. This model uses machine learning algorithms to identify and predict the user's psychological states, such as stress, anxiety, and depression. For example, it uses a high-performance GPU to quickly analyze large amounts of data. The input of this step is the preprocessed EEG data, and the output is the psychological state analysis result.

[0554] Step 6:

[0555] The server uses an emotion engine to recognize the user's emotional state from the analysis results and EEG data. This emotion engine also uses a machine learning algorithm to analyze emotions such as happiness, sadness, and anger from EEG patterns. The input of this step is the psychological state analysis results and EEG data, and the output is the resulting emotional state.

[0556] Step 7:

[0557] The server notifies the user and healthcare professionals of the analysis results and the recognized emotional state. The results are provided through a dedicated web interface or mobile application. For example, users can check the results on their own devices, and healthcare professionals can use them as reference information for treatment. The input of this step is the emotional state result, and the output is the analysis results provided to the user and healthcare professionals.

[0558] (Application example 2)

[0559] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0560] Conventional autonomous vehicle systems have had the challenge of being unable to grasp the emotional and psychological states of drivers and passengers in real time and provide appropriate feedback accordingly. This has made it difficult to ensure maximum passenger safety and comfort. There has also been a lack of concrete measures to reduce psychological stress and anxiety.

[0561] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 2 is realized by the following means.

[0562] In this invention, the server includes means for acquiring electroencephalogram data, means for storing the acquired electroencephalogram data in a storage device, means for analyzing the electroencephalogram data stored in the storage device using artificial intelligence, and means for providing feedback to the vehicle control system based on the generated analysis results, thereby enabling real-time feedback based on the user's psychological and emotional states.

[0563] "Electroencephalogram data" refers to data that is generated by electronically collecting a user's electroencephalogram activity and converting that information into a digital format.

[0564] A "storage device" is a hardware or software component used to store acquired data.

[0565] "Artificial intelligence" refers to algorithms and systems that learn from large amounts of data and perform analysis and predictions.

[0566] "Analysis results" are the information obtained from the analysis of acquired EEG data by artificial intelligence.

[0567] An "automobile control system" is a system for controlling an autonomous vehicle, and is a device that has the function of automatically operating the vehicle.

[0568] "Feedback" refers to various responses and instructions given to the system or user based on the analysis results.

[0569] "Mental state" is information that indicates the user's mental state and emotional fluctuations.

[0570] This invention collects and analyzes the EEG data of passengers in an autonomous vehicle in real time, and provides feedback to the vehicle's control system based on the analysis results. To achieve this, an EEG data acquisition device, a server, a storage device, an artificial intelligence (AI) analysis system, and an automobile control system are required.

[0571] First, the user wears an EEG data acquisition device and EEG data is collected in real time. This data is transmitted to a server via wireless communication or the Internet. The server then stores the received EEG data in a storage device.

[0572] The server then transmits the stored EEG data to an AI analysis system for analysis. The AI ​​analysis system uses a pre-trained generative AI model to analyze the EEG data and identify the user's psychological and emotional state. For example, specific EEG patterns can be used to detect a user's stress level, anxiety, or relaxation state.

[0573] The analysis results are sent back to the server, which then provides feedback to the car's control system. For example, if the user is feeling highly stressed, the vehicle's control system can automatically select a relaxing route and adjust the in-car environment. It can also play ambient music or provide interesting information when the user is in a relaxed state.

[0574] This allows the driving experience of autonomous vehicles to be optimized based on the user's psychological and emotional state, improving safety and comfort.

[0575] For example, if a user feels high stress during a long drive, the server will automatically recommend a route change based on the analysis results, and the vehicle will switch to a route with better scenery.The system will also automatically adjust the in-car music selection and air conditioning settings to promote relaxation for the user.

[0576] Here are some examples of prompts to input to the generative AI model:

[0577] "Please suggest the best machine learning model to analyze the stress level and emotional state of the user from their EEG data. Also, I would like your advice on how to provide the best vehicle navigation action when high stress is detected."

[0578] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0579] Step 1:

[0580] The user wears an EEG data acquisition device. This device has the function of collecting the user's EEG in real time and converting it into a digital format. The input is the user's EEG signal, and the output is digitized EEG data.

[0581] Step 2:

[0582] The digitized EEG data collected by the EEG data acquisition device is sent to the user's device using wireless communication or Bluetooth. The input is the digitized EEG data, and the output is the data sent to the user's device.

[0583] Step 3:

[0584] The EEG data received by the user's device is sent to the server. The data is sent via the Internet. The input is the EEG data stored on the user's device, and the output is the EEG data sent to the server.

[0585] Step 4:

[0586] The EEG data received by the server is stored in a storage device. Specifically, a database management system is used. The input is the EEG data sent to the server, and the output is the data stored in the storage device.

[0587] Step 5:

[0588] The server uses artificial intelligence to analyze the EEG data stored in the storage device. A generative AI model is used for this analysis, and it performs feature extraction and data normalization. The input is the EEG data stored in the storage device, and the output is the analysis results.

[0589] Step 6:

[0590] The server receives the analysis results from the AI ​​and identifies the user's psychological and emotional state. Specifically, it determines the user's stress level, anxiety, relaxation state, etc. The input is the analysis results, and the output is the identified psychological and emotional state.

[0591] Step 7:

[0592] The server then provides feedback to the vehicle's control system based on the analysis results. For example, if high stress is detected, a relaxing route will be automatically selected. The input is the identified psychological and emotional state, and the output is vehicle control instructions as feedback.

[0593] Step 8:

[0594] The vehicle's control system receives feedback from the server and adjusts the vehicle's behavior and environmental settings, such as changing the navigation route, adjusting the air conditioning temperature, or selecting in-car music. The input is vehicle control instructions, and the output is the actual vehicle adjustment behavior.

[0595] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0596] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0597] In the above embodiment, an example in which the specific processing is performed by the data processing device 12 has been given, but the technology of the present disclosure is not limited to this, and the specific processing may be performed by the smart glasses 214.

[0598] [Third embodiment]

[0599] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0600] 5, the data processing system 310 includes the data processing device 12 and a headset type terminal 314. An example of the data processing device 12 is a server.

[0601] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0602] The headset type terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a display 343. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the display 343 are also connected to the bus 52.

[0603] The microphone 238 receives instructions and the like from the user 20 by receiving voice uttered by the user 20. The microphone 238 captures the voice uttered by the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio in accordance with instructions from the processor 46.

[0604] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the surroundings of user 20 (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0605] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0606] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset type terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0607] The specific processing program 56 is an example of a "program" according to the technology of the present disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0608] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0609] In the headset type terminal 314, a reception output process is performed by the processor 46. A reception output program 60 is stored in the storage 50. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[0610] Next, a description will be given of the identification process performed by the identification processing unit 290 of the data processing device 12. In the following description, the data processing device 12 will be referred to as the "server" and the headset type terminal 314 will be referred to as the "terminal."

[0611] The system of the present invention collects the user's brain wave data and analyzes it using artificial intelligence to identify and predict unconscious stress, anxiety, and depression, and provides the results to medical professionals and psychotherapists. This system consists of the following components:

[0612] First, a device is required to acquire EEG data. The user attaches electrodes to their scalp to measure EEG signals, which are then captured as digital data. This digital data is then sent to a server and stored in a storage device.

[0613] The server then stores the received EEG data in a storage device, where it is prepared for analysis. The server then analyzes the data using an artificial intelligence model, which uses pre-trained machine learning algorithms that have learned subconscious patterns of stress, anxiety, and depression from a large number of sample data.

[0614] Once the analysis is complete, the server outputs the results, which may include whether or not the patient is stressed, their level of anxiety, their level of depression, etc. The server has the means to provide these results to the user, and can also transmit the results over the Internet for use by medical professionals and psychotherapists.

[0615] Furthermore, by allowing users to check the results through an interface, it can promote self-insight and be used as reference information for necessary treatment or counseling. The analysis results are displayed in a concise and easy-to-understand format, and it can also suggest countermeasures depending on the state of stress, anxiety, and depression.

[0616] As a concrete example, consider a case where a user wears an EEG measuring device to capture EEG data while sleeping. This data is sent to a server and stored in a storage device. The server uses an artificial intelligence model to analyze this data and generate a result, such as "the user exhibits high stress levels." This result is provided to the user through a web interface and, if necessary, automatically sent to a medical professional. Medical professionals can use this information to recommend appropriate treatment methods for the user.

[0617] In this way, the system of the present invention enables highly accurate assessment of psychological states by analyzing electroencephalogram data, realizing a new approach to providing appropriate psychotherapy and treatment for individual users.

[0618] The processing flow will be explained below.

[0619] Step 1:

[0620] The user uses an electroencephalogram (EEG) measuring device to obtain brainwave data while sleeping. This device collects brainwave signals through electrodes attached to the scalp and converts them into digital data.

[0621] Step 2:

[0622] The user's device transmits this EEG data to a server, typically transferred over a network in real time or in batches.

[0623] Step 3:

[0624] The server stores the received EEG data in a storage device, and at this time, it also checks for errors to ensure data integrity and accurate storage.

[0625] Step 4:

[0626] The server prepares the EEG data stored in the storage device to be input into the artificial intelligence model (AI model), performing preprocessing such as data normalization and feature extraction.

[0627] Step 5:

[0628] The server analyzes the EEG data using a pre-trained AI model, which is trained with machine learning algorithms to identify unconscious patterns of stress, anxiety, and depression.

[0629] Step 6:

[0630] The server generates the analysis results output by the AI ​​model, specifically summarizing the results in the form of numerical values ​​and reports that indicate psychological states such as stress levels, depression levels, and anxiety levels.

[0631] Step 7:

[0632] The server outputs the analysis results to the user via a display device or a web interface, and the user can access a dedicated web page to check their own analysis results.

[0633] Step 8:

[0634] Users can use the analysis results to deepen their self-insight and share it with medical professionals or psychotherapists as needed. Medical professionals can then receive the analysis results and propose optimal treatment plans and counseling methods for the user.

[0635] Step 9:

[0636] The server continuously collects and stores data for further analysis and trend analysis, and this data is used to improve future models and analyze new psychological states.

[0637] Step 10:

[0638] The server provides analysis results in real time via the internet to those who need them, and is equipped with a function that allows data to be smoothly shared with medical professionals in remote locations, for example, through remote counseling or telemedicine.

[0639] In this way, this system realizes a series of processes that collect and analyze the user's EEG data and provide the results to the user and medical professionals, thereby supporting more effective psychotherapy and treatment.

[0640] Example 1

[0641] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[0642] Conventional EEG data analysis systems have had difficulty accurately detecting and predicting unconscious stress, anxiety, and depression. Furthermore, there was a lack of a means to quickly and intuitively provide analysis results to users and medical professionals, hindering their linkage to appropriate psychotherapy and treatment. Furthermore, insufficient pre-processing, such as data normalization and noise removal, often limited the accuracy of the analysis.

[0643] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[0644] In this invention, the server

[0645] a means for acquiring electroencephalogram data;

[0646] means for storing the acquired electroencephalogram data in a storage device;

[0647] means for pre-processing the electroencephalogram data stored in the storage device;

[0648] A means for analyzing the pre-processed EEG data using a generative AI model;

[0649] A means for outputting the analysis results;

[0650] a display device for providing the analysis results to a user or a medical professional;

[0651] This makes it possible to detect unconscious psychological states with high accuracy and provide the results to relevant parties at the appropriate time.

[0652] "Electroencephalogram data" refers to information obtained by quantifying signals that represent the electrical activity of the user's brain, obtained by a measuring device.

[0653] A "storage device" is a physical or virtual storage that holds digital data and allows it to be accessed and manipulated as needed.

[0654] "Preprocessing" refers to the procedure of normalizing, removing noise, segmenting, and otherwise processing the acquired EEG data to convert it into a format suitable for analysis.

[0655] A "generative AI model" is a model that uses artificial intelligence algorithms that have been pre-trained with large amounts of data and are capable of detecting specific patterns and anomalies.

[0656] "Analysis" refers to computational processes performed to assess subconscious psychological states based on pre-processed EEG data.

[0657] The "analysis results" are judgment information regarding stress, anxiety, and depression obtained from the analysis of EEG data by the generative AI model.

[0658] A "display device" is a device or software interface that visually presents analysis results to a user or medical professional.

[0659] The present invention is a system that collects a user's electroencephalogram data, analyzes it using artificial intelligence, identifies and predicts unconscious stress, anxiety, and depression, and provides the results to medical professionals and psychotherapists. This system is composed of the following components:

[0660] First, the user needs a device to collect brainwave data. The user wears an EEG measuring device on their scalp. This device measures the electrical signals of the brainwaves and converts them into digital data. The EEG measuring device itself can be a commercially available electroencephalograph.

[0661] The acquired digital data is then sent over the Internet to a server. The server receives the data, first stores it temporarily in memory, and then saves it in a storage device such as a database. The stored data is then pre-processed for analysis. This pre-processing includes data normalization, noise removal, segmentation, etc.

[0662] Once pre-processed, the data is fed into a generative AI model, which has been pre-trained with a large amount of data and is capable of detecting specific patterns and anomalies, specifically neural networks that identify subconscious states of stress, anxiety, and depression.

[0663] The analysis results are generated on the server and provided to users and healthcare professionals. The results are displayed through a web interface, allowing users to check their own status and share it with healthcare professionals as needed. The analysis results are presented in a visually understandable format, such as graphs and charts.

[0664] As a concrete example, consider a case where a user wears an EEG device at night to capture EEG data while sleeping. This data is transmitted in real time to a server and stored in a storage device. The server immediately pre-processes the data and inputs it into a generative AI model. The analysis results in a judgment that "the user exhibits high stress levels." This result is provided to the user through a web interface and, if necessary, automatically sent to medical professionals.

[0665] An example prompt might be, "Implement a program that uses a user's EEG data to analyze their subconscious stress levels. The EEG data will be sent digitally to a server and stored on a storage device. An artificial intelligence model will be used to analyze the data to determine the presence or absence of stress, the level of anxiety, and the level of depression. The results of the analysis will be provided to the user through a web interface and automatically sent to a medical professional if necessary."

[0666] This system can detect unconscious psychological states with high accuracy and provide users with appropriate insights and medical information.

[0667] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0668] Overall processing flow

[0669] The processing flow of this system is shown below in specific steps. At each step, the specific operation, input, and output are also explained.

[0670] Step 1: Collect EEG data

[0671] The user wears the EEG measurement device and begins measuring. The device converts the brain's electrical signals into digital data.

[0672] Input: User's EEG signal

[0673] Processing: The EEG signal is converted into digital data by the EEG measuring device.

[0674] Output: Digital EEG data

[0675] Step 2: Sending EEG data

[0676] The electroencephalogram measuring device transmits the acquired digital data to a server via the Internet.

[0677] Input: Digital EEG data

[0678] Processing: Data encryption and secure transmission over the Internet

[0679] Output: EEG data sent to the server

[0680] Step 3: Save the EEG data

[0681] The server temporarily stores the received brain wave data in memory, and then saves it in a storage device such as a database.

[0682] Input: Received EEG data

[0683] Processing: Temporarily storing data and storing it in a database

[0684] Output: EEG data stored in a database

[0685] Step 4: Preprocessing the data

[0686] The server retrieves the EEG data stored in the storage device and performs pre-processing for analysis.

[0687] Input: Stored EEG data

[0688] Processing: Data normalization, denoising, segmentation (e.g. splitting data by time)

[0689] Output: Pre-processed EEG data

[0690] Step 5: Analyze the data

[0691] The server inputs the pre-processed EEG data into a generative AI model for analysis.

[0692] Input: Pre-processed EEG data

[0693] Processing: Generative AI models analyze data and detect patterns of stress, anxiety, and depression

[0694] Output: Analysis results (stress level, anxiety level, depression level)

[0695] Step 6: Generate analysis results

[0696] The server converts the analysis results obtained by the generative AI model into a format that is easy for humans to understand.

[0697] Input: Analysis results from a generative AI model

[0698] Processing: Converting analysis results into different formats, generating graphs and charts

[0699] Output: Visually easy-to-understand analysis results

[0700] Step 7: Provide analysis results

[0701] The server provides the generated analysis results to users and medical professionals via a web interface.

[0702] Input: Visualized analysis results

[0703] Processing: Displaying results via web interface

[0704] Output: User and healthcare professional review of results

[0705] Specific operation example

[0706] 1. The user wears the EEG measurement device and presses a button to start measurement.

[0707] 2. The EEG measuring device automatically converts the EEG into digital data and sends it to the server.

[0708] 3. The server temporarily stores the received data in memory and then records it in the database.

[0709] 4. The server denoises the data, splits it into time segments, and feeds it into a generative AI model.

[0710] 5. The server uses machine learning algorithms to analyze your stress and anxiety levels.

[0711] 6. The server converts the analysis results into graphs and charts and displays them in a web interface.

[0712] 7. Users and healthcare professionals can review the results through a web interface and take action as necessary.

[0713] The above are the processing steps of the program of this system and their specific operations.

[0714] (Application example 1)

[0715] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[0716] Conventional EEG analysis systems were limited to analyzing a user's EEG data to evaluate their psychological state, but lacked the ability to immediately grasp fluctuations in the user's psychological state in real time and provide appropriate countermeasures. This meant that they were unable to quickly respond to the stress and psychological burden experienced by customers and employees, particularly in brick-and-mortar stores, posing challenges to improving customer experience and employee mental health care.

[0717] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[0718] In this invention, the server includes means for acquiring electroencephalogram data, means for storing the acquired electroencephalogram data in a storage device, means for analyzing the electroencephalogram data stored in the storage device using artificial intelligence, means for outputting the analysis results, and means for sending notifications in real time based on the analysis results. This enables real-time monitoring of the psychological states of customers and employees, allows appropriate countermeasures to be immediately provided, and enables an improved customer experience and mental health care for employees.

[0719] "EEG data" is digital data of electrical signals that measure the user's brain activity.

[0720] The "storage device" is a device for storing acquired electroencephalogram data.

[0721] "Artificial intelligence" is a technology that uses machine learning algorithms to analyze brainwave data and predict and identify subconscious psychological states.

[0722] The "analysis results" are information on stress levels and psychological state generated based on brain wave data analyzed by artificial intelligence.

[0723] "Notifications" are messages or alerts sent in real time via a display or other device with appropriate countermeasures based on the analysis results.

[0724] A "display device" is a device that visually presents analysis results and notifications to users.

[0725] "Real-time" means that the process from acquiring EEG data to notifying the analysis results is carried out instantly.

[0726] The system of this invention acquires a user's brainwave data in real time and analyzes it using artificial intelligence (AI) to identify stress, anxiety, and depression. Based on the analysis results, it also notifies appropriate countermeasures in real time, thereby improving the customer experience and mental health care for employees, primarily in brick-and-mortar stores.

[0727] Program Generation

[0728] In the system of the present invention, the program performs the following processing.

[0729] First, a user (customer or employee) wears an EEG measurement device, and EEG data is acquired from the device. EEG data is collected using hardware such as a wearable device or headset. This EEG data is sent to a server in real time and stored in a storage device.

[0730] The server analyzes the stored EEG data using a pre-trained AI model (e.g., a neural network model using Keras), which estimates the user's stress level and subconscious psychological state. The analysis uses a machine learning algorithm using a large amount of sample data.

[0731] The analysis results are generated in real time from the server, with specific output such as "The user's stress level is high." Based on this result, the system generates a notification suggesting appropriate countermeasures.

[0732] Processing Description

[0733] The server normalizes the acquired data, inputs it into an AI model, and outputs the analysis results. Specifically, a numerical calculation library such as Numpy is used for data normalization, and a deep learning framework such as Keras is used for machine learning.

[0734] The means for sending notifications to users based on the analysis results includes the ability to send messages in real time via a REST API, which are delivered to browsers and smartphone displays.

[0735] For example, if a store detects a customer's high stress level, it may play relaxing music or suggest a specific product for that customer. Similarly, if an employee is found to be under high stress, a notification will be sent to the manager, who will recommend appropriate breaks.

[0736] Specific examples

[0737] For example, suppose a customer wears a wearable device while selecting a product in a physical store, and brain wave data is collected at that time. This data is sent to a server in real time, and the stress level is analyzed. If the analysis result indicates that the customer's stress level is high, the system will notify the store staff to play relaxing music.

[0738] An example of a prompt sentence is, "Please suggest measures to take if customer M has a high stress level at a healthcare store. For example, this could include playing relaxing music, suggesting specific products, or guiding the customer through the store."

[0739] In this way, by using the system of the present invention, it is possible to grasp the psychological state of customers and respond appropriately in real time, thereby improving customer experience and providing mental health care for employees.

[0740] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0741] Step 1:

[0742] EEG data acquisition

[0743] The user wears an EEG measurement device, which captures brainwaves in real time. The device captures brainwaves as electrical signals, converts them into digital data, and transmits them to a server.

[0744] Input: User's EEG signal

[0745] Output: Digitized EEG data

[0746] Step 2:

[0747] Data storage

[0748] The server stores the received digitized EEG data in a storage device. In this step, the data is verified and stored accurately to ensure its reliability.

[0749] Input: Digitized EEG data

[0750] Output: EEG data stored in a memory device

[0751] Step 3:

[0752] Data Preprocessing

[0753] The server normalizes the EEG data stored in the storage device, a process that uses a numerical computing library such as Numpy to standardize the data and convert it into a format that can be input into an AI model.

[0754] Input: EEG data stored in a memory device

[0755] Output: Normalized EEG data

[0756] Step 4:

[0757] AI-based analysis

[0758] The server then feeds the normalized EEG data into a pre-trained AI model that analyzes stress levels and subconscious psychological states using deep learning frameworks such as Keras.

[0759] Input: Normalized EEG data

[0760] Output: Analysis results of stress level and psychological state

[0761] Step 5:

[0762] Saving and monitoring analysis results

[0763] The server stores the analysis results in a storage device and monitors them in real time. In this step, the server continuously monitors the user's psychological state based on the analysis results.

[0764] Input: Analysis results

[0765] Output: Analysis results and monitoring data stored in a storage device

[0766] Step 6:

[0767] Generate notifications

[0768] The server generates notifications based on the analysis, suggesting appropriate actions to take, which are provided to users and store staff in real time and sent via a REST API.

[0769] Input: Analysis results

[0770] Output: Notification message with workaround

[0771] Step 7:

[0772] Sending notifications

[0773] The server then sends the generated notification message to a display device or smartphone, allowing users and store staff to immediately understand the appropriate countermeasures based on the analysis results.

[0774] Input: Notification message

[0775] Output: Notification display to users and store staff

[0776] In this way, the system of the present invention collects and analyzes users' brainwave data and provides appropriate countermeasures in real time, thereby improving the customer experience in physical stores and providing mental health care for employees.

[0777] Furthermore, an emotion engine that estimates the user's emotion may be further combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59, and perform identification processing using the user's emotion.

[0778] The system of the present invention collects and analyzes the user's brain wave data, identifies and predicts the user's subconscious psychological state, and then uses an emotion engine to recognize the user's emotional state, providing the results to the user and medical professionals.

[0779] First, a device for acquiring EEG data is prepared. This device collects EEG signals through electrodes attached to the user's scalp and converts them into digital data. The user uses this device to acquire EEG data while sleeping or relaxing.

[0780] The user's device then transmits the acquired EEG data to a server, typically transferred in real time or in batches over the internet. The server stores the received EEG data in a storage device and performs error checking to ensure data integrity.

[0781] The server prepares the EEG data stored in the storage device for analysis. Specifically, it normalizes the data, extracts features, and performs preprocessing. The EEG data is then analyzed using a pre-trained artificial intelligence model. This allows it to identify and predict subconscious psychological states such as stress, anxiety, and depression.

[0782] Furthermore, the server uses an emotion engine to recognize the user's emotional state from the analysis of the brainwave data and psychological state. The emotion engine uses a machine learning algorithm and can analyze emotions from the user's brainwave patterns and other physiological data.

[0783] The analysis results include stress levels, depression levels, and anxiety levels, as well as emotional states (e.g., happiness, sadness, anger, etc.). The server outputs these results to the user via a display device or web interface. Users can access a dedicated web page or application to check their own analysis results and emotional states.

[0784] As a concrete example, consider the case where a user wears an EEG measuring device and acquires EEG data while relaxing at the end of the day. This data is sent to a server and stored in a storage device. The server analyzes the data using an artificial intelligence model and generates an analysis result such as, "The user has a high stress level and exhibits anxiety and mild depression." Furthermore, the emotion engine determines that "The user is currently experiencing strong feelings of anger." This result is provided to the user through a web interface and is also automatically sent to a medical professional. Based on this information, the medical professional can suggest the most appropriate treatment or counseling method for the user.

[0785] In this way, by analyzing EEG data and combining it with an emotion engine, this system can identify and predict the user's psychological and emotional state in detail and provide appropriate feedback and countermeasures, thereby improving the accuracy and effectiveness of psychotherapy and therapeutic support.

[0786] The processing flow will be explained below.

[0787] Step 1:

[0788] The user wears an EEG measuring device on their scalp to obtain brain wave data during sleep, relaxation, etc. This device electrically collects brain wave signals through electrodes and converts them into digital data.

[0789] Step 2:

[0790] The user's device transmits the acquired EEG data to a server. This data is transferred to the server via the Internet in real time or in batches. Security protocols and data compression techniques are used to ensure the data transmission is secure and efficient.

[0791] Step 3:

[0792] The server stores the received EEG data in a storage device. During the storage process, an error check is performed to confirm the integrity and accuracy of the data. The server notifies the user's device that the data has been successfully stored.

[0793] Step 4:

[0794] The server prepares the EEG data stored in the storage device for analysis. Specifically, it performs preprocessing such as data normalization, filtering, and feature extraction to prepare the data for appropriate analysis.

[0795] Step 5:

[0796] The server analyzes the pre-processed EEG data using a pre-trained artificial intelligence model that uses machine learning algorithms to identify unconscious patterns of stress, anxiety, and depression.

[0797] Step 6:

[0798] The server generates the analysis results from the AI ​​model. Specifically, the results are summarized in the form of numerical values ​​and reports that indicate psychological states such as stress levels, anxiety levels, and depression levels. These analysis results are also used as the base data for subsequent emotion analysis.

[0799] Step 7:

[0800] The server uses an emotion engine to recognize the user's emotional state from the EEG data and the results of the previous analysis. The emotion engine uses a machine learning algorithm to identify emotions from the user's EEG patterns. The results include the user's state of happiness, sadness, anger, etc.

[0801] Step 8:

[0802] The server integrates the analyzed psychological and emotional state results and outputs a single report that contains a complete picture of the user's psychological and emotional state in a format that is easy for users and medical professionals to understand.

[0803] Step 9:

[0804] The server displays the output analysis results via an interface for providing them to the user, who can then access a dedicated web page or application to check their own analysis results.

[0805] Step 10:

[0806] Users can deepen their self-insight based on the analysis results provided and share them with medical professionals or psychotherapists as needed, allowing medical professionals to propose optimal treatment plans and counseling methods for the user based on the analysis results.

[0807] In this way, this system collects and analyzes the user's brainwave data and combines it with an emotion engine to identify the user's subconscious psychological and emotional states in detail and provide appropriate feedback, enabling more accurate psychotherapy and treatment support.

[0808] Example 2

[0809] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[0810] Conventional psychological and emotional state analysis systems have low accuracy, making it difficult to provide users with appropriate feedback or therapeutic support. Furthermore, obtaining highly accurate analysis results requires extensive specialized knowledge and expensive equipment, making them difficult for average users to use. The purpose of this invention is to solve these problems and provide a system that can obtain more accurate analysis results and is easy to use.

[0811] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[0812] In this invention, the server includes means for preprocessing EEG data, means for analyzing the preprocessed EEG data using an artificial intelligence model, and means for using an emotion engine that recognizes an emotional state from the EEG data and the analysis results. This enables accurate preprocessing of the EEG data, realizes highly accurate analysis of psychological and emotional states, and makes it possible to provide appropriate feedback and therapeutic support to the user.

[0813] "Electroencephalogram data" refers to electrical signals generated from the user's brain, acquired as digital data.

[0814] "Storage device" refers to a database or other digital storage medium for storing acquired EEG data.

[0815] "Preprocessing" refers to processes such as data normalization, feature extraction, and noise removal to prepare EEG data in a form that is easier to analyze.

[0816] An "artificial intelligence model" is a model trained using machine learning algorithms to analyze EEG data and identify psychological states.

[0817] The "emotion engine" is an algorithm that recognizes the user's emotional state based on the analysis results of brainwave data and artificial intelligence models.

[0818] "Analysis results" are data representing the user's psychological and emotional state obtained by the artificial intelligence model and emotion engine.

[0819] This invention is a system that collects and analyzes a user's electroencephalogram data to identify and predict their subconscious psychological and emotional states. This system includes a device for acquiring electroencephalogram data, a terminal for transmitting the data to a server, a server for storing and analyzing the data, and a function for outputting the analysis results.

[0820] First, the user uses a device to acquire brain waves. This device has the function of collecting brain wave signals through electrodes attached to the scalp and converting them into digital data. Specific devices include an electroencephalograph and scalp electrodes. The user uses this device to acquire brain wave data, usually while relaxed or asleep.

[0821] The user's device then transmits the acquired EEG data to a server via the internet. The device is equipped with Wi-Fi or mobile data capabilities to ensure stable communication. The data is sent in real time or in batches at the end of the day.

[0822] The server stores the received EEG data in a storage device (e.g., a database) and performs error checking. It then preprocesses the stored data. This preprocessing includes normalization, feature extraction, and noise removal. The server uses high-performance hardware (e.g., a CPU or GPU) to quickly perform these preprocessing steps.

[0823] The server then analyzes the pre-processed EEG data using a pre-trained artificial intelligence model that employs machine learning algorithms to identify and predict the user's subconscious psychological states, such as stress, anxiety, and depression.

[0824] After the analysis is complete, the server uses an emotion engine to recognize the user's emotional state from the analysis of the EEG data and psychological state. The emotion engine also uses machine learning algorithms to analyze emotions (e.g., happiness, sadness, anger, etc.) from the user's EEG patterns and other physiological data.

[0825] Finally, the psychological and emotional states obtained as analysis results are provided to the user via a dedicated web interface or mobile application. Users can check their own psychological and emotional states through this interface. The analysis results can also be automatically sent to medical professionals as needed to be used as reference information for treatment and counseling.

[0826] As a concrete example, consider a case where a user wears an EEG measuring device at the end of the day and captures EEG data while relaxing. This data is sent by the device to a server and stored in a storage device. The server analyzes the data using an artificial intelligence model and generates an analysis result such as "The user has a high stress level and exhibits anxiety and mild depression." The emotion engine then determines that "The user is currently experiencing strong emotions of anger." These results are notified to the user through a web interface and are also sent to medical professionals.

[0827] Example prompt sentence:

[0828] "The user wore an EEG measuring device and, while relaxed, brain wave data was collected. The data was sent to a server and stored. The server analyzed the data using an artificial intelligence model and produced results indicating the user's high stress levels, anxiety, and mild depression. Furthermore, the emotion engine determined that the user was experiencing strong feelings of anger. The analysis results were provided to the user through a web interface and also sent to medical professionals."

[0829] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0830] Step 1:

[0831] The user wears an EEG measurement device to acquire EEG data. The device collects EEG signals as digital data through electrodes attached to the scalp. For example, EEG data can be acquired by wearing the device while the user is relaxing at night. The input of this step is the user's EEG signals, and the output is digital EEG data.

[0832] Step 2:

[0833] The device transmits the acquired EEG data to a server via the internet. The device is equipped with Wi-Fi or mobile data capabilities to ensure stable communication. The data is transmitted in real time or in batches at the end of the day. The input of this step is digital EEG data, and the output is the EEG data transmitted to the server.

[0834] Step 3:

[0835] The server stores the received EEG data in a storage device. At this time, the server performs an error check to confirm the consistency of the data. For example, it maintains the integrity of the data by checking whether the data format is correct and whether there is any missing data. The input of this step is the EEG data sent from the terminal, and the output is the EEG data stored in the storage device.

[0836] Step 4:

[0837] The server preprocesses the EEG data stored in the storage device. Preprocessing includes data normalization, feature extraction, and noise removal. For example, applying a noise removal algorithm to improve the data quality. The input of this step is the EEG data stored in the storage device, and the output is the preprocessed EEG data.

[0838] Step 5:

[0839] The server analyzes the preprocessed EEG data using a pre-trained artificial intelligence model. This model uses machine learning algorithms to identify and predict the user's psychological states, such as stress, anxiety, and depression. For example, it uses a high-performance GPU to quickly analyze large amounts of data. The input of this step is the preprocessed EEG data, and the output is the psychological state analysis result.

[0840] Step 6:

[0841] The server uses an emotion engine to recognize the user's emotional state from the analysis results and EEG data. This emotion engine also uses a machine learning algorithm to analyze emotions such as happiness, sadness, and anger from EEG patterns. The input of this step is the psychological state analysis results and EEG data, and the output is the resulting emotional state.

[0842] Step 7:

[0843] The server notifies the user and healthcare professionals of the analysis results and the recognized emotional state. The results are provided through a dedicated web interface or mobile application. For example, users can check the results on their own devices, and healthcare professionals can use them as reference information for treatment. The input of this step is the emotional state result, and the output is the analysis results provided to the user and healthcare professionals.

[0844] (Application example 2)

[0845] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[0846] Conventional autonomous vehicle systems have had the challenge of being unable to grasp the emotional and psychological states of drivers and passengers in real time and provide appropriate feedback accordingly. This has made it difficult to ensure maximum passenger safety and comfort. There has also been a lack of concrete measures to reduce psychological stress and anxiety.

[0847] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 2 is realized by the following means.

[0848] In this invention, the server includes means for acquiring electroencephalogram data, means for storing the acquired electroencephalogram data in a storage device, means for analyzing the electroencephalogram data stored in the storage device using artificial intelligence, and means for providing feedback to the vehicle control system based on the generated analysis results, thereby enabling real-time feedback based on the user's psychological and emotional states.

[0849] "Electroencephalogram data" refers to data that is generated by electronically collecting a user's electroencephalogram activity and converting that information into a digital format.

[0850] A "storage device" is a hardware or software component used to store acquired data.

[0851] "Artificial intelligence" refers to algorithms and systems that learn from large amounts of data and perform analysis and predictions.

[0852] "Analysis results" are the information obtained from the analysis of acquired EEG data by artificial intelligence.

[0853] An "automobile control system" is a system for controlling an autonomous vehicle, and is a device that has the function of automatically operating the vehicle.

[0854] "Feedback" refers to various responses and instructions given to the system or user based on the analysis results.

[0855] "Mental state" is information that indicates the user's mental state and emotional fluctuations.

[0856] This invention collects and analyzes the EEG data of passengers in an autonomous vehicle in real time, and provides feedback to the vehicle's control system based on the analysis results. To achieve this, an EEG data acquisition device, a server, a storage device, an artificial intelligence (AI) analysis system, and an automobile control system are required.

[0857] First, the user wears an EEG data acquisition device and EEG data is collected in real time. This data is transmitted to a server via wireless communication or the Internet. The server then stores the received EEG data in a storage device.

[0858] The server then transmits the stored EEG data to an AI analysis system for analysis. The AI ​​analysis system uses a pre-trained generative AI model to analyze the EEG data and identify the user's psychological and emotional state. For example, specific EEG patterns can be used to detect a user's stress level, anxiety, or relaxation state.

[0859] The analysis results are sent back to the server, which then provides feedback to the car's control system. For example, if the user is feeling highly stressed, the vehicle's control system can automatically select a relaxing route and adjust the in-car environment. It can also play ambient music or provide interesting information when the user is in a relaxed state.

[0860] This allows the driving experience of autonomous vehicles to be optimized based on the user's psychological and emotional state, improving safety and comfort.

[0861] For example, if a user feels high stress during a long drive, the server will automatically recommend a route change based on the analysis results, and the vehicle will switch to a route with better scenery.The system will also automatically adjust the in-car music selection and air conditioning settings to promote relaxation for the user.

[0862] Here are some examples of prompts to input to the generative AI model:

[0863] "Please suggest the best machine learning model to analyze the stress level and emotional state of the user from their EEG data. Also, I would like your advice on how to provide the best vehicle navigation action when high stress is detected."

[0864] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0865] Step 1:

[0866] The user wears an EEG data acquisition device. This device has the function of collecting the user's EEG in real time and converting it into a digital format. The input is the user's EEG signal, and the output is digitized EEG data.

[0867] Step 2:

[0868] The digitized EEG data collected by the EEG data acquisition device is sent to the user's device using wireless communication or Bluetooth. The input is the digitized EEG data, and the output is the data sent to the user's device.

[0869] Step 3:

[0870] The EEG data received by the user's device is sent to the server. The data is sent via the Internet. The input is the EEG data stored on the user's device, and the output is the EEG data sent to the server.

[0871] Step 4:

[0872] The EEG data received by the server is stored in a storage device. Specifically, a database management system is used. The input is the EEG data sent to the server, and the output is the data stored in the storage device.

[0873] Step 5:

[0874] The server uses artificial intelligence to analyze the EEG data stored in the storage device. A generative AI model is used for this analysis, and it performs feature extraction and data normalization. The input is the EEG data stored in the storage device, and the output is the analysis results.

[0875] Step 6:

[0876] The server receives the analysis results from the AI ​​and identifies the user's psychological and emotional state. Specifically, it determines the user's stress level, anxiety, relaxation state, etc. The input is the analysis results, and the output is the identified psychological and emotional state.

[0877] Step 7:

[0878] The server then provides feedback to the vehicle's control system based on the analysis results. For example, if high stress is detected, a relaxing route will be automatically selected. The input is the identified psychological and emotional state, and the output is vehicle control instructions as feedback.

[0879] Step 8:

[0880] The vehicle's control system receives feedback from the server and adjusts the vehicle's behavior and environmental settings, such as changing the navigation route, adjusting the air conditioning temperature, or selecting in-car music. The input is vehicle control instructions, and the output is the actual vehicle adjustment behavior.

[0881] The specific processing unit 290 transmits the result of the specific processing to the headset type terminal 314. In the headset type terminal 314, the control unit 46A causes the speaker 240 and the display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0882] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0883] In the above embodiment, an example was given in which the specific processing is performed by the data processing device 12, but the technology of the present disclosure is not limited to this, and the specific processing may be performed by the headset type terminal 314.

[0884] [Fourth embodiment]

[0885] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0886] 7, a data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0887] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0888] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a control target 443. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the control target 443 are also connected to the bus 52.

[0889] The microphone 238 receives instructions and the like from the user 20 by receiving voice uttered by the user 20. The microphone 238 captures the voice uttered by the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio in accordance with instructions from the processor 46.

[0890] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the surroundings of user 20 (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0891] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0892] The control object 443 includes a display device, LEDs in the eyes, and motors for driving the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

[0893] Fig. 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Fig. 8, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0894] The specific processing program 56 is an example of a "program" according to the technology of the present disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0895] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0896] In the robot 414, the processor 46 performs the reception output process. A reception output program 60 is stored in the storage 50. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[0897] Next, a description will be given of the specific processing performed by the specific processing unit 290 of the data processing device 12. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[0898] The system of the present invention collects the user's brain wave data and analyzes it using artificial intelligence to identify and predict unconscious stress, anxiety, and depression, and provides the results to medical professionals and psychotherapists. This system consists of the following components:

[0899] First, a device is required to acquire EEG data. The user attaches electrodes to their scalp to measure EEG signals, which are then captured as digital data. This digital data is then sent to a server and stored in a storage device.

[0900] The server then stores the received EEG data in a storage device, where it is prepared for analysis. The server then analyzes the data using an artificial intelligence model, which uses pre-trained machine learning algorithms that have learned subconscious patterns of stress, anxiety, and depression from a large number of sample data.

[0901] Once the analysis is complete, the server outputs the results, which may include whether or not the patient is stressed, their level of anxiety, their level of depression, etc. The server has the means to provide these results to the user, and can also transmit the results over the Internet for use by medical professionals and psychotherapists.

[0902] Furthermore, by allowing users to check the results through an interface, it can promote self-insight and be used as reference information for necessary treatment or counseling. The analysis results are displayed in a concise and easy-to-understand format, and it can also suggest countermeasures depending on the state of stress, anxiety, and depression.

[0903] As a concrete example, consider a case where a user wears an EEG measuring device to capture EEG data while sleeping. This data is sent to a server and stored in a storage device. The server uses an artificial intelligence model to analyze this data and generate a result, such as "the user exhibits high stress levels." This result is provided to the user through a web interface and, if necessary, automatically sent to a medical professional. Medical professionals can use this information to recommend appropriate treatment methods for the user.

[0904] In this way, the system of the present invention enables highly accurate assessment of psychological states by analyzing electroencephalogram data, realizing a new approach to providing appropriate psychotherapy and treatment for individual users.

[0905] The processing flow will be explained below.

[0906] Step 1:

[0907] The user uses an electroencephalogram (EEG) measuring device to obtain brainwave data while sleeping. This device collects brainwave signals through electrodes attached to the scalp and converts them into digital data.

[0908] Step 2:

[0909] The user's device transmits this EEG data to a server, typically transferred over a network in real time or in batches.

[0910] Step 3:

[0911] The server stores the received EEG data in a storage device, and at this time, it also checks for errors to ensure data integrity and accurate storage.

[0912] Step 4:

[0913] The server prepares the EEG data stored in the storage device to be input into the artificial intelligence model (AI model), performing preprocessing such as data normalization and feature extraction.

[0914] Step 5:

[0915] The server analyzes the EEG data using a pre-trained AI model, which is trained with machine learning algorithms to identify unconscious patterns of stress, anxiety, and depression.

[0916] Step 6:

[0917] The server generates the analysis results output by the AI ​​model, specifically summarizing the results in the form of numerical values ​​and reports that indicate psychological states such as stress levels, depression levels, and anxiety levels.

[0918] Step 7:

[0919] The server outputs the analysis results to the user via a display device or a web interface, and the user can access a dedicated web page to check their own analysis results.

[0920] Step 8:

[0921] Users can use the analysis results to deepen their self-insight and share it with medical professionals or psychotherapists as needed. Medical professionals can then receive the analysis results and propose optimal treatment plans and counseling methods for the user.

[0922] Step 9:

[0923] The server continuously collects and stores data for further analysis and trend analysis, and this data is used to improve future models and analyze new psychological states.

[0924] Step 10:

[0925] The server provides analysis results in real time via the internet to those who need them, and is equipped with a function that allows data to be smoothly shared with medical professionals in remote locations, for example, through remote counseling or telemedicine.

[0926] In this way, this system realizes a series of processes that collect and analyze the user's EEG data and provide the results to the user and medical professionals, thereby supporting more effective psychotherapy and treatment.

[0927] Example 1

[0928] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[0929] Conventional EEG data analysis systems have had difficulty accurately detecting and predicting unconscious stress, anxiety, and depression. Furthermore, there was a lack of a means to quickly and intuitively provide analysis results to users and medical professionals, hindering their linkage to appropriate psychotherapy and treatment. Furthermore, insufficient pre-processing, such as data normalization and noise removal, often limited the accuracy of the analysis.

[0930] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[0931] In this invention, the server

[0932] a means for acquiring electroencephalogram data;

[0933] means for storing the acquired electroencephalogram data in a storage device;

[0934] means for pre-processing the electroencephalogram data stored in the storage device;

[0935] A means for analyzing the pre-processed EEG data using a generative AI model;

[0936] A means for outputting the analysis results;

[0937] a display device for providing the analysis results to a user or a medical professional;

[0938] This makes it possible to detect unconscious psychological states with high accuracy and provide the results to relevant parties at the appropriate time.

[0939] "Electroencephalogram data" refers to information obtained by quantifying signals that represent the electrical activity of the user's brain, obtained by a measuring device.

[0940] A "storage device" is a physical or virtual storage that holds digital data and allows it to be accessed and manipulated as needed.

[0941] "Preprocessing" refers to the procedure of normalizing, removing noise, segmenting, and otherwise processing the acquired EEG data to convert it into a format suitable for analysis.

[0942] A "generative AI model" is a model that uses artificial intelligence algorithms that have been pre-trained with large amounts of data and are capable of detecting specific patterns and anomalies.

[0943] "Analysis" refers to computational processes performed to assess subconscious psychological states based on pre-processed EEG data.

[0944] The "analysis results" are judgment information regarding stress, anxiety, and depression obtained from the analysis of EEG data by the generative AI model.

[0945] A "display device" is a device or software interface that visually presents analysis results to a user or medical professional.

[0946] The present invention is a system that collects a user's electroencephalogram data, analyzes it using artificial intelligence, identifies and predicts unconscious stress, anxiety, and depression, and provides the results to medical professionals and psychotherapists. This system is composed of the following components:

[0947] First, the user needs a device to collect brainwave data. The user wears an EEG measuring device on their scalp. This device measures the electrical signals of the brainwaves and converts them into digital data. The EEG measuring device itself can be a commercially available electroencephalograph.

[0948] The acquired digital data is then sent over the Internet to a server. The server receives the data, first stores it temporarily in memory, and then saves it in a storage device such as a database. The stored data is then pre-processed for analysis. This pre-processing includes data normalization, noise removal, segmentation, etc.

[0949] Once pre-processed, the data is fed into a generative AI model, which has been pre-trained with a large amount of data and is capable of detecting specific patterns and anomalies, specifically neural networks that identify subconscious states of stress, anxiety, and depression.

[0950] The analysis results are generated on the server and provided to users and healthcare professionals. The results are displayed through a web interface, allowing users to check their own status and share it with healthcare professionals as needed. The analysis results are presented in a visually understandable format, such as graphs and charts.

[0951] As a concrete example, consider a case where a user wears an EEG device at night to capture EEG data while sleeping. This data is transmitted in real time to a server and stored in a storage device. The server immediately pre-processes the data and inputs it into a generative AI model. The analysis results in a judgment that "the user exhibits high stress levels." This result is provided to the user through a web interface and, if necessary, automatically sent to medical professionals.

[0952] An example prompt might be, "Implement a program that uses a user's EEG data to analyze their subconscious stress levels. The EEG data will be sent digitally to a server and stored on a storage device. An artificial intelligence model will be used to analyze the data to determine the presence or absence of stress, the level of anxiety, and the level of depression. The results of the analysis will be provided to the user through a web interface and automatically sent to a medical professional if necessary."

[0953] This system can detect unconscious psychological states with high accuracy and provide users with appropriate insights and medical information.

[0954] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0955] Overall processing flow

[0956] The processing flow of this system is shown below in specific steps. At each step, the specific operation, input, and output are also explained.

[0957] Step 1: Collect EEG data

[0958] The user wears the EEG measurement device and begins measuring. The device converts the brain's electrical signals into digital data.

[0959] Input: User's EEG signal

[0960] Processing: The EEG signal is converted into digital data by the EEG measuring device.

[0961] Output: Digital EEG data

[0962] Step 2: Sending EEG data

[0963] The electroencephalogram measuring device transmits the acquired digital data to a server via the Internet.

[0964] Input: Digital EEG data

[0965] Processing: Data encryption and secure transmission over the Internet

[0966] Output: EEG data sent to the server

[0967] Step 3: Save the EEG data

[0968] The server temporarily stores the received brain wave data in memory, and then saves it in a storage device such as a database.

[0969] Input: Received EEG data

[0970] Processing: Temporarily storing data and storing it in a database

[0971] Output: EEG data stored in a database

[0972] Step 4: Preprocessing the data

[0973] The server retrieves the EEG data stored in the storage device and performs pre-processing for analysis.

[0974] Input: Stored EEG data

[0975] Processing: Data normalization, denoising, segmentation (e.g. splitting data by time)

[0976] Output: Pre-processed EEG data

[0977] Step 5: Analyze the data

[0978] The server inputs the pre-processed EEG data into a generative AI model for analysis.

[0979] Input: Pre-processed EEG data

[0980] Processing: Generative AI models analyze data and detect patterns of stress, anxiety, and depression

[0981] Output: Analysis results (stress level, anxiety level, depression level)

[0982] Step 6: Generate analysis results

[0983] The server converts the analysis results obtained by the generative AI model into a format that is easy for humans to understand.

[0984] Input: Analysis results from a generative AI model

[0985] Processing: Converting analysis results into different formats, generating graphs and charts

[0986] Output: Visually easy-to-understand analysis results

[0987] Step 7: Provide analysis results

[0988] The server provides the generated analysis results to users and medical professionals via a web interface.

[0989] Input: Visualized analysis results

[0990] Processing: Displaying results via web interface

[0991] Output: User and healthcare professional review of results

[0992] Specific operation example

[0993] 1. The user wears the EEG measurement device and presses a button to start measurement.

[0994] 2. The EEG measuring device automatically converts the EEG into digital data and sends it to the server.

[0995] 3. The server temporarily stores the received data in memory and then records it in the database.

[0996] 4. The server denoises the data, splits it into time segments, and feeds it into a generative AI model.

[0997] 5. The server uses machine learning algorithms to analyze your stress and anxiety levels.

[0998] 6. The server converts the analysis results into graphs and charts and displays them in a web interface.

[0999] 7. Users and healthcare professionals can review the results through a web interface and take action as necessary.

[1000] The above are the processing steps of the program of this system and their specific operations.

[1001] (Application example 1)

[1002] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1003] Conventional EEG analysis systems were limited to analyzing a user's EEG data to evaluate their psychological state, but lacked the ability to immediately grasp fluctuations in the user's psychological state in real time and provide appropriate countermeasures. This meant that they were unable to quickly respond to the stress and psychological burden experienced by customers and employees, particularly in brick-and-mortar stores, posing challenges to improving customer experience and employee mental health care.

[1004] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[1005] In this invention, the server includes means for acquiring electroencephalogram data, means for storing the acquired electroencephalogram data in a storage device, means for analyzing the electroencephalogram data stored in the storage device using artificial intelligence, means for outputting the analysis results, and means for sending notifications in real time based on the analysis results. This enables real-time monitoring of the psychological states of customers and employees, allows appropriate countermeasures to be immediately provided, and enables an improved customer experience and mental health care for employees.

[1006] "EEG data" is digital data of electrical signals that measure the user's brain activity.

[1007] The "storage device" is a device for storing acquired electroencephalogram data.

[1008] "Artificial intelligence" is a technology that uses machine learning algorithms to analyze brainwave data and predict and identify subconscious psychological states.

[1009] The "analysis results" are information on stress levels and psychological state generated based on brain wave data analyzed by artificial intelligence.

[1010] "Notifications" are messages or alerts sent in real time via a display or other device with appropriate countermeasures based on the analysis results.

[1011] A "display device" is a device that visually presents analysis results and notifications to users.

[1012] "Real-time" means that the process from acquiring EEG data to notifying the analysis results is carried out instantly.

[1013] The system of this invention acquires a user's brainwave data in real time and analyzes it using artificial intelligence (AI) to identify stress, anxiety, and depression. Based on the analysis results, it also notifies appropriate countermeasures in real time, thereby improving the customer experience and mental health care for employees, primarily in brick-and-mortar stores.

[1014] Program Generation

[1015] In the system of the present invention, the program performs the following processing.

[1016] First, a user (customer or employee) wears an EEG measurement device, and EEG data is acquired from the device. EEG data is collected using hardware such as a wearable device or headset. This EEG data is sent to a server in real time and stored in a storage device.

[1017] The server analyzes the stored EEG data using a pre-trained AI model (e.g., a neural network model using Keras), which estimates the user's stress level and subconscious psychological state. The analysis uses a machine learning algorithm using a large amount of sample data.

[1018] The analysis results are generated in real time from the server, with specific output such as "The user's stress level is high." Based on this result, the system generates a notification suggesting appropriate countermeasures.

[1019] Processing Description

[1020] The server normalizes the acquired data, inputs it into an AI model, and outputs the analysis results. Specifically, a numerical calculation library such as Numpy is used for data normalization, and a deep learning framework such as Keras is used for machine learning.

[1021] The means for sending notifications to users based on the analysis results includes the ability to send messages in real time via a REST API, which are delivered to browsers and smartphone displays.

[1022] For example, if a store detects a customer's high stress level, it may play relaxing music or suggest a specific product for that customer. Similarly, if an employee is found to be under high stress, a notification will be sent to the manager, who will recommend appropriate breaks.

[1023] Specific examples

[1024] For example, suppose a customer wears a wearable device while selecting a product in a physical store, and brain wave data is collected at that time. This data is sent to a server in real time, and the stress level is analyzed. If the analysis result indicates that the customer's stress level is high, the system will notify the store staff to play relaxing music.

[1025] An example of a prompt sentence is, "Please suggest measures to take if customer M has a high stress level at a healthcare store. For example, this could include playing relaxing music, suggesting specific products, or guiding the customer through the store."

[1026] In this way, by using the system of the present invention, it is possible to grasp the psychological state of customers and respond appropriately in real time, thereby improving customer experience and providing mental health care for employees.

[1027] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1028] Step 1:

[1029] EEG data acquisition

[1030] The user wears an EEG measurement device, which captures brainwaves in real time. The device captures brainwaves as electrical signals, converts them into digital data, and transmits them to a server.

[1031] Input: User's EEG signal

[1032] Output: Digitized EEG data

[1033] Step 2:

[1034] Data storage

[1035] The server stores the received digitized EEG data in a storage device. In this step, the data is verified and stored accurately to ensure its reliability.

[1036] Input: Digitized EEG data

[1037] Output: EEG data stored in a memory device

[1038] Step 3:

[1039] Data Preprocessing

[1040] The server normalizes the EEG data stored in the storage device, a process that uses a numerical computing library such as Numpy to standardize the data and convert it into a format that can be input into an AI model.

[1041] Input: EEG data stored in a memory device

[1042] Output: Normalized EEG data

[1043] Step 4:

[1044] AI-based analysis

[1045] The server then feeds the normalized EEG data into a pre-trained AI model that analyzes stress levels and subconscious psychological states using deep learning frameworks such as Keras.

[1046] Input: Normalized EEG data

[1047] Output: Analysis results of stress level and psychological state

[1048] Step 5:

[1049] Saving and monitoring analysis results

[1050] The server stores the analysis results in a storage device and monitors them in real time. In this step, the server continuously monitors the user's psychological state based on the analysis results.

[1051] Input: Analysis results

[1052] Output: Analysis results and monitoring data stored in a storage device

[1053] Step 6:

[1054] Generate notifications

[1055] The server generates notifications based on the analysis, suggesting appropriate actions to take, which are provided to users and store staff in real time and sent via a REST API.

[1056] Input: Analysis results

[1057] Output: Notification message with workaround

[1058] Step 7:

[1059] Sending notifications

[1060] The server then sends the generated notification message to a display device or smartphone, allowing users and store staff to immediately understand the appropriate countermeasures based on the analysis results.

[1061] Input: Notification message

[1062] Output: Notification display to users and store staff

[1063] In this way, the system of the present invention collects and analyzes users' brainwave data and provides appropriate countermeasures in real time, thereby improving the customer experience in physical stores and providing mental health care for employees.

[1064] Furthermore, an emotion engine that estimates the user's emotion may be further combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59, and perform identification processing using the user's emotion.

[1065] The system of the present invention collects and analyzes the user's brain wave data, identifies and predicts the user's subconscious psychological state, and then uses an emotion engine to recognize the user's emotional state, providing the results to the user and medical professionals.

[1066] First, a device for acquiring EEG data is prepared. This device collects EEG signals through electrodes attached to the user's scalp and converts them into digital data. The user uses this device to acquire EEG data while sleeping or relaxing.

[1067] The user's device then transmits the acquired EEG data to a server, typically transferred in real time or in batches over the internet. The server stores the received EEG data in a storage device and performs error checking to ensure data integrity.

[1068] The server prepares the EEG data stored in the storage device for analysis. Specifically, it normalizes the data, extracts features, and performs preprocessing. The EEG data is then analyzed using a pre-trained artificial intelligence model. This allows it to identify and predict subconscious psychological states such as stress, anxiety, and depression.

[1069] Furthermore, the server uses an emotion engine to recognize the user's emotional state from the analysis of the brainwave data and psychological state. The emotion engine uses a machine learning algorithm and can analyze emotions from the user's brainwave patterns and other physiological data.

[1070] The analysis results include stress levels, depression levels, and anxiety levels, as well as emotional states (e.g., happiness, sadness, anger, etc.). The server outputs these results to the user via a display device or web interface. Users can access a dedicated web page or application to check their own analysis results and emotional states.

[1071] As a concrete example, consider the case where a user wears an EEG measuring device and acquires EEG data while relaxing at the end of the day. This data is sent to a server and stored in a storage device. The server analyzes the data using an artificial intelligence model and generates an analysis result such as, "The user has a high stress level and exhibits anxiety and mild depression." Furthermore, the emotion engine determines that "The user is currently experiencing strong feelings of anger." This result is provided to the user through a web interface and is also automatically sent to a medical professional. Based on this information, the medical professional can suggest the most appropriate treatment or counseling method for the user.

[1072] In this way, by analyzing EEG data and combining it with an emotion engine, this system can identify and predict the user's psychological and emotional state in detail and provide appropriate feedback and countermeasures, thereby improving the accuracy and effectiveness of psychotherapy and therapeutic support.

[1073] The processing flow will be explained below.

[1074] Step 1:

[1075] The user wears an EEG measuring device on their scalp to obtain brain wave data during sleep, relaxation, etc. This device electrically collects brain wave signals through electrodes and converts them into digital data.

[1076] Step 2:

[1077] The user's device transmits the acquired EEG data to a server. This data is transferred to the server via the Internet in real time or in batches. Security protocols and data compression techniques are used to ensure the data transmission is secure and efficient.

[1078] Step 3:

[1079] The server stores the received EEG data in a storage device. During the storage process, an error check is performed to confirm the integrity and accuracy of the data. The server notifies the user's device that the data has been successfully stored.

[1080] Step 4:

[1081] The server prepares the EEG data stored in the storage device for analysis. Specifically, it performs preprocessing such as data normalization, filtering, and feature extraction to prepare the data for appropriate analysis.

[1082] Step 5:

[1083] The server analyzes the pre-processed EEG data using a pre-trained artificial intelligence model that uses machine learning algorithms to identify unconscious patterns of stress, anxiety, and depression.

[1084] Step 6:

[1085] The server generates the analysis results from the AI ​​model. Specifically, the results are summarized in the form of numerical values ​​and reports that indicate psychological states such as stress levels, anxiety levels, and depression levels. These analysis results are also used as the base data for subsequent emotion analysis.

[1086] Step 7:

[1087] The server uses an emotion engine to recognize the user's emotional state from the EEG data and the results of the previous analysis. The emotion engine uses a machine learning algorithm to identify emotions from the user's EEG patterns. The results include the user's state of happiness, sadness, anger, etc.

[1088] Step 8:

[1089] The server integrates the analyzed psychological and emotional state results and outputs a single report that contains a complete picture of the user's psychological and emotional state in a format that is easy for users and medical professionals to understand.

[1090] Step 9:

[1091] The server displays the output analysis results via an interface for providing them to the user, who can then access a dedicated web page or application to check their own analysis results.

[1092] Step 10:

[1093] Users can deepen their self-insight based on the analysis results provided and share them with medical professionals or psychotherapists as needed, allowing medical professionals to propose optimal treatment plans and counseling methods for the user based on the analysis results.

[1094] In this way, this system collects and analyzes the user's brainwave data and combines it with an emotion engine to identify the user's subconscious psychological and emotional states in detail and provide appropriate feedback, enabling more accurate psychotherapy and treatment support.

[1095] Example 2

[1096] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1097] Conventional psychological and emotional state analysis systems have low accuracy, making it difficult to provide users with appropriate feedback or therapeutic support. Furthermore, obtaining highly accurate analysis results requires extensive specialized knowledge and expensive equipment, making them difficult for average users to use. The purpose of this invention is to solve these problems and provide a system that can obtain more accurate analysis results and is easy to use.

[1098] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[1099] In this invention, the server includes means for preprocessing EEG data, means for analyzing the preprocessed EEG data using an artificial intelligence model, and means for using an emotion engine that recognizes an emotional state from the EEG data and the analysis results. This enables accurate preprocessing of the EEG data, realizes highly accurate analysis of psychological and emotional states, and makes it possible to provide appropriate feedback and therapeutic support to the user.

[1100] "Electroencephalogram data" refers to electrical signals generated from the user's brain, acquired as digital data.

[1101] "Storage device" refers to a database or other digital storage medium for storing acquired EEG data.

[1102] "Preprocessing" refers to processes such as data normalization, feature extraction, and noise removal to prepare EEG data in a form that is easier to analyze.

[1103] An "artificial intelligence model" is a model trained using machine learning algorithms to analyze EEG data and identify psychological states.

[1104] The "emotion engine" is an algorithm that recognizes the user's emotional state based on the analysis results of brainwave data and artificial intelligence models.

[1105] "Analysis results" are data representing the user's psychological and emotional state obtained by the artificial intelligence model and emotion engine.

[1106] This invention is a system that collects and analyzes a user's electroencephalogram data to identify and predict their subconscious psychological and emotional states. This system includes a device for acquiring electroencephalogram data, a terminal for transmitting the data to a server, a server for storing and analyzing the data, and a function for outputting the analysis results.

[1107] First, the user uses a device to acquire brain waves. This device has the function of collecting brain wave signals through electrodes attached to the scalp and converting them into digital data. Specific devices include an electroencephalograph and scalp electrodes. The user uses this device to acquire brain wave data, usually while relaxed or asleep.

[1108] The user's device then transmits the acquired EEG data to a server via the internet. The device is equipped with Wi-Fi or mobile data capabilities to ensure stable communication. The data is sent in real time or in batches at the end of the day.

[1109] The server stores the received EEG data in a storage device (e.g., a database) and performs error checking. It then preprocesses the stored data. This preprocessing includes normalization, feature extraction, and noise removal. The server uses high-performance hardware (e.g., a CPU or GPU) to quickly perform these preprocessing steps.

[1110] The server then analyzes the pre-processed EEG data using a pre-trained artificial intelligence model that employs machine learning algorithms to identify and predict the user's subconscious psychological states, such as stress, anxiety, and depression.

[1111] After the analysis is complete, the server uses an emotion engine to recognize the user's emotional state from the analysis of the EEG data and psychological state. The emotion engine also uses machine learning algorithms to analyze emotions (e.g., happiness, sadness, anger, etc.) from the user's EEG patterns and other physiological data.

[1112] Finally, the psychological and emotional states obtained as analysis results are provided to the user via a dedicated web interface or mobile application. Users can check their own psychological and emotional states through this interface. The analysis results can also be automatically sent to medical professionals as needed to be used as reference information for treatment and counseling.

[1113] As a concrete example, consider a case where a user wears an EEG measuring device at the end of the day and captures EEG data while relaxing. This data is sent by the device to a server and stored in a storage device. The server analyzes the data using an artificial intelligence model and generates an analysis result such as "The user has a high stress level and exhibits anxiety and mild depression." The emotion engine then determines that "The user is currently experiencing strong emotions of anger." These results are notified to the user through a web interface and are also sent to medical professionals.

[1114] Example prompt sentence:

[1115] "The user wore an EEG measuring device and, while relaxed, brain wave data was collected. The data was sent to a server and stored. The server analyzed the data using an artificial intelligence model and produced results indicating the user's high stress levels, anxiety, and mild depression. Furthermore, the emotion engine determined that the user was experiencing strong feelings of anger. The analysis results were provided to the user through a web interface and also sent to medical professionals."

[1116] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1117] Step 1:

[1118] The user wears an EEG measurement device to acquire EEG data. The device collects EEG signals as digital data through electrodes attached to the scalp. For example, EEG data can be acquired by wearing the device while the user is relaxing at night. The input of this step is the user's EEG signals, and the output is digital EEG data.

[1119] Step 2:

[1120] The device transmits the acquired EEG data to a server via the internet. The device is equipped with Wi-Fi or mobile data capabilities to ensure stable communication. The data is transmitted in real time or in batches at the end of the day. The input of this step is digital EEG data, and the output is the EEG data transmitted to the server.

[1121] Step 3:

[1122] The server stores the received EEG data in a storage device. At this time, the server performs an error check to confirm the consistency of the data. For example, it maintains the integrity of the data by checking whether the data format is correct and whether there is any missing data. The input of this step is the EEG data sent from the terminal, and the output is the EEG data stored in the storage device.

[1123] Step 4:

[1124] The server preprocesses the EEG data stored in the storage device. Preprocessing includes data normalization, feature extraction, and noise removal. For example, applying a noise removal algorithm to improve the data quality. The input of this step is the EEG data stored in the storage device, and the output is the preprocessed EEG data.

[1125] Step 5:

[1126] The server analyzes the preprocessed EEG data using a pre-trained artificial intelligence model. This model uses machine learning algorithms to identify and predict the user's psychological states, such as stress, anxiety, and depression. For example, it uses a high-performance GPU to quickly analyze large amounts of data. The input of this step is the preprocessed EEG data, and the output is the psychological state analysis result.

[1127] Step 6:

[1128] The server uses an emotion engine to recognize the user's emotional state from the analysis results and EEG data. This emotion engine also uses a machine learning algorithm to analyze emotions such as happiness, sadness, and anger from EEG patterns. The input of this step is the psychological state analysis results and EEG data, and the output is the resulting emotional state.

[1129] Step 7:

[1130] The server notifies the user and healthcare professionals of the analysis results and the recognized emotional state. The results are provided through a dedicated web interface or mobile application. For example, users can check the results on their own devices, and healthcare professionals can use them as reference information for treatment. The input of this step is the emotional state result, and the output is the analysis results provided to the user and healthcare professionals.

[1131] (Application example 2)

[1132] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1133] Conventional autonomous vehicle systems have had the challenge of being unable to grasp the emotional and psychological states of drivers and passengers in real time and provide appropriate feedback accordingly. This has made it difficult to ensure maximum passenger safety and comfort. There has also been a lack of concrete measures to reduce psychological stress and anxiety.

[1134] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 2 is realized by the following means.

[1135] In this invention, the server includes means for acquiring electroencephalogram data, means for storing the acquired electroencephalogram data in a storage device, means for analyzing the electroencephalogram data stored in the storage device using artificial intelligence, and means for providing feedback to the vehicle control system based on the generated analysis results, thereby enabling real-time feedback based on the user's psychological and emotional states.

[1136] "Electroencephalogram data" refers to data that is generated by electronically collecting a user's electroencephalogram activity and converting that information into a digital format.

[1137] A "storage device" is a hardware or software component used to store acquired data.

[1138] "Artificial intelligence" refers to algorithms and systems that learn from large amounts of data and perform analysis and predictions.

[1139] "Analysis results" are the information obtained from the analysis of acquired EEG data by artificial intelligence.

[1140] An "automobile control system" is a system for controlling an autonomous vehicle, and is a device that has the function of automatically operating the vehicle.

[1141] "Feedback" refers to various responses and instructions given to the system or user based on the analysis results.

[1142] "Mental state" is information that indicates the user's mental state and emotional fluctuations.

[1143] This invention collects and analyzes the EEG data of passengers in an autonomous vehicle in real time, and provides feedback to the vehicle's control system based on the analysis results. To achieve this, an EEG data acquisition device, a server, a storage device, an artificial intelligence (AI) analysis system, and an automobile control system are required.

[1144] First, the user wears an EEG data acquisition device and EEG data is collected in real time. This data is transmitted to a server via wireless communication or the Internet. The server then stores the received EEG data in a storage device.

[1145] The server then transmits the stored EEG data to an AI analysis system for analysis. The AI ​​analysis system uses a pre-trained generative AI model to analyze the EEG data and identify the user's psychological and emotional state. For example, specific EEG patterns can be used to detect a user's stress level, anxiety, or relaxation state.

[1146] The analysis results are sent back to the server, which then provides feedback to the car's control system. For example, if the user is feeling highly stressed, the vehicle's control system can automatically select a relaxing route and adjust the in-car environment. It can also play ambient music or provide interesting information when the user is in a relaxed state.

[1147] This allows the driving experience of autonomous vehicles to be optimized based on the user's psychological and emotional state, improving safety and comfort.

[1148] For example, if a user feels high stress during a long drive, the server will automatically recommend a route change based on the analysis results, and the vehicle will switch to a route with better scenery.The system will also automatically adjust the in-car music selection and air conditioning settings to promote relaxation for the user.

[1149] Here are some examples of prompts to input to the generative AI model:

[1150] "Please suggest the best machine learning model to analyze the stress level and emotional state of the user from their EEG data. Also, I would like your advice on how to provide the best vehicle navigation action when high stress is detected."

[1151] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1152] Step 1:

[1153] The user wears an EEG data acquisition device. This device has the function of collecting the user's EEG in real time and converting it into a digital format. The input is the user's EEG signal, and the output is digitized EEG data.

[1154] Step 2:

[1155] The digitized EEG data collected by the EEG data acquisition device is sent to the user's device using wireless communication or Bluetooth. The input is the digitized EEG data, and the output is the data sent to the user's device.

[1156] Step 3:

[1157] The EEG data received by the user's device is sent to the server. The data is sent via the Internet. The input is the EEG data stored on the user's device, and the output is the EEG data sent to the server.

[1158] Step 4:

[1159] The EEG data received by the server is stored in a storage device. Specifically, a database management system is used. The input is the EEG data sent to the server, and the output is the data stored in the storage device.

[1160] Step 5:

[1161] The server uses artificial intelligence to analyze the EEG data stored in the storage device. A generative AI model is used for this analysis, and it performs feature extraction and data normalization. The input is the EEG data stored in the storage device, and the output is the analysis results.

[1162] Step 6:

[1163] The server receives the analysis results from the AI ​​and identifies the user's psychological and emotional state. Specifically, it determines the user's stress level, anxiety, relaxation state, etc. The input is the analysis results, and the output is the identified psychological and emotional state.

[1164] Step 7:

[1165] The server then provides feedback to the vehicle's control system based on the analysis results. For example, if high stress is detected, a relaxing route will be automatically selected. The input is the identified psychological and emotional state, and the output is vehicle control instructions as feedback.

[1166] Step 8:

[1167] The vehicle's control system receives feedback from the server and adjusts the vehicle's behavior and environmental settings, such as changing the navigation route, adjusting the air conditioning temperature, or selecting in-car music. The input is vehicle control instructions, and the output is the actual vehicle adjustment behavior.

[1168] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the control target 443 to output the result of the specific processing. The microphone 238 acquires voice indicating a user input regarding the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[1169] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[1170] In the above embodiment, an example was given in which the specific processing is performed by the data processing device 12, but the technology of the present disclosure is not limited to this, and the specific processing may be performed by the robot 414.

[1171] The emotion identification model 59 as an emotion engine may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[1172] FIG. 9 illustrates an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. Emotions closer to the center of the concentric circles are more primitive. Emotions representing states and behaviors arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion includes both affect and mental states. Emotions generally generated from reactions occurring in the brain are arranged on the left side of the concentric circles. Emotions generally induced by situational judgment are arranged on the right side of the concentric circles. Emotions generally generated from reactions occurring in the brain and induced by situational judgment are arranged on the upper and lower sides of the concentric circles. Furthermore, the emotion of "pleasure" is arranged on the upper side of the concentric circles, and the emotion of "discomfort" is arranged on the lower side. In this way, in the emotion map 400, multiple emotions are mapped based on the structure by which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[1173] These emotions are distributed in the 3 o'clock direction on emotion map 400, and typically fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

[1174] The inside of emotion map 400 represents what is going on in the mind, and the outside of emotion map 400 represents behavior, so the further you go outside emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[1175] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is indicated, and when they approach the ideal, a state of pleasure is indicated. Emotions can also be created for robots, automobiles, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is indicated, and when they approach the ideal, a state of pleasure is indicated. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on Voice Emotion Recognition and Emotional Brain Physiological Signal Analysis Systems, Tokushima University, Doctoral Dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the "reaction" domain, where sensation is dominant. The right half of the emotion map lists emotions belonging to the "situation" domain, where situational awareness is dominant.

[1176] The emotion map defines two emotions that promote learning. One is a negative emotion on the situation side, around the middle of "repentance" or "reflection." In other words, this occurs when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is a positive emotion on the response side, around "desire." In other words, this occurs when the robot experiences positive feelings such as "I want more" or "I want to know more."

[1177] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple pieces of training data that are combinations of user input and emotion values ​​indicating each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions that are located close to each other have similar values, as in the emotion map 900 shown in FIG. 10. FIG. 10 shows an example in which multiple emotions, "relieved," "calm," and "reassuring," have similar emotion values.

[1178] The system according to the present disclosure has been described above mainly with respect to the functions of the data processing device 12, but the system according to the present disclosure is not necessarily implemented on a server. The system according to the present disclosure may be implemented as a general information processing system. The present disclosure may be implemented, for example, as a software program running on a personal computer or an application running on a smartphone, etc. The method according to the present disclosure may be provided to users in the form of SaaS (Software as a Service).

[1179] In the above embodiment, an example was given in which the specific processing is performed by one computer 22, but the technology of the present disclosure is not limited to this, and the specific processing may be distributed and performed by a plurality of computers including the computer 22. For example, the data generation model 58 may be provided in an external device of the data processing device 12, and data may be generated in the external device in accordance with input data.

[1180] In the above embodiment, an example in which the specific processing program 56 is stored in the storage 32 has been described, but the technology of the present disclosure is not limited to this. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-transitory storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-transitory storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes the specific processing in accordance with the specific processing program 56.

[1181] 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.

[1182] It is not necessary to store all of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store all of the specific processing program 56 in the storage 32; only a portion of the specific processing program 56 may be stored.

[1183] The hardware resource for executing a specific process can be any of the following processors: An example of a processor is a CPU, which is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. Another example of a processor is a dedicated electrical circuit, such as an FPGA (Field-Programmable Gate Array), a PLD (Programmable Logic Device), or an ASIC (Application Specific Integrated Circuit), which is a processor with a circuit configuration designed specifically for executing a specific process. Each processor has built-in or connected memory, and each processor uses the memory to execute the specific process.

[1184] The hardware resource that executes the specific processing may be configured with one of these various processors, or may be configured with a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Also, the hardware resource that executes the specific processing may be a single processor.

[1185] As an example of a system configured with a single processor, first, one processor is configured by combining one or more CPUs and software, and this processor functions as a hardware resource that executes a specific process. Second, there is a system that uses a processor that realizes the functions of an entire system including multiple hardware resources that execute a specific process on a single IC chip, as typified by SoC (System-on-a-chip). In this way, a specific process is realized using one or more of the above-mentioned various processors as hardware resources.

[1186] Furthermore, the hardware structure of these various processors can be, more specifically, an electric circuit that combines circuit elements such as semiconductor devices. The specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps may be deleted, new steps may be added, or the processing order may be rearranged, without departing from the spirit of the invention.

[1187] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, to avoid confusion and facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[1188] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference.

[1189] The following is further disclosed regarding the above embodiment.

[1190] (Claim 1)

[1191] a means for acquiring electroencephalogram data;

[1192] means for storing the acquired electroencephalogram data in a storage device;

[1193] means for analyzing the brain wave data stored in the storage device by artificial intelligence;

[1194] A means for outputting the analysis results;

[1195] A system including:

[1196] (Claim 2)

[1197] 2. The system according to claim 1, wherein the system generates an analysis result indicating that the acquired electroencephalogram data reflects an unconscious psychological state.

[1198] (Claim 3)

[1199] 10. The system of claim 1, wherein the analysis results are provided to the user via a display device.

[1200] (Claim 4)

[1201] 10. The system according to claim 1, further comprising a communication means for transmitting and receiving electroencephalogram data via the Internet.

[1202] (Claim 5)

[1203] The system of claim 1, wherein the analyzed electroencephalogram data is provided as information for diagnosing the mental health condition of the subject and providing treatment support.

[1204] (Claim 6)

[1205] 10. The system of claim 1, further comprising means for generating psychotherapy or counseling recommendations based on the analysis.

[1206] "Example 1"

[1207] (Claim 1)

[1208] a means for acquiring electroencephalogram data;

[1209] means for storing the acquired electroencephalogram data in a storage device;

[1210] means for pre-processing the electroencephalogram data stored in the storage device;

[1211] A means for analyzing the pre-processed EEG data using a generative AI model;

[1212] A means for outputting the analysis results;

[1213] a display device for providing the analysis results to a user or a medical professional;

[1214] A system including:

[1215] (Claim 2)

[1216] 10. The system of claim 1, which uses a trained generative AI model to analyze subconscious psychological states based on acquired EEG data.

[1217] (Claim 3)

[1218] 10. The system according to claim 1, wherein the analysis results are visually presented to the user via a display device.

[1219] "Application Example 1"

[1220] (Claim 1)

[1221] a means for acquiring electroencephalogram data;

[1222] means for storing the acquired electroencephalogram data in a storage device;

[1223] means for analyzing the brain wave data stored in the storage device by artificial intelligence;

[1224] A means for outputting the analysis results;

[1225] A means for sending notifications in real time based on the analysis results;

[1226] A system including:

[1227] (Claim 2)

[1228] The system of claim 1 generates an analysis result indicating that the acquired EEG data reflects an unconscious psychological state, estimates stress levels based on the analysis result, and generates a notification suggesting countermeasures for customers and employees.

[1229] (Claim 3)

[1230] 2. The system according to claim 1, wherein the analysis results and countermeasures are provided to the user via a display device.

[1231] "Example 2: Combining Emotion Engines"

[1232] Rewritten claims

[1233] (Claim 1)

[1234] a means for acquiring electroencephalogram data;

[1235] means for storing the acquired electroencephalogram data in a storage device;

[1236] means for preprocessing the electroencephalogram data stored in the storage device;

[1237] means for analyzing the preprocessed EEG data using an artificial intelligence model;

[1238] a means for using an emotion engine to recognize an emotional state from the electroencephalogram data and analysis results;

[1239] means for outputting the analysis results and the recognized emotional state;

[1240] A system including:

[1241] (Claim 2)

[1242] 2. The system according to claim 1, wherein the system generates an analysis result indicating that the acquired electroencephalogram data reflects an unconscious psychological state.

[1243] (Claim 3)

[1244] 10. The system of claim 1, wherein the analysis results and the recognized emotional state are provided to the user via a display device.

[1245] "Application example 2 when combining emotion engines"

[1246] (Claim 1)

[1247] a means for acquiring electroencephalogram data;

[1248] means for storing the acquired electroencephalogram data in a storage device;

[1249] means for analyzing the brain wave data stored in the storage device by artificial intelligence;

[1250] A means for outputting the analysis results;

[1251] a means for providing feedback to a vehicle control system based on the generated analysis results;

[1252] A system including:

[1253] (Claim 2)

[1254] 2. The system according to claim 1, wherein the system generates an analysis result indicating that the acquired electroencephalogram data reflects an unconscious psychological state.

[1255] (Claim 3)

[1256] 10. The system of claim 1, wherein the analysis results are provided to a user via a display device and are fed back to a vehicle control system. [Explanation of symbols]

[1257] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot< / url:> < / url:> < / url:> < / url:>

Claims

1. a means for acquiring electroencephalogram data; means for storing the acquired electroencephalogram data in a storage device; means for analyzing the brain wave data stored in the storage device by artificial intelligence; A means for outputting the analysis results; A system including:

2. 2. The system according to claim 1, wherein the system generates an analysis result indicating that the acquired electroencephalogram data reflects an unconscious psychological state.

3. The system according to claim 1, wherein the analysis results are provided to the user via a display device.

4. 2. The system according to claim 1, further comprising a communication means for transmitting and receiving electroencephalogram data via the Internet.

5. The system according to claim 1, wherein the analyzed electroencephalogram data is provided as information for diagnosing the mental health condition of the subject and providing treatment support.

6. The system of claim 1 further comprising means for generating psychotherapy or counseling recommendations based on the analysis.

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