system

Abstract

We provide the system. [Solution] A sensor means for collecting the user's biometric information, A processing means for analyzing the aforementioned biological information and evaluating emotional state and stress level, An advice means for generating and providing relaxation techniques to the user based on the evaluation, A feedback mechanism for receiving user feedback, An update means that adjusts the processing means and the advice means based on the feedback, A system that includes this.

Description

Technical Field

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

Background Art

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

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In modern society, problems related to stress and mental health are expanding, but there is a lack of means to provide effective and individualized support for this. Conventional mental care methods have difficulty in quickly and effectively responding to individual situations, and in particular, it is required to respond without delay to changes in emotional state and increases or decreases in stress levels in real time.

Means for Solving the Problems

[0005] This invention provides a sensor means for collecting the user's biometric information in real time, and a processing means for analyzing the data obtained thereby, enabling the evaluation of the user's emotional state and stress level. Furthermore, by including an advice means for generating and providing appropriate relaxation techniques to the user based on the evaluation results, it realizes personalized and rapid mental support. In addition, by including a feedback means for utilizing user feedback and an update means for adjusting the analysis and advice based on this information, the invention aims to improve service quality.

[0006] A "user" is someone who provides biometric information and receives relaxation techniques and feedback from the system.

[0007] "Biometric information" refers to data related to the user's physical condition, such as heart rate, skin potential response, and voice tone.

[0008] "Sensing means" refers to devices or equipment used to collect a user's biometric information in real time.

[0009] "Processing means" refers to a function within the system that analyzes biometric information and evaluates the user's emotional state and stress level.

[0010] "Advice tool" refers to a function that suggests appropriate relaxation techniques to the user based on the results of biometric data analysis.

[0011] "Feedback mechanism" refers to a function that collects opinions and feedback from users regarding relaxation techniques and other related matters.

[0012] "Update mechanisms" refer to functions that improve the quality of service by adjusting the system's processing and advice mechanisms based on user feedback. [Brief explanation of the drawing]

[0013] [Figure 1]This is a conceptual diagram showing an example of the configuration of a data processing system according to the first embodiment. [Figure 2] This is a conceptual diagram showing an example of the essential functions of a data processing device and a smart device according to the first embodiment. [Figure 3] This is a conceptual diagram showing an example of the configuration of a data processing system according to the second embodiment. [Figure 4] This is a conceptual diagram showing an example of the main functions of a data processing device and smart glasses according to the second embodiment. [Figure 5] This is a conceptual diagram showing an example of the configuration of a data processing system according to the third embodiment. [Figure 6] This is a conceptual diagram showing an example of the main functions of a data processing device and a headset-type terminal according to the third embodiment. [Figure 7] This is a conceptual diagram showing an example of the configuration of a data processing system according to the fourth embodiment. [Figure 8] This is a conceptual diagram showing an example of the main functions of a data processing device and a robot according to the fourth embodiment. [Figure 9] This shows an emotion map where multiple emotions are mapped. [Figure 10] This shows an emotion map where multiple emotions are mapped. [Figure 11] This is a sequence diagram showing the processing flow of the data processing system in Example 1. [Figure 12] This is a sequence diagram showing the processing flow of the data processing system in Application Example 1. [Figure 13] This is a sequence diagram showing the processing flow of the data processing system in Example 2, which incorporates an emotion engine. [Figure 14] This is a sequence diagram showing the processing flow of the data processing system in Application Example 2, which combines an emotion engine. [Modes for carrying out the invention]

[0014] Hereinafter, an example of an embodiment of the system relating to the technology of this disclosure will be described with reference to the attached drawings.

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

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

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

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

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

[0020] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B." That is, "A and / or B" means that it may be A alone, or B alone, or a combination of A and B. Furthermore, in this specification, the same concept as "A and / or B" applies when expressing three or more things linked by "and / or."

[0021] [First Embodiment]

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

[0023] As shown in Figure 1, the data processing system 10 includes a data processing device 12 and a smart device 14. An example of the data processing device 12 is a server.

[0024] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0025] The smart device 14 comprises a computer 36, a reception device 38, an output device 40, a camera 42, and a communication interface 44. The computer 36 comprises a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The reception device 38, output device 40, and camera 42 are also connected to the bus 52.

[0026] The reception device 38 is equipped with a touch panel 38A and a microphone 38B, etc., and receives user input. The touch panel 38A receives user input by detecting contact with an object (e.g., a pen or finger). The microphone 38B receives user input by detecting the user's voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the data indicating the user input.

[0027] The output device 40 includes a display 40A and a speaker 40B, and presents data to the user 20 by outputting the data in a form perceptible to the user 20 (e.g., audio and / or text). The display 40A displays visible information such as text and images according to instructions from the processor 46. The speaker 40B outputs audio according to instructions from the processor 46. The camera 42 is a small digital camera equipped with an optical system such as a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0028] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various types of information between processor 46 and processor 28 via network 54.

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

[0030] As shown in Figure 2, in the data processing device 12, a specific processing is performed by the processor 28. A specific processing program 56 is stored in the storage 32. The specific processing program 56 is an example of a "program" related to the technology of this disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0031] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0032] In the smart device 14, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The reception output program 60 is used in conjunction with a specific processing program 56 by the data processing system 10. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0033] Next, the specific processing performed by the specific processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the smart device 14 as the "terminal".

[0034] This invention relates to an AI system for supporting a user's mental health, comprising sensor means, processing means, advice means, feedback means, and update means. This system continuously collects biometric information through wearable devices or smartphones worn by the user, analyzes this data on a server to evaluate emotional state and stress levels, and proposes appropriate relaxation techniques to the user based on the results.

[0035] Sensor data collection

[0036] The device uses sensors built into the smartwatch to collect biometric information in real time, such as the user's heart rate, skin potential response, and ambient sounds. This allows for the acquisition of data on the user's physical and mental state in a natural state during their daily activities.

[0037] Data Analysis

[0038] The server receives biometric information transmitted from the terminal and analyzes the data using advanced machine learning algorithms. This analysis detects changes in the user's emotions and signs of stress, and determines whether intervention is necessary if required.

[0039] Providing advice

[0040] The server selects the most suitable relaxation method for the user based on the analysis results and sends that method to the terminal. The terminal then guides the user in real time through voice guidance and screen displays, showing the specific steps of the relaxation technique (e.g., deep breathing and simple stretching).

[0041] Processing user feedback

[0042] The user provides feedback on the effectiveness of the suggested relaxation techniques. The device sends this feedback to the server, which then adjusts the analysis model and suggested methods based on it to provide better personalized support.

[0043] Specific example

[0044] For example, if a user in a stressful office environment has a higher-than-normal heart rate and increased skin electrical activity, the server recognizes this as a sign of stress. The server suggests the user try deep breathing and sends a guidance message to the device. After the user practices the suggested method and confirms its effectiveness, they provide feedback, which the server then uses to refine future suggestions.

[0045] Thus, the present invention provides mental care tailored to the individual needs of users and serves as a valuable tool for supporting their mental health.

[0046] The following describes the processing flow.

[0047] Step 1:

[0048] The terminal uses sensors built into the user's smart device to collect biometric information in real time. This includes data such as heart rate, skin potential response, and voice tone. The collected data is sent to a server for the next analysis step.

[0049] Step 2:

[0050] The server receives biometric information transmitted from the terminal and stores it. The stored data is then preprocessed to establish a baseline by comparing it with the user's past history data.

[0051] Step 3:

[0052] The server applies advanced machine learning algorithms to analyze the received biometric data. This analysis detects changes in the user's emotional state and increases in stress levels.

[0053] Step 4:

[0054] Based on the analysis results, the server selects appropriate relaxation techniques for the user. The selected advice is sent to the terminal as specific guidelines in text or audio format.

[0055] Step 5:

[0056] The device notifies the user of relaxation technique advice sent from the server. The notification is communicated to the user via a pop-up message or voice assistant.

[0057] Step 6:

[0058] The user performs the suggested relaxation techniques and checks their effects. Based on the results, they enter feedback on their device.

[0059] Step 7:

[0060] The terminal sends user feedback data to the server. This data is used to analyze the system and improve the advice techniques.

[0061] Step 8:

[0062] The server analyzes the feedback data and uses it to update the machine learning model, improving the accuracy of future suggestions. This prepares it for more advanced personalization in the next user session.

[0063] (Example 1)

[0064] 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."

[0065] In modern society, stress and emotional fluctuations are major factors that significantly impact health. Conventional methods have made it difficult to accurately assess these conditions in real time and implement optimal individual countermeasures. Furthermore, there has been a lack of sufficient means to continuously improve the system by incorporating user feedback. Therefore, this invention aims to evaluate stress levels based on the user's biometric information, provide individually tailored relaxation methods, and optimize the system based on feedback.

[0066] 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.

[0067] In this invention, the server includes information acquisition means for collecting the user's biometric information, information processing means for analyzing the biometric information to evaluate the emotional state and stress level, and guidance means for generating a relaxation method based on the evaluation using a generation AI model and providing it to the user. This enables the provision of optimal mental care tailored to the user's individual needs in real time, thereby improving their health.

[0068] "Information acquisition means" refers to devices and technologies for collecting biometric information from users, and includes sensors that detect heart rate, skin potential responses, environmental sounds, etc.

[0069] "Information processing means" refers to software and algorithms for analyzing collected biometric information and evaluating the user's emotional state and stress level.

[0070] A "generative AI model" is a system of programs that uses machine learning to generate relaxation methods from data, and is a model trained to provide suggestions tailored to the user.

[0071] A "guidance tool" is an interface that provides users with generated relaxation methods, guiding them through audio guides and text displays.

[0072] A "response acquisition mechanism" is a system that allows users to provide feedback on the effectiveness of relaxation methods, and acquires this information through input devices or software.

[0073] "Information update means" refers to means for adjusting and optimizing the content of information processing means and guidance means within the system based on user feedback.

[0074] The present invention is a system for supporting the mental health of users, and includes means for acquiring information, means for processing information, means for providing guidance using a generative AI model, means for acquiring responses, and means for updating information.

[0075] This system collects biometric information using smart devices, which are portable digital devices worn by users on a daily basis. Multiple sensing devices built into the terminal acquire heart rate, skin potential responses, and ambient sounds in real time.

[0076] The server uses TENSORFLOW®, a machine learning library that utilizes Python as its information processing tool, to analyze the collected biometric data. This allows it to evaluate the user's emotional state and stress level, and to derive appropriate relaxation methods.

[0077] The guidance system using a generative AI model generates relaxation methods in real time based on analysis results and provides them to the user in audio data or text format. For example, when stress is detected, it suggests a simple instruction such as, "Take deep breaths for one minute."

[0078] The user performs the provided relaxation method and provides feedback on the results. The terminal sends the acquired feedback to the server, and based on the feedback, adjusts the content of the information processing means and guidance means to improve the accuracy of the system.

[0079] This process adapts to the user's situation and needs, individually optimizing mental care. The generative AI model learns from user data and evolves to provide more precise advice. Specific prompts might include phrases like, "Assess the user's stress level and suggest appropriate relaxation techniques if necessary."

[0080] Through such embodiments, the present invention provides a function that supports the mental health of the user.

[0081] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0082] Step 1:

[0083] The device collects the user's biometric information. Specifically, sensors built into the smart device measure heart rate, skin potential response, and ambient sound in real time. The input is sensor data, and the output is organized user biometric information. This data is temporarily stored in a process within the smart device.

[0084] Step 2:

[0085] The device transmits the collected biometric information to the server. The data is securely encrypted and transmitted via Wi-Fi or a mobile network. The input is the biometric information collected in the previous step, and the output is the received data for processing on the server. At this stage, the data integrity is checked, and it is processed to ensure that no missing or abnormal data is detected.

[0086] Step 3:

[0087] The server uses TensorFlow to analyze the received biometric data. Here, machine learning algorithms operate to assess stress levels and emotional state. The input is biometric data sent from the terminal, and the output is an assessment of the user's mental state based on the analysis. This assessment is recorded in a database and forms the basis for subsequent actions.

[0088] Step 4:

[0089] The server uses a generative AI model to select the optimal relaxation method based on the analysis results. The generative AI model considers the user's emotional state and derives specific relaxation techniques. The input is the evaluation result of the emotional state, and the output is the generated relaxation prompt. The prompt includes suggestions such as "Take deep breaths for one minute."

[0090] Step 5:

[0091] The terminal guides the user through relaxation methods sent from the server via voice and text. The terminal screen displays the steps and visual instructions, while voice guidance is provided simultaneously. The input is the generated prompt, and the output is a call to action from the user by sequentially conveying instructions.

[0092] Step 6:

[0093] Users perform activities according to relaxation instructions and then input feedback about their state and effects into a terminal. The input consists of the user's own feelings and results after relaxation, while the output is data sent to the server as feedback. Users record the changes they experienced using a simple input format.

[0094] Step 7:

[0095] The server updates its analysis models and guidance methods based on user feedback data. It retrains machine learning models to improve overall system accuracy by considering the feedback information. The input is user feedback, and the output is the improved analysis model. This update evolves to make subsequent relaxation suggestions more personalized.

[0096] (Application Example 1)

[0097] 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."

[0098] In recent years, stress and mental health problems have increased dramatically, creating a need for appropriate care that users can easily access in their daily lives. However, existing systems make it difficult for users to receive personalized mental care tailored to their individual circumstances within their homes. Solving this problem is essential.

[0099] 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.

[0100] In this invention, the server includes a sensor device that collects the user's biometric information, an information processing device that analyzes the biometric information to evaluate the emotional state and stress level, and an advisory device that generates and provides relaxation techniques to the user based on the evaluation. This makes it possible to analyze the user's biometric information in real time and provide appropriate relaxation methods tailored to the individual's condition through a home-use device.

[0101] "User biometric information" refers to information that indicates the physiological state of the body, such as the user's heart rate and skin potential response.

[0102] A "sensor device" is a device used to detect a user's biometric information in real time.

[0103] An "information processing device" is a device that analyzes collected biometric information and evaluates the user's emotional state and stress level.

[0104] "Relaxation techniques" refer to specific methods and techniques used to reduce the mental and physical stress of users.

[0105] An "advice device" is a device that provides users with relaxation techniques generated based on evaluations performed by an information processing device.

[0106] An "update device" is a device that has the function of adjusting the information processing device and the advisory device based on feedback from the user.

[0107] "Household appliances" refer to multi-functional devices and equipment used in the home, particularly those designed to support the user's mental health.

[0108] A "multifunctional sensor" is a device that has sensors capable of simultaneously acquiring multiple pieces of biological information.

[0109] To realize this invention, the user needs to wear a sensor device that collects biometric information in real time. The sensor device acquires data such as heart rate and skin potential response and transmits it to a server via a mobile device. The server analyzes this biometric data using an information processing device to evaluate the user's emotional state and stress level.

[0110] Based on the evaluation results, the server utilizes a generated AI model to select the optimal relaxation technique. This technique is presented to the user in audio or visual format via an advisory device. The advice is transmitted using the user's mobile device or home equipment and is provided as a visual or audio guide.

[0111] For example, if a user's heart rate exceeds a certain threshold, the server will suggest relaxation techniques such as deep breathing or stretching. This allows the user to immediately practice the appropriate methods and improve their mental health.

[0112] Furthermore, a response device receives user feedback, which is then processed by a server using an update device. This feedback is reflected in the analysis model and used to improve the accuracy of future relaxation technology proposals.

[0113] As a concrete example, using a generated AI prompt such as, "Create a prompt suggesting appropriate relaxation techniques when the user's heart rate and skin potential response are elevated," the system can select the optimal relaxation method and communicate it to the user.

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

[0115] Step 1:

[0116] The device acquires the user's biometric information via sensor devices (heart rate sensor and skin potential sensor). The input is data obtained from the biosensors, and the output is real-time collected biometric information. This data is transmitted to the server via a secure protocol.

[0117] Step 2:

[0118] The server receives biometric information transmitted from the terminal. The input is biometric information from the terminal, and the output is biometric data ready for analysis. Data cleaning is performed here, removing outliers and sorting by time.

[0119] Step 3:

[0120] The server uses an information processing device to analyze biometric data and evaluate the user's emotional state and stress level. The input is cleaned biometric data, and the output is the evaluation result of the stress level and emotional state. This analysis utilizes a machine learning model to perform pattern recognition based on past data.

[0121] Step 4:

[0122] The server uses a generated AI model to select the optimal relaxation technique based on the evaluation results. The input is the evaluation results of emotional state and stress level, and the output is the selected relaxation technique. By generating prompt sentences and feeding them into this model, a specific technique is identified.

[0123] Step 5:

[0124] The server transmits the selected relaxation technology to the terminal via an advisory device. The input is detailed information about the relaxation technology, and the output is the data to be transmitted to the terminal. In this process, audio or visual content is selected as the means of transmission.

[0125] Step 6:

[0126] The user practices the suggested relaxation techniques and sends feedback about their effects to the server via their device. The input is the user's feedback, and the output is the feedback information received by the server. The feedback is in the form of a simple questionnaire, inquiring about the effectiveness and satisfaction level.

[0127] Step 7:

[0128] The server receives feedback via a response device, and the update device adjusts the information processing device and the advisory device. The input is user feedback information, and the output is updated system parameters. This update improves the accuracy of suggestions in subsequent sessions.

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

[0130] This invention relates to an AI mental care system incorporating an emotion engine that recognizes the user's emotions, and is comprised of sensor means, processing means, advice means, feedback means, update means, and the emotion engine. This system has the ability to collect biometric information through a smart device worn by the user, analyze it on a server to identify the user's emotional state and stress level, and propose relaxation techniques accordingly.

[0131] Sensor data collection and emotion recognition

[0132] The device collects biometric information, including heart rate, skin potential, and voice, from smartwatches and smartphones. In addition, it uses the device's camera to monitor the user's facial expressions. This data is transmitted to a server in real time.

[0133] Data analysis and emotional judgment

[0134] The server receives multimodal biometric data transmitted from the terminal and uses an emotion engine to recognize the user's emotional state. The emotion engine combines voice tone analysis, facial expression recognition, and emotion estimation from text to comprehensively understand the user's current emotional state.

[0135] Suggestions for relaxation techniques

[0136] Based on the analysis results from the emotion engine, the server generates appropriate relaxation techniques for the user. For example, for users experiencing heightened tension or anxiety, it suggests relaxation activities such as meditation or deep breathing. This advice is communicated to the user via voice guidance and text messages through the device.

[0137] Collecting user feedback and updating the system

[0138] Users can provide feedback on the effectiveness and satisfaction level of the relaxation techniques they have practiced. This feedback is sent from the device to the server and used to improve the system and the emotion engine's algorithms. This process allows the system to continuously provide personalized care for each user.

[0139] Specific example

[0140] For example, if a user experiences stress while working at the office and their heart rate spikes, the emotion engine on the server, based on the simultaneously detected tense facial expression, determines that the user is in an anxious state. Based on this result, the device notifies the user via voice and text with advice such as, "Take a short meditation to calm your mind." By providing feedback on the user's meditation experience, the relaxation plan provided in subsequent sessions can be further optimized.

[0141] In this way, the present invention makes it possible to grasp the user's real-time emotional changes and continuously provide personalized mental care.

[0142] The following describes the processing flow.

[0143] Step 1:

[0144] The device utilizes the smartwatch's sensors to collect the user's biometric information, acquiring heart rate, skin potential responses, voice data, and other data in real time. It also monitors the user's facial expressions via the camera and transmits this data to a server.

[0145] Step 2:

[0146] The server integrates biometric information and facial expression data received from the terminal and analyzes it using an emotion engine. The emotion engine combines voice tone, facial features, and text data analysis to identify the user's emotional state. In this process, emotional categories such as "anxiety," "happiness," and "anger" are recognized.

[0147] Step 3:

[0148] Based on the output of the emotion engine, the server selects appropriate relaxation techniques according to the user's current emotional state and stress level. For example, if anxiety is detected, it may recommend deep breathing exercises or short meditation sessions. These suggestions are then formatted and sent to the terminal.

[0149] Step 4:

[0150] The device displays relaxation techniques received from the server on the user's smartphone or smartwatch screen, and also provides voice guidance. The user receives instructions through the screen and performs the relaxation techniques.

[0151] Step 5:

[0152] Users provide feedback on the effectiveness and satisfaction level of relaxation techniques they have tried, entering the feedback via their device. This feedback is often provided through text input or questionnaires.

[0153] Step 6:

[0154] The device sends feedback data received from the user to the server. The server analyzes this feedback information, incorporates it into the emotion engine and advice methods, and updates the algorithm to improve the accuracy of support in the future.

[0155] Throughout this entire process, the system continuously provides individualized, real-time support for the user's mental health.

[0156] (Example 2)

[0157] 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".

[0158] In modern society, it is crucial to recognize users' emotional states and stress levels in real time and provide personalized mental care. However, conventional technologies have struggled to accurately grasp users' emotions and precisely suggest appropriate relaxation techniques. In particular, integrating information from multiple data sources to achieve highly accurate emotion recognition remains an unresolved challenge.

[0159] 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.

[0160] In this invention, the server includes sensor means for collecting the user's biometric information, processing means for analyzing the biometric information to evaluate the user's emotional state and stress level, and means equipped with an emotion engine that performs emotion recognition by combining voice analysis, facial expression recognition, and text estimation. This makes it possible to evaluate the user's emotions from multiple perspectives and provide appropriate relaxation technology tailored to each individual.

[0161] "Sensor means" refers to devices or functions for collecting a user's biometric information, enabling the acquisition of information such as heart rate, skin potential, and voice.

[0162] "Processing means" refers to a part of a system that has the function of analyzing collected biometric information and evaluating the user's emotional state and stress level.

[0163] An "emotion engine" refers to technologies and programs that combine voice analysis, facial recognition, and text estimation to comprehensively recognize a user's emotions.

[0164] "Advice means" refers to a function that provides users with relaxation techniques generated based on evaluations by processing means, and is particularly presented in the form of audio or text.

[0165] A "feedback mechanism" is a function for receiving opinions and feedback from users, which is used to improve and optimize the system.

[0166] "Update mechanism" refers to a function that adjusts processing and advice mechanisms based on user feedback to improve the overall operation of the system.

[0167] A "multimodal sensor" refers to a sensor that can acquire and integrate data from different types of sensors, enabling it to process information from multiple data sources in an integrated manner.

[0168] This invention relates to a system for analyzing a user's emotional state and stress level in real time and providing appropriate relaxation techniques. The system mainly consists of sensor means, processing means, emotion engine, advice means, feedback means, and update means.

[0169] The device uses a smartwatch or smartphone attached to the user's body to collect a variety of biosignals, including heart rate, skin potential, voice data, and even facial expressions captured using a camera. This data is transmitted to a server in real time.

[0170] The server analyzes the data received from these terminals. This analysis utilizes an emotion engine that integrates voice tone analysis, facial expression recognition, and emotion estimation from text. This emotion engine combines a wide variety of data to comprehensively recognize the user's emotional state.

[0171] After evaluating the user's emotional state, the server generates relaxation techniques tailored to that state. This process uses specific algorithms and generative AI models to suggest the most suitable methods for the user, such as meditation or deep breathing. The server sends these suggestions to the device, which then notifies the user of the advice via voice or text.

[0172] Subsequently, users try these relaxation techniques and provide feedback on their effects via their device. This feedback is received by the server and used to improve the system's overall processing and advice mechanisms. By repeating this process, the system can provide more personalized care to each user.

[0173] As a concrete example of its use, if a user experiences stress at work, this accumulated stress is detected as an increase in heart rate or facial tension. The server analyzes this and sequentially notifies the user of appropriate relaxation techniques. The user follows the suggestions and provides feedback on the results, which further optimizes the advice for future sessions.

[0174] An example of a prompt message for the generating AI model is, "Based on heart rate, facial expressions, and voice data, identify the user's emotions and generate appropriate relaxation techniques." This enables optimal care tailored to the user's emotions and stress levels.

[0175] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0176] Step 1:

[0177] The device collects the user's biometric information. Smartwatches and smartphone sensors capture heart rate, skin potential, voice, and facial expressions using cameras as input. This data is transmitted to a server in real time. Specifically, the device collects biometric signals captured by sensors at regular intervals, bundles them into data packets, and sends them to the server.

[0178] Step 2:

[0179] The server receives biometric information from the terminal. It integrates heart rate changes, skin potential fluctuations, voice tone, and facial expression image data as input. The server preprocesses this data, standardizing it according to its respective data format, in preparation for processing. As output, it converts the biometric data into an analyzable format.

[0180] Step 3:

[0181] The server analyzes biometric information using an emotion engine. It uses pre-processed multimodal data as input, performing voice analysis, facial expression recognition, and emotion estimation from text. Specifically, the emotion engine reads emotions from the rhythm and intonation of speech, analyzes facial expressions based on a model, and estimates emotional tendencies from text information. The output is numerical data representing the user's emotional state.

[0182] Step 4:

[0183] The server generates appropriate relaxation techniques based on the user's emotional state. It uses the emotional state and stress level, output from an emotional engine, as input. The server utilizes a generative AI model to algorithmically determine the appropriate relaxation method for each emotional state. Specifically, the generative AI model selects the most suitable action for the user's current situation and outputs it as a feasible relaxation plan.

[0184] Step 5:

[0185] The server sends the generated relaxation technology to the terminal, and the terminal notifies the user of the relaxation technology. The input is the generated relaxation plan, formatted as voice guidance and text messages. The terminal utilizes its notification function to provide the user with specific instructions and advice. The output is the relaxation advice received by the user.

[0186] Step 6:

[0187] The user performs the recommended relaxation technique and provides feedback. The input is the user's opinion on the effectiveness and satisfaction level of the relaxation technique performed, entered into a terminal. This feedback is collected by the terminal and sent to the server. The output is the feedback information recorded on the server for future service optimization.

[0188] Step 7:

[0189] The server updates the system using user feedback. It analyzes feedback data as input and adjusts processing and advice methods. Specifically, it uses feedback information as training data for the AI ​​model to improve recognition accuracy and suggestion quality in subsequent uses. The output includes the updated model and system settings.

[0190] (Application Example 2)

[0191] 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".

[0192] In modern urban environments, many people experience stress and emotional instability, but there is a lack of effective systems to detect this in real time and respond to individual needs. Furthermore, there is a need for a system that effectively understands and utilizes stress levels across the entire city.

[0193] 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.

[0194] In this invention, the server includes detection means for collecting user biometric information, analysis means for analyzing the biometric information to evaluate emotional state and stress level, and display means for visualizing stress levels in an urban environment. This makes it possible to monitor the emotional state of individual users in real time and provide personalized relaxation methods, while simultaneously enabling stress management and environmental improvement throughout the city.

[0195] A "detection means for collecting user biometric information" is a device that uses multiple sensors installed in a smart device to collect biometric data such as heart rate, facial expressions, and voice.

[0196] "Analysis means for analyzing biometric information to evaluate emotional state and stress level" refers to a device or program that performs processing to comprehensively determine the user's emotions and stress levels based on collected biometric data.

[0197] A "means for generating and providing relaxation methods to users" is a device that has the function of recommending actions to relieve tension, such as meditation or deep breathing, to the user according to their analyzed emotional state.

[0198] A "feedback receiving mechanism" is a device that has the function of obtaining information from users regarding the effectiveness and satisfaction level of relaxation methods, and using that information to improve the overall system.

[0199] "Update means for adjusting analysis means and proposal means based on feedback" refers to a device or program for controlling the process of improving the accuracy of analysis and refining proposals based on feedback information from users.

[0200] A "display method for visualizing stress levels in urban environments" is a system that visually shows stress levels within a city using maps, graphs, etc., based on collected data.

[0201] This invention is a system that provides personalized relaxation methods by monitoring a user's biometric information and analyzing their emotional and stress levels in real time. This is achieved by utilizing the user's smart devices and terminals installed in urban facilities.

[0202] The server uses multiple hardware and software components to process biometric information collected from smart devices. Specifically, it employs a composite sensor embedded in the smart device as a detection means. This allows for the real-time collection of data such as heart rate, facial expressions, and voice. Next, the analysis means uses natural language processing APIs from Google Cloud and IBM Watson to analyze this biometric data and evaluate the user's emotional state.

[0203] Based on the analysis results, the server generates relaxation methods through suggestion mechanisms. For example, it may suggest methods such as meditation or deep breathing to the user. These suggestions are notified to the user's device in the form of voice or text. If the user feels stressed while jogging or walking, the device can notify them with a prompt message such as, "Your heart rate is rising rapidly. Take a deep breath now to reset your emotions."

[0204] After performing these relaxation methods, users provide feedback to the server through a response mechanism regarding their effectiveness and satisfaction level. This feedback information is used to improve the overall system performance using an update mechanism and is stored as data to improve the accuracy of future analyses and suggestions.

[0205] Furthermore, to visualize stress levels in urban environments, the data analyzed via display devices can be visualized in map and graph formats and shared on displays installed in public facilities. This allows urban administrators and the general public to identify areas where stress is concentrated and take necessary countermeasures.

[0206] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0207] Step 1:

[0208] The device collects biometric information such as the user's heart rate, facial expressions, and voice in real time using a combination of sensors. This data becomes input, and the device packages it and sends it to the server.

[0209] Step 2:

[0210] The server processes the received biometric data using analytical tools. Using natural language processing APIs from Google Cloud and IBM Watson, it evaluates emotional states based on heart rate, voice tone, and changes in facial expressions. This analysis visualizes stress levels and emotional changes, and emotional state data is output.

[0211] Step 3:

[0212] The server generates appropriate relaxation methods based on this emotional state data. This process determines the content of the relaxation suggestions. Specifically, a prompt message using a generative AI model, such as "Take a deep breath and relax," is created and output.

[0213] Step 4:

[0214] The server delivers the generated prompt message to the terminal as both an audio and text message. The terminal receives this message and notifies the user. In this step, the user can learn the appropriate relaxation method.

[0215] Step 5:

[0216] The user performs the suggested relaxation method and sends feedback regarding its effectiveness and satisfaction level to the server via their device. This feedback data is then used as input for the next step.

[0217] Step 6:

[0218] The server receives feedback from users and updates the analysis and suggestion methods based on that feedback. This update process improves the accuracy of subsequent analyses and suggestions. The updated algorithm is output and applied to the entire system.

[0219] Step 7:

[0220] The server uses the analyzed data and feedback to visualize the distribution of stress in the urban environment. This is then output as maps and graphs using display devices and shown on displays placed in public facilities. This allows for a visual understanding of stress hotspots within the city.

[0221] 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.

[0222] 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.

[0223] 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.

[0224] [Second Embodiment]

[0225] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0226] 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.

[0227] 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).

[0228] 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.

[0229] 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.

[0230] 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).

[0231] 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.

[0232] 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.

[0233] 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.

[0234] 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.

[0235] 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.

[0236] 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".

[0237] This invention relates to an AI system for supporting a user's mental health, comprising sensor means, processing means, advice means, feedback means, and update means. This system continuously collects biometric information through wearable devices or smartphones worn by the user, analyzes this data on a server to evaluate emotional state and stress levels, and proposes appropriate relaxation techniques to the user based on the results.

[0238] Sensor data collection

[0239] The device uses sensors built into the smartwatch to collect biometric information in real time, such as the user's heart rate, skin potential response, and ambient sounds. This allows for the acquisition of data on the user's physical and mental state in a natural state during their daily activities.

[0240] Data Analysis

[0241] The server receives biometric information transmitted from the terminal and analyzes the data using advanced machine learning algorithms. This analysis detects changes in the user's emotions and signs of stress, and determines whether intervention is necessary if required.

[0242] Providing advice

[0243] The server selects the most suitable relaxation method for the user based on the analysis results and sends that method to the terminal. The terminal then guides the user in real time through voice guidance and screen displays, showing the specific steps of the relaxation technique (e.g., deep breathing and simple stretching).

[0244] Processing user feedback

[0245] The user provides feedback on the effectiveness of the suggested relaxation techniques. The device sends this feedback to the server, which then adjusts the analysis model and suggested methods based on it to provide better personalized support.

[0246] Specific example

[0247] For example, if a user in a stressful office environment has a higher-than-normal heart rate and increased skin electrical activity, the server recognizes this as a sign of stress. The server suggests the user try deep breathing and sends a guidance message to the device. After the user practices the suggested method and confirms its effectiveness, they provide feedback, which the server then uses to refine future suggestions.

[0248] Thus, the present invention provides mental care tailored to the individual needs of users and serves as a valuable tool for supporting their mental health.

[0249] The following describes the processing flow.

[0250] Step 1:

[0251] The terminal uses sensors built into the user's smart device to collect biometric information in real time. This includes data such as heart rate, skin potential response, and voice tone. The collected data is sent to a server for the next analysis step.

[0252] Step 2:

[0253] The server receives biometric information transmitted from the terminal and stores it. The stored data is then preprocessed to establish a baseline by comparing it with the user's past history data.

[0254] Step 3:

[0255] The server applies advanced machine learning algorithms to analyze the received biometric data. This analysis detects changes in the user's emotional state and increases in stress levels.

[0256] Step 4:

[0257] Based on the analysis results, the server selects appropriate relaxation techniques for the user. The selected advice is sent to the terminal as specific guidelines in text or audio format.

[0258] Step 5:

[0259] The device notifies the user of relaxation technique advice sent from the server. The notification is communicated to the user via a pop-up message or voice assistant.

[0260] Step 6:

[0261] The user performs the suggested relaxation techniques and checks their effects. Based on the results, they enter feedback on their device.

[0262] Step 7:

[0263] The terminal sends user feedback data to the server. This data is used to analyze the system and improve the advice techniques.

[0264] Step 8:

[0265] The server analyzes the feedback data and uses it to update the machine learning model, improving the accuracy of future suggestions. This prepares it for more advanced personalization in the next user session.

[0266] (Example 1)

[0267] 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."

[0268] In modern society, stress and emotional fluctuations are major factors that significantly impact health. Conventional methods have made it difficult to accurately assess these conditions in real time and implement optimal individual countermeasures. Furthermore, there has been a lack of sufficient means to continuously improve the system by incorporating user feedback. Therefore, this invention aims to evaluate stress levels based on the user's biometric information, provide individually tailored relaxation methods, and optimize the system based on feedback.

[0269] 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.

[0270] In this invention, the server includes information acquisition means for collecting the user's biometric information, information processing means for analyzing the biometric information to evaluate the emotional state and stress level, and guidance means for generating a relaxation method based on the evaluation using a generation AI model and providing it to the user. This enables the provision of optimal mental care tailored to the user's individual needs in real time, thereby improving their health.

[0271] "Information acquisition means" refers to devices and technologies for collecting biometric information from users, and includes sensors that detect heart rate, skin potential responses, environmental sounds, etc.

[0272] "Information processing means" refers to software and algorithms for analyzing collected biometric information and evaluating the user's emotional state and stress level.

[0273] A "generative AI model" is a system of programs that uses machine learning to generate relaxation methods from data, and is a model trained to provide suggestions tailored to the user.

[0274] A "guidance tool" is an interface that provides users with generated relaxation methods, guiding them through audio guides and text displays.

[0275] A "response acquisition mechanism" is a system that allows users to provide feedback on the effectiveness of relaxation methods, and acquires this information through input devices or software.

[0276] "Information update means" refers to means for adjusting and optimizing the content of information processing means and guidance means within the system based on user feedback.

[0277] The present invention is a system for supporting the mental health of users, and includes means for acquiring information, means for processing information, means for providing guidance using a generative AI model, means for acquiring responses, and means for updating information.

[0278] This system uses a smart device, a portable digital device that users wear daily, to collect biological information. Multiple sensing devices installed on the terminal acquire heart rate, skin potential response, and environmental sound in real time.

[0279] The server applies TensorFlow, a machine learning library using Python as the information processing means, to analyze the collected biological information. Thereby, it evaluates the user's emotional state and stress level and derives appropriate relaxation methods.

[0280] The guidance means using the generative AI model generates relaxation methods in real time based on the analysis results and provides them to the user in the form of voice data or character information. For example, when stress is detected, it proposes a simple instruction such as "Take a deep breath for one minute."

[0281] The user implements the provided relaxation method and gives feedback on the result. The terminal sends the acquired feedback to the server, adjusts the content of the information processing means and the guidance means based on the feedback, and improves the accuracy of the system.

[0282] This process adapts according to the user's situation and needs and optimizes mental care individually. The generative AI model learns based on user data and evolves to provide more refined advice. Specific prompt sentences include "Evaluate the user's stress level and propose appropriate relaxation techniques if necessary."

[0283] According to such an embodiment, the present invention provides a function of supporting the mental health of users.

[0284] The flow of the specific process in Example 1 will be described using FIG. 11.

[0285] Step 1:

[0286] The terminal collects the user's biometric information. Specifically, sensors installed on the smart device measure the heart rate, galvanic skin response, and ambient sound in real time. The input is sensor data, and the output is the organized user biometric information. This data is temporarily stored in a process within the smart device.

[0287] Step 2:

[0288] The terminal sends the collected biometric information to the server. The data is securely encrypted and transmitted via Wi-Fi or a mobile network. The input is the biometric information collected in the previous step, and the output is the received data for processing on the server. Data integrity checks are performed at this stage and processed so that no missing or abnormal data is detected.

[0289] Step 3:

[0290] The server uses TensorFlow to analyze the received biometric information. Here, a machine learning algorithm operates to evaluate the stress level and emotional state. The input is the biometric information sent from the terminal, and the output is the evaluation result of the user's mental state based on the analysis. This evaluation is recorded in the database and serves as the basis for the next action.

[0291] Step 4:

[0292] The server selects the optimal relaxation method using a generated AI model based on the analysis results. The generated AI model takes into account the user's emotional state and derives a specific relaxation method. The input is the evaluation result of the emotional state, and the output is the generated relaxation prompt. The prompt includes suggestions such as "Take a deep breath for one minute."

[0293] Step 5:

[0294] The terminal guides the user through relaxation methods sent from the server via voice and text. The terminal screen displays the steps and visual instructions, while voice guidance is provided simultaneously. The input is the generated prompt, and the output is a call to action from the user by sequentially conveying instructions.

[0295] Step 6:

[0296] Users perform activities according to relaxation instructions and then input feedback about their state and effects into a terminal. The input consists of the user's own feelings and results after relaxation, while the output is data sent to the server as feedback. Users record the changes they experienced using a simple input format.

[0297] Step 7:

[0298] The server updates its analysis models and guidance methods based on user feedback data. It retrains machine learning models to improve overall system accuracy by considering the feedback information. The input is user feedback, and the output is the improved analysis model. This update evolves to make subsequent relaxation suggestions more personalized.

[0299] (Application Example 1)

[0300] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server," and the smart glasses 214 will be referred to as the "terminal."

[0301] In recent years, stress and mental health problems have increased dramatically, creating a need for appropriate care that users can easily access in their daily lives. However, existing systems make it difficult for users to receive personalized mental care tailored to their individual circumstances within their homes. Solving this problem is essential.

[0302] The specific processing by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0303] In this invention, the server includes a sensor device that collects the user's biological information, an information processing device that analyzes the biological information to evaluate the emotional state and stress level, and an advice device that generates relaxation techniques based on the evaluation and provides them to the user. As a result, it becomes possible to analyze the user's biological information in real time and provide an appropriate relaxation method according to the individual's state through household devices.

[0304] "The user's biological information" refers to information indicating the physiological state of the body, such as the user's heart rate and galvanic skin response.

[0305] "The sensor device" is a device used to detect the user's biological information in real time.

[0306] "The information processing device" is a device that analyzes the collected biological information and plays a role in evaluating the user's emotional state and stress level.

[0307] "The relaxation technique" refers to specific methods and techniques used to reduce the user's mental and physical stress.

[0308] "The advice device" is a device for providing the relaxation technique generated based on the evaluation in the information processing device to the user.

[0309] "The update device" is a device having a function of adjusting the information processing device and the advice device based on feedback from the user.

[0310] "The household device" refers to multifunctional devices and equipment used within the home, especially for supporting the user's mental care.

[0311] "The multifunctional sensor" refers to a device having a sensor capable of simultaneously acquiring a plurality of biological information.

[0312] To realize this invention, the user needs to wear a sensor device that collects biometric information in real time. The sensor device acquires data such as heart rate and skin potential response and transmits it to a server via a mobile device. The server analyzes this biometric data using an information processing device to evaluate the user's emotional state and stress level.

[0313] Based on the evaluation results, the server utilizes a generated AI model to select the optimal relaxation technique. This technique is presented to the user in audio or visual format via an advisory device. The advice is transmitted using the user's mobile device or home equipment and is provided as a visual or audio guide.

[0314] For example, if a user's heart rate exceeds a certain threshold, the server will suggest relaxation techniques such as deep breathing or stretching. This allows the user to immediately practice the appropriate methods and improve their mental health.

[0315] Furthermore, a response device receives user feedback, which is then processed by a server using an update device. This feedback is reflected in the analysis model and used to improve the accuracy of future relaxation technology proposals.

[0316] As a concrete example, using a generated AI prompt such as, "Create a prompt suggesting appropriate relaxation techniques when the user's heart rate and skin potential response are elevated," the system can select the optimal relaxation method and communicate it to the user.

[0317] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0318] Step 1:

[0319] The device acquires the user's biometric information via sensor devices (heart rate sensor and skin potential sensor). The input is data obtained from the biosensors, and the output is real-time collected biometric information. This data is transmitted to the server via a secure protocol.

[0320] Step 2:

[0321] The server receives biometric information transmitted from the terminal. The input is biometric information from the terminal, and the output is biometric data ready for analysis. Data cleaning is performed here, removing outliers and sorting by time.

[0322] Step 3:

[0323] The server uses an information processing device to analyze biometric data and evaluate the user's emotional state and stress level. The input is cleaned biometric data, and the output is the evaluation result of the stress level and emotional state. This analysis utilizes a machine learning model to perform pattern recognition based on past data.

[0324] Step 4:

[0325] The server uses a generated AI model to select the optimal relaxation technique based on the evaluation results. The input is the evaluation results of emotional state and stress level, and the output is the selected relaxation technique. By generating prompt sentences and feeding them into this model, a specific technique is identified.

[0326] Step 5:

[0327] The server transmits the selected relaxation technology to the terminal via an advisory device. The input is detailed information about the relaxation technology, and the output is the data to be transmitted to the terminal. In this process, audio or visual content is selected as the means of transmission.

[0328] Step 6:

[0329] The user practices the suggested relaxation techniques and sends feedback about their effects to the server via their device. The input is the user's feedback, and the output is the feedback information received by the server. The feedback is in the form of a simple questionnaire, inquiring about the effectiveness and satisfaction level.

[0330] Step 7:

[0331] The server receives feedback via a response device, and the update device adjusts the information processing device and the advisory device. The input is user feedback information, and the output is updated system parameters. This update improves the accuracy of suggestions in subsequent sessions.

[0332] 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.

[0333] This invention relates to an AI mental care system incorporating an emotion engine that recognizes the user's emotions, and is comprised of sensor means, processing means, advice means, feedback means, update means, and the emotion engine. This system has the ability to collect biometric information through a smart device worn by the user, analyze it on a server to identify the user's emotional state and stress level, and propose relaxation techniques accordingly.

[0334] Sensor data collection and emotion recognition

[0335] The device collects biometric information, including heart rate, skin potential, and voice, from smartwatches and smartphones. In addition, it uses the device's camera to monitor the user's facial expressions. This data is transmitted to a server in real time.

[0336] Data analysis and emotional judgment

[0337] The server receives multimodal biometric data transmitted from the terminal and uses an emotion engine to recognize the user's emotional state. The emotion engine combines voice tone analysis, facial expression recognition, and emotion estimation from text to comprehensively understand the user's current emotional state.

[0338] Suggestions for relaxation techniques

[0339] Based on the analysis results from the emotion engine, the server generates appropriate relaxation techniques for the user. For example, for users experiencing heightened tension or anxiety, it suggests relaxation activities such as meditation or deep breathing. This advice is communicated to the user via voice guidance and text messages through the device.

[0340] Collecting user feedback and updating the system

[0341] Users can provide feedback on the effectiveness and satisfaction level of the relaxation techniques they have practiced. This feedback is sent from the device to the server and used to improve the system and the emotion engine's algorithms. This process allows the system to continuously provide personalized care for each user.

[0342] Specific example

[0343] For example, if a user experiences stress while working at the office and their heart rate spikes, the emotion engine on the server, based on the simultaneously detected tense facial expression, determines that the user is in an anxious state. Based on this result, the device notifies the user via voice and text with advice such as, "Take a short meditation to calm your mind." By providing feedback on the user's meditation experience, the relaxation plan provided in subsequent sessions can be further optimized.

[0344] In this way, the present invention makes it possible to grasp the user's real-time emotional changes and continuously provide personalized mental care.

[0345] The following describes the processing flow.

[0346] Step 1:

[0347] The device utilizes the smartwatch's sensors to collect the user's biometric information, acquiring heart rate, skin potential responses, voice data, and other data in real time. It also monitors the user's facial expressions via the camera and transmits this data to a server.

[0348] Step 2:

[0349] The server integrates biometric information and facial expression data received from the terminal and analyzes it using an emotion engine. The emotion engine combines voice tone, facial features, and text data analysis to identify the user's emotional state. In this process, emotional categories such as "anxiety," "happiness," and "anger" are recognized.

[0350] Step 3:

[0351] Based on the output of the emotion engine, the server selects appropriate relaxation techniques according to the user's current emotional state and stress level. For example, if anxiety is detected, it may recommend deep breathing exercises or short meditation sessions. These suggestions are then formatted and sent to the terminal.

[0352] Step 4:

[0353] The device displays relaxation techniques received from the server on the user's smartphone or smartwatch screen, and also provides voice guidance. The user receives instructions through the screen and performs the relaxation techniques.

[0354] Step 5:

[0355] Users provide feedback on the effectiveness and satisfaction level of relaxation techniques they have tried, entering the feedback via their device. This feedback is often provided through text input or questionnaires.

[0356] Step 6:

[0357] The device sends feedback data received from the user to the server. The server analyzes this feedback information, incorporates it into the emotion engine and advice methods, and updates the algorithm to improve the accuracy of support in the future.

[0358] Throughout this entire process, the system continuously provides individualized, real-time support for the user's mental health.

[0359] (Example 2)

[0360] 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".

[0361] In modern society, it is crucial to recognize users' emotional states and stress levels in real time and provide personalized mental care. However, conventional technologies have struggled to accurately grasp users' emotions and precisely suggest appropriate relaxation techniques. In particular, integrating information from multiple data sources to achieve highly accurate emotion recognition remains an unresolved challenge.

[0362] 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.

[0363] In this invention, the server includes sensor means for collecting the user's biometric information, processing means for analyzing the biometric information to evaluate the user's emotional state and stress level, and means equipped with an emotion engine that performs emotion recognition by combining voice analysis, facial expression recognition, and text estimation. This makes it possible to evaluate the user's emotions from multiple perspectives and provide appropriate relaxation technology tailored to each individual.

[0364] "Sensor means" refers to devices or functions for collecting a user's biometric information, enabling the acquisition of information such as heart rate, skin potential, and voice.

[0365] "Processing means" refers to a part of a system that has the function of analyzing collected biometric information and evaluating the user's emotional state and stress level.

[0366] An "emotion engine" refers to technologies and programs that combine voice analysis, facial recognition, and text estimation to comprehensively recognize a user's emotions.

[0367] "Advice means" refers to a function that provides users with relaxation techniques generated based on evaluations by processing means, and is particularly presented in the form of audio or text.

[0368] A "feedback mechanism" is a function for receiving opinions and feedback from users, which is used to improve and optimize the system.

[0369] "Update mechanism" refers to a function that adjusts processing and advice mechanisms based on user feedback to improve the overall operation of the system.

[0370] A "multimodal sensor" refers to a sensor that can acquire and integrate data from different types of sensors, enabling it to process information from multiple data sources in an integrated manner.

[0371] This invention relates to a system for analyzing a user's emotional state and stress level in real time and providing appropriate relaxation techniques. The system mainly consists of sensor means, processing means, emotion engine, advice means, feedback means, and update means.

[0372] The device uses a smartwatch or smartphone attached to the user's body to collect a variety of biosignals, including heart rate, skin potential, voice data, and even facial expressions captured using a camera. This data is transmitted to a server in real time.

[0373] The server analyzes the data received from these terminals. This analysis utilizes an emotion engine that integrates voice tone analysis, facial expression recognition, and emotion estimation from text. This emotion engine combines a wide variety of data to comprehensively recognize the user's emotional state.

[0374] After evaluating the user's emotional state, the server generates relaxation techniques tailored to that state. This process uses specific algorithms and generative AI models to suggest the most suitable methods for the user, such as meditation or deep breathing. The server sends these suggestions to the device, which then notifies the user of the advice via voice or text.

[0375] Subsequently, users try these relaxation techniques and provide feedback on their effects via their device. This feedback is received by the server and used to improve the system's overall processing and advice mechanisms. By repeating this process, the system can provide more personalized care to each user.

[0376] As a concrete example of its use, if a user experiences stress at work, this accumulated stress is detected as an increase in heart rate or facial tension. The server analyzes this and sequentially notifies the user of appropriate relaxation techniques. The user follows the suggestions and provides feedback on the results, which further optimizes the advice for future sessions.

[0377] An example of a prompt message for the generating AI model is, "Based on heart rate, facial expressions, and voice data, identify the user's emotions and generate appropriate relaxation techniques." This enables optimal care tailored to the user's emotions and stress levels.

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

[0379] Step 1:

[0380] The device collects the user's biometric information. Smartwatches and smartphone sensors capture heart rate, skin potential, voice, and facial expressions using cameras as input. This data is transmitted to a server in real time. Specifically, the device collects biometric signals captured by sensors at regular intervals, bundles them into data packets, and sends them to the server.

[0381] Step 2:

[0382] The server receives biometric information from the terminal. It integrates heart rate changes, skin potential fluctuations, voice tone, and facial expression image data as input. The server preprocesses this data, standardizing it according to its respective data format, in preparation for processing. As output, it converts the biometric data into an analyzable format.

[0383] Step 3:

[0384] The server analyzes biometric information using an emotion engine. It uses pre-processed multimodal data as input, performing voice analysis, facial expression recognition, and emotion estimation from text. Specifically, the emotion engine reads emotions from the rhythm and intonation of speech, analyzes facial expressions based on a model, and estimates emotional tendencies from text information. The output is numerical data representing the user's emotional state.

[0385] Step 4:

[0386] The server generates appropriate relaxation techniques based on the user's emotional state. It uses the emotional state and stress level, output from an emotional engine, as input. The server utilizes a generative AI model to algorithmically determine the appropriate relaxation method for each emotional state. Specifically, the generative AI model selects the most suitable action for the user's current situation and outputs it as a feasible relaxation plan.

[0387] Step 5:

[0388] The server sends the generated relaxation technology to the terminal, and the terminal notifies the user of the relaxation technology. The input is the generated relaxation plan, formatted as voice guidance and text messages. The terminal utilizes its notification function to provide the user with specific instructions and advice. The output is the relaxation advice received by the user.

[0389] Step 6:

[0390] The user performs the recommended relaxation technique and provides feedback. The input is the user's opinion on the effectiveness and satisfaction level of the relaxation technique performed, entered into a terminal. This feedback is collected by the terminal and sent to the server. The output is the feedback information recorded on the server for future service optimization.

[0391] Step 7:

[0392] The server updates the system using user feedback. It analyzes feedback data as input and adjusts processing and advice methods. Specifically, it uses feedback information as training data for the AI ​​model to improve recognition accuracy and suggestion quality in subsequent uses. The output includes the updated model and system settings.

[0393] (Application Example 2)

[0394] 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."

[0395] In modern urban environments, many people experience stress and emotional instability, but there is a lack of effective systems to detect this in real time and respond to individual needs. Furthermore, there is a need for a system that effectively understands and utilizes stress levels across the entire city.

[0396] 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.

[0397] In this invention, the server includes detection means for collecting user biometric information, analysis means for analyzing the biometric information to evaluate emotional state and stress level, and display means for visualizing stress levels in an urban environment. This makes it possible to monitor the emotional state of individual users in real time and provide personalized relaxation methods, while simultaneously enabling stress management and environmental improvement throughout the city.

[0398] A "detection means for collecting user biometric information" is a device that uses multiple sensors installed in a smart device to collect biometric data such as heart rate, facial expressions, and voice.

[0399] "Analysis means for analyzing biometric information to evaluate emotional state and stress level" refers to a device or program that performs processing to comprehensively determine the user's emotions and stress levels based on collected biometric data.

[0400] A "means for generating and providing relaxation methods to users" is a device that has the function of recommending actions to relieve tension, such as meditation or deep breathing, to the user according to their analyzed emotional state.

[0401] A "feedback receiving mechanism" is a device that has the function of obtaining information from users regarding the effectiveness and satisfaction level of relaxation methods, and using that information to improve the overall system.

[0402] "Update means for adjusting analysis means and proposal means based on feedback" refers to a device or program for controlling the process of improving the accuracy of analysis and refining proposals based on feedback information from users.

[0403] A "display method for visualizing stress levels in urban environments" is a system that visually shows stress levels within a city using maps, graphs, etc., based on collected data.

[0404] This invention is a system that provides personalized relaxation methods by monitoring a user's biometric information and analyzing their emotional and stress levels in real time. This is achieved by utilizing the user's smart devices and terminals installed in urban facilities.

[0405] The server uses multiple hardware and software components to process biometric information collected from smart devices. Specifically, it employs a composite sensor embedded in the smart device as a detection means, collecting data such as heart rate, facial expressions, and voice in real time. Next, the analysis means uses natural language processing APIs from Google Cloud and IBM Watson to analyze this biometric data and evaluate the user's emotional state.

[0406] Based on the analysis results, the server generates relaxation methods through suggestion mechanisms. For example, it may suggest methods such as meditation or deep breathing to the user. These suggestions are notified to the user's device in the form of voice or text. If the user feels stressed while jogging or walking, the device can notify them with a prompt message such as, "Your heart rate is rising rapidly. Take a deep breath now to reset your emotions."

[0407] After performing these relaxation methods, users provide feedback to the server through a response mechanism regarding their effectiveness and satisfaction level. This feedback information is used to improve the overall system performance using an update mechanism and is stored as data to improve the accuracy of future analyses and suggestions.

[0408] Furthermore, to visualize stress levels in urban environments, the data analyzed via display devices can be visualized in map and graph formats and shared on displays installed in public facilities. This allows urban administrators and the general public to identify areas where stress is concentrated and take necessary countermeasures.

[0409] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0410] Step 1:

[0411] The device collects biometric information such as the user's heart rate, facial expressions, and voice in real time using a combination of sensors. This data becomes input, and the device packages it and sends it to the server.

[0412] Step 2:

[0413] The server processes the received biometric data using analytical tools. Using natural language processing APIs from Google Cloud and IBM Watson, it evaluates emotional states based on heart rate, voice tone, and changes in facial expressions. This analysis visualizes stress levels and emotional changes, and emotional state data is output.

[0414] Step 3:

[0415] The server generates appropriate relaxation methods based on this emotional state data. This process determines the content of the relaxation suggestions. Specifically, a prompt message using a generative AI model, such as "Take a deep breath and relax," is created and output.

[0416] Step 4:

[0417] The server delivers the generated prompt message to the terminal as both an audio and text message. The terminal receives this message and notifies the user. In this step, the user can learn the appropriate relaxation method.

[0418] Step 5:

[0419] The user performs the suggested relaxation method and sends feedback regarding its effectiveness and satisfaction level to the server via their device. This feedback data is then used as input for the next step.

[0420] Step 6:

[0421] The server receives feedback from users and updates the analysis and suggestion methods based on that feedback. This update process improves the accuracy of subsequent analyses and suggestions. The updated algorithm is output and applied to the entire system.

[0422] Step 7:

[0423] The server uses the analyzed data and feedback to visualize the distribution of stress in the urban environment. This is then output as maps and graphs using display devices and shown on displays placed in public facilities. This allows for a visual understanding of stress hotspots within the city.

[0424] 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.

[0425] 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.

[0426] 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.

[0427] [Third Embodiment]

[0428] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0429] 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.

[0430] 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).

[0431] 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.

[0432] 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.

[0433] 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).

[0434] 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.

[0435] 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.

[0436] 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.

[0437] 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.

[0438] 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.

[0439] 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".

[0440] This invention relates to an AI system for supporting a user's mental health, comprising sensor means, processing means, advice means, feedback means, and update means. This system continuously collects biometric information through wearable devices or smartphones worn by the user, analyzes this data on a server to evaluate emotional state and stress levels, and proposes appropriate relaxation techniques to the user based on the results.

[0441] Sensor data collection

[0442] The device uses sensors built into the smartwatch to collect biometric information in real time, such as the user's heart rate, skin potential response, and ambient sounds. This allows for the acquisition of data on the user's physical and mental state in a natural state during their daily activities.

[0443] Data Analysis

[0444] The server receives biometric information transmitted from the terminal and analyzes the data using advanced machine learning algorithms. This analysis detects changes in the user's emotions and signs of stress, and determines whether intervention is necessary if required.

[0445] Providing advice

[0446] The server selects the most suitable relaxation method for the user based on the analysis results and sends that method to the terminal. The terminal then guides the user in real time through voice guidance and screen displays, showing the specific steps of the relaxation technique (e.g., deep breathing and simple stretching).

[0447] Processing user feedback

[0448] The user provides feedback on the effectiveness of the suggested relaxation techniques. The device sends this feedback to the server, which then adjusts the analysis model and suggested methods based on it to provide better personalized support.

[0449] Specific example

[0450] For example, if a user in a stressful office environment has a higher-than-normal heart rate and increased skin electrical activity, the server recognizes this as a sign of stress. The server suggests the user try deep breathing and sends a guidance message to the device. After the user practices the suggested method and confirms its effectiveness, they provide feedback, which the server then uses to refine future suggestions.

[0451] Thus, the present invention provides mental care tailored to the individual needs of users and serves as a valuable tool for supporting their mental health.

[0452] The following describes the processing flow.

[0453] Step 1:

[0454] The terminal uses sensors built into the user's smart device to collect biometric information in real time. This includes data such as heart rate, skin potential response, and voice tone. The collected data is sent to a server for the next analysis step.

[0455] Step 2:

[0456] The server receives biometric information transmitted from the terminal and stores it. The stored data is then preprocessed to establish a baseline by comparing it with the user's past history data.

[0457] Step 3:

[0458] The server applies advanced machine learning algorithms to analyze the received biometric data. This analysis detects changes in the user's emotional state and increases in stress levels.

[0459] Step 4:

[0460] Based on the analysis results, the server selects appropriate relaxation techniques for the user. The selected advice is sent to the terminal as specific guidelines in text or audio format.

[0461] Step 5:

[0462] The device notifies the user of relaxation technique advice sent from the server. The notification is communicated to the user via a pop-up message or voice assistant.

[0463] Step 6:

[0464] The user performs the suggested relaxation techniques and checks their effects. Based on the results, they enter feedback on their device.

[0465] Step 7:

[0466] The terminal sends user feedback data to the server. This data is used to analyze the system and improve the advice techniques.

[0467] Step 8:

[0468] The server analyzes the feedback data and uses it to update the machine learning model, improving the accuracy of future suggestions. This prepares it for more advanced personalization in the next user session.

[0469] (Example 1)

[0470] 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."

[0471] In modern society, stress and emotional fluctuations are major factors that significantly impact health. Conventional methods have made it difficult to accurately assess these conditions in real time and implement optimal individual countermeasures. Furthermore, there has been a lack of sufficient means to continuously improve the system by incorporating user feedback. Therefore, this invention aims to evaluate stress levels based on the user's biometric information, provide individually tailored relaxation methods, and optimize the system based on feedback.

[0472] 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.

[0473] In this invention, the server includes information acquisition means for collecting the user's biometric information, information processing means for analyzing the biometric information to evaluate the emotional state and stress level, and guidance means for generating a relaxation method based on the evaluation using a generation AI model and providing it to the user. This enables the provision of optimal mental care tailored to the user's individual needs in real time, thereby improving their health.

[0474] "Information acquisition means" refers to devices and technologies for collecting biometric information from users, and includes sensors that detect heart rate, skin potential responses, environmental sounds, etc.

[0475] "Information processing means" refers to software and algorithms for analyzing collected biometric information and evaluating the user's emotional state and stress level.

[0476] A "generative AI model" is a system of programs that uses machine learning to generate relaxation methods from data, and is a model trained to provide suggestions tailored to the user.

[0477] A "guidance tool" is an interface that provides users with generated relaxation methods, guiding them through audio guides and text displays.

[0478] A "response acquisition mechanism" is a system that allows users to provide feedback on the effectiveness of relaxation methods, and acquires this information through input devices or software.

[0479] "Information update means" refers to means for adjusting and optimizing the content of information processing means and guidance means within the system based on user feedback.

[0480] The present invention is a system for supporting the mental health of users, and includes means for acquiring information, means for processing information, means for providing guidance using a generative AI model, means for acquiring responses, and means for updating information.

[0481] This system collects biometric information using smart devices, which are portable digital devices worn by users on a daily basis. Multiple sensing devices built into the terminal acquire heart rate, skin potential responses, and ambient sounds in real time.

[0482] The server uses TensorFlow, a machine learning library based on Python, to analyze collected biometric data. This allows it to assess the user's emotional state and stress level, and determine appropriate relaxation methods.

[0483] The guidance system using a generative AI model generates relaxation methods in real time based on analysis results and provides them to the user in audio data or text format. For example, when stress is detected, it suggests a simple instruction such as, "Take deep breaths for one minute."

[0484] The user performs the provided relaxation method and provides feedback on the results. The terminal sends the acquired feedback to the server, and based on the feedback, adjusts the content of the information processing means and guidance means to improve the accuracy of the system.

[0485] This process adapts to the user's situation and needs, individually optimizing mental care. The generative AI model learns from user data and evolves to provide more precise advice. Specific prompts might include phrases like, "Assess the user's stress level and suggest appropriate relaxation techniques if necessary."

[0486] Through such embodiments, the present invention provides a function that supports the mental health of the user.

[0487] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0488] Step 1:

[0489] The device collects the user's biometric information. Specifically, sensors built into the smart device measure heart rate, skin potential response, and ambient sound in real time. The input is sensor data, and the output is organized user biometric information. This data is temporarily stored in a process within the smart device.

[0490] Step 2:

[0491] The device transmits the collected biometric information to the server. The data is securely encrypted and transmitted via Wi-Fi or a mobile network. The input is the biometric information collected in the previous step, and the output is the received data for processing on the server. At this stage, the data integrity is checked, and it is processed to ensure that no missing or abnormal data is detected.

[0492] Step 3:

[0493] The server uses TensorFlow to analyze the received biometric data. Here, machine learning algorithms operate to assess stress levels and emotional state. The input is biometric data sent from the terminal, and the output is an assessment of the user's mental state based on the analysis. This assessment is recorded in a database and forms the basis for subsequent actions.

[0494] Step 4:

[0495] The server uses a generative AI model to select the optimal relaxation method based on the analysis results. The generative AI model considers the user's emotional state and derives specific relaxation techniques. The input is the evaluation result of the emotional state, and the output is the generated relaxation prompt. The prompt includes suggestions such as "Take deep breaths for one minute."

[0496] Step 5:

[0497] The terminal guides the user through relaxation methods sent from the server via voice and text. The terminal screen displays the steps and visual instructions, while voice guidance is provided simultaneously. The input is the generated prompt, and the output is a call to action from the user by sequentially conveying instructions.

[0498] Step 6:

[0499] Users perform activities according to relaxation instructions and then input feedback about their state and effects into a terminal. The input consists of the user's own feelings and results after relaxation, while the output is data sent to the server as feedback. Users record the changes they experienced using a simple input format.

[0500] Step 7:

[0501] The server updates its analysis models and guidance methods based on user feedback data. It retrains machine learning models to improve overall system accuracy by considering the feedback information. The input is user feedback, and the output is the improved analysis model. This update evolves to make subsequent relaxation suggestions more personalized.

[0502] (Application Example 1)

[0503] 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."

[0504] In recent years, stress and mental health problems have increased dramatically, creating a need for appropriate care that users can easily access in their daily lives. However, existing systems make it difficult for users to receive personalized mental care tailored to their individual circumstances within their homes. Solving this problem is essential.

[0505] 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.

[0506] In this invention, the server includes a sensor device that collects the user's biometric information, an information processing device that analyzes the biometric information to evaluate the emotional state and stress level, and an advisory device that generates and provides relaxation techniques to the user based on the evaluation. This makes it possible to analyze the user's biometric information in real time and provide appropriate relaxation methods tailored to the individual's condition through a home-use device.

[0507] "User biometric information" refers to information that indicates the physiological state of the body, such as the user's heart rate and skin potential response.

[0508] A "sensor device" is a device used to detect a user's biometric information in real time.

[0509] An "information processing device" is a device that analyzes collected biometric information and evaluates the user's emotional state and stress level.

[0510] "Relaxation techniques" refer to specific methods and techniques used to reduce the mental and physical stress of users.

[0511] An "advice device" is a device that provides users with relaxation techniques generated based on evaluations performed by an information processing device.

[0512] An "update device" is a device that has the function of adjusting the information processing device and the advisory device based on feedback from the user.

[0513] "Household appliances" refer to multi-functional devices and equipment used in the home, particularly those designed to support the user's mental health.

[0514] A "multifunctional sensor" is a device that has sensors capable of simultaneously acquiring multiple pieces of biological information.

[0515] To realize this invention, the user needs to wear a sensor device that collects biometric information in real time. The sensor device acquires data such as heart rate and skin potential response and transmits it to a server via a mobile device. The server analyzes this biometric data using an information processing device to evaluate the user's emotional state and stress level.

[0516] Based on the evaluation results, the server utilizes a generated AI model to select the optimal relaxation technique. This technique is presented to the user in audio or visual format via an advisory device. The advice is transmitted using the user's mobile device or home equipment and is provided as a visual or audio guide.

[0517] For example, if a user's heart rate exceeds a certain threshold, the server will suggest relaxation techniques such as deep breathing or stretching. This allows the user to immediately practice the appropriate methods and improve their mental health.

[0518] Furthermore, a response device receives user feedback, which is then processed by a server using an update device. This feedback is reflected in the analysis model and used to improve the accuracy of future relaxation technology proposals.

[0519] As a concrete example, using a generated AI prompt such as, "Create a prompt suggesting appropriate relaxation techniques when the user's heart rate and skin potential response are elevated," the system can select the optimal relaxation method and communicate it to the user.

[0520] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0521] Step 1:

[0522] The device acquires the user's biometric information via sensor devices (heart rate sensor and skin potential sensor). The input is data obtained from the biosensors, and the output is real-time collected biometric information. This data is transmitted to the server via a secure protocol.

[0523] Step 2:

[0524] The server receives biometric information transmitted from the terminal. The input is biometric information from the terminal, and the output is biometric data ready for analysis. Data cleaning is performed here, removing outliers and sorting by time.

[0525] Step 3:

[0526] The server uses an information processing device to analyze biometric data and evaluate the user's emotional state and stress level. The input is cleaned biometric data, and the output is the evaluation result of the stress level and emotional state. This analysis utilizes a machine learning model to perform pattern recognition based on past data.

[0527] Step 4:

[0528] The server uses a generated AI model to select the optimal relaxation technique based on the evaluation results. The input is the evaluation results of emotional state and stress level, and the output is the selected relaxation technique. By generating prompt sentences and feeding them into this model, a specific technique is identified.

[0529] Step 5:

[0530] The server transmits the selected relaxation technology to the terminal via an advisory device. The input is detailed information about the relaxation technology, and the output is the data to be transmitted to the terminal. In this process, audio or visual content is selected as the means of transmission.

[0531] Step 6:

[0532] The user practices the suggested relaxation techniques and sends feedback about their effects to the server via their device. The input is the user's feedback, and the output is the feedback information received by the server. The feedback is in the form of a simple questionnaire, inquiring about the effectiveness and satisfaction level.

[0533] Step 7:

[0534] The server receives feedback via a response device, and the update device adjusts the information processing device and the advisory device. The input is user feedback information, and the output is updated system parameters. This update improves the accuracy of suggestions in subsequent sessions.

[0535] 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.

[0536] This invention relates to an AI mental care system incorporating an emotion engine that recognizes the user's emotions, and is comprised of sensor means, processing means, advice means, feedback means, update means, and the emotion engine. This system has the ability to collect biometric information through a smart device worn by the user, analyze it on a server to identify the user's emotional state and stress level, and propose relaxation techniques accordingly.

[0537] Sensor data collection and emotion recognition

[0538] The device collects biometric information, including heart rate, skin potential, and voice, from smartwatches and smartphones. In addition, it uses the device's camera to monitor the user's facial expressions. This data is transmitted to a server in real time.

[0539] Data analysis and emotional judgment

[0540] The server receives multimodal biometric data transmitted from the terminal and uses an emotion engine to recognize the user's emotional state. The emotion engine combines voice tone analysis, facial expression recognition, and emotion estimation from text to comprehensively understand the user's current emotional state.

[0541] Suggestions for relaxation techniques

[0542] Based on the analysis results from the emotion engine, the server generates appropriate relaxation techniques for the user. For example, for users experiencing heightened tension or anxiety, it suggests relaxation activities such as meditation or deep breathing. This advice is communicated to the user via voice guidance and text messages through the device.

[0543] Collecting user feedback and updating the system

[0544] Users can provide feedback on the effectiveness and satisfaction level of the relaxation techniques they have practiced. This feedback is sent from the device to the server and used to improve the system and the emotion engine's algorithms. This process allows the system to continuously provide personalized care for each user.

[0545] Specific example

[0546] For example, if a user experiences stress while working at the office and their heart rate spikes, the emotion engine on the server, based on the simultaneously detected tense facial expression, determines that the user is in an anxious state. Based on this result, the device notifies the user via voice and text with advice such as, "Take a short meditation to calm your mind." By providing feedback on the user's meditation experience, the relaxation plan provided in subsequent sessions can be further optimized.

[0547] In this way, the present invention makes it possible to grasp the user's real-time emotional changes and continuously provide personalized mental care.

[0548] The following describes the processing flow.

[0549] Step 1:

[0550] The device utilizes the smartwatch's sensors to collect the user's biometric information, acquiring heart rate, skin potential responses, voice data, and other data in real time. It also monitors the user's facial expressions via the camera and transmits this data to a server.

[0551] Step 2:

[0552] The server integrates biometric information and facial expression data received from the terminal and analyzes it using an emotion engine. The emotion engine combines voice tone, facial features, and text data analysis to identify the user's emotional state. In this process, emotional categories such as "anxiety," "happiness," and "anger" are recognized.

[0553] Step 3:

[0554] Based on the output of the emotion engine, the server selects appropriate relaxation techniques according to the user's current emotional state and stress level. For example, if anxiety is detected, it may recommend deep breathing exercises or short meditation sessions. These suggestions are then formatted and sent to the terminal.

[0555] Step 4:

[0556] The device displays relaxation techniques received from the server on the user's smartphone or smartwatch screen, and also provides voice guidance. The user receives instructions through the screen and performs the relaxation techniques.

[0557] Step 5:

[0558] Users provide feedback on the effectiveness and satisfaction level of relaxation techniques they have tried, entering the feedback via their device. This feedback is often provided through text input or questionnaires.

[0559] Step 6:

[0560] The device sends feedback data received from the user to the server. The server analyzes this feedback information, incorporates it into the emotion engine and advice methods, and updates the algorithm to improve the accuracy of support in the future.

[0561] Throughout this entire process, the system continuously provides individualized, real-time support for the user's mental health.

[0562] (Example 2)

[0563] 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."

[0564] In modern society, it is crucial to recognize users' emotional states and stress levels in real time and provide personalized mental care. However, conventional technologies have struggled to accurately grasp users' emotions and precisely suggest appropriate relaxation techniques. In particular, integrating information from multiple data sources to achieve highly accurate emotion recognition remains an unresolved challenge.

[0565] 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.

[0566] In this invention, the server includes sensor means for collecting the user's biometric information, processing means for analyzing the biometric information to evaluate the user's emotional state and stress level, and means equipped with an emotion engine that performs emotion recognition by combining voice analysis, facial expression recognition, and text estimation. This makes it possible to evaluate the user's emotions from multiple perspectives and provide appropriate relaxation technology tailored to each individual.

[0567] "Sensor means" refers to devices or functions for collecting a user's biometric information, enabling the acquisition of information such as heart rate, skin potential, and voice.

[0568] "Processing means" refers to a part of a system that has the function of analyzing collected biometric information and evaluating the user's emotional state and stress level.

[0569] An "emotion engine" refers to technologies and programs that combine voice analysis, facial recognition, and text estimation to comprehensively recognize a user's emotions.

[0570] "Advice means" refers to a function that provides users with relaxation techniques generated based on evaluations by processing means, and is particularly presented in the form of audio or text.

[0571] A "feedback mechanism" is a function for receiving opinions and feedback from users, which is used to improve and optimize the system.

[0572] "Update mechanism" refers to a function that adjusts processing and advice mechanisms based on user feedback to improve the overall operation of the system.

[0573] A "multimodal sensor" refers to a sensor that can acquire and integrate data from different types of sensors, enabling it to process information from multiple data sources in an integrated manner.

[0574] This invention relates to a system for analyzing a user's emotional state and stress level in real time and providing appropriate relaxation techniques. The system mainly consists of sensor means, processing means, emotion engine, advice means, feedback means, and update means.

[0575] The device uses a smartwatch or smartphone attached to the user's body to collect a variety of biosignals, including heart rate, skin potential, voice data, and even facial expressions captured using a camera. This data is transmitted to a server in real time.

[0576] The server analyzes the data received from these terminals. This analysis utilizes an emotion engine that integrates voice tone analysis, facial expression recognition, and emotion estimation from text. This emotion engine combines a wide variety of data to comprehensively recognize the user's emotional state.

[0577] After evaluating the user's emotional state, the server generates relaxation techniques tailored to that state. This process uses specific algorithms and generative AI models to suggest the most suitable methods for the user, such as meditation or deep breathing. The server sends these suggestions to the device, which then notifies the user of the advice via voice or text.

[0578] Subsequently, users try these relaxation techniques and provide feedback on their effects via their device. This feedback is received by the server and used to improve the system's overall processing and advice mechanisms. By repeating this process, the system can provide more personalized care to each user.

[0579] As a concrete example of its use, if a user experiences stress at work, this accumulated stress is detected as an increase in heart rate or facial tension. The server analyzes this and sequentially notifies the user of appropriate relaxation techniques. The user follows the suggestions and provides feedback on the results, which further optimizes the advice for future sessions.

[0580] An example of a prompt message for the generating AI model is, "Based on heart rate, facial expressions, and voice data, identify the user's emotions and generate appropriate relaxation techniques." This enables optimal care tailored to the user's emotions and stress levels.

[0581] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0582] Step 1:

[0583] The device collects the user's biometric information. Smartwatches and smartphone sensors capture heart rate, skin potential, voice, and facial expressions using cameras as input. This data is transmitted to a server in real time. Specifically, the device collects biometric signals captured by sensors at regular intervals, bundles them into data packets, and sends them to the server.

[0584] Step 2:

[0585] The server receives biometric information from the terminal. It integrates heart rate changes, skin potential fluctuations, voice tone, and facial expression image data as input. The server preprocesses this data, standardizing it according to its respective data format, in preparation for processing. As output, it converts the biometric data into an analyzable format.

[0586] Step 3:

[0587] The server analyzes biometric information using an emotion engine. It uses pre-processed multimodal data as input, performing voice analysis, facial expression recognition, and emotion estimation from text. Specifically, the emotion engine reads emotions from the rhythm and intonation of speech, analyzes facial expressions based on a model, and estimates emotional tendencies from text information. The output is numerical data representing the user's emotional state.

[0588] Step 4:

[0589] The server generates appropriate relaxation techniques based on the user's emotional state. It uses the emotional state and stress level, output from an emotional engine, as input. The server utilizes a generative AI model to algorithmically determine the appropriate relaxation method for each emotional state. Specifically, the generative AI model selects the most suitable action for the user's current situation and outputs it as a feasible relaxation plan.

[0590] Step 5:

[0591] The server sends the generated relaxation technology to the terminal, and the terminal notifies the user of the relaxation technology. The input is the generated relaxation plan, formatted as voice guidance and text messages. The terminal utilizes its notification function to provide the user with specific instructions and advice. The output is the relaxation advice received by the user.

[0592] Step 6:

[0593] The user performs the recommended relaxation technique and provides feedback. The input is the user's opinion on the effectiveness and satisfaction level of the relaxation technique performed, entered into a terminal. This feedback is collected by the terminal and sent to the server. The output is the feedback information recorded on the server for future service optimization.

[0594] Step 7:

[0595] The server updates the system using user feedback. It analyzes feedback data as input and adjusts processing and advice methods. Specifically, it uses feedback information as training data for the AI ​​model to improve recognition accuracy and suggestion quality in subsequent uses. The output includes the updated model and system settings.

[0596] (Application Example 2)

[0597] 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."

[0598] In modern urban environments, many people experience stress and emotional instability, but there is a lack of effective systems to detect this in real time and respond to individual needs. Furthermore, there is a need for a system that effectively understands and utilizes stress levels across the entire city.

[0599] 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.

[0600] In this invention, the server includes detection means for collecting user biometric information, analysis means for analyzing the biometric information to evaluate emotional state and stress level, and display means for visualizing stress levels in an urban environment. This makes it possible to monitor the emotional state of individual users in real time and provide personalized relaxation methods, while simultaneously enabling stress management and environmental improvement throughout the city.

[0601] A "detection means for collecting user biometric information" is a device that uses multiple sensors installed in a smart device to collect biometric data such as heart rate, facial expressions, and voice.

[0602] "Analysis means for analyzing biometric information to evaluate emotional state and stress level" refers to a device or program that performs processing to comprehensively determine the user's emotions and stress levels based on collected biometric data.

[0603] A "means for generating and providing relaxation methods to users" is a device that has the function of recommending actions to relieve tension, such as meditation or deep breathing, to the user according to their analyzed emotional state.

[0604] A "feedback receiving mechanism" is a device that has the function of obtaining information from users regarding the effectiveness and satisfaction level of relaxation methods, and using that information to improve the overall system.

[0605] "Update means for adjusting analysis means and proposal means based on feedback" refers to a device or program for controlling the process of improving the accuracy of analysis and refining proposals based on feedback information from users.

[0606] A "display method for visualizing stress levels in urban environments" is a system that visually shows stress levels within a city using maps, graphs, etc., based on collected data.

[0607] This invention is a system that provides personalized relaxation methods by monitoring a user's biometric information and analyzing their emotional and stress levels in real time. This is achieved by utilizing the user's smart devices and terminals installed in urban facilities.

[0608] The server uses multiple hardware and software components to process biometric information collected from smart devices. Specifically, it employs a composite sensor embedded in the smart device as a detection means, collecting data such as heart rate, facial expressions, and voice in real time. Next, the analysis means uses natural language processing APIs from Google Cloud and IBM Watson to analyze this biometric data and evaluate the user's emotional state.

[0609] Based on the analysis results, the server generates relaxation methods through suggestion mechanisms. For example, it may suggest methods such as meditation or deep breathing to the user. These suggestions are notified to the user's device in the form of voice or text. If the user feels stressed while jogging or walking, the device can notify them with a prompt message such as, "Your heart rate is rising rapidly. Take a deep breath now to reset your emotions."

[0610] After performing these relaxation methods, users provide feedback to the server through a response mechanism regarding their effectiveness and satisfaction level. This feedback information is used to improve the overall system performance using an update mechanism and is stored as data to improve the accuracy of future analyses and suggestions.

[0611] Furthermore, to visualize stress levels in urban environments, the data analyzed via display devices can be visualized in map and graph formats and shared on displays installed in public facilities. This allows urban administrators and the general public to identify areas where stress is concentrated and take necessary countermeasures.

[0612] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0613] Step 1:

[0614] The device collects biometric information such as the user's heart rate, facial expressions, and voice in real time using a combination of sensors. This data becomes input, and the device packages it and sends it to the server.

[0615] Step 2:

[0616] The server processes the received biometric data using analytical tools. Using natural language processing APIs from Google Cloud and IBM Watson, it evaluates emotional states based on heart rate, voice tone, and changes in facial expressions. This analysis visualizes stress levels and emotional changes, and emotional state data is output.

[0617] Step 3:

[0618] The server generates appropriate relaxation methods based on this emotional state data. This process determines the content of the relaxation suggestions. Specifically, a prompt message using a generative AI model, such as "Take a deep breath and relax," is created and output.

[0619] Step 4:

[0620] The server delivers the generated prompt message to the terminal as both an audio and text message. The terminal receives this message and notifies the user. In this step, the user can learn the appropriate relaxation method.

[0621] Step 5:

[0622] The user performs the suggested relaxation method and sends feedback regarding its effectiveness and satisfaction level to the server via their device. This feedback data is then used as input for the next step.

[0623] Step 6:

[0624] The server receives feedback from users and updates the analysis and suggestion methods based on that feedback. This update process improves the accuracy of subsequent analyses and suggestions. The updated algorithm is output and applied to the entire system.

[0625] Step 7:

[0626] The server uses the analyzed data and feedback to visualize the distribution of stress in the urban environment. This is then output as maps and graphs using display devices and shown on displays placed in public facilities. This allows for a visual understanding of stress hotspots within the city.

[0627] 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.

[0628] 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.

[0629] 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.

[0630] [Fourth Embodiment]

[0631] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0632] 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.

[0633] 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).

[0634] 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.

[0635] 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.

[0636] 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).

[0637] 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.

[0638] 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.

[0639] 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.

[0640] 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.

[0641] 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.

[0642] 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.

[0643] 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".

[0644] This invention relates to an AI system for supporting a user's mental health, comprising sensor means, processing means, advice means, feedback means, and update means. This system continuously collects biometric information through wearable devices or smartphones worn by the user, analyzes this data on a server to evaluate emotional state and stress levels, and proposes appropriate relaxation techniques to the user based on the results.

[0645] Sensor data collection

[0646] The device uses sensors built into the smartwatch to collect biometric information in real time, such as the user's heart rate, skin potential response, and ambient sounds. This allows for the acquisition of data on the user's physical and mental state in a natural state during their daily activities.

[0647] Data Analysis

[0648] The server receives biometric information transmitted from the terminal and analyzes the data using advanced machine learning algorithms. This analysis detects changes in the user's emotions and signs of stress, and determines whether intervention is necessary if required.

[0649] Providing advice

[0650] The server selects the most suitable relaxation method for the user based on the analysis results and sends that method to the terminal. The terminal then guides the user in real time through voice guidance and screen displays, showing the specific steps of the relaxation technique (e.g., deep breathing and simple stretching).

[0651] Processing user feedback

[0652] The user provides feedback on the effectiveness of the suggested relaxation techniques. The device sends this feedback to the server, which then adjusts the analysis model and suggested methods based on it to provide better personalized support.

[0653] Specific example

[0654] For example, if a user in a stressful office environment has a higher-than-normal heart rate and increased skin electrical activity, the server recognizes this as a sign of stress. The server suggests the user try deep breathing and sends a guidance message to the device. After the user practices the suggested method and confirms its effectiveness, they provide feedback, which the server then uses to refine future suggestions.

[0655] Thus, the present invention provides mental care tailored to the individual needs of users and serves as a valuable tool for supporting their mental health.

[0656] The following describes the processing flow.

[0657] Step 1:

[0658] The terminal uses sensors built into the user's smart device to collect biometric information in real time. This includes data such as heart rate, skin potential response, and voice tone. The collected data is sent to a server for the next analysis step.

[0659] Step 2:

[0660] The server receives biometric information transmitted from the terminal and stores it. The stored data is then preprocessed to establish a baseline by comparing it with the user's past history data.

[0661] Step 3:

[0662] The server applies advanced machine learning algorithms to analyze the received biometric data. This analysis detects changes in the user's emotional state and increases in stress levels.

[0663] Step 4:

[0664] Based on the analysis results, the server selects appropriate relaxation techniques for the user. The selected advice is sent to the terminal as specific guidelines in text or audio format.

[0665] Step 5:

[0666] The device notifies the user of relaxation technique advice sent from the server. The notification is communicated to the user via a pop-up message or voice assistant.

[0667] Step 6:

[0668] The user performs the suggested relaxation techniques and checks their effects. Based on the results, they enter feedback on their device.

[0669] Step 7:

[0670] The terminal sends user feedback data to the server. This data is used to analyze the system and improve the advice techniques.

[0671] Step 8:

[0672] The server analyzes the feedback data and uses it to update the machine learning model, improving the accuracy of future suggestions. This prepares it for more advanced personalization in the next user session.

[0673] (Example 1)

[0674] 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".

[0675] In modern society, stress and emotional fluctuations are major factors that significantly impact health. Conventional methods have made it difficult to accurately assess these conditions in real time and implement optimal individual countermeasures. Furthermore, there has been a lack of sufficient means to continuously improve the system by incorporating user feedback. Therefore, this invention aims to evaluate stress levels based on the user's biometric information, provide individually tailored relaxation methods, and optimize the system based on feedback.

[0676] 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.

[0677] In this invention, the server includes information acquisition means for collecting the user's biometric information, information processing means for analyzing the biometric information to evaluate the emotional state and stress level, and guidance means for generating a relaxation method based on the evaluation using a generation AI model and providing it to the user. This enables the provision of optimal mental care tailored to the user's individual needs in real time, thereby improving their health.

[0678] "Information acquisition means" refers to devices and technologies for collecting biometric information from users, and includes sensors that detect heart rate, skin potential responses, environmental sounds, etc.

[0679] "Information processing means" refers to software and algorithms for analyzing collected biometric information and evaluating the user's emotional state and stress level.

[0680] A "generative AI model" is a system of programs that uses machine learning to generate relaxation methods from data, and is a model trained to provide suggestions tailored to the user.

[0681] A "guidance tool" is an interface that provides users with generated relaxation methods, guiding them through audio guides and text displays.

[0682] A "response acquisition mechanism" is a system that allows users to provide feedback on the effectiveness of relaxation methods, and acquires this information through input devices or software.

[0683] "Information update means" refers to means for adjusting and optimizing the content of information processing means and guidance means within the system based on user feedback.

[0684] The present invention is a system for supporting the mental health of users, and includes means for acquiring information, means for processing information, means for providing guidance using a generative AI model, means for acquiring responses, and means for updating information.

[0685] This system collects biometric information using smart devices, which are portable digital devices worn by users on a daily basis. Multiple sensing devices built into the terminal acquire heart rate, skin potential responses, and ambient sounds in real time.

[0686] The server uses TensorFlow, a machine learning library based on Python, to analyze collected biometric data. This allows it to assess the user's emotional state and stress level, and determine appropriate relaxation methods.

[0687] The guidance system using a generative AI model generates relaxation methods in real time based on analysis results and provides them to the user in audio data or text format. For example, when stress is detected, it suggests a simple instruction such as, "Take deep breaths for one minute."

[0688] The user performs the provided relaxation method and provides feedback on the results. The terminal sends the acquired feedback to the server, and based on the feedback, adjusts the content of the information processing means and guidance means to improve the accuracy of the system.

[0689] This process adapts to the user's situation and needs, individually optimizing mental care. The generative AI model learns from user data and evolves to provide more precise advice. Specific prompts might include phrases like, "Assess the user's stress level and suggest appropriate relaxation techniques if necessary."

[0690] Through such embodiments, the present invention provides a function that supports the mental health of the user.

[0691] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0692] Step 1:

[0693] The device collects the user's biometric information. Specifically, sensors built into the smart device measure heart rate, skin potential response, and ambient sound in real time. The input is sensor data, and the output is organized user biometric information. This data is temporarily stored in a process within the smart device.

[0694] Step 2:

[0695] The device transmits the collected biometric information to the server. The data is securely encrypted and transmitted via Wi-Fi or a mobile network. The input is the biometric information collected in the previous step, and the output is the received data for processing on the server. At this stage, the data integrity is checked, and it is processed to ensure that no missing or abnormal data is detected.

[0696] Step 3:

[0697] The server uses TensorFlow to analyze the received biometric data. Here, machine learning algorithms operate to assess stress levels and emotional state. The input is biometric data sent from the terminal, and the output is an assessment of the user's mental state based on the analysis. This assessment is recorded in a database and forms the basis for subsequent actions.

[0698] Step 4:

[0699] The server uses a generative AI model to select the optimal relaxation method based on the analysis results. The generative AI model considers the user's emotional state and derives specific relaxation techniques. The input is the evaluation result of the emotional state, and the output is the generated relaxation prompt. The prompt includes suggestions such as "Take deep breaths for one minute."

[0700] Step 5:

[0701] The terminal guides the user through relaxation methods sent from the server via voice and text. The terminal screen displays the steps and visual instructions, while voice guidance is provided simultaneously. The input is the generated prompt, and the output is a call to action from the user by sequentially conveying instructions.

[0702] Step 6:

[0703] Users perform activities according to relaxation instructions and then input feedback about their state and effects into a terminal. The input consists of the user's own feelings and results after relaxation, while the output is data sent to the server as feedback. Users record the changes they experienced using a simple input format.

[0704] Step 7:

[0705] The server updates its analysis models and guidance methods based on user feedback data. It retrains machine learning models to improve overall system accuracy by considering the feedback information. The input is user feedback, and the output is the improved analysis model. This update evolves to make subsequent relaxation suggestions more personalized.

[0706] (Application Example 1)

[0707] 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".

[0708] In recent years, stress and mental health problems have increased dramatically, creating a need for appropriate care that users can easily access in their daily lives. However, existing systems make it difficult for users to receive personalized mental care tailored to their individual circumstances within their homes. Solving this problem is essential.

[0709] 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.

[0710] In this invention, the server includes a sensor device that collects the user's biometric information, an information processing device that analyzes the biometric information to evaluate the emotional state and stress level, and an advisory device that generates and provides relaxation techniques to the user based on the evaluation. This makes it possible to analyze the user's biometric information in real time and provide appropriate relaxation methods tailored to the individual's condition through a home-use device.

[0711] "User biometric information" refers to information that indicates the physiological state of the body, such as the user's heart rate and skin potential response.

[0712] A "sensor device" is a device used to detect a user's biometric information in real time.

[0713] An "information processing device" is a device that analyzes collected biometric information and evaluates the user's emotional state and stress level.

[0714] "Relaxation techniques" refer to specific methods and techniques used to reduce the mental and physical stress of users.

[0715] An "advice device" is a device that provides users with relaxation techniques generated based on evaluations performed by an information processing device.

[0716] An "update device" is a device that has the function of adjusting the information processing device and the advisory device based on feedback from the user.

[0717] "Household appliances" refer to multi-functional devices and equipment used in the home, particularly those designed to support the user's mental health.

[0718] A "multifunctional sensor" is a device that has sensors capable of simultaneously acquiring multiple pieces of biological information.

[0719] To realize this invention, the user needs to wear a sensor device that collects biometric information in real time. The sensor device acquires data such as heart rate and skin potential response and transmits it to a server via a mobile device. The server analyzes this biometric data using an information processing device to evaluate the user's emotional state and stress level.

[0720] Based on the evaluation results, the server utilizes a generated AI model to select the optimal relaxation technique. This technique is presented to the user in audio or visual format via an advisory device. The advice is transmitted using the user's mobile device or home equipment and is provided as a visual or audio guide.

[0721] For example, if a user's heart rate exceeds a certain threshold, the server will suggest relaxation techniques such as deep breathing or stretching. This allows the user to immediately practice the appropriate methods and improve their mental health.

[0722] Furthermore, a response device receives user feedback, which is then processed by a server using an update device. This feedback is reflected in the analysis model and used to improve the accuracy of future relaxation technology proposals.

[0723] As a concrete example, using a generated AI prompt such as, "Create a prompt suggesting appropriate relaxation techniques when the user's heart rate and skin potential response are elevated," the system can select the optimal relaxation method and communicate it to the user.

[0724] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0725] Step 1:

[0726] The device acquires the user's biometric information via sensor devices (heart rate sensor and skin potential sensor). The input is data obtained from the biosensors, and the output is real-time collected biometric information. This data is transmitted to the server via a secure protocol.

[0727] Step 2:

[0728] The server receives biometric information transmitted from the terminal. The input is biometric information from the terminal, and the output is biometric data ready for analysis. Data cleaning is performed here, removing outliers and sorting by time.

[0729] Step 3:

[0730] The server uses an information processing device to analyze biometric data and evaluate the user's emotional state and stress level. The input is cleaned biometric data, and the output is the evaluation result of the stress level and emotional state. This analysis utilizes a machine learning model to perform pattern recognition based on past data.

[0731] Step 4:

[0732] The server uses a generated AI model to select the optimal relaxation technique based on the evaluation results. The input is the evaluation results of emotional state and stress level, and the output is the selected relaxation technique. By generating prompt sentences and feeding them into this model, a specific technique is identified.

[0733] Step 5:

[0734] The server transmits the selected relaxation technology to the terminal via an advisory device. The input is detailed information about the relaxation technology, and the output is the data to be transmitted to the terminal. In this process, audio or visual content is selected as the means of transmission.

[0735] Step 6:

[0736] The user practices the suggested relaxation techniques and sends feedback about their effects to the server via their device. The input is the user's feedback, and the output is the feedback information received by the server. The feedback is in the form of a simple questionnaire, inquiring about the effectiveness and satisfaction level.

[0737] Step 7:

[0738] The server receives feedback via a response device, and the update device adjusts the information processing device and the advisory device. The input is user feedback information, and the output is updated system parameters. This update improves the accuracy of suggestions in subsequent sessions.

[0739] 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.

[0740] This invention relates to an AI mental care system incorporating an emotion engine that recognizes the user's emotions, and is comprised of sensor means, processing means, advice means, feedback means, update means, and the emotion engine. This system has the ability to collect biometric information through a smart device worn by the user, analyze it on a server to identify the user's emotional state and stress level, and propose relaxation techniques accordingly.

[0741] Sensor data collection and emotion recognition

[0742] The device collects biometric information, including heart rate, skin potential, and voice, from smartwatches and smartphones. In addition, it uses the device's camera to monitor the user's facial expressions. This data is transmitted to a server in real time.

[0743] Data analysis and emotional judgment

[0744] The server receives multimodal biometric data transmitted from the terminal and uses an emotion engine to recognize the user's emotional state. The emotion engine combines voice tone analysis, facial expression recognition, and emotion estimation from text to comprehensively understand the user's current emotional state.

[0745] Suggestions for relaxation techniques

[0746] Based on the analysis results from the emotion engine, the server generates appropriate relaxation techniques for the user. For example, for users experiencing heightened tension or anxiety, it suggests relaxation activities such as meditation or deep breathing. This advice is communicated to the user via voice guidance and text messages through the device.

[0747] Collecting user feedback and updating the system

[0748] Users can provide feedback on the effectiveness and satisfaction level of the relaxation techniques they have practiced. This feedback is sent from the device to the server and used to improve the system and the emotion engine's algorithms. This process allows the system to continuously provide personalized care for each user.

[0749] Specific example

[0750] For example, if a user experiences stress while working at the office and their heart rate spikes, the emotion engine on the server, based on the simultaneously detected tense facial expression, determines that the user is in an anxious state. Based on this result, the device notifies the user via voice and text with advice such as, "Take a short meditation to calm your mind." By providing feedback on the user's meditation experience, the relaxation plan provided in subsequent sessions can be further optimized.

[0751] In this way, the present invention makes it possible to grasp the user's real-time emotional changes and continuously provide personalized mental care.

[0752] The following describes the processing flow.

[0753] Step 1:

[0754] The device utilizes the smartwatch's sensors to collect the user's biometric information, acquiring heart rate, skin potential responses, voice data, and other data in real time. It also monitors the user's facial expressions via the camera and transmits this data to a server.

[0755] Step 2:

[0756] The server integrates biometric information and facial expression data received from the terminal and analyzes it using an emotion engine. The emotion engine combines voice tone, facial features, and text data analysis to identify the user's emotional state. In this process, emotional categories such as "anxiety," "happiness," and "anger" are recognized.

[0757] Step 3:

[0758] Based on the output of the emotion engine, the server selects appropriate relaxation techniques according to the user's current emotional state and stress level. For example, if anxiety is detected, it may recommend deep breathing exercises or short meditation sessions. These suggestions are then formatted and sent to the terminal.

[0759] Step 4:

[0760] The device displays relaxation techniques received from the server on the user's smartphone or smartwatch screen, and also provides voice guidance. The user receives instructions through the screen and performs the relaxation techniques.

[0761] Step 5:

[0762] Users provide feedback on the effectiveness and satisfaction level of relaxation techniques they have tried, entering the feedback via their device. This feedback is often provided through text input or questionnaires.

[0763] Step 6:

[0764] The device sends feedback data received from the user to the server. The server analyzes this feedback information, incorporates it into the emotion engine and advice methods, and updates the algorithm to improve the accuracy of support in the future.

[0765] Throughout this entire process, the system continuously provides individualized, real-time support for the user's mental health.

[0766] (Example 2)

[0767] 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".

[0768] In modern society, it is crucial to recognize users' emotional states and stress levels in real time and provide personalized mental care. However, conventional technologies have struggled to accurately grasp users' emotions and precisely suggest appropriate relaxation techniques. In particular, integrating information from multiple data sources to achieve highly accurate emotion recognition remains an unresolved challenge.

[0769] 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.

[0770] In this invention, the server includes sensor means for collecting the user's biometric information, processing means for analyzing the biometric information to evaluate the user's emotional state and stress level, and means equipped with an emotion engine that performs emotion recognition by combining voice analysis, facial expression recognition, and text estimation. This makes it possible to evaluate the user's emotions from multiple perspectives and provide appropriate relaxation technology tailored to each individual.

[0771] "Sensor means" refers to devices or functions for collecting a user's biometric information, enabling the acquisition of information such as heart rate, skin potential, and voice.

[0772] "Processing means" refers to a part of a system that has the function of analyzing collected biometric information and evaluating the user's emotional state and stress level.

[0773] An "emotion engine" refers to technologies and programs that combine voice analysis, facial recognition, and text estimation to comprehensively recognize a user's emotions.

[0774] "Advice means" refers to a function that provides users with relaxation techniques generated based on evaluations by processing means, and is particularly presented in the form of audio or text.

[0775] A "feedback mechanism" is a function for receiving opinions and feedback from users, which is used to improve and optimize the system.

[0776] "Update mechanism" refers to a function that adjusts processing and advice mechanisms based on user feedback to improve the overall operation of the system.

[0777] A "multimodal sensor" refers to a sensor that can acquire and integrate data from different types of sensors, enabling it to process information from multiple data sources in an integrated manner.

[0778] This invention relates to a system for analyzing a user's emotional state and stress level in real time and providing appropriate relaxation techniques. The system mainly consists of sensor means, processing means, emotion engine, advice means, feedback means, and update means.

[0779] The device uses a smartwatch or smartphone attached to the user's body to collect a variety of biosignals, including heart rate, skin potential, voice data, and even facial expressions captured using a camera. This data is transmitted to a server in real time.

[0780] The server analyzes the data received from these terminals. This analysis utilizes an emotion engine that integrates voice tone analysis, facial expression recognition, and emotion estimation from text. This emotion engine combines a wide variety of data to comprehensively recognize the user's emotional state.

[0781] After evaluating the user's emotional state, the server generates relaxation techniques tailored to that state. This process uses specific algorithms and generative AI models to suggest the most suitable methods for the user, such as meditation or deep breathing. The server sends these suggestions to the device, which then notifies the user of the advice via voice or text.

[0782] Subsequently, users try these relaxation techniques and provide feedback on their effects via their device. This feedback is received by the server and used to improve the system's overall processing and advice mechanisms. By repeating this process, the system can provide more personalized care to each user.

[0783] As a concrete example of its use, if a user experiences stress at work, this accumulated stress is detected as an increase in heart rate or facial tension. The server analyzes this and sequentially notifies the user of appropriate relaxation techniques. The user follows the suggestions and provides feedback on the results, which further optimizes the advice for future sessions.

[0784] An example of a prompt message for the generating AI model is, "Based on heart rate, facial expressions, and voice data, identify the user's emotions and generate appropriate relaxation techniques." This enables optimal care tailored to the user's emotions and stress levels.

[0785] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0786] Step 1:

[0787] The device collects the user's biometric information. Smartwatches and smartphone sensors capture heart rate, skin potential, voice, and facial expressions using cameras as input. This data is transmitted to a server in real time. Specifically, the device collects biometric signals captured by sensors at regular intervals, bundles them into data packets, and sends them to the server.

[0788] Step 2:

[0789] The server receives biometric information from the terminal. It integrates heart rate changes, skin potential fluctuations, voice tone, and facial expression image data as input. The server preprocesses this data, standardizing it according to its respective data format, in preparation for processing. As output, it converts the biometric data into an analyzable format.

[0790] Step 3:

[0791] The server analyzes biometric information using an emotion engine. It uses pre-processed multimodal data as input, performing voice analysis, facial expression recognition, and emotion estimation from text. Specifically, the emotion engine reads emotions from the rhythm and intonation of speech, analyzes facial expressions based on a model, and estimates emotional tendencies from text information. The output is numerical data representing the user's emotional state.

[0792] Step 4:

[0793] The server generates appropriate relaxation techniques based on the user's emotional state. It uses the emotional state and stress level, output from an emotional engine, as input. The server utilizes a generative AI model to algorithmically determine the appropriate relaxation method for each emotional state. Specifically, the generative AI model selects the most suitable action for the user's current situation and outputs it as a feasible relaxation plan.

[0794] Step 5:

[0795] The server sends the generated relaxation technology to the terminal, and the terminal notifies the user of the relaxation technology. The input is the generated relaxation plan, formatted as voice guidance and text messages. The terminal utilizes its notification function to provide the user with specific instructions and advice. The output is the relaxation advice received by the user.

[0796] Step 6:

[0797] The user performs the recommended relaxation technique and provides feedback. The input is the user's opinion on the effectiveness and satisfaction level of the relaxation technique performed, entered into a terminal. This feedback is collected by the terminal and sent to the server. The output is the feedback information recorded on the server for future service optimization.

[0798] Step 7:

[0799] The server updates the system using user feedback. It analyzes feedback data as input and adjusts processing and advice methods. Specifically, it uses feedback information as training data for the AI ​​model to improve recognition accuracy and suggestion quality in subsequent uses. The output includes the updated model and system settings.

[0800] (Application Example 2)

[0801] 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".

[0802] In modern urban environments, many people experience stress and emotional instability, but there is a lack of effective systems to detect this in real time and respond to individual needs. Furthermore, there is a need for a system that effectively understands and utilizes stress levels across the entire city.

[0803] 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.

[0804] In this invention, the server includes detection means for collecting user biometric information, analysis means for analyzing the biometric information to evaluate emotional state and stress level, and display means for visualizing stress levels in an urban environment. This makes it possible to monitor the emotional state of individual users in real time and provide personalized relaxation methods, while simultaneously enabling stress management and environmental improvement throughout the city.

[0805] A "detection means for collecting user biometric information" is a device that uses multiple sensors installed in a smart device to collect biometric data such as heart rate, facial expressions, and voice.

[0806] "Analysis means for analyzing biometric information to evaluate emotional state and stress level" refers to a device or program that performs processing to comprehensively determine the user's emotions and stress levels based on collected biometric data.

[0807] A "means for generating and providing relaxation methods to users" is a device that has the function of recommending actions to relieve tension, such as meditation or deep breathing, to the user according to their analyzed emotional state.

[0808] A "feedback receiving mechanism" is a device that has the function of obtaining information from users regarding the effectiveness and satisfaction level of relaxation methods, and using that information to improve the overall system.

[0809] "Update means for adjusting analysis means and proposal means based on feedback" refers to a device or program for controlling the process of improving the accuracy of analysis and refining proposals based on feedback information from users.

[0810] A "display method for visualizing stress levels in urban environments" is a system that visually shows stress levels within a city using maps, graphs, etc., based on collected data.

[0811] This invention is a system that provides personalized relaxation methods by monitoring a user's biometric information and analyzing their emotional and stress levels in real time. This is achieved by utilizing the user's smart devices and terminals installed in urban facilities.

[0812] The server uses multiple hardware and software components to process biometric information collected from smart devices. Specifically, it employs a composite sensor embedded in the smart device as a detection means, collecting data such as heart rate, facial expressions, and voice in real time. Next, the analysis means uses natural language processing APIs from Google Cloud and IBM Watson to analyze this biometric data and evaluate the user's emotional state.

[0813] Based on the analysis results, the server generates relaxation methods through suggestion mechanisms. For example, it may suggest methods such as meditation or deep breathing to the user. These suggestions are notified to the user's device in the form of voice or text. If the user feels stressed while jogging or walking, the device can notify them with a prompt message such as, "Your heart rate is rising rapidly. Take a deep breath now to reset your emotions."

[0814] After performing these relaxation methods, users provide feedback to the server through a response mechanism regarding their effectiveness and satisfaction level. This feedback information is used to improve the overall system performance using an update mechanism and is stored as data to improve the accuracy of future analyses and suggestions.

[0815] Furthermore, to visualize stress levels in urban environments, the data analyzed via display devices can be visualized in map and graph formats and shared on displays installed in public facilities. This allows urban administrators and the general public to identify areas where stress is concentrated and take necessary countermeasures.

[0816] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0817] Step 1:

[0818] The device collects biometric information such as the user's heart rate, facial expressions, and voice in real time using a combination of sensors. This data becomes input, and the device packages it and sends it to the server.

[0819] Step 2:

[0820] The server processes the received biometric data using analytical tools. Using natural language processing APIs from Google Cloud and IBM Watson, it evaluates emotional states based on heart rate, voice tone, and changes in facial expressions. This analysis visualizes stress levels and emotional changes, and emotional state data is output.

[0821] Step 3:

[0822] The server generates appropriate relaxation methods based on this emotional state data. This process determines the content of the relaxation suggestions. Specifically, a prompt message using a generative AI model, such as "Take a deep breath and relax," is created and output.

[0823] Step 4:

[0824] The server delivers the generated prompt message to the terminal as both an audio and text message. The terminal receives this message and notifies the user. In this step, the user can learn the appropriate relaxation method.

[0825] Step 5:

[0826] The user performs the suggested relaxation method and sends feedback regarding its effectiveness and satisfaction level to the server via their device. This feedback data is then used as input for the next step.

[0827] Step 6:

[0828] The server receives feedback from users and updates the analysis and suggestion methods based on that feedback. This update process improves the accuracy of subsequent analyses and suggestions. The updated algorithm is output and applied to the entire system.

[0829] Step 7:

[0830] The server uses the analyzed data and feedback to visualize the distribution of stress in the urban environment. This is then output as maps and graphs using display devices and shown on displays placed in public facilities. This allows for a visual understanding of stress hotspots within the city.

[0831] 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.

[0832] 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.

[0833] 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.

[0834] 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.

[0835] 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.

[0836] 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.

[0837] 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.

[0838] 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.

[0839] 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."

[0840] 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.

[0841] 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.

[0842] 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.

[0843] 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.

[0844] 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.

[0845] 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.

[0846] 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.

[0847] 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.

[0848] 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.

[0849] 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.

[0850] 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.

[0851] 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.

[0852] The following is further disclosed regarding the embodiments described above.

[0853] (Claim 1)

[0854] A sensor means for collecting the user's biometric information,

[0855] A processing means for analyzing the aforementioned biological information and evaluating emotional state and stress level,

[0856] An advice means for generating and providing relaxation techniques to the user based on the evaluation,

[0857] A feedback mechanism for receiving user feedback,

[0858] An update means that adjusts the processing means and the advice means based on the feedback,

[0859] A system that includes this.

[0860] (Claim 2)

[0861] The system according to claim 1, wherein the advice means provides relaxation techniques in the form of audio and text.

[0862] (Claim 3)

[0863] The system according to claim 1, wherein the sensor means uses a multimodal sensor incorporated into a smart device.

[0864] "Example 1"

[0865] (Claim 1)

[0866] A means for acquiring information to collect the user's biometric information,

[0867] Information processing means for analyzing the aforementioned biological information and evaluating emotional state and stress level,

[0868] A means of providing guidance to the user, which generates a relaxation method based on the evaluation using an AI model,

[0869] A means of receiving feedback from users regarding the effectiveness,

[0870] Information updating means for adjusting and updating the information processing means and guidance means based on the feedback,

[0871] A system that includes this.

[0872] (Claim 2)

[0873] The system according to claim 1, wherein the guidance means provides a relaxation method in the form of audio data and text information.

[0874] (Claim 3)

[0875] The system according to claim 1, wherein the information acquisition means uses a plurality of sensing devices incorporated into a portable digital device.

[0876] "Application Example 1"

[0877] (Claim 1)

[0878] A sensor device that collects the user's biometric information,

[0879] An information processing device that analyzes the aforementioned biological information to evaluate emotional state and stress level,

[0880] An advisory device that generates and provides relaxation techniques to the user based on the evaluation,

[0881] A response device that receives feedback from users,

[0882] An update device that adjusts the information processing device and the advisory device based on the feedback,

[0883] A system that includes a device integrated into a home appliance that suggests relaxation methods to the user via voice and display based on their emotional state and stress level.

[0884] (Claim 2)

[0885] The system according to claim 1, wherein the advisory device provides relaxation technology in audio and visual formats.

[0886] (Claim 3)

[0887] The system according to claim 1, wherein the sensor device uses a multi-functional sensor incorporated into a portable information terminal.

[0888] "Example 2 of combining an emotion engine"

[0889] (Claim 1)

[0890] A sensor means for collecting the user's biometric information,

[0891] Processing means for analyzing the aforementioned biometric information to evaluate the user's emotional state and stress level,

[0892] A means equipped with an emotion engine that performs emotion recognition by combining voice analysis, facial expression recognition, and text estimation,

[0893] An advisory means for generating and providing relaxation techniques based on the evaluation to the user,

[0894] A feedback mechanism for receiving user feedback,

[0895] A system including an update means that adjusts the processing means and the advice means based on the feedback.

[0896] (Claim 2)

[0897] The system according to claim 1, wherein the advice means provides relaxation techniques in the form of voice and text.

[0898] (Claim 3)

[0899] The system according to claim 1, wherein the sensor means uses a multimodal sensor built into a portable device.

[0900] "Application example 2 when combining with an emotional engine"

[0901] (Claim 1)

[0902] A detection means for collecting the user's biometric information,

[0903] An analytical means for analyzing the aforementioned biological information to evaluate emotional state and stress level,

[0904] A proposal means for generating and providing a relaxation method based on the evaluation to the user,

[0905] A means of receiving user feedback,

[0906] An update means that adjusts the analysis means and the proposal means based on the feedback,

[0907] A display method for visualizing stress levels in urban environments,

[0908] A system that includes this.

[0909] (Claim 2)

[0910] The system according to claim 1, wherein the proposed means provides a relaxation method in the form of audio and text.

[0911] (Claim 3)

[0912] The system according to claim 1, wherein the detection means uses a composite sensor incorporated into a smart device. [Explanation of Symbols]

[0913] 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 sensor means for collecting the user's biometric information, A processing means for analyzing the aforementioned biological information and evaluating emotional state and stress level, An advice means for generating and providing relaxation techniques to the user based on the evaluation, A feedback mechanism for receiving user feedback, An update means that adjusts the processing means and the advice means based on the feedback, A system that includes this.

2. The system according to claim 1, wherein the advice means provides relaxation techniques in the form of audio and text.

3. The system according to claim 1, wherein the sensor means uses a multimodal sensor incorporated into a smart device.

Images