system

Abstract

We provide the system. [Solution] A wearable device for collecting biometric data, A server device for analyzing this biological data and evaluating stress levels, A server device that generates individual stress management advice for users based on their stress levels, A system including a user terminal for notifying the user of the generated advice.

Description

Technical Field

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

Background Art

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

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In modern society, many people are burdened with stress at work, which causes a decline in productivity and health problems. Especially in a remote work environment, stress management suitable for individual work styles is required, but there is a lack of a system that can respond individually in real time. As this problem, it is to provide a system that can accurately grasp the stress situation of individual users and provide appropriate countermeasures.

Means for Solving the Problems

[0005] This invention provides a system that includes a wearable device for collecting biometric data, a server device for evaluating stress levels based on this data, and a user terminal that generates and notifies each user of stress management advice according to their stress level. In this system, the server device analyzes the acquired biometric data in real time and prioritizes generating advice when the stress level exceeds a threshold. Furthermore, it is possible to personalize subsequent advice based on user feedback. As a result, users can effectively manage their stress and work in an environment that is optimal for them.

[0006] "Biometric data" refers to data that indicates an individual's physiological state, including information such as heart rate, body temperature, skin conductance, and oxygen saturation.

[0007] A "wearable device" refers to a device that a user wears on a daily basis, allowing for the continuous acquisition of biometric data. Functionally, it collects and temporarily stores data.

[0008] A "server device" is a computer system that analyzes biological data and evaluates stress levels, and is responsible for generating personalized advice for each user.

[0009] A "user terminal" is a device that has the function of receiving advice sent from a server device and notifying the user, and provides an interface that accepts user operations.

[0010] "Stress level" is a numerical indicator obtained as a result of analysis by the server device, and it indicates the degree of stress experienced by individual users.

[0011] "Advice" refers to suggestions and instructions generated by the server device for the user, including specific actions to reduce stress and improve work style.

[0012] A "threshold" is a reference value used by server equipment to evaluate stress levels, and exceeding this value requires special action.

[0013] "Feedback" refers to the evaluations and comments that users provide regarding advice, and this information is used by the system to improve future advice. [Brief explanation of the drawing]

[0014] [Figure 1] This is a conceptual diagram showing an example of the configuration of a data processing system according to the first embodiment. [Figure 2] This is a conceptual diagram showing an example of the essential functions of a data processing device and a smart device according to the first embodiment. [Figure 3] This is a conceptual diagram showing an example of the configuration of a data processing system according to the second embodiment. [Figure 4] This is a conceptual diagram showing an example of the main functions of a data processing device and smart glasses according to the second embodiment. [Figure 5] This is a conceptual diagram showing an example of the configuration of a data processing system according to the third embodiment. [Figure 6] This is a conceptual diagram showing an example of the main functions of a data processing device and a headset-type terminal according to the third embodiment. [Figure 7] This is a conceptual diagram showing an example of the configuration of a data processing system according to the fourth embodiment. [Figure 8] This is a conceptual diagram showing an example of the main functions of a data processing device and a robot according to the fourth embodiment. [Figure 9] This shows an emotion map where multiple emotions are mapped. [Figure 10] This shows an emotion map where multiple emotions are mapped. [Figure 11] This is a sequence diagram showing the processing flow of the data processing system in Example 1. [Figure 12] This is a sequence diagram showing the processing flow of the data processing system in Application Example 1. [Figure 13] It is a sequence diagram showing the processing flow of the data processing system in Example 2 when combined with an emotion engine. [Figure 14] It is a sequence diagram showing the processing flow of the data processing system in Application Example 2 when combined with an emotion engine.

Mode for Carrying Out the Invention

[0015] 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.

[0016] First, the language used in the following description will be explained.

[0017] In the following embodiments, a processor with a reference numeral (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.

[0018] In the following embodiments, a RAM (Random Access Memory) with a reference numeral is a memory in which information is temporarily stored and is used as a work memory by the processor.

[0019] In the following embodiments, a storage with a reference numeral 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.

[0020] In the following embodiments, the signed communication interface (I / F) is an interface that includes a communication processor and an antenna, etc. The communication interface manages communication between multiple computers. Examples of communication standards applicable to the communication interface include wireless communication standards such as 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), or Bluetooth (registered trademark).

[0021] 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."

[0022] [First Embodiment]

[0023] Figure 1 shows an example of the configuration of the data processing system 10 according to the first embodiment.

[0024] 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.

[0025] 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).

[0026] 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.

[0027] 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.

[0028] 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.

[0029] 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.

[0030] Figure 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0031] 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.

[0032] 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.

[0033] 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.

[0034] 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".

[0035] The system of this invention begins with the collection of biometric data in real time via a wearable device used by the user on a daily basis. This device has the function of continuously monitoring various biometric data such as heart rate, body temperature, and skin conductance 24 hours a day. The terminal collects this data at regular intervals and temporarily stores it.

[0036] Next, the device periodically sends the collected data to the server. The server analyzes the received biometric data and evaluates the user's stress level. In this analysis, a pre-set stress threshold is used to quantify the user's stress state. Based on the results of this evaluation, the server generates stress management advice tailored to each individual user.

[0037] The generated advice is communicated to the user via their device. This notification is provided to the user in the form of a push notification or in-app message. Based on the advice received, the user can take actions to reduce stress. For example, they may be suggested to practice deep breathing or take a short break.

[0038] Furthermore, users can send feedback on the advice to the server via their device. This feedback will be considered in future advice generation and used as data to provide more personalized suggestions.

[0039] For example, if a user is experiencing excessive stress, the server will analyze their stress level and recommend relaxation methods such as stretching or listening to music. Furthermore, if a user is working remotely, the server can proactively suggest face-to-face communication.

[0040] In this way, the system can effectively manage stress by monitoring the user's condition and providing personalized advice.

[0041] The following describes the processing flow.

[0042] Step 1:

[0043] The device acquires biometric data from the wearable device in real time. This data includes heart rate, body temperature, and skin conductance, and is temporarily stored within the device.

[0044] Step 2:

[0045] The device periodically transmits the collected biometric data to a server via the network. This transmission is encrypted to protect privacy.

[0046] Step 3:

[0047] The server analyzes biometric data received from the terminal and calculates the user's stress level. This analysis uses historical data and algorithms to quantify the current stress level.

[0048] Step 4:

[0049] The server evaluates whether the stress level exceeds a set threshold. If the threshold is exceeded, it is determined that a situation requiring special attention is necessary, and countermeasures are considered promptly.

[0050] Step 5:

[0051] The server generates specific stress management advice for the user based on their stress level. This advice includes suggestions for relaxation techniques and adjustments to the work environment.

[0052] Step 6:

[0053] The server sends the generated advice to the device. When the device receives the advice, it is displayed to the user as a push notification or in-app message.

[0054] Step 7:

[0055] Users can review advice through their devices and take action as needed. They can also send feedback, including results and comments, to the server via their devices.

[0056] Step 8:

[0057] The server collects user feedback, stores it in a database to make future advice more appropriate and personalized, and uses it for subsequent analyses.

[0058] By following these steps, the system can provide users with effective stress management.

[0059] (Example 1)

[0060] 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."

[0061] Modern people are exposed to various stressors in their living and working environments, and the lack of effective ways to manage these stressors is a problem. Traditional methods have made it difficult to provide stress management optimized for individual users, and there has also been a lack of mechanisms for quickly incorporating user feedback.

[0062] 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.

[0063] In this invention, the server includes means for analyzing biological information to evaluate the stress state, means for generating individual stress relief instructions based on the stress state, and means for receiving feedback from the user and modifying subsequent instructions based on that feedback. This enables the optimization of stress management tailored to each individual user and the rapid incorporation of feedback.

[0064] A "wearable device" is a device that automatically collects biometric information when worn by the user on a daily basis.

[0065] "Biometric information" refers to data that indicates the user's physical condition, such as heart rate, body temperature, and skin conductance.

[0066] An "information processing device" is a computer system that analyzes biological information and evaluates the user's stress level based on the results.

[0067] "Stress level" is an evaluation index that indicates the degree of physical or mental burden on the user.

[0068] "Stress reduction instructions" refer to specific action plans or suggestions provided to reduce the user's stress.

[0069] A "user communication device" is a terminal with communication capabilities that notifies the user of the generated stress relief instructions.

[0070] "Opinions" refer to feedback from users expressing their evaluations or thoughts on stress relief instructions.

[0071] A "reference value" is a predetermined numerical value or condition that serves as a guideline when evaluating a stress level.

[0072] This invention relates to a system for managing a user's stress level in real time. Specific embodiments for carrying out the invention are described below.

[0073] Users collect biometric information on a daily basis using wearable devices. A general-purpose smartwatch can be used as a concrete example of a device that monitors heart rate, body temperature, skin conductance, etc., and automatically records this data.

[0074] The terminal receives biometric information from the wearable device using Bluetooth or Wi-Fi and temporarily stores it in its storage device. A typical smartphone or tablet could be used as this terminal. These terminals manage the data through application software installed on them.

[0075] The server receives and analyzes biometric information transmitted from the terminal. Data processing scripts using programming languages ​​such as Python and R are implemented for data analysis. Based on the analysis, the server evaluates the user's stress level and generates stress relief instructions. This generation uses a generative AI model, allowing for the creation of personalized instructions by inputting prompts into the AI. A concrete example of a prompt might be, "What are effective ways to cope with my current stress level?"

[0076] The generated stress relief instructions are sent to the device and the user is notified. This notification is provided as a push notification or in-app message. Based on this information, the user can take specific stress-relief measures.

[0077] Furthermore, users input their feedback on the instructions into their device. This feedback is sent to the server via the device and incorporated into future instruction generation, enabling more accurate and personalized responses.

[0078] Based on the above, this invention realizes a system that provides stress management optimized for individual users and contributes to improving the user's health.

[0079] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0080] Step 1:

[0081] The user wears a wearable device while going about their daily life. This device continuously monitors heart rate, body temperature, and skin conductance, and periodically records the data. The device transmits biometric information to a terminal in digital format. The input is the user's biometric information, and the output is digital data.

[0082] Step 2:

[0083] The terminal receives biometric information from a wearable device via wireless communication. The received data is temporarily stored in the terminal's internal memory. The terminal records the biometric information in a database to retain data necessary for later analysis. The input is the biometric information transmitted from the device, and the output is the stored data.

[0084] Step 3:

[0085] The device transmits biometric information to the server at regular intervals. A secure communication protocol is used to ensure data security. The server acknowledges receipt and stores the biometric information in storage. The input is the biometric information sent from the device, and the output is the data stored on the server.

[0086] Step 4:

[0087] The server performs data analysis based on stored biometric information. It uses Python or R analysis scripts to assess stress levels. The server quantifies the stress level as a result of the analysis and compares it to a baseline value. The input is stored biometric information, and the output is the quantified stress level.

[0088] Step 5:

[0089] The server generates stress relief instructions based on the stress level. A generative AI model is used for generation, creating instructions by inputting prompt sentences into the generative AI. For example, a prompt sentence such as "What are effective ways to reduce the user's stress?" might be used. The input is a numerical stress level, and the output is the generated stress relief instruction.

[0090] Step 6:

[0091] The device notifies the user of generated advice sent from the server. This notification is used as a push notification or in-app message. The user can then implement stress management strategies based on this advice. The input is the generated stress relief instructions, and the output is the notification delivered to the user.

[0092] Step 7:

[0093] The user inputs feedback into the terminal based on their experience trying stress relief instructions. The terminal sends the feedback data to the server. The server analyzes the feedback and incorporates it into generating the next set of instructions. The input is user feedback, and the output is corrected data that will be used to improve the next set of instructions.

[0094] (Application Example 1)

[0095] 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."

[0096] In modern care facilities, stress management for the elderly is a critical issue. However, there is a lack of adequate means to monitor stress indicators based on individual elderly individuals' biometric information in real time and to provide prompt and appropriate management advice based on that data. As a result, it is difficult for care staff to make timely decisions on effective stress reduction methods for the elderly, hindering improvements in their quality of life.

[0097] 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.

[0098] In this invention, the server includes means for analyzing biometric information to evaluate stress indicators, means for generating individualized management advice based on the stress indicators, and means for modifying the management advice based on feedback provided by the supervisor. This enables real-time assessment of stress in elderly individuals and the rapid provision of appropriate management advice.

[0099] "Devices and means for collecting biological information" refer to equipment for continuously measuring and acquiring biological information such as heart rate, body temperature, and skin conductance as data.

[0100] "A computational device for analyzing biological information and evaluating stress indicators" refers to a device that uses acquired biological information to quantify or index stress levels based on a specific algorithm.

[0101] "Means for generating individualized management advice" refers to a process for generating optimal stress management methods and suggestions for each individual user based on analyzed stress indicators.

[0102] An "information terminal" is an electronic device used to notify care staff and other supervisors of the generated management advice.

[0103] "Calculation device means for modifying management advice based on feedback" refers to a process or device that reflects feedback information from care staff and users to optimize management advice for subsequent sessions.

[0104] The system for implementing this invention first requires the use of a device that continuously collects biological information. This device is a wearable device that has the function of recording biological information such as heart rate, body temperature, and skin conductance in real time and transmitting the data to an information terminal.

[0105] Upon receiving this biometric information, the server analyzes the data using a specific algorithm and calculates the user's stress index. Specifically, it uses programming languages ​​such as Python and data analysis libraries such as SciPy and NumPy to convert the biometric information into a numerical stress index. For server-side analysis, resources such as AWS® Lambda and Google® Cloud Functions can be used as cloud services.

[0106] Next, the server generates personalized stress management advice based on the calculated stress indicators. Using a generation AI model, it sends user-specific prompts such as, "Your heart rate is higher than normal. Please suggest relaxation methods for elderly individuals." The generated advice is immediately communicated to care staff via information terminals. Based on this information, care staff can suggest music therapy or stretching exercises, effectively alleviating stress in elderly individuals.

[0107] For example, in the case of elderly person A whose average heart rate has increased by 20% compared to normal, and who also experiences changes in skin conductance, listening to relaxing music may be recommended. This allows care staff to provide prompt and appropriate care based on rich information from AI.

[0108] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0109] Step 1:

[0110] The device collects biometric information such as heart rate, body temperature, and skin conductance from wearable devices in real time. The biometric information obtained from each sensor is used as input. This data is temporarily stored on the device and prepared for transmission to a server via communication protocols such as Bluetooth.

[0111] Step 2:

[0112] The server receives biometric information transmitted from the terminal and performs preprocessing for data analysis. The input is the received biometric information. Based on this, the server processes the data by removing unnecessary noise and normalizing it. The output is a clean dataset that can be analyzed.

[0113] Step 3:

[0114] The server takes a clean dataset as input and uses Python and libraries such as SciPy and NumPy to calculate stress indicators based on biometric data. Specifically, it performs tasks such as heart rate variability analysis and calculation of the rate of change in skin conductance. The output is the user's stress index.

[0115] Step 4:

[0116] The server uses a generation AI model based on the generated stress indicators to create optimal stress management advice for the user. The input consists of stress indicators, and data is sent to the AI ​​model using prompts such as, "Please suggest relaxation methods for the elderly." The output is personalized stress management advice.

[0117] Step 5:

[0118] The server sends the generated advice to the terminal, which then notifies the care staff. Specifically, the terminal uses a push notification function to attract the care staff's attention. Advice is the input, and visualized management information is provided to the staff as the output.

[0119] Step 6:

[0120] Upon receiving a notification, the user takes action based on the advice. Feedback is sent to the server via the device. The input is the result of implementing the advice, and the output is feedback. This information is used to generate advice in the future.

[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 provides a system that enables more precise and personalized stress management for users by integrating and utilizing biometric data and emotional data. The system consists of a wearable device, a user terminal, a server, and an emotional engine.

[0123] First, biometric data is continuously collected by a wearable device. Data such as heart rate, body temperature, and skin conductance are acquired and transmitted to the terminal with the user's permission. The terminal simultaneously uses a microphone and camera to acquire the user's voice and facial expression data and estimate their emotional state.

[0124] Next, the device sends this data to the server. The server analyzes the biometric and emotional data to assess the user's stress level and emotional state. In this assessment, an emotion engine plays a role, improving the accuracy of the analysis by incorporating emotional information into the stress analysis. In particular, if the stress level exceeds a threshold and a negative emotional state is detected, the server quickly generates countermeasures.

[0125] The server then generates personalized stress management advice based on the user's current emotions and stress level. This advice is sent to the user's device and delivered to them via push notifications or in-app messages. For example, if a user is feeling anxious, the server might recommend deep breathing or a short meditation. The server can also suggest listening to music or going for a walk to users who tend to be negative.

[0126] Users receive advice from their devices and reduce stress by acting on that advice. Furthermore, users can send feedback on the advice to the server via their devices. This feedback is recorded on the server to further tailor future advice to their specific needs.

[0127] This system allows users to manage their stress autonomously and continuously, and receive optimal support tailored to their individual emotional state.

[0128] The following describes the processing flow.

[0129] Step 1:

[0130] The device acquires biometric data such as heart rate, body temperature, and skin conductance from wearable devices in real time and temporarily stores it.

[0131] Step 2:

[0132] The device uses its built-in microphone and camera to acquire user voice data and facial image data. This prepares the device for estimating the user's emotional state.

[0133] Step 3:

[0134] The device encrypts the acquired biometric data and voice / facial expression data before transmitting it to the server.

[0135] Step 4:

[0136] The server analyzes the received biometric data and quantifies the user's current stress level. This is done using a known algorithm.

[0137] Step 5:

[0138] The server uses an emotion engine to analyze voice data and facial expression data to identify the user's emotional state (e.g., joy, sadness, anger, etc.).

[0139] Step 6:

[0140] The server integrates the analyzed stress level and emotional state to assess the user's overall condition. If the stress level exceeds a threshold, the server prioritizes considering countermeasures, taking the emotional state into account.

[0141] Step 7:

[0142] The server generates specific stress management advice based on the user's stress level and emotional information. For example, if the user is stressed and angry, it will suggest relaxation techniques and actions to help them calm down.

[0143] Step 8:

[0144] The server immediately sends the generated advice to the device, and the device then provides the advice to the user as a push notification or in-app message.

[0145] Step 9:

[0146] Users act according to the advice provided on their device and send feedback on the results of their actions and the advice back to the server via their device.

[0147] Step 10:

[0148] The server records user feedback in a database and uses it as data to improve future stress management advice.

[0149] Through these steps, the system can provide advanced stress management that takes into account the user's stress and emotions.

[0150] (Example 2)

[0151] 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".

[0152] In modern society, individuals are experiencing increasing psychological stress, and there is a need for systems to appropriately address this. However, existing systems struggle to provide precise stress assessments and individualized countermeasures that integrate biometric and emotional information. To solve this problem, more accurate analysis and rapid responses tailored to individual conditions are required.

[0153] 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.

[0154] In this invention, the server includes means for comprehensively analyzing biometric and emotional information to evaluate psychological load, means for rapidly generating countermeasures when the psychological load exceeds a certain threshold and negative emotions are detected, and means for individualizing the generated countermeasures and notifying the user. This enables highly accurate stress assessment and the provision of rapid, individualized countermeasures.

[0155] "Biometric information" refers to data about an organism's body, including measurable information such as heart rate, body temperature, and skin conductance.

[0156] A "portable device" is a device that can be easily carried and is designed to perform a specific function.

[0157] "Emotional information" refers to data used to evaluate an individual's psychological state, specifically information about emotions obtained from voice and facial expressions.

[0158] An "information processing device" refers to an electronic device or system that analyzes received data and outputs specific results.

[0159] "Psychological burden" refers to mental pressure or strain that affects an individual, such as stress or anxiety.

[0160] "Specific criteria" refers to clearly defined boundary values ​​or conditions that, when exceeded, trigger evaluations or actions.

[0161] "Negative emotions" refer to negative feelings such as anxiety, sadness, and anger that indicate the user's mental state.

[0162] A "user terminal" is an electronic device designed for use by users, providing a means for displaying and inputting information.

[0163] This invention provides a system that offers personalized stress management by comprehensively utilizing biometric and emotional information. This system consists of a portable device, terminals, a server, and an emotion analysis engine. The hardware and software required to effectively implement the system are described below.

[0164] Portable devices continuously measure biometric information such as heart rate, body temperature, and skin conductance, enabling real-time monitoring of the user's physical condition. These devices transmit this data to a terminal via wireless communication.

[0165] The device receives biometric information and uses its built-in microphone and camera to acquire voice and facial expressions. This collects emotional information to estimate the user's psychological state. The device's built-in voice recognition system and image analysis algorithms support this process.

[0166] When the server receives data transmitted from the terminal, it uses an emotion analysis engine to comprehensively analyze biometric and emotional information. This analysis assesses the user's psychological burden. For example, if the tone of voice changes in addition to an increase in heart rate, it suggests that anxiety is increasing.

[0167] Based on the analysis results, the server quickly generates personalized countermeasures if the user's psychological burden is high. These countermeasures are tailored to the user's current emotions and stress level and are sent to the device.

[0168] For example, the system can use the following prompt to ask the generating AI model for appropriate advice: "If the user's heart rate is elevated and it is determined that they are anxious, please tell me how to take deep breaths."

[0169] This system allows users to receive personalized stress management advice and make further adjustments based on the feedback. By following the advice received via the device and taking actions to reduce psychological stress, users can autonomously manage their own health.

[0170] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0171] Step 1:

[0172] A portable device measures the user's biometric data in real time. This device acquires heart rate, body temperature, and skin conductance, and transmits this data to a terminal via wireless communication. It receives biometric data as input and provides data to the terminal as output. Specifically, the device continues to collect data non-invasively even during daily activities.

[0173] Step 2:

[0174] The terminal receives biometric data sent from portable devices, while simultaneously acquiring voice and facial expressions using a microphone and camera, and generating emotional information. It receives the acquired voice and video data as input and sends the analyzed emotional information as output to the server. Specifically, it analyzes the user's emotional state using voice recognition software and image recognition algorithms.

[0175] Step 3:

[0176] The server receives biometric data and emotional information transmitted from the terminal. This input data is analyzed by an emotion analysis engine to assess the user's psychological burden. The server then generates an output representing the level of the assessed psychological burden and determines appropriate countermeasures. Statistical analysis of the data and pattern recognition techniques are used in this process.

[0177] Step 4:

[0178] The server generates personalized countermeasures tailored to the user's current situation based on the psychological stress assessment results. The input is the level of psychological stress, and the output is specific stress management advice. A generative AI model is used to create advice in the form of prompts that are appropriate to each individual's state.

[0179] Step 5:

[0180] The server sends the generated advice to the device. The device delivers this advice to the user as a push notification or in-app message. The user receives the advice as input and acts based on it as output. Specific actions suggested include guidance on deep breathing or meditation, or playing music.

[0181] Step 6:

[0182] Users manage their stress by acting according to the advice provided. Users record their feedback in the app and send it to the server through that system. Inputs are the user's actions and impressions, and output is evaluation data that will be reflected in future advice. Specific examples of feedback include responses to in-app surveys.

[0183] (Application Example 2)

[0184] 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".

[0185] In recent years, the increasing mental burden stemming from daily life and work has become a serious problem for many people. In response, there is a need for accurate and individualized stress management based on each user's emotional state and biosignals. However, conventional methods struggle to accurately grasp a user's specific emotional state and provide immediate support. Furthermore, it is difficult to flexibly adjust advice based on user feedback. This challenge requires not merely the analysis of quantified data, but the integration of more comprehensive emotional information.

[0186] 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.

[0187] In this invention, the server includes an information processing device for analyzing biosignals and emotional signals to evaluate emotional state and mental burden; an information processing device for generating individual stress management guidelines for the user based on the mental burden and emotional state; and an information processing device for modifying the guidelines for subsequent uses based on the user's response. This enables real-time stress management tailored to the user and optimization of continuous advice through feedback.

[0188] "Biosignals" refer to information generated from the user's body, such as heart rate, body temperature, and skin electrical resistance, and are primarily used to evaluate health status and mental stress.

[0189] A "portable device" is a small, portable electronic device that can be worn by a user on a daily basis to collect biosignals.

[0190] "Emotional signals" refer to information obtained to estimate a user's emotional state, such as their voice and facial expressions, and are data analyzed for stress management purposes.

[0191] An "information processing device" refers to a computer system that receives biological signals and emotional signals, analyzes them, and evaluates and processes the user's emotional state and mental burden.

[0192] "Mental burden" refers to the degree of mental pressure and stress that users experience in their daily lives and work.

[0193] "Guidelines" refer to specific advice and suggestions provided based on the user's current situation to promote stress management and emotional stabilization.

[0194] A "user terminal" is an electronic device that users directly operate to receive information and guidance, and includes smartphones and tablets.

[0195] To implement this invention, it is necessary to construct a system in which a portable device, an information processing device, and a user terminal work together. The portable device is worn constantly by the user during their daily life and collects biosignals (such as heart rate, body temperature, and skin electrical resistance) in real time.

[0196] The information processing device analyzes biometric and emotional signals transmitted from portable devices and user terminals. The emotional signals used here include information acquired from sources such as voice and cameras. The information processing device utilizes this data to evaluate the user's mental burden and emotional state. Generative AI models such as TENSORFLOW® can be used for the analysis. This generates optimal stress management guidelines for the user.

[0197] The user terminal receives and displays the guidelines output by the user. The terminal can provide the guidelines to the user via push notifications or in-app messages through the application. Furthermore, it receives user feedback and transmits it to the information processing device. This allows the guidelines to be continuously improved based on user responses.

[0198] For example, if a user shows signs of mental stress while getting ready in the morning, the information processing device will generate a message such as, "Take a break and try a 3-minute meditation to cherish the quiet time in the morning," and notify the user through their terminal. In this case, an example of a prompt message generated by the AI ​​model would be, "The user's heart rate is elevated, and facial analysis also indicates stress. Please suggest ways to relax in this situation."

[0199] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0200] Step 1:

[0201] The portable device continuously collects the user's heart rate, body temperature, and skin electrical resistance. This biosignal data is transmitted to the user's terminal in real time. The input is biosignal data, and the output is data transmission to the user's terminal.

[0202] Step 2:

[0203] The terminal processes the received biometric signal data and uses voice and camera to collect the user's emotional signals (data based on facial expressions and voice). At this stage, it receives biometric and emotional signal data as input and generates data to send to the information processing device as output.

[0204] Step 3:

[0205] The server analyzes biometric and emotional signals transmitted from the terminal. This analysis uses generative AI models such as TensorFlow to perform integrated data analysis and evaluate the user's mental burden and emotional state. The input is data from the terminal, and the output is the evaluation result of the mental burden.

[0206] Step 4:

[0207] The server generates optimal stress management guidelines for the user based on the evaluation results. It utilizes a generation AI model to generate prompts and formulate specific advice. The input is the evaluation results of mental burden, and the output is specific guidance for the user.

[0208] Step 5:

[0209] The device receives instructions sent from the server and displays them to the user as push notifications or in-app messages. The input is the instructions from the server, and the output is the notification to the user. The specific action of the device is to display the instructions in an appropriate format and prompt the user to take action.

[0210] Step 6:

[0211] The user acts based on guidelines from their device and sends feedback on the effects of those actions to the server via the device. The input is the user's feedback, and the output is the feedback data sent to the server. The server then takes this data into consideration when generating the next set of guidelines.

[0212] 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.

[0213] 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.

[0214] 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.

[0215] [Second Embodiment]

[0216] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0217] 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.

[0218] 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).

[0219] 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.

[0220] 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.

[0221] 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).

[0222] 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.

[0223] 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.

[0224] 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.

[0225] 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.

[0226] 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.

[0227] 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".

[0228] The system of this invention begins with the collection of biometric data in real time via a wearable device used by the user on a daily basis. This device has the function of continuously monitoring various biometric data such as heart rate, body temperature, and skin conductance 24 hours a day. The terminal collects this data at regular intervals and temporarily stores it.

[0229] Next, the device periodically sends the collected data to the server. The server analyzes the received biometric data and evaluates the user's stress level. In this analysis, a pre-set stress threshold is used to quantify the user's stress state. Based on the results of this evaluation, the server generates stress management advice tailored to each individual user.

[0230] The generated advice is communicated to the user via their device. This notification is provided to the user in the form of a push notification or in-app message. Based on the advice received, the user can take actions to reduce stress. For example, they may be suggested to practice deep breathing or take a short break.

[0231] Furthermore, users can send feedback on the advice to the server via their device. This feedback will be considered in future advice generation and used as data to provide more personalized suggestions.

[0232] For example, if a user is experiencing excessive stress, the server will analyze their stress level and recommend relaxation methods such as stretching or listening to music. Furthermore, if a user is working remotely, the server can proactively suggest face-to-face communication.

[0233] In this way, the system can effectively manage stress by monitoring the user's condition and providing personalized advice.

[0234] The following describes the processing flow.

[0235] Step 1:

[0236] The device acquires biometric data from the wearable device in real time. This data includes heart rate, body temperature, and skin conductance, and is temporarily stored within the device.

[0237] Step 2:

[0238] The device periodically transmits the collected biometric data to a server via the network. This transmission is encrypted to protect privacy.

[0239] Step 3:

[0240] The server analyzes biometric data received from the terminal and calculates the user's stress level. This analysis uses historical data and algorithms to quantify the current stress level.

[0241] Step 4:

[0242] The server evaluates whether the stress level exceeds a set threshold. If the threshold is exceeded, it is determined that a situation requiring special attention is necessary, and countermeasures are considered promptly.

[0243] Step 5:

[0244] The server generates specific stress management advice for the user based on their stress level. This advice includes suggestions for relaxation techniques and adjustments to the work environment.

[0245] Step 6:

[0246] The server sends the generated advice to the device. When the device receives the advice, it is displayed to the user as a push notification or in-app message.

[0247] Step 7:

[0248] Users can review advice through their devices and take action as needed. They can also send feedback, including results and comments, to the server via their devices.

[0249] Step 8:

[0250] The server collects user feedback, stores it in a database to make future advice more appropriate and personalized, and uses it for subsequent analyses.

[0251] By following these steps, the system can provide users with effective stress management.

[0252] (Example 1)

[0253] 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."

[0254] Modern people are exposed to various stressors in their living and working environments, and the lack of effective ways to manage these stressors is a problem. Traditional methods have made it difficult to provide stress management optimized for individual users, and there has also been a lack of mechanisms for quickly incorporating user feedback.

[0255] 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.

[0256] In this invention, the server includes means for analyzing biological information to evaluate the stress state, means for generating individual stress relief instructions based on the stress state, and means for receiving feedback from the user and modifying subsequent instructions based on that feedback. This enables the optimization of stress management tailored to each individual user and the rapid incorporation of feedback.

[0257] A "wearable device" is a device that automatically collects biometric information when worn by the user on a daily basis.

[0258] "Biometric information" refers to data that indicates the user's physical condition, such as heart rate, body temperature, and skin conductance.

[0259] An "information processing device" is a computer system that analyzes biological information and evaluates the user's stress level based on the results.

[0260] "Stress level" is an evaluation index that indicates the degree of physical or mental burden on the user.

[0261] "Stress reduction instructions" refer to specific action plans or suggestions provided to reduce the user's stress.

[0262] A "user communication device" is a terminal with communication capabilities that notifies the user of the generated stress relief instructions.

[0263] "Opinions" refer to feedback from users expressing their evaluations or thoughts on stress relief instructions.

[0264] A "reference value" is a predetermined numerical value or condition that serves as a guideline when evaluating a stress level.

[0265] This invention relates to a system for managing a user's stress level in real time. Specific embodiments for carrying out the invention are described below.

[0266] Users collect biometric information on a daily basis using wearable devices. A general-purpose smartwatch can be used as a concrete example of a device that monitors heart rate, body temperature, skin conductance, etc., and automatically records this data.

[0267] The terminal receives biometric information from the wearable device using Bluetooth or Wi-Fi and temporarily stores it in its storage device. A typical smartphone or tablet could be used as this terminal. These terminals manage the data through application software installed on them.

[0268] The server receives and analyzes biometric information transmitted from the terminal. Data processing scripts using programming languages ​​such as Python and R are implemented for data analysis. Based on the analysis, the server evaluates the user's stress level and generates stress relief instructions. This generation uses a generative AI model, allowing for the creation of personalized instructions by inputting prompts into the AI. A concrete example of a prompt might be, "What are effective ways to cope with my current stress level?"

[0269] The generated stress relief instructions are sent to the device and the user is notified. This notification is provided as a push notification or in-app message. Based on this information, the user can take specific stress-relief measures.

[0270] Furthermore, users input their feedback on the instructions into their device. This feedback is sent to the server via the device and incorporated into future instruction generation, enabling more accurate and personalized responses.

[0271] Based on the above, this invention realizes a system that provides stress management optimized for individual users and contributes to improving the user's health.

[0272] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0273] Step 1:

[0274] The user wears a wearable device while going about their daily life. This device continuously monitors heart rate, body temperature, and skin conductance, and periodically records the data. The device transmits biometric information to a terminal in digital format. The input is the user's biometric information, and the output is digital data.

[0275] Step 2:

[0276] The terminal receives biometric information from a wearable device via wireless communication. The received data is temporarily stored in the terminal's internal memory. The terminal records the biometric information in a database to retain data necessary for later analysis. The input is the biometric information transmitted from the device, and the output is the stored data.

[0277] Step 3:

[0278] The device transmits biometric information to the server at regular intervals. A secure communication protocol is used to ensure data security. The server acknowledges receipt and stores the biometric information in storage. The input is the biometric information sent from the device, and the output is the data stored on the server.

[0279] Step 4:

[0280] The server performs data analysis based on stored biometric information. It uses Python or R analysis scripts to assess stress levels. The server quantifies the stress level as a result of the analysis and compares it to a baseline value. The input is stored biometric information, and the output is the quantified stress level.

[0281] Step 5:

[0282] The server generates stress relief instructions based on the stress level. A generation AI model is used for the generation, and the instructions are created by inputting a prompt sentence into the generation AI. For example, a prompt sentence such as "What are effective ways to reduce the stress of the user?" may be used. The input is the quantified stress level, and the output is the generated stress relief instruction.

[0283] Step 6:

[0284] The terminal notifies the user of the generated advice sent from the server. The notification is used as a push notification or an in-app message. The user can implement stress countermeasures based on this advice. The input is the generated stress relief instruction, and the output is the notification delivered to the user.

[0285] Step 7:

[0286] The user inputs the result of trying the stress relief instruction as feedback into the terminal. The terminal sends the feedback data to the server. The server analyzes the feedback and reflects it in the generation of the next instruction. The input is the feedback from the user, and the output is the correction data utilized in the next instruction.

[0287] (Application Example 1)

[0288] Next, Application Example 1 will be described. In the following description, the data processing device 12 is referred to as the "server", and the smart glasses 214 are referred to as the "terminal".

[0289] In modern care facilities, stress management for the elderly is an important issue. However, means for monitoring stress indicators based on the biometric information of individual elderly people in real time and providing appropriate management advice promptly based on it are not sufficiently developed. For this reason, it is difficult for care staff to timely determine an effective management method for reducing the stress of the elderly, and the improvement of the quality of life of the elderly is hindered.

[0290] 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.

[0291] In this invention, the server includes means for analyzing biometric information to evaluate stress indicators, means for generating individualized management advice based on the stress indicators, and means for modifying the management advice based on feedback provided by the supervisor. This enables real-time assessment of stress in elderly individuals and the rapid provision of appropriate management advice.

[0292] "Devices and means for collecting biological information" refer to equipment for continuously measuring and acquiring biological information such as heart rate, body temperature, and skin conductance as data.

[0293] "A computational device for analyzing biological information and evaluating stress indicators" refers to a device that uses acquired biological information to quantify or index stress levels based on a specific algorithm.

[0294] "Means for generating individualized management advice" refers to a process for generating optimal stress management methods and suggestions for each individual user based on analyzed stress indicators.

[0295] An "information terminal" is an electronic device used to notify care staff and other supervisors of the generated management advice.

[0296] "Calculation device means for modifying management advice based on feedback" refers to a process or device that reflects feedback information from care staff and users to optimize management advice for subsequent sessions.

[0297] The system for implementing this invention first requires the use of a device that continuously collects biological information. This device is a wearable device that has the function of recording biological information such as heart rate, body temperature, and skin conductance in real time and transmitting the data to an information terminal.

[0298] Upon receiving this biometric information, the server analyzes the data using a specific algorithm and calculates the user's stress index. Specifically, it uses programming languages ​​such as Python and data analysis libraries such as SciPy and NumPy to convert the biometric information into a numerical stress index. For server-side analysis, resources such as AWS Lambda and Google Cloud Functions can be used as cloud services.

[0299] Next, the server generates personalized stress management advice based on the calculated stress indicators. Using a generation AI model, it sends user-specific prompts such as, "Your heart rate is higher than normal. Please suggest relaxation methods for elderly individuals." The generated advice is immediately communicated to care staff via information terminals. Based on this information, care staff can suggest music therapy or stretching exercises, effectively alleviating stress in elderly individuals.

[0300] For example, in the case of elderly person A whose average heart rate has increased by 20% compared to normal, and who also experiences changes in skin conductance, listening to relaxing music may be recommended. This allows care staff to provide prompt and appropriate care based on rich information from AI.

[0301] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0302] Step 1:

[0303] The terminal collects biometric information such as heart rate, body temperature, and skin conductance from the wearable device in real time. The biometric information obtained from each sensor is used as input. This data is temporarily stored in the terminal and prepared to be sent to the server through a communication protocol such as Bluetooth.

[0304] Step 2:

[0305] The server receives the biometric information sent from the terminal and performs preprocessing for data analysis. There is the biometric information received as input. Based on this, the server performs processes such as removing unnecessary noise and normalizing the data. As output, a clean dataset that can be analyzed is obtained.

[0306] Step 3:

[0307] The server uses libraries such as Python, SciPy, and NumPy with the clean dataset as input to calculate stress indices based on the biometric information. Specific operations include performing heart rate variability analysis and calculating the rate of change of skin conductance. As output, the user's stress index is generated.

[0308] Step 4:

[0309] The server uses the generated stress index to generate personalized stress management advice for the user by leveraging the generative AI model. The stress index is the input, and data is sent to the AI model using a prompt sentence such as "Please propose relaxation methods for the elderly." As output, personalized stress management advice is obtained.

[0310] Step 5:

[0311] The server sends the generated advice to the terminal, which then notifies the care staff. Specifically, the terminal uses a push notification function to attract the care staff's attention. Advice is the input, and visualized management information is provided to the staff as the output.

[0312] Step 6:

[0313] Upon receiving a notification, the user takes action based on the advice. Feedback is sent to the server via the device. The input is the result of implementing the advice, and the output is feedback. This information is used to generate advice in the future.

[0314] 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.

[0315] This invention provides a system that enables more precise and personalized stress management for users by integrating and utilizing biometric data and emotional data. The system consists of a wearable device, a user terminal, a server, and an emotional engine.

[0316] First, biometric data is continuously collected by a wearable device. Data such as heart rate, body temperature, and skin conductance are acquired and transmitted to the terminal with the user's permission. The terminal simultaneously uses a microphone and camera to acquire the user's voice and facial expression data and estimate their emotional state.

[0317] Next, the device sends this data to the server. The server analyzes the biometric and emotional data to assess the user's stress level and emotional state. In this assessment, an emotion engine plays a role, improving the accuracy of the analysis by incorporating emotional information into the stress analysis. In particular, if the stress level exceeds a threshold and a negative emotional state is detected, the server quickly generates countermeasures.

[0318] The server then generates personalized stress management advice based on the user's current emotions and stress level. This advice is sent to the user's device and delivered to them via push notifications or in-app messages. For example, if a user is feeling anxious, the server might recommend deep breathing or a short meditation. The server can also suggest listening to music or going for a walk to users who tend to be negative.

[0319] Users receive advice from their devices and reduce stress by acting on that advice. Furthermore, users can send feedback on the advice to the server via their devices. This feedback is recorded on the server to further tailor future advice to their specific needs.

[0320] This system allows users to manage their stress autonomously and continuously, and receive optimal support tailored to their individual emotional state.

[0321] The following describes the processing flow.

[0322] Step 1:

[0323] The device acquires biometric data such as heart rate, body temperature, and skin conductance from wearable devices in real time and temporarily stores it.

[0324] Step 2:

[0325] The device uses its built-in microphone and camera to acquire user voice data and facial image data. This prepares the device for estimating the user's emotional state.

[0326] Step 3:

[0327] The device encrypts the acquired biometric data and voice / facial expression data before transmitting it to the server.

[0328] Step 4:

[0329] The server analyzes the received biometric data and quantifies the user's current stress level. This is done using a known algorithm.

[0330] Step 5:

[0331] The server uses an emotion engine to analyze voice data and facial expression data to identify the user's emotional state (e.g., joy, sadness, anger, etc.).

[0332] Step 6:

[0333] The server integrates the analyzed stress level and emotional state to assess the user's overall condition. If the stress level exceeds a threshold, the server prioritizes considering countermeasures, taking the emotional state into account.

[0334] Step 7:

[0335] The server generates specific stress management advice based on the user's stress level and emotional information. For example, if the user is stressed and angry, it will suggest relaxation techniques and actions to help them calm down.

[0336] Step 8:

[0337] The server immediately sends the generated advice to the device, and the device then provides the advice to the user as a push notification or in-app message.

[0338] Step 9:

[0339] Users act according to the advice provided on their device and send feedback on the results of their actions and the advice back to the server via their device.

[0340] Step 10:

[0341] The server records user feedback in a database and uses it as data to improve future stress management advice.

[0342] Through these steps, the system can provide advanced stress management that takes into account the user's stress and emotions.

[0343] (Example 2)

[0344] 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".

[0345] In modern society, individuals are experiencing increasing psychological stress, and there is a need for systems to appropriately address this. However, existing systems struggle to provide precise stress assessments and individualized countermeasures that integrate biometric and emotional information. To solve this problem, more accurate analysis and rapid responses tailored to individual conditions are required.

[0346] 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.

[0347] In this invention, the server includes means for comprehensively analyzing biometric and emotional information to evaluate psychological load, means for rapidly generating countermeasures when the psychological load exceeds a certain threshold and negative emotions are detected, and means for individualizing the generated countermeasures and notifying the user. This enables highly accurate stress assessment and the provision of rapid, individualized countermeasures.

[0348] "Biometric information" refers to data about an organism's body, including measurable information such as heart rate, body temperature, and skin conductance.

[0349] A "portable device" is a device that can be easily carried and is designed to perform a specific function.

[0350] "Emotional information" refers to data used to evaluate an individual's psychological state, specifically information about emotions obtained from voice and facial expressions.

[0351] An "information processing device" refers to an electronic device or system that analyzes received data and outputs specific results.

[0352] "Psychological burden" refers to mental pressure or strain that affects an individual, such as stress or anxiety.

[0353] "Specific criteria" refers to clearly defined boundary values ​​or conditions that, when exceeded, trigger evaluations or actions.

[0354] "Negative emotions" refer to negative feelings such as anxiety, sadness, and anger that indicate the user's mental state.

[0355] A "user terminal" is an electronic device designed for use by users, providing a means for displaying and inputting information.

[0356] This invention provides a system that offers personalized stress management by comprehensively utilizing biometric and emotional information. This system consists of a portable device, terminals, a server, and an emotion analysis engine. The hardware and software required to effectively implement the system are described below.

[0357] Portable devices continuously measure biometric information such as heart rate, body temperature, and skin conductance, enabling real-time monitoring of the user's physical condition. These devices transmit this data to a terminal via wireless communication.

[0358] The device receives biometric information and uses its built-in microphone and camera to acquire voice and facial expressions. This collects emotional information to estimate the user's psychological state. The device's built-in voice recognition system and image analysis algorithms support this process.

[0359] When the server receives data transmitted from the terminal, it uses an emotion analysis engine to comprehensively analyze biometric and emotional information. This analysis assesses the user's psychological burden. For example, if the tone of voice changes in addition to an increase in heart rate, it suggests that anxiety is increasing.

[0360] Based on the analysis results, the server quickly generates personalized countermeasures if the user's psychological burden is high. These countermeasures are tailored to the user's current emotions and stress level and are sent to the device.

[0361] For example, the system can use the following prompt to ask the generating AI model for appropriate advice: "If the user's heart rate is elevated and it is determined that they are anxious, please tell me how to take deep breaths."

[0362] This system allows users to receive personalized stress management advice and make further adjustments based on the feedback. By following the advice received via the device and taking actions to reduce psychological stress, users can autonomously manage their own health.

[0363] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0364] Step 1:

[0365] A portable device measures the user's biometric data in real time. This device acquires heart rate, body temperature, and skin conductance, and transmits this data to a terminal via wireless communication. It receives biometric data as input and provides data to the terminal as output. Specifically, the device continues to collect data non-invasively even during daily activities.

[0366] Step 2:

[0367] The terminal receives biometric data sent from portable devices, while simultaneously acquiring voice and facial expressions using a microphone and camera, and generating emotional information. It receives the acquired voice and video data as input and sends the analyzed emotional information as output to the server. Specifically, it analyzes the user's emotional state using voice recognition software and image recognition algorithms.

[0368] Step 3:

[0369] The server receives biometric data and emotional information transmitted from the terminal. This input data is analyzed by an emotion analysis engine to assess the user's psychological burden. The server then generates an output representing the level of the assessed psychological burden and determines appropriate countermeasures. Statistical analysis of the data and pattern recognition techniques are used in this process.

[0370] Step 4:

[0371] The server generates personalized countermeasures tailored to the user's current situation based on the psychological stress assessment results. The input is the level of psychological stress, and the output is specific stress management advice. A generative AI model is used to create advice in the form of prompts that are appropriate to each individual's state.

[0372] Step 5:

[0373] The server sends the generated advice to the device. The device delivers this advice to the user as a push notification or in-app message. The user receives the advice as input and acts based on it as output. Specific actions suggested include guidance on deep breathing or meditation, or playing music.

[0374] Step 6:

[0375] Users manage their stress by acting according to the advice provided. Users record their feedback in the app and send it to the server through that system. Inputs are the user's actions and impressions, and output is evaluation data that will be reflected in future advice. Specific examples of feedback include responses to in-app surveys.

[0376] (Application Example 2)

[0377] 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."

[0378] In recent years, the increasing mental burden stemming from daily life and work has become a serious problem for many people. In response, there is a need for accurate and individualized stress management based on each user's emotional state and biosignals. However, conventional methods struggle to accurately grasp a user's specific emotional state and provide immediate support. Furthermore, it is difficult to flexibly adjust advice based on user feedback. This challenge requires not merely the analysis of quantified data, but the integration of more comprehensive emotional information.

[0379] 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.

[0380] In this invention, the server includes an information processing device for analyzing biosignals and emotional signals to evaluate emotional state and mental burden; an information processing device for generating individual stress management guidelines for the user based on the mental burden and emotional state; and an information processing device for modifying the guidelines for subsequent uses based on the user's response. This enables real-time stress management tailored to the user and optimization of continuous advice through feedback.

[0381] "Biosignals" refer to information generated from the user's body, such as heart rate, body temperature, and skin electrical resistance, and are primarily used to evaluate health status and mental stress.

[0382] A "portable device" is a small, portable electronic device that can be worn by a user on a daily basis to collect biosignals.

[0383] "Emotional signals" refer to information obtained to estimate a user's emotional state, such as their voice and facial expressions, and are data analyzed for stress management purposes.

[0384] An "information processing device" refers to a computer system that receives biological signals and emotional signals, analyzes them, and evaluates and processes the user's emotional state and mental burden.

[0385] "Mental burden" refers to the degree of mental pressure and stress that users experience in their daily lives and work.

[0386] "Guidelines" refer to specific advice and suggestions provided based on the user's current situation to promote stress management and emotional stabilization.

[0387] A "user terminal" is an electronic device that users directly operate to receive information and guidance, and includes smartphones and tablets.

[0388] To implement this invention, it is necessary to construct a system in which a portable device, an information processing device, and a user terminal work together. The portable device is worn constantly by the user during their daily life and collects biosignals (such as heart rate, body temperature, and skin electrical resistance) in real time.

[0389] The information processing device analyzes biometric and emotional signals transmitted from portable devices and user terminals. The emotional signals used here include information acquired from sources such as voice and cameras. The information processing device utilizes this data to evaluate the user's mental burden and emotional state. Generative AI models such as TensorFlow can be used for the analysis. This generates optimal stress management guidelines for the user.

[0390] The user terminal receives and displays the guidelines output by the user. The terminal can provide the guidelines to the user via push notifications or in-app messages through the application. Furthermore, it receives user feedback and transmits it to the information processing device. This allows the guidelines to be continuously improved based on user responses.

[0391] For example, if a user shows signs of mental stress while getting ready in the morning, the information processing device will generate a message such as, "Take a break and try a 3-minute meditation to cherish the quiet time in the morning," and notify the user through their terminal. In this case, an example of a prompt message generated by the AI ​​model would be, "The user's heart rate is elevated, and facial analysis also indicates stress. Please suggest ways to relax in this situation."

[0392] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0393] Step 1:

[0394] The portable device continuously collects the user's heart rate, body temperature, and skin electrical resistance. This biosignal data is transmitted to the user's terminal in real time. The input is biosignal data, and the output is data transmission to the user's terminal.

[0395] Step 2:

[0396] The terminal processes the received biometric signal data and uses voice and camera to collect the user's emotional signals (data based on facial expressions and voice). At this stage, it receives biometric and emotional signal data as input and generates data to send to the information processing device as output.

[0397] Step 3:

[0398] The server analyzes biometric and emotional signals transmitted from the terminal. This analysis uses generative AI models such as TensorFlow to perform integrated data analysis and evaluate the user's mental burden and emotional state. The input is data from the terminal, and the output is the evaluation result of the mental burden.

[0399] Step 4:

[0400] The server generates optimal stress management guidelines for the user based on the evaluation results. It utilizes a generation AI model to generate prompts and formulate specific advice. The input is the evaluation results of mental burden, and the output is specific guidance for the user.

[0401] Step 5:

[0402] The device receives instructions sent from the server and displays them to the user as push notifications or in-app messages. The input is the instructions from the server, and the output is the notification to the user. The specific action of the device is to display the instructions in an appropriate format and prompt the user to take action.

[0403] Step 6:

[0404] The user acts based on guidelines from their device and sends feedback on the effects of those actions to the server via the device. The input is the user's feedback, and the output is the feedback data sent to the server. The server then takes this data into consideration when generating the next set of guidelines.

[0405] 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.

[0406] 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.

[0407] 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.

[0408] [Third Embodiment]

[0409] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0410] 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.

[0411] 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).

[0412] 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.

[0413] 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.

[0414] 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).

[0415] 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.

[0416] 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.

[0417] 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.

[0418] 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.

[0419] 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.

[0420] 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".

[0421] The system of this invention begins with the collection of biometric data in real time via a wearable device used by the user on a daily basis. This device has the function of continuously monitoring various biometric data such as heart rate, body temperature, and skin conductance 24 hours a day. The terminal collects this data at regular intervals and temporarily stores it.

[0422] Next, the device periodically sends the collected data to the server. The server analyzes the received biometric data and evaluates the user's stress level. In this analysis, a pre-set stress threshold is used to quantify the user's stress state. Based on the results of this evaluation, the server generates stress management advice tailored to each individual user.

[0423] The generated advice is communicated to the user via their device. This notification is provided to the user in the form of a push notification or in-app message. Based on the advice received, the user can take actions to reduce stress. For example, they may be suggested to practice deep breathing or take a short break.

[0424] Furthermore, users can send feedback on the advice to the server via their device. This feedback will be considered in future advice generation and used as data to provide more personalized suggestions.

[0425] For example, if a user is experiencing excessive stress, the server will analyze their stress level and recommend relaxation methods such as stretching or listening to music. Furthermore, if a user is working remotely, the server can proactively suggest face-to-face communication.

[0426] In this way, the system can effectively manage stress by monitoring the user's condition and providing personalized advice.

[0427] The following describes the processing flow.

[0428] Step 1:

[0429] The device acquires biometric data from the wearable device in real time. This data includes heart rate, body temperature, and skin conductance, and is temporarily stored within the device.

[0430] Step 2:

[0431] The device periodically transmits the collected biometric data to a server via the network. This transmission is encrypted to protect privacy.

[0432] Step 3:

[0433] The server analyzes biometric data received from the terminal and calculates the user's stress level. This analysis uses historical data and algorithms to quantify the current stress level.

[0434] Step 4:

[0435] The server evaluates whether the stress level exceeds a set threshold. If the threshold is exceeded, it is determined that a situation requiring special attention is necessary, and countermeasures are considered promptly.

[0436] Step 5:

[0437] The server generates specific stress management advice for the user based on their stress level. This advice includes suggestions for relaxation techniques and adjustments to the work environment.

[0438] Step 6:

[0439] The server sends the generated advice to the device. When the device receives the advice, it is displayed to the user as a push notification or in-app message.

[0440] Step 7:

[0441] Users can review advice through their devices and take action as needed. They can also send feedback, including results and comments, to the server via their devices.

[0442] Step 8:

[0443] The server collects user feedback, stores it in a database to make future advice more appropriate and personalized, and uses it for subsequent analyses.

[0444] By following these steps, the system can provide users with effective stress management.

[0445] (Example 1)

[0446] 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."

[0447] Modern people are exposed to various stressors in their living and working environments, and the lack of effective ways to manage these stressors is a problem. Traditional methods have made it difficult to provide stress management optimized for individual users, and there has also been a lack of mechanisms for quickly incorporating user feedback.

[0448] 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.

[0449] In this invention, the server includes means for analyzing biological information to evaluate the stress state, means for generating individual stress relief instructions based on the stress state, and means for receiving feedback from the user and modifying subsequent instructions based on that feedback. This enables the optimization of stress management tailored to each individual user and the rapid incorporation of feedback.

[0450] A "wearable device" is a device that automatically collects biometric information when worn by the user on a daily basis.

[0451] "Biometric information" refers to data that indicates the user's physical condition, such as heart rate, body temperature, and skin conductance.

[0452] An "information processing device" is a computer system that analyzes biological information and evaluates the user's stress level based on the results.

[0453] "Stress level" is an evaluation index that indicates the degree of physical or mental burden on the user.

[0454] "Stress reduction instructions" refer to specific action plans or suggestions provided to reduce the user's stress.

[0455] A "user communication device" is a terminal with communication capabilities that notifies the user of the generated stress relief instructions.

[0456] "Opinions" refer to feedback from users expressing their evaluations or thoughts on stress relief instructions.

[0457] A "reference value" is a predetermined numerical value or condition that serves as a guideline when evaluating a stress level.

[0458] This invention relates to a system for managing a user's stress level in real time. Specific embodiments for carrying out the invention are described below.

[0459] Users collect biometric information on a daily basis using wearable devices. A general-purpose smartwatch can be used as a concrete example of a device that monitors heart rate, body temperature, skin conductance, etc., and automatically records this data.

[0460] The terminal receives biometric information from the wearable device using Bluetooth or Wi-Fi and temporarily stores it in its storage device. A typical smartphone or tablet could be used as this terminal. These terminals manage the data through application software installed on them.

[0461] The server receives and analyzes biometric information transmitted from the terminal. Data processing scripts using programming languages ​​such as Python and R are implemented for data analysis. Based on the analysis, the server evaluates the user's stress level and generates stress relief instructions. This generation uses a generative AI model, allowing for the creation of personalized instructions by inputting prompts into the AI. A concrete example of a prompt might be, "What are effective ways to cope with my current stress level?"

[0462] The generated stress relief instructions are sent to the device and the user is notified. This notification is provided as a push notification or in-app message. Based on this information, the user can take specific stress-relief measures.

[0463] Furthermore, users input their feedback on the instructions into their device. This feedback is sent to the server via the device and incorporated into future instruction generation, enabling more accurate and personalized responses.

[0464] Based on the above, this invention realizes a system that provides stress management optimized for individual users and contributes to improving the user's health.

[0465] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0466] Step 1:

[0467] The user wears a wearable device while going about their daily life. This device continuously monitors heart rate, body temperature, and skin conductance, and periodically records the data. The device transmits biometric information to a terminal in digital format. The input is the user's biometric information, and the output is digital data.

[0468] Step 2:

[0469] The terminal receives biometric information from a wearable device via wireless communication. The received data is temporarily stored in the terminal's internal memory. The terminal records the biometric information in a database to retain data necessary for later analysis. The input is the biometric information transmitted from the device, and the output is the stored data.

[0470] Step 3:

[0471] The device transmits biometric information to the server at regular intervals. A secure communication protocol is used to ensure data security. The server acknowledges receipt and stores the biometric information in storage. The input is the biometric information sent from the device, and the output is the data stored on the server.

[0472] Step 4:

[0473] The server performs data analysis based on stored biometric information. It uses Python or R analysis scripts to assess stress levels. The server quantifies the stress level as a result of the analysis and compares it to a baseline value. The input is stored biometric information, and the output is the quantified stress level.

[0474] Step 5:

[0475] The server generates stress relief instructions based on the stress level. A generative AI model is used for generation, creating instructions by inputting prompt sentences into the generative AI. For example, a prompt sentence such as "What are effective ways to reduce the user's stress?" might be used. The input is a numerical stress level, and the output is the generated stress relief instruction.

[0476] Step 6:

[0477] The device notifies the user of generated advice sent from the server. This notification is used as a push notification or in-app message. The user can then implement stress management strategies based on this advice. The input is the generated stress relief instructions, and the output is the notification delivered to the user.

[0478] Step 7:

[0479] The user inputs feedback into the terminal based on their experience trying stress relief instructions. The terminal sends the feedback data to the server. The server analyzes the feedback and incorporates it into generating the next set of instructions. The input is user feedback, and the output is corrected data that will be used to improve the next set of instructions.

[0480] (Application Example 1)

[0481] 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."

[0482] In modern care facilities, stress management for the elderly is a critical issue. However, there is a lack of adequate means to monitor stress indicators based on individual elderly individuals' biometric information in real time and to provide prompt and appropriate management advice based on that data. As a result, it is difficult for care staff to make timely decisions on effective stress reduction methods for the elderly, hindering improvements in their quality of life.

[0483] 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.

[0484] In this invention, the server includes means for analyzing biometric information to evaluate stress indicators, means for generating individualized management advice based on the stress indicators, and means for modifying the management advice based on feedback provided by the supervisor. This enables real-time assessment of stress in elderly individuals and the rapid provision of appropriate management advice.

[0485] "Devices and means for collecting biological information" refer to equipment for continuously measuring and acquiring biological information such as heart rate, body temperature, and skin conductance as data.

[0486] "A computational device for analyzing biological information and evaluating stress indicators" refers to a device that uses acquired biological information to quantify or index stress levels based on a specific algorithm.

[0487] "Means for generating individualized management advice" refers to a process for generating optimal stress management methods and suggestions for each individual user based on analyzed stress indicators.

[0488] An "information terminal" is an electronic device used to notify care staff and other supervisors of the generated management advice.

[0489] "Calculation device means for modifying management advice based on feedback" refers to a process or device that reflects feedback information from care staff and users to optimize management advice for subsequent sessions.

[0490] The system for implementing this invention first requires the use of a device that continuously collects biological information. This device is a wearable device that has the function of recording biological information such as heart rate, body temperature, and skin conductance in real time and transmitting the data to an information terminal.

[0491] Upon receiving this biometric information, the server analyzes the data using a specific algorithm and calculates the user's stress index. Specifically, it uses programming languages ​​such as Python and data analysis libraries such as SciPy and NumPy to convert the biometric information into a numerical stress index. For server-side analysis, resources such as AWS Lambda and Google Cloud Functions can be used as cloud services.

[0492] Next, the server generates personalized stress management advice based on the calculated stress indicators. Using a generation AI model, it sends user-specific prompts such as, "Your heart rate is higher than normal. Please suggest relaxation methods for elderly individuals." The generated advice is immediately communicated to care staff via information terminals. Based on this information, care staff can suggest music therapy or stretching exercises, effectively alleviating stress in elderly individuals.

[0493] For example, in the case of elderly person A whose average heart rate has increased by 20% compared to normal, and who also experiences changes in skin conductance, listening to relaxing music may be recommended. This allows care staff to provide prompt and appropriate care based on rich information from AI.

[0494] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0495] Step 1:

[0496] The device collects biometric information such as heart rate, body temperature, and skin conductance from wearable devices in real time. The biometric information obtained from each sensor is used as input. This data is temporarily stored on the device and prepared for transmission to a server via communication protocols such as Bluetooth.

[0497] Step 2:

[0498] The server receives biometric information transmitted from the terminal and performs preprocessing for data analysis. The input is the received biometric information. Based on this, the server processes the data by removing unnecessary noise and normalizing it. The output is a clean dataset that can be analyzed.

[0499] Step 3:

[0500] The server takes a clean dataset as input and uses Python and libraries such as SciPy and NumPy to calculate stress indicators based on biometric data. Specifically, it performs tasks such as heart rate variability analysis and calculation of the rate of change in skin conductance. The output is the user's stress index.

[0501] Step 4:

[0502] The server uses a generation AI model based on the generated stress indicators to create optimal stress management advice for the user. The input consists of stress indicators, and data is sent to the AI ​​model using prompts such as, "Please suggest relaxation methods for the elderly." The output is personalized stress management advice.

[0503] Step 5:

[0504] The server sends the generated advice to the terminal, which then notifies the care staff. Specifically, the terminal uses a push notification function to attract the care staff's attention. Advice is the input, and visualized management information is provided to the staff as the output.

[0505] Step 6:

[0506] Upon receiving a notification, the user takes action based on the advice. Feedback is sent to the server via the device. The input is the result of implementing the advice, and the output is feedback. This information is used to generate advice in the future.

[0507] 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.

[0508] This invention provides a system that enables more precise and personalized stress management for users by integrating and utilizing biometric data and emotional data. The system consists of a wearable device, a user terminal, a server, and an emotional engine.

[0509] First, biometric data is continuously collected by a wearable device. Data such as heart rate, body temperature, and skin conductance are acquired and transmitted to the terminal with the user's permission. The terminal simultaneously uses a microphone and camera to acquire the user's voice and facial expression data and estimate their emotional state.

[0510] Next, the device sends this data to the server. The server analyzes the biometric and emotional data to assess the user's stress level and emotional state. In this assessment, an emotion engine plays a role, improving the accuracy of the analysis by incorporating emotional information into the stress analysis. In particular, if the stress level exceeds a threshold and a negative emotional state is detected, the server quickly generates countermeasures.

[0511] The server then generates personalized stress management advice based on the user's current emotions and stress level. This advice is sent to the user's device and delivered to them via push notifications or in-app messages. For example, if a user is feeling anxious, the server might recommend deep breathing or a short meditation. The server can also suggest listening to music or going for a walk to users who tend to be negative.

[0512] Users receive advice from their devices and reduce stress by acting on that advice. Furthermore, users can send feedback on the advice to the server via their devices. This feedback is recorded on the server to further tailor future advice to their specific needs.

[0513] This system allows users to manage their stress autonomously and continuously, and receive optimal support tailored to their individual emotional state.

[0514] The following describes the processing flow.

[0515] Step 1:

[0516] The device acquires biometric data such as heart rate, body temperature, and skin conductance from wearable devices in real time and temporarily stores it.

[0517] Step 2:

[0518] The device uses its built-in microphone and camera to acquire user voice data and facial image data. This prepares the device for estimating the user's emotional state.

[0519] Step 3:

[0520] The device encrypts the acquired biometric data and voice / facial expression data before transmitting it to the server.

[0521] Step 4:

[0522] The server analyzes the received biometric data and quantifies the user's current stress level. This is done using a known algorithm.

[0523] Step 5:

[0524] The server uses an emotion engine to analyze voice data and facial expression data to identify the user's emotional state (e.g., joy, sadness, anger, etc.).

[0525] Step 6:

[0526] The server integrates the analyzed stress level and emotional state to assess the user's overall condition. If the stress level exceeds a threshold, the server prioritizes considering countermeasures, taking the emotional state into account.

[0527] Step 7:

[0528] The server generates specific stress management advice based on the user's stress level and emotional information. For example, if the user is stressed and angry, it will suggest relaxation techniques and actions to help them calm down.

[0529] Step 8:

[0530] The server immediately sends the generated advice to the device, and the device then provides the advice to the user as a push notification or in-app message.

[0531] Step 9:

[0532] Users act according to the advice provided on their device and send feedback on the results of their actions and the advice back to the server via their device.

[0533] Step 10:

[0534] The server records user feedback in a database and uses it as data to improve future stress management advice.

[0535] Through these steps, the system can provide advanced stress management that takes into account the user's stress and emotions.

[0536] (Example 2)

[0537] 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."

[0538] In modern society, individuals are experiencing increasing psychological stress, and there is a need for systems to appropriately address this. However, existing systems struggle to provide precise stress assessments and individualized countermeasures that integrate biometric and emotional information. To solve this problem, more accurate analysis and rapid responses tailored to individual conditions are required.

[0539] 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.

[0540] In this invention, the server includes means for comprehensively analyzing biometric and emotional information to evaluate psychological load, means for rapidly generating countermeasures when the psychological load exceeds a certain threshold and negative emotions are detected, and means for individualizing the generated countermeasures and notifying the user. This enables highly accurate stress assessment and the provision of rapid, individualized countermeasures.

[0541] "Biometric information" refers to data about an organism's body, including measurable information such as heart rate, body temperature, and skin conductance.

[0542] A "portable device" is a device that can be easily carried and is designed to perform a specific function.

[0543] "Emotional information" refers to data used to evaluate an individual's psychological state, specifically information about emotions obtained from voice and facial expressions.

[0544] An "information processing device" refers to an electronic device or system that analyzes received data and outputs specific results.

[0545] "Psychological burden" refers to mental pressure or strain that affects an individual, such as stress or anxiety.

[0546] "Specific criteria" refers to clearly defined boundary values ​​or conditions that, when exceeded, trigger evaluations or actions.

[0547] "Negative emotions" refer to negative feelings such as anxiety, sadness, and anger that indicate the user's mental state.

[0548] A "user terminal" is an electronic device designed for use by users, providing a means for displaying and inputting information.

[0549] This invention provides a system that offers personalized stress management by comprehensively utilizing biometric and emotional information. This system consists of a portable device, terminals, a server, and an emotion analysis engine. The hardware and software required to effectively implement the system are described below.

[0550] Portable devices continuously measure biometric information such as heart rate, body temperature, and skin conductance, enabling real-time monitoring of the user's physical condition. These devices transmit this data to a terminal via wireless communication.

[0551] The device receives biometric information and uses its built-in microphone and camera to acquire voice and facial expressions. This collects emotional information to estimate the user's psychological state. The device's built-in voice recognition system and image analysis algorithms support this process.

[0552] When the server receives data transmitted from the terminal, it uses an emotion analysis engine to comprehensively analyze biometric and emotional information. This analysis assesses the user's psychological burden. For example, if the tone of voice changes in addition to an increase in heart rate, it suggests that anxiety is increasing.

[0553] Based on the analysis results, the server quickly generates personalized countermeasures if the user's psychological burden is high. These countermeasures are tailored to the user's current emotions and stress level and are sent to the device.

[0554] For example, the system can use the following prompt to ask the generating AI model for appropriate advice: "If the user's heart rate is elevated and it is determined that they are anxious, please tell me how to take deep breaths."

[0555] This system allows users to receive personalized stress management advice and make further adjustments based on the feedback. By following the advice received via the device and taking actions to reduce psychological stress, users can autonomously manage their own health.

[0556] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0557] Step 1:

[0558] A portable device measures the user's biometric data in real time. This device acquires heart rate, body temperature, and skin conductance, and transmits this data to a terminal via wireless communication. It receives biometric data as input and provides data to the terminal as output. Specifically, the device continues to collect data non-invasively even during daily activities.

[0559] Step 2:

[0560] The terminal receives biometric data sent from portable devices, while simultaneously acquiring voice and facial expressions using a microphone and camera, and generating emotional information. It receives the acquired voice and video data as input and sends the analyzed emotional information as output to the server. Specifically, it analyzes the user's emotional state using voice recognition software and image recognition algorithms.

[0561] Step 3:

[0562] The server receives biometric data and emotional information transmitted from the terminal. This input data is analyzed by an emotion analysis engine to assess the user's psychological burden. The server then generates an output representing the level of the assessed psychological burden and determines appropriate countermeasures. Statistical analysis of the data and pattern recognition techniques are used in this process.

[0563] Step 4:

[0564] The server generates personalized countermeasures tailored to the user's current situation based on the psychological stress assessment results. The input is the level of psychological stress, and the output is specific stress management advice. A generative AI model is used to create advice in the form of prompts that are appropriate to each individual's state.

[0565] Step 5:

[0566] The server sends the generated advice to the device. The device delivers this advice to the user as a push notification or in-app message. The user receives the advice as input and acts based on it as output. Specific actions suggested include guidance on deep breathing or meditation, or playing music.

[0567] Step 6:

[0568] Users manage their stress by acting according to the advice provided. Users record their feedback in the app and send it to the server through that system. Inputs are the user's actions and impressions, and output is evaluation data that will be reflected in future advice. Specific examples of feedback include responses to in-app surveys.

[0569] (Application Example 2)

[0570] 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."

[0571] In recent years, the increasing mental burden stemming from daily life and work has become a serious problem for many people. In response, there is a need for accurate and individualized stress management based on each user's emotional state and biosignals. However, conventional methods struggle to accurately grasp a user's specific emotional state and provide immediate support. Furthermore, it is difficult to flexibly adjust advice based on user feedback. This challenge requires not merely the analysis of quantified data, but the integration of more comprehensive emotional information.

[0572] 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.

[0573] In this invention, the server includes an information processing device for analyzing biosignals and emotional signals to evaluate emotional state and mental burden; an information processing device for generating individual stress management guidelines for the user based on the mental burden and emotional state; and an information processing device for modifying the guidelines for subsequent uses based on the user's response. This enables real-time stress management tailored to the user and optimization of continuous advice through feedback.

[0574] "Biosignals" refer to information generated from the user's body, such as heart rate, body temperature, and skin electrical resistance, and are primarily used to evaluate health status and mental stress.

[0575] A "portable device" is a small, portable electronic device that can be worn by a user on a daily basis to collect biosignals.

[0576] "Emotional signals" refer to information obtained to estimate a user's emotional state, such as their voice and facial expressions, and are data analyzed for stress management purposes.

[0577] An "information processing device" refers to a computer system that receives biological signals and emotional signals, analyzes them, and evaluates and processes the user's emotional state and mental burden.

[0578] "Mental burden" refers to the degree of mental pressure and stress that users experience in their daily lives and work.

[0579] "Guidelines" refer to specific advice and suggestions provided based on the user's current situation to promote stress management and emotional stabilization.

[0580] A "user terminal" is an electronic device that users directly operate to receive information and guidance, and includes smartphones and tablets.

[0581] To implement this invention, it is necessary to construct a system in which a portable device, an information processing device, and a user terminal work together. The portable device is worn constantly by the user during their daily life and collects biosignals (such as heart rate, body temperature, and skin electrical resistance) in real time.

[0582] The information processing device analyzes biometric and emotional signals transmitted from portable devices and user terminals. The emotional signals used here include information acquired from sources such as voice and cameras. The information processing device utilizes this data to evaluate the user's mental burden and emotional state. Generative AI models such as TensorFlow can be used for the analysis. This generates optimal stress management guidelines for the user.

[0583] The user terminal receives and displays the guidelines output by the user. The terminal can provide the guidelines to the user via push notifications or in-app messages through the application. Furthermore, it receives user feedback and transmits it to the information processing device. This allows the guidelines to be continuously improved based on user responses.

[0584] For example, if a user shows signs of mental stress while getting ready in the morning, the information processing device will generate a message such as, "Take a break and try a 3-minute meditation to cherish the quiet time in the morning," and notify the user through their terminal. In this case, an example of a prompt message generated by the AI ​​model would be, "The user's heart rate is elevated, and facial analysis also indicates stress. Please suggest ways to relax in this situation."

[0585] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0586] Step 1:

[0587] The portable device continuously collects the user's heart rate, body temperature, and skin electrical resistance. This biosignal data is transmitted to the user's terminal in real time. The input is biosignal data, and the output is data transmission to the user's terminal.

[0588] Step 2:

[0589] The terminal processes the received biometric signal data and uses voice and camera to collect the user's emotional signals (data based on facial expressions and voice). At this stage, it receives biometric and emotional signal data as input and generates data to send to the information processing device as output.

[0590] Step 3:

[0591] The server analyzes biometric and emotional signals transmitted from the terminal. This analysis uses generative AI models such as TensorFlow to perform integrated data analysis and evaluate the user's mental burden and emotional state. The input is data from the terminal, and the output is the evaluation result of the mental burden.

[0592] Step 4:

[0593] The server generates optimal stress management guidelines for the user based on the evaluation results. It utilizes a generation AI model to generate prompts and formulate specific advice. The input is the evaluation results of mental burden, and the output is specific guidance for the user.

[0594] Step 5:

[0595] The device receives instructions sent from the server and displays them to the user as push notifications or in-app messages. The input is the instructions from the server, and the output is the notification to the user. The specific action of the device is to display the instructions in an appropriate format and prompt the user to take action.

[0596] Step 6:

[0597] The user acts based on guidelines from their device and sends feedback on the effects of those actions to the server via the device. The input is the user's feedback, and the output is the feedback data sent to the server. The server then takes this data into consideration when generating the next set of guidelines.

[0598] 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.

[0599] 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.

[0600] 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.

[0601] [Fourth Embodiment]

[0602] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0603] 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.

[0604] 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).

[0605] 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.

[0606] 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.

[0607] 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).

[0608] 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.

[0609] 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.

[0610] 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.

[0611] 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.

[0612] 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.

[0613] 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.

[0614] 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".

[0615] The system of this invention begins with the collection of biometric data in real time via a wearable device used by the user on a daily basis. This device has the function of continuously monitoring various biometric data such as heart rate, body temperature, and skin conductance 24 hours a day. The terminal collects this data at regular intervals and temporarily stores it.

[0616] Next, the device periodically sends the collected data to the server. The server analyzes the received biometric data and evaluates the user's stress level. In this analysis, a pre-set stress threshold is used to quantify the user's stress state. Based on the results of this evaluation, the server generates stress management advice tailored to each individual user.

[0617] The generated advice is communicated to the user via their device. This notification is provided to the user in the form of a push notification or in-app message. Based on the advice received, the user can take actions to reduce stress. For example, they may be suggested to practice deep breathing or take a short break.

[0618] Furthermore, users can send feedback on the advice to the server via their device. This feedback will be considered in future advice generation and used as data to provide more personalized suggestions.

[0619] For example, if a user is experiencing excessive stress, the server will analyze their stress level and recommend relaxation methods such as stretching or listening to music. Furthermore, if a user is working remotely, the server can proactively suggest face-to-face communication.

[0620] In this way, the system can effectively manage stress by monitoring the user's condition and providing personalized advice.

[0621] The following describes the processing flow.

[0622] Step 1:

[0623] The device acquires biometric data from the wearable device in real time. This data includes heart rate, body temperature, and skin conductance, and is temporarily stored within the device.

[0624] Step 2:

[0625] The device periodically transmits the collected biometric data to a server via the network. This transmission is encrypted to protect privacy.

[0626] Step 3:

[0627] The server analyzes biometric data received from the terminal and calculates the user's stress level. This analysis uses historical data and algorithms to quantify the current stress level.

[0628] Step 4:

[0629] The server evaluates whether the stress level exceeds a set threshold. If the threshold is exceeded, it is determined that a situation requiring special attention is necessary, and countermeasures are considered promptly.

[0630] Step 5:

[0631] The server generates specific stress management advice for the user based on their stress level. This advice includes suggestions for relaxation techniques and adjustments to the work environment.

[0632] Step 6:

[0633] The server sends the generated advice to the device. When the device receives the advice, it is displayed to the user as a push notification or in-app message.

[0634] Step 7:

[0635] Users can review advice through their devices and take action as needed. They can also send feedback, including results and comments, to the server via their devices.

[0636] Step 8:

[0637] The server collects user feedback, stores it in a database to make future advice more appropriate and personalized, and uses it for subsequent analyses.

[0638] By following these steps, the system can provide users with effective stress management.

[0639] (Example 1)

[0640] 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".

[0641] Modern people are exposed to various stressors in their living and working environments, and the lack of effective ways to manage these stressors is a problem. Traditional methods have made it difficult to provide stress management optimized for individual users, and there has also been a lack of mechanisms for quickly incorporating user feedback.

[0642] 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.

[0643] In this invention, the server includes means for analyzing biological information to evaluate the stress state, means for generating individual stress relief instructions based on the stress state, and means for receiving feedback from the user and modifying subsequent instructions based on that feedback. This enables the optimization of stress management tailored to each individual user and the rapid incorporation of feedback.

[0644] A "wearable device" is a device that automatically collects biometric information when worn by the user on a daily basis.

[0645] "Biometric information" refers to data that indicates the user's physical condition, such as heart rate, body temperature, and skin conductance.

[0646] An "information processing device" is a computer system that analyzes biological information and evaluates the user's stress level based on the results.

[0647] "Stress level" is an evaluation index that indicates the degree of physical or mental burden on the user.

[0648] "Stress reduction instructions" refer to specific action plans or suggestions provided to reduce the user's stress.

[0649] A "user communication device" is a terminal with communication capabilities that notifies the user of the generated stress relief instructions.

[0650] "Opinions" refer to feedback from users expressing their evaluations or thoughts on stress relief instructions.

[0651] A "reference value" is a predetermined numerical value or condition that serves as a guideline when evaluating a stress level.

[0652] This invention relates to a system for managing a user's stress level in real time. Specific embodiments for carrying out the invention are described below.

[0653] Users collect biometric information on a daily basis using wearable devices. A general-purpose smartwatch can be used as a concrete example of a device that monitors heart rate, body temperature, skin conductance, etc., and automatically records this data.

[0654] The terminal receives biometric information from the wearable device using Bluetooth or Wi-Fi and temporarily stores it in its storage device. A typical smartphone or tablet could be used as this terminal. These terminals manage the data through application software installed on them.

[0655] The server receives and analyzes biometric information transmitted from the terminal. Data processing scripts using programming languages ​​such as Python and R are implemented for data analysis. Based on the analysis, the server evaluates the user's stress level and generates stress relief instructions. This generation uses a generative AI model, allowing for the creation of personalized instructions by inputting prompts into the AI. A concrete example of a prompt might be, "What are effective ways to cope with my current stress level?"

[0656] The generated stress relief instructions are sent to the device and the user is notified. This notification is provided as a push notification or in-app message. Based on this information, the user can take specific stress-relief measures.

[0657] Furthermore, users input their feedback on the instructions into their device. This feedback is sent to the server via the device and incorporated into future instruction generation, enabling more accurate and personalized responses.

[0658] Based on the above, this invention realizes a system that provides stress management optimized for individual users and contributes to improving the user's health.

[0659] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0660] Step 1:

[0661] The user wears a wearable device while going about their daily life. This device continuously monitors heart rate, body temperature, and skin conductance, and periodically records the data. The device transmits biometric information to a terminal in digital format. The input is the user's biometric information, and the output is digital data.

[0662] Step 2:

[0663] The terminal receives biometric information from a wearable device via wireless communication. The received data is temporarily stored in the terminal's internal memory. The terminal records the biometric information in a database to retain data necessary for later analysis. The input is the biometric information transmitted from the device, and the output is the stored data.

[0664] Step 3:

[0665] The device transmits biometric information to the server at regular intervals. A secure communication protocol is used to ensure data security. The server acknowledges receipt and stores the biometric information in storage. The input is the biometric information sent from the device, and the output is the data stored on the server.

[0666] Step 4:

[0667] The server performs data analysis based on stored biometric information. It uses Python or R analysis scripts to assess stress levels. The server quantifies the stress level as a result of the analysis and compares it to a baseline value. The input is stored biometric information, and the output is the quantified stress level.

[0668] Step 5:

[0669] The server generates stress relief instructions based on the stress level. A generative AI model is used for generation, creating instructions by inputting prompt sentences into the generative AI. For example, a prompt sentence such as "What are effective ways to reduce the user's stress?" might be used. The input is a numerical stress level, and the output is the generated stress relief instruction.

[0670] Step 6:

[0671] The device notifies the user of generated advice sent from the server. This notification is used as a push notification or in-app message. The user can then implement stress management strategies based on this advice. The input is the generated stress relief instructions, and the output is the notification delivered to the user.

[0672] Step 7:

[0673] The user inputs feedback into the terminal based on their experience trying stress relief instructions. The terminal sends the feedback data to the server. The server analyzes the feedback and incorporates it into generating the next set of instructions. The input is user feedback, and the output is corrected data that will be used to improve the next set of instructions.

[0674] (Application Example 1)

[0675] 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".

[0676] In modern care facilities, stress management for the elderly is a critical issue. However, there is a lack of adequate means to monitor stress indicators based on individual elderly individuals' biometric information in real time and to provide prompt and appropriate management advice based on that data. As a result, it is difficult for care staff to make timely decisions on effective stress reduction methods for the elderly, hindering improvements in their quality of life.

[0677] 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.

[0678] In this invention, the server includes means for analyzing biometric information to evaluate stress indicators, means for generating individualized management advice based on the stress indicators, and means for modifying the management advice based on feedback provided by the supervisor. This enables real-time assessment of stress in elderly individuals and the rapid provision of appropriate management advice.

[0679] "Devices and means for collecting biological information" refer to equipment for continuously measuring and acquiring biological information such as heart rate, body temperature, and skin conductance as data.

[0680] "A computational device for analyzing biological information and evaluating stress indicators" refers to a device that uses acquired biological information to quantify or index stress levels based on a specific algorithm.

[0681] "Means for generating individualized management advice" refers to a process for generating optimal stress management methods and suggestions for each individual user based on analyzed stress indicators.

[0682] An "information terminal" is an electronic device used to notify care staff and other supervisors of the generated management advice.

[0683] "Calculation device means for modifying management advice based on feedback" refers to a process or device that reflects feedback information from care staff and users to optimize management advice for subsequent sessions.

[0684] The system for implementing this invention first requires the use of a device that continuously collects biological information. This device is a wearable device that has the function of recording biological information such as heart rate, body temperature, and skin conductance in real time and transmitting the data to an information terminal.

[0685] Upon receiving this biometric information, the server analyzes the data using a specific algorithm and calculates the user's stress index. Specifically, it uses programming languages ​​such as Python and data analysis libraries such as SciPy and NumPy to convert the biometric information into a numerical stress index. For server-side analysis, resources such as AWS Lambda and Google Cloud Functions can be used as cloud services.

[0686] Next, the server generates personalized stress management advice based on the calculated stress indicators. Using a generation AI model, it sends user-specific prompts such as, "Your heart rate is higher than normal. Please suggest relaxation methods for elderly individuals." The generated advice is immediately communicated to care staff via information terminals. Based on this information, care staff can suggest music therapy or stretching exercises, effectively alleviating stress in elderly individuals.

[0687] For example, in the case of elderly person A whose average heart rate has increased by 20% compared to normal, and who also experiences changes in skin conductance, listening to relaxing music may be recommended. This allows care staff to provide prompt and appropriate care based on rich information from AI.

[0688] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0689] Step 1:

[0690] The device collects biometric information such as heart rate, body temperature, and skin conductance from wearable devices in real time. The biometric information obtained from each sensor is used as input. This data is temporarily stored on the device and prepared for transmission to a server via communication protocols such as Bluetooth.

[0691] Step 2:

[0692] The server receives biometric information transmitted from the terminal and performs preprocessing for data analysis. The input is the received biometric information. Based on this, the server processes the data by removing unnecessary noise and normalizing it. The output is a clean dataset that can be analyzed.

[0693] Step 3:

[0694] The server takes a clean dataset as input and uses Python and libraries such as SciPy and NumPy to calculate stress indicators based on biometric data. Specifically, it performs tasks such as heart rate variability analysis and calculation of the rate of change in skin conductance. The output is the user's stress index.

[0695] Step 4:

[0696] The server uses a generation AI model based on the generated stress indicators to create optimal stress management advice for the user. The input consists of stress indicators, and data is sent to the AI ​​model using prompts such as, "Please suggest relaxation methods for the elderly." The output is personalized stress management advice.

[0697] Step 5:

[0698] The server sends the generated advice to the terminal, which then notifies the care staff. Specifically, the terminal uses a push notification function to attract the care staff's attention. Advice is the input, and visualized management information is provided to the staff as the output.

[0699] Step 6:

[0700] Upon receiving a notification, the user takes action based on the advice. Feedback is sent to the server via the device. The input is the result of implementing the advice, and the output is feedback. This information is used to generate advice in the future.

[0701] 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.

[0702] This invention provides a system that enables more precise and personalized stress management for users by integrating and utilizing biometric data and emotional data. The system consists of a wearable device, a user terminal, a server, and an emotional engine.

[0703] First, biometric data is continuously collected by a wearable device. Data such as heart rate, body temperature, and skin conductance are acquired and transmitted to the terminal with the user's permission. The terminal simultaneously uses a microphone and camera to acquire the user's voice and facial expression data and estimate their emotional state.

[0704] Next, the device sends this data to the server. The server analyzes the biometric and emotional data to assess the user's stress level and emotional state. In this assessment, an emotion engine plays a role, improving the accuracy of the analysis by incorporating emotional information into the stress analysis. In particular, if the stress level exceeds a threshold and a negative emotional state is detected, the server quickly generates countermeasures.

[0705] The server then generates personalized stress management advice based on the user's current emotions and stress level. This advice is sent to the user's device and delivered to them via push notifications or in-app messages. For example, if a user is feeling anxious, the server might recommend deep breathing or a short meditation. The server can also suggest listening to music or going for a walk to users who tend to be negative.

[0706] Users receive advice from their devices and reduce stress by acting on that advice. Furthermore, users can send feedback on the advice to the server via their devices. This feedback is recorded on the server to further tailor future advice to their specific needs.

[0707] This system allows users to manage their stress autonomously and continuously, and receive optimal support tailored to their individual emotional state.

[0708] The following describes the processing flow.

[0709] Step 1:

[0710] The device acquires biometric data such as heart rate, body temperature, and skin conductance from wearable devices in real time and temporarily stores it.

[0711] Step 2:

[0712] The device uses its built-in microphone and camera to acquire user voice data and facial image data. This prepares the device for estimating the user's emotional state.

[0713] Step 3:

[0714] The device encrypts the acquired biometric data and voice / facial expression data before transmitting it to the server.

[0715] Step 4:

[0716] The server analyzes the received biometric data and quantifies the user's current stress level. This is done using a known algorithm.

[0717] Step 5:

[0718] The server uses an emotion engine to analyze voice data and facial expression data to identify the user's emotional state (e.g., joy, sadness, anger, etc.).

[0719] Step 6:

[0720] The server integrates the analyzed stress level and emotional state to assess the user's overall condition. If the stress level exceeds a threshold, the server prioritizes considering countermeasures, taking the emotional state into account.

[0721] Step 7:

[0722] The server generates specific stress management advice based on the user's stress level and emotional information. For example, if the user is stressed and angry, it will suggest relaxation techniques and actions to help them calm down.

[0723] Step 8:

[0724] The server immediately sends the generated advice to the device, and the device then provides the advice to the user as a push notification or in-app message.

[0725] Step 9:

[0726] Users act according to the advice provided on their device and send feedback on the results of their actions and the advice back to the server via their device.

[0727] Step 10:

[0728] The server records user feedback in a database and uses it as data to improve future stress management advice.

[0729] Through these steps, the system can provide advanced stress management that takes into account the user's stress and emotions.

[0730] (Example 2)

[0731] 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".

[0732] In modern society, individuals are experiencing increasing psychological stress, and there is a need for systems to appropriately address this. However, existing systems struggle to provide precise stress assessments and individualized countermeasures that integrate biometric and emotional information. To solve this problem, more accurate analysis and rapid responses tailored to individual conditions are required.

[0733] 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.

[0734] In this invention, the server includes means for comprehensively analyzing biometric and emotional information to evaluate psychological load, means for rapidly generating countermeasures when the psychological load exceeds a certain threshold and negative emotions are detected, and means for individualizing the generated countermeasures and notifying the user. This enables highly accurate stress assessment and the provision of rapid, individualized countermeasures.

[0735] "Biometric information" refers to data about an organism's body, including measurable information such as heart rate, body temperature, and skin conductance.

[0736] A "portable device" is a device that can be easily carried and is designed to perform a specific function.

[0737] "Emotional information" refers to data used to evaluate an individual's psychological state, specifically information about emotions obtained from voice and facial expressions.

[0738] An "information processing device" refers to an electronic device or system that analyzes received data and outputs specific results.

[0739] "Psychological burden" refers to mental pressure or strain that affects an individual, such as stress or anxiety.

[0740] "Specific criteria" refers to clearly defined boundary values ​​or conditions that, when exceeded, trigger evaluations or actions.

[0741] "Negative emotions" refer to negative feelings such as anxiety, sadness, and anger that indicate the user's mental state.

[0742] A "user terminal" is an electronic device designed for use by users, providing a means for displaying and inputting information.

[0743] This invention provides a system that offers personalized stress management by comprehensively utilizing biometric and emotional information. This system consists of a portable device, terminals, a server, and an emotion analysis engine. The hardware and software required to effectively implement the system are described below.

[0744] Portable devices continuously measure biometric information such as heart rate, body temperature, and skin conductance, enabling real-time monitoring of the user's physical condition. These devices transmit this data to a terminal via wireless communication.

[0745] The device receives biometric information and uses its built-in microphone and camera to acquire voice and facial expressions. This collects emotional information to estimate the user's psychological state. The device's built-in voice recognition system and image analysis algorithms support this process.

[0746] When the server receives data transmitted from the terminal, it uses an emotion analysis engine to comprehensively analyze biometric and emotional information. This analysis assesses the user's psychological burden. For example, if the tone of voice changes in addition to an increase in heart rate, it suggests that anxiety is increasing.

[0747] Based on the analysis results, the server quickly generates personalized countermeasures if the user's psychological burden is high. These countermeasures are tailored to the user's current emotions and stress level and are sent to the device.

[0748] For example, the system can use the following prompt to ask the generating AI model for appropriate advice: "If the user's heart rate is elevated and it is determined that they are anxious, please tell me how to take deep breaths."

[0749] This system allows users to receive personalized stress management advice and make further adjustments based on the feedback. By following the advice received via the device and taking actions to reduce psychological stress, users can autonomously manage their own health.

[0750] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0751] Step 1:

[0752] A portable device measures the user's biometric data in real time. This device acquires heart rate, body temperature, and skin conductance, and transmits this data to a terminal via wireless communication. It receives biometric data as input and provides data to the terminal as output. Specifically, the device continues to collect data non-invasively even during daily activities.

[0753] Step 2:

[0754] The terminal receives biometric data sent from portable devices, while simultaneously acquiring voice and facial expressions using a microphone and camera, and generating emotional information. It receives the acquired voice and video data as input and sends the analyzed emotional information as output to the server. Specifically, it analyzes the user's emotional state using voice recognition software and image recognition algorithms.

[0755] Step 3:

[0756] The server receives biometric data and emotional information transmitted from the terminal. This input data is analyzed by an emotion analysis engine to assess the user's psychological burden. The server then generates an output representing the level of the assessed psychological burden and determines appropriate countermeasures. Statistical analysis of the data and pattern recognition techniques are used in this process.

[0757] Step 4:

[0758] The server generates personalized countermeasures tailored to the user's current situation based on the psychological stress assessment results. The input is the level of psychological stress, and the output is specific stress management advice. A generative AI model is used to create advice in the form of prompts that are appropriate to each individual's state.

[0759] Step 5:

[0760] The server sends the generated advice to the device. The device delivers this advice to the user as a push notification or in-app message. The user receives the advice as input and acts based on it as output. Specific actions suggested include guidance on deep breathing or meditation, or playing music.

[0761] Step 6:

[0762] Users manage their stress by acting according to the advice provided. Users record their feedback in the app and send it to the server through that system. Inputs are the user's actions and impressions, and output is evaluation data that will be reflected in future advice. Specific examples of feedback include responses to in-app surveys.

[0763] (Application Example 2)

[0764] 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".

[0765] In recent years, the increasing mental burden stemming from daily life and work has become a serious problem for many people. In response, there is a need for accurate and individualized stress management based on each user's emotional state and biosignals. However, conventional methods struggle to accurately grasp a user's specific emotional state and provide immediate support. Furthermore, it is difficult to flexibly adjust advice based on user feedback. This challenge requires not merely the analysis of quantified data, but the integration of more comprehensive emotional information.

[0766] 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.

[0767] In this invention, the server includes an information processing device for analyzing biosignals and emotional signals to evaluate emotional state and mental burden; an information processing device for generating individual stress management guidelines for the user based on the mental burden and emotional state; and an information processing device for modifying the guidelines for subsequent uses based on the user's response. This enables real-time stress management tailored to the user and optimization of continuous advice through feedback.

[0768] "Biosignals" refer to information generated from the user's body, such as heart rate, body temperature, and skin electrical resistance, and are primarily used to evaluate health status and mental stress.

[0769] A "portable device" is a small, portable electronic device that can be worn by a user on a daily basis to collect biosignals.

[0770] "Emotional signals" refer to information obtained to estimate a user's emotional state, such as their voice and facial expressions, and are data analyzed for stress management purposes.

[0771] An "information processing device" refers to a computer system that receives biological signals and emotional signals, analyzes them, and evaluates and processes the user's emotional state and mental burden.

[0772] "Mental burden" refers to the degree of mental pressure and stress that users experience in their daily lives and work.

[0773] "Guidelines" refer to specific advice and suggestions provided based on the user's current situation to promote stress management and emotional stabilization.

[0774] A "user terminal" is an electronic device that users directly operate to receive information and guidance, and includes smartphones and tablets.

[0775] To implement this invention, it is necessary to construct a system in which a portable device, an information processing device, and a user terminal work together. The portable device is worn constantly by the user during their daily life and collects biosignals (such as heart rate, body temperature, and skin electrical resistance) in real time.

[0776] The information processing device analyzes biometric and emotional signals transmitted from portable devices and user terminals. The emotional signals used here include information acquired from sources such as voice and cameras. The information processing device utilizes this data to evaluate the user's mental burden and emotional state. Generative AI models such as TensorFlow can be used for the analysis. This generates optimal stress management guidelines for the user.

[0777] The user terminal receives and displays the guidelines output by the user. The terminal can provide the guidelines to the user via push notifications or in-app messages through the application. Furthermore, it receives user feedback and transmits it to the information processing device. This allows the guidelines to be continuously improved based on user responses.

[0778] For example, if a user shows signs of mental stress while getting ready in the morning, the information processing device will generate a message such as, "Take a break and try a 3-minute meditation to cherish the quiet time in the morning," and notify the user through their terminal. In this case, an example of a prompt message generated by the AI ​​model would be, "The user's heart rate is elevated, and facial analysis also indicates stress. Please suggest ways to relax in this situation."

[0779] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0780] Step 1:

[0781] The portable device continuously collects the user's heart rate, body temperature, and skin electrical resistance. This biosignal data is transmitted to the user's terminal in real time. The input is biosignal data, and the output is data transmission to the user's terminal.

[0782] Step 2:

[0783] The terminal processes the received biometric signal data and uses voice and camera to collect the user's emotional signals (data based on facial expressions and voice). At this stage, it receives biometric and emotional signal data as input and generates data to send to the information processing device as output.

[0784] Step 3:

[0785] The server analyzes biometric and emotional signals transmitted from the terminal. This analysis uses generative AI models such as TensorFlow to perform integrated data analysis and evaluate the user's mental burden and emotional state. The input is data from the terminal, and the output is the evaluation result of the mental burden.

[0786] Step 4:

[0787] The server generates optimal stress management guidelines for the user based on the evaluation results. It utilizes a generation AI model to generate prompts and formulate specific advice. The input is the evaluation results of mental burden, and the output is specific guidance for the user.

[0788] Step 5:

[0789] The device receives instructions sent from the server and displays them to the user as push notifications or in-app messages. The input is the instructions from the server, and the output is the notification to the user. The specific action of the device is to display the instructions in an appropriate format and prompt the user to take action.

[0790] Step 6:

[0791] The user acts based on guidelines from their device and sends feedback on the effects of those actions to the server via the device. The input is the user's feedback, and the output is the feedback data sent to the server. The server then takes this data into consideration when generating the next set of guidelines.

[0792] 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.

[0793] 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.

[0794] 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.

[0795] 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.

[0796] 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.

[0797] 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.

[0798] 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.

[0799] 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.

[0800] 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."

[0801] 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.

[0802] 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.

[0803] 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.

[0804] 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.

[0805] 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.

[0806] 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.

[0807] 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.

[0808] 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.

[0809] 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.

[0810] 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.

[0811] 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.

[0812] 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.

[0813] The following is further disclosed regarding the embodiments described above.

[0814] (Claim 1)

[0815] A wearable device for collecting biometric data,

[0816] A server device for analyzing this biological data and evaluating stress levels,

[0817] A server device that generates individual stress management advice for users based on their stress levels,

[0818] A system including a user terminal for notifying the user of the generated advice.

[0819] (Claim 2)

[0820] The server device will, if the stress level exceeds a pre-set threshold,

[0821] The system according to claim 1, which prioritizes the generation of advice.

[0822] (Claim 3)

[0823] We receive user feedback and use that feedback to revise our advice for future updates.

[0824] The system according to claim 1, comprising a server device.

[0825] "Example 1"

[0826] (Claim 1)

[0827] A wearable device for collecting biometric information,

[0828] An information processing device for analyzing this biological information and evaluating the stress state,

[0829] An information processing device that generates individual stress relief instructions for the user based on their stress level,

[0830] A system including a user communication device for notifying the user of generated instructions.

[0831] (Claim 2)

[0832] The information processing device, when the stress level exceeds a predetermined threshold,

[0833] The system according to claim 1, which prioritizes the generation of instructions.

[0834] (Claim 3)

[0835] We receive feedback from users and revise future instructions based on that feedback.

[0836] The system according to claim 1, comprising an information processing device.

[0837] "Application Example 1"

[0838] (Claim 1)

[0839] A device and means for collecting biological information,

[0840] A computing device for analyzing biological information and evaluating stress indicators,

[0841] A computing device means for generating individualized management advice based on stress indicators,

[0842] Information terminal means for notifying supervisors of generated management advice,

[0843] A computing device means for modifying management advice based on feedback provided by the supervisor,

[0844] A system that includes this.

[0845] (Claim 2)

[0846] The system according to claim 1, wherein the calculation device means preferentially generates advice when the stress index exceeds a pre-set threshold value.

[0847] (Claim 3)

[0848] The system according to claim 1, comprising a modification function that proposes stress relief methods based on feedback.

[0849] "Example 2 of combining an emotion engine"

[0850] (Claim 1)

[0851] Portable devices for measuring biological information,

[0852] An information processing device for evaluating psychological load by integrating and analyzing biological and emotional information,

[0853] An information processing device that rapidly generates countermeasures when psychological stress exceeds a certain threshold and negative emotions are detected,

[0854] A system including a user terminal for individualizing the generated countermeasures and notifying users.

[0855] (Claim 2)

[0856] The system according to claim 1, comprising an information processing function for improving the accuracy of analysis using emotional information.

[0857] (Claim 3)

[0858] The system according to claim 1, comprising an information processing device that receives evaluations from users and adjusts subsequent countermeasures based on those evaluations.

[0859] "Application example 2 when combining with an emotional engine"

[0860] (Claim 1)

[0861] A portable device for collecting biological signals,

[0862] An information processing device for analyzing these biosignals and emotional signals to evaluate emotional state and mental burden,

[0863] An information processing device that generates individual stress management guidelines for users based on their mental burden and emotional state,

[0864] A system including a user terminal for notifying users of the generated guidelines.

[0865] (Claim 2)

[0866] The information processing device will, if the mental burden exceeds a pre-set threshold,

[0867] The system according to claim 1, which preferentially generates guidelines.

[0868] (Claim 3)

[0869] We receive feedback from users and revise our guidelines for future updates based on that feedback.

[0870] The system according to claim 1, comprising an information processing device. [Explanation of Symbols]

[0871] 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 wearable device for collecting biometric data, A server device for analyzing this biological data and evaluating stress levels, A server device that generates individual stress management advice for users based on their stress levels, A system including a user terminal for notifying the user of the generated advice.

2. The server device will, if the stress level exceeds a pre-set threshold, The system according to claim 1, which prioritizes the generation of advice.

3. We receive user feedback and use that feedback to revise our advice for future updates. The system according to claim 1, comprising a server device.

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