system

Abstract

We provide the system. [Solution] A means of acquiring personal biometric data using biometric sensors, A means for transmitting acquired biometric data to an information processing device via a communication device, A means of analyzing biometric data received by an information processing device and evaluating an individual's mental state, A means of generating personalized care guidance based on an individual's mental state using a generative AI model, A means of providing generated care guidance to individuals in an interactive format through personal devices, A system that includes a means of receiving feedback from individuals and incorporating it into future care guidance.

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, the method including the steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to an explanation of a character of the chatbot, 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 are living under high stress, but the means to accurately understand their own emotions and mental state are limited. As a result, appropriate care methods cannot be found, and the risk of damaging physical and mental health is increasing. It is an object of the present invention to solve this problem and provide a means for an individual to appropriately understand their mental state and find a care method suitable for themselves.

Means for Solving the Problems

[0005] This invention provides means for collecting personal biometric information using a biosensor and means for transmitting the collected biometric information to a processing device. Furthermore, it includes means for analyzing the received biometric information in the processing device and evaluating the individual's psychological state. Based on the evaluation, it generates personalized advice and provides it to the individual, thereby realizing a system that supports individual mental health.

[0006] A "biosensor" is a device used to measure an individual's biological information, such as heart rate, skin electrical response, and body temperature, in real time.

[0007] "Biometric information" refers to data that indicates an individual's physical condition, and specifically includes heart rate, stress level, body temperature, etc.

[0008] A "processing device" is a computer system that receives and analyzes collected biological information.

[0009] "Analysis" is the process of evaluating an individual's psychological state based on received biometric information and extracting meaningful information.

[0010] "Psychological state" refers to an individual's emotional or mental health.

[0011] "Advice" refers to suggestions or guidelines generated based on an individual's psychological state, with the aim of promoting improvement in mental health.

[0012] "Personalized" refers to having specifications and content optimized based on the individual user's situation and history. [Brief explanation of the drawing]

[0013] [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] It is a conceptual diagram showing an example of the configuration of a data processing system according to the second embodiment. [Figure 4] It is a conceptual diagram showing an example of the main functions of a data processing device and smart glasses according to the second embodiment. [Figure 5] It is a conceptual diagram showing an example of the configuration of a data processing system according to the third embodiment. [Figure 6] It is a conceptual diagram showing an example of the main functions of a data processing device and a headset-type terminal according to the third embodiment. [Figure 7] It is a conceptual diagram showing an example of the configuration of a data processing system according to the fourth embodiment. [Figure 8] It is a conceptual diagram showing an example of the main functions of a data processing device and a robot according to the fourth embodiment. [Figure 9] It shows an emotion map to which a plurality of emotions are mapped. [Figure 10] It shows an emotion map to which a plurality of emotions are mapped. [Figure 11] It is a sequence diagram showing the processing flow of the data processing system in Example 1. [Figure 12] It is a sequence diagram showing the processing flow of the data processing system in Application Example 1. [Figure 13] It is a sequence diagram showing the processing flow of the data processing system in Example 2 when an emotion engine is combined. [Figure 14] It is a sequence diagram showing the processing flow of the data processing system in Application Example 2 when an emotion engine is combined.

Embodiments for Carrying Out the Invention

[0014] 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.

[0015] First, the terms used in the following description will be explained.

[0016] In the following embodiments, the numbered processor (hereinafter simply referred to as "processor") may be a single arithmetic unit or a combination of multiple arithmetic units. Also, the processor may be a single type of arithmetic unit or a combination of multiple types of arithmetic units. Examples of arithmetic units include a CPU (Central Processing Unit), a GPU (Graphics Processing Unit), a GPGPU (General-Purpose computing on Graphics Processing Units), an APU (Accelerated Processing Unit), and the like.

[0017] In the following embodiments, the numbered RAM (Random Access Memory) is a memory in which information is temporarily stored and is used as a work memory by the processor.

[0018] In the following embodiments, the numbered storage is one or more non-volatile storage devices that store various programs and various parameters, etc. Examples of non-volatile storage devices include flash memory (SSD (Solid State Drive)), magnetic disks (e.g., hard disks), or magnetic tapes, and the like.

[0019] In the following embodiments, the numbered communication I / F (Interface) is an interface including a communication processor and an antenna, etc. The communication I / F controls communication between multiple computers. Examples of communication standards applied to the communication I / F include wireless communication standards including 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), or Bluetooth (registered trademark), and the like.

[0020] 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."

[0021] [First Embodiment]

[0022] Figure 1 shows an example of the configuration of the data processing system 10 according to the first embodiment.

[0023] 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.

[0024] 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).

[0025] 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.

[0026] 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.

[0027] 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.

[0028] 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.

[0029] Figure 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0030] 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.

[0031] 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.

[0032] 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.

[0033] 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".

[0034] This invention is a system that supports an individual's mental health by combining a wearable device, a server, and a user terminal. The wearable device collects biometric information such as heart rate and stress level in real time. This data is transferred to the user terminal and then sent to the server. The server uses the received data to analyze the individual's psychological state. By using machine learning algorithms and natural language processing techniques, it is possible to evaluate the individual's mental health state in detail and generate necessary advice.

[0035] The generated advice is notified to the user's device and presented to the user in an interactive format through a chatbot, which serves as a communication tool. This system allows users to gain a deeper understanding of their own mental state and implement appropriate care methods.

[0036] As a concrete example, when user A is in a stressful environment, a wearable device detects an increase in heart rate and changes in skin electrical responses. This data is sent to a server, which determines that user A's stress level is higher than normal. The server generates advice on relaxation methods and suggests to user A, via their device, to "take a few minutes of deep breathing." By providing feedback from the user, the server incorporates this information into subsequent analyses and advice, enabling more personalized care.

[0037] This system features real-time data analysis and an interactive feedback mechanism with users, supporting mental health management based on individual needs.

[0038] The following describes the processing flow.

[0039] Step 1:

[0040] The user wears a wearable device. This device measures various biometric information, such as heart rate, skin electrical response, and body temperature, in seconds.

[0041] Step 2:

[0042] The user's wearable device transmits measured biometric information to the user's terminal via Bluetooth or Wi-Fi. The user's terminal temporarily stores this data.

[0043] Step 3:

[0044] The user's device sends biometric information stored on it to the server using a secure communication protocol (e.g., HTTPS). The server receives this information and stores it in a database.

[0045] Step 4:

[0046] The server analyzes the received biometric data using a pre-trained AI model. The analysis evaluates how the biometric data deviates from predetermined baseline values ​​to identify the user's psychological state.

[0047] Step 5:

[0048] The server generates personalized advice based on the analysis results, taking into account the user's psychological state. The advice also considers the user's past behavioral history and feedback.

[0049] Step 6:

[0050] The server generates advice and sends it to the user's terminal. The user's terminal receives this information and notifies the user through the chatbot.

[0051] Step 7:

[0052] The user reviews the advice provided through interaction with the chatbot. The user then inputs the results and their impressions of following the advice into the chatbot and sends the feedback to the server.

[0053] Step 8:

[0054] The server analyzes user feedback and readjusts the AI ​​model. This allows it to provide more appropriate advice to the user in the future.

[0055] (Example 1)

[0056] 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."

[0057] In modern society, individual mental health management is a crucial issue, but many individuals lack the means to assess their own psychological state in real time and take appropriate measures.

[0058] 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.

[0059] In this invention, the server includes means for collecting an individual's biometric information using a device for measuring biometric information, means for analyzing the received biometric information with a computer and using machine learning techniques to evaluate the individual's psychological state, and means for generating personalized advice using natural language processing techniques based on the individual's psychological state. This enables individuals to understand their own psychological state in real time and receive appropriate advice, thereby enabling them to manage their mental health in their daily lives on their own initiative.

[0060] "Biometric information" refers to data obtained from the body that indicates a person's health and psychological state, such as heart rate and skin electrical responses.

[0061] A "device for measuring biometric information" refers to a device, such as a wearable device, that measures an individual's physical condition in real time and collects biometric information.

[0062] A "communication device" is a device that has the function of sending and receiving data via the internet or wireless communication.

[0063] A "computer" is a device or system for receiving and analyzing biological information, and in particular, a device with processing power for machine learning and data analysis.

[0064] "Machine learning techniques" are a collection of algorithms and methods for analyzing data and performing predictions and classifications.

[0065] "Natural language processing technology" is a technique that analyzes text data to understand or generate linguistic meaning.

[0066] "Personalized advice" refers to individualized information that provides appropriate suggestions and guidance based on the psychological state and circumstances of each user.

[0067] "Interactive information processing" is an information technology that collects information through interaction with the user and processes it according to its purpose.

[0068] "Feedback" refers to the reactions and opinions that users provide regarding the services or suggestions they receive.

[0069] This invention is a system for supporting an individual's mental health, and is primarily implemented using a biometric information measuring device, a communication device, and a computer. The user wears a biometric information measuring device to collect their own biometric information in real time. This device non-invasively measures heart rate, skin electrical responses, etc., and transmits the data to the user's terminal using Bluetooth® or Wi-Fi.

[0070] The terminal transfers the received data to a computer acting as a server via a communication device. The server uses machine learning libraries such as TENSORFLOW® and PyTorch to analyze the received biometric information and evaluate the individual's psychological state. This evaluation utilizes natural language processing technology to generate personalized advice tailored to the individual's psychological state.

[0071] The generated advice is sent to the user's terminal via a communication device. The terminal provides the generated advice to the user using an interactive interface. The user can manage their mental health by receiving the advice from the terminal and taking action accordingly. The user also provides feedback on the advice through the terminal. The server collects this feedback and incorporates it into the next advice generation to improve the system's accuracy.

[0072] As a concrete example, consider a scenario where a user is in a stressful work environment. If a biometric device detects an increase in heart rate, that data is sent to a server, and advice such as "Try meditating for a few minutes" is generated. If the user provides feedback such as "I felt more relaxed after meditating," this information will be taken into consideration in the next analysis.

[0073] An example of a prompt message is: "Create a program that analyzes a large amount of heart rate data to assess the user's stress level. Explain how to provide appropriate feedback to the user based on the results."

[0074] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0075] Step 1:

[0076] The user wears a device that measures biometric information while going about their daily life. This device measures biometric information such as heart rate and skin electrical response in real time. The measured data is transmitted to the user's terminal via Bluetooth or Wi-Fi. The input is biometric information obtained from the wearable device, and the output is biometric information transferred to the terminal. The device uses sensors to quickly and accurately collect the necessary data.

[0077] Step 2:

[0078] The terminal transmits the received biometric information to the server via the internet using a communication device. The input is the biometric information stored on the terminal, and the output is the biometric information transferred to the server. The terminal ensures a stable internet connection and operates to smoothly transmit data to the server.

[0079] Step 3:

[0080] The server processes the received biometric information using machine learning techniques. Specifically, it analyzes an individual's psychological state using a neural network model with libraries such as TensorFlow. The input is the biometric information received by the server, and the output is the analysis result. The server utilizes its processing power to quickly calculate each data point and derive accurate results.

[0081] Step 4:

[0082] The server generates personalized advice using natural language processing techniques based on the analysis results. The input is the analysis of the psychological state, and the output is the generated advice. The server creates the advice using a rule-based approach, incorporating additional knowledge from experts as needed.

[0083] Step 5:

[0084] The generated advice is sent from the server to the user's terminal via a communication device. The terminal uses an interactive chatbot to notify the user and present the advice. The input is the advice from the server, and the output is the advice displayed on the user's terminal. The terminal displays the information in a user-friendly interface and operates in a way that allows the user to understand it properly.

[0085] Step 6:

[0086] The user implements the suggested advice and provides feedback on its effectiveness via the terminal. The input is the user's feedback, and the output is feedback data sent to the server. The user inputs their experiences and impressions according to the terminal's instructions.

[0087] Step 7:

[0088] The server receives feedback from users and analyzes it to help generate enhanced advice for the next time. The input is feedback data, and the output is an improved advice generation model. The server analyzes the feedback and uses that data to improve the algorithm.

[0089] (Application Example 1)

[0090] 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."

[0091] Traditional individual mental health management systems struggle to provide personalized care guidance tailored to each individual in real time, and to incorporate that feedback into future recommendations.

[0092] 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.

[0093] In this invention, the server includes means for analyzing biometric data and evaluating an individual's mental state, means for generating personalized care guidance using a generative AI model, and means for providing care guidance to the individual in an interactive format through a personal device. This enables the provision of timely mental health care guidance tailored to each individual and improves the accuracy of care guidance through the use of feedback.

[0094] A "biometric information sensor" is a physical or electronic device used to detect and collect biological data from living organisms in real time.

[0095] A "communication device" is an electronic device used to send and receive data between different devices.

[0096] An "information processing device" is a computer system used to analyze and process received data.

[0097] A "generative AI model" is an algorithm trained to perform a specific task using artificial intelligence.

[0098] "Care guidance" refers to specific advice or recommended procedures based on an individual's mental state, aimed at promoting health maintenance and recovery.

[0099] A "personal device" is an electronic device that an individual can directly operate or use.

[0100] "Feedback" refers to the evaluation or comments that users provide regarding the system's suggestions.

[0101] The system for carrying out the present invention includes a biometric sensor, a communication device, an information processing device, and a personal device as its main components. The biometric sensor collects biometric data such as an individual's heart rate and stress level, and transmits this data to the information processing device via the communication device. The information processing device analyzes the received biometric data and evaluates the individual's mental state using a generated AI model. Based on this, it generates personalized care guidance, sends it to the personal device, and presents it to the user in an interactive format.

[0102] In this system, for example, when a user experiences stress at work, a biometric sensor detects an increase in heart rate. The server analyzes this data and immediately generates care instructions, such as "take a few minutes of deep breathing," which are then sent to the user's smartphone. This process allows users to understand their condition in real time and take appropriate action.

[0103] The hardware used includes wearable devices (e.g., wristbands with heart rate monitors), smartphones, and tablets. For software, TensorFlow, a machine learning framework, is used for data analysis, and spaCy, a natural language processing library, is employed. Generative AI models provide specific advice to users, and feedback is received to further improve the accuracy of future care guidance.

[0104] Examples of specific prompt messages are as follows:

[0105] If a user's current stress level is higher than normal, speculate on the cause and suggest three simple stress-relieving methods they can use in their daily life. These methods should be quick and require no special equipment.

[0106] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0107] Step 1:

[0108] The biometric sensor acquires biometric data such as the user's heart rate and stress level in real time. This data is transmitted as digital signals to a communication device. The input is biometric data, and the output is the digital data transmitted to the communication device.

[0109] Step 2:

[0110] The terminal transfers the biometric data received via the communication device to the information processing device. At this stage, the data is formatted according to the communication protocol and standardized into a form that the information processing device can process. The input is formatted digital data, and the output is analyzable data sent to the information processing device.

[0111] Step 3:

[0112] The server analyzes the received biometric data using machine learning algorithms. A generative AI model evaluates the user's mental state from the data. This analysis process uses standardized biometric data as input and yields the evaluated mental state as output.

[0113] Step 4:

[0114] The server uses a generative AI model to generate personalized care guidance based on the user's mental state. The guidance content is constructed as conversational text using natural language processing techniques. The input is the assessed mental state, and the output is the generated care guidance text.

[0115] Step 5:

[0116] The terminal notifies the user of care instructions received from the server. Information is displayed on a GUI to make it easy for the user to review the instructions. Input is the text of the care instructions, and output is a visual representation of the instructions that the user can confirm.

[0117] Step 6:

[0118] Users provide feedback on the care instructions they receive. This feedback is sent from the terminal to the server. The input is the user's feedback, and the output is the feedback data sent to the server.

[0119] Step 7:

[0120] The server analyzes the received feedback and makes adjustments to improve the accuracy of future care guidance. In this process, feedback data is used as input, and the adjusted algorithm is obtained as output.

[0121] 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.

[0122] This invention is a system that comprehensively supports an individual's mental health by combining a wearable device, a server, a user terminal, and an emotion engine. The wearable device collects biometric information, including heart rate and stress levels, in real time. This data is transmitted to the server via the user terminal. Based on the received data, the server uses an AI model to analyze the user's psychological state. The analysis also includes biofeedback and comparison of feedback data to improve the accuracy of the user's emotion recognition.

[0123] The emotion engine analyzes voice and text data provided by the user and evaluates their emotions. This allows the emotion engine to integrate biometric fluctuation patterns with emotional history to gain a detailed understanding of the user's emotions. Based on this information, the server generates personalized advice corresponding to the user's psychological state and notifies the user's device of the results.

[0124] As a concrete example, consider a case where user B is typically busy and emotionally exhausted. The wearable device collects data indicating high stress levels and sends it to a server. The server, through its emotion engine, analyzes the user's voice expressions of anxiety and negative expressions in text, and assesses that the user's psychological state is unstable. As a result, the server generates specific advice, such as "set aside time to relax at the start of the day," and notifies the user's device.

[0125] By incorporating an emotion engine, this system can capture users' emotions from multiple perspectives, enabling more precise and personalized care. This approach allows for advice tailored to each user's mental health condition, achieving comprehensive mental health support.

[0126] The following describes the processing flow.

[0127] Step 1:

[0128] The user wears a wearable device that measures biometric information such as heart rate, stress level, and body temperature in real time. This device periodically updates the measurement data and stores it in a buffer.

[0129] Step 2:

[0130] The wearable device transmits collected biometric information to the user's mobile device via Bluetooth or Wi-Fi. The user's device temporarily stores this data.

[0131] Step 3:

[0132] The user's device securely transmits stored biometric information to the server. During this process, data integrity is maintained, and the information is safely transported via a communication protocol.

[0133] Step 4:

[0134] The server receives biometric information, which is then processed into an analysis queue. An AI model is used to analyze the user's psychological state. Here, patterns in the biometric information are compared with past data to evaluate the user's stress level and emotional changes.

[0135] Step 5:

[0136] The server activates the emotion engine and analyzes the voice and text data provided by the user. The emotion engine recognizes the user's emotional state from the tone of voice and the content of the text.

[0137] Step 6:

[0138] The server integrates biometric and emotional data based on the analysis results from the emotion engine to evaluate the overall psychological state. Based on this information, it generates personalized advice.

[0139] Step 7:

[0140] The generated advice is sent to the user's device. The user's device then uses its notification function to interactively present the advice to the user.

[0141] Step 8:

[0142] The user evaluates the advice provided and enters feedback into the device. This feedback includes the results of implementing the advice and their impressions.

[0143] Step 9:

[0144] The server receives user feedback and updates its analysis algorithms. This will enable the provision of more effective and personalized advice in the future.

[0145] (Example 2)

[0146] 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".

[0147] In modern society, many individuals experience daily stress and mental fatigue, which threatens their mental health. Conventional technologies have struggled to provide specific and personalized support tailored to each individual's psychological state in real time. To solve this problem, a system is needed that enables highly accurate emotion analysis and the generation of personalized support suggestions.

[0148] 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.

[0149] In this invention, the server includes means for collecting an individual's biometric information using a sensor device, means for transmitting the biometric information collected via a terminal to a data analysis device, and means for analyzing the biometric information received by the data analysis device using a machine learning model to evaluate the individual's mental state. This makes it possible to provide specific and timely advice tailored to each individual's mental state.

[0150] A "sensor device" is a device used to collect an individual's biometric information in real time, measuring data such as heart rate and stress level.

[0151] A "terminal" is a device used to transmit biometric information collected from sensor devices to a data analysis device, and typically a smartphone or tablet is used for this purpose.

[0152] A "data analysis device" is a device used to analyze received biological information and is used to evaluate an individual's mental state using machine learning models.

[0153] A "machine learning model" is an algorithm used in data analysis devices to analyze an individual's mental state based on biological information, learning data patterns and making predictions.

[0154] An "emotion analysis device" is a device that analyzes voice and text information and integrates data used to evaluate mental state.

[0155] A "notification device" is a device that directly delivers generated suggestions to an individual, typically by displaying alerts or messages on a digital device.

[0156] "Responses" refer to feedback information from individuals, which is used to improve the accuracy of system analysis.

[0157] To implement this invention, a system to support an individual's mental health is necessary. This system consists of a combination of sensor devices, terminals, a server, an emotion analyzer, and a notification device.

[0158] The user wears a sensor device, which collects biometric information such as the user's heart rate and stress level in real time. The collected data is transmitted to a terminal using Bluetooth technology. The terminal is typically a smartphone or tablet, and these devices are responsible for sending the data to the server. The HTTPS protocol is used for transmission, ensuring the security of the communication.

[0159] The server utilizes a data analysis device to analyze the received biometric data. A program developed in Python uses a machine learning model to perform the data analysis. This model employs machine learning libraries such as TensorFlow. The analysis process includes quantifying the user's mental state by comparing it with past data. Furthermore, the emotion analysis device analyzes the user's voice and text data, integrating the results into the server's evaluation.

[0160] Subsequently, using a generative AI model, the server creates specific and personalized suggestions tailored to the user's mental state. The process involves inputting prompts into the generative AI and generating appropriate advice. An example prompt might be, "Please suggest relaxation methods for when the user is feeling stressed."

[0161] Finally, the notification device provides the user with suggestions generated on the device. Push notifications are used, allowing users to receive advice and take action immediately. This system enables users to receive appropriate advice tailored to their mental health status and lead a healthier life.

[0162] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0163] Step 1:

[0164] By wearing a sensor device, the user's biometric information, such as heart rate and stress level, is collected in real time. The input is the user's current biometric information, which the sensor device records. The data is transmitted to the terminal via Bluetooth. The output in this step is the biometric information transferred to the terminal.

[0165] Step 2:

[0166] The device packages the biometric data received via Bluetooth using the HTTPS protocol in order to send it to the server. The input is the biometric information present in the device, and the output is the data sent to the server. In this step, SSL / TLS encryption is performed to ensure the security of the information.

[0167] Step 3:

[0168] The server stores the received biometric information in a data analysis device and starts the analysis using a Python program. The input is the biometric information sent to the server, and the output from the data analysis device is an evaluation of the biological state. In this process, a machine learning model is used to quantify the user's mental state through comparison with past data.

[0169] Step 4:

[0170] The server further analyzes the voice and text data collected from the user using an emotion analysis device. The input is voice and text data, and the output is an evaluation of the user's emotions based on this data. By utilizing natural language processing technology, the emotional tone of the text is analyzed and integrated to more accurately determine the user's mental state.

[0171] Step 5:

[0172] The server uses a generative AI model based on these evaluations to generate personalized suggestions suitable for the user. The input is mental state and emotional data, and the output is the suggested content. A prompt is sent to the generative AI model to generate specific advice such as, "Please tell me some relaxation methods that would be good for when the user is feeling stressed."

[0173] Step 6:

[0174] The device provides the user with suggestions received from the server. The input is the generated suggestion content, and the output is the advice displayed to the user. Using push notifications, users can immediately check the suggestions and utilize them in their daily lives.

[0175] (Application Example 2)

[0176] 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".

[0177] In modern society, individual mental health care has become a crucial issue. However, conventional methods make it difficult to analyze an individual's emotional state in detail and provide appropriate advice. Therefore, there is a need to develop a system that can accurately assess a user's psychological state and provide personalized feedback quickly and effectively.

[0178] 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.

[0179] In this invention, the server includes means for collecting an individual's biometric information using biosensors, means for providing personalized information tailored to the psychological state evaluated by voice and text output devices, and means for a home automated machine to communicate personalized advice to the user through voice and music output. This makes it possible to analyze an individual's psychological state in detail and provide appropriate advice immediately based on the results.

[0180] A "biosensor" is a device that acquires biometric information such as an individual's heart rate and stress level in real time.

[0181] A "processing device" is a device, including a server, that analyzes received biometric information and evaluates the psychological state.

[0182] A "speech and text output device" is a device that generates personalized information according to the analyzed psychological state and provides it to the user.

[0183] A "household automated machine" is a mechanical device that delivers advice based on the user's psychological state through voice or music within their living environment.

[0184] "Personalized advice" refers to specific suggestions and guidance designed to adapt to the user's current psychological state.

[0185] This invention provides a system for analyzing biometric information collected from wearable devices to support an individual's mental health. A server receives data such as heart rate and stress levels collected using biosensors. This data is analyzed using an emotion analysis engine running on the server to evaluate the individual's psychological state. Personalized information tailored to the evaluated psychological state is then provided through voice and text output devices.

[0186] Furthermore, a home-use automated machine will deliver personalized advice based on analysis results to the user through voice and music output within the user's living environment. This system is based on hardware such as Raspberry Pi and Arduino, and uses programming languages ​​such as Python to perform emotion analysis and output control. As a result, users can receive appropriate feedback according to their psychological state.

[0187] For example, imagine a situation where a user is feeling stressed after a busy day. When a wearable device detects an increased stress level, the server uses a generative AI model to instruct a home automation device to play music to promote relaxation. The user is then given advice such as, "Why not take a break and listen to some music to relax today?"

[0188] An example of a prompt for a generative AI model would be: "Predict the emotional state of a user with a heart rate of 95 and a stress level of 8, and generate appropriate advice."

[0189] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0190] Step 1:

[0191] Personal biometric information is collected from wearable devices. The wearable device acquires data such as the user's heart rate and stress level in real time and transmits this data to a server via the device. The biometric input, which indicates the user's current health status, is transmitted to the server as output.

[0192] Step 2:

[0193] The server stores the received biometric information in a database and uses that information to evaluate the user's psychological state. Using a generative AI model, it analyzes input data such as heart rate and stress level and outputs the user's emotional state. Specifically, the process involves normalizing and filtering the data before processing it as input to the model.

[0194] Step 3:

[0195] The server generates prompt messages based on the analyzed psychological state. These prompt messages are designed to adapt to the user's current emotional state and serve as instructions for deciding on the next action. Subsequently, a generative AI model is used to generate advice to provide to the user. Here, the prompt messages are input into the model, and the output is advice.

[0196] Step 4:

[0197] The server sends the generated advice to the terminal, which then relays it to the home-use automated machine. The terminal controls specific actions such as voice output and music playback, providing appropriate feedback to the user. The output advice is designed to promote user relaxation.

[0198] Step 5:

[0199] Users receive audio and music from their devices and follow the instructions to reduce stress. This allows the system to receive user feedback, which is then used for further analysis and advice generation. This feedback contributes to the continuous improvement of the system.

[0200] 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.

[0201] 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.

[0202] 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.

[0203] [Second Embodiment]

[0204] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0205] 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.

[0206] 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).

[0207] 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.

[0208] 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.

[0209] 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).

[0210] 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.

[0211] 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.

[0212] 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.

[0213] 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.

[0214] 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.

[0215] 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".

[0216] This invention is a system that supports an individual's mental health by combining a wearable device, a server, and a user terminal. The wearable device collects biometric information such as heart rate and stress level in real time. This data is transferred to the user terminal and then sent to the server. The server uses the received data to analyze the individual's psychological state. By using machine learning algorithms and natural language processing techniques, it is possible to evaluate the individual's mental health state in detail and generate necessary advice.

[0217] The generated advice is notified to the user's device and presented to the user in an interactive format through a chatbot, which serves as a communication tool. This system allows users to gain a deeper understanding of their own mental state and implement appropriate care methods.

[0218] As a concrete example, when user A is in a stressful environment, a wearable device detects an increase in heart rate and changes in skin electrical responses. This data is sent to a server, which determines that user A's stress level is higher than normal. The server generates advice on relaxation methods and suggests to user A, via their device, to "take a few minutes of deep breathing." By providing feedback from the user, the server incorporates this information into subsequent analyses and advice, enabling more personalized care.

[0219] This system features real-time data analysis and an interactive feedback mechanism with users, supporting mental health management based on individual needs.

[0220] The following describes the processing flow.

[0221] Step 1:

[0222] The user wears a wearable device. This device measures various biometric information, such as heart rate, skin electrical response, and body temperature, in seconds.

[0223] Step 2:

[0224] The user's wearable device transmits measured biometric information to the user's terminal via Bluetooth or Wi-Fi. The user's terminal temporarily stores this data.

[0225] Step 3:

[0226] The user's device sends biometric information stored on it to the server using a secure communication protocol (e.g., HTTPS). The server receives this information and stores it in a database.

[0227] Step 4:

[0228] The server analyzes the received biometric data using a pre-trained AI model. The analysis evaluates how the biometric data deviates from predetermined baseline values ​​to identify the user's psychological state.

[0229] Step 5:

[0230] The server generates personalized advice based on the analysis results, taking into account the user's psychological state. The advice also considers the user's past behavioral history and feedback.

[0231] Step 6:

[0232] The server generates advice and sends it to the user's terminal. The user's terminal receives this information and notifies the user through the chatbot.

[0233] Step 7:

[0234] The user reviews the advice provided through interaction with the chatbot. The user then inputs the results and their impressions of following the advice into the chatbot and sends the feedback to the server.

[0235] Step 8:

[0236] The server analyzes user feedback and readjusts the AI ​​model. This allows it to provide more appropriate advice to the user in the future.

[0237] (Example 1)

[0238] 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".

[0239] In modern society, individual mental health management is a crucial issue, but many individuals lack the means to assess their own psychological state in real time and take appropriate measures.

[0240] 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.

[0241] In this invention, the server includes means for collecting an individual's biometric information using a device for measuring biometric information, means for analyzing the received biometric information with a computer and using machine learning techniques to evaluate the individual's psychological state, and means for generating personalized advice using natural language processing techniques based on the individual's psychological state. This enables individuals to understand their own psychological state in real time and receive appropriate advice, thereby enabling them to manage their mental health in their daily lives on their own initiative.

[0242] "Biometric information" refers to data obtained from the body that indicates a person's health and psychological state, such as heart rate and skin electrical responses.

[0243] A "device for measuring biometric information" refers to a device, such as a wearable device, that measures an individual's physical condition in real time and collects biometric information.

[0244] A "communication device" is a device that has the function of sending and receiving data via the internet or wireless communication.

[0245] A "computer" is a device or system for receiving and analyzing biological information, and in particular, a device with processing power for machine learning and data analysis.

[0246] "Machine learning techniques" are a collection of algorithms and methods for analyzing data and performing predictions and classifications.

[0247] "Natural language processing technology" is a technique that analyzes text data to understand or generate linguistic meaning.

[0248] "Personalized advice" refers to individualized information that provides appropriate suggestions and guidance based on the psychological state and circumstances of each user.

[0249] "Interactive information processing" is an information technology that collects information through interaction with the user and processes it according to its purpose.

[0250] "Feedback" refers to the reactions and opinions that users provide regarding the services or suggestions they receive.

[0251] This invention is a system for supporting an individual's mental health, and is primarily implemented using a biometric device, a communication device, and a computer. The user wears a biometric device to collect their own biometric data in real time. This device non-invasively measures heart rate, skin electrical responses, etc., and transmits the data to the user's terminal using Bluetooth or Wi-Fi.

[0252] The terminal transfers the received data to a computer acting as a server via a communication device. The server uses machine learning libraries such as TensorFlow and PyTorch to analyze the received biometric information and evaluate the individual's psychological state. This evaluation utilizes natural language processing techniques to generate personalized advice tailored to the individual's psychological state.

[0253] The generated advice is sent to the user's terminal via a communication device. The terminal provides the generated advice to the user using an interactive interface. The user can manage their mental health by receiving the advice from the terminal and taking action accordingly. The user also provides feedback on the advice through the terminal. The server collects this feedback and incorporates it into the next advice generation to improve the system's accuracy.

[0254] As a concrete example, consider a scenario where a user is in a stressful work environment. If a biometric device detects an increase in heart rate, that data is sent to a server, and advice such as "Try meditating for a few minutes" is generated. If the user provides feedback such as "I felt more relaxed after meditating," this information will be taken into consideration in the next analysis.

[0255] An example of a prompt message is: "Create a program that analyzes a large amount of heart rate data to assess the user's stress level. Explain how to provide appropriate feedback to the user based on the results."

[0256] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0257] Step 1:

[0258] The user wears a device that measures biometric information while going about their daily life. This device measures biometric information such as heart rate and skin electrical response in real time. The measured data is transmitted to the user's terminal via Bluetooth or Wi-Fi. The input is biometric information obtained from the wearable device, and the output is biometric information transferred to the terminal. The device uses sensors to quickly and accurately collect the necessary data.

[0259] Step 2:

[0260] The terminal transmits the received biometric information to the server via the internet using a communication device. The input is the biometric information stored on the terminal, and the output is the biometric information transferred to the server. The terminal ensures a stable internet connection and operates to smoothly transmit data to the server.

[0261] Step 3:

[0262] The server processes the received biometric information using machine learning techniques. Specifically, it analyzes an individual's psychological state using a neural network model with libraries such as TensorFlow. The input is the biometric information received by the server, and the output is the analysis result. The server utilizes its processing power to quickly calculate each data point and derive accurate results.

[0263] Step 4:

[0264] The server generates personalized advice using natural language processing techniques based on the analysis results. The input is the analysis of the psychological state, and the output is the generated advice. The server creates the advice using a rule-based approach, incorporating additional knowledge from experts as needed.

[0265] Step 5:

[0266] The generated advice is sent from the server to the user's terminal via a communication device. The terminal uses an interactive chatbot to notify the user and present the advice. The input is the advice from the server, and the output is the advice displayed on the user's terminal. The terminal displays the information in a user-friendly interface and operates in a way that allows the user to understand it properly.

[0267] Step 6:

[0268] The user implements the suggested advice and provides feedback on its effectiveness via the terminal. The input is the user's feedback, and the output is feedback data sent to the server. The user inputs their experiences and impressions according to the terminal's instructions.

[0269] Step 7:

[0270] The server receives feedback from users and analyzes it to help generate enhanced advice for the next time. The input is feedback data, and the output is an improved advice generation model. The server analyzes the feedback and uses that data to improve the algorithm.

[0271] (Application Example 1)

[0272] 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."

[0273] Traditional individual mental health management systems struggle to provide personalized care guidance tailored to each individual in real time, and to incorporate that feedback into future recommendations.

[0274] 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.

[0275] In this invention, the server includes means for analyzing biometric data and evaluating an individual's mental state, means for generating personalized care guidance using a generative AI model, and means for providing care guidance to the individual in an interactive format through a personal device. This enables the provision of timely mental health care guidance tailored to each individual and improves the accuracy of care guidance through the use of feedback.

[0276] A "biometric information sensor" is a physical or electronic device used to detect and collect biological data from living organisms in real time.

[0277] A "communication device" is an electronic device used to send and receive data between different devices.

[0278] An "information processing device" is a computer system used to analyze and process received data.

[0279] A "generative AI model" is an algorithm trained to perform a specific task using artificial intelligence.

[0280] "Care guidance" refers to specific advice or recommended procedures based on an individual's mental state, aimed at promoting health maintenance and recovery.

[0281] A "personal device" is an electronic device that an individual can directly operate or use.

[0282] "Feedback" refers to the evaluation or comments that users provide regarding the system's suggestions.

[0283] The system for carrying out the present invention includes a biometric sensor, a communication device, an information processing device, and a personal device as its main components. The biometric sensor collects biometric data such as an individual's heart rate and stress level, and transmits this data to the information processing device via the communication device. The information processing device analyzes the received biometric data and evaluates the individual's mental state using a generated AI model. Based on this, it generates personalized care guidance, sends it to the personal device, and presents it to the user in an interactive format.

[0284] In this system, as a specific example, when a user feels stressed at work, the biometric sensor detects an increase in heart rate. The server analyzes this and immediately generates care guidance such as "take a few minutes of deep breaths" and notifies the user's smartphone. Through this process, the user can understand their own state in real time and take appropriate actions.

[0285] The hardware used includes wearable devices (e.g., a wristband with a heart rate monitor), smartphones, and tablets. As software, the machine learning framework TensorFlow is used for data analysis, and the natural language processing library spaCy is utilized. By providing specific advice to the user through the generative AI model and receiving feedback, the accuracy of future care guidance can be further improved.

[0286] Examples of specific prompt texts are as follows:

[0287] If the user's current stress level is higher than normal, please speculate on the cause and propose three stress-relieving methods that can be easily done in daily life. Please make sure these methods do not take a long time and do not require special tools.

[0288] The flow of the specific process in Application Example 1 will be described using Figure 12.

[0289] Step 1:

[0290] The biometric sensor acquires biometric data such as the user's heart rate and stress level in real time. These data are transmitted to the communication device as digital signals. The input is biometric data, and the output is the digital data transmitted to the communication device.

[0291] Step 2:

[0292] The terminal transfers the biometric data received via the communication device to the information processing device. At this stage, the data is formatted according to the communication protocol and standardized into a form that the information processing device can process. The input is formatted digital data, and the output is analyzable data sent to the information processing device.

[0293] Step 3:

[0294] The server analyzes the received biometric data using machine learning algorithms. A generative AI model evaluates the user's mental state from the data. This analysis process uses standardized biometric data as input and yields the evaluated mental state as output.

[0295] Step 4:

[0296] The server uses a generative AI model to generate personalized care guidance based on the user's mental state. The guidance content is constructed as conversational text using natural language processing techniques. The input is the assessed mental state, and the output is the generated care guidance text.

[0297] Step 5:

[0298] The terminal notifies the user of care instructions received from the server. Information is displayed on a GUI to make it easy for the user to review the instructions. Input is the text of the care instructions, and output is a visual representation of the instructions that the user can confirm.

[0299] Step 6:

[0300] Users provide feedback on the care instructions they receive. This feedback is sent from the terminal to the server. The input is the user's feedback, and the output is the feedback data sent to the server.

[0301] Step 7:

[0302] The server analyzes the received feedback and makes adjustments to improve the accuracy of future care guidance. In this process, feedback data is used as input, and the adjusted algorithm is obtained as output.

[0303] 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.

[0304] This invention is a system that comprehensively supports an individual's mental health by combining a wearable device, a server, a user terminal, and an emotion engine. The wearable device collects biometric information, including heart rate and stress levels, in real time. This data is transmitted to the server via the user terminal. Based on the received data, the server uses an AI model to analyze the user's psychological state. The analysis also includes biofeedback and comparison of feedback data to improve the accuracy of the user's emotion recognition.

[0305] The emotion engine analyzes voice and text data provided by the user and evaluates their emotions. This allows the emotion engine to integrate biometric fluctuation patterns with emotional history to gain a detailed understanding of the user's emotions. Based on this information, the server generates personalized advice corresponding to the user's psychological state and notifies the user's device of the results.

[0306] As a concrete example, consider a case where user B is typically busy and emotionally exhausted. The wearable device collects data indicating high stress levels and sends it to a server. The server, through its emotion engine, analyzes the user's voice expressions of anxiety and negative expressions in text, and assesses that the user's psychological state is unstable. As a result, the server generates specific advice, such as "set aside time to relax at the start of the day," and notifies the user's device.

[0307] By incorporating an emotion engine, this system can comprehensively capture the user's emotions and provide more refined and personalized care. This method enables advice tailored to the mental health status of individual users and realizes comprehensive mental health support.

[0308] The processing flow will be described below.

[0309] Step 1:

[0310] The user wears a wearable device and measures biometric information such as heart rate, stress level, and body temperature in real time. This device periodically updates the measurement data and stores it in a buffer.

[0311] Step 2:

[0312] The wearable device transmits the collected biometric information to the user's mobile terminal via Bluetooth or Wi-Fi. The user's terminal temporarily stores this data.

[0313] Step 3:

[0314] The user's terminal securely transmits the accumulated biometric information to the server. At this time, while maintaining the data integrity, it is safely carried via the communication protocol.

[0315] Step 4:

[0316] The server takes the received biometric information into the analysis queue and analyzes the user's mental state using an AI model. Here, a comparison is made between the pattern of biometric information and past data to evaluate changes in the user's stress level and emotions.

[0317] Step 5:

[0318] The server activates the emotion engine and analyzes the voice and text data provided by the user. The emotion engine recognizes the user's emotional state from the tone of voice and the content of the text.

[0319] Step 6:

[0320] The server integrates biometric and emotional data based on the analysis results from the emotion engine to evaluate the overall psychological state. Based on this information, it generates personalized advice.

[0321] Step 7:

[0322] The generated advice is sent to the user's device. The user's device then uses its notification function to interactively present the advice to the user.

[0323] Step 8:

[0324] The user evaluates the advice provided and enters feedback into the device. This feedback includes the results of implementing the advice and their impressions.

[0325] Step 9:

[0326] The server receives user feedback and updates its analysis algorithms. This will enable the provision of more effective and personalized advice in the future.

[0327] (Example 2)

[0328] 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".

[0329] In modern society, many individuals experience daily stress and mental fatigue, which threatens their mental health. Conventional technologies have struggled to provide specific and personalized support tailored to each individual's psychological state in real time. To solve this problem, a system is needed that enables highly accurate emotion analysis and the generation of personalized support suggestions.

[0330] 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.

[0331] In this invention, the server includes means for collecting an individual's biometric information using a sensor device, means for transmitting the biometric information collected via a terminal to a data analysis device, and means for analyzing the biometric information received by the data analysis device using a machine learning model to evaluate the individual's mental state. This makes it possible to provide specific and timely advice tailored to each individual's mental state.

[0332] A "sensor device" is a device used to collect an individual's biometric information in real time, measuring data such as heart rate and stress level.

[0333] A "terminal" is a device used to transmit biometric information collected from sensor devices to a data analysis device, and typically a smartphone or tablet is used for this purpose.

[0334] A "data analysis device" is a device used to analyze received biological information and is used to evaluate an individual's mental state using machine learning models.

[0335] A "machine learning model" is an algorithm used in data analysis devices to analyze an individual's mental state based on biological information, learning data patterns and making predictions.

[0336] An "emotion analysis device" is a device that analyzes voice and text information and integrates data used to evaluate mental state.

[0337] A "notification device" is a device that directly delivers generated suggestions to an individual, typically by displaying alerts or messages on a digital device.

[0338] "Responses" refer to feedback information from individuals, which is used to improve the accuracy of system analysis.

[0339] To implement this invention, a system to support an individual's mental health is necessary. This system consists of a combination of sensor devices, terminals, a server, an emotion analyzer, and a notification device.

[0340] The user wears a sensor device, which collects biometric information such as the user's heart rate and stress level in real time. The collected data is transmitted to a terminal using Bluetooth technology. The terminal is typically a smartphone or tablet, and these devices are responsible for sending the data to the server. The HTTPS protocol is used for transmission, ensuring the security of the communication.

[0341] The server utilizes a data analysis device to analyze the received biometric data. A program developed in Python uses a machine learning model to perform the data analysis. This model employs machine learning libraries such as TensorFlow. The analysis process includes quantifying the user's mental state by comparing it with past data. Furthermore, the emotion analysis device analyzes the user's voice and text data, integrating the results into the server's evaluation.

[0342] Subsequently, using a generative AI model, the server creates specific and personalized suggestions tailored to the user's mental state. The process involves inputting prompts into the generative AI and generating appropriate advice. An example prompt might be, "Please suggest relaxation methods for when the user is feeling stressed."

[0343] Finally, the notification device provides the user with suggestions generated on the device. Push notifications are used, allowing users to receive advice and take action immediately. This system enables users to receive appropriate advice tailored to their mental health status and lead a healthier life.

[0344] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0345] Step 1:

[0346] By wearing a sensor device, the user's biometric information, such as heart rate and stress level, is collected in real time. The input is the user's current biometric information, which the sensor device records. The data is transmitted to the terminal via Bluetooth. The output in this step is the biometric information transferred to the terminal.

[0347] Step 2:

[0348] The device packages the biometric data received via Bluetooth using the HTTPS protocol in order to send it to the server. The input is the biometric information present in the device, and the output is the data sent to the server. In this step, SSL / TLS encryption is performed to ensure the security of the information.

[0349] Step 3:

[0350] The server stores the received biometric information in a data analysis device and starts the analysis using a Python program. The input is the biometric information sent to the server, and the output from the data analysis device is an evaluation of the biological state. In this process, a machine learning model is used to quantify the user's mental state through comparison with past data.

[0351] Step 4:

[0352] The server further analyzes the voice and text data collected from the user using an emotion analysis device. The input is voice and text data, and the output is an evaluation of the user's emotions based on this data. By utilizing natural language processing technology, the emotional tone of the text is analyzed and integrated to more accurately determine the user's mental state.

[0353] Step 5:

[0354] The server uses a generative AI model based on these evaluations to generate personalized suggestions suitable for the user. The input is mental state and emotional data, and the output is the suggested content. A prompt is sent to the generative AI model to generate specific advice such as, "Please tell me some relaxation methods that would be good for when the user is feeling stressed."

[0355] Step 6:

[0356] The device provides the user with suggestions received from the server. The input is the generated suggestion content, and the output is the advice displayed to the user. Using push notifications, users can immediately check the suggestions and utilize them in their daily lives.

[0357] (Application Example 2)

[0358] 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."

[0359] In modern society, individual mental health care has become a crucial issue. However, conventional methods make it difficult to analyze an individual's emotional state in detail and provide appropriate advice. Therefore, there is a need to develop a system that can accurately assess a user's psychological state and provide personalized feedback quickly and effectively.

[0360] 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.

[0361] In this invention, the server includes means for collecting an individual's biometric information using biosensors, means for providing personalized information tailored to the psychological state evaluated by voice and text output devices, and means for a home automated machine to communicate personalized advice to the user through voice and music output. This makes it possible to analyze an individual's psychological state in detail and provide appropriate advice immediately based on the results.

[0362] A "biosensor" is a device that acquires biometric information such as an individual's heart rate and stress level in real time.

[0363] A "processing device" is a device, including a server, that analyzes received biometric information and evaluates the psychological state.

[0364] A "speech and text output device" is a device that generates personalized information according to the analyzed psychological state and provides it to the user.

[0365] A "household automated machine" is a mechanical device that delivers advice based on the user's psychological state through voice or music within their living environment.

[0366] "Personalized advice" refers to specific suggestions and guidance designed to adapt to the user's current psychological state.

[0367] This invention provides a system for analyzing biometric information collected from wearable devices to support an individual's mental health. A server receives data such as heart rate and stress levels collected using biosensors. This data is analyzed using an emotion analysis engine running on the server to evaluate the individual's psychological state. Personalized information tailored to the evaluated psychological state is then provided through voice and text output devices.

[0368] Furthermore, a home-use automated machine will deliver personalized advice based on analysis results to the user through voice and music output within the user's living environment. This system is based on hardware such as Raspberry Pi and Arduino, and uses programming languages ​​such as Python to perform emotion analysis and output control. As a result, users can receive appropriate feedback according to their psychological state.

[0369] For example, imagine a situation where a user is feeling stressed after a busy day. When a wearable device detects an increased stress level, the server uses a generative AI model to instruct a home automation device to play music to promote relaxation. The user is then given advice such as, "Why not take a break and listen to some music to relax today?"

[0370] An example of a prompt for a generative AI model would be: "Predict the emotional state of a user with a heart rate of 95 and a stress level of 8, and generate appropriate advice."

[0371] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0372] Step 1:

[0373] Personal biometric information is collected from wearable devices. The wearable device acquires data such as the user's heart rate and stress level in real time and transmits this data to a server via the device. The biometric input, which indicates the user's current health status, is transmitted to the server as output.

[0374] Step 2:

[0375] The server stores the received biometric information in a database and uses that information to evaluate the user's psychological state. Using a generative AI model, it analyzes input data such as heart rate and stress level and outputs the user's emotional state. Specifically, the process involves normalizing and filtering the data before processing it as input to the model.

[0376] Step 3:

[0377] The server generates prompt messages based on the analyzed psychological state. These prompt messages are designed to adapt to the user's current emotional state and serve as instructions for deciding on the next action. Subsequently, a generative AI model is used to generate advice to provide to the user. Here, the prompt messages are input into the model, and the output is advice.

[0378] Step 4:

[0379] The server sends the generated advice to the terminal, which then relays it to the home-use automated machine. The terminal controls specific actions such as voice output and music playback, providing appropriate feedback to the user. The output advice is designed to promote user relaxation.

[0380] Step 5:

[0381] Users receive audio and music from their devices and follow the instructions to reduce stress. This allows the system to receive user feedback, which is then used for further analysis and advice generation. This feedback contributes to the continuous improvement of the system.

[0382] 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.

[0383] 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.

[0384] 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.

[0385] [Third Embodiment]

[0386] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0387] 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.

[0388] 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).

[0389] 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.

[0390] 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.

[0391] 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).

[0392] 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.

[0393] 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.

[0394] 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.

[0395] 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.

[0396] 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.

[0397] 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".

[0398] This invention is a system that supports an individual's mental health by combining a wearable device, a server, and a user terminal. The wearable device collects biometric information such as heart rate and stress level in real time. This data is transferred to the user terminal and then sent to the server. The server uses the received data to analyze the individual's psychological state. By using machine learning algorithms and natural language processing techniques, it is possible to evaluate the individual's mental health state in detail and generate necessary advice.

[0399] The generated advice is notified to the user's device and presented to the user in an interactive format through a chatbot, which serves as a communication tool. This system allows users to gain a deeper understanding of their own mental state and implement appropriate care methods.

[0400] As a concrete example, when user A is in a stressful environment, a wearable device detects an increase in heart rate and changes in skin electrical responses. This data is sent to a server, which determines that user A's stress level is higher than normal. The server generates advice on relaxation methods and suggests to user A, via their device, to "take a few minutes of deep breathing." By providing feedback from the user, the server incorporates this information into subsequent analyses and advice, enabling more personalized care.

[0401] This system features real-time data analysis and an interactive feedback mechanism with users, supporting mental health management based on individual needs.

[0402] The following describes the processing flow.

[0403] Step 1:

[0404] The user wears a wearable device. This device measures various biometric information, such as heart rate, skin electrical response, and body temperature, in seconds.

[0405] Step 2:

[0406] The user's wearable device transmits measured biometric information to the user's terminal via Bluetooth or Wi-Fi. The user's terminal temporarily stores this data.

[0407] Step 3:

[0408] The user's device sends biometric information stored on it to the server using a secure communication protocol (e.g., HTTPS). The server receives this information and stores it in a database.

[0409] Step 4:

[0410] The server analyzes the received biometric data using a pre-trained AI model. The analysis evaluates how the biometric data deviates from predetermined baseline values ​​to identify the user's psychological state.

[0411] Step 5:

[0412] The server generates personalized advice based on the analysis results, taking into account the user's psychological state. The advice also considers the user's past behavioral history and feedback.

[0413] Step 6:

[0414] The server generates advice and sends it to the user's terminal. The user's terminal receives this information and notifies the user through the chatbot.

[0415] Step 7:

[0416] The user reviews the advice provided through interaction with the chatbot. The user then inputs the results and their impressions of following the advice into the chatbot and sends the feedback to the server.

[0417] Step 8:

[0418] The server analyzes user feedback and readjusts the AI ​​model. This allows it to provide more appropriate advice to the user in the future.

[0419] (Example 1)

[0420] 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."

[0421] In modern society, individual mental health management is a crucial issue, but many individuals lack the means to assess their own psychological state in real time and take appropriate measures.

[0422] 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.

[0423] In this invention, the server includes means for collecting an individual's biometric information using a device for measuring biometric information, means for analyzing the received biometric information with a computer and using machine learning techniques to evaluate the individual's psychological state, and means for generating personalized advice using natural language processing techniques based on the individual's psychological state. This enables individuals to understand their own psychological state in real time and receive appropriate advice, thereby enabling them to manage their mental health in their daily lives on their own initiative.

[0424] "Biometric information" refers to data obtained from the body that indicates a person's health and psychological state, such as heart rate and skin electrical responses.

[0425] A "device for measuring biometric information" refers to a device, such as a wearable device, that measures an individual's physical condition in real time and collects biometric information.

[0426] A "communication device" is a device that has the function of sending and receiving data via the internet or wireless communication.

[0427] A "computer" is a device or system for receiving and analyzing biological information, and in particular, a device with processing power for machine learning and data analysis.

[0428] "Machine learning techniques" are a collection of algorithms and methods for analyzing data and performing predictions and classifications.

[0429] "Natural language processing technology" is a technique that analyzes text data to understand or generate linguistic meaning.

[0430] "Personalized advice" refers to individualized information that provides appropriate suggestions and guidance based on the psychological state and circumstances of each user.

[0431] "Interactive information processing" is an information technology that collects information through interaction with the user and processes it according to its purpose.

[0432] "Feedback" refers to the reactions and opinions that users provide regarding the services or suggestions they receive.

[0433] This invention is a system for supporting an individual's mental health, and is primarily implemented using a biometric device, a communication device, and a computer. The user wears a biometric device to collect their own biometric data in real time. This device non-invasively measures heart rate, skin electrical responses, etc., and transmits the data to the user's terminal using Bluetooth or Wi-Fi.

[0434] The terminal transfers the received data to a computer acting as a server via a communication device. The server uses machine learning libraries such as TensorFlow and PyTorch to analyze the received biometric information and evaluate the individual's psychological state. This evaluation utilizes natural language processing techniques to generate personalized advice tailored to the individual's psychological state.

[0435] The generated advice is sent to the user's terminal via a communication device. The terminal provides the generated advice to the user using an interactive interface. The user can manage their mental health by receiving the advice from the terminal and taking action accordingly. The user also provides feedback on the advice through the terminal. The server collects this feedback and incorporates it into the next advice generation to improve the system's accuracy.

[0436] As a concrete example, consider a scenario where a user is in a stressful work environment. If a biometric device detects an increase in heart rate, that data is sent to a server, and advice such as "Try meditating for a few minutes" is generated. If the user provides feedback such as "I felt more relaxed after meditating," this information will be taken into consideration in the next analysis.

[0437] An example of a prompt message is: "Create a program that analyzes a large amount of heart rate data to assess the user's stress level. Explain how to provide appropriate feedback to the user based on the results."

[0438] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0439] Step 1:

[0440] The user wears a device that measures biometric information while going about their daily life. This device measures biometric information such as heart rate and skin electrical response in real time. The measured data is transmitted to the user's terminal via Bluetooth or Wi-Fi. The input is biometric information obtained from the wearable device, and the output is biometric information transferred to the terminal. The device uses sensors to quickly and accurately collect the necessary data.

[0441] Step 2:

[0442] The terminal transmits the received biometric information to the server via the internet using a communication device. The input is the biometric information stored on the terminal, and the output is the biometric information transferred to the server. The terminal ensures a stable internet connection and operates to smoothly transmit data to the server.

[0443] Step 3:

[0444] The server processes the received biometric information using machine learning techniques. Specifically, it analyzes an individual's psychological state using a neural network model with libraries such as TensorFlow. The input is the biometric information received by the server, and the output is the analysis result. The server utilizes its processing power to quickly calculate each data point and derive accurate results.

[0445] Step 4:

[0446] The server generates personalized advice using natural language processing techniques based on the analysis results. The input is the analysis of the psychological state, and the output is the generated advice. The server creates the advice using a rule-based approach, incorporating additional knowledge from experts as needed.

[0447] Step 5:

[0448] The generated advice is sent from the server to the user's terminal via a communication device. The terminal uses an interactive chatbot to notify the user and present the advice. The input is the advice from the server, and the output is the advice displayed on the user's terminal. The terminal displays the information in a user-friendly interface and operates in a way that allows the user to understand it properly.

[0449] Step 6:

[0450] The user implements the suggested advice and provides feedback on its effectiveness via the terminal. The input is the user's feedback, and the output is feedback data sent to the server. The user inputs their experiences and impressions according to the terminal's instructions.

[0451] Step 7:

[0452] The server receives feedback from users and analyzes it to help generate enhanced advice for the next time. The input is feedback data, and the output is an improved advice generation model. The server analyzes the feedback and uses that data to improve the algorithm.

[0453] (Application Example 1)

[0454] 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."

[0455] Traditional individual mental health management systems struggle to provide personalized care guidance tailored to each individual in real time, and to incorporate that feedback into future recommendations.

[0456] 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.

[0457] In this invention, the server includes means for analyzing biometric data and evaluating an individual's mental state, means for generating personalized care guidance using a generative AI model, and means for providing care guidance to the individual in an interactive format through a personal device. This enables the provision of timely mental health care guidance tailored to each individual and improves the accuracy of care guidance through the use of feedback.

[0458] A "biometric information sensor" is a physical or electronic device used to detect and collect biological data from living organisms in real time.

[0459] A "communication device" is an electronic device used to send and receive data between different devices.

[0460] An "information processing device" is a computer system used to analyze and process received data.

[0461] A "generative AI model" is an algorithm trained to perform a specific task using artificial intelligence.

[0462] "Care guidance" refers to specific advice or recommended procedures based on an individual's mental state, aimed at promoting health maintenance and recovery.

[0463] A "personal device" is an electronic device that an individual can directly operate or use.

[0464] "Feedback" refers to the evaluation or comments that users provide regarding the system's suggestions.

[0465] The system for carrying out the present invention includes a biometric sensor, a communication device, an information processing device, and a personal device as its main components. The biometric sensor collects biometric data such as an individual's heart rate and stress level, and transmits this data to the information processing device via the communication device. The information processing device analyzes the received biometric data and evaluates the individual's mental state using a generated AI model. Based on this, it generates personalized care guidance, sends it to the personal device, and presents it to the user in an interactive format.

[0466] In this system, for example, when a user experiences stress at work, a biometric sensor detects an increase in heart rate. The server analyzes this data and immediately generates care instructions, such as "take a few minutes of deep breathing," which are then sent to the user's smartphone. This process allows users to understand their condition in real time and take appropriate action.

[0467] The hardware used includes wearable devices (e.g., wristbands with heart rate monitors), smartphones, and tablets. For software, TensorFlow, a machine learning framework, is used for data analysis, and spaCy, a natural language processing library, is employed. Generative AI models provide specific advice to users, and feedback is received to further improve the accuracy of future care guidance.

[0468] Examples of specific prompt messages are as follows:

[0469] If a user's current stress level is higher than normal, speculate on the cause and suggest three simple stress-relieving methods they can use in their daily life. These methods should be quick and require no special equipment.

[0470] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0471] Step 1:

[0472] The biometric sensor acquires biometric data such as the user's heart rate and stress level in real time. This data is transmitted as digital signals to a communication device. The input is biometric data, and the output is the digital data transmitted to the communication device.

[0473] Step 2:

[0474] The terminal transfers the biometric data received via the communication device to the information processing device. At this stage, the data is formatted according to the communication protocol and standardized into a form that the information processing device can process. The input is formatted digital data, and the output is analyzable data sent to the information processing device.

[0475] Step 3:

[0476] The server analyzes the received biometric data using machine learning algorithms. A generative AI model evaluates the user's mental state from the data. This analysis process uses standardized biometric data as input and yields the evaluated mental state as output.

[0477] Step 4:

[0478] The server uses a generative AI model to generate personalized care guidance based on the user's mental state. The guidance content is constructed as conversational text using natural language processing techniques. The input is the assessed mental state, and the output is the generated care guidance text.

[0479] Step 5:

[0480] The terminal notifies the user of care instructions received from the server. Information is displayed on a GUI to make it easy for the user to review the instructions. Input is the text of the care instructions, and output is a visual representation of the instructions that the user can confirm.

[0481] Step 6:

[0482] Users provide feedback on the care instructions they receive. This feedback is sent from the terminal to the server. The input is the user's feedback, and the output is the feedback data sent to the server.

[0483] Step 7:

[0484] The server analyzes the received feedback and makes adjustments to improve the accuracy of future care guidance. In this process, feedback data is used as input, and the adjusted algorithm is obtained as output.

[0485] 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.

[0486] This invention is a system that comprehensively supports an individual's mental health by combining a wearable device, a server, a user terminal, and an emotion engine. The wearable device collects biometric information, including heart rate and stress levels, in real time. This data is transmitted to the server via the user terminal. Based on the received data, the server uses an AI model to analyze the user's psychological state. The analysis also includes biofeedback and comparison of feedback data to improve the accuracy of the user's emotion recognition.

[0487] The emotion engine analyzes voice and text data provided by the user and evaluates their emotions. This allows the emotion engine to integrate biometric fluctuation patterns with emotional history to gain a detailed understanding of the user's emotions. Based on this information, the server generates personalized advice corresponding to the user's psychological state and notifies the user's device of the results.

[0488] As a concrete example, consider a case where user B is typically busy and emotionally exhausted. The wearable device collects data indicating high stress levels and sends it to a server. The server, through its emotion engine, analyzes the user's voice expressions of anxiety and negative expressions in text, and assesses that the user's psychological state is unstable. As a result, the server generates specific advice, such as "set aside time to relax at the start of the day," and notifies the user's device.

[0489] By incorporating an emotion engine, this system can capture users' emotions from multiple perspectives, enabling more precise and personalized care. This approach allows for advice tailored to each user's mental health condition, achieving comprehensive mental health support.

[0490] The following describes the processing flow.

[0491] Step 1:

[0492] The user wears a wearable device that measures biometric information such as heart rate, stress level, and body temperature in real time. This device periodically updates the measurement data and stores it in a buffer.

[0493] Step 2:

[0494] The wearable device transmits collected biometric information to the user's mobile device via Bluetooth or Wi-Fi. The user's device temporarily stores this data.

[0495] Step 3:

[0496] The user's device securely transmits stored biometric information to the server. During this process, data integrity is maintained, and the information is safely transported via a communication protocol.

[0497] Step 4:

[0498] The server receives biometric information, which is then processed into an analysis queue. An AI model is used to analyze the user's psychological state. Here, patterns in the biometric information are compared with past data to evaluate the user's stress level and emotional changes.

[0499] Step 5:

[0500] The server activates the emotion engine and analyzes the voice and text data provided by the user. The emotion engine recognizes the user's emotional state from the tone of voice and the content of the text.

[0501] Step 6:

[0502] The server integrates biometric and emotional data based on the analysis results from the emotion engine to evaluate the overall psychological state. Based on this information, it generates personalized advice.

[0503] Step 7:

[0504] The generated advice is sent to the user's device. The user's device then uses its notification function to interactively present the advice to the user.

[0505] Step 8:

[0506] The user evaluates the advice provided and enters feedback into the device. This feedback includes the results of implementing the advice and their impressions.

[0507] Step 9:

[0508] The server receives user feedback and updates its analysis algorithms. This will enable the provision of more effective and personalized advice in the future.

[0509] (Example 2)

[0510] 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."

[0511] In modern society, many individuals experience daily stress and mental fatigue, which threatens their mental health. Conventional technologies have struggled to provide specific and personalized support tailored to each individual's psychological state in real time. To solve this problem, a system is needed that enables highly accurate emotion analysis and the generation of personalized support suggestions.

[0512] 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.

[0513] In this invention, the server includes means for collecting an individual's biometric information using a sensor device, means for transmitting the biometric information collected via a terminal to a data analysis device, and means for analyzing the biometric information received by the data analysis device using a machine learning model to evaluate the individual's mental state. This makes it possible to provide specific and timely advice tailored to each individual's mental state.

[0514] A "sensor device" is a device used to collect an individual's biometric information in real time, measuring data such as heart rate and stress level.

[0515] A "terminal" is a device used to transmit biometric information collected from sensor devices to a data analysis device, and typically a smartphone or tablet is used for this purpose.

[0516] A "data analysis device" is a device used to analyze received biological information and is used to evaluate an individual's mental state using machine learning models.

[0517] A "machine learning model" is an algorithm used in data analysis devices to analyze an individual's mental state based on biological information, learning data patterns and making predictions.

[0518] An "emotion analysis device" is a device that analyzes voice and text information and integrates data used to evaluate mental state.

[0519] A "notification device" is a device that directly delivers generated suggestions to an individual, typically by displaying alerts or messages on a digital device.

[0520] "Responses" refer to feedback information from individuals, which is used to improve the accuracy of system analysis.

[0521] To implement this invention, a system to support an individual's mental health is necessary. This system consists of a combination of sensor devices, terminals, a server, an emotion analyzer, and a notification device.

[0522] The user wears a sensor device, which collects biometric information such as the user's heart rate and stress level in real time. The collected data is transmitted to a terminal using Bluetooth technology. The terminal is typically a smartphone or tablet, and these devices are responsible for sending the data to the server. The HTTPS protocol is used for transmission, ensuring the security of the communication.

[0523] The server utilizes a data analysis device to analyze the received biometric data. A program developed in Python uses a machine learning model to perform the data analysis. This model employs machine learning libraries such as TensorFlow. The analysis process includes quantifying the user's mental state by comparing it with past data. Furthermore, the emotion analysis device analyzes the user's voice and text data, integrating the results into the server's evaluation.

[0524] Subsequently, using a generative AI model, the server creates specific and personalized suggestions tailored to the user's mental state. The process involves inputting prompts into the generative AI and generating appropriate advice. An example prompt might be, "Please suggest relaxation methods for when the user is feeling stressed."

[0525] Finally, the notification device provides the user with suggestions generated on the device. Push notifications are used, allowing users to receive advice and take action immediately. This system enables users to receive appropriate advice tailored to their mental health status and lead a healthier life.

[0526] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0527] Step 1:

[0528] By wearing a sensor device, the user's biometric information, such as heart rate and stress level, is collected in real time. The input is the user's current biometric information, which the sensor device records. The data is transmitted to the terminal via Bluetooth. The output in this step is the biometric information transferred to the terminal.

[0529] Step 2:

[0530] The device packages the biometric data received via Bluetooth using the HTTPS protocol in order to send it to the server. The input is the biometric information present in the device, and the output is the data sent to the server. In this step, SSL / TLS encryption is performed to ensure the security of the information.

[0531] Step 3:

[0532] The server stores the received biometric information in a data analysis device and starts the analysis using a Python program. The input is the biometric information sent to the server, and the output from the data analysis device is an evaluation of the biological state. In this process, a machine learning model is used to quantify the user's mental state through comparison with past data.

[0533] Step 4:

[0534] The server further analyzes the voice and text data collected from the user using an emotion analysis device. The input is voice and text data, and the output is an evaluation of the user's emotions based on this data. By utilizing natural language processing technology, the emotional tone of the text is analyzed and integrated to more accurately determine the user's mental state.

[0535] Step 5:

[0536] The server uses a generative AI model based on these evaluations to generate personalized suggestions suitable for the user. The input is mental state and emotional data, and the output is the suggested content. A prompt is sent to the generative AI model to generate specific advice such as, "Please tell me some relaxation methods that would be good for when the user is feeling stressed."

[0537] Step 6:

[0538] The device provides the user with suggestions received from the server. The input is the generated suggestion content, and the output is the advice displayed to the user. Using push notifications, users can immediately check the suggestions and utilize them in their daily lives.

[0539] (Application Example 2)

[0540] 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."

[0541] In modern society, individual mental health care has become a crucial issue. However, conventional methods make it difficult to analyze an individual's emotional state in detail and provide appropriate advice. Therefore, there is a need to develop a system that can accurately assess a user's psychological state and provide personalized feedback quickly and effectively.

[0542] 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.

[0543] In this invention, the server includes means for collecting an individual's biometric information using biosensors, means for providing personalized information tailored to the psychological state evaluated by voice and text output devices, and means for a home automated machine to communicate personalized advice to the user through voice and music output. This makes it possible to analyze an individual's psychological state in detail and provide appropriate advice immediately based on the results.

[0544] A "biosensor" is a device that acquires biometric information such as an individual's heart rate and stress level in real time.

[0545] A "processing device" is a device, including a server, that analyzes received biometric information and evaluates the psychological state.

[0546] A "speech and text output device" is a device that generates personalized information according to the analyzed psychological state and provides it to the user.

[0547] A "household automated machine" is a mechanical device that delivers advice based on the user's psychological state through voice or music within their living environment.

[0548] "Personalized advice" refers to specific suggestions and guidance designed to adapt to the user's current psychological state.

[0549] This invention provides a system for analyzing biometric information collected from wearable devices to support an individual's mental health. A server receives data such as heart rate and stress levels collected using biosensors. This data is analyzed using an emotion analysis engine running on the server to evaluate the individual's psychological state. Personalized information tailored to the evaluated psychological state is then provided through voice and text output devices.

[0550] Furthermore, a home-use automated machine will deliver personalized advice based on analysis results to the user through voice and music output within the user's living environment. This system is based on hardware such as Raspberry Pi and Arduino, and uses programming languages ​​such as Python to perform emotion analysis and output control. As a result, users can receive appropriate feedback according to their psychological state.

[0551] For example, imagine a situation where a user is feeling stressed after a busy day. When a wearable device detects an increased stress level, the server uses a generative AI model to instruct a home automation device to play music to promote relaxation. The user is then given advice such as, "Why not take a break and listen to some music to relax today?"

[0552] An example of a prompt for a generative AI model would be: "Predict the emotional state of a user with a heart rate of 95 and a stress level of 8, and generate appropriate advice."

[0553] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0554] Step 1:

[0555] Personal biometric information is collected from wearable devices. The wearable device acquires data such as the user's heart rate and stress level in real time and transmits this data to a server via the device. The biometric input, which indicates the user's current health status, is transmitted to the server as output.

[0556] Step 2:

[0557] The server stores the received biometric information in a database and uses that information to evaluate the user's psychological state. Using a generative AI model, it analyzes input data such as heart rate and stress level and outputs the user's emotional state. Specifically, the process involves normalizing and filtering the data before processing it as input to the model.

[0558] Step 3:

[0559] The server generates prompt messages based on the analyzed psychological state. These prompt messages are designed to adapt to the user's current emotional state and serve as instructions for deciding on the next action. Subsequently, a generative AI model is used to generate advice to provide to the user. Here, the prompt messages are input into the model, and the output is advice.

[0560] Step 4:

[0561] The server sends the generated advice to the terminal, which then relays it to the home-use automated machine. The terminal controls specific actions such as voice output and music playback, providing appropriate feedback to the user. The output advice is designed to promote user relaxation.

[0562] Step 5:

[0563] Users receive audio and music from their devices and follow the instructions to reduce stress. This allows the system to receive user feedback, which is then used for further analysis and advice generation. This feedback contributes to the continuous improvement of the system.

[0564] 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.

[0565] 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.

[0566] 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.

[0567] [Fourth Embodiment]

[0568] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0569] 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.

[0570] 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).

[0571] 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.

[0572] 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.

[0573] 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).

[0574] 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.

[0575] 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.

[0576] 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.

[0577] 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.

[0578] 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.

[0579] 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.

[0580] 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".

[0581] This invention is a system that supports an individual's mental health by combining a wearable device, a server, and a user terminal. The wearable device collects biometric information such as heart rate and stress level in real time. This data is transferred to the user terminal and then sent to the server. The server uses the received data to analyze the individual's psychological state. By using machine learning algorithms and natural language processing techniques, it is possible to evaluate the individual's mental health state in detail and generate necessary advice.

[0582] The generated advice is notified to the user's device and presented to the user in an interactive format through a chatbot, which serves as a communication tool. This system allows users to gain a deeper understanding of their own mental state and implement appropriate care methods.

[0583] As a concrete example, when user A is in a stressful environment, a wearable device detects an increase in heart rate and changes in skin electrical responses. This data is sent to a server, which determines that user A's stress level is higher than normal. The server generates advice on relaxation methods and suggests to user A, via their device, to "take a few minutes of deep breathing." By providing feedback from the user, the server incorporates this information into subsequent analyses and advice, enabling more personalized care.

[0584] This system features real-time data analysis and an interactive feedback mechanism with users, supporting mental health management based on individual needs.

[0585] The following describes the processing flow.

[0586] Step 1:

[0587] The user wears a wearable device. This device measures various biometric information, such as heart rate, skin electrical response, and body temperature, in seconds.

[0588] Step 2:

[0589] The user's wearable device transmits measured biometric information to the user's terminal via Bluetooth or Wi-Fi. The user's terminal temporarily stores this data.

[0590] Step 3:

[0591] The user's device sends biometric information stored on it to the server using a secure communication protocol (e.g., HTTPS). The server receives this information and stores it in a database.

[0592] Step 4:

[0593] The server analyzes the received biometric data using a pre-trained AI model. The analysis evaluates how the biometric data deviates from predetermined baseline values ​​to identify the user's psychological state.

[0594] Step 5:

[0595] The server generates personalized advice based on the analysis results, taking into account the user's psychological state. The advice also considers the user's past behavioral history and feedback.

[0596] Step 6:

[0597] The server generates advice and sends it to the user's terminal. The user's terminal receives this information and notifies the user through the chatbot.

[0598] Step 7:

[0599] The user reviews the advice provided through interaction with the chatbot. The user then inputs the results and their impressions of following the advice into the chatbot and sends the feedback to the server.

[0600] Step 8:

[0601] The server analyzes user feedback and readjusts the AI ​​model. This allows it to provide more appropriate advice to the user in the future.

[0602] (Example 1)

[0603] 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".

[0604] In modern society, individual mental health management is a crucial issue, but many individuals lack the means to assess their own psychological state in real time and take appropriate measures.

[0605] 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.

[0606] In this invention, the server includes means for collecting an individual's biometric information using a device for measuring biometric information, means for analyzing the received biometric information with a computer and using machine learning techniques to evaluate the individual's psychological state, and means for generating personalized advice using natural language processing techniques based on the individual's psychological state. This enables individuals to understand their own psychological state in real time and receive appropriate advice, thereby enabling them to manage their mental health in their daily lives on their own initiative.

[0607] "Biometric information" refers to data obtained from the body that indicates a person's health and psychological state, such as heart rate and skin electrical responses.

[0608] A "device for measuring biometric information" refers to a device, such as a wearable device, that measures an individual's physical condition in real time and collects biometric information.

[0609] A "communication device" is a device that has the function of sending and receiving data via the internet or wireless communication.

[0610] A "computer" is a device or system for receiving and analyzing biological information, and in particular, a device with processing power for machine learning and data analysis.

[0611] "Machine learning techniques" are a collection of algorithms and methods for analyzing data and performing predictions and classifications.

[0612] "Natural language processing technology" is a technique that analyzes text data to understand or generate linguistic meaning.

[0613] "Personalized advice" refers to individualized information that provides appropriate suggestions and guidance based on the psychological state and circumstances of each user.

[0614] "Interactive information processing" is an information technology that collects information through interaction with the user and processes it according to its purpose.

[0615] "Feedback" refers to the reactions and opinions that users provide regarding the services or suggestions they receive.

[0616] This invention is a system for supporting an individual's mental health, and is primarily implemented using a biometric device, a communication device, and a computer. The user wears a biometric device to collect their own biometric data in real time. This device non-invasively measures heart rate, skin electrical responses, etc., and transmits the data to the user's terminal using Bluetooth or Wi-Fi.

[0617] The terminal transfers the received data to a computer acting as a server via a communication device. The server uses machine learning libraries such as TensorFlow and PyTorch to analyze the received biometric information and evaluate the individual's psychological state. This evaluation utilizes natural language processing techniques to generate personalized advice tailored to the individual's psychological state.

[0618] The generated advice is sent to the user's terminal via a communication device. The terminal provides the generated advice to the user using an interactive interface. The user can manage their mental health by receiving the advice from the terminal and taking action accordingly. The user also provides feedback on the advice through the terminal. The server collects this feedback and incorporates it into the next advice generation to improve the system's accuracy.

[0619] As a concrete example, consider a scenario where a user is in a stressful work environment. If a biometric device detects an increase in heart rate, that data is sent to a server, and advice such as "Try meditating for a few minutes" is generated. If the user provides feedback such as "I felt more relaxed after meditating," this information will be taken into consideration in the next analysis.

[0620] An example of a prompt message is: "Create a program that analyzes a large amount of heart rate data to assess the user's stress level. Explain how to provide appropriate feedback to the user based on the results."

[0621] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0622] Step 1:

[0623] The user wears a device that measures biometric information while going about their daily life. This device measures biometric information such as heart rate and skin electrical response in real time. The measured data is transmitted to the user's terminal via Bluetooth or Wi-Fi. The input is biometric information obtained from the wearable device, and the output is biometric information transferred to the terminal. The device uses sensors to quickly and accurately collect the necessary data.

[0624] Step 2:

[0625] The terminal transmits the received biometric information to the server via the internet using a communication device. The input is the biometric information stored on the terminal, and the output is the biometric information transferred to the server. The terminal ensures a stable internet connection and operates to smoothly transmit data to the server.

[0626] Step 3:

[0627] The server processes the received biometric information using machine learning techniques. Specifically, it analyzes an individual's psychological state using a neural network model with libraries such as TensorFlow. The input is the biometric information received by the server, and the output is the analysis result. The server utilizes its processing power to quickly calculate each data point and derive accurate results.

[0628] Step 4:

[0629] The server generates personalized advice using natural language processing techniques based on the analysis results. The input is the analysis of the psychological state, and the output is the generated advice. The server creates the advice using a rule-based approach, incorporating additional knowledge from experts as needed.

[0630] Step 5:

[0631] The generated advice is sent from the server to the user's terminal via a communication device. The terminal uses an interactive chatbot to notify the user and present the advice. The input is the advice from the server, and the output is the advice displayed on the user's terminal. The terminal displays the information in a user-friendly interface and operates in a way that allows the user to understand it properly.

[0632] Step 6:

[0633] The user implements the suggested advice and provides feedback on its effectiveness via the terminal. The input is the user's feedback, and the output is feedback data sent to the server. The user inputs their experiences and impressions according to the terminal's instructions.

[0634] Step 7:

[0635] The server receives feedback from users and analyzes it to help generate enhanced advice for the next time. The input is feedback data, and the output is an improved advice generation model. The server analyzes the feedback and uses that data to improve the algorithm.

[0636] (Application Example 1)

[0637] 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".

[0638] Traditional individual mental health management systems struggle to provide personalized care guidance tailored to each individual in real time, and to incorporate that feedback into future recommendations.

[0639] 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.

[0640] In this invention, the server includes means for analyzing biometric data and evaluating an individual's mental state, means for generating personalized care guidance using a generative AI model, and means for providing care guidance to the individual in an interactive format through a personal device. This enables the provision of timely mental health care guidance tailored to each individual and improves the accuracy of care guidance through the use of feedback.

[0641] A "biometric information sensor" is a physical or electronic device used to detect and collect biological data from living organisms in real time.

[0642] A "communication device" is an electronic device used to send and receive data between different devices.

[0643] An "information processing device" is a computer system used to analyze and process received data.

[0644] A "generative AI model" is an algorithm trained to perform a specific task using artificial intelligence.

[0645] "Care guidance" refers to specific advice or recommended procedures based on an individual's mental state, aimed at promoting health maintenance and recovery.

[0646] A "personal device" is an electronic device that an individual can directly operate or use.

[0647] "Feedback" refers to the evaluation or comments that users provide regarding the system's suggestions.

[0648] The system for carrying out the present invention includes a biometric sensor, a communication device, an information processing device, and a personal device as its main components. The biometric sensor collects biometric data such as an individual's heart rate and stress level, and transmits this data to the information processing device via the communication device. The information processing device analyzes the received biometric data and evaluates the individual's mental state using a generated AI model. Based on this, it generates personalized care guidance, sends it to the personal device, and presents it to the user in an interactive format.

[0649] In this system, for example, when a user experiences stress at work, a biometric sensor detects an increase in heart rate. The server analyzes this data and immediately generates care instructions, such as "take a few minutes of deep breathing," which are then sent to the user's smartphone. This process allows users to understand their condition in real time and take appropriate action.

[0650] The hardware used includes wearable devices (e.g., wristbands with heart rate monitors), smartphones, and tablets. For software, TensorFlow, a machine learning framework, is used for data analysis, and spaCy, a natural language processing library, is employed. Generative AI models provide specific advice to users, and feedback is received to further improve the accuracy of future care guidance.

[0651] Examples of specific prompt messages are as follows:

[0652] If a user's current stress level is higher than normal, speculate on the cause and suggest three simple stress-relieving methods they can use in their daily life. These methods should be quick and require no special equipment.

[0653] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0654] Step 1:

[0655] The biometric sensor acquires biometric data such as the user's heart rate and stress level in real time. This data is transmitted as digital signals to a communication device. The input is biometric data, and the output is the digital data transmitted to the communication device.

[0656] Step 2:

[0657] The terminal transfers the biometric data received via the communication device to the information processing device. At this stage, the data is formatted according to the communication protocol and standardized into a form that the information processing device can process. The input is formatted digital data, and the output is analyzable data sent to the information processing device.

[0658] Step 3:

[0659] The server analyzes the received biometric data using machine learning algorithms. A generative AI model evaluates the user's mental state from the data. This analysis process uses standardized biometric data as input and yields the evaluated mental state as output.

[0660] Step 4:

[0661] The server uses a generative AI model to generate personalized care guidance based on the user's mental state. The guidance content is constructed as conversational text using natural language processing techniques. The input is the assessed mental state, and the output is the generated care guidance text.

[0662] Step 5:

[0663] The terminal notifies the user of care instructions received from the server. Information is displayed on a GUI to make it easy for the user to review the instructions. Input is the text of the care instructions, and output is a visual representation of the instructions that the user can confirm.

[0664] Step 6:

[0665] Users provide feedback on the care instructions they receive. This feedback is sent from the terminal to the server. The input is the user's feedback, and the output is the feedback data sent to the server.

[0666] Step 7:

[0667] The server analyzes the received feedback and makes adjustments to improve the accuracy of future care guidance. In this process, feedback data is used as input, and the adjusted algorithm is obtained as output.

[0668] 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.

[0669] This invention is a system that comprehensively supports an individual's mental health by combining a wearable device, a server, a user terminal, and an emotion engine. The wearable device collects biometric information, including heart rate and stress levels, in real time. This data is transmitted to the server via the user terminal. Based on the received data, the server uses an AI model to analyze the user's psychological state. The analysis also includes biofeedback and comparison of feedback data to improve the accuracy of the user's emotion recognition.

[0670] The emotion engine analyzes voice and text data provided by the user and evaluates their emotions. This allows the emotion engine to integrate biometric fluctuation patterns with emotional history to gain a detailed understanding of the user's emotions. Based on this information, the server generates personalized advice corresponding to the user's psychological state and notifies the user's device of the results.

[0671] As a concrete example, consider a case where user B is typically busy and emotionally exhausted. The wearable device collects data indicating high stress levels and sends it to a server. The server, through its emotion engine, analyzes the user's voice expressions of anxiety and negative expressions in text, and assesses that the user's psychological state is unstable. As a result, the server generates specific advice, such as "set aside time to relax at the start of the day," and notifies the user's device.

[0672] By incorporating an emotion engine, this system can capture users' emotions from multiple perspectives, enabling more precise and personalized care. This approach allows for advice tailored to each user's mental health condition, achieving comprehensive mental health support.

[0673] The following describes the processing flow.

[0674] Step 1:

[0675] The user wears a wearable device that measures biometric information such as heart rate, stress level, and body temperature in real time. This device periodically updates the measurement data and stores it in a buffer.

[0676] Step 2:

[0677] The wearable device transmits collected biometric information to the user's mobile device via Bluetooth or Wi-Fi. The user's device temporarily stores this data.

[0678] Step 3:

[0679] The user's device securely transmits stored biometric information to the server. During this process, data integrity is maintained, and the information is safely transported via a communication protocol.

[0680] Step 4:

[0681] The server receives biometric information, which is then processed into an analysis queue. An AI model is used to analyze the user's psychological state. Here, patterns in the biometric information are compared with past data to evaluate the user's stress level and emotional changes.

[0682] Step 5:

[0683] The server activates the emotion engine and analyzes the voice and text data provided by the user. The emotion engine recognizes the user's emotional state from the tone of voice and the content of the text.

[0684] Step 6:

[0685] The server integrates biometric and emotional data based on the analysis results from the emotion engine to evaluate the overall psychological state. Based on this information, it generates personalized advice.

[0686] Step 7:

[0687] The generated advice is sent to the user's device. The user's device then uses its notification function to interactively present the advice to the user.

[0688] Step 8:

[0689] The user evaluates the advice provided and enters feedback into the device. This feedback includes the results of implementing the advice and their impressions.

[0690] Step 9:

[0691] The server receives user feedback and updates its analysis algorithms. This will enable the provision of more effective and personalized advice in the future.

[0692] (Example 2)

[0693] 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".

[0694] In modern society, many individuals experience daily stress and mental fatigue, which threatens their mental health. Conventional technologies have struggled to provide specific and personalized support tailored to each individual's psychological state in real time. To solve this problem, a system is needed that enables highly accurate emotion analysis and the generation of personalized support suggestions.

[0695] 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.

[0696] In this invention, the server includes means for collecting an individual's biometric information using a sensor device, means for transmitting the biometric information collected via a terminal to a data analysis device, and means for analyzing the biometric information received by the data analysis device using a machine learning model to evaluate the individual's mental state. This makes it possible to provide specific and timely advice tailored to each individual's mental state.

[0697] A "sensor device" is a device used to collect an individual's biometric information in real time, measuring data such as heart rate and stress level.

[0698] A "terminal" is a device used to transmit biometric information collected from sensor devices to a data analysis device, and typically a smartphone or tablet is used for this purpose.

[0699] A "data analysis device" is a device used to analyze received biological information and is used to evaluate an individual's mental state using machine learning models.

[0700] A "machine learning model" is an algorithm used in data analysis devices to analyze an individual's mental state based on biological information, learning data patterns and making predictions.

[0701] An "emotion analysis device" is a device that analyzes voice and text information and integrates data used to evaluate mental state.

[0702] A "notification device" is a device that directly delivers generated suggestions to an individual, typically by displaying alerts or messages on a digital device.

[0703] "Responses" refer to feedback information from individuals, which is used to improve the accuracy of system analysis.

[0704] To implement this invention, a system to support an individual's mental health is necessary. This system consists of a combination of sensor devices, terminals, a server, an emotion analyzer, and a notification device.

[0705] The user wears a sensor device, which collects biometric information such as the user's heart rate and stress level in real time. The collected data is transmitted to a terminal using Bluetooth technology. The terminal is typically a smartphone or tablet, and these devices are responsible for sending the data to the server. The HTTPS protocol is used for transmission, ensuring the security of the communication.

[0706] The server utilizes a data analysis device to analyze the received biometric data. A program developed in Python uses a machine learning model to perform the data analysis. This model employs machine learning libraries such as TensorFlow. The analysis process includes quantifying the user's mental state by comparing it with past data. Furthermore, the emotion analysis device analyzes the user's voice and text data, integrating the results into the server's evaluation.

[0707] Subsequently, using a generative AI model, the server creates specific and personalized suggestions tailored to the user's mental state. The process involves inputting prompts into the generative AI and generating appropriate advice. An example prompt might be, "Please suggest relaxation methods for when the user is feeling stressed."

[0708] Finally, the notification device provides the user with suggestions generated on the device. Push notifications are used, allowing users to receive advice and take action immediately. This system enables users to receive appropriate advice tailored to their mental health status and lead a healthier life.

[0709] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0710] Step 1:

[0711] By wearing a sensor device, the user's biometric information, such as heart rate and stress level, is collected in real time. The input is the user's current biometric information, which the sensor device records. The data is transmitted to the terminal via Bluetooth. The output in this step is the biometric information transferred to the terminal.

[0712] Step 2:

[0713] The device packages the biometric data received via Bluetooth using the HTTPS protocol in order to send it to the server. The input is the biometric information present in the device, and the output is the data sent to the server. In this step, SSL / TLS encryption is performed to ensure the security of the information.

[0714] Step 3:

[0715] The server stores the received biometric information in a data analysis device and starts the analysis using a Python program. The input is the biometric information sent to the server, and the output from the data analysis device is an evaluation of the biological state. In this process, a machine learning model is used to quantify the user's mental state through comparison with past data.

[0716] Step 4:

[0717] The server further analyzes the voice and text data collected from the user using an emotion analysis device. The input is voice and text data, and the output is an evaluation of the user's emotions based on this data. By utilizing natural language processing technology, the emotional tone of the text is analyzed and integrated to more accurately determine the user's mental state.

[0718] Step 5:

[0719] The server uses a generative AI model based on these evaluations to generate personalized suggestions suitable for the user. The input is mental state and emotional data, and the output is the suggested content. A prompt is sent to the generative AI model to generate specific advice such as, "Please tell me some relaxation methods that would be good for when the user is feeling stressed."

[0720] Step 6:

[0721] The device provides the user with suggestions received from the server. The input is the generated suggestion content, and the output is the advice displayed to the user. Using push notifications, users can immediately check the suggestions and utilize them in their daily lives.

[0722] (Application Example 2)

[0723] 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".

[0724] In modern society, individual mental health care has become a crucial issue. However, conventional methods make it difficult to analyze an individual's emotional state in detail and provide appropriate advice. Therefore, there is a need to develop a system that can accurately assess a user's psychological state and provide personalized feedback quickly and effectively.

[0725] 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.

[0726] In this invention, the server includes means for collecting an individual's biometric information using biosensors, means for providing personalized information tailored to the psychological state evaluated by voice and text output devices, and means for a home automated machine to communicate personalized advice to the user through voice and music output. This makes it possible to analyze an individual's psychological state in detail and provide appropriate advice immediately based on the results.

[0727] A "biosensor" is a device that acquires biometric information such as an individual's heart rate and stress level in real time.

[0728] A "processing device" is a device, including a server, that analyzes received biometric information and evaluates the psychological state.

[0729] A "speech and text output device" is a device that generates personalized information according to the analyzed psychological state and provides it to the user.

[0730] A "household automated machine" is a mechanical device that delivers advice based on the user's psychological state through voice or music within their living environment.

[0731] "Personalized advice" refers to specific suggestions and guidance designed to adapt to the user's current psychological state.

[0732] This invention provides a system for analyzing biometric information collected from wearable devices to support an individual's mental health. A server receives data such as heart rate and stress levels collected using biosensors. This data is analyzed using an emotion analysis engine running on the server to evaluate the individual's psychological state. Personalized information tailored to the evaluated psychological state is then provided through voice and text output devices.

[0733] Furthermore, a home-use automated machine will deliver personalized advice based on analysis results to the user through voice and music output within the user's living environment. This system is based on hardware such as Raspberry Pi and Arduino, and uses programming languages ​​such as Python to perform emotion analysis and output control. As a result, users can receive appropriate feedback according to their psychological state.

[0734] For example, imagine a situation where a user is feeling stressed after a busy day. When a wearable device detects an increased stress level, the server uses a generative AI model to instruct a home automation device to play music to promote relaxation. The user is then given advice such as, "Why not take a break and listen to some music to relax today?"

[0735] An example of a prompt for a generative AI model would be: "Predict the emotional state of a user with a heart rate of 95 and a stress level of 8, and generate appropriate advice."

[0736] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0737] Step 1:

[0738] Personal biometric information is collected from wearable devices. The wearable device acquires data such as the user's heart rate and stress level in real time and transmits this data to a server via the device. The biometric input, which indicates the user's current health status, is transmitted to the server as output.

[0739] Step 2:

[0740] The server stores the received biometric information in a database and uses that information to evaluate the user's psychological state. Using a generative AI model, it analyzes input data such as heart rate and stress level and outputs the user's emotional state. Specifically, the process involves normalizing and filtering the data before processing it as input to the model.

[0741] Step 3:

[0742] The server generates prompt messages based on the analyzed psychological state. These prompt messages are designed to adapt to the user's current emotional state and serve as instructions for deciding on the next action. Subsequently, a generative AI model is used to generate advice to provide to the user. Here, the prompt messages are input into the model, and the output is advice.

[0743] Step 4:

[0744] The server sends the generated advice to the terminal, which then relays it to the home-use automated machine. The terminal controls specific actions such as voice output and music playback, providing appropriate feedback to the user. The output advice is designed to promote user relaxation.

[0745] Step 5:

[0746] Users receive audio and music from their devices and follow the instructions to reduce stress. This allows the system to receive user feedback, which is then used for further analysis and advice generation. This feedback contributes to the continuous improvement of the system.

[0747] 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.

[0748] 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.

[0749] 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.

[0750] 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.

[0751] 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.

[0752] 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.

[0753] 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.

[0754] 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.

[0755] 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."

[0756] 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.

[0757] 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.

[0758] 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.

[0759] 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.

[0760] 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.

[0761] 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.

[0762] 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.

[0763] 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.

[0764] 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.

[0765] 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.

[0766] 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.

[0767] 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.

[0768] The following is further disclosed regarding the embodiments described above.

[0769] (Claim 1)

[0770] A means of collecting personal biometric information using biosensors,

[0771] A means for transmitting collected biological information to a processing device,

[0772] A means for analyzing biological information received by a processing device and evaluating an individual's psychological state,

[0773] A means of generating personalized advice based on an individual's psychological state,

[0774] A system that includes means of providing generated advice to individuals.

[0775] (Claim 2)

[0776] The system according to claim 1, wherein the processing device updates an individual's behavioral history based on the evaluation results and reflects this in the next advice.

[0777] (Claim 3)

[0778] The system according to claim 1, which analyzes individual feedback and improves the accuracy of a biometric information analysis algorithm.

[0779] "Example 1"

[0780] (Claim 1)

[0781] A means of collecting an individual's biometric information using a device that measures biometric information,

[0782] A means for transmitting collected biometric information to a computer via a communication device,

[0783] A means of using machine learning techniques to analyze biometric information received by a computer and evaluate an individual's psychological state,

[0784] A means of generating personalized advice using natural language processing technology based on an individual's psychological state,

[0785] A system that includes means for presenting generated advice to an individual via a communication device and for collecting feedback using interactive information processing.

[0786] (Claim 2)

[0787] The system according to claim 1, wherein a computer updates an individual's behavioral history based on the evaluation results and analyzes the feedback to reflect it in future advice.

[0788] (Claim 3)

[0789] The system according to claim 1, which analyzes individual feedback and improves the accuracy of machine learning algorithms for biometric information analysis.

[0790] "Application Example 1"

[0791] (Claim 1)

[0792] A means of acquiring personal biometric data using biometric sensors,

[0793] A means for transmitting acquired biometric data to an information processing device via a communication device,

[0794] A means of analyzing biometric data received by an information processing device and evaluating an individual's mental state,

[0795] A means of generating personalized care guidance based on an individual's mental state using a generative AI model,

[0796] A means of providing generated care guidance to individuals in an interactive format through personal devices,

[0797] A system that includes a means of receiving feedback from individuals and incorporating it into future care guidance.

[0798] (Claim 2)

[0799] The system according to claim 1, wherein the information processing device modifies an individual's behavioral record based on the evaluation results and applies it to the next care guidance.

[0800] (Claim 3)

[0801] The system according to claim 1, which analyzes individual feedback and improves the precision of the biometric data analysis algorithm.

[0802] "Example 2 of combining an emotion engine"

[0803] (Claim 1)

[0804] A means of collecting personal biometric information using a sensor device,

[0805] A means for transmitting biometric information collected via a terminal to a data analysis device,

[0806] A method for analyzing biological information received by a data analysis device using a machine learning model to evaluate an individual's mental state,

[0807] A means for analyzing voice and text information using an emotion analysis device and integrating it with psychological data,

[0808] A means of generating personalized suggestions based on an individual's mental state,

[0809] A system that includes means of providing generated suggestions to individuals through a notification device.

[0810] (Claim 2)

[0811] The system according to claim 1, wherein the data analysis device uses the evaluation results and individual behavioral data to improve the content of the next proposal.

[0812] (Claim 3)

[0813] The system according to claim 1, which analyzes responses from individuals and improves the accuracy of the bioinformation analysis mechanism.

[0814] "Application example 2 when combining with an emotional engine"

[0815] (Claim 1)

[0816] A means of collecting personal biometric information using biosensors,

[0817] A means for transmitting collected biological information to a processing device,

[0818] A means for analyzing biological information received by a processing device and evaluating an individual's psychological state,

[0819] A means for providing personalized information tailored to the psychological state assessed by a voice and text output device,

[0820] A means by which a home automated machine communicates personalized advice to the user through voice and music output,

[0821] A system that includes means of providing generated advice to individuals.

[0822] (Claim 2)

[0823] The system according to claim 1, wherein the processing device updates an individual's activity history based on the evaluation results and reflects this in the next advice.

[0824] (Claim 3)

[0825] The system according to claim 1, which analyzes individual feedback and improves the accuracy of computational methods for biometric information analysis. [Explanation of symbols]

[0826] 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 personal biometric data using biometric sensors, A means for transmitting acquired biometric data to an information processing device via a communication device, A means of analyzing biometric data received by an information processing device and evaluating an individual's mental state, A means of generating personalized care guidance based on an individual's mental state using a generative AI model, A means of providing generated care guidance to individuals in an interactive format through personal devices, A system that includes a means of receiving feedback from individuals and incorporating it into future care guidance.

2. The system according to claim 1, wherein the information processing device modifies an individual's behavioral record based on the evaluation results and applies it to the next care guidance.

3. The system according to claim 1, which analyzes individual feedback and improves the precision of the biometric data analysis algorithm.

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