system

Abstract

We provide the system. [Solution] Means for acquiring biometric data, A means for evaluating psychological state and level of pressure based on the aforementioned biometric data, Based on the aforementioned evaluation results, means for providing psychological health advice to users, An automated machine means for presenting the provided advice to the user, 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 method for controlling a persona chatbot, which is 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 character of the chatbot, encoding the prompt, and inputting the encoded prompt into a language model to generate a chatbot utterance in response to the user utterance.

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In modern society, stress and mental health problems have a great impact on people's lives. However, there is a lack of real-time systems for providing appropriate mental care according to individual needs. As a result, it is difficult for users to accurately grasp their own stress levels and quickly find appropriate coping methods. To solve these problems, there is a need for a system that provides automatic and individualized mental care support.

Means for Solving the Problems

[0005] This invention provides a system that includes a device for collecting a user's biometric information in real time. This system also includes a device for analyzing the collected biometric information and provides the user with appropriate mental care advice based on the analysis results. Furthermore, it continuously improves the advice by collecting user feedback and incorporating it into modeling. In this way, it enables personalized mental care tailored to the user's characteristics and circumstances.

[0006] "Biometric information" refers to data about the user's physical condition, such as heart rate, skin electrical activity, and voice tone.

[0007] "Device" refers to the totality of hardware and software used for collecting, analyzing, and providing mental health advice based on biological information.

[0008] "Analysis" is the process of using collected biometric information to evaluate the user's emotional state and stress level.

[0009] "Mental care advice" refers to specific suggestions and relaxation techniques aimed at alleviating user stress and stabilizing their mental state.

[0010] "Feedback" refers to information returned to the system after a user has performed a suggested action, including its effects and implementation status.

[0011] "Modeling" is the process of adjusting algorithms based on user data to improve the accuracy and effectiveness of advice.

[0012] "Real-time" refers to a process where data collection, analysis, and advice provision are performed instantly and without delay.

[0013] "Personalization" refers to adjusting the system's output to suit the individual user's characteristics and needs. [Brief explanation of the drawing]

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

MODE FOR CARRYING OUT THE INVENTION

[0015] Hereinafter, an example of an embodiment of a system according to the technology of the present disclosure will be described with reference to the accompanying drawings.

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

[0017] In the following embodiments, the numbered processor (hereinafter simply referred to as "processor") may be one arithmetic unit or a combination of multiple arithmetic units. Also, the processor may be one type of arithmetic unit or a combination of multiple types of arithmetic units. Examples of arithmetic units include a CPU (Central Processing Unit), a GPU (Graphics Processing Unit), a GPGPU (General-Purpose computing on Graphics Processing Units), an APU (Accelerated Processing Unit), and the like.

[0018] In the following embodiments, 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.

[0019] 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, etc.

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

[0021] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B." That is, "A and / or B" means that it may be A alone, or B alone, or a combination of A and B. Furthermore, in this specification, the same concept as "A and / or B" applies when expressing three or more things linked by "and / or."

[0022] [First Embodiment]

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

[0024] As shown in Figure 1, the data processing system 10 includes a data processing device 12 and a smart device 14. An example of the data processing device 12 is a server.

[0025] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0026] The smart device 14 comprises a computer 36, a reception device 38, an output device 40, a camera 42, and a communication interface 44. The computer 36 comprises a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The reception device 38, output device 40, and camera 42 are also connected to the bus 52.

[0027] The reception device 38 is equipped with a touch panel 38A and a microphone 38B, etc., and receives user input. The touch panel 38A receives user input by detecting contact with an object (e.g., a pen or finger). The microphone 38B receives user input by detecting the user's voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the data indicating the user input.

[0028] The output device 40 includes a display 40A and a speaker 40B, and presents data to the user 20 by outputting the data in a form perceptible to the user 20 (e.g., audio and / or text). The display 40A displays visible information such as text and images according to instructions from the processor 46. The speaker 40B outputs audio according to instructions from the processor 46. The camera 42 is a small digital camera equipped with an optical system such as a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0029] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various types of information between processor 46 and processor 28 via network 54.

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

[0031] As shown in Figure 2, in the data processing device 12, a specific processing is performed by the processor 28. A specific processing program 56 is stored in the storage 32. The specific processing program 56 is an example of a "program" related to the technology of this disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0032] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0033] In the smart device 14, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The reception output program 60 is used in conjunction with a specific processing program 56 by the data processing system 10. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0034] Next, the specific processing performed by the specific processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the smart device 14 as the "terminal".

[0035] To implement this invention, it is necessary to construct a system that collects and analyzes biometric information in real time and provides appropriate mental care advice to the user based on the results. This system consists of three main components: a "terminal," a "server," and a "user."

[0036] The terminal functions as a device for collecting the user's biometric information. This could be a wearable device or smartphone that measures heart rate or voice tone, for example. The terminal collects this data in real time, performs initial processing, and then sends it to a server in an encrypted form.

[0037] The server analyzes the user's stress level based on the received biometric information. Using machine learning algorithms, it detects changes in heart rate and abnormalities in voice tone to understand the user's emotional state. Based on the analysis results, it generates personalized mental care advice and sends this information back to the device.

[0038] Users receive advice through their devices and perform the suggested relaxation techniques and suggestions. Feedback on these actions is entered into the device, which is then stored in the user's profile and used to update the server's data model.

[0039] For example, if a user's heart rate remains higher than normal, the server will determine that their stress level is rising. As a result, specific advice such as "Take deep breaths" or "Try a short meditation" will be sent to the device. By performing these actions and providing feedback, the server will adjust subsequent advice to be more appropriate for that user.

[0040] In this way, the system can continuously provide users with the most suitable mental care in real time and sequentially.

[0041] The following describes the processing flow.

[0042] Step 1:

[0043] The device acquires biometric information such as heart rate, skin electrical activity, and voice tone from the user's wearable device or smartphone.

[0044] Step 2:

[0045] The device preprocesses the collected biometric information, removes noise, encrypts it, and then sends it to the server.

[0046] Step 3:

[0047] The server stores the biometric information received from the terminal into a database and immediately begins analysis.

[0048] Step 4:

[0049] The server uses machine learning algorithms to assess the user's emotional state and stress level from biometric data.

[0050] Step 5:

[0051] The server generates personalized mental health advice based on the analysis results.

[0052] Step 6:

[0053] The server then sends the generated advice to the terminal.

[0054] Step 7:

[0055] The device displays a message on the screen to notify the user of received mental health advice.

[0056] Step 8:

[0057] The user performs relaxation techniques according to the provided advice and inputs feedback on the results and effectiveness into the device.

[0058] Step 9:

[0059] The device sends the feedback entered by the user to the server.

[0060] Step 10:

[0061] The server receives feedback from users, updates the data model, and incorporates it into future analyses and advice generation.

[0062] (Example 1)

[0063] 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."

[0064] In modern society, there is a demand for real-time mental healthcare tailored to individual users. However, conventional systems are limited to uniform advice, and have the challenge of providing detailed support based on individual biometric data. Furthermore, mechanisms for improving the quality of advice based on feedback have not been adequately established.

[0065] 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.

[0066] In this invention, the server includes means for analyzing stress indicators based on biometric data, means for evaluating emotional states using machine learning algorithms, and means for generating personalized mental care guidance using a generative AI model. This enables the provision of real-time and personalized mental care to individual users.

[0067] "Biometric data" refers to a collection of information obtained from a user's body, such as their heart rate and voice tone.

[0068] A "stress index" is a numerical value or evaluation criterion that indicates a user's stress level, calculated based on biometric data.

[0069] A "machine learning algorithm" refers to mathematical methods and models used to learn patterns from data and perform analysis and prediction.

[0070] A "generative AI model" is an artificial intelligence technology that generates specific advice and guidance for users in natural language.

[0071] "Personalized mental care" refers to mental health guidance and support tailored to the individual user's condition and feedback.

[0072] "Feedback" refers to information provided by users regarding their impressions and results after implementation, which is useful for improving the system.

[0073] This system is primarily composed of three elements: "terminals," "servers," and "users."

[0074] The terminal is a device for collecting the user's biometric data in real time. This includes wearable devices that measure heart rate and smartphones that capture voice tones. After acquiring the biometric data, the terminal performs noise reduction and filtering, encrypts the information, and sends it to a server. This ensures the security of the data using advanced encryption algorithms.

[0075] The server receives biometric data sent from the terminal and uses machine learning algorithms to analyze the user's stress indicators. The goal of this analysis is to capture fluctuations in heart rate and differences in voice tone within the data to understand the user's emotional state. Based on the analysis results, the server uses a generative AI model to generate personalized mental care advice for the user. In this process, an example of a prompt might be, "Please tell me effective relaxation methods when the user's heart rate is high." Based on such prompts, the generative AI model constructs appropriate advice.

[0076] Users receive mental health advice from the server via their device and then act on it. For example, users might receive specific suggestions such as "take deep breaths" or "do a short meditation." After acting on the advice, users input feedback into their device, and this information is sent to the server. This feedback data is used by the server to improve the data model, helping to make future advice even more personalized.

[0077] This system is configured as described above and provides users with real-time, individually adapted mental care.

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

[0079] Step 1:

[0080] The device collects biometric data entered by the user. During this process, the device uses a heart rate sensor and microphone to capture data, and extracts useful data through a noise reduction filter. This processed data is then prepared for the next step and output to the server in a securely protected form using an encryption algorithm.

[0081] Step 2:

[0082] The server receives and decrypts encrypted biometric data transmitted from the terminal. After decrypting the data, it passes the analysis of heart rate variability and voice tone as input to a machine learning algorithm. The analysis results in the output of numerical and categorical data that evaluates the user's emotional state, such as stress indicators. This evaluation data is then used in the next step.

[0083] Step 3:

[0084] The server utilizes a generative AI model based on the analyzed results, generating advice in the form of a prompt. An example of a prompt might be, "Please tell me an effective relaxation method when the user's heart rate is high." Based on this input, the generative AI model outputs personalized mental care advice to the user.

[0085] Step 4:

[0086] The server sends the generated mental health advice to the terminal. The terminal displays the advice received from the server to the user and prompts them to take action.

[0087] Step 5:

[0088] Users follow mental care advice from their device and take actions that promote relaxation. For example, users complete specific actions such as "taking deep breaths" or "trying a short meditation." After completion, they input the results and their impressions as feedback into the device and output the data to the server.

[0089] Step 6:

[0090] The server processes feedback data received from users and uses it to improve its machine learning model. This allows the server to update the data model and generate output that improves the accuracy of subsequent analyses and advice. In this way, the system continuously evolves, providing more tailored mental care to each user.

[0091] (Application Example 1)

[0092] 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."

[0093] In recent years, many people experience stress in their daily lives, which impacts their health. However, providing effective mental care tailored to individual circumstances is difficult, and conventional technologies have struggled to respond in real time and on an individual basis. In particular, there is a need for a system that can automatically provide mental support within the home.

[0094] 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.

[0095] In this invention, the server includes means for acquiring biometric data, means for evaluating psychological state and stress levels, and automated mechanical means for presenting the provided advice to the user. This makes it possible to provide users with appropriate mental health advice in real time and to automatically and individually realize mental care tailored to each individual's stress level.

[0096] "Biometric data" refers to data obtained from the human body, specifically information such as heart rate and voice tone.

[0097] "Psychological state" refers to the user's emotions and mental health, including emotional states such as stress and anxiety.

[0098] The "pressure level" is an indicator that shows the degree of psychological stress and pressure experienced by the user.

[0099] "Evaluation methods" refer to a series of processes for analyzing psychological state and stress levels based on biometric data, and for deriving appropriate mental health advice.

[0100] "Automated mechanical means" refers to mechanical devices used to provide psychological health advice to users, and specifically includes robots.

[0101] "Psychological health advice" refers to suggestions for relaxation and improvement tailored to the user's current psychological state, and is expressed in natural language.

[0102] The system for implementing this invention mainly consists of three elements: a "server," a "terminal," and a "user."

[0103] The server is responsible for evaluating the user's psychological state and level of stress based on biometric data. To do this, the server uses machine learning algorithms to analyze the collected data (such as heart rate and voice tone). Based on the analysis results, it generates psychological health advice tailored to the user and sends it to the terminal.

[0104] The terminal functions as a device for collecting the user's biometric data. Specifically, wearable devices equipped with sensors to measure heart rate and voice tone, or smartphones, are used. The terminal collects this data in real time and transmits it to a server. The terminal also has a display and voice output function to present advice received from the server to the user.

[0105] Users receive mental health advice through their device, enabling them to take appropriate action based on their mental state and stress levels. Specific examples include suggestions for relaxation techniques such as "practicing short meditations" or "trying deep breathing." Users input their progress and feedback into the device, which is used to update the server's data model.

[0106] As a concrete example of this system, if a user feels stressed after work one day, the terminal will detect an increase in heart rate, and the server will generate advice such as, "Please sit quietly for 5 minutes to relax." This advice will be presented to the user from the terminal, and the user will follow it and perform relaxation.

[0107] An example of a prompt for a generative AI model is, "Generate the optimal relaxation method to provide when the user's heart rate increases."

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

[0109] Step 1:

[0110] The device measures the user's heart rate and voice tone using sensors. Biometric data acquired from the user is used as input. This data is initially processed and encrypted in real time. This processing prepares the user's biometric data for secure transmission to the server.

[0111] Step 2:

[0112] The device sends encrypted biometric data to the server. The data the server receives as input is the encrypted biometric data sent from the device. This data is decrypted on the server. The server analyzes the decrypted data and uses machine learning algorithms to calculate the user's psychological state and level of stress. This process determines the user's stress and emotional state.

[0113] Step 3:

[0114] The server utilizes a generative AI model based on the analysis results to create personalized psychological health advice for the user. The input is data on the user's psychological state and stress levels, and the output is advice expressed in natural language. An example of this prompt is, "Please suggest relaxation methods recommended when the user's heart rate is high." This process generates the advice to be delivered to the user.

[0115] Step 4:

[0116] The server sends the generated advice to the terminal. The data the terminal receives as input is the advice information sent from the server. The terminal uses its display and audio output functions to present this to the user. This process allows the user to receive and respond to advice in real time.

[0117] Step 5:

[0118] Users implement the advice they receive through their device. The implementation status and feedback are entered into the device. This becomes new input data and is resent to the server. This feedback data is used to update the server's information model, helping to improve the accuracy of future advice. This process enhances personalized support tailored to each user's individual circumstances across the entire system.

[0119] 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.

[0120] To implement this invention, it is necessary to construct a system that includes a biometric information collection device, a device for analyzing emotional states and stress levels, a device for providing mental care advice, and an emotion engine that recognizes the user's emotions.

[0121] The device collects biometric information in real time, acquiring biometric data such as heart rate, skin electrical activity, and voice tone. This data is de-noised during initial processing, encrypted, and then sent to the server.

[0122] The server analyzes biometric information received from the terminal and uses machine learning algorithms to evaluate the user's emotional state and stress level. An emotion engine plays a crucial role in this process. The emotion engine analyzes the user's voice tone and, if necessary, facial expressions from camera footage to identify the user's emotions. This emotional data is incorporated into the stress assessment and used to generate more accurate mental health advice.

[0123] Users act on the advice they receive via their device and provide feedback on the results. This feedback is used to confirm changes in the user's emotions and the effectiveness of stress reduction. User feedback is crucial data for the server to update its data model and improve future advice.

[0124] For example, if a user speaks in a tense tone, the emotion engine detects this tone and determines that their anxiety is heightened. Based on this result, the server generates specific advice such as "Take deep breaths to reduce anxiety" or "Try listening to calming music" and notifies the device. If the user follows this advice and feels it is effective, they provide feedback, which the emotion engine uses to improve future analyses.

[0125] This system aims to provide personalized mental care by gaining a deeper understanding of the user's characteristics and the situation at hand. The integration of an emotion engine enables more nuanced responses tailored to the user's emotions, contributing to stress management and maintaining mental health.

[0126] The following describes the processing flow.

[0127] Step 1:

[0128] The device collects heart rate, skin electrical activity, voice tone, and even facial expressions via a camera in real time from the user's wearable device or smartphone.

[0129] Step 2:

[0130] The device preprocesses the collected biometric information and facial expression data, removes noise, then encrypts this data and sends it to the server.

[0131] Step 3:

[0132] The server stores the data received from the terminal in a database and immediately begins analysis using the emotion engine.

[0133] Step 4:

[0134] The emotion engine uses machine learning algorithms to analyze the user's emotional state based on their voice tone and facial expressions.

[0135] Step 5:

[0136] The server takes in the results of the emotion engine's analysis and integrates them with heart rate and skin electrical activity data to assess the user's stress level.

[0137] Step 6:

[0138] Based on the analysis results, the server generates mental care advice optimized for the emotional state and stress level.

[0139] Step 7:

[0140] The server then expresses the generated advice in natural language and sends it to the terminal.

[0141] Step 8:

[0142] The device displays messages on the screen or provides voice guidance to notify the user of received mental health advice visually or audibly.

[0143] Step 9:

[0144] Users follow the advice received from their device and implement relaxation techniques and other measures. Afterward, they evaluate the results and the effects they experienced, and input the feedback into the device.

[0145] Step 10:

[0146] The device collects user feedback and sends it back to the server.

[0147] Step 11:

[0148] The server analyzes user feedback and updates its data model to improve the accuracy of sentiment analysis and advice generation in the future.

[0149] (Example 2)

[0150] 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".

[0151] Traditional mental healthcare systems have challenges in accurately assessing users' emotions and stress levels, and the advice they offer is often generic, resulting in ineffective stress relief. Furthermore, there are insufficient mechanisms for effectively utilizing user feedback to improve the system.

[0152] 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.

[0153] In this invention, the server includes means for acquiring the user's biometric indicators, means for removing noise from the acquired biometric indicators, encrypting them, and transmitting them, and means for analyzing the encrypted biometric indicators and evaluating the emotional state and stress level. This enables accurate evaluation of the emotional state and stress level based on the user's biometric indicators, and realizes the provision of personalized mental care.

[0154] A "user" is defined as someone who is subject to the acquisition of biometric data and the provision of mental healthcare.

[0155] "Biometric indicators" are data necessary to evaluate a user's emotional state and stress level, such as heart rate, skin electrical activity, and voice tone.

[0156] "Noise reduction" is a process that removes unwanted signals contained in biometric indicators to improve the accuracy of the data.

[0157] "Encryption" is a technology that protects information to ensure the secure transmission of biometric data, preventing its contents from being read by third parties.

[0158] "Emotional state" is an indicator that shows the user's psychological state, and includes emotions such as anxiety and relaxation.

[0159] "Stress level" is an indicator that shows the degree of stress a user is experiencing.

[0160] "Evaluation" is the process of analyzing emotional state and stress levels based on acquired biometric indicators, and then judging the results.

[0161] "Mental care" refers to support and advice provided to improve a user's emotional state and stress levels.

[0162] An "emotion engine" is an algorithm and technology that analyzes voice tone, facial expressions, and other factors to recognize a user's emotions.

[0163] To implement this invention, it is necessary to construct a comprehensive system that collects and analyzes biometric information and provides appropriate mental care. This system includes a terminal for acquiring the user's biometric indicators, a server that analyzes the biometric indicators and evaluates emotional state and stress levels, and a function to provide personalized mental care to the user based on the evaluation results.

[0164] The device uses a wearable form factor and is equipped with biometric data collection devices such as a heart rate sensor, skin electrical activity sensor, and microphone. These sensors acquire the user's biometric data in real time, and after initial processing to remove noise, the data is encrypted using AES encryption technology and sent to the server.

[0165] Upon decrypting the received encrypted data, the server utilizes machine learning algorithms and an emotion engine to assess the user's emotional state and stress level. The emotion engine analyzes subtle changes in voice tone and facial expressions to identify the user's emotions. Based on these analysis results, it generates personalized mental health advice. This advice is provided in natural language and sent to the user via their device.

[0166] Users receive advice via their device and take action accordingly. Specific instructions are given, such as practicing deep breathing or playing relaxing music. Users return the results of these actions to the system as feedback. This feedback is used by the server to update its data model and improve the quality of advice provided in the future.

[0167] For example, when a user speaks in a tense voice, the emotion engine analyzes the tone of voice and determines that the user is experiencing heightened anxiety. Based on this assessment, the server generates and provides advice to the user, such as "Take a deep breath to alleviate your anxiety" or "Listen to calming music to relax."

[0168] Examples of prompts to input into a generative AI model:

[0169] "Please explain how to analyze a user's emotional state based on their voice data and biometric information, and provide personalized advice to reduce stress."

[0170] The introduction of this system will enable more effective and personalized mental care based on the user's characteristics and current condition.

[0171] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0172] Step 1:

[0173] The device collects the user's biometric data. Specifically, it uses a heart rate sensor, a skin electrical activity sensor, and a microphone to acquire biometric data in real time. The input is biometric information from the user, and the output is biometric data before noise reduction.

[0174] Step 2:

[0175] The device performs noise reduction on the collected biometric data. Specifically, it uses digital filtering technology to remove noise and improve data accuracy. The input is biometric data, and the output is clean data after noise reduction.

[0176] Step 3:

[0177] The terminal encrypts the noise-removed data using AES encryption technology and sends it to the server using a secure protocol. Specifically, it applies an encryption algorithm to ensure data confidentiality. The input is noise-removed data, and the output is encrypted data.

[0178] Step 4:

[0179] The server decrypts the received encrypted data. Specifically, it applies a decryption algorithm to retrieve the original data. The input is encrypted data, and the output is the decrypted biometric data.

[0180] Step 5:

[0181] The server uses machine learning algorithms to analyze biometric data and assess the user's emotional state and stress level. Specifically, it performs voice tone and facial expression analysis using an emotion engine. The input is decoded biometric data, and the output is the assessment result of the emotional state and stress level.

[0182] Step 6:

[0183] The server generates personalized mental health advice for the user based on the analysis results. Specifically, it creates advice in natural language according to a template and provides instructions tailored to the user's situation. The input is the evaluation results, and the output is the mental health advice.

[0184] Step 7:

[0185] The terminal notifies the user of advice received from the server. The user receives this advice and takes action according to the instructions. The input is the advice sent from the server, and the output is the information obtained by the user.

[0186] Step 8:

[0187] Users provide feedback on the effectiveness of the advice they receive via their device. Specifically, they use a feedback input interface to record the degree of the advice's effectiveness and their impressions. The input is the user's feedback information, and the output is the feedback data received by the server.

[0188] Step 9:

[0189] The server updates its data model using user feedback to improve the accuracy of subsequent analysis results and the quality of advice. Specifically, it incorporates the feedback data into a machine learning model and performs training. The input is the feedback data, and the output is the improved data model.

[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 life, residents are experiencing increasing stress and mental health problems, and there is a need for appropriate support to address these issues. However, a system that accurately assesses mental stress levels at the individual and community levels and provides appropriate advice has not yet been put into practical use. In particular, methods for quickly proposing concrete measures to reduce stress throughout an entire city are still immature.

[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 means for collecting biometric information, means for analyzing emotional states and mental stress levels, and means for analyzing mental stress levels in each region and proposing stress reduction measures. This makes it possible to support the mental health of not only individuals but also entire communities and improve the quality of life for residents.

[0195] "Biometric information" refers to data related to human bodily functions, such as heart rate, skin electrical activity, and voice tone.

[0196] "Emotional state" refers to information that indicates a person's internal emotional condition, and is analyzed from factors such as voice tone and facial expressions.

[0197] "Mental tension level" is an indicator that shows the degree of psychological tension caused by stress, anxiety, etc.

[0198] "Psychological care suggestions" refer to specific advice aimed at improving mental health, provided based on the user's emotional state and level of mental stress.

[0199] "Regional mental stress levels" refer to the levels of mental stress and tension among residents in a specific geographical area, and are data that indicates the state of mental health in that region.

[0200] "Stress reduction measures" refer to specific methods and activities proposed to alleviate the mental tension of residents.

[0201] To implement this invention, a system is constructed using a biometric data collection device such as a smartwatch and a server that utilizes cloud computing.

[0202] The device collects biometric information such as heart rate, skin electrical activity, and voice tone in real time. This data is securely transmitted from the device to a server in the cloud using an API. Noise reduction and encryption are performed on the device, ensuring the confidentiality of the transmitted data.

[0203] The server utilizes machine learning models built in Python (e.g., TENSORFLOW®) to analyze received data. To assess emotional state and mental stress levels, it uses an emotion analysis API to analyze not only voice tone but also facial expressions from camera footage as needed. This allows the server to identify the mental stress level of the user or region and generate psychological care suggestions. These suggestions are generated using natural language processing algorithms and provided to the user in a concrete form.

[0204] For example, if the system determines that a user's stress level is high in their local area, the device will display information such as "recommended places to relax" or "information on relaxation events held in the area." In this way, it can support not only individual mental health care but also stress management for the entire community.

[0205] An example of a prompt could be: "Develop an AI model that monitors citizens' biometric information and emotional state in real time, analyzes stress levels for each region, and proposes specific stress reduction measures to support the mental health of citizens in smart cities."

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

[0207] Step 1:

[0208] The device uses a smartwatch and sensors to collect biometric information such as the user's heart rate, skin electrical activity, and voice tone. The input data is biometric information obtained from biosensors, and after data collection, a noise reduction filter is applied to improve the accuracy of the data. The output of this process is biometric information with noise removed.

[0209] Step 2:

[0210] The terminal encrypts denoised biometric information and transmits it to the server while maintaining security. The input data is denoised biometric information, and confidentiality is ensured using an encryption algorithm. The output is encrypted biometric information.

[0211] Step 3:

[0212] The server decodes the received biometric information and analyzes emotional state and mental stress levels using a machine learning model built in Python. The input data is encrypted biometric information, which is then decoded and processed using an emotion analysis API and machine learning algorithms. The output is an evaluation of the emotional state and mental stress levels of the user or region.

[0213] Step 4:

[0214] The server generates psychological care suggestions for the user based on the evaluation results of their emotional state and mental stress level. The input data is the result of emotion analysis, and a natural language processing algorithm is used to generate specific advice. The output is the psychological care suggestion.

[0215] Step 5:

[0216] The server sends the generated psychological care suggestions to the terminal and notifies the user. The input data is the psychological care suggestions, which are output in a format that allows for real-time feedback. The output is the psychological care suggestions displayed on the user interface of the terminal.

[0217] Step 6:

[0218] The user acts on the provided psychological care suggestions and inputs feedback into the terminal. The input data is the user's feedback on the effectiveness of the psychological care suggestions and becomes new training data for improving the system's performance. The output is sent to the server as user feedback.

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

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

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

[0222] [Second Embodiment]

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

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

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

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

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

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

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

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

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

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

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

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

[0235] To implement this invention, it is necessary to construct a system that collects and analyzes biometric information in real time and provides appropriate mental care advice to the user based on the results. This system consists of three main components: a "terminal," a "server," and a "user."

[0236] The terminal functions as a device for collecting the user's biometric information. This could be a wearable device or smartphone that measures heart rate or voice tone, for example. The terminal collects this data in real time, performs initial processing, and then sends it to a server in an encrypted form.

[0237] The server analyzes the user's stress level based on the received biometric information. Using machine learning algorithms, it detects changes in heart rate and abnormalities in voice tone to understand the user's emotional state. Based on the analysis results, it generates personalized mental care advice and sends this information back to the device.

[0238] Users receive advice through their devices and perform the suggested relaxation techniques and suggestions. Feedback on these actions is entered into the device, which is then stored in the user's profile and used to update the server's data model.

[0239] For example, if a user's heart rate remains higher than normal, the server will determine that their stress level is rising. As a result, specific advice such as "Take deep breaths" or "Try a short meditation" will be sent to the device. By performing these actions and providing feedback, the server will adjust subsequent advice to be more appropriate for that user.

[0240] In this way, the system can continuously provide users with the most suitable mental care in real time and sequentially.

[0241] The following describes the processing flow.

[0242] Step 1:

[0243] The device acquires biometric information such as heart rate, skin electrical activity, and voice tone from the user's wearable device or smartphone.

[0244] Step 2:

[0245] The device preprocesses the collected biometric information, removes noise, encrypts it, and then sends it to the server.

[0246] Step 3:

[0247] The server stores the biometric information received from the terminal into a database and immediately begins analysis.

[0248] Step 4:

[0249] The server uses machine learning algorithms to assess the user's emotional state and stress level from biometric data.

[0250] Step 5:

[0251] The server generates personalized mental health advice based on the analysis results.

[0252] Step 6:

[0253] The server then sends the generated advice to the terminal.

[0254] Step 7:

[0255] The device displays a message on the screen to notify the user of received mental health advice.

[0256] Step 8:

[0257] The user performs relaxation techniques according to the provided advice and inputs feedback on the results and effectiveness into the device.

[0258] Step 9:

[0259] The device sends the feedback entered by the user to the server.

[0260] Step 10:

[0261] The server receives feedback from users, updates the data model, and incorporates it into future analyses and advice generation.

[0262] (Example 1)

[0263] 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."

[0264] In modern society, there is a demand for real-time mental healthcare tailored to individual users. However, conventional systems are limited to uniform advice, and have the challenge of providing detailed support based on individual biometric data. Furthermore, mechanisms for improving the quality of advice based on feedback have not been adequately established.

[0265] 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.

[0266] In this invention, the server includes means for analyzing stress indicators based on biometric data, means for evaluating emotional states using machine learning algorithms, and means for generating personalized mental care guidance using a generative AI model. This enables the provision of real-time and personalized mental care to individual users.

[0267] "Biometric data" refers to a collection of information obtained from a user's body, such as their heart rate and voice tone.

[0268] A "stress index" is a numerical value or evaluation criterion that indicates a user's stress level, calculated based on biometric data.

[0269] A "machine learning algorithm" refers to mathematical methods and models used to learn patterns from data and perform analysis and prediction.

[0270] A "generative AI model" is an artificial intelligence technology that generates specific advice and guidance for users in natural language.

[0271] "Personalized mental care" refers to mental health guidance and support tailored to the individual user's condition and feedback.

[0272] "Feedback" refers to information provided by users regarding their impressions and results after implementation, which is useful for improving the system.

[0273] This system is primarily composed of three elements: "terminals," "servers," and "users."

[0274] The terminal is a device for collecting the user's biometric data in real time. This includes wearable devices that measure heart rate and smartphones that capture voice tones. After acquiring the biometric data, the terminal performs noise reduction and filtering, encrypts the information, and sends it to a server. This ensures the security of the data using advanced encryption algorithms.

[0275] The server receives biometric data sent from the terminal and uses machine learning algorithms to analyze the user's stress indicators. The goal of this analysis is to capture fluctuations in heart rate and differences in voice tone within the data to understand the user's emotional state. Based on the analysis results, the server uses a generative AI model to generate personalized mental care advice for the user. In this process, an example of a prompt might be, "Please tell me effective relaxation methods when the user's heart rate is high." Based on such prompts, the generative AI model constructs appropriate advice.

[0276] Users receive mental health advice from the server via their device and then act on it. For example, users might receive specific suggestions such as "take deep breaths" or "do a short meditation." After acting on the advice, users input feedback into their device, and this information is sent to the server. This feedback data is used by the server to improve the data model, helping to make future advice even more personalized.

[0277] This system is configured as described above to provide users with real-time, individually adapted mental care.

[0278] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0279] Step 1:

[0280] The device collects biometric data entered by the user. During this process, the device uses a heart rate sensor and microphone to capture data, and extracts useful data through a noise reduction filter. This processed data is then prepared for the next step and output to the server in a securely protected form using an encryption algorithm.

[0281] Step 2:

[0282] The server receives and decrypts encrypted biometric data transmitted from the terminal. After decrypting the data, it passes the analysis of heart rate variability and voice tone as input to a machine learning algorithm. The analysis results in the output of numerical and categorical data that evaluates the user's emotional state, such as stress indicators. This evaluation data is then used in the next step.

[0283] Step 3:

[0284] The server utilizes the generated AI model based on the analyzed results to generate advice with the prompt text as the input. As an example of the prompt text, something like "Please tell me effective relaxation methods when the user's heart rate is high" is used. Based on this input, the generated AI model outputs mental care advice individualized for the user.

[0285] Step 4:

[0286] The server sends the generated mental care advice to the terminal. The terminal displays the advice received from the server to the user and outputs it in a form that prompts execution.

[0287] Step 5:

[0288] The user takes actions to actually promote relaxation according to the mental care advice from the terminal. For example, the user completes specific actions such as "performing deep breathing" or "trying short meditation". After completion, the results and feelings are input into the terminal as feedback and output to the server as data.

[0289] Step 6:

[0290] The server processes the feedback data received from the user and uses it to improve the machine learning model. Thereby, the server updates the data model and generates output for improving the accuracy of subsequent analysis and advice. As a result, the system continuously evolves and continues to provide more suitable mental care for each user.

[0291] (Application Example 1)

[0292] Next, Application Example 1 will be described. In the following description, the data processing device 12 is referred to as the "server", and the smart glasses 214 are referred to as the "terminal".

[0293] In recent years, many people experience stress in their daily lives, which impacts their health. However, providing effective mental care tailored to individual circumstances is difficult, and conventional technologies have struggled to respond in real time and on an individual basis. In particular, there is a need for a system that can automatically provide mental support within the home.

[0294] 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.

[0295] In this invention, the server includes means for acquiring biometric data, means for evaluating psychological state and stress levels, and automated mechanical means for presenting the provided advice to the user. This makes it possible to provide users with appropriate mental health advice in real time and to automatically and individually realize mental care tailored to each individual's stress level.

[0296] "Biometric data" refers to data obtained from the human body, specifically information such as heart rate and voice tone.

[0297] "Psychological state" refers to the user's emotions and mental health, including emotional states such as stress and anxiety.

[0298] The "pressure level" is an indicator that shows the degree of psychological stress and pressure experienced by the user.

[0299] "Evaluation methods" refer to a series of processes for analyzing psychological state and stress levels based on biometric data, and for deriving appropriate mental health advice.

[0300] "Automated mechanical means" refers to mechanical devices used to provide psychological health advice to users, and specifically includes robots.

[0301] "Psychological health advice" refers to suggestions for relaxation and improvement tailored to the user's current psychological state, and is expressed in natural language.

[0302] The system for implementing this invention mainly consists of three elements: a "server," a "terminal," and a "user."

[0303] The server is responsible for evaluating the user's psychological state and level of stress based on biometric data. To do this, the server uses machine learning algorithms to analyze the collected data (such as heart rate and voice tone). Based on the analysis results, it generates psychological health advice tailored to the user and sends it to the terminal.

[0304] The terminal functions as a device for collecting the user's biometric data. Specifically, wearable devices equipped with sensors to measure heart rate and voice tone, or smartphones, are used. The terminal collects this data in real time and transmits it to a server. The terminal also has a display and voice output function to present advice received from the server to the user.

[0305] Users receive mental health advice through their device, enabling them to take appropriate action based on their mental state and stress levels. Specific examples include suggestions for relaxation techniques such as "practicing short meditations" or "trying deep breathing." Users input their progress and feedback into the device, which is used to update the server's data model.

[0306] As a concrete example of this system, if a user feels stressed after work one day, the terminal will detect an increase in heart rate, and the server will generate advice such as, "Please sit quietly for 5 minutes to relax." This advice will be presented to the user from the terminal, and the user will follow it and perform relaxation.

[0307] Examples of prompt texts for the generative AI model include "Please generate the optimal relaxation method to provide when the user's heart rate increases."

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

[0309] Step 1:

[0310] The terminal measures the user's heart rate and voice tone with sensors. There is biometric data obtained from the user as input. This data is initially processed and encrypted in real time. This process prepares the user's biometric data to be securely sent to the server.

[0311] Step 2:

[0312] The terminal sends the encrypted biometric data to the server. The data received by the server as input is the encrypted biometric data sent from the terminal. This data is decrypted by the server. The server analyzes the decrypted data and calculates the mental state and stress level using a machine learning algorithm. This process determines the user's stress and emotional state.

[0313] Step 3:

[0314] The server utilizes the generative AI model based on the analysis results to create mental health advice suitable for the user. The input is data on the mental state and stress level, and the output is advice expressed in natural language. An example of this prompt text is "Please propose a relaxation method recommended when the user's heart rate is high." This process generates the advice to be delivered to the user.

[0315] Step 4:

[0316] The server sends the generated advice to the terminal. The data the terminal receives as input is the advice information sent from the server. The terminal uses its display and audio output functions to present this to the user. This process allows the user to receive and respond to advice in real time.

[0317] Step 5:

[0318] Users implement the advice they receive through their device. The implementation status and feedback are entered into the device. This becomes new input data and is resent to the server. This feedback data is used to update the server's information model, helping to improve the accuracy of future advice. This process enhances personalized support tailored to each user's individual circumstances across the entire system.

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

[0320] To implement this invention, it is necessary to construct a system that includes a biometric information collection device, a device for analyzing emotional states and stress levels, a device for providing mental care advice, and an emotion engine that recognizes the user's emotions.

[0321] The device collects biometric information in real time, acquiring biometric data such as heart rate, skin electrical activity, and voice tone. This data is de-noised during initial processing, encrypted, and then sent to the server.

[0322] The server analyzes biometric information received from the terminal and uses machine learning algorithms to evaluate the user's emotional state and stress level. An emotion engine plays a crucial role in this process. The emotion engine analyzes the user's voice tone and, if necessary, facial expressions from camera footage to identify the user's emotions. This emotional data is incorporated into the stress assessment and used to generate more accurate mental health advice.

[0323] Users act on the advice they receive via their device and provide feedback on the results. This feedback is used to confirm changes in the user's emotions and the effectiveness of stress reduction. User feedback is crucial data for the server to update its data model and improve future advice.

[0324] For example, if a user speaks in a tense tone, the emotion engine detects this tone and determines that their anxiety is heightened. Based on this result, the server generates specific advice such as "Take deep breaths to reduce anxiety" or "Try listening to calming music" and notifies the device. If the user follows this advice and feels it is effective, they provide feedback, which the emotion engine uses to improve future analyses.

[0325] This system aims to provide personalized mental care by gaining a deeper understanding of the user's characteristics and the situation at hand. The integration of an emotion engine enables more nuanced responses tailored to the user's emotions, contributing to stress management and maintaining mental health.

[0326] The following describes the processing flow.

[0327] Step 1:

[0328] The device collects heart rate, skin electrical activity, voice tone, and even facial expressions via a camera in real time from the user's wearable device or smartphone.

[0329] Step 2:

[0330] The device preprocesses the collected biometric information and facial expression data, removes noise, then encrypts this data and sends it to the server.

[0331] Step 3:

[0332] The server stores the data received from the terminal in a database and immediately begins analysis using the emotion engine.

[0333] Step 4:

[0334] The emotion engine uses machine learning algorithms to analyze the user's emotional state based on their voice tone and facial expressions.

[0335] Step 5:

[0336] The server takes in the results of the emotion engine's analysis and integrates them with heart rate and skin electrical activity data to assess the user's stress level.

[0337] Step 6:

[0338] Based on the analysis results, the server generates mental care advice optimized for the emotional state and stress level.

[0339] Step 7:

[0340] The server then expresses the generated advice in natural language and sends it to the terminal.

[0341] Step 8:

[0342] The device displays messages on the screen or provides voice guidance to notify the user of received mental health advice visually or audibly.

[0343] Step 9:

[0344] Users follow the advice received from their device and implement relaxation techniques and other measures. Afterward, they evaluate the results and the effects they experienced, and input the feedback into the device.

[0345] Step 10:

[0346] The device collects user feedback and sends it back to the server.

[0347] Step 11:

[0348] The server analyzes user feedback and updates its data model to improve the accuracy of sentiment analysis and advice generation in the future.

[0349] (Example 2)

[0350] 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".

[0351] Traditional mental healthcare systems have challenges in accurately assessing users' emotions and stress levels, and the advice they offer is often generic, resulting in ineffective stress relief. Furthermore, there are insufficient mechanisms for effectively utilizing user feedback to improve the system.

[0352] 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.

[0353] In this invention, the server includes means for acquiring the user's biometric indicators, means for removing noise from the acquired biometric indicators, encrypting them, and transmitting them, and means for analyzing the encrypted biometric indicators and evaluating the emotional state and stress level. This enables accurate evaluation of the emotional state and stress level based on the user's biometric indicators, and realizes the provision of personalized mental care.

[0354] A "user" is defined as someone who is subject to the acquisition of biometric data and the provision of mental healthcare.

[0355] "Biometric indicators" are data necessary to evaluate a user's emotional state and stress level, such as heart rate, skin electrical activity, and voice tone.

[0356] "Noise reduction" is a process that removes unwanted signals contained in biometric indicators to improve the accuracy of the data.

[0357] "Encryption" is a technology that protects information to ensure the secure transmission of biometric data, preventing its contents from being read by third parties.

[0358] "Emotional state" is an indicator that shows the user's psychological state, and includes emotions such as anxiety and relaxation.

[0359] "Stress level" is an indicator that shows the degree of stress a user is experiencing.

[0360] "Evaluation" is the process of analyzing emotional state and stress levels based on acquired biometric indicators, and then judging the results.

[0361] "Mental care" refers to support and advice provided to improve a user's emotional state and stress levels.

[0362] An "emotion engine" is an algorithm and technology that analyzes voice tone, facial expressions, and other factors to recognize a user's emotions.

[0363] To implement this invention, it is necessary to construct a comprehensive system that collects and analyzes biometric information and provides appropriate mental care. This system includes a terminal for acquiring the user's biometric indicators, a server that analyzes the biometric indicators and evaluates emotional state and stress levels, and a function to provide personalized mental care to the user based on the evaluation results.

[0364] The device uses a wearable form factor and is equipped with biometric data collection devices such as a heart rate sensor, skin electrical activity sensor, and microphone. These sensors acquire the user's biometric data in real time, and after initial processing to remove noise, the data is encrypted using AES encryption technology and sent to the server.

[0365] Upon decrypting the received encrypted data, the server utilizes machine learning algorithms and an emotion engine to assess the user's emotional state and stress level. The emotion engine analyzes subtle changes in voice tone and facial expressions to identify the user's emotions. Based on these analysis results, it generates personalized mental health advice. This advice is provided in natural language and sent to the user via their device.

[0366] Users receive advice via their device and take action accordingly. Specific instructions are given, such as practicing deep breathing or playing relaxing music. Users return the results of these actions to the system as feedback. This feedback is used by the server to update its data model and improve the quality of advice provided in the future.

[0367] For example, when a user speaks in a tense voice, the emotion engine analyzes the tone of voice and determines that the user is experiencing heightened anxiety. Based on this assessment, the server generates and provides advice to the user, such as "Take a deep breath to alleviate your anxiety" or "Listen to calming music to relax."

[0368] Examples of prompts to input into a generative AI model:

[0369] "Please explain how to analyze a user's emotional state based on their voice data and biometric information, and provide personalized advice to reduce stress."

[0370] The introduction of this system will enable more effective and personalized mental care based on the user's characteristics and current condition.

[0371] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0372] Step 1:

[0373] The device collects the user's biometric data. Specifically, it uses a heart rate sensor, a skin electrical activity sensor, and a microphone to acquire biometric data in real time. The input is biometric information from the user, and the output is biometric data before noise reduction.

[0374] Step 2:

[0375] The device performs noise reduction on the collected biometric data. Specifically, it uses digital filtering technology to remove noise and improve data accuracy. The input is biometric data, and the output is clean data after noise reduction.

[0376] Step 3:

[0377] The terminal encrypts the noise-removed data using AES encryption technology and sends it to the server using a secure protocol. Specifically, it applies an encryption algorithm to ensure data confidentiality. The input is noise-removed data, and the output is encrypted data.

[0378] Step 4:

[0379] The server decrypts the received encrypted data. Specifically, it applies a decryption algorithm to retrieve the original data. The input is encrypted data, and the output is the decrypted biometric data.

[0380] Step 5:

[0381] The server uses machine learning algorithms to analyze biometric data and assess the user's emotional state and stress level. Specifically, it performs voice tone and facial expression analysis using an emotion engine. The input is decoded biometric data, and the output is the assessment result of the emotional state and stress level.

[0382] Step 6:

[0383] The server generates personalized mental health advice for the user based on the analysis results. Specifically, it creates advice in natural language according to a template and provides instructions tailored to the user's situation. The input is the evaluation results, and the output is the mental health advice.

[0384] Step 7:

[0385] The terminal notifies the user of advice received from the server. The user receives this advice and takes action according to the instructions. The input is the advice sent from the server, and the output is the information obtained by the user.

[0386] Step 8:

[0387] Users provide feedback on the effectiveness of the advice they receive via their device. Specifically, they use a feedback input interface to record the degree of the advice's effectiveness and their impressions. The input is the user's feedback information, and the output is the feedback data received by the server.

[0388] Step 9:

[0389] The server updates its data model using user feedback to improve the accuracy of subsequent analysis results and the quality of advice. Specifically, it incorporates the feedback data into a machine learning model and performs training. The input is the feedback data, and the output is the improved data model.

[0390] (Application Example 2)

[0391] 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."

[0392] In modern urban life, residents are experiencing increasing stress and mental health problems, and there is a need for appropriate support to address these issues. However, a system that accurately assesses mental stress levels at the individual and community levels and provides appropriate advice has not yet been put into practical use. In particular, methods for quickly proposing concrete measures to reduce stress throughout an entire city are still immature.

[0393] 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.

[0394] In this invention, the server includes means for collecting biometric information, means for analyzing emotional states and mental stress levels, and means for analyzing mental stress levels in each region and proposing stress reduction measures. This makes it possible to support the mental health of not only individuals but also entire communities and improve the quality of life for residents.

[0395] "Biometric information" refers to data related to human bodily functions, such as heart rate, skin electrical activity, and voice tone.

[0396] "Emotional state" refers to information that indicates a person's internal emotional condition, and is analyzed from factors such as voice tone and facial expressions.

[0397] "Mental tension level" is an indicator that shows the degree of psychological tension caused by stress, anxiety, etc.

[0398] "Psychological care suggestions" refer to specific advice aimed at improving mental health, provided based on the user's emotional state and level of mental stress.

[0399] "Regional mental stress levels" refer to the levels of mental stress and tension among residents in a specific geographical area, and are data that indicates the state of mental health in that region.

[0400] "Stress reduction measures" refer to specific methods and activities proposed to alleviate the mental tension of residents.

[0401] To implement this invention, a system is constructed using a biometric data collection device such as a smartwatch and a server that utilizes cloud computing.

[0402] The device collects biometric information such as heart rate, skin electrical activity, and voice tone in real time. This data is securely transmitted from the device to a server in the cloud using an API. Noise reduction and encryption are performed on the device, ensuring the confidentiality of the transmitted data.

[0403] The server utilizes machine learning models built in Python (e.g., TensorFlow) to analyze the received data. To assess emotional state and mental stress levels, it uses an emotion analysis API to analyze not only voice tone but also facial expressions from camera footage as needed. This allows the server to identify the mental stress level of the user or region and generate psychological care suggestions. These suggestions are generated using natural language processing algorithms and provided to the user in a concrete form.

[0404] For example, if the system determines that a user's stress level is high in their local area, the device will display information such as "recommended places to relax" or "information on relaxation events held in the area." In this way, it can support not only individual mental health care but also stress management for the entire community.

[0405] An example of a prompt could be: "Develop an AI model that monitors citizens' biometric information and emotional state in real time, analyzes stress levels for each region, and proposes specific stress reduction measures to support the mental health of citizens in smart cities."

[0406] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0407] Step 1:

[0408] The device uses a smartwatch and sensors to collect biometric information such as the user's heart rate, skin electrical activity, and voice tone. The input data is biometric information obtained from biosensors, and after data collection, a noise reduction filter is applied to improve the accuracy of the data. The output of this process is biometric information with noise removed.

[0409] Step 2:

[0410] The terminal encrypts denoised biometric information and transmits it to the server while maintaining security. The input data is denoised biometric information, and confidentiality is ensured using an encryption algorithm. The output is encrypted biometric information.

[0411] Step 3:

[0412] The server decodes the received biometric information and analyzes emotional state and mental stress levels using a machine learning model built in Python. The input data is encrypted biometric information, which is then decoded and processed using an emotion analysis API and machine learning algorithms. The output is an evaluation of the emotional state and mental stress levels of the user or region.

[0413] Step 4:

[0414] The server generates psychological care suggestions for the user based on the evaluation results of their emotional state and mental stress level. The input data is the result of emotion analysis, and a natural language processing algorithm is used to generate specific advice. The output is the psychological care suggestion.

[0415] Step 5:

[0416] The server sends the generated psychological care suggestions to the terminal and notifies the user. The input data is the psychological care suggestions, which are output in a format that allows for real-time feedback. The output is the psychological care suggestions displayed on the user interface of the terminal.

[0417] Step 6:

[0418] The user acts on the provided psychological care suggestions and inputs feedback into the terminal. The input data is the user's feedback on the effectiveness of the psychological care suggestions and becomes new training data for improving the system's performance. The output is sent to the server as user feedback.

[0419] 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.

[0420] 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.

[0421] 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.

[0422] [Third Embodiment]

[0423] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0424] 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.

[0425] 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).

[0426] 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.

[0427] 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.

[0428] 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).

[0429] 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.

[0430] 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.

[0431] 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.

[0432] 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.

[0433] 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.

[0434] 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".

[0435] To implement this invention, it is necessary to construct a system that collects and analyzes biometric information in real time and provides appropriate mental care advice to the user based on the results. This system consists of three main components: a "terminal," a "server," and a "user."

[0436] The terminal functions as a device for collecting the user's biometric information. This could be a wearable device or smartphone that measures heart rate or voice tone, for example. The terminal collects this data in real time, performs initial processing, and then sends it to a server in an encrypted form.

[0437] The server analyzes the user's stress level based on the received biometric information. Using machine learning algorithms, it detects changes in heart rate and abnormalities in voice tone to understand the user's emotional state. Based on the analysis results, it generates personalized mental care advice and sends this information back to the device.

[0438] Users receive advice through their devices and perform the suggested relaxation techniques and suggestions. Feedback on these actions is entered into the device, which is then stored in the user's profile and used to update the server's data model.

[0439] For example, if a user's heart rate remains higher than normal, the server will determine that their stress level is rising. As a result, specific advice such as "Take deep breaths" or "Try a short meditation" will be sent to the device. By performing these actions and providing feedback, the server will adjust subsequent advice to be more appropriate for that user.

[0440] In this way, the system can continuously provide users with the most suitable mental care in real time and sequentially.

[0441] The following describes the processing flow.

[0442] Step 1:

[0443] The device acquires biometric information such as heart rate, skin electrical activity, and voice tone from the user's wearable device or smartphone.

[0444] Step 2:

[0445] The device preprocesses the collected biometric information, removes noise, encrypts it, and then sends it to the server.

[0446] Step 3:

[0447] The server stores the biometric information received from the terminal into a database and immediately begins analysis.

[0448] Step 4:

[0449] The server uses machine learning algorithms to assess the user's emotional state and stress level from biometric data.

[0450] Step 5:

[0451] The server generates personalized mental health advice based on the analysis results.

[0452] Step 6:

[0453] The server then sends the generated advice to the terminal.

[0454] Step 7:

[0455] The device displays a message on the screen to notify the user of received mental health advice.

[0456] Step 8:

[0457] The user performs relaxation techniques according to the provided advice and inputs feedback on the results and effectiveness into the device.

[0458] Step 9:

[0459] The device sends the feedback entered by the user to the server.

[0460] Step 10:

[0461] The server receives feedback from users, updates the data model, and incorporates it into future analyses and advice generation.

[0462] (Example 1)

[0463] 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."

[0464] In modern society, there is a demand for real-time mental healthcare tailored to individual users. However, conventional systems are limited to uniform advice, and have the challenge of providing detailed support based on individual biometric data. Furthermore, mechanisms for improving the quality of advice based on feedback have not been adequately established.

[0465] 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.

[0466] In this invention, the server includes means for analyzing stress indicators based on biometric data, means for evaluating emotional states using machine learning algorithms, and means for generating personalized mental care guidance using a generative AI model. This enables the provision of real-time and personalized mental care to individual users.

[0467] "Biometric data" refers to a collection of information obtained from a user's body, such as their heart rate and voice tone.

[0468] A "stress index" is a numerical value or evaluation criterion that indicates a user's stress level, calculated based on biometric data.

[0469] A "machine learning algorithm" refers to mathematical methods and models used to learn patterns from data and perform analysis and prediction.

[0470] A "generative AI model" is an artificial intelligence technology that generates specific advice and guidance for users in natural language.

[0471] "Personalized mental care" refers to mental health guidance and support tailored to the individual user's condition and feedback.

[0472] "Feedback" refers to information provided by users regarding their impressions and results after implementation, which is useful for improving the system.

[0473] This system is primarily composed of three elements: "terminals," "servers," and "users."

[0474] The terminal is a device for collecting the user's biometric data in real time. This includes wearable devices that measure heart rate and smartphones that capture voice tones. After acquiring the biometric data, the terminal performs noise reduction and filtering, encrypts the information, and sends it to a server. This ensures the security of the data using advanced encryption algorithms.

[0475] The server receives biometric data sent from the terminal and uses machine learning algorithms to analyze the user's stress indicators. The goal of this analysis is to capture fluctuations in heart rate and differences in voice tone within the data to understand the user's emotional state. Based on the analysis results, the server uses a generative AI model to generate personalized mental care advice for the user. In this process, an example of a prompt might be, "Please tell me effective relaxation methods when the user's heart rate is high." Based on such prompts, the generative AI model constructs appropriate advice.

[0476] Users receive mental health advice from the server via their device and then act on it. For example, users might receive specific suggestions such as "take deep breaths" or "do a short meditation." After acting on the advice, users input feedback into their device, and this information is sent to the server. This feedback data is used by the server to improve the data model, helping to make future advice even more personalized.

[0477] This system is configured as described above to provide users with real-time, individually adapted mental care.

[0478] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0479] Step 1:

[0480] The device collects biometric data entered by the user. During this process, the device uses a heart rate sensor and microphone to capture data, and extracts useful data through a noise reduction filter. This processed data is then prepared for the next step and output to the server in a securely protected form using an encryption algorithm.

[0481] Step 2:

[0482] The server receives and decrypts encrypted biometric data transmitted from the terminal. After decrypting the data, it passes the analysis of heart rate variability and voice tone as input to a machine learning algorithm. The analysis results in the output of numerical and categorical data that evaluates the user's emotional state, such as stress indicators. This evaluation data is then used in the next step.

[0483] Step 3:

[0484] The server utilizes a generative AI model based on the analyzed results, generating advice in the form of a prompt. An example of a prompt might be, "Please tell me an effective relaxation method when the user's heart rate is high." Based on this input, the generative AI model outputs personalized mental care advice to the user.

[0485] Step 4:

[0486] The server sends the generated mental health advice to the terminal. The terminal displays the advice received from the server to the user and prompts them to take action.

[0487] Step 5:

[0488] Users follow mental care advice from their device and take actions that promote relaxation. For example, users complete specific actions such as "taking deep breaths" or "trying a short meditation." After completion, they input the results and their impressions as feedback into the device and output the data to the server.

[0489] Step 6:

[0490] The server processes feedback data received from users and uses it to improve its machine learning model. This allows the server to update the data model and generate output that improves the accuracy of subsequent analyses and advice. In this way, the system continuously evolves, providing more tailored mental care to each user.

[0491] (Application Example 1)

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

[0493] In recent years, many people experience stress in their daily lives, which impacts their health. However, providing effective mental care tailored to individual circumstances is difficult, and conventional technologies have struggled to respond in real time and on an individual basis. In particular, there is a need for a system that can automatically provide mental support within the home.

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

[0495] In this invention, the server includes means for acquiring biometric data, means for evaluating psychological state and stress levels, and automated mechanical means for presenting the provided advice to the user. This makes it possible to provide users with appropriate mental health advice in real time and to automatically and individually realize mental care tailored to each individual's stress level.

[0496] "Biometric data" refers to data obtained from the human body, specifically information such as heart rate and voice tone.

[0497] "Psychological state" refers to the user's emotions and mental health, including emotional states such as stress and anxiety.

[0498] The "pressure level" is an indicator that shows the degree of psychological stress and pressure experienced by the user.

[0499] "Evaluation methods" refer to a series of processes for analyzing psychological state and stress levels based on biometric data, and for deriving appropriate mental health advice.

[0500] "Automated mechanical means" refers to mechanical devices used to provide psychological health advice to users, and specifically includes robots.

[0501] "Psychological health advice" refers to suggestions for relaxation and improvement tailored to the user's current psychological state, and is expressed in natural language.

[0502] The system for implementing this invention mainly consists of three elements: a "server," a "terminal," and a "user."

[0503] The server is responsible for evaluating the user's psychological state and level of stress based on biometric data. To do this, the server uses machine learning algorithms to analyze the collected data (such as heart rate and voice tone). Based on the analysis results, it generates psychological health advice tailored to the user and sends it to the terminal.

[0504] The terminal functions as a device for collecting the user's biometric data. Specifically, wearable devices equipped with sensors to measure heart rate and voice tone, or smartphones, are used. The terminal collects this data in real time and transmits it to a server. The terminal also has a display and voice output function to present advice received from the server to the user.

[0505] Users receive mental health advice through their device, enabling them to take appropriate action based on their mental state and stress levels. Specific examples include suggestions for relaxation techniques such as "practicing short meditations" or "trying deep breathing." Users input their progress and feedback into the device, which is used to update the server's data model.

[0506] As a concrete example of this system, if a user feels stressed after work one day, the terminal will detect an increase in heart rate, and the server will generate advice such as, "Please sit quietly for 5 minutes to relax." This advice will be presented to the user from the terminal, and the user will follow it and perform relaxation.

[0507] An example of a prompt for a generative AI model is, "Generate the optimal relaxation method to provide when the user's heart rate increases."

[0508] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0509] Step 1:

[0510] The device measures the user's heart rate and voice tone using sensors. Biometric data acquired from the user is used as input. This data is initially processed and encrypted in real time. This processing prepares the user's biometric data for secure transmission to the server.

[0511] Step 2:

[0512] The device sends encrypted biometric data to the server. The data the server receives as input is the encrypted biometric data sent from the device. This data is decrypted on the server. The server analyzes the decrypted data and uses machine learning algorithms to calculate the user's psychological state and level of stress. This process determines the user's stress and emotional state.

[0513] Step 3:

[0514] The server utilizes a generative AI model based on the analysis results to create personalized psychological health advice for the user. The input is data on the user's psychological state and stress levels, and the output is advice expressed in natural language. An example of this prompt is, "Please suggest relaxation methods recommended when the user's heart rate is high." This process generates the advice to be delivered to the user.

[0515] Step 4:

[0516] The server sends the generated advice to the terminal. The data the terminal receives as input is the advice information sent from the server. The terminal uses its display and audio output functions to present this to the user. This process allows the user to receive and respond to advice in real time.

[0517] Step 5:

[0518] Users implement the advice they receive through their device. The implementation status and feedback are entered into the device. This becomes new input data and is resent to the server. This feedback data is used to update the server's information model, helping to improve the accuracy of future advice. This process enhances personalized support tailored to each user's individual circumstances across the entire system.

[0519] 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.

[0520] To implement this invention, it is necessary to construct a system that includes a biometric information collection device, a device for analyzing emotional states and stress levels, a device for providing mental care advice, and an emotion engine that recognizes the user's emotions.

[0521] The device collects biometric information in real time, acquiring biometric data such as heart rate, skin electrical activity, and voice tone. This data is de-noised during initial processing, encrypted, and then sent to the server.

[0522] The server analyzes biometric information received from the terminal and uses machine learning algorithms to evaluate the user's emotional state and stress level. An emotion engine plays a crucial role in this process. The emotion engine analyzes the user's voice tone and, if necessary, facial expressions from camera footage to identify the user's emotions. This emotional data is incorporated into the stress assessment and used to generate more accurate mental health advice.

[0523] Users act on the advice they receive via their device and provide feedback on the results. This feedback is used to confirm changes in the user's emotions and the effectiveness of stress reduction. User feedback is crucial data for the server to update its data model and improve future advice.

[0524] For example, if a user speaks in a tense tone, the emotion engine detects this tone and determines that their anxiety is heightened. Based on this result, the server generates specific advice such as "Take deep breaths to reduce anxiety" or "Try listening to calming music" and notifies the device. If the user follows this advice and feels it is effective, they provide feedback, which the emotion engine uses to improve future analyses.

[0525] This system aims to provide personalized mental care by gaining a deeper understanding of the user's characteristics and the situation at hand. The integration of an emotion engine enables more nuanced responses tailored to the user's emotions, contributing to stress management and maintaining mental health.

[0526] The following describes the processing flow.

[0527] Step 1:

[0528] The device collects heart rate, skin electrical activity, voice tone, and even facial expressions via a camera in real time from the user's wearable device or smartphone.

[0529] Step 2:

[0530] The device preprocesses the collected biometric information and facial expression data, removes noise, then encrypts this data and sends it to the server.

[0531] Step 3:

[0532] The server stores the data received from the terminal in a database and immediately begins analysis using the emotion engine.

[0533] Step 4:

[0534] The emotion engine uses machine learning algorithms to analyze the user's emotional state based on their voice tone and facial expressions.

[0535] Step 5:

[0536] The server takes in the results of the emotion engine's analysis and integrates them with heart rate and skin electrical activity data to assess the user's stress level.

[0537] Step 6:

[0538] Based on the analysis results, the server generates mental care advice optimized for the emotional state and stress level.

[0539] Step 7:

[0540] The server then expresses the generated advice in natural language and sends it to the terminal.

[0541] Step 8:

[0542] The device displays messages on the screen or provides voice guidance to notify the user of received mental health advice visually or audibly.

[0543] Step 9:

[0544] Users follow the advice received from their device and implement relaxation techniques and other measures. Afterward, they evaluate the results and the effects they experienced, and input the feedback into the device.

[0545] Step 10:

[0546] The device collects user feedback and sends it back to the server.

[0547] Step 11:

[0548] The server analyzes user feedback and updates its data model to improve the accuracy of sentiment analysis and advice generation in the future.

[0549] (Example 2)

[0550] 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."

[0551] Traditional mental healthcare systems have challenges in accurately assessing users' emotions and stress levels, and the advice they offer is often generic, resulting in ineffective stress relief. Furthermore, there are insufficient mechanisms for effectively utilizing user feedback to improve the system.

[0552] 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.

[0553] In this invention, the server includes means for acquiring the user's biometric indicators, means for removing noise from the acquired biometric indicators, encrypting them, and transmitting them, and means for analyzing the encrypted biometric indicators and evaluating the emotional state and stress level. This enables accurate evaluation of the emotional state and stress level based on the user's biometric indicators, and realizes the provision of personalized mental care.

[0554] A "user" is defined as someone who is subject to the acquisition of biometric data and the provision of mental healthcare.

[0555] "Biometric indicators" are data necessary to evaluate a user's emotional state and stress level, such as heart rate, skin electrical activity, and voice tone.

[0556] "Noise reduction" is a process that removes unwanted signals contained in biometric indicators to improve the accuracy of the data.

[0557] "Encryption" is a technology that protects information to ensure the secure transmission of biometric data, preventing its contents from being read by third parties.

[0558] "Emotional state" is an indicator that shows the user's psychological state, and includes emotions such as anxiety and relaxation.

[0559] "Stress level" is an indicator that shows the degree of stress a user is experiencing.

[0560] "Evaluation" is the process of analyzing emotional state and stress levels based on acquired biometric indicators, and then judging the results.

[0561] "Mental care" refers to support and advice provided to improve a user's emotional state and stress levels.

[0562] An "emotion engine" is an algorithm and technology that analyzes voice tone, facial expressions, and other factors to recognize a user's emotions.

[0563] To implement this invention, it is necessary to construct a comprehensive system that collects and analyzes biometric information and provides appropriate mental care. This system includes a terminal for acquiring the user's biometric indicators, a server that analyzes the biometric indicators and evaluates emotional state and stress levels, and a function to provide personalized mental care to the user based on the evaluation results.

[0564] The device uses a wearable form factor and is equipped with biometric data collection devices such as a heart rate sensor, skin electrical activity sensor, and microphone. These sensors acquire the user's biometric data in real time, and after initial processing to remove noise, the data is encrypted using AES encryption technology and sent to the server.

[0565] Upon decrypting the received encrypted data, the server utilizes machine learning algorithms and an emotion engine to assess the user's emotional state and stress level. The emotion engine analyzes subtle changes in voice tone and facial expressions to identify the user's emotions. Based on these analysis results, it generates personalized mental health advice. This advice is provided in natural language and sent to the user via their device.

[0566] Users receive advice via their device and take action accordingly. Specific instructions are given, such as practicing deep breathing or playing relaxing music. Users return the results of these actions to the system as feedback. This feedback is used by the server to update its data model and improve the quality of advice provided in the future.

[0567] For example, when a user speaks in a tense voice, the emotion engine analyzes the tone of voice and determines that the user is experiencing heightened anxiety. Based on this assessment, the server generates and provides advice to the user, such as "Take a deep breath to alleviate your anxiety" or "Listen to calming music to relax."

[0568] Examples of prompts to input into a generative AI model:

[0569] "Please explain how to analyze a user's emotional state based on their voice data and biometric information, and provide personalized advice to reduce stress."

[0570] The introduction of this system will enable more effective and personalized mental care based on the user's characteristics and current condition.

[0571] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0572] Step 1:

[0573] The device collects the user's biometric data. Specifically, it uses a heart rate sensor, a skin electrical activity sensor, and a microphone to acquire biometric data in real time. The input is biometric information from the user, and the output is biometric data before noise reduction.

[0574] Step 2:

[0575] The device performs noise reduction on the collected biometric data. Specifically, it uses digital filtering technology to remove noise and improve data accuracy. The input is biometric data, and the output is clean data after noise reduction.

[0576] Step 3:

[0577] The terminal encrypts the noise-removed data using AES encryption technology and sends it to the server using a secure protocol. Specifically, it applies an encryption algorithm to ensure data confidentiality. The input is noise-removed data, and the output is encrypted data.

[0578] Step 4:

[0579] The server decrypts the received encrypted data. Specifically, it applies a decryption algorithm to retrieve the original data. The input is encrypted data, and the output is decrypted biometric data.

[0580] Step 5:

[0581] The server uses machine learning algorithms to analyze biometric data and assess the user's emotional state and stress level. Specifically, it performs voice tone and facial expression analysis using an emotion engine. The input is decoded biometric data, and the output is the assessment result of the emotional state and stress level.

[0582] Step 6:

[0583] The server generates personalized mental health advice for the user based on the analysis results. Specifically, it creates advice in natural language according to a template and provides instructions tailored to the user's situation. The input is the evaluation results, and the output is the mental health advice.

[0584] Step 7:

[0585] The terminal notifies the user of advice received from the server. The user receives this advice and takes action according to the instructions. The input is the advice sent from the server, and the output is the information obtained by the user.

[0586] Step 8:

[0587] Users provide feedback on the effectiveness of the advice they receive via their device. Specifically, they use a feedback input interface to record the degree of the advice's effectiveness and their impressions. The input is the user's feedback information, and the output is the feedback data received by the server.

[0588] Step 9:

[0589] The server updates its data model using user feedback to improve the accuracy of subsequent analysis results and the quality of advice. Specifically, it incorporates the feedback data into a machine learning model and performs training. The input is the feedback data, and the output is the improved data model.

[0590] (Application Example 2)

[0591] 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."

[0592] In modern urban life, residents are experiencing increasing stress and mental health problems, and there is a need for appropriate support to address these issues. However, a system that accurately assesses mental stress levels at the individual and community levels and provides appropriate advice has not yet been put into practical use. In particular, methods for quickly proposing concrete measures to reduce stress throughout an entire city are still immature.

[0593] 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.

[0594] In this invention, the server includes means for collecting biometric information, means for analyzing emotional states and mental stress levels, and means for analyzing mental stress levels in each region and proposing stress reduction measures. This makes it possible to support the mental health of not only individuals but also entire communities and improve the quality of life for residents.

[0595] "Biometric information" refers to data related to human bodily functions, such as heart rate, skin electrical activity, and voice tone.

[0596] "Emotional state" refers to information that indicates a person's internal emotional condition, and is analyzed from factors such as voice tone and facial expressions.

[0597] "Mental tension level" is an indicator that shows the degree of psychological tension caused by stress, anxiety, etc.

[0598] "Psychological care suggestions" refer to specific advice aimed at improving mental health, provided based on the user's emotional state and level of mental stress.

[0599] "Regional mental stress levels" refer to the levels of mental stress and tension among residents in a specific geographical area, and are data that indicates the state of mental health in that region.

[0600] "Stress reduction measures" refer to specific methods and activities proposed to alleviate the mental tension of residents.

[0601] To implement this invention, a system is constructed using a biometric data collection device such as a smartwatch and a server that utilizes cloud computing.

[0602] The device collects biometric information such as heart rate, skin electrical activity, and voice tone in real time. This data is securely transmitted from the device to a server in the cloud using an API. Noise reduction and encryption are performed on the device, ensuring the confidentiality of the transmitted data.

[0603] The server utilizes machine learning models built in Python (e.g., TensorFlow) to analyze the received data. To assess emotional state and mental stress levels, it uses an emotion analysis API to analyze not only voice tone but also facial expressions from camera footage as needed. This allows the server to identify the mental stress level of the user or region and generate psychological care suggestions. These suggestions are generated using natural language processing algorithms and provided to the user in a concrete form.

[0604] For example, if the system determines that a user's stress level is high in their local area, the device will display information such as "recommended places to relax" or "information on relaxation events held in the area." In this way, it can support not only individual mental health care but also stress management for the entire community.

[0605] An example of a prompt could be: "Develop an AI model that monitors citizens' biometric information and emotional state in real time, analyzes stress levels for each region, and proposes specific stress reduction measures to support the mental health of citizens in smart cities."

[0606] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0607] Step 1:

[0608] The device uses a smartwatch and sensors to collect biometric information such as the user's heart rate, skin electrical activity, and voice tone. The input data is biometric information obtained from biosensors, and after data collection, a noise reduction filter is applied to improve the accuracy of the data. The output of this process is biometric information with noise removed.

[0609] Step 2:

[0610] The terminal encrypts denoised biometric information and transmits it to the server while maintaining security. The input data is denoised biometric information, and confidentiality is ensured using an encryption algorithm. The output is encrypted biometric information.

[0611] Step 3:

[0612] The server decodes the received biometric information and analyzes emotional state and mental stress levels using a machine learning model built in Python. The input data is encrypted biometric information, which is then decoded and processed using an emotion analysis API and machine learning algorithms. The output is an evaluation of the emotional state and mental stress levels of the user or region.

[0613] Step 4:

[0614] The server generates psychological care suggestions for the user based on the evaluation results of their emotional state and mental stress level. The input data is the result of emotion analysis, and a natural language processing algorithm is used to generate specific advice. The output is the psychological care suggestion.

[0615] Step 5:

[0616] The server sends the generated psychological care suggestions to the terminal and notifies the user. The input data is the psychological care suggestions, which are output in a format that allows for real-time feedback. The output is the psychological care suggestions displayed on the user interface of the terminal.

[0617] Step 6:

[0618] The user acts on the provided psychological care suggestions and inputs feedback into the terminal. The input data is the user's feedback on the effectiveness of the psychological care suggestions and becomes new training data for improving the system's performance. The output is sent to the server as user feedback.

[0619] 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.

[0620] 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.

[0621] 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.

[0622] [Fourth Embodiment]

[0623] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0624] 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.

[0625] 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).

[0626] 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.

[0627] 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.

[0628] 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).

[0629] 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.

[0630] 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.

[0631] 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.

[0632] 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.

[0633] 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.

[0634] 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.

[0635] 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".

[0636] To implement this invention, it is necessary to construct a system that collects and analyzes biometric information in real time and provides appropriate mental care advice to the user based on the results. This system consists of three main components: a "terminal," a "server," and a "user."

[0637] The terminal functions as a device for collecting the user's biometric information. This could be a wearable device or smartphone that measures heart rate or voice tone, for example. The terminal collects this data in real time, performs initial processing, and then sends it to a server in an encrypted form.

[0638] The server analyzes the user's stress level based on the received biometric information. Using machine learning algorithms, it detects changes in heart rate and abnormalities in voice tone to understand the user's emotional state. Based on the analysis results, it generates personalized mental care advice and sends this information back to the device.

[0639] Users receive advice through their devices and perform the suggested relaxation techniques and suggestions. Feedback on these actions is entered into the device, which is then stored in the user's profile and used to update the server's data model.

[0640] For example, if a user's heart rate remains higher than normal, the server will determine that their stress level is rising. As a result, specific advice such as "Take deep breaths" or "Try a short meditation" will be sent to the device. By performing these actions and providing feedback, the server will adjust subsequent advice to be more appropriate for that user.

[0641] In this way, the system can continuously provide users with the most suitable mental care in real time and sequentially.

[0642] The following describes the processing flow.

[0643] Step 1:

[0644] The device acquires biometric information such as heart rate, skin electrical activity, and voice tone from the user's wearable device or smartphone.

[0645] Step 2:

[0646] The device preprocesses the collected biometric information, removes noise, encrypts it, and then sends it to the server.

[0647] Step 3:

[0648] The server stores the biometric information received from the terminal into a database and immediately begins analysis.

[0649] Step 4:

[0650] The server uses machine learning algorithms to assess the user's emotional state and stress level from biometric data.

[0651] Step 5:

[0652] The server generates personalized mental health advice based on the analysis results.

[0653] Step 6:

[0654] The server then sends the generated advice to the terminal.

[0655] Step 7:

[0656] The device displays a message on the screen to notify the user of received mental health advice.

[0657] Step 8:

[0658] The user performs relaxation techniques according to the provided advice and inputs feedback on the results and effectiveness into the device.

[0659] Step 9:

[0660] The device sends the feedback entered by the user to the server.

[0661] Step 10:

[0662] The server receives feedback from users, updates the data model, and incorporates it into future analyses and advice generation.

[0663] (Example 1)

[0664] 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".

[0665] In modern society, there is a demand for real-time mental healthcare tailored to individual users. However, conventional systems are limited to uniform advice, and have the challenge of providing detailed support based on individual biometric data. Furthermore, mechanisms for improving the quality of advice based on feedback have not been adequately established.

[0666] 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.

[0667] In this invention, the server includes means for analyzing stress indicators based on biometric data, means for evaluating emotional states using machine learning algorithms, and means for generating personalized mental care guidance using a generative AI model. This enables the provision of real-time and personalized mental care to individual users.

[0668] "Biometric data" refers to a collection of information obtained from a user's body, such as their heart rate and voice tone.

[0669] A "stress index" is a numerical value or evaluation criterion that indicates a user's stress level, calculated based on biometric data.

[0670] A "machine learning algorithm" refers to mathematical methods and models used to learn patterns from data and perform analysis and prediction.

[0671] A "generative AI model" is an artificial intelligence technology that generates specific advice and guidance for users in natural language.

[0672] "Personalized mental care" refers to mental health guidance and support tailored to the individual user's condition and feedback.

[0673] "Feedback" refers to information provided by users regarding their impressions and results after implementation, which is useful for improving the system.

[0674] This system is primarily composed of three elements: "terminals," "servers," and "users."

[0675] The terminal is a device for collecting the user's biometric data in real time. This includes wearable devices that measure heart rate and smartphones that capture voice tones. After acquiring the biometric data, the terminal performs noise reduction and filtering, encrypts the information, and sends it to a server. This ensures the security of the data using advanced encryption algorithms.

[0676] The server receives biometric data sent from the terminal and uses machine learning algorithms to analyze the user's stress indicators. The goal of this analysis is to capture fluctuations in heart rate and differences in voice tone within the data to understand the user's emotional state. Based on the analysis results, the server uses a generative AI model to generate personalized mental care advice for the user. In this process, an example of a prompt might be, "Please tell me effective relaxation methods when the user's heart rate is high." Based on such prompts, the generative AI model constructs appropriate advice.

[0677] Users receive mental health advice from the server via their device and then act on it. For example, users might receive specific suggestions such as "take deep breaths" or "do a short meditation." After acting on the advice, users input feedback into their device, and this information is sent to the server. This feedback data is used by the server to improve the data model, helping to make future advice even more personalized.

[0678] This system is configured as described above to provide users with real-time, individually adapted mental care.

[0679] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0680] Step 1:

[0681] The device collects biometric data entered by the user. During this process, the device uses a heart rate sensor and microphone to capture data, and extracts useful data through a noise reduction filter. This processed data is then prepared for the next step and output to the server in a securely protected form using an encryption algorithm.

[0682] Step 2:

[0683] The server receives and decrypts encrypted biometric data transmitted from the terminal. After decrypting the data, it passes the analysis of heart rate variability and voice tone as input to a machine learning algorithm. The analysis results in the output of numerical and categorical data that evaluates the user's emotional state, such as stress indicators. This evaluation data is then used in the next step.

[0684] Step 3:

[0685] The server utilizes a generative AI model based on the analyzed results, generating advice in the form of a prompt. An example of a prompt might be, "Please tell me an effective relaxation method when the user's heart rate is high." Based on this input, the generative AI model outputs personalized mental care advice to the user.

[0686] Step 4:

[0687] The server sends the generated mental health advice to the terminal. The terminal displays the advice received from the server to the user and prompts them to take action.

[0688] Step 5:

[0689] Users follow mental care advice from their device and take actions that promote relaxation. For example, users complete specific actions such as "taking deep breaths" or "trying a short meditation." After completion, they input the results and their impressions as feedback into the device and output the data to the server.

[0690] Step 6:

[0691] The server processes feedback data received from users and uses it to improve its machine learning model. This allows the server to update the data model and generate output that improves the accuracy of subsequent analyses and advice. In this way, the system continuously evolves, providing more tailored mental care to each user.

[0692] (Application Example 1)

[0693] 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".

[0694] In recent years, many people experience stress in their daily lives, which impacts their health. However, providing effective mental care tailored to individual circumstances is difficult, and conventional technologies have struggled to respond in real time and on an individual basis. In particular, there is a need for a system that can automatically provide mental support within the home.

[0695] 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.

[0696] In this invention, the server includes means for acquiring biometric data, means for evaluating psychological state and stress levels, and automated mechanical means for presenting the provided advice to the user. This makes it possible to provide users with appropriate mental health advice in real time and to automatically and individually realize mental care tailored to each individual's stress level.

[0697] "Biometric data" refers to data obtained from the human body, specifically information such as heart rate and voice tone.

[0698] "Psychological state" refers to the user's emotions and mental health, including emotional states such as stress and anxiety.

[0699] The "pressure level" is an indicator that shows the degree of psychological stress and pressure experienced by the user.

[0700] "Evaluation methods" refer to a series of processes for analyzing psychological state and stress levels based on biometric data, and for deriving appropriate mental health advice.

[0701] "Automated mechanical means" refers to mechanical devices used to provide psychological health advice to users, and specifically includes robots.

[0702] "Psychological health advice" refers to suggestions for relaxation and improvement tailored to the user's current psychological state, and is expressed in natural language.

[0703] The system for implementing this invention mainly consists of three elements: a "server," a "terminal," and a "user."

[0704] The server is responsible for evaluating the user's psychological state and level of stress based on biometric data. To do this, the server uses machine learning algorithms to analyze the collected data (such as heart rate and voice tone). Based on the analysis results, it generates psychological health advice tailored to the user and sends it to the terminal.

[0705] The terminal functions as a device for collecting the user's biometric data. Specifically, wearable devices equipped with sensors to measure heart rate and voice tone, or smartphones, are used. The terminal collects this data in real time and transmits it to a server. The terminal also has a display and voice output function to present advice received from the server to the user.

[0706] Users receive mental health advice through their device, enabling them to take appropriate action based on their mental state and stress levels. Specific examples include suggestions for relaxation techniques such as "practicing short meditations" or "trying deep breathing." Users input their progress and feedback into the device, which is used to update the server's data model.

[0707] As a concrete example of this system, if a user feels stressed after work one day, the terminal will detect an increase in heart rate, and the server will generate advice such as, "Please sit quietly for 5 minutes to relax." This advice will be presented to the user from the terminal, and the user will follow it and perform relaxation.

[0708] An example of a prompt for a generative AI model is, "Generate the optimal relaxation method to provide when the user's heart rate increases."

[0709] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0710] Step 1:

[0711] The device measures the user's heart rate and voice tone using sensors. Biometric data acquired from the user is used as input. This data is initially processed and encrypted in real time. This processing prepares the user's biometric data for secure transmission to the server.

[0712] Step 2:

[0713] The device sends encrypted biometric data to the server. The data the server receives as input is the encrypted biometric data sent from the device. This data is decrypted on the server. The server analyzes the decrypted data and uses machine learning algorithms to calculate the user's psychological state and level of stress. This process determines the user's stress and emotional state.

[0714] Step 3:

[0715] The server utilizes a generative AI model based on the analysis results to create personalized psychological health advice for the user. The input is data on the user's psychological state and stress levels, and the output is advice expressed in natural language. An example of this prompt is, "Please suggest relaxation methods recommended when the user's heart rate is high." This process generates the advice to be delivered to the user.

[0716] Step 4:

[0717] The server sends the generated advice to the terminal. The data the terminal receives as input is the advice information sent from the server. The terminal uses its display and audio output functions to present this to the user. This process allows the user to receive and respond to advice in real time.

[0718] Step 5:

[0719] Users implement the advice they receive through their device. The implementation status and feedback are entered into the device. This becomes new input data and is resent to the server. This feedback data is used to update the server's information model, helping to improve the accuracy of future advice. This process enhances personalized support tailored to each user's individual circumstances across the entire system.

[0720] 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.

[0721] To implement this invention, it is necessary to construct a system that includes a biometric information collection device, a device for analyzing emotional states and stress levels, a device for providing mental care advice, and an emotion engine that recognizes the user's emotions.

[0722] The device collects biometric information in real time, acquiring biometric data such as heart rate, skin electrical activity, and voice tone. This data is de-noised during initial processing, encrypted, and then sent to the server.

[0723] The server analyzes biometric information received from the terminal and uses machine learning algorithms to evaluate the user's emotional state and stress level. An emotion engine plays a crucial role in this process. The emotion engine analyzes the user's voice tone and, if necessary, facial expressions from camera footage to identify the user's emotions. This emotional data is incorporated into the stress assessment and used to generate more accurate mental health advice.

[0724] Users act on the advice they receive via their device and provide feedback on the results. This feedback is used to confirm changes in the user's emotions and the effectiveness of stress reduction. User feedback is crucial data for the server to update its data model and improve future advice.

[0725] For example, if a user speaks in a tense tone, the emotion engine detects this tone and determines that their anxiety is heightened. Based on this result, the server generates specific advice such as "Take deep breaths to reduce anxiety" or "Try listening to calming music" and notifies the device. If the user follows this advice and feels it is effective, they provide feedback, which the emotion engine uses to improve future analyses.

[0726] This system aims to provide personalized mental care by gaining a deeper understanding of the user's characteristics and the situation at hand. The integration of an emotion engine enables more nuanced responses tailored to the user's emotions, contributing to stress management and maintaining mental health.

[0727] The following describes the processing flow.

[0728] Step 1:

[0729] The device collects heart rate, skin electrical activity, voice tone, and even facial expressions via a camera in real time from the user's wearable device or smartphone.

[0730] Step 2:

[0731] The device preprocesses the collected biometric information and facial expression data, removes noise, then encrypts this data and sends it to the server.

[0732] Step 3:

[0733] The server stores the data received from the terminal in a database and immediately begins analysis using the emotion engine.

[0734] Step 4:

[0735] The emotion engine uses machine learning algorithms to analyze the user's emotional state based on their voice tone and facial expressions.

[0736] Step 5:

[0737] The server takes in the results of the emotion engine's analysis and integrates them with heart rate and skin electrical activity data to assess the user's stress level.

[0738] Step 6:

[0739] Based on the analysis results, the server generates mental care advice optimized for the emotional state and stress level.

[0740] Step 7:

[0741] The server then expresses the generated advice in natural language and sends it to the terminal.

[0742] Step 8:

[0743] The device displays messages on the screen or provides voice guidance to notify the user of received mental health advice visually or audibly.

[0744] Step 9:

[0745] Users follow the advice received from their device and implement relaxation techniques and other measures. Afterward, they evaluate the results and the effects they experienced, and input the feedback into the device.

[0746] Step 10:

[0747] The device collects user feedback and sends it back to the server.

[0748] Step 11:

[0749] The server analyzes user feedback and updates its data model to improve the accuracy of sentiment analysis and advice generation in the future.

[0750] (Example 2)

[0751] 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".

[0752] Traditional mental healthcare systems have challenges in accurately assessing users' emotions and stress levels, and the advice they offer is often generic, resulting in ineffective stress relief. Furthermore, there are insufficient mechanisms for effectively utilizing user feedback to improve the system.

[0753] 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.

[0754] In this invention, the server includes means for acquiring the user's biometric indicators, means for removing noise from the acquired biometric indicators, encrypting them, and transmitting them, and means for analyzing the encrypted biometric indicators and evaluating the emotional state and stress level. This enables accurate evaluation of the emotional state and stress level based on the user's biometric indicators, and realizes the provision of personalized mental care.

[0755] A "user" is defined as someone who is subject to the acquisition of biometric data and the provision of mental healthcare.

[0756] "Biometric indicators" are data necessary to evaluate a user's emotional state and stress level, such as heart rate, skin electrical activity, and voice tone.

[0757] "Noise reduction" is a process that removes unwanted signals contained in biometric indicators to improve the accuracy of the data.

[0758] "Encryption" is a technology that protects information to ensure the secure transmission of biometric data, preventing its contents from being read by third parties.

[0759] "Emotional state" is an indicator that shows the user's psychological state, and includes emotions such as anxiety and relaxation.

[0760] "Stress level" is an indicator that shows the degree of stress a user is experiencing.

[0761] "Evaluation" is the process of analyzing emotional state and stress levels based on acquired biometric indicators, and then judging the results.

[0762] "Mental care" refers to support and advice provided to improve a user's emotional state and stress levels.

[0763] An "emotion engine" is an algorithm and technology that analyzes voice tone, facial expressions, and other factors to recognize a user's emotions.

[0764] To implement this invention, it is necessary to construct a comprehensive system that collects and analyzes biometric information and provides appropriate mental care. This system includes a terminal for acquiring the user's biometric indicators, a server that analyzes the biometric indicators and evaluates emotional state and stress levels, and a function to provide personalized mental care to the user based on the evaluation results.

[0765] The device uses a wearable form factor and is equipped with biometric data collection devices such as a heart rate sensor, skin electrical activity sensor, and microphone. These sensors acquire the user's biometric data in real time, and after initial processing to remove noise, the data is encrypted using AES encryption technology and sent to the server.

[0766] Upon decrypting the received encrypted data, the server utilizes machine learning algorithms and an emotion engine to assess the user's emotional state and stress level. The emotion engine analyzes subtle changes in voice tone and facial expressions to identify the user's emotions. Based on these analysis results, it generates personalized mental health advice. This advice is provided in natural language and sent to the user via their device.

[0767] Users receive advice via their device and take action accordingly. Specific instructions are given, such as practicing deep breathing or playing relaxing music. Users return the results of these actions to the system as feedback. This feedback is used by the server to update its data model and improve the quality of advice provided in the future.

[0768] For example, when a user speaks in a tense voice, the emotion engine analyzes the tone of voice and determines that the user is experiencing heightened anxiety. Based on this assessment, the server generates and provides advice to the user, such as "Take a deep breath to alleviate your anxiety" or "Listen to calming music to relax."

[0769] Examples of prompts to input into a generative AI model:

[0770] "Please explain how to analyze a user's emotional state based on their voice data and biometric information, and provide personalized advice to reduce stress."

[0771] The introduction of this system will enable more effective and personalized mental care based on the user's characteristics and current condition.

[0772] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0773] Step 1:

[0774] The device collects the user's biometric data. Specifically, it uses a heart rate sensor, a skin electrical activity sensor, and a microphone to acquire biometric data in real time. The input is biometric information from the user, and the output is biometric data before noise reduction.

[0775] Step 2:

[0776] The device performs noise reduction on the collected biometric data. Specifically, it uses digital filtering technology to remove noise and improve data accuracy. The input is biometric data, and the output is clean data after noise reduction.

[0777] Step 3:

[0778] The terminal encrypts the noise-removed data using AES encryption technology and sends it to the server using a secure protocol. Specifically, it applies an encryption algorithm to ensure data confidentiality. The input is noise-removed data, and the output is encrypted data.

[0779] Step 4:

[0780] The server decrypts the received encrypted data. Specifically, it applies a decryption algorithm to retrieve the original data. The input is encrypted data, and the output is decrypted biometric data.

[0781] Step 5:

[0782] The server uses machine learning algorithms to analyze biometric data and assess the user's emotional state and stress level. Specifically, it performs voice tone and facial expression analysis using an emotion engine. The input is decoded biometric data, and the output is the assessment result of the emotional state and stress level.

[0783] Step 6:

[0784] The server generates personalized mental health advice for the user based on the analysis results. Specifically, it creates advice in natural language according to a template and provides instructions tailored to the user's situation. The input is the evaluation results, and the output is the mental health advice.

[0785] Step 7:

[0786] The terminal notifies the user of advice received from the server. The user receives this advice and takes action according to the instructions. The input is the advice sent from the server, and the output is the information obtained by the user.

[0787] Step 8:

[0788] Users provide feedback on the effectiveness of the advice they receive via their device. Specifically, they use a feedback input interface to record the degree of the advice's effectiveness and their impressions. The input is the user's feedback information, and the output is the feedback data received by the server.

[0789] Step 9:

[0790] The server updates its data model using user feedback to improve the accuracy of subsequent analysis results and the quality of advice. Specifically, it incorporates the feedback data into a machine learning model and performs training. The input is the feedback data, and the output is the improved data model.

[0791] (Application Example 2)

[0792] 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".

[0793] In modern urban life, residents are experiencing increasing stress and mental health problems, and there is a need for appropriate support to address these issues. However, a system that accurately assesses mental stress levels at the individual and community levels and provides appropriate advice has not yet been put into practical use. In particular, methods for quickly proposing concrete measures to reduce stress throughout an entire city are still immature.

[0794] 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.

[0795] In this invention, the server includes means for collecting biometric information, means for analyzing emotional states and mental stress levels, and means for analyzing mental stress levels in each region and proposing stress reduction measures. This makes it possible to support the mental health of not only individuals but also entire communities and improve the quality of life for residents.

[0796] "Biometric information" refers to data related to human bodily functions, such as heart rate, skin electrical activity, and voice tone.

[0797] "Emotional state" refers to information that indicates a person's internal emotional condition, and is analyzed from factors such as voice tone and facial expressions.

[0798] "Mental tension level" is an indicator that shows the degree of psychological tension caused by stress, anxiety, etc.

[0799] "Psychological care suggestions" refer to specific advice aimed at improving mental health, provided based on the user's emotional state and level of mental stress.

[0800] "Regional mental stress levels" refer to the levels of mental stress and tension among residents in a specific geographical area, and are data that indicates the state of mental health in that region.

[0801] "Stress reduction measures" refer to specific methods and activities proposed to alleviate the mental tension of residents.

[0802] To implement this invention, a system is constructed using a biometric data collection device such as a smartwatch and a server that utilizes cloud computing.

[0803] The device collects biometric information such as heart rate, skin electrical activity, and voice tone in real time. This data is securely transmitted from the device to a server in the cloud using an API. Noise reduction and encryption are performed on the device, ensuring the confidentiality of the transmitted data.

[0804] The server utilizes machine learning models built in Python (e.g., TensorFlow) to analyze the received data. To assess emotional state and mental stress levels, it uses an emotion analysis API to analyze not only voice tone but also facial expressions from camera footage as needed. This allows the server to identify the mental stress level of the user or region and generate psychological care suggestions. These suggestions are generated using natural language processing algorithms and provided to the user in a concrete form.

[0805] For example, if the system determines that a user's stress level is high in their local area, the device will display information such as "recommended places to relax" or "information on relaxation events held in the area." In this way, it can support not only individual mental health care but also stress management for the entire community.

[0806] An example of a prompt could be: "Develop an AI model that monitors citizens' biometric information and emotional state in real time, analyzes stress levels for each region, and proposes specific stress reduction measures to support the mental health of citizens in smart cities."

[0807] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0808] Step 1:

[0809] The device uses a smartwatch and sensors to collect biometric information such as the user's heart rate, skin electrical activity, and voice tone. The input data is biometric information obtained from biosensors, and after data collection, a noise reduction filter is applied to improve the accuracy of the data. The output of this process is biometric information with noise removed.

[0810] Step 2:

[0811] The terminal encrypts denoised biometric information and transmits it to the server while maintaining security. The input data is denoised biometric information, and confidentiality is ensured using an encryption algorithm. The output is encrypted biometric information.

[0812] Step 3:

[0813] The server decodes the received biometric information and analyzes emotional state and mental stress levels using a machine learning model built in Python. The input data is encrypted biometric information, which is then decoded and processed using an emotion analysis API and machine learning algorithms. The output is an evaluation of the emotional state and mental stress levels of the user or region.

[0814] Step 4:

[0815] The server generates psychological care suggestions for the user based on the evaluation results of their emotional state and mental stress level. The input data is the result of emotion analysis, and a natural language processing algorithm is used to generate specific advice. The output is the psychological care suggestion.

[0816] Step 5:

[0817] The server sends the generated psychological care suggestions to the terminal and notifies the user. The input data is the psychological care suggestions, which are output in a format that allows for real-time feedback. The output is the psychological care suggestions displayed on the user interface of the terminal.

[0818] Step 6:

[0819] The user acts on the provided psychological care suggestions and inputs feedback into the terminal. The input data is the user's feedback on the effectiveness of the psychological care suggestions and becomes new training data for improving the system's performance. The output is sent to the server as user feedback.

[0820] 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.

[0821] 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.

[0822] 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.

[0823] 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.

[0824] 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.

[0825] 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.

[0826] 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.

[0827] 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.

[0828] 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."

[0829] 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.

[0830] 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.

[0831] 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.

[0832] 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.

[0833] 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.

[0834] 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.

[0835] 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.

[0836] 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.

[0837] 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.

[0838] 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.

[0839] 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.

[0840] 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 as being incorporated by reference.

[0841] The following is further disclosed regarding the embodiments described above.

[0842] (Claim 1)

[0843] A device for collecting biological information,

[0844] A device for analyzing emotional state and stress level based on the aforementioned biological information,

[0845] Based on the aforementioned analysis results, a device for providing mental care advice to the user,

[0846] A system that includes this.

[0847] (Claim 2)

[0848] The system according to claim 1, characterized in that it collects user feedback and the analysis device updates the data model.

[0849] (Claim 3)

[0850] The system according to claim 1, characterized in that the mental care advice is provided in natural language.

[0851] "Example 1"

[0852] (Claim 1)

[0853] A terminal means for collecting and performing initial processing of biometric data,

[0854] A terminal means that encrypts the aforementioned biometric data and transmits it to a server means,

[0855] A server means for analyzing stress indicators based on the aforementioned biological data,

[0856] A server that uses a machine learning algorithm to evaluate emotional states,

[0857] A server means for generating personalized mental care guidance using a generative AI model,

[0858] A terminal means that provides the aforementioned instruction to the user in natural language,

[0859] A system that includes this.

[0860] (Claim 2)

[0861] The system according to claim 1, characterized in that it collects user feedback and the server means continuously updates the data model.

[0862] (Claim 3)

[0863] The system according to claim 1, characterized in that it receives a proposal from a generative AI model using the aforementioned prompt statement.

[0864] "Application Example 1"

[0865] (Claim 1)

[0866] Means for acquiring biometric data,

[0867] A means for evaluating psychological state and level of pressure based on the aforementioned biometric data,

[0868] Based on the aforementioned evaluation results, means for providing psychological health advice to users,

[0869] An automated machine means for presenting the provided advice to the user,

[0870] A system that includes this.

[0871] (Claim 2)

[0872] The system according to claim 1, characterized in that the user's response is obtained and the evaluation means updates the information model.

[0873] (Claim 3)

[0874] The system according to claim 1, characterized in that the aforementioned psychological health advice is presented in natural language.

[0875] "Example 2 of combining an emotion engine"

[0876] (Claim 1)

[0877] Means for obtaining user biometric indicators,

[0878] A means of removing noise from acquired biometric indicators, encrypting them, and transmitting them,

[0879] A means for analyzing encrypted biometric indicators to evaluate emotional state and stress levels,

[0880] Based on the evaluation results, means of providing individualized mental care,

[0881] A means including an emotion engine that recognizes the user's emotions,

[0882] A system that includes this.

[0883] (Claim 2)

[0884] The system according to claim 1, characterized by collecting user feedback, updating the data model, and improving the accuracy of subsequent analyses.

[0885] (Claim 3)

[0886] The system according to claim 1, characterized in that mental care is provided in natural language.

[0887] "Application example 2 when combining with an emotional engine"

[0888] (Claim 1)

[0889] Means for collecting biological information,

[0890] A means for analyzing emotional state and mental tension based on the aforementioned biological information,

[0891] Based on the aforementioned analysis results, a means for providing psychological care suggestions to the user,

[0892] A means for analyzing the level of mental tension in each region and proposing stress reduction measures appropriate to the said region,

[0893] A system that includes this.

[0894] (Claim 2)

[0895] The system according to claim 1, characterized in that the user's response is collected and the analysis means updates the aggregate.

[0896] (Claim 3)

[0897] The system according to claim 1, characterized in that the aforementioned psychological care proposal is provided in natural language. [Explanation of Symbols]

[0898] 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. Means for acquiring biometric data, A means for evaluating psychological state and level of pressure based on the aforementioned biometric data, Based on the aforementioned evaluation results, means for providing psychological health advice to users, An automated machine means for presenting the provided advice to the user, A system that includes this.

2. The system according to claim 1, characterized in that the user's response is obtained and the evaluation means updates the information model.

3. The system according to claim 1, characterized in that the aforementioned psychological health advice is presented in natural language.

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