system

Abstract

Provide a system. 【Solution means】 Means for acquiring a user's biological signal in real time, Means for analyzing the acquired biological signal to determine an emotional state, Means for generating support information for the user based on the emotional state, Means for notifying the user of the generated support information, Means for collecting feedback from the user, Means for analyzing the collected feedback to improve the analysis accuracy, Means for providing support information through a visual assistance device worn by field workers and enabling support based on individual needs, A system including.

Description

Technical Field

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

Background Art

[0002] Patent Document 1 discloses a method for controlling a persona chatbot, which is performed by at least one processor and includes steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to an explanation of a chatbot character, encoding the prompt, and inputting the encoded prompt into a language model to generate a chatbot utterance in response to the user utterance.

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In modern society, many people live in a state of long working hours and high stress environment, and as a result, it has become a problem that emotional control is difficult. Such a situation may lead to deterioration of mental health and decline in productivity, which is particularly problematic for individuals who do not have appropriate self-care time and methods. Therefore, there is a need for a system that allows users to grasp their own emotional state in real time and obtain appropriate coping methods immediately.

Means for Solving the Problems

[0005] This invention provides a system that acquires a user's biosignals in real time, analyzes the data, and determines their emotional state. Based on the emotional state, the system generates appropriate advice and notifies the user, thereby supporting emotional control. Furthermore, it improves the system's analysis accuracy by collecting and analyzing user feedback. It also provides more personalized support to the user through the provision of positive messages tailored to their emotional state and the use of natural language processing technology.

[0006] "Biosignals" are electrical or physical information that reflects an individual's physiological activities, such as heart rate, brain waves, and respiration.

[0007] "Real-time" is a concept that refers to the temporal responsiveness of processing and analyzing data immediately at the moment it is generated.

[0008] "Emotional state" refers to the psychological state experienced by the user, such as joy, anger, sadness, and stress.

[0009] "Analysis" is the process of examining data in detail and making certain judgments or evaluations.

[0010] "Advice information" refers to information that provides users with specific instructions and suggestions for dealing with their own problems or situations.

[0011] "Notification" refers to the means and process by which a system communicates information or results to a user.

[0012] "Feedback" refers to information that users use to communicate their reactions and results regarding the information and advice provided by the system.

[0013] "Natural language processing technology" refers to the technology used by computers to analyze, understand, and generate natural human language.

[0014] A "positive message" is a forward-looking message intended to improve the user's mental state and boost their self-esteem. [Brief explanation of the drawing]

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

Embodiments for Carrying Out the Invention

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

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

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

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

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

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

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

[0023] [First Embodiment]

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

[0025] As shown in Figure 1, the data processing system 10 includes a data processing device 12 and a smart device 14. An example of the data processing device 12 is a server.

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

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

[0028] The reception device 38 is equipped with a touch panel 38A and a microphone 38B, etc., and receives user input. The touch panel 38A receives user input by detecting contact with an object (e.g., a pen or finger). The microphone 38B receives user input by detecting the user's voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the data indicating the user input.

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

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

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

[0032] As shown in Figure 2, in the data processing device 12, a specific processing is performed by the processor 28. A specific processing program 56 is stored in the storage 32. The specific processing program 56 is an example of a "program" related to the technology of this disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

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

[0034] In the smart device 14, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The reception output program 60 is used in conjunction with a specific processing program 56 by the data processing system 10. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

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

[0036] The system of the present invention acquires a user's biosignals in real time, analyzes that data, and determines their emotional state. Specifically, the following components work together.

[0037] First, the device uses an electroencephalogram (EEG) sensor to collect the user's biosignals. This collected data forms the basis of information indicating the user's emotional state. The device immediately transmits the acquired biosignal data to a server, where the data is analyzed.

[0038] Next, the server analyzes the received biometric signals based on an AI algorithm. The analysis determines the user's current emotional state (e.g., anger, sadness, stress). Based on this determination, the server begins the process of generating personalized advice for the user.

[0039] Once the analysis of biosignals is complete and appropriate advice is generated, the server uses natural language processing technology to create feedback in a human-readable format. This feedback is highly specific because it is personalized based on the user's emotional state. The generated feedback is sent back to the device and notified to the user. This notification allows the user to understand their emotional state and take appropriate action.

[0040] For example, if a user is experiencing stress at work, the server generates advice based on past data and analysis results, such as, "Try taking a short break and taking some deep breaths." The terminal then notifies the user, who can then act according to the system's advice.

[0041] Furthermore, users can provide feedback to the server via their device regarding the results and reactions of following the provided advice. This feedback information is aggregated by the server and used to improve the accuracy of the AI ​​algorithm, leading to improved quality of future advice.

[0042] This system is designed to help users recognize their emotions in real time and provide support to enhance their ability to control their emotions. In this way, users can maximize the effectiveness of self-care and achieve emotional stability.

[0043] The following describes the processing flow.

[0044] Step 1:

[0045] The device acquires the user's biosignals in real time through an electroencephalogram (EEG) sensor attached to the user. The acquired biosignals are temporarily stored as digital data within the device.

[0046] Step 2:

[0047] The device transmits temporarily stored biometric signal data to the server using a secure communication protocol. This data is uploaded periodically and placed in a waiting state for analysis.

[0048] Step 3:

[0049] The server applies an AI model to analyze the received biosignal data. The AI ​​model extracts specific brainwave patterns and determines the user's emotional state. This step classifies what emotions the user is currently experiencing (e.g., anger or stress).

[0050] Step 4:

[0051] The server generates appropriate advice based on the analysis results. This advice includes specific suggestions for improving the user's emotional state. Natural language processing techniques are used in this process to format the feedback into a human-readable format.

[0052] Step 5:

[0053] The server sends formatted advice information to the terminal. The terminal immediately notifies the user of the received information. This notification may be made via screen display or audio output.

[0054] Step 6:

[0055] The user checks notifications from their device and selects actions or responses based on the provided advice. This step requires proactive action from the user.

[0056] Step 7:

[0057] Users input the results of following the advice and their own reactions into their device and leave them as feedback. This feedback is recorded through the user interface.

[0058] Step 8:

[0059] The server receives aggregated feedback data and uses it to improve future algorithms. This makes the AI ​​model increasingly accurate and provides more personalized support to users.

[0060] (Example 1)

[0061] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server," and the smart device 14 will be referred to as the "terminal."

[0062] It is necessary to recognize users' emotional states in real time and provide effective ways to respond. However, conventional technologies have not adequately achieved the rapid collection and analysis of biometric data and the provision of appropriate feedback. There is a need for technology that supports users in understanding their own emotions and making self-improvement efforts.

[0063] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0064] In this invention, the server includes means for acquiring the user's biometric data in real time using an information processing device, means for analyzing the acquired biometric data to determine the user's psychological state, and means for generating guidance information for the user based on the psychological state. This makes it possible to quickly and accurately grasp the user's emotional state and provide specific guidance information tailored to their individual needs.

[0065] An "information processing device" is a device used to collect, process, and analyze data, and mainly refers to computers and servers.

[0066] "Biometric data" refers to signals that indicate a person's physiological state, and specifically includes vital data such as brain waves and heart rate.

[0067] "Psychological state" refers to a user's current emotions and mood, and is determined by a specific algorithm.

[0068] "Guidance information" refers to advice and suggestions for the user generated based on their assessed psychological state.

[0069] "Evaluation information" refers to feedback regarding the results and reactions of users based on the guidance information provided.

[0070] "Interpretation accuracy" refers to the ability to accurately determine a person's psychological state from analyzed biometric data.

[0071] "Language processing technology" refers to the technology of processing natural language using computers and providing information in a format that is easy for humans to understand.

[0072] The embodiments for carrying out the present invention will be described in detail below.

[0073] The device is equipped with a biosensor for measuring brainwaves and acquires real-time biometric data from the user. This device can be worn on the user's forehead, where it continuously measures brainwave data. The acquired data is processed digitally within the device to remove noise and is converted into an analyzable format.

[0074] Next, the device transmits the acquired biometric data to a server in real time using wireless communication technology (e.g., Bluetooth or Wi-Fi). This server functions as an information processing device and uses a high-performance computer to analyze the biometric data. The server uses AI algorithms and machine learning models (e.g., deep learning frameworks) to determine the user's psychological state from the received data. For example, in response to a prompt such as, "Suggest advice to generate if the user's emotional state is stressed," the generative AI model obtains appropriate advice.

[0075] Furthermore, the server generates guidance information based on the user's psychological state using natural language processing technology and sends it back to the terminal. The terminal then notifies the user of this guidance information, providing helpful feedback. For example, if the user is feeling stressed, the server might generate advice such as "Try taking a short break" and transmit this information to the user via the terminal.

[0076] Users act based on the provided guidance information and then send feedback to the server by entering evaluation information on their device. This evaluation information helps improve the accuracy of the AI ​​algorithms on the server and contributes to improving future guidance information.

[0077] This invention recognizes the user's emotional state in real time and provides support for effectively managing their emotions. In this way, users can achieve emotional stability in their daily lives and promote self-care.

[0078] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0079] Step 1:

[0080] The device acquires the user's brainwave data using biosensors. The input is the user's brainwave signal, and the output is a denoised digital signal. This data is processed in real time by a built-in digital signal processing unit and converted into an analyzable format.

[0081] Step 2:

[0082] The terminal transmits a denoised digital signal to the server in real time using wireless communication technology (e.g., Bluetooth or Wi-Fi). The input is the digital signal within the terminal, and the output is biometric data transmitted to the server. A high-speed protocol is used in this communication to prevent data loss.

[0083] Step 3:

[0084] The server analyzes the received biometric data using an AI algorithm. The input is biometric data transmitted from the terminal, and the output is a determination of the user's psychological state. This analysis includes specific operations such as extracting features from time-series brainwave patterns using a deep learning model.

[0085] Step 4:

[0086] The server uses a generative AI model to generate guidance information tailored to the user based on the psychological state assessment results. The input is the psychological state assessment results, and the output is guidance information in natural language format. Based on the keywords "generative AI model, prompt sentence," the language generation model operates to provide specific advice to the user.

[0087] Step 5:

[0088] The server uses natural language processing technology to format the generated guidance information in a way that is easy for the user to understand, and then sends it to the terminal. The input is the guidance information obtained by the generation AI model, and the output is the formatted text sent to the terminal. This feedback is immediately notified to the user.

[0089] Step 6:

[0090] Users act according to the provided guidance information and input the results as feedback into the device. The input includes user evaluation information, and the output is evaluation data sent to the server via the device. This information is used to improve the AI ​​algorithm.

[0091] Step 7:

[0092] The server analyzes evaluation data submitted by users to improve the accuracy of the AI ​​model's interpretation. The input is evaluation data, and the output is the improved performance of the AI ​​algorithm. This allows the system to evolve, resulting in higher quality guidance information in the future and more accurate feedback for users.

[0093] (Application Example 1)

[0094] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server," and the smart device 14 will be referred to as the "terminal."

[0095] In today's aging society, providing care tailored to individual needs is a crucial challenge. However, understanding the emotional state of elderly individuals in real time and providing appropriate support is not easy. Therefore, a decline in the quality of care due to a lack of emotionally-based approaches becomes a problem.

[0096] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0097] In this invention, the server includes means for acquiring the user's biosignals in real time, means for analyzing the acquired biosignals to determine the emotional state, and means for providing advisory information through a visual assistance device worn by field workers, thereby enabling support based on individual needs. This makes it possible to provide appropriate care based on the emotional state of elderly people.

[0098] "User biosignals" refer to electrical or physiological signals emitted from an individual's body, including information such as brain waves and heart rate, which can be used to infer emotional states.

[0099] "Real-time acquisition" means that data is acquired almost simultaneously with its generation, collecting data instantly without any time lag.

[0100] "Determining emotional state" is the process of analyzing and identifying the user's current psychological or emotional state based on acquired biosignals.

[0101] "Advice information" refers to information that provides guidance and suggestions tailored to the user's emotional state, aiming to improve the situation.

[0102] A "visual assistance device" is a device that visually presents information to the wearer, such as smart glasses, which display information overlaid on the real-world appearance.

[0103] "Feedback" refers to information about user reactions and results, which is used as data to improve the accuracy of system analysis.

[0104] The system for implementing this invention aims to support the improvement of elderly care by understanding the user's emotional state in real time and providing information tailored to individual needs. The server is designed to use an electroencephalogram (EEG) sensor to acquire biosignals in real time. This makes it possible to continuously collect electrical signals emitted from the user's body.

[0105] The acquired biosignals are sent to a server, where an AI algorithm is used to analyze the data. This analysis identifies the user's emotional state. The analysis uses programming languages ​​such as Python and machine learning libraries such as TENSORFLOW® and PyTorch.

[0106] After the emotional state is determined, natural language processing technology is used to generate specific advice for the user. This information is then visually presented through a visual aid, such as smart glasses. This enables care staff to provide appropriate care tailored to the emotional state of the elderly person.

[0107] For example, the system can detect if an elderly person is confused or stressed and generate prompt messages offering advice such as, "Shall we talk for a bit? Playing some relaxing music might also be helpful." It can also use prompt messages like, "Elderly person A seems anxious. What care approach would be effective?" This kind of feedback is also used to improve the accuracy of future analyses by the AI ​​system.

[0108] As described above, this system functions as a means of providing individualized care based on emotional states through real-time analysis of biological signals.

[0109] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0110] Step 1:

[0111] The device uses an electroencephalogram (EEG) sensor to measure the user's brainwaves. The biosignals obtained from the sensor become input and are acquired as digital signals in real time. These signals are then immediately transmitted to the server as data packets.

[0112] Step 2:

[0113] The server analyzes the received biometric signal data. It processes the input signals using an AI algorithm to identify the user's emotional state. The data is filtered, and a feature extraction algorithm outputs emotional states such as anger, sadness, and stress.

[0114] Step 3:

[0115] The server generates advice information based on the analysis results. Understanding the emotional state, it uses natural language processing techniques to create advice messages tailored to the user. At this stage, the input is emotional state data, and the output is a prompt message.

[0116] Step 4:

[0117] Advice information generated from the server is sent to the terminal. The information is displayed on smart glasses that function as a visual aid, and care staff are notified. This allows the input advice to be visually output, making it possible to encourage specific actions.

[0118] Step 5:

[0119] The user takes action based on the advice provided. The results of this action and feedback are returned to the server via the device. This input is stored in a database to improve the accuracy of the next analysis and is used to retrain the AI ​​model.

[0120] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0121] This invention is a system that acquires and analyzes a user's biosignals in real time and combines them with an emotion engine to provide more accurate recognition of emotional states and advice. This system is realized through the collaboration of hardware and software.

[0122] Specifically, an electroencephalogram (EEG) sensor attached to the device acquires the user's biosignals in real time. The acquired biosignal data is immediately sent to a server where it is analyzed. This analysis determines the user's current emotional state.

[0123] A key feature of this invention is that the server incorporates an emotion engine, which records the user's emotional history. The emotion engine utilizes the accumulated emotional data to learn the user's emotional patterns and predict changes in their emotional state. This makes it possible to recognize potential future emotional fluctuations in advance and prepare advice to address them proactively.

[0124] Furthermore, the emotion engine utilizes machine learning algorithms, enabling advanced pattern recognition and the generation of more precise feedback tailored to the user's specific emotions.

[0125] For example, if a user is facing a difficult project, the server uses an emotion engine to predict the user's past severe stress responses. Based on these results, it generates a positive message such as, "Take a short break and create a relaxing environment," and notifies the user through their device.

[0126] Furthermore, users can contribute to the system's learning process and improve the quality of future advice by providing feedback on the results of following these suggestions.

[0127] As described above, the present invention recognizes emotional states in real time while simultaneously providing users with optimal emotional control support through continuous data accumulation and machine learning. This system enables users to effectively manage their emotions and enhance their ability to cope with stressful situations.

[0128] The following describes the processing flow.

[0129] Step 1:

[0130] The device acquires biosignals in real time from an electroencephalogram (EEG) sensor attached to the user. This biosignal data is an important indicator of the user's emotional state.

[0131] Step 2:

[0132] The device securely transmits the acquired biometric data to the server using wireless or wired communication protocols. This process employs encryption technology to maintain data integrity and privacy.

[0133] Step 3:

[0134] The server uses an AI model to analyze the received biosignal data. The AI ​​model extracts specific patterns from the data and classifies the user's emotional state. This allows for an instantaneous determination of the user's mental state.

[0135] Step 4:

[0136] The server uses an emotion engine to analyze incoming data and past emotional history. The emotion engine detects changes in the user's emotional state and uses machine learning algorithms to predict future emotional fluctuations. This process makes it possible to proactively detect potential problems.

[0137] Step 5:

[0138] The server generates appropriate advice based on an analysis and prediction of the user's emotional state. This advice is presented in an easy-to-understand manner using natural language processing technology and is designed to help stabilize the user's emotions.

[0139] Step 6:

[0140] The device receives the generated advisory information and immediately notifies the user. Notifications are typically made using a visual display and an audible alert to draw the user's attention.

[0141] Step 7:

[0142] Users receive advice presented by their devices and act accordingly. The user's responses and actions are later used to improve system performance.

[0143] Step 8:

[0144] Users input feedback on the results of their actions and changes in their emotions into the device. This feedback is stored in the emotion engine and used as data to improve the quality of subsequent advice.

[0145] Step 9:

[0146] The server tunes its algorithms based on aggregated feedback, continuously improving the accuracy of its analysis. This allows the system to provide more personalized emotional support to each user.

[0147] (Example 2)

[0148] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server" and the smart device 14 as the "terminal".

[0149] Conventional emotion recognition systems have struggled to accurately grasp a user's real-time emotional state and provide appropriate advice immediately. Furthermore, they lacked the functionality to fully utilize collected feedback and improve the system's analytical accuracy. Additionally, their use of natural language generation technology to generate appropriate messages based on emotional states was insufficient, making it impossible to address individual user needs.

[0150] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0151] In this invention, the server includes means for acquiring the user's biometric information in real time, means for analyzing the acquired biometric information to determine the emotional state, and means for generating advice information for the user based on the emotional state. This makes it possible to accurately recognize the user's emotions in real time and provide personalized advice information based on predicted emotional fluctuations.

[0152] "Users" refer to end-users who use this system and are the entities that provide biometric information in real time.

[0153] "Biometric information" refers to data that indicates the user's physiological state, and includes, but is not limited to, electroencephalograms (EEGs).

[0154] "Real-time" refers to a state in which information is acquired and processed almost simultaneously or instantaneously.

[0155] "Emotional state" refers to the user's internal emotional condition and is determined based on analyzed biometric information.

[0156] "Advice information" refers to advice on actions and thoughts presented to the user based on their analyzed emotional state.

[0157] "Response" refers to the feedback or reaction that a user gives to advice from the system.

[0158] "Analytical accuracy" is a term that indicates the degree of accuracy and reliability in the analysis of biological information and responses.

[0159] A "machine learning algorithm" is a method of learning patterns from data and is a technology for making predictions about the future.

[0160] "Natural language generation technology" refers to the technology that enables systems to generate human language and form appropriate messages.

[0161] A "positive message" is information that is constructive and forward-looking to the user.

[0162] This invention is a system that acquires and analyzes a user's biometric information in real time to recognize their emotional state and provide appropriate advice. This system mainly consists of three elements: a terminal, a server, and the user.

[0163] The terminal is equipped with sensors to acquire the user's biometric information. These sensors, such as electroencephalogram (EEG) sensors, measure the user's biometric information and convert it into digital data. The acquired data is transmitted from the terminal to the server using a secure communication protocol.

[0164] The server is responsible for analyzing the received biometric information. It uses data analysis software such as Python's pandas library and scikit-learn to clean the biometric information and extract features. Next, it uses machine learning algorithms to determine the user's emotional state. Machine learning algorithms such as TensorFlow and PyTorch can be used. Furthermore, the server has an emotion engine built in that learns the user's emotional history and predicts future emotional fluctuations.

[0165] Based on the predicted emotional state, the server uses natural language generation technology to generate personalized advice for the user. This generation process is based on pre-stored data and a generation AI model. Appropriate advice is then notified to the user via their device.

[0166] For example, if a user is feeling stressed, the system will notify them with a positive message such as, "Take a short break and create a relaxing environment." Another example of a prompt to be input to the generative AI model is, "Based on the user's current emotional state and past history, suggest the next action for the user."

[0167] Users can contribute to the system's learning by responding to the advice provided. The device then sends this feedback back to the server, which uses it to improve the accuracy of future analyses and advice generation. This allows users to more effectively manage their emotions and improve their ability to cope appropriately with stressful situations.

[0168] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0169] Step 1:

[0170] Sensors attached to the device acquire the user's biometric information. The sensors measure physiological data such as brain waves in real time and convert it into digital data. The input is biometric information, and the output is biometric data in digital format.

[0171] Step 2:

[0172] The device transmits the acquired digital data to the server. A secure protocol (e.g., HTTPS) is used for communication to ensure data confidentiality. The input is biometric data in digital format, and the output is the biometric data transmitted to the server.

[0173] Step 3:

[0174] The server analyzes the received biometric data. The server uses the Python pandas library to clean and preprocess the data, removing noise. The input is the biometric data sent to the server, and the output is clean, feature-extracted data.

[0175] Step 4:

[0176] The server uses machine learning algorithms such as scikit-learn and TensorFlow to analyze preprocessed data and determine the user's emotional state. The input is clean, feature-extracted data, and the output is the result of the emotional state determination.

[0177] Step 5:

[0178] The server's emotion engine predicts future emotional fluctuations using historical data and a generative AI model. The input is the result of the emotional state assessment and historical data, and the output is the predicted emotional fluctuation.

[0179] Step 6:

[0180] The server uses natural language generation technology to generate appropriate advice based on the user's emotional state. The input is the predicted emotional fluctuation, and the output is the generated advice.

[0181] Step 7:

[0182] The device notifies the user of the generated advice information. Notification methods include display on the screen and audio alerts. The input is the generated advice information, and the output is the notification to the user.

[0183] Step 8:

[0184] After the user follows the advice, they provide feedback. The user inputs the results and their impressions through their device. The input is the user's feedback, and the output is feedback data.

[0185] Step 9:

[0186] The server analyzes user feedback and uses it as training data for the system. This improves the accuracy of subsequent analyses and advice generation. The input is feedback data, and the output is the analyzed feedback information and the learning results based on it.

[0187] (Application Example 2)

[0188] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as a "server" and the smart device 14 as a "terminal".

[0189] In modern society, many individuals are experiencing a decline in their quality of life due to stress and emotional fluctuations. Therefore, there is a need for effective ways to recognize and appropriately address these emotional states. In particular, enabling home-based assistive devices used by users in their daily lives to autonomously adjust their operation based on the user's emotional state is a crucial issue for providing a comfortable living environment.

[0190] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0191] In this invention, the server includes means for acquiring the user's biosignals in real time, means for generating advice information for the user based on their emotional state, and means for controlling a life support device that adjusts its operation according to the user's emotional state. This enables accurate recognition of the user's emotional state, provision of optimal advice and entertainment content, and autonomous adjustment of the life support device's operation.

[0192] "Means of acquiring a user's biosignals in real time" refers to sensors and devices for collecting physiological data such as the user's brainwaves and heart rate.

[0193] "Means for analyzing acquired biosignals to determine emotional state" refers to algorithms or programs that analyze collected biosignal data and identify the user's emotional state based on the results.

[0194] "Means for generating advice information for users based on their emotional state" refers to a system that generates information and instructions that enable users to respond appropriately according to their determined emotional state.

[0195] "Means for notifying the user of generated advisory information" refers to an interface for communicating the generated information to the user in voice, text, or other formats.

[0196] "Means for collecting user feedback" refers to a system for collecting responses and opinions provided by users.

[0197] "Means of analyzing collected feedback to improve analysis accuracy" refers to methods of analyzing feedback data obtained from users to improve the accuracy of the system's sentiment recognition and advice.

[0198] "Means for controlling life support devices that adjust their operation according to the user's emotional state" refers to a control system that automatically optimizes the operation of home support devices and electrical appliances based on the determined emotional state.

[0199] This invention includes an emotional support system that recognizes a user's emotional state in real time and provides optimal feedback. The system consists of a sensor device that acquires the user's biosignals in real time, an analysis server, and a life support device that serves as a user interface. Specifically, an electroencephalogram (EEG) sensor is used to acquire the user's brainwaves, and the data is immediately transmitted to the server. The server uses a machine learning algorithm to analyze the emotional state and generate appropriate advice. The generated information is notified to the user, and if necessary, life support devices such as room lighting and music players adjust their operation according to the user's emotional state.

[0200] The specific hardware includes a portable device for acquiring biosignals, and the data is transmitted wirelessly to a server. The software includes algorithms using programming languages ​​such as Python and R for data analysis. Based on the analysis results, natural language processing techniques are applied to generate optimal feedback for the user. For example, if the server determines that the user is tired, it might suggest, "I'll play some relaxing music," and after the user approves, appropriate music is played through the support device.

[0201] The detailed usage procedure for this system involves the server notifying the user via their device with a pre-prepared advice message when a change in emotional state is predicted. User feedback further improves the system's analysis accuracy, enabling more personalized support.

[0202] Specific examples of prompt statements are as follows:

[0203] "Please describe how a robot can detect a user's stress level in real time from biosignals and provide appropriate relaxation content."

[0204] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0205] Step 1:

[0206] The device acquires biosignals through the user's electroencephalogram (EEG) sensor. The acquired signal data is preprocessed and converted into the required analysis format. At this stage, the input is raw biosignal data, and the output is signal data in an analyzable format.

[0207] Step 2:

[0208] The device sends pre-processed data to the server. The server analyzes the received data and uses a machine learning model to determine the user's emotional state. At this stage, the input is pre-processed signal data, and the output is information indicating the user's emotional state.

[0209] Step 3:

[0210] The server uses a generative AI model to generate optimal advice for the user based on the determined emotional state. This generation process uses natural language processing to create prompts and positive messages. The input is emotional state information, and the output is advice information.

[0211] Step 4:

[0212] The server sends the generated advice information to the terminal. The terminal notifies the user of this information as voice or text. At this stage, the input is the advice information, and the output is the notification message to the user.

[0213] Step 5:

[0214] Users provide feedback on the advice and messages presented. This feedback is sent to the server via the terminal. The input is the user's feedback information, and the output is a new dataset for analysis.

[0215] Step 6:

[0216] The server analyzes the collected feedback to improve the sentiment analysis model. This feedback analysis is used to add new data points and improve the model's predictive accuracy. The input is the feedback information, and the output is the updated analysis algorithm.

[0217] Step 7:

[0218] The server generates instructions to control the assistive devices based on the user's emotional state and transmits them via the terminal. The terminal adjusts the operation of the devices based on these instructions. At this stage, the inputs are the emotional assessment results and feedback results, and the output is the control instructions for the assistive devices.

[0219] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0220] Data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of data generation model 58 is ChatGPT (registered trademark) (Internet search).<URL: https: / / openai.com / blog / chatgpt> ), Gemini (registered trademark) (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0221] In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and the specific processing may also be performed by the smart device 14.

[0222] [Second Embodiment]

[0223] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0224] As shown in Figure 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0225] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0226] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication interface 44. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, and camera 42 are also connected to the bus 52.

[0227] The microphone 238 receives voice signals from the user 20 and receives instructions from the user 20. The microphone 238 captures the voice signals from the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0228] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the area around the user 20 (for example, an imaging range defined by a field of view equivalent to the width of a typical healthy person's field of vision).

[0229] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0230] Figure 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Figure 4, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0231] The specific processing program 56 is an example of a "program" relating to the technology of this disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0232] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0233] In the smart glasses 214, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0234] Next, the identification processing performed by the identification processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the smart glasses 214 will be referred to as the "terminal".

[0235] The system of the present invention acquires a user's biosignals in real time, analyzes that data, and determines their emotional state. Specifically, the following components work together.

[0236] First, the device uses an electroencephalogram (EEG) sensor to collect the user's biosignals. This collected data forms the basis of information indicating the user's emotional state. The device immediately transmits the acquired biosignal data to a server, where the data is analyzed.

[0237] Next, the server analyzes the received biometric signals based on an AI algorithm. The analysis determines the user's current emotional state (e.g., anger, sadness, stress). Based on this determination, the server begins the process of generating personalized advice for the user.

[0238] Once the analysis of biosignals is complete and appropriate advice is generated, the server uses natural language processing technology to create feedback in a human-readable format. This feedback is highly specific because it is personalized based on the user's emotional state. The generated feedback is sent back to the device and notified to the user. This notification allows the user to understand their emotional state and take appropriate action.

[0239] For example, if a user is experiencing stress at work, the server generates advice based on past data and analysis results, such as, "Try taking a short break and taking some deep breaths." The terminal then notifies the user, who can then act according to the system's advice.

[0240] Furthermore, users can provide feedback to the server via their device regarding the results and reactions of following the provided advice. This feedback information is aggregated by the server and used to improve the accuracy of the AI ​​algorithm, leading to improved quality of future advice.

[0241] This system is designed to help users recognize their emotions in real time and provide support to enhance their ability to control their emotions. In this way, users can maximize the effectiveness of self-care and achieve emotional stability.

[0242] The following describes the processing flow.

[0243] Step 1:

[0244] The device acquires the user's biosignals in real time through an electroencephalogram (EEG) sensor attached to the user. The acquired biosignals are temporarily stored as digital data within the device.

[0245] Step 2:

[0246] The device transmits temporarily stored biometric signal data to the server using a secure communication protocol. This data is uploaded periodically and placed in a waiting state for analysis.

[0247] Step 3:

[0248] The server applies an AI model to analyze the received biosignal data. The AI ​​model extracts specific brainwave patterns and determines the user's emotional state. This step classifies what emotions the user is currently experiencing (e.g., anger or stress).

[0249] Step 4:

[0250] The server generates appropriate advice based on the analysis results. This advice includes specific suggestions for improving the user's emotional state. Natural language processing techniques are used in this process to format the feedback into a human-readable format.

[0251] Step 5:

[0252] The server sends formatted advice information to the terminal. The terminal immediately notifies the user of the received information. This notification may be made via screen display or audio output.

[0253] Step 6:

[0254] The user checks notifications from their device and selects actions or responses based on the provided advice. This step requires proactive action from the user.

[0255] Step 7:

[0256] Users input the results of following the advice and their own reactions into their device and leave them as feedback. This feedback is recorded through the user interface.

[0257] Step 8:

[0258] The server receives aggregated feedback data and uses it to improve future algorithms. This makes the AI ​​model increasingly accurate and provides more personalized support to users.

[0259] (Example 1)

[0260] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server" and the smart glasses 214 will be referred to as the "terminal".

[0261] It is necessary to recognize users' emotional states in real time and provide effective ways to respond. However, conventional technologies have not adequately achieved the rapid collection and analysis of biometric data and the provision of appropriate feedback. There is a need for technology that supports users in understanding their own emotions and making self-improvement efforts.

[0262] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0263] In this invention, the server includes means for acquiring the user's biometric data in real time using an information processing device, means for analyzing the acquired biometric data to determine the user's psychological state, and means for generating guidance information for the user based on the psychological state. This makes it possible to quickly and accurately grasp the user's emotional state and provide specific guidance information tailored to their individual needs.

[0264] An "information processing device" is a device used to collect, process, and analyze data, and mainly refers to computers and servers.

[0265] "Biometric data" refers to signals that indicate a person's physiological state, and specifically includes vital data such as brain waves and heart rate.

[0266] "Psychological state" refers to a user's current emotions and mood, and is determined by a specific algorithm.

[0267] "Guidance information" refers to advice and suggestions for the user generated based on their assessed psychological state.

[0268] "Evaluation information" refers to feedback regarding the results and reactions of users based on the guidance information provided.

[0269] "Interpretation accuracy" refers to the ability to accurately determine a person's psychological state from analyzed biometric data.

[0270] "Language processing technology" refers to the technology of processing natural language using computers and providing information in a format that is easy for humans to understand.

[0271] The embodiments for carrying out the present invention will be described in detail below.

[0272] The device is equipped with a biosensor for measuring brainwaves and acquires real-time biometric data from the user. This device can be worn on the user's forehead, where it continuously measures brainwave data. The acquired data is processed digitally within the device to remove noise and is converted into an analyzable format.

[0273] Next, the device transmits the acquired biometric data to a server in real time using wireless communication technology (e.g., Bluetooth or Wi-Fi). This server functions as an information processing device and uses a high-performance computer to analyze the biometric data. The server uses AI algorithms and machine learning models (e.g., deep learning frameworks) to determine the user's psychological state from the received data. For example, in response to a prompt such as, "Suggest advice to generate if the user's emotional state is stressed," the generative AI model obtains appropriate advice.

[0274] Furthermore, the server generates guidance information based on the user's psychological state using natural language processing technology and sends it back to the terminal. The terminal then notifies the user of this guidance information, providing helpful feedback. For example, if the user is feeling stressed, the server might generate advice such as "Try taking a short break" and transmit this information to the user via the terminal.

[0275] Users act based on the provided guidance information and then send feedback to the server by entering evaluation information on their device. This evaluation information helps improve the accuracy of the AI ​​algorithms on the server and contributes to improving future guidance information.

[0276] This invention recognizes the user's emotional state in real time and provides support for effectively managing their emotions. In this way, users can achieve emotional stability in their daily lives and promote self-care.

[0277] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0278] Step 1:

[0279] The terminal acquires the user's electroencephalogram data using a biosensor. The input is the user's electroencephalogram signal, and the output is a digital signal after noise removal. This data is processed in real time by the built-in digital signal processing unit and converted into an analyzable format.

[0280] Step 2:

[0281] The terminal transmits the digital signal after noise removal to the server in real time using wireless communication technologies (e.g., Bluetooth or Wi-Fi). The input is the digital signal in the terminal, and the output is the biological data transmitted to the server. A high-speed protocol is used in this communication to prevent data loss.

[0282] Step 3:

[0283] The server analyzes the received biological data using an AI algorithm. The input is the biological data transmitted from the terminal, and the output is the determination result of the user's mental state. This analysis uses a deep learning model and includes specific operations for feature extraction from the time-series electroencephalogram pattern.

[0284] Step 4:

[0285] The server generates guidance information suitable for the user using a generative AI model based on the determination result of the mental state. The input is the determination result of the mental state, and the output is guidance information in natural language format. Based on the keywords "generative AI model, prompt text", the language generation model operates to provide specific advice to the user.

[0286] Step 5:

[0287] The server uses natural language processing technology to format the generated guidance information in a way that is easy for the user to understand, and then sends it to the terminal. The input is the guidance information obtained by the generation AI model, and the output is the formatted text sent to the terminal. This feedback is immediately notified to the user.

[0288] Step 6:

[0289] Users act according to the provided guidance information and input the results as feedback into the device. The input includes user evaluation information, and the output is evaluation data sent to the server via the device. This information is used to improve the AI ​​algorithm.

[0290] Step 7:

[0291] The server analyzes evaluation data submitted by users to improve the accuracy of the AI ​​model's interpretation. The input is evaluation data, and the output is the improved performance of the AI ​​algorithm. This allows the system to evolve, resulting in higher quality guidance information in the future and more accurate feedback for users.

[0292] (Application Example 1)

[0293] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server," and the smart glasses 214 will be referred to as the "terminal."

[0294] In today's aging society, providing care tailored to individual needs is a crucial challenge. However, understanding the emotional state of elderly individuals in real time and providing appropriate support is not easy. Therefore, a decline in the quality of care due to a lack of emotionally-based approaches becomes a problem.

[0295] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0296] In this invention, the server includes means for acquiring the user's biosignals in real time, means for analyzing the acquired biosignals to determine the emotional state, and means for providing advisory information through a visual assistance device worn by field workers, thereby enabling support based on individual needs. This makes it possible to provide appropriate care based on the emotional state of elderly people.

[0297] "User biosignals" refer to electrical or physiological signals emitted from an individual's body, including information such as brain waves and heart rate, which can be used to infer emotional states.

[0298] "Real-time acquisition" means that data is acquired almost simultaneously with its generation, collecting data instantly without any time lag.

[0299] "Determining emotional state" is the process of analyzing and identifying the user's current psychological or emotional state based on acquired biosignals.

[0300] "Advice information" refers to information that provides guidance and suggestions tailored to the user's emotional state, aiming to improve the situation.

[0301] A "visual assistance device" is a device that visually presents information to the wearer, such as smart glasses, which display information overlaid on the real-world appearance.

[0302] "Feedback" refers to information about user reactions and results, which is used as data to improve the accuracy of system analysis.

[0303] The system for implementing this invention aims to support the improvement of elderly care by understanding the user's emotional state in real time and providing information tailored to individual needs. The server is designed to use an electroencephalogram (EEG) sensor to acquire biosignals in real time. This makes it possible to continuously collect electrical signals emitted from the user's body.

[0304] The acquired biological signals are transmitted to the server, where AI algorithms are used to analyze the data. Through this analysis, the user's emotional state is identified. For the analysis, programming languages such as Python and machine learning libraries such as TensorFlow and PyTorch are used.

[0305] After the emotional state is determined, specific advice information for the user is generated by leveraging natural language processing technology. This information is visually presented to, for example, smart glasses through a visual assistance device. As a result, the care staff can provide appropriate care according to the emotional state of the elderly.

[0306] As a specific example, the system detects that the elderly person is confused or stressed and generates a prompt sentence such as "Would you like to talk a little? Playing music for relaxation is also recommended." Prompt sentences such as "It seems that elderly person A is feeling anxious. What kind of care approach is effective?" can be used. Such feedback is also utilized to improve the future analysis accuracy of the AI system.

[0307] As described above, this system functions as a means to provide individual care based on the emotional state through real-time analysis of biological signals.

[0308] The flow of the specific process in Application Example 1 will be described using FIG. 12.

[0309] Step 1:

[0310] The terminal uses an electroencephalogram sensor to measure the user's electroencephalogram. The biological signals obtained from the sensor serve as the input and are acquired as digital signals in real time. Next, these signals are immediately transmitted to the server as data packets.

[0311] Step 2:

[0312] The server analyzes the received biometric signal data. It processes the input signals using an AI algorithm to identify the user's emotional state. The data is filtered, and a feature extraction algorithm outputs emotional states such as anger, sadness, and stress.

[0313] Step 3:

[0314] The server generates advice information based on the analysis results. Understanding the emotional state, it uses natural language processing techniques to create advice messages tailored to the user. At this stage, the input is emotional state data, and the output is a prompt message.

[0315] Step 4:

[0316] Advice information generated from the server is sent to the terminal. The information is displayed on smart glasses that function as a visual aid, and care staff are notified. This allows the input advice to be visually output, making it possible to encourage specific actions.

[0317] Step 5:

[0318] The user takes action based on the advice provided. The results of this action and feedback are returned to the server via the device. This input is stored in a database to improve the accuracy of the next analysis and is used to retrain the AI ​​model.

[0319] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0320] This invention is a system that acquires and analyzes a user's biosignals in real time and combines them with an emotion engine to provide more accurate recognition of emotional states and advice. This system is realized through the collaboration of hardware and software.

[0321] Specifically, an electroencephalogram (EEG) sensor attached to the device acquires the user's biosignals in real time. The acquired biosignal data is immediately sent to a server where it is analyzed. This analysis determines the user's current emotional state.

[0322] A key feature of this invention is that the server incorporates an emotion engine, which records the user's emotional history. The emotion engine utilizes the accumulated emotional data to learn the user's emotional patterns and predict changes in their emotional state. This makes it possible to recognize potential future emotional fluctuations in advance and prepare advice to address them proactively.

[0323] Furthermore, the emotion engine utilizes machine learning algorithms, enabling advanced pattern recognition and the generation of more precise feedback tailored to the user's specific emotions.

[0324] For example, if a user is facing a difficult project, the server uses an emotion engine to predict the user's past severe stress responses. Based on these results, it generates a positive message such as, "Take a short break and create a relaxing environment," and notifies the user through their device.

[0325] Furthermore, users can contribute to the system's learning process and improve the quality of future advice by providing feedback on the results of following these suggestions.

[0326] As described above, the present invention recognizes emotional states in real time while simultaneously providing users with optimal emotional control support through continuous data accumulation and machine learning. This system enables users to effectively manage their emotions and enhance their ability to cope with stressful situations.

[0327] The following describes the processing flow.

[0328] Step 1:

[0329] The device acquires biosignals in real time from an electroencephalogram (EEG) sensor attached to the user. This biosignal data is an important indicator of the user's emotional state.

[0330] Step 2:

[0331] The device securely transmits the acquired biometric data to the server using wireless or wired communication protocols. This process employs encryption technology to maintain data integrity and privacy.

[0332] Step 3:

[0333] The server uses an AI model to analyze the received biosignal data. The AI ​​model extracts specific patterns from the data and classifies the user's emotional state. This allows for an instantaneous determination of the user's mental state.

[0334] Step 4:

[0335] The server uses an emotion engine to analyze incoming data and past emotional history. The emotion engine detects changes in the user's emotional state and uses machine learning algorithms to predict future emotional fluctuations. This process makes it possible to proactively detect potential problems.

[0336] Step 5:

[0337] The server generates appropriate advice based on an analysis and prediction of the user's emotional state. This advice is presented in an easy-to-understand manner using natural language processing technology and is designed to help stabilize the user's emotions.

[0338] Step 6:

[0339] The device receives the generated advisory information and immediately notifies the user. Notifications are typically made using a visual display and an audible alert to draw the user's attention.

[0340] Step 7:

[0341] Users receive advice presented by their devices and act accordingly. The user's responses and actions are later used to improve system performance.

[0342] Step 8:

[0343] Users input feedback on the results of their actions and changes in their emotions into the device. This feedback is stored in the emotion engine and used as data to improve the quality of subsequent advice.

[0344] Step 9:

[0345] The server tunes its algorithms based on aggregated feedback, continuously improving the accuracy of its analysis. This allows the system to provide more personalized emotional support to each user.

[0346] (Example 2)

[0347] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server" and the smart glasses 214 will be referred to as the "terminal".

[0348] Conventional emotion recognition systems have struggled to accurately grasp a user's real-time emotional state and provide appropriate advice immediately. Furthermore, they lacked the functionality to fully utilize collected feedback and improve the system's analytical accuracy. Additionally, their use of natural language generation technology to generate appropriate messages based on emotional states was insufficient, making it impossible to address individual user needs.

[0349] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0350] In this invention, the server includes means for acquiring the user's biometric information in real time, means for analyzing the acquired biometric information to determine the emotional state, and means for generating advice information for the user based on the emotional state. This makes it possible to accurately recognize the user's emotions in real time and provide personalized advice information based on predicted emotional fluctuations.

[0351] "Users" refer to end-users who use this system and are the entities that provide biometric information in real time.

[0352] "Biometric information" refers to data that indicates the user's physiological state, and includes, but is not limited to, electroencephalograms (EEGs).

[0353] "Real-time" refers to a state in which information is acquired and processed almost simultaneously or instantaneously.

[0354] "Emotional state" refers to the user's internal emotional condition and is determined based on analyzed biometric information.

[0355] "Advice information" refers to advice on actions and thoughts presented to the user based on their analyzed emotional state.

[0356] "Response" refers to the feedback or reaction that a user gives to advice from the system.

[0357] "Analytical accuracy" is a term that indicates the degree of accuracy and reliability in the analysis of biological information and responses.

[0358] A "machine learning algorithm" is a method of learning patterns from data and is a technology for making predictions about the future.

[0359] "Natural language generation technology" refers to the technology that enables systems to generate human language and form appropriate messages.

[0360] A "positive message" is information that is constructive and forward-looking to the user.

[0361] This invention is a system that acquires and analyzes a user's biometric information in real time to recognize their emotional state and provide appropriate advice. This system mainly consists of three elements: a terminal, a server, and the user.

[0362] The terminal is equipped with sensors to acquire the user's biometric information. These sensors, such as electroencephalogram (EEG) sensors, measure the user's biometric information and convert it into digital data. The acquired data is transmitted from the terminal to the server using a secure communication protocol.

[0363] The server is responsible for analyzing the received biometric information. It uses data analysis software such as Python's pandas library and scikit-learn to clean the biometric information and extract features. Next, it uses machine learning algorithms to determine the user's emotional state. Machine learning algorithms such as TensorFlow and PyTorch can be used. Furthermore, the server has an emotion engine built in that learns the user's emotional history and predicts future emotional fluctuations.

[0364] Based on the predicted emotional state, the server uses natural language generation technology to generate personalized advice for the user. This generation process is based on pre-stored data and a generation AI model. Appropriate advice is then notified to the user via their device.

[0365] For example, if a user is feeling stressed, the system will notify them with a positive message such as, "Take a short break and create a relaxing environment." Another example of a prompt to be input to the generative AI model is, "Based on the user's current emotional state and past history, suggest the next action for the user."

[0366] Users can contribute to the system's learning by responding to the advice provided. The device then sends this feedback back to the server, which uses it to improve the accuracy of future analyses and advice generation. This allows users to more effectively manage their emotions and improve their ability to cope appropriately with stressful situations.

[0367] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0368] Step 1:

[0369] Sensors attached to the device acquire the user's biometric information. The sensors measure physiological data such as brain waves in real time and convert it into digital data. The input is biometric information, and the output is biometric data in digital format.

[0370] Step 2:

[0371] The device transmits the acquired digital data to the server. A secure protocol (e.g., HTTPS) is used for communication to ensure data confidentiality. The input is biometric data in digital format, and the output is the biometric data transmitted to the server.

[0372] Step 3:

[0373] The server analyzes the received biometric data. The server uses the Python pandas library to clean and preprocess the data, removing noise. The input is the biometric data sent to the server, and the output is clean, feature-extracted data.

[0374] Step 4:

[0375] The server uses machine learning algorithms such as scikit-learn and TensorFlow to analyze preprocessed data and determine the user's emotional state. The input is clean, feature-extracted data, and the output is the result of the emotional state determination.

[0376] Step 5:

[0377] The server's emotion engine predicts future emotional fluctuations using historical data and a generative AI model. The input is the result of the emotional state assessment and historical data, and the output is the predicted emotional fluctuation.

[0378] Step 6:

[0379] The server uses natural language generation technology to generate appropriate advice based on the user's emotional state. The input is the predicted emotional fluctuation, and the output is the generated advice.

[0380] Step 7:

[0381] The device notifies the user of the generated advice information. Notification methods include display on the screen and audio alerts. The input is the generated advice information, and the output is the notification to the user.

[0382] Step 8:

[0383] After the user follows the advice, they provide feedback. The user inputs the results and their impressions through their device. The input is the user's feedback, and the output is feedback data.

[0384] Step 9:

[0385] The server analyzes user feedback and uses it as training data for the system. This improves the accuracy of subsequent analyses and advice generation. The input is feedback data, and the output is the analyzed feedback information and the learning results based on it.

[0386] (Application Example 2)

[0387] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as the "server," and the smart glasses 214 will be referred to as the "terminal."

[0388] In modern society, many individuals are experiencing a decline in their quality of life due to stress and emotional fluctuations. Therefore, there is a need for effective ways to recognize and appropriately address these emotional states. In particular, enabling home-based assistive devices used by users in their daily lives to autonomously adjust their operation based on the user's emotional state is a crucial issue for providing a comfortable living environment.

[0389] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0390] In this invention, the server includes means for acquiring the user's biosignals in real time, means for generating advice information for the user based on their emotional state, and means for controlling a life support device that adjusts its operation according to the user's emotional state. This enables accurate recognition of the user's emotional state, provision of optimal advice and entertainment content, and autonomous adjustment of the life support device's operation.

[0391] "Means of acquiring a user's biosignals in real time" refers to sensors and devices for collecting physiological data such as the user's brainwaves and heart rate.

[0392] "Means for analyzing acquired biosignals to determine emotional state" refers to algorithms or programs that analyze collected biosignal data and identify the user's emotional state based on the results.

[0393] "Means for generating advice information for users based on their emotional state" refers to a system that generates information and instructions that enable users to respond appropriately according to their determined emotional state.

[0394] "Means for notifying the user of generated advisory information" refers to an interface for communicating the generated information to the user in voice, text, or other formats.

[0395] "Means for collecting user feedback" refers to a system for collecting responses and opinions provided by users.

[0396] "Means of analyzing collected feedback to improve analysis accuracy" refers to methods of analyzing feedback data obtained from users to improve the accuracy of the system's sentiment recognition and advice.

[0397] "Means for controlling life support devices that adjust their operation according to the user's emotional state" refers to a control system that automatically optimizes the operation of home support devices and electrical appliances based on the determined emotional state.

[0398] This invention includes an emotional support system that recognizes a user's emotional state in real time and provides optimal feedback. The system consists of a sensor device that acquires the user's biosignals in real time, an analysis server, and a life support device that serves as a user interface. Specifically, an electroencephalogram (EEG) sensor is used to acquire the user's brainwaves, and the data is immediately transmitted to the server. The server uses a machine learning algorithm to analyze the emotional state and generate appropriate advice. The generated information is notified to the user, and if necessary, life support devices such as room lighting and music players adjust their operation according to the user's emotional state.

[0399] The specific hardware includes a portable device for acquiring biosignals, and the data is transmitted wirelessly to a server. The software includes algorithms using programming languages ​​such as Python and R for data analysis. Based on the analysis results, natural language processing techniques are applied to generate optimal feedback for the user. For example, if the server determines that the user is tired, it might suggest, "I'll play some relaxing music," and after the user approves, appropriate music is played through the support device.

[0400] The detailed usage procedure for this system involves the server notifying the user via their device with a pre-prepared advice message when a change in emotional state is predicted. User feedback further improves the system's analysis accuracy, enabling more personalized support.

[0401] Specific examples of prompt statements are as follows:

[0402] "Please describe how a robot can detect a user's stress level in real time from biosignals and provide appropriate relaxation content."

[0403] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0404] Step 1:

[0405] The device acquires biosignals through the user's electroencephalogram (EEG) sensor. The acquired signal data is preprocessed and converted into the required analysis format. At this stage, the input is raw biosignal data, and the output is signal data in an analyzable format.

[0406] Step 2:

[0407] The device sends pre-processed data to the server. The server analyzes the received data and uses a machine learning model to determine the user's emotional state. At this stage, the input is pre-processed signal data, and the output is information indicating the user's emotional state.

[0408] Step 3:

[0409] The server uses a generative AI model to generate optimal advice for the user based on the determined emotional state. This generation process uses natural language processing to create prompts and positive messages. The input is emotional state information, and the output is advice information.

[0410] Step 4:

[0411] The server sends the generated advice information to the terminal. The terminal notifies the user of this information as voice or text. At this stage, the input is the advice information, and the output is the notification message to the user.

[0412] Step 5:

[0413] Users provide feedback on the advice and messages presented. This feedback is sent to the server via the terminal. The input is the user's feedback information, and the output is a new dataset for analysis.

[0414] Step 6:

[0415] The server analyzes the collected feedback to improve the sentiment analysis model. This feedback analysis is used to add new data points and improve the model's predictive accuracy. The input is the feedback information, and the output is the updated analysis algorithm.

[0416] Step 7:

[0417] The server generates instructions to control the assistive devices based on the user's emotional state and transmits them via the terminal. The terminal adjusts the operation of the devices based on these instructions. At this stage, the inputs are the emotional assessment results and feedback results, and the output is the control instructions for the assistive devices.

[0418] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0419] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). One example of data generation model 58 is ChatGPT (Internet search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0420] In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and the specific processing may also be performed by the smart glasses 214.

[0421] [Third Embodiment]

[0422] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0423] As shown in Figure 5, the data processing system 310 includes a data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0424] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0425] The headset terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a display 343. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and display 343 are also connected to the bus 52.

[0426] The microphone 238 receives voice signals from the user 20 and receives instructions from the user 20. The microphone 238 captures the voice signals from the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0427] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the area around the user 20 (for example, an imaging range defined by a field of view equivalent to the width of a typical healthy person's field of vision).

[0428] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0429] Figure 6 shows an example of the main functions of the data processing device 12 and the headset terminal 314. As shown in Figure 6, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0430] The specific processing program 56 is an example of a "program" relating to the technology of this disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0431] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0432] In the headset terminal 314, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0433] Next, the specific processing performed by the specific processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the headset terminal 314 will be referred to as the "terminal".

[0434] The system of the present invention acquires a user's biosignals in real time, analyzes that data, and determines their emotional state. Specifically, the following components work together.

[0435] First, the device uses an electroencephalogram (EEG) sensor to collect the user's biosignals. This collected data forms the basis of information indicating the user's emotional state. The device immediately transmits the acquired biosignal data to a server, where the data is analyzed.

[0436] Next, the server analyzes the received biometric signals based on an AI algorithm. The analysis determines the user's current emotional state (e.g., anger, sadness, stress). Based on this determination, the server begins the process of generating personalized advice for the user.

[0437] Once the analysis of biosignals is complete and appropriate advice is generated, the server uses natural language processing technology to create feedback in a human-readable format. This feedback is highly specific because it is personalized based on the user's emotional state. The generated feedback is sent back to the device and notified to the user. This notification allows the user to understand their emotional state and take appropriate action.

[0438] For example, if a user is experiencing stress at work, the server generates advice based on past data and analysis results, such as, "Try taking a short break and taking some deep breaths." The terminal then notifies the user, who can then act according to the system's advice.

[0439] Furthermore, users can provide feedback to the server via their device regarding the results and reactions of following the provided advice. This feedback information is aggregated by the server and used to improve the accuracy of the AI ​​algorithm, leading to improved quality of future advice.

[0440] This system is designed to help users recognize their emotions in real time and provide support to enhance their ability to control their emotions. In this way, users can maximize the effectiveness of self-care and achieve emotional stability.

[0441] The following describes the processing flow.

[0442] Step 1:

[0443] The device acquires the user's biosignals in real time through an electroencephalogram (EEG) sensor attached to the user. The acquired biosignals are temporarily stored as digital data within the device.

[0444] Step 2:

[0445] The device transmits temporarily stored biometric signal data to the server using a secure communication protocol. This data is uploaded periodically and placed in a waiting state for analysis.

[0446] Step 3:

[0447] The server applies an AI model to analyze the received biosignal data. The AI ​​model extracts specific brainwave patterns and determines the user's emotional state. This step classifies what emotions the user is currently experiencing (e.g., anger or stress).

[0448] Step 4:

[0449] The server generates appropriate advice based on the analysis results. This advice includes specific suggestions for improving the user's emotional state. Natural language processing techniques are used in this process to format the feedback into a human-readable format.

[0450] Step 5:

[0451] The server sends formatted advice information to the terminal. The terminal immediately notifies the user of the received information. This notification may be made via screen display or audio output.

[0452] Step 6:

[0453] The user checks notifications from their device and selects actions or responses based on the provided advice. This step requires proactive action from the user.

[0454] Step 7:

[0455] Users input the results of following the advice and their own reactions into their device and leave them as feedback. This feedback is recorded through the user interface.

[0456] Step 8:

[0457] The server receives aggregated feedback data and uses it to improve future algorithms. This makes the AI ​​model increasingly accurate and provides more personalized support to users.

[0458] (Example 1)

[0459] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server," and the headset-type terminal 314 will be referred to as the "terminal."

[0460] It is necessary to recognize users' emotional states in real time and provide effective ways to respond. However, conventional technologies have not adequately achieved the rapid collection and analysis of biometric data and the provision of appropriate feedback. There is a need for technology that supports users in understanding their own emotions and making self-improvement efforts.

[0461] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0462] In this invention, the server includes means for acquiring the user's biometric data in real time using an information processing device, means for analyzing the acquired biometric data to determine the user's psychological state, and means for generating guidance information for the user based on the psychological state. This makes it possible to quickly and accurately grasp the user's emotional state and provide specific guidance information tailored to their individual needs.

[0463] An "information processing device" is a device used to collect, process, and analyze data, and mainly refers to computers and servers.

[0464] "Biometric data" refers to signals that indicate a person's physiological state, and specifically includes vital data such as brain waves and heart rate.

[0465] "Psychological state" refers to a user's current emotions and mood, and is determined by a specific algorithm.

[0466] "Guidance information" refers to advice and suggestions for the user generated based on their assessed psychological state.

[0467] "Evaluation information" refers to feedback regarding the results and reactions of users based on the guidance information provided.

[0468] "Interpretation accuracy" refers to the ability to accurately determine a person's psychological state from analyzed biometric data.

[0469] "Language processing technology" refers to the technology of processing natural language using computers and providing information in a format that is easy for humans to understand.

[0470] The embodiments for carrying out the present invention will be described in detail below.

[0471] The device is equipped with a biosensor for measuring brainwaves and acquires real-time biometric data from the user. This device can be worn on the user's forehead, where it continuously measures brainwave data. The acquired data is processed digitally within the device to remove noise and is converted into an analyzable format.

[0472] Next, the device transmits the acquired biometric data to a server in real time using wireless communication technology (e.g., Bluetooth or Wi-Fi). This server functions as an information processing device and uses a high-performance computer to analyze the biometric data. The server uses AI algorithms and machine learning models (e.g., deep learning frameworks) to determine the user's psychological state from the received data. For example, in response to a prompt such as, "Suggest advice to generate if the user's emotional state is stressed," the generative AI model obtains appropriate advice.

[0473] Furthermore, the server generates guidance information based on the user's psychological state using natural language processing technology and sends it back to the terminal. The terminal then notifies the user of this guidance information, providing helpful feedback. For example, if the user is feeling stressed, the server might generate advice such as "Try taking a short break" and transmit this information to the user via the terminal.

[0474] Users act based on the provided guidance information and then send feedback to the server by entering evaluation information on their device. This evaluation information helps improve the accuracy of the AI ​​algorithms on the server and contributes to improving future guidance information.

[0475] This invention recognizes the user's emotional state in real time and provides support for effectively managing their emotions. In this way, users can achieve emotional stability in their daily lives and promote self-care.

[0476] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0477] Step 1:

[0478] The device acquires the user's brainwave data using biosensors. The input is the user's brainwave signal, and the output is a denoised digital signal. This data is processed in real time by a built-in digital signal processing unit and converted into an analyzable format.

[0479] Step 2:

[0480] The terminal transmits a denoised digital signal to the server in real time using wireless communication technology (e.g., Bluetooth or Wi-Fi). The input is the digital signal within the terminal, and the output is biometric data transmitted to the server. A high-speed protocol is used in this communication to prevent data loss.

[0481] Step 3:

[0482] The server analyzes the received biometric data using an AI algorithm. The input is biometric data transmitted from the terminal, and the output is a determination of the user's psychological state. This analysis includes specific operations such as extracting features from time-series brainwave patterns using a deep learning model.

[0483] Step 4:

[0484] The server uses a generative AI model to generate guidance information tailored to the user based on the psychological state assessment results. The input is the psychological state assessment results, and the output is guidance information in natural language format. Based on the keywords "generative AI model, prompt sentence," the language generation model operates to provide specific advice to the user.

[0485] Step 5:

[0486] The server uses natural language processing technology to format the generated guidance information in a way that is easy for the user to understand, and then sends it to the terminal. The input is the guidance information obtained by the generation AI model, and the output is the formatted text sent to the terminal. This feedback is immediately notified to the user.

[0487] Step 6:

[0488] Users act according to the provided guidance information and input the results as feedback into the device. The input includes user evaluation information, and the output is evaluation data sent to the server via the device. This information is used to improve the AI ​​algorithm.

[0489] Step 7:

[0490] The server analyzes evaluation data submitted by users to improve the accuracy of the AI ​​model's interpretation. The input is evaluation data, and the output is the improved performance of the AI ​​algorithm. This allows the system to evolve, resulting in higher quality guidance information in the future and more accurate feedback for users.

[0491] (Application Example 1)

[0492] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server," and the headset-type terminal 314 will be referred to as the "terminal."

[0493] In today's aging society, providing care tailored to individual needs is a crucial challenge. However, understanding the emotional state of elderly individuals in real time and providing appropriate support is not easy. Therefore, a decline in the quality of care due to a lack of emotionally-based approaches becomes a problem.

[0494] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0495] In this invention, the server includes means for acquiring the user's biosignals in real time, means for analyzing the acquired biosignals to determine the emotional state, and means for providing advisory information through a visual assistance device worn by field workers, thereby enabling support based on individual needs. This makes it possible to provide appropriate care based on the emotional state of elderly people.

[0496] "User biosignals" refer to electrical or physiological signals emitted from an individual's body, including information such as brain waves and heart rate, which can be used to infer emotional states.

[0497] "Real-time acquisition" means that data is acquired almost simultaneously with its generation, collecting data instantly without any time lag.

[0498] "Determining emotional state" is the process of analyzing and identifying the user's current psychological or emotional state based on acquired biosignals.

[0499] "Advice information" refers to information that provides guidance and suggestions tailored to the user's emotional state, aiming to improve the situation.

[0500] A "visual assistance device" is a device that visually presents information to the wearer, such as smart glasses, which display information overlaid on the real-world appearance.

[0501] "Feedback" refers to information about user reactions and results, which is used as data to improve the accuracy of system analysis.

[0502] The system for implementing this invention aims to support the improvement of elderly care by understanding the user's emotional state in real time and providing information tailored to individual needs. The server is designed to use an electroencephalogram (EEG) sensor to acquire biosignals in real time. This makes it possible to continuously collect electrical signals emitted from the user's body.

[0503] The acquired biometric signals are sent to a server, where an AI algorithm is used to analyze the data. This analysis identifies the user's emotional state. The analysis uses programming languages ​​such as Python and machine learning libraries such as TensorFlow and PyTorch.

[0504] After the emotional state is determined, natural language processing technology is used to generate specific advice for the user. This information is then visually presented through a visual aid, such as smart glasses. This enables care staff to provide appropriate care tailored to the emotional state of the elderly person.

[0505] For example, the system can detect if an elderly person is confused or stressed and generate prompt messages offering advice such as, "Shall we talk for a bit? Playing some relaxing music might also be helpful." It can also use prompt messages like, "Elderly person A seems anxious. What care approach would be effective?" This kind of feedback is also used to improve the accuracy of future analyses by the AI ​​system.

[0506] As described above, this system functions as a means of providing individualized care based on emotional states through real-time analysis of biological signals.

[0507] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0508] Step 1:

[0509] The device uses an electroencephalogram (EEG) sensor to measure the user's brainwaves. The biosignals obtained from the sensor become input and are acquired as digital signals in real time. These signals are then immediately transmitted to the server as data packets.

[0510] Step 2:

[0511] The server analyzes the received biometric signal data. It processes the input signals using an AI algorithm to identify the user's emotional state. The data is filtered, and a feature extraction algorithm outputs emotional states such as anger, sadness, and stress.

[0512] Step 3:

[0513] The server generates advice information based on the analysis results. Understanding the emotional state, it uses natural language processing techniques to create advice messages tailored to the user. At this stage, the input is emotional state data, and the output is a prompt message.

[0514] Step 4:

[0515] Advice information generated from the server is sent to the terminal. The information is displayed on smart glasses that function as a visual aid, and care staff are notified. This allows the input advice to be visually output, making it possible to encourage specific actions.

[0516] Step 5:

[0517] The user takes action based on the advice provided. The results of this action and feedback are returned to the server via the device. This input is stored in a database to improve the accuracy of the next analysis and is used to retrain the AI ​​model.

[0518] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0519] This invention is a system that acquires and analyzes a user's biosignals in real time and combines them with an emotion engine to provide more accurate recognition of emotional states and advice. This system is realized through the collaboration of hardware and software.

[0520] Specifically, an electroencephalogram (EEG) sensor attached to the device acquires the user's biosignals in real time. The acquired biosignal data is immediately sent to a server where it is analyzed. This analysis determines the user's current emotional state.

[0521] A key feature of this invention is that the server incorporates an emotion engine, which records the user's emotional history. The emotion engine utilizes the accumulated emotional data to learn the user's emotional patterns and predict changes in their emotional state. This makes it possible to recognize potential future emotional fluctuations in advance and prepare advice to address them proactively.

[0522] Furthermore, the emotion engine utilizes machine learning algorithms, enabling advanced pattern recognition and the generation of more precise feedback tailored to the user's specific emotions.

[0523] For example, if a user is facing a difficult project, the server uses an emotion engine to predict the user's past severe stress responses. Based on these results, it generates a positive message such as, "Take a short break and create a relaxing environment," and notifies the user through their device.

[0524] Furthermore, users can contribute to the system's learning process and improve the quality of future advice by providing feedback on the results of following these suggestions.

[0525] As described above, the present invention recognizes emotional states in real time while simultaneously providing users with optimal emotional control support through continuous data accumulation and machine learning. This system enables users to effectively manage their emotions and enhance their ability to cope with stressful situations.

[0526] The following describes the processing flow.

[0527] Step 1:

[0528] The device acquires biosignals in real time from an electroencephalogram (EEG) sensor attached to the user. This biosignal data is an important indicator of the user's emotional state.

[0529] Step 2:

[0530] The device securely transmits the acquired biometric data to the server using wireless or wired communication protocols. This process employs encryption technology to maintain data integrity and privacy.

[0531] Step 3:

[0532] The server uses an AI model to analyze the received biosignal data. The AI ​​model extracts specific patterns from the data and classifies the user's emotional state. This allows for an instantaneous determination of the user's mental state.

[0533] Step 4:

[0534] The server uses an emotion engine to analyze incoming data and past emotional history. The emotion engine detects changes in the user's emotional state and uses machine learning algorithms to predict future emotional fluctuations. This process makes it possible to proactively detect potential problems.

[0535] Step 5:

[0536] The server generates appropriate advice based on an analysis and prediction of the user's emotional state. This advice is presented in an easy-to-understand manner using natural language processing technology and is designed to help stabilize the user's emotions.

[0537] Step 6:

[0538] The device receives the generated advisory information and immediately notifies the user. Notifications are typically made using a visual display and an audible alert to draw the user's attention.

[0539] Step 7:

[0540] Users receive advice presented by their devices and act accordingly. The user's responses and actions are later used to improve system performance.

[0541] Step 8:

[0542] Users input feedback on the results of their actions and changes in their emotions into the device. This feedback is stored in the emotion engine and used as data to improve the quality of subsequent advice.

[0543] Step 9:

[0544] The server tunes its algorithms based on aggregated feedback, continuously improving the accuracy of its analysis. This allows the system to provide more personalized emotional support to each user.

[0545] (Example 2)

[0546] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server," and the headset-type terminal 314 will be referred to as the "terminal."

[0547] Conventional emotion recognition systems have struggled to accurately grasp a user's real-time emotional state and provide appropriate advice immediately. Furthermore, they lacked the functionality to fully utilize collected feedback and improve the system's analytical accuracy. Additionally, their use of natural language generation technology to generate appropriate messages based on emotional states was insufficient, making it impossible to address individual user needs.

[0548] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0549] In this invention, the server includes means for acquiring the user's biometric information in real time, means for analyzing the acquired biometric information to determine the emotional state, and means for generating advice information for the user based on the emotional state. This makes it possible to accurately recognize the user's emotions in real time and provide personalized advice information based on predicted emotional fluctuations.

[0550] "Users" refer to end-users who use this system and are the entities that provide biometric information in real time.

[0551] "Biometric information" refers to data that indicates the user's physiological state, and includes, but is not limited to, electroencephalograms (EEGs).

[0552] "Real-time" refers to a state in which information is acquired and processed almost simultaneously or instantaneously.

[0553] "Emotional state" refers to the user's internal emotional condition and is determined based on analyzed biometric information.

[0554] "Advice information" refers to advice on actions and thoughts presented to the user based on their analyzed emotional state.

[0555] "Response" refers to the feedback or reaction that a user gives to advice from the system.

[0556] "Analytical accuracy" is a term that indicates the degree of accuracy and reliability in the analysis of biological information and responses.

[0557] A "machine learning algorithm" is a method of learning patterns from data and is a technology for making predictions about the future.

[0558] "Natural language generation technology" refers to the technology that enables systems to generate human language and form appropriate messages.

[0559] A "positive message" is information that is constructive and forward-looking to the user.

[0560] This invention is a system that acquires and analyzes a user's biometric information in real time to recognize their emotional state and provide appropriate advice. This system mainly consists of three elements: a terminal, a server, and the user.

[0561] The terminal is equipped with sensors to acquire the user's biometric information. These sensors, such as electroencephalogram (EEG) sensors, measure the user's biometric information and convert it into digital data. The acquired data is transmitted from the terminal to the server using a secure communication protocol.

[0562] The server is responsible for analyzing the received biometric information. It uses data analysis software such as Python's pandas library and scikit-learn to clean the biometric information and extract features. Next, it uses machine learning algorithms to determine the user's emotional state. Machine learning algorithms such as TensorFlow and PyTorch can be used. Furthermore, the server has an emotion engine built in that learns the user's emotional history and predicts future emotional fluctuations.

[0563] Based on the predicted emotional state, the server uses natural language generation technology to generate personalized advice for the user. This generation process is based on pre-stored data and a generation AI model. Appropriate advice is then notified to the user via their device.

[0564] For example, if a user is feeling stressed, the system will notify them with a positive message such as, "Take a short break and create a relaxing environment." Another example of a prompt to be input to the generative AI model is, "Based on the user's current emotional state and past history, suggest the next action for the user."

[0565] Users can contribute to the system's learning by responding to the advice provided. The device then sends this feedback back to the server, which uses it to improve the accuracy of future analyses and advice generation. This allows users to more effectively manage their emotions and improve their ability to cope appropriately with stressful situations.

[0566] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0567] Step 1:

[0568] Sensors attached to the device acquire the user's biometric information. The sensors measure physiological data such as brain waves in real time and convert it into digital data. The input is biometric information, and the output is biometric data in digital format.

[0569] Step 2:

[0570] The device transmits the acquired digital data to the server. A secure protocol (e.g., HTTPS) is used for communication to ensure data confidentiality. The input is biometric data in digital format, and the output is the biometric data transmitted to the server.

[0571] Step 3:

[0572] The server analyzes the received biometric data. The server uses the Python pandas library to clean and preprocess the data, removing noise. The input is the biometric data sent to the server, and the output is clean, feature-extracted data.

[0573] Step 4:

[0574] The server uses machine learning algorithms such as scikit-learn and TensorFlow to analyze preprocessed data and determine the user's emotional state. The input is clean, feature-extracted data, and the output is the result of the emotional state determination.

[0575] Step 5:

[0576] The server's emotion engine predicts future emotional fluctuations using historical data and a generative AI model. The input is the result of the emotional state assessment and historical data, and the output is the predicted emotional fluctuation.

[0577] Step 6:

[0578] The server uses natural language generation technology to generate appropriate advice based on the user's emotional state. The input is the predicted emotional fluctuation, and the output is the generated advice.

[0579] Step 7:

[0580] The device notifies the user of the generated advice information. Notification methods include display on the screen and audio alerts. The input is the generated advice information, and the output is the notification to the user.

[0581] Step 8:

[0582] After the user follows the advice, they provide feedback. The user inputs the results and their impressions through their device. The input is the user's feedback, and the output is feedback data.

[0583] Step 9:

[0584] The server analyzes user feedback and uses it as training data for the system. This improves the accuracy of subsequent analyses and advice generation. The input is feedback data, and the output is the analyzed feedback information and the learning results based on it.

[0585] (Application Example 2)

[0586] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as the "server," and the headset-type terminal 314 will be referred to as the "terminal."

[0587] In modern society, many individuals are experiencing a decline in their quality of life due to stress and emotional fluctuations. Therefore, there is a need for effective ways to recognize and appropriately address these emotional states. In particular, enabling home-based assistive devices used by users in their daily lives to autonomously adjust their operation based on the user's emotional state is a crucial issue for providing a comfortable living environment.

[0588] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0589] In this invention, the server includes means for acquiring the user's biosignals in real time, means for generating advice information for the user based on their emotional state, and means for controlling a life support device that adjusts its operation according to the user's emotional state. This enables accurate recognition of the user's emotional state, provision of optimal advice and entertainment content, and autonomous adjustment of the life support device's operation.

[0590] "Means of acquiring a user's biosignals in real time" refers to sensors and devices for collecting physiological data such as the user's brainwaves and heart rate.

[0591] "Means for analyzing acquired biosignals to determine emotional state" refers to algorithms or programs that analyze collected biosignal data and identify the user's emotional state based on the results.

[0592] "Means for generating advice information for users based on their emotional state" refers to a system that generates information and instructions that enable users to respond appropriately according to their determined emotional state.

[0593] "Means for notifying the user of generated advisory information" refers to an interface for communicating the generated information to the user in voice, text, or other formats.

[0594] "Means for collecting user feedback" refers to a system for collecting responses and opinions provided by users.

[0595] "Means of analyzing collected feedback to improve analysis accuracy" refers to methods of analyzing feedback data obtained from users to improve the accuracy of the system's sentiment recognition and advice.

[0596] "Means for controlling life support devices that adjust their operation according to the user's emotional state" refers to a control system that automatically optimizes the operation of home support devices and electrical appliances based on the determined emotional state.

[0597] This invention includes an emotional support system that recognizes a user's emotional state in real time and provides optimal feedback. The system consists of a sensor device that acquires the user's biosignals in real time, an analysis server, and a life support device that serves as a user interface. Specifically, an electroencephalogram (EEG) sensor is used to acquire the user's brainwaves, and the data is immediately transmitted to the server. The server uses a machine learning algorithm to analyze the emotional state and generate appropriate advice. The generated information is notified to the user, and if necessary, life support devices such as room lighting and music players adjust their operation according to the user's emotional state.

[0598] The specific hardware includes a portable device for acquiring biosignals, and the data is transmitted wirelessly to a server. The software includes algorithms using programming languages ​​such as Python and R for data analysis. Based on the analysis results, natural language processing techniques are applied to generate optimal feedback for the user. For example, if the server determines that the user is tired, it might suggest, "I'll play some relaxing music," and after the user approves, appropriate music is played through the support device.

[0599] The detailed usage procedure for this system involves the server notifying the user via their device with a pre-prepared advice message when a change in emotional state is predicted. User feedback further improves the system's analysis accuracy, enabling more personalized support.

[0600] Specific examples of prompt statements are as follows:

[0601] "Please describe how a robot can detect a user's stress level in real time from biosignals and provide appropriate relaxation content."

[0602] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0603] Step 1:

[0604] The device acquires biosignals through the user's electroencephalogram (EEG) sensor. The acquired signal data is preprocessed and converted into the required analysis format. At this stage, the input is raw biosignal data, and the output is signal data in an analyzable format.

[0605] Step 2:

[0606] The device sends pre-processed data to the server. The server analyzes the received data and uses a machine learning model to determine the user's emotional state. At this stage, the input is pre-processed signal data, and the output is information indicating the user's emotional state.

[0607] Step 3:

[0608] The server uses a generative AI model to generate optimal advice for the user based on the determined emotional state. This generation process uses natural language processing to create prompts and positive messages. The input is emotional state information, and the output is advice information.

[0609] Step 4:

[0610] The server sends the generated advice information to the terminal. The terminal notifies the user of this information as voice or text. At this stage, the input is the advice information, and the output is the notification message to the user.

[0611] Step 5:

[0612] Users provide feedback on the advice and messages presented. This feedback is sent to the server via the terminal. The input is the user's feedback information, and the output is a new dataset for analysis.

[0613] Step 6:

[0614] The server analyzes the collected feedback to improve the sentiment analysis model. This feedback analysis is used to add new data points and improve the model's predictive accuracy. The input is the feedback information, and the output is the updated analysis algorithm.

[0615] Step 7:

[0616] The server generates instructions to control the assistive devices based on the user's emotional state and transmits them via the terminal. The terminal adjusts the operation of the devices based on these instructions. At this stage, the inputs are the emotional assessment results and feedback results, and the output is the control instructions for the assistive devices.

[0617] The specific processing unit 290 transmits the result of the specific processing to the headset terminal 314. In the headset terminal 314, the control unit 46A causes the speaker 240 and display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0618] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). One example of data generation model 58 is ChatGPT (Internet search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0619] In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and specific processing may also be performed by the headset terminal 314.

[0620] [Fourth Embodiment]

[0621] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0622] As shown in Figure 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0623] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0624] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a controlled object 443. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and controlled object 443 are also connected to the bus 52.

[0625] The microphone 238 receives voice signals from the user 20 and receives instructions from the user 20. The microphone 238 captures the voice signals from the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0626] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the area around the user 20 (for example, an imaging range defined by a field of view equivalent to the width of a typical healthy person's field of vision).

[0627] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0628] The controlled object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the robot 414's emotions can be expressed by controlling these motors. Furthermore, the robot 414's facial expressions can also be expressed by controlling the illumination state of the LEDs in its eyes.

[0629] Figure 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Figure 8, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0630] The specific processing program 56 is an example of a "program" relating to the technology of this disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0631] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0632] In robot 414, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0633] Next, the specific processing performed by the specific processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0634] The system of the present invention acquires a user's biosignals in real time, analyzes that data, and determines their emotional state. Specifically, the following components work together.

[0635] First, the device uses an electroencephalogram (EEG) sensor to collect the user's biosignals. This collected data forms the basis of information indicating the user's emotional state. The device immediately transmits the acquired biosignal data to a server, where the data is analyzed.

[0636] Next, the server analyzes the received biometric signals based on an AI algorithm. The analysis determines the user's current emotional state (e.g., anger, sadness, stress). Based on this determination, the server begins the process of generating personalized advice for the user.

[0637] Once the analysis of biosignals is complete and appropriate advice is generated, the server uses natural language processing technology to create feedback in a human-readable format. This feedback is highly specific because it is personalized based on the user's emotional state. The generated feedback is sent back to the device and notified to the user. This notification allows the user to understand their emotional state and take appropriate action.

[0638] For example, if a user is experiencing stress at work, the server generates advice based on past data and analysis results, such as, "Try taking a short break and taking some deep breaths." The terminal then notifies the user, who can then act according to the system's advice.

[0639] Furthermore, users can provide feedback to the server via their device regarding the results and reactions of following the provided advice. This feedback information is aggregated by the server and used to improve the accuracy of the AI ​​algorithm, leading to improved quality of future advice.

[0640] This system is designed to help users recognize their emotions in real time and provide support to enhance their ability to control their emotions. In this way, users can maximize the effectiveness of self-care and achieve emotional stability.

[0641] The following describes the processing flow.

[0642] Step 1:

[0643] The device acquires the user's biosignals in real time through an electroencephalogram (EEG) sensor attached to the user. The acquired biosignals are temporarily stored as digital data within the device.

[0644] Step 2:

[0645] The device transmits temporarily stored biometric signal data to the server using a secure communication protocol. This data is uploaded periodically and placed in a waiting state for analysis.

[0646] Step 3:

[0647] The server applies an AI model to analyze the received biosignal data. The AI ​​model extracts specific brainwave patterns and determines the user's emotional state. This step classifies what emotions the user is currently experiencing (e.g., anger or stress).

[0648] Step 4:

[0649] The server generates appropriate advice based on the analysis results. This advice includes specific suggestions for improving the user's emotional state. Natural language processing techniques are used in this process to format the feedback into a human-readable format.

[0650] Step 5:

[0651] The server sends formatted advice information to the terminal. The terminal immediately notifies the user of the received information. This notification may be made via screen display or audio output.

[0652] Step 6:

[0653] The user checks notifications from their device and selects actions or responses based on the provided advice. This step requires proactive action from the user.

[0654] Step 7:

[0655] Users input the results of following the advice and their own reactions into their device and leave them as feedback. This feedback is recorded through the user interface.

[0656] Step 8:

[0657] The server receives aggregated feedback data and uses it to improve future algorithms. This makes the AI ​​model increasingly accurate and provides more personalized support to users.

[0658] (Example 1)

[0659] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0660] It is necessary to recognize users' emotional states in real time and provide effective ways to respond. However, conventional technologies have not adequately achieved the rapid collection and analysis of biometric data and the provision of appropriate feedback. There is a need for technology that supports users in understanding their own emotions and making self-improvement efforts.

[0661] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0662] In this invention, the server includes means for acquiring the user's biometric data in real time using an information processing device, means for analyzing the acquired biometric data to determine the user's psychological state, and means for generating guidance information for the user based on the psychological state. This makes it possible to quickly and accurately grasp the user's emotional state and provide specific guidance information tailored to their individual needs.

[0663] An "information processing device" is a device used to collect, process, and analyze data, and mainly refers to computers and servers.

[0664] "Biometric data" refers to signals that indicate a person's physiological state, and specifically includes vital data such as brain waves and heart rate.

[0665] "Psychological state" refers to a user's current emotions and mood, and is determined by a specific algorithm.

[0666] "Guidance information" refers to advice and suggestions for the user generated based on their assessed psychological state.

[0667] "Evaluation information" refers to feedback regarding the results and reactions of users based on the guidance information provided.

[0668] "Interpretation accuracy" refers to the ability to accurately determine a person's psychological state from analyzed biometric data.

[0669] "Language processing technology" refers to the technology of processing natural language using computers and providing information in a format that is easy for humans to understand.

[0670] The embodiments for carrying out the present invention will be described in detail below.

[0671] The device is equipped with a biosensor for measuring brainwaves and acquires real-time biometric data from the user. This device can be worn on the user's forehead, where it continuously measures brainwave data. The acquired data is processed digitally within the device to remove noise and is converted into an analyzable format.

[0672] Next, the device transmits the acquired biometric data to a server in real time using wireless communication technology (e.g., Bluetooth or Wi-Fi). This server functions as an information processing device and uses a high-performance computer to analyze the biometric data. The server uses AI algorithms and machine learning models (e.g., deep learning frameworks) to determine the user's psychological state from the received data. For example, in response to a prompt such as, "Suggest advice to generate if the user's emotional state is stressed," the generative AI model obtains appropriate advice.

[0673] Furthermore, the server generates guidance information based on the user's psychological state using natural language processing technology and sends it back to the terminal. The terminal then notifies the user of this guidance information, providing helpful feedback. For example, if the user is feeling stressed, the server might generate advice such as "Try taking a short break" and transmit this information to the user via the terminal.

[0674] Users act based on the provided guidance information and then send feedback to the server by entering evaluation information on their device. This evaluation information helps improve the accuracy of the AI ​​algorithms on the server and contributes to improving future guidance information.

[0675] This invention recognizes the user's emotional state in real time and provides support for effectively managing their emotions. In this way, users can achieve emotional stability in their daily lives and promote self-care.

[0676] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0677] Step 1:

[0678] The device acquires the user's brainwave data using biosensors. The input is the user's brainwave signal, and the output is a denoised digital signal. This data is processed in real time by a built-in digital signal processing unit and converted into an analyzable format.

[0679] Step 2:

[0680] The terminal transmits a denoised digital signal to the server in real time using wireless communication technology (e.g., Bluetooth or Wi-Fi). The input is the digital signal within the terminal, and the output is biometric data transmitted to the server. A high-speed protocol is used in this communication to prevent data loss.

[0681] Step 3:

[0682] The server analyzes the received biometric data using an AI algorithm. The input is biometric data transmitted from the terminal, and the output is a determination of the user's psychological state. This analysis includes specific operations such as extracting features from time-series brainwave patterns using a deep learning model.

[0683] Step 4:

[0684] The server uses a generative AI model to generate guidance information tailored to the user based on the psychological state assessment results. The input is the psychological state assessment results, and the output is guidance information in natural language format. Based on the keywords "generative AI model, prompt sentence," the language generation model operates to provide specific advice to the user.

[0685] Step 5:

[0686] The server uses natural language processing technology to format the generated guidance information in a way that is easy for the user to understand, and then sends it to the terminal. The input is the guidance information obtained by the generation AI model, and the output is the formatted text sent to the terminal. This feedback is immediately notified to the user.

[0687] Step 6:

[0688] Users act according to the provided guidance information and input the results as feedback into the device. The input includes user evaluation information, and the output is evaluation data sent to the server via the device. This information is used to improve the AI ​​algorithm.

[0689] Step 7:

[0690] The server analyzes evaluation data submitted by users to improve the accuracy of the AI ​​model's interpretation. The input is evaluation data, and the output is the improved performance of the AI ​​algorithm. This allows the system to evolve, resulting in higher quality guidance information in the future and more accurate feedback for users.

[0691] (Application Example 1)

[0692] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0693] In today's aging society, providing care tailored to individual needs is a crucial challenge. However, understanding the emotional state of elderly individuals in real time and providing appropriate support is not easy. Therefore, a decline in the quality of care due to a lack of emotionally-based approaches becomes a problem.

[0694] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0695] In this invention, the server includes means for acquiring the user's biosignals in real time, means for analyzing the acquired biosignals to determine the emotional state, and means for providing advisory information through a visual assistance device worn by field workers, thereby enabling support based on individual needs. This makes it possible to provide appropriate care based on the emotional state of elderly people.

[0696] "User biosignals" refer to electrical or physiological signals emitted from an individual's body, including information such as brain waves and heart rate, which can be used to infer emotional states.

[0697] "Real-time acquisition" means that data is acquired almost simultaneously with its generation, collecting data instantly without any time lag.

[0698] "Determining emotional state" is the process of analyzing and identifying the user's current psychological or emotional state based on acquired biosignals.

[0699] "Advice information" refers to information that provides guidance and suggestions tailored to the user's emotional state, aiming to improve the situation.

[0700] A "visual assistance device" is a device that visually presents information to the wearer, such as smart glasses, which display information overlaid on the real-world appearance.

[0701] "Feedback" refers to information about user reactions and results, which is used as data to improve the accuracy of system analysis.

[0702] The system for implementing this invention aims to support the improvement of elderly care by understanding the user's emotional state in real time and providing information tailored to individual needs. The server is designed to use an electroencephalogram (EEG) sensor to acquire biosignals in real time. This makes it possible to continuously collect electrical signals emitted from the user's body.

[0703] The acquired biometric signals are sent to a server, where an AI algorithm is used to analyze the data. This analysis identifies the user's emotional state. The analysis uses programming languages ​​such as Python and machine learning libraries such as TensorFlow and PyTorch.

[0704] After the emotional state is determined, natural language processing technology is used to generate specific advice for the user. This information is then visually presented through a visual aid, such as smart glasses. This enables care staff to provide appropriate care tailored to the emotional state of the elderly person.

[0705] For example, the system can detect if an elderly person is confused or stressed and generate prompt messages offering advice such as, "Shall we talk for a bit? Playing some relaxing music might also be helpful." It can also use prompt messages like, "Elderly person A seems anxious. What care approach would be effective?" This kind of feedback is also used to improve the accuracy of future analyses by the AI ​​system.

[0706] As described above, this system functions as a means of providing individualized care based on emotional states through real-time analysis of biological signals.

[0707] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0708] Step 1:

[0709] The device uses an electroencephalogram (EEG) sensor to measure the user's brainwaves. The biosignals obtained from the sensor become input and are acquired as digital signals in real time. These signals are then immediately transmitted to the server as data packets.

[0710] Step 2:

[0711] The server analyzes the received biometric signal data. It processes the input signals using an AI algorithm to identify the user's emotional state. The data is filtered, and a feature extraction algorithm outputs emotional states such as anger, sadness, and stress.

[0712] Step 3:

[0713] The server generates advice information based on the analysis results. Understanding the emotional state, it uses natural language processing techniques to create advice messages tailored to the user. At this stage, the input is emotional state data, and the output is a prompt message.

[0714] Step 4:

[0715] Advice information generated from the server is sent to the terminal. The information is displayed on smart glasses that function as a visual aid, and care staff are notified. This allows the input advice to be visually output, making it possible to encourage specific actions.

[0716] Step 5:

[0717] The user takes action based on the advice provided. The results of this action and feedback are returned to the server via the device. This input is stored in a database to improve the accuracy of the next analysis and is used to retrain the AI ​​model.

[0718] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0719] This invention is a system that acquires and analyzes a user's biosignals in real time and combines them with an emotion engine to provide more accurate recognition of emotional states and advice. This system is realized through the collaboration of hardware and software.

[0720] Specifically, an electroencephalogram (EEG) sensor attached to the device acquires the user's biosignals in real time. The acquired biosignal data is immediately sent to a server where it is analyzed. This analysis determines the user's current emotional state.

[0721] A key feature of this invention is that the server incorporates an emotion engine, which records the user's emotional history. The emotion engine utilizes the accumulated emotional data to learn the user's emotional patterns and predict changes in their emotional state. This makes it possible to recognize potential future emotional fluctuations in advance and prepare advice to address them proactively.

[0722] Furthermore, the emotion engine utilizes machine learning algorithms, enabling advanced pattern recognition and the generation of more precise feedback tailored to the user's specific emotions.

[0723] For example, if a user is facing a difficult project, the server uses an emotion engine to predict the user's past severe stress responses. Based on these results, it generates a positive message such as, "Take a short break and create a relaxing environment," and notifies the user through their device.

[0724] Furthermore, users can contribute to the system's learning process and improve the quality of future advice by providing feedback on the results of following these suggestions.

[0725] As described above, the present invention recognizes emotional states in real time while simultaneously providing users with optimal emotional control support through continuous data accumulation and machine learning. This system enables users to effectively manage their emotions and enhance their ability to cope with stressful situations.

[0726] The following describes the processing flow.

[0727] Step 1:

[0728] The device acquires biosignals in real time from an electroencephalogram (EEG) sensor attached to the user. This biosignal data is an important indicator of the user's emotional state.

[0729] Step 2:

[0730] The device securely transmits the acquired biometric data to the server using wireless or wired communication protocols. This process employs encryption technology to maintain data integrity and privacy.

[0731] Step 3:

[0732] The server uses an AI model to analyze the received biosignal data. The AI ​​model extracts specific patterns from the data and classifies the user's emotional state. This allows for an instantaneous determination of the user's mental state.

[0733] Step 4:

[0734] The server uses an emotion engine to analyze incoming data and past emotional history. The emotion engine detects changes in the user's emotional state and uses machine learning algorithms to predict future emotional fluctuations. This process makes it possible to proactively detect potential problems.

[0735] Step 5:

[0736] The server generates appropriate advice based on an analysis and prediction of the user's emotional state. This advice is presented in an easy-to-understand manner using natural language processing technology and is designed to help stabilize the user's emotions.

[0737] Step 6:

[0738] The device receives the generated advisory information and immediately notifies the user. Notifications are typically made using a visual display and an audible alert to draw the user's attention.

[0739] Step 7:

[0740] Users receive advice presented by their devices and act accordingly. The user's responses and actions are later used to improve system performance.

[0741] Step 8:

[0742] Users input feedback on the results of their actions and changes in their emotions into the device. This feedback is stored in the emotion engine and used as data to improve the quality of subsequent advice.

[0743] Step 9:

[0744] The server tunes its algorithms based on aggregated feedback, continuously improving the accuracy of its analysis. This allows the system to provide more personalized emotional support to each user.

[0745] (Example 2)

[0746] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0747] Conventional emotion recognition systems have struggled to accurately grasp a user's real-time emotional state and provide appropriate advice immediately. Furthermore, they lacked the functionality to fully utilize collected feedback and improve the system's analytical accuracy. Additionally, their use of natural language generation technology to generate appropriate messages based on emotional states was insufficient, making it impossible to address individual user needs.

[0748] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0749] In this invention, the server includes means for acquiring the user's biometric information in real time, means for analyzing the acquired biometric information to determine the emotional state, and means for generating advice information for the user based on the emotional state. This makes it possible to accurately recognize the user's emotions in real time and provide personalized advice information based on predicted emotional fluctuations.

[0750] "Users" refer to end-users who use this system and are the entities that provide biometric information in real time.

[0751] "Biometric information" refers to data that indicates the user's physiological state, and includes, but is not limited to, electroencephalograms (EEGs).

[0752] "Real-time" refers to a state in which information is acquired and processed almost simultaneously or instantaneously.

[0753] "Emotional state" refers to the user's internal emotional condition and is determined based on analyzed biometric information.

[0754] "Advice information" refers to advice on actions and thoughts presented to the user based on their analyzed emotional state.

[0755] "Response" refers to the feedback or reaction that a user gives to advice from the system.

[0756] "Analytical accuracy" is a term that indicates the degree of accuracy and reliability in the analysis of biological information and responses.

[0757] A "machine learning algorithm" is a method of learning patterns from data and is a technology for making predictions about the future.

[0758] "Natural language generation technology" refers to the technology that enables systems to generate human language and form appropriate messages.

[0759] A "positive message" is information that is constructive and forward-looking to the user.

[0760] This invention is a system that acquires and analyzes a user's biometric information in real time to recognize their emotional state and provide appropriate advice. This system mainly consists of three elements: a terminal, a server, and the user.

[0761] The terminal is equipped with sensors to acquire the user's biometric information. These sensors, such as electroencephalogram (EEG) sensors, measure the user's biometric information and convert it into digital data. The acquired data is transmitted from the terminal to the server using a secure communication protocol.

[0762] The server is responsible for analyzing the received biometric information. It uses data analysis software such as Python's pandas library and scikit-learn to clean the biometric information and extract features. Next, it uses machine learning algorithms to determine the user's emotional state. Machine learning algorithms such as TensorFlow and PyTorch can be used. Furthermore, the server has an emotion engine built in that learns the user's emotional history and predicts future emotional fluctuations.

[0763] Based on the predicted emotional state, the server uses natural language generation technology to generate personalized advice for the user. This generation process is based on pre-stored data and a generation AI model. Appropriate advice is then notified to the user via their device.

[0764] For example, if a user is feeling stressed, the system will notify them with a positive message such as, "Take a short break and create a relaxing environment." Another example of a prompt to be input to the generative AI model is, "Based on the user's current emotional state and past history, suggest the next action for the user."

[0765] Users can contribute to the system's learning by responding to the advice provided. The device then sends this feedback back to the server, which uses it to improve the accuracy of future analyses and advice generation. This allows users to more effectively manage their emotions and improve their ability to cope appropriately with stressful situations.

[0766] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0767] Step 1:

[0768] Sensors attached to the device acquire the user's biometric information. The sensors measure physiological data such as brain waves in real time and convert it into digital data. The input is biometric information, and the output is biometric data in digital format.

[0769] Step 2:

[0770] The device transmits the acquired digital data to the server. A secure protocol (e.g., HTTPS) is used for communication to ensure data confidentiality. The input is biometric data in digital format, and the output is the biometric data transmitted to the server.

[0771] Step 3:

[0772] The server analyzes the received biometric data. The server uses the Python pandas library to clean and preprocess the data, removing noise. The input is the biometric data sent to the server, and the output is clean, feature-extracted data.

[0773] Step 4:

[0774] The server uses machine learning algorithms such as scikit-learn and TensorFlow to analyze preprocessed data and determine the user's emotional state. The input is clean, feature-extracted data, and the output is the result of the emotional state determination.

[0775] Step 5:

[0776] The server's emotion engine predicts future emotional fluctuations using historical data and a generative AI model. The input is the result of the emotional state assessment and historical data, and the output is the predicted emotional fluctuation.

[0777] Step 6:

[0778] The server uses natural language generation technology to generate appropriate advice based on the user's emotional state. The input is the predicted emotional fluctuation, and the output is the generated advice.

[0779] Step 7:

[0780] The device notifies the user of the generated advice information. Notification methods include display on the screen and audio alerts. The input is the generated advice information, and the output is the notification to the user.

[0781] Step 8:

[0782] After the user follows the advice, they provide feedback. The user inputs the results and their impressions through their device. The input is the user's feedback, and the output is feedback data.

[0783] Step 9:

[0784] The server analyzes user feedback and uses it as training data for the system. This improves the accuracy of subsequent analyses and advice generation. The input is feedback data, and the output is the analyzed feedback information and the learning results based on it.

[0785] (Application Example 2)

[0786] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0787] In modern society, many individuals are experiencing a decline in their quality of life due to stress and emotional fluctuations. Therefore, there is a need for effective ways to recognize and appropriately address these emotional states. In particular, enabling home-based assistive devices used by users in their daily lives to autonomously adjust their operation based on the user's emotional state is a crucial issue for providing a comfortable living environment.

[0788] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0789] In this invention, the server includes means for acquiring the user's biosignals in real time, means for generating advice information for the user based on their emotional state, and means for controlling a life support device that adjusts its operation according to the user's emotional state. This enables accurate recognition of the user's emotional state, provision of optimal advice and entertainment content, and autonomous adjustment of the life support device's operation.

[0790] "Means of acquiring a user's biosignals in real time" refers to sensors and devices for collecting physiological data such as the user's brainwaves and heart rate.

[0791] "Means for analyzing acquired biosignals to determine emotional state" refers to algorithms or programs that analyze collected biosignal data and identify the user's emotional state based on the results.

[0792] "Means for generating advice information for users based on their emotional state" refers to a system that generates information and instructions that enable users to respond appropriately according to their determined emotional state.

[0793] "Means for notifying the user of generated advisory information" refers to an interface for communicating the generated information to the user in voice, text, or other formats.

[0794] "Means for collecting user feedback" refers to a system for collecting responses and opinions provided by users.

[0795] "Means of analyzing collected feedback to improve analysis accuracy" refers to methods of analyzing feedback data obtained from users to improve the accuracy of the system's sentiment recognition and advice.

[0796] "Means for controlling life support devices that adjust their operation according to the user's emotional state" refers to a control system that automatically optimizes the operation of home support devices and electrical appliances based on the determined emotional state.

[0797] This invention includes an emotional support system that recognizes a user's emotional state in real time and provides optimal feedback. The system consists of a sensor device that acquires the user's biosignals in real time, an analysis server, and a life support device that serves as a user interface. Specifically, an electroencephalogram (EEG) sensor is used to acquire the user's brainwaves, and the data is immediately transmitted to the server. The server uses a machine learning algorithm to analyze the emotional state and generate appropriate advice. The generated information is notified to the user, and if necessary, life support devices such as room lighting and music players adjust their operation according to the user's emotional state.

[0798] The specific hardware includes a portable device for acquiring biosignals, and the data is transmitted wirelessly to a server. The software includes algorithms using programming languages ​​such as Python and R for data analysis. Based on the analysis results, natural language processing techniques are applied to generate optimal feedback for the user. For example, if the server determines that the user is tired, it might suggest, "I'll play some relaxing music," and after the user approves, appropriate music is played through the support device.

[0799] The detailed usage procedure for this system involves the server notifying the user via their device with a pre-prepared advice message when a change in emotional state is predicted. User feedback further improves the system's analysis accuracy, enabling more personalized support.

[0800] Specific examples of prompt statements are as follows:

[0801] "Please describe how a robot can detect a user's stress level in real time from biosignals and provide appropriate relaxation content."

[0802] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0803] Step 1:

[0804] The device acquires biosignals through the user's electroencephalogram (EEG) sensor. The acquired signal data is preprocessed and converted into the required analysis format. At this stage, the input is raw biosignal data, and the output is signal data in an analyzable format.

[0805] Step 2:

[0806] The device sends pre-processed data to the server. The server analyzes the received data and uses a machine learning model to determine the user's emotional state. At this stage, the input is pre-processed signal data, and the output is information indicating the user's emotional state.

[0807] Step 3:

[0808] The server uses a generative AI model to generate optimal advice for the user based on the determined emotional state. This generation process uses natural language processing to create prompts and positive messages. The input is emotional state information, and the output is advice information.

[0809] Step 4:

[0810] The server sends the generated advice information to the terminal. The terminal notifies the user of this information as voice or text. At this stage, the input is the advice information, and the output is the notification message to the user.

[0811] Step 5:

[0812] Users provide feedback on the advice and messages presented. This feedback is sent to the server via the terminal. The input is the user's feedback information, and the output is a new dataset for analysis.

[0813] Step 6:

[0814] The server analyzes the collected feedback to improve the sentiment analysis model. This feedback analysis is used to add new data points and improve the model's predictive accuracy. The input is the feedback information, and the output is the updated analysis algorithm.

[0815] Step 7:

[0816] The server generates instructions to control the assistive devices based on the user's emotional state and transmits them via the terminal. The terminal adjusts the operation of the devices based on these instructions. At this stage, the inputs are the emotional assessment results and feedback results, and the output is the control instructions for the assistive devices.

[0817] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the controlled object 443 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0818] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). One example of data generation model 58 is ChatGPT (Internet search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0819] In the above embodiment, an example was given in which the specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and the specific processing may also be performed by the robot 414.

[0820] Furthermore, the emotion identification model 59, acting as an emotion engine, may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to a specific mapping, which is an emotion map (see Figure 9). Similarly, the emotion identification model 59 may also determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0821] Figure 9 shows an emotion map 400 in which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. The closer to the center of the concentric circles, the more primitive the emotions are located. Further out of the concentric circles, emotions representing states and actions arising from mental states are located. Emotion is a concept that includes feelings and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions occurring in the brain are located. On the right side of the concentric circles, emotions that are generally induced by situational judgment are located. Above and below the concentric circles, emotions that are generally generated from reactions occurring in the brain and induced by situational judgment are located. In addition, the emotion of "pleasure" is located on the upper side of the concentric circles, and the emotion of "displeasure" is located on the lower side. Thus, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions arise, and emotions that are likely to occur simultaneously are mapped close together.

[0822] These emotions are distributed at the 3 o'clock position on the Emotion Map 400, and usually fluctuate between feelings of security and anxiety. In the right half of the Emotion Map 400, situational awareness takes precedence over internal feelings, resulting in a calm impression.

[0823] The inside of the Emotion Map 400 represents inner thoughts, while the outside represents actions. Therefore, the further you go from the outside of the Emotion Map 400, the more visible (expressed in actions) your emotions become.

[0824] Here, human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. Similarly, in robots, cars, motorcycles, etc., emotions can be created based on various balances, such as posture and battery level. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. The emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on a system for analyzing brain physiological signals of speech emotion recognition and emotion, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map contains emotions belonging to a region called "response," where sensation is dominant. The right half of the emotion map contains emotions belonging to a region called "situation," where situational awareness is dominant.

[0825] The emotion map defines two emotions that promote learning. One is the emotion around the middle of the negative "repentance" and "reflection" on the situation side. In other words, it is when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is the emotion around the positive "desire" on the reaction side. In other words, it is when the robot has positive feelings such as "I want more" or "I want to know more."

[0826] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​representing each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple training data sets, which are combinations of user input and emotion values ​​representing each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions located close together have similar values, as shown in the emotion map 900 in Figure 10. Figure 10 shows an example where multiple emotions such as "reassured," "calm," and "confident" have similar emotion values.

[0827] The above description primarily focuses on the functions of the data processing device 12 in relation to this disclosure. However, the system related to this disclosure is not necessarily implemented on a server. The system related to this disclosure may be implemented as a general information processing system. This disclosure may be implemented, for example, as a software program that runs on a personal computer or as an application that runs on a smartphone. The method related to this disclosure may be provided to users in SaaS (Software as a Service) format.

[0828] In the above embodiment, an example was given in which a specific process is performed by a single computer 22. However, the technology of this disclosure is not limited thereto, and a distributed processing of the specific process may be performed by multiple computers, including computer 22. For example, a data generation model 58 may be provided in an external device of the data processing device 12, and the external device may generate data according to the input data.

[0829] In the above embodiment, an example was given in which the specific processing program 56 is stored in the storage 32, but the technology of this disclosure is not limited thereto. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-temporary storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-temporary storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes specific processing according to the specific processing program 56.

[0830] 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.

[0831] Furthermore, it is not necessary to store the entirety of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store the entirety of the specific processing program 56 in the storage 32; it is acceptable to store only a portion of the specific processing program 56.

[0832] The following types of processors can be used as hardware resources to perform specific processing. Examples of processors include a CPU, a general-purpose processor that functions as a hardware resource to perform specific processing by executing software, i.e., a program. Other examples of processors include dedicated electrical circuits, such as FPGAs (Field-Programmable Gate Arrays), PLDs (Programmable Logic Devices), or ASICs (Application Specific Integrated Circuits), which have circuit configurations specifically designed to perform specific processing. All of these processors have built-in or connected memory, and all of them perform specific processing by using memory.

[0833] The hardware resource that performs a specific process may consist of one of these various processors, or it may consist of a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Alternatively, the hardware resource that performs a specific process may consist of a single processor.

[0834] Examples of configurations using a single processor include, firstly, a configuration in which one or more CPUs and software are combined to form a single processor, and this processor functions as a hardware resource that performs a specific process. Secondly, there is a configuration using a processor that realizes the functions of the entire system, including multiple hardware resources that perform a specific process, on a single IC chip, as exemplified by SoCs (System-on-a-chip). In this way, a specific process is realized using one or more of the above types of processors as hardware resources.

[0835] Furthermore, the hardware structure of these various processors can more specifically utilize electrical circuits that combine circuit elements such as semiconductor devices. Also, the specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps can be deleted, new steps added, or the processing order rearranged, as long as it does not deviate from the main purpose.

[0836] The descriptions and illustrations presented above are detailed explanations of the technical aspects of this disclosure and are merely examples of the technical aspects. For example, the above descriptions of the structure, function, operation, and effect are examples of the structure, function, operation, and effect of the technical aspects of this disclosure. Therefore, it goes without saying that you may delete unnecessary parts, add new elements, or replace elements in the descriptions and illustrations presented above, as long as you do not deviate from the essence of the technical aspects of this disclosure. Furthermore, in order to avoid confusion and facilitate understanding of the technical aspects of this disclosure, explanations of common technical knowledge and the like that do not require special explanation to enable the implementation of the technical aspects of this disclosure have been omitted from the descriptions and illustrations presented above.

[0837] All documents, patent applications, and technical standards described herein are incorporated by reference to the same extent as if each individual document, patent application, and technical standard were specifically and individually noted to be incorporated by reference.

[0838] The following is further disclosed regarding the embodiments described above.

[0839] (Claim 1)

[0840] A means of acquiring a user's biometric signals in real time,

[0841] A means for analyzing acquired biosignals to determine emotional state,

[0842] A means for generating advice information for the user based on their emotional state,

[0843] A means of notifying the user of the generated advice information,

[0844] A means of collecting user feedback,

[0845] A means to improve the accuracy of the analysis by analyzing the collected feedback,

[0846] A system that includes this.

[0847] (Claim 2)

[0848] The system according to claim 1, further comprising means for providing a user with a positive message that corresponds to their emotional state.

[0849] (Claim 3)

[0850] The system according to claim 1, further comprising means for providing appropriate advisory information to a user using natural language processing technology.

[0851] "Example 1"

[0852] (Claim 1)

[0853] A means of acquiring a user's biometric data in real time using an information processing device,

[0854] A means of determining psychological state by analyzing acquired biometric data,

[0855] A means for generating guidance information for the user based on their psychological state,

[0856] A means of notifying the user of the generated guideline information,

[0857] A means of collecting user evaluation information,

[0858] A means to analyze the collected evaluation information and improve the accuracy of interpretation,

[0859] A system that includes this.

[0860] (Claim 2)

[0861] The system according to claim 1, further comprising means for providing a user with positive information that corresponds to their psychological state.

[0862] (Claim 3)

[0863] The system according to claim 1, further comprising means for providing appropriate guidance information to a user using language processing technology.

[0864] "Application Example 1"

[0865] (Claim 1)

[0866] A means of acquiring a user's biometric signals in real time,

[0867] A means for analyzing acquired biosignals to determine emotional state,

[0868] A means for generating advice information for the user based on their emotional state,

[0869] A means of notifying the user of the generated advice information,

[0870] A means of collecting user feedback,

[0871] A means to improve the accuracy of the analysis by analyzing the collected feedback,

[0872] A means of providing advisory information through visual assistance devices worn by field workers, enabling support based on individual needs,

[0873] A system that includes this.

[0874] (Claim 2)

[0875] The system according to claim 1, further comprising means for providing a user with a positive message that corresponds to their emotional state.

[0876] (Claim 3)

[0877] The system according to claim 1, further comprising means for providing appropriate advisory information to a user using natural language processing technology.

[0878] "Example 2 of combining an emotion engine"

[0879] (Claim 1)

[0880] A means of acquiring users' biometric information in real time,

[0881] A means of determining emotional state by analyzing acquired biometric information,

[0882] A means of generating advice information for users based on their emotional state,

[0883] A means of notifying the user of the generated advice information,

[0884] Means for collecting responses from users,

[0885] A means for analyzing collected responses to improve analysis accuracy,

[0886] A method for analyzing a user's emotional history using machine learning algorithms and predicting future emotional fluctuations,

[0887] A means of using natural language generation technology to ensure that the generated advice information matches the user,

[0888] A system that includes this.

[0889] (Claim 2)

[0890] The system according to claim 1, further comprising means for providing a positive message to a user that corresponds to their emotional state.

[0891] (Claim 3)

[0892] The system according to claim 1, further comprising means for providing appropriate advisory information to a user using generation technology.

[0893] "Application example 2 when combining with an emotional engine"

[0894] (Claim 1)

[0895] A means of acquiring a user's biometric signals in real time,

[0896] A means for analyzing acquired biosignals to determine emotional state,

[0897] A means for generating advice information for the user based on their emotional state,

[0898] A means of notifying the user of the generated advice information,

[0899] A means of collecting user feedback,

[0900] A means to improve the accuracy of the analysis by analyzing the collected feedback,

[0901] A means of controlling a life support device that adjusts its operation according to the user's emotional state,

[0902] A system that includes this.

[0903] (Claim 2)

[0904] The system according to claim 1, further comprising means for providing a positive message to the user in accordance with their emotional state and for playing selected entertainment content.

[0905] (Claim 3)

[0906] The system according to claim 1, further comprising means for providing appropriate advisory information to the user using natural language processing technology and enhancing real-time interaction. [Explanation of symbols]

[0907] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots< / url:> < / url:> < / url:> < / url:>

Claims

1. A means of acquiring a user's biometric signals in real time, A means for analyzing acquired biosignals to determine emotional state, A means for generating advice information for the user based on their emotional state, A means of notifying the user of the generated advice information, A means of collecting user feedback, A means to improve the accuracy of the analysis by analyzing the collected feedback, A means of providing advisory information through visual assistance devices worn by field workers, enabling support based on individual needs, A system that includes this.

2. The system according to claim 1, further comprising means for providing a user with a positive message that corresponds to their emotional state.

3. The system according to claim 1, further comprising means for providing appropriate advisory information to a user using natural language processing technology.

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