system

Abstract

Provide a system. The present invention includes a means for collecting user lifestyle and activity data from IoT devices; means for transmitting the data to a server via the user's terminal; means for analyzing the data in the server and detecting a disruption of life rhythm or a decrease in activity; a means for the server to send a link to a web questionnaire to the user based on the detection result; a means for receiving the results of the web questionnaire from the user, conducting a psychoanalysis, and determining whether the user is at risk of depression; A means for providing AI counseling based on the determination result; A system including:

Description

[Technical Field]

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

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

[0003] [Patent Document 1] Japanese Patent Publication No. 2022-180282 Summary of the Invention [Problem to be solved by the invention]

[0004] In today's busy society, many people's physical and mental health is being affected by disruptions to their daily routines and reduced activity levels. Early detection of mental health issues, such as depression, is particularly important, but many people are unaware of their condition and miss opportunities to receive appropriate counseling. Therefore, there is a need for a system that can automatically detect disruptions to daily routines and reduced activity levels in daily life, identify mental health issues at an early stage, and take appropriate measures. [Means for solving the problem]

[0005] To solve the above problems, the present invention provides the following means: a system including means for collecting a user's daily rhythm and activity data from an IoT device, means for transmitting the data to a server via the user's terminal, means for the server to analyze the data and detect disruptions to the daily rhythm or declines in activity, means for the server to send a link to an online questionnaire to the user based on the detection results, means for receiving the results of the online questionnaire from the user, conducting a psychoanalysis, and determining whether the user is at risk of developing depression, and means for providing AI counseling based on the determination results. This allows users to grasp their own mental health at an early stage and take appropriate measures.

[0006] An "IoT device" is a device that can be connected to the Internet and uses sensors to measure and record the environment and user behavior.

[0007] "Life rhythm" refers to the cycle of activity and rest in a person's daily life, and typically includes the timing of sleep, waking, eating, exercise, etc.

[0008] "Activity data" refers to data related to the user's physical activity and movements, including the amount of exercise, number of steps, calories burned, and the like.

[0009] A "terminal" is a device that is operated directly by a user or that receives data from a device and transmits it to a server, and examples of such devices include smartphones and tablets.

[0010] "Server" means a central management system that receives, analyzes, stores, and provides services to other systems.

[0011] "Analysis" is the process of processing collected data to extract useful information and discover specific patterns or anomalies.

[0012] "Detection" refers to confirming the existence of specific conditions from the analysis results and recognizing abnormalities or important events.

[0013] "Web questionnaire" is a method of assessing a user's health and psychological state through a questionnaire form accessible via the Internet.

[0014] "Psychoanalysis" is the process of assessing a user's psychological state and mental health based on the results of a web questionnaire.

[0015] The term "pro-depression group" refers to users who are not currently diagnosed with depression, but who are in a state that is thought to be highly likely to develop depression in the future.

[0016] "AI counseling" is the process of using artificial intelligence to provide users with mental health support and advice.

[0017] A "chatbot" is a program that interacts with users via a text-based chat interface, specifically designed for counseling and question-answering purposes. [Brief explanation of the drawings]

[0018] [Figure 1] 1 is a conceptual diagram showing an example of the configuration of a data processing system according to a first embodiment. [Figure 2] 1 is a conceptual diagram showing an example of main functions of a data processing device and a smart device according to a first embodiment. [Figure 3] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a second embodiment. [Figure 4] FIG. 10 is a conceptual diagram showing an example of main functions of a data processing device and smart glasses according to a second embodiment. [Figure 5] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a third embodiment. [Figure 6]FIG. 11 is a conceptual diagram showing an example of main functions of a data processing device and a headset-type terminal according to a third embodiment. [Figure 7] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a fourth embodiment. [Figure 8] FIG. 10 is a conceptual diagram showing an example of main functions of a data processing device and a robot according to a fourth embodiment. [Figure 9] 1 shows an emotion map onto which multiple emotions are mapped. [Figure 10] 1 shows an emotion map onto which multiple emotions are mapped. [Figure 11] FIG. 3 is a sequence diagram showing a processing flow of the data processing system according to the first embodiment. [Figure 12] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system in Application Example 1. [Figure 13] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system according to the second embodiment when an emotion engine is combined. [Figure 14] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system in Application Example 2 when an emotion engine is combined. DETAILED DESCRIPTION OF THE INVENTION

[0019] An example of an embodiment of a system according to the technology of the present disclosure will be described below with reference to the accompanying drawings.

[0020] First, the terms used in the following description will be explained.

[0021] In the following embodiments, a coded processor (hereinafter simply referred to as a "processor") may be a single arithmetic device or a combination of multiple arithmetic devices. Furthermore, a processor may be a single type of arithmetic device or a combination of multiple types of arithmetic devices. Examples of arithmetic devices include a CPU (Central Processing Unit), a GPU (Graphics Processing Unit), a GPGPU (General-Purpose computing on Graphics Processing Units), and an APU (Accelerated Processing Unit).

[0022] In the following embodiments, a coded RAM (Random Access Memory) is a memory in which information is temporarily stored and is used as a working memory by a processor.

[0023] In the following embodiments, the coded storage is one or more non-volatile storage devices that store various programs, various parameters, etc. Examples of non-volatile storage devices include flash memory (SSD (Solid State Drive)), magnetic disks (e.g., hard disks), and magnetic tapes.

[0024] In the following embodiments, a communication I / F (Interface) with a symbol is an interface including a communication processor, an antenna, etc. The communication I / F controls communication between multiple computers. Examples of communication standards applied to the communication I / F include wireless communication standards including 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), Bluetooth (registered trademark), etc.

[0025] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B." In other words, "A and / or B" means that it may be only A, only B, or a combination of A and B. Furthermore, in this specification, the same concept as "A and / or B" is also applied when three or more things are expressed connected by "and / or."

[0026] [First embodiment]

[0027] FIG. 1 shows an example of the configuration of a data processing system 10 according to the first embodiment.

[0028] 1, a 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.

[0029] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0030] The smart device 14 includes a computer 36, a reception device 38, an output device 40, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The reception device 38, the output device 40, and the camera 42 are also connected to the bus 52.

[0031] The reception device 38 includes a touch panel 38A, a microphone 38B, and the like, and receives user input. The touch panel 38A detects contact with an indicator (for example, a pen or a finger) to receive user input by the touch of the indicator. The microphone 38B detects the user's voice to receive user input by voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and the 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.

[0032] 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 of expression that the user 20 can perceive (for example, audio and / or text). The display 40A displays visible information such as text and images in accordance with instructions from the processor 46. The speaker 40B outputs audio in accordance with instructions from the processor 46. The camera 42 is a compact digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0033] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 control the exchange of various information between the processor 46 and the processor 28 via the network 54.

[0034] FIG. 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0035] 2, in the data processing device 12, a specific process 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" according to the technology of the present 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 process 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.

[0036] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0037] In the smart device 14, the processor 46 performs the reception output process. The storage 50 stores a reception output program 60. The reception output program 60 is used in conjunction with the 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 process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[0038] Next, a description will be given of the specific processing performed by the specific processing unit 290 of the data processing device 12. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0039] This invention configures a system including an IoT device, a user terminal, and a server, and by implementing the means described below, monitors the user's daily rhythm and activity data and effectively manages their mental health.

[0040] Data collection

[0041] The terminal collects the user's daily rhythm and activity data from IoT devices. Specifically, IoT devices such as smartwatches and fitness trackers record data such as heart rate, number of steps, and sleep time in real time. As users wear these devices on a daily basis, data is collected periodically.

[0042] Data transmission

[0043] A terminal (e.g., a smartphone) periodically transmits data collected from IoT devices to a server. Data transmission is performed using wireless communication methods such as Bluetooth or Wi-Fi, and is set so that data is automatically uploaded from the user's smartphone to the server at specific intervals.

[0044] Data analysis

[0045] The server receives the transmitted data and uses AI models to analyze the rhythm and activity data. As part of the analysis, anomaly detection algorithms are applied to detect unusual patterns or disruptions to rhythm, such as a sudden drop in activity compared to normal activity or a significant change in sleep duration.

[0046] Anomaly detection notification

[0047] If the server detects an abnormality, it includes a means to send a notification to the user based on the results. Specifically, the server sends a push notification or email to the user's smartphone to inform them that an abnormality has been detected in their daily rhythm. A message such as "Your activity level has decreased recently. Please answer the online questionnaire to confirm this" is sent.

[0048] Conducting online questionnaires

[0049] The user receives a notification and answers the online questionnaire. The user clicks on the link in the notification using their smartphone to access the questionnaire form in a web browser. The questionnaire form contains a wide range of questions, and the user reports their current mental health status by answering them.

[0050] psychoanalysis

[0051] The server receives the user's responses to the online questionnaire and performs a psychological analysis. This involves using an AI model to analyze the questionnaire results and determine whether the user is at risk of depression. For example, the responses can be used to detect signs of depression or loss of interest.

[0052] Providing AI counseling

[0053] If the server determines that the user is at risk of developing depression, it provides AI counseling. The server then activates a chatbot and provides counseling to the user. Through dialogue with the user, the chatbot provides advice on stress factors and how to manage mood. It asks questions such as, "Is there anything that's been bothering you lately at work or in your personal life?" and provides counseling tailored to the user's situation.

[0054] Specific examples

[0055] Scenario: A user

[0056] 1. Data Collection

[0057] The device (smartwatch) records the user's heart rate, sleep time, and exercise volume throughout the day.

[0058] 2. Data Transmission

[0059] The device (smartphone) automatically collects data at night and sends it to the server.

[0060] 3. Data Analysis

[0061] The server analyzes the received data and detects a significant decrease in exercise volume from the data from the past week.

[0062] 4. Anomaly detection notification

[0063] The server detects the abnormality and sends the user a notification saying, "Your activity has decreased recently. Please answer the online questionnaire to confirm."

[0064] 5. Conducting an online questionnaire

[0065] The user checks the notification, clicks the link to the online medical questionnaire, and answers the medical questionnaire form.

[0066] 6. Psychoanalysis

[0067] The server analyzes the results of the interview, evaluates the user's mental health status, and determines whether the user is at risk of developing depression.

[0068] 7. Providing AI counseling

[0069] The server begins counseling the user through a chatbot, offering advice such as, "Is there anything that's been bothering you lately, either at work or in your personal life?"

[0070] In this way, by implementing the present invention, users can recognize mental health problems early on and take appropriate measures.

[0071] The processing flow will be explained below.

[0072] Step 1:

[0073] The device (smartwatch) records the user's activity data, such as heart rate, sleep time, and exercise volume, in real time. This data is temporarily stored in the internal memory.

[0074] Step 2:

[0075] The device (smartwatch) transmits data to the smartphone via Bluetooth, including heart rate, sleep time, exercise volume, as well as date and time information.

[0076] Step 3:

[0077] The device (smartphone) automatically sends this data to the server at regular intervals via Wi-Fi or mobile data communication.

[0078] Step 4:

[0079] The server receives the data sent from the user's smartphone, stores it in a database, and passes it to an AI model for analysis.

[0080] Step 5:

[0081] The server analyzes the received data and uses an AI model to detect disruptions to daily rhythms and declines in activity, for example, by assessing the degree to which a person's daily rhythm has deviated from normal based on the amount of exercise, heart rate, and sleep patterns over the past week.

[0082] Step 6:

[0083] If the server detects an abnormality, it sends a notification to the user's smartphone. The notification contains a message stating that a disruption in daily rhythm or a decrease in activity level has been detected, along with a request to "answer a web questionnaire to confirm the status."

[0084] Step 7:

[0085] The user receives a notification on their smartphone and clicks on a link to access a web questionnaire that includes questions about their current mood, stress level, sleep quality, and more.

[0086] Step 8:

[0087] The user answers the questionnaire form and presses the send button. The user's answers are sent to the server via the Internet.

[0088] Step 9:

[0089] The server receives the results of the medical interview sent by the user and performs a psychological analysis using an AI model. Based on the analysis results, it determines whether the user is at risk of developing depression.

[0090] Step 10:

[0091] If the server determines that the individual is at risk of developing depression, it will launch a chatbot as a means of providing AI counseling.

[0092] Step 11:

[0093] The server (chatbot) begins counseling by asking the user questions such as, "Is there anything that's been bothering you lately, either at work or in your personal life?" The chatbot then provides appropriate advice and support based on the user's answers.

[0094] Through this series of steps, users can understand their own mental health at an early stage and take the necessary measures quickly.

[0095] Example 1

[0096] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0097] In modern society, mental health problems caused by stress and poor lifestyle habits are on the rise. In response to this, there is a need to manage users' mental states by detecting abnormalities early and providing appropriate counseling, thereby preventing and improving mental illnesses such as depression. However, conventional systems have been unable to provide sufficiently effective measures because the processes of data collection, analysis, notification, and counseling are fragmented and manual. The objective of this invention is to provide a system that utilizes IoT devices and AI technology to automatically and continuously monitor users' mental health states and take appropriate measures early.

[0098] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[0099] In this invention, the server includes means for collecting user life rhythm and activity data from IoT devices, means for transmitting the data to the server via the user's terminal, means for analyzing the data in the server and detecting disruption of the user's life rhythm or decline in activity using an anomaly detection algorithm, means for the server to send a link to an online questionnaire to the user based on the detection results, means for receiving the web questionnaire results from the user and performing psychoanalysis using an AI model to determine whether the user is at risk of depression, and means for providing counseling to the user using an AI counseling function based on the determination results. This makes it possible to detect abnormalities in the user's life rhythm and activity early and provide professional counseling in a timely manner.

[0100] An "IoT device" is a device that has the ability to collect and transmit data via the Internet, and specifically includes smartwatches and fitness trackers.

[0101] A "user device" is an electronic device that a user uses on a daily basis and has functions such as collecting and sending data and receiving notifications. Specifically, this applies to smartphones and tablets.

[0102] A "server" is a computer system that provides services to multiple terminals via a network, and receives, analyzes, stores, and manages notifications of data.

[0103] "Life rhythm" refers to the activity patterns in the user's daily life, such as sleep time, meal time, and exercise time.

[0104] "Activity data" refers to data related to the user's physical activity, including the number of steps taken, heart rate, and exercise intensity.

[0105] An "anomaly detection algorithm" is a computational method for detecting abnormal data that deviates from normal data patterns, and uses machine learning and statistical methods.

[0106] "Web questionnaire" refers to a user answering questions about their mental and physical state via a web browser.

[0107] An "AI model" is a mathematical model that uses artificial intelligence technology to analyze data and make predictions.

[0108] "Psychoanalysis" is the process of analyzing the results of a user's medical interview and assessing their mental state.

[0109] "Pre-depression" refers to people who show signs of depression but have not yet been definitively diagnosed.

[0110] The "AI counseling function" uses artificial intelligence to provide counseling to users, offering advice on stress management and mood improvement through a chatbot.

[0111] A "chatbot" is a software program that uses natural language processing to interact with users and provide counseling and support.

[0112] A "notification" is an information transmission message sent by a server to a user's device, and can take the form of a push notification or email.

[0113] The system according to the present invention is composed of a combination of IoT devices, a user's terminal, and a server. The main purpose of this system is to monitor the user's daily rhythm and activity data and effectively manage their mental health.

[0114] Data collection

[0115] The devices used are IoT devices such as smartwatches and fitness trackers. These devices record daily life data such as the user's heart rate, number of steps, and sleep time in real time and store the data in their internal memory. When users wear these devices on a daily basis, data is collected periodically.

[0116] Data transmission

[0117] The device (e.g., a smartphone) periodically sends the collected data to a server. Data is transferred from the smartwatch to the smartphone using Bluetooth, and then uploaded to the server using Wi-Fi or mobile data. This process is performed automatically at specific intervals (e.g., every day at 10 p.m.).

[0118] Data analysis

[0119] The server then analyzes the received data using AI models. During this analysis, anomaly detection algorithms are applied to the circadian rhythm and activity data to detect unusual patterns or disruptions to circadian rhythms, such as a sudden drop in activity compared to normal activity levels or significant fluctuations in sleep duration.

[0120] Anomaly detection notification

[0121] When the server detects an abnormality, it sends a notification to the user based on the results. This notification is sent to the user's smartphone as a push notification or email. The content of the notification might be, for example, a message such as, "Your activity level has decreased recently. Please answer the online questionnaire to confirm."

[0122] Conducting online questionnaires

[0123] The user receives the notification and clicks on the link to the online questionnaire, which opens a web browser and accesses the questionnaire form. The questionnaire form contains several questions about the user's physical and mental condition. The user answers the questions and submits the form.

[0124] psychoanalysis

[0125] The server receives the user's responses to the online questionnaire and performs a psychological analysis using an AI model. The analysis uses an algorithm to detect signs of depression from the responses, such as low mood or a lack of interest in daily activities.

[0126] Providing AI counseling

[0127] If the server determines that the user is at risk of depression, it activates an AI counseling function. A chatbot within the server is activated and begins counseling the user. The chatbot uses natural language processing technology to converse with the user and provide advice on stress management and mood improvement.

[0128] (Example)

[0129] Scenario: A user

[0130] 1. Data Collection

[0131] The device (smartwatch) records the user's heart rate, sleep time, and exercise volume throughout the day.

[0132] 2. Data Transmission

[0133] The device (smartphone) automatically collects data at night and sends it to the server.

[0134] 3. Data Analysis

[0135] The server analyzes the received data and detects a significant decrease in exercise volume from the data from the past week.

[0136] 4. Anomaly detection notification

[0137] The server detects the abnormality and sends the user a notification saying, "Your activity has decreased recently. Please answer the online questionnaire to confirm."

[0138] 5. Conducting an online questionnaire

[0139] The user checks the notification, clicks the link to the online medical questionnaire, and answers the medical questionnaire form.

[0140] 6. Psychoanalysis

[0141] The server analyzes the results of the interview, evaluates the user's mental health status, and determines whether the user is at risk of developing depression.

[0142] 7. Providing AI counseling

[0143] The server begins counseling the user through a chatbot, offering advice such as, "Is there anything that's been bothering you lately, either at work or in your personal life?"

[0144] Prompt Sentence Examples

[0145] "Have you been worried about anything recently at work or in your personal life? Please answer a few questions so we can provide you with counseling tailored to your situation."

[0146] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0147] Step 1: Data collection

[0148] The device (smartwatch or fitness tracker) records the user's daily life data, such as heart rate, steps, and sleep time, in real time. Sensors within the device detect physical activity and store it in internal memory. Specifically, when the user wakes up in the morning, the smartwatch records the amount of sleep the previous day and continuously monitors heart rate and steps throughout the day. Input: Raw data from the sensor. Output: Life rhythm and activity data stored in internal memory.

[0149] Step 2: Send data

[0150] The device (smartphone) receives the data collected from the smartwatch via Bluetooth. It then uploads it to the server using Wi-Fi or mobile data communication. This transmission process is carried out automatically at specific intervals (e.g., 10 p.m. every day). Specifically, at 10 p.m., the smartphone app automatically collects data from the smartwatch and sends it to the server. Input: Data transferred from the smartwatch. Output: Data uploaded to the server.

[0151] Step 3: Data analysis

[0152] The server analyzes the received data using an AI model. During this analysis process, an anomaly detection algorithm is applied to the lifestyle and activity data to detect unusual patterns or disruptions to lifestyle rhythms. For example, a week's worth of data can be compiled to identify sudden drops in activity levels or significant fluctuations in sleep duration. Input: Data uploaded to the server. Output: Analysis results (anomaly detection results).

[0153] Step 4: Anomaly detection notification

[0154] If the server detects an abnormality based on the analysis results, it will send a notification to the user. The notification is sent to the smartphone as a push notification or email, and includes a message saying, "Your activity level has decreased recently. Please answer the online questionnaire to confirm." Specifically, if a significant decrease in activity level is detected, the server will automatically generate and send a notification. Input: Data analysis results. Output: Notification sent to the user's smartphone.

[0155] Step 5: Conducting an online questionnaire

[0156] The user receives the notification, clicks the web questionnaire link to open a web browser, and accesses the questionnaire form. The questionnaire form contains multiple questions about the user's physical and mental condition, and the user answers each question and submits the form. Specifically, the user checks the notification on their smartphone, clicks the link to access the questionnaire form in a web browser, and answers the questions. Input: The questionnaire form displayed in the web browser. Output: The submitted questionnaire answers.

[0157] Step 6: Psychoanalysis

[0158] The server receives the user's responses to the web questionnaire and performs a psychoanalysis using an AI model. Signals such as depressed mood and lack of interest in daily activities are analyzed to detect signs of depression from the responses. Specifically, the server collects the questionnaire responses, analyzes them using an AI model, and checks for signs of pre-depression. Input: Questionnaire results. Output: Psychoanalysis results.

[0159] Step 7: Providing AI counseling

[0160] The server provides AI counseling functions to users who are determined to be at risk of depression. A chatbot within the server is activated and begins counseling the user. The chatbot uses natural language processing technology to converse with the user and provide advice on stress management and mood improvement. Specifically, the chatbot asks the user, "Is there anything at work that's been bothering you lately?" and provides counseling appropriate to the situation. Input: Psychoanalysis results. Output: Contents of the counseling conversation.

[0161] (Application example 1)

[0162] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0163] In recent years, with the spread of IoT devices and smartphones, it has become possible to collect data on users' daily rhythms and activities and manage their mental health. However, conventional systems only monitor users' mental health status and often do not suggest specific actions for lifestyle improvement. In particular, they lack the functionality to suggest meal menus based on the user's health status, and more comprehensive health management is required. Therefore, a system that can improve not only a user's mental health but also their overall health is needed.

[0164] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[0165] In this invention, the server includes means for collecting user's life rhythm and activity data from IoT devices, means for transmitting the data to the server via the user's terminal, means for analyzing the data and detecting disruption of the user's life rhythm or decrease in activity, means for transmitting a link to an online questionnaire to the user based on the detection results, means for receiving the web questionnaire results from the user and performing a psychoanalysis to determine whether the user is at risk of depression, means for providing AI counseling based on the determination results, and means for suggesting an appropriate meal menu based on the user's health condition. This enables specific measures to improve the user's overall health condition as well as their mental health condition.

[0166] An "IoT device" is a physical device that is connected to the Internet and has the ability to collect and communicate data.

[0167] "Life rhythm" refers to the time patterns of various activities in the user's daily life.

[0168] "Activity data" refers to data related to the user's physical activity, such as the amount of exercise, heart rate, and sleep time.

[0169] A "user's terminal" is a device that receives data from an IoT device and sends it to a server, and generally refers to a smartphone.

[0170] A "server" is a computer system for storing, managing, and analyzing data.

[0171] "Data analysis" is the process of detecting specific patterns or anomalies based on collected data.

[0172] "Disruption of daily rhythm" refers to an abnormality in the pattern of normal daily activities.

[0173] "Decreased activity" refers to a state in which the user's physical activity is less than usual.

[0174] "Web medical questionnaire" refers to a medical questionnaire conducted online, and is used to obtain a detailed understanding of a user's health condition.

[0175] "Psychoanalysis" is the assessment of a user's mental health based on collected data and the results of a web questionnaire.

[0176] "Pre-depression" refers to a condition in which symptoms of depression are present but the diagnostic criteria are not yet met.

[0177] "AI counseling" refers to providing counseling services to users using artificial intelligence.

[0178] A "meal menu" is a combination of meals suggested based on your health condition.

[0179] This invention configures a system including an IoT device, a user terminal, and a server, and by implementing the following means, monitors the user's daily rhythm and activity data, effectively managing their mental and overall health.

[0180] 1. Data Collection

[0181] The device collects data on the user's daily rhythm and activity from IoT devices such as smartwatches and fitness trackers, which record data such as heart rate, steps taken, and sleep time in real time. Data is collected periodically as users wear these devices daily.

[0182] 2. Data Transmission

[0183] A terminal (e.g., a smartphone) periodically transmits data collected from IoT devices to a server. Data transmission is performed using wireless communication methods such as Bluetooth or Wi-Fi, and data is uploaded from the user's smartphone to the server at regular intervals.

[0184] 3. Data Analysis

[0185] The server receives the data and uses AI models to analyze the rhythm and activity data. This analysis detects unusual patterns and disruptions to rhythm, such as a sudden drop in activity compared to normal activity levels or significant fluctuations in sleep duration.

[0186] 4. Anomaly detection notification

[0187] If the server detects an abnormality, it will send a notification to the user based on the results. Specifically, the server will send a push notification or email to the user's smartphone to inform them that an abnormality has been detected in their daily rhythm. A message such as "Your activity level has decreased recently. Please answer the online questionnaire to confirm this" will be sent.

[0188] 5. Conducting an online questionnaire

[0189] The user receives a notification and answers the online questionnaire. The user clicks on the link in the notification using their smartphone to access the questionnaire form in a web browser. The questionnaire form contains a wide range of questions, and by answering these questions, the user reports their current mental health status.

[0190] 6. Psychoanalysis

[0191] The server receives the user's responses to the online questionnaire and performs a psychological analysis. This involves using an AI model to analyze the questionnaire results and determine whether the user is at risk of depression. For example, the responses can be used to detect signs of depression or loss of interest.

[0192] 7. Providing AI counseling

[0193] If the server determines that the user is at risk of developing depression, it provides AI counseling. The server then activates a chatbot and provides counseling to the user. Through dialogue with the user, the chatbot provides advice on stress factors and how to manage mood. It asks questions such as, "Is there anything that's been bothering you lately at work or in your personal life?" and provides counseling tailored to the user's situation.

[0194] 8. Meal suggestions based on health status

[0195] The server proposes an appropriate meal menu based on the user's health condition and lifestyle. Specifically, it identifies nutritional deficiencies based on activity data and the results of an online questionnaire, and proposes meal menus to compensate for these. For example, it suggests a menu with a relaxing effect to a user with a high heart rate and lack of sleep.

[0196] Specific examples

[0197] For example, if a user's recent activity data shows that their physical activity has decreased significantly and their sleep schedule has become irregular, the server will make the following food delivery suggestions to the user:

[0198] "Recently, you've been showing signs of less exercise and sleep deprivation. To boost your immune system, I recommend a salad rich in vitamin C or a smoothie made with antioxidant-rich berries."

[0199] Example prompts to input to a generative AI model:

[0200] "Please provide appropriate advice to suggest meal menus to users who have recently decreased their exercise and have irregular sleep schedules."

[0201] The hardware used is a smartwatch (Apple Watch, Fitbit) or smartphone (iPhone (registered trademark), Android (registered trademark) device), and the server uses a cloud service such as AWS (registered trademark) or GCP. AI models such as TENSORFLOW (registered trademark) or PyTorch are used for analysis, and it is suitable to use MySQL (registered trademark) or PostgreSQL as the database.

[0202] By taking these measures, users can receive comprehensive health management.

[0203] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0204] Step 1:

[0205] The device collects user's daily rhythm and activity data (heart rate, number of steps, sleep time, etc.) from IoT devices such as smartwatches and fitness trackers. This data is acquired in real time and stored in a local database.

[0206] Input: Sensor data from IoT devices.

[0207] Output: User's daily rhythm and activity data stored in a local database.

[0208] Step 2:

[0209] The device (smartphone) periodically sends collected data to the server using Bluetooth or Wi-Fi. The timing of the transmission is set for each interval.

[0210] Input: Circadian rhythm and activity data stored in a local database.

[0211] Output: Data uploaded to the server.

[0212] Step 3:

[0213] The server analyzes the received data and uses AI models to detect disruptions in daily rhythms and declines in activity, thereby identifying abnormal patterns.

[0214] Input: Life rhythm and activity data uploaded to the server.

[0215] Output: Anomaly detection results.

[0216] Step 4:

[0217] The server sends a push notification to the user's device based on the anomaly detection result, which includes a link to the online questionnaire.

[0218] Input: Anomaly detection results.

[0219] Output: The push notification sent to the user's device.

[0220] Step 5:

[0221] The user receives the notification and clicks on the link to the online questionnaire, which opens a web browser and allows the user to access the questionnaire form and answer each question.

[0222] Input: Web questionnaire link in the push notification.

[0223] Output: User's questionnaire response data.

[0224] Step 6:

[0225] The server receives the results of the web questionnaire from the user and performs a psychological analysis using an AI model to determine whether the user is at risk of developing depression.

[0226] Input: Web questionnaire result data.

[0227] Output: psychoanalysis results.

[0228] Step 7:

[0229] If the server determines that the user is at risk of developing depression, it will launch a chatbot to provide AI counseling. The chatbot will then provide advice in a dialogue format with the user.

[0230] Input: Psychoanalysis results.

[0231] Output: Counseling content via chatbot.

[0232] Step 8:

[0233] The server then proposes an appropriate meal plan based on the user's health status, including nutritional analysis based on activity data and online questionnaire results, and notifies the user's food delivery app of the proposed plan.

[0234] Input: Activity data and web questionnaire results.

[0235] Output: Meal suggestions notified to the user.

[0236] Through these steps, the entire system functions to comprehensively manage and improve the user's lifestyle and health.

[0237] Furthermore, an emotion engine that estimates the user's emotion may be combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0238] This invention configures a system that includes an IoT device, a user terminal, and a server, and by combining these systems with an emotion engine, it monitors the user's daily rhythm and activity data, and also manages their mental health with high accuracy.

[0239] Data collection

[0240] The terminal collects data on the user's daily rhythm and activity from IoT devices. Specifically, smartwatches and fitness trackers record data such as heart rate, number of steps, and sleep time in real time. As users wear these devices on a daily basis, data is collected periodically.

[0241] Data transmission

[0242] The terminal (smartphone) periodically sends data collected from IoT devices to a server. Data transmission is performed using wireless communication methods such as Bluetooth or Wi-Fi, and is set to automatically upload data from the smartphone to the server at specific intervals.

[0243] Data analysis

[0244] The server receives the transmitted data and uses AI models to analyze the rhythm and activity data, comparing it with past data to detect, for example, a sudden drop in activity or a change in sleep patterns.

[0245] Anomaly detection notification

[0246] If the server detects an abnormality, it includes a means for sending a notification to the user based on the results. The notification is sent to the smartphone via push notification or email, and displays a message such as "Your activity level has decreased recently. Please answer the online questionnaire to confirm."

[0247] Conducting online questionnaires

[0248] The user receives a notification and answers the online questionnaire. The user clicks on the link in the notification on their smartphone to access the questionnaire form in a web browser. The questionnaire form contains a wide range of questions, such as current mood, stress level, and sleep quality.

[0249] psychoanalysis

[0250] The server receives the user's web questionnaire results and performs a psychological analysis using an AI model and emotion engine. Based on the analysis results, it determines whether the user is at risk of developing depression. This emotion engine analyzes the text data and voice data extracted from the user's questionnaire responses to recognize the user's emotional state. This improves the accuracy of the analysis results.

[0251] Providing AI counseling

[0252] If the server determines that the user is at risk of developing depression, it provides AI counseling. The server then activates a chatbot and provides counseling to the user. The chatbot converses with the user based on the emotional state recognized by the emotion engine, and provides advice on stress factors and how to manage mood. It provides appropriate counseling according to the user's current situation, such as asking questions like, "Is there anything that's bothering you recently at work or in your personal life?"

[0253] Specific examples

[0254] Scenario: A user

[0255] 1. Data Collection

[0256] The device (smartwatch) records the user's heart rate, sleep time, and exercise volume throughout the day.

[0257] 2. Data Transmission

[0258] The device (smartphone) automatically collects data at night and sends it to the server.

[0259] 3. Data Analysis

[0260] The server analyzes the received data and detects a significant decrease in exercise volume from the data from the past week.

[0261] 4. Anomaly detection notification

[0262] The server detects the abnormality and sends the user a notification saying, "Your activity has decreased recently. Please answer the online questionnaire to confirm."

[0263] 5. Conducting an online questionnaire

[0264] The user checks the notification, clicks the link to the online medical questionnaire, and answers the medical questionnaire form.

[0265] 6. Psychoanalysis

[0266] The server analyzes the results of the interview, evaluates the user's mental health, and determines whether they are at risk of developing depression. The emotion engine recognizes the user's emotional state from their responses and reflects this in the analysis results.

[0267] 7. Providing AI counseling

[0268] The server starts counseling the user through a chatbot, offering advice such as, "Is there anything that's been bothering you lately, either at work or in your personal life?" The server then provides more appropriate counseling based on the emotional state recognized by the emotion engine.

[0269] In this way, by implementing the system of the present invention in combination with an emotion engine, users can more accurately understand their own mental health and take appropriate and prompt measures.

[0270] The processing flow will be explained below.

[0271] Step 1:

[0272] The device (smartwatch) records the user's activity data, such as heart rate, sleep time, and exercise volume, in real time. This data is stored in the internal memory.

[0273] Step 2:

[0274] The device (smartwatch) transmits the collected data via Bluetooth to a smartphone at regular intervals, including heart rate, sleep time, exercise volume, date, and time.

[0275] Step 3:

[0276] The device (smartphone) transmits the data received via Bluetooth to the server via Wi-Fi or mobile data communication at regular intervals.

[0277] Step 4:

[0278] The server receives the data sent from the user's smartphone and stores it in a database, which is then passed to the AI ​​model.

[0279] Step 5:

[0280] The server uses AI models to analyze the incoming data and compare it with past data to detect changes or anomalies in daily rhythms, such as a sudden drop in normal activity or a significant deviation from normal sleep patterns.

[0281] Step 6:

[0282] If the server detects an abnormality, such as a disruption in daily rhythm or a decrease in activity, it will send a notification to the user. The notification will be sent via push notification or email to the smartphone and will include a message such as, "Your activity level has decreased recently. Please answer the online questionnaire to confirm."

[0283] Step 7:

[0284] The user sees the notification on their smartphone and clicks on a link to access a web questionnaire, which includes questions about their current mood, stress level, sleep quality, loss of interest, etc.

[0285] Step 8:

[0286] The user fills in the medical interview form and presses the send button to send the results of the medical interview to the server. The results of the medical interview are sent to the server via the Internet.

[0287] Step 9:

[0288] The server receives the user's web questionnaire results and performs psychological analysis using an AI model and emotion engine. The emotion engine analyzes the user's text and voice data to recognize their emotional state, resulting in a more accurate mental health assessment.

[0289] Step 10:

[0290] The server determines whether the user is at risk of depression based on the psychoanalysis results. If the user is at risk of depression, the AI ​​counseling process begins.

[0291] Step 11:

[0292] The server launches a chatbot to provide AI counseling. The chatbot provides counseling based on the user's emotional state. For example, it asks questions such as, "Is there anything bothering you recently at work or in your personal life?"

[0293] Step 12:

[0294] The server (chatbot) provides appropriate advice and support based on the user's responses. For example, it provides advice on stress management and relaxation. Furthermore, if the user's emotional state changes due to the emotion engine, the content of the counseling will be adjusted accordingly.

[0295] Through this series of steps, the system can continuously monitor the user's daily rhythm and emotional state, detect mental health problems early, and take appropriate measures.

[0296] Example 2

[0297] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0298] In recent years, the risk of mental illness has increased due to disruptions in daily rhythms and reduced activity levels. However, there are limited systems that can detect these fluctuations early and provide appropriate responses. Conventional technologies have not only been able to collect and analyze users' biometric data, but also have struggled to comprehensively manage their mental health. In other words, there is a need for the development of a system that can quickly and accurately assess a user's mental state and provide appropriate counseling.

[0299] The identification process by the identification processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means. In this invention, the server includes means for collecting biometric data of a user from an IoT device, means for transmitting the data to the server via the user's terminal, and means for analyzing the data in the server and detecting a disturbance in biorhythm or a decrease in activity level. This makes it possible to quickly and accurately detect changes in the user's mental state through the collection and analysis of the user's biometric data and take appropriate measures.

[0300] Furthermore, the server includes a means for using a generative AI model to analyze the data and a means for using an emotion engine to analyze the emotional state from the results of the web questionnaire, which enables detailed analysis of the user's lifestyle, activity fluctuations, and emotional state, realizing highly accurate psychoanalysis and early detection of pre-depression.

[0301] The server also includes a means for providing AI counseling based on the results of the assessment, which enables counseling using an emotion engine to generate dialogue suited to the user, thereby effectively supporting the user's mental health.

[0302] An "IoT device" is a physical device that is connected to the Internet and is capable of collecting and transmitting data.

[0303] "Biometric data" refers to data related to the user's daily rhythm and activity, such as heart rate, number of steps, and sleep time.

[0304] A "terminal" is a device used by a user to send data collected from an IoT device to a server, and specifically refers to a smartphone or tablet.

[0305] A "server" is a computer system that receives, analyzes, and stores collected data.

[0306] A "generative AI model" is an artificial intelligence model for analyzing data and recognizing patterns.

[0307] An "emotion engine" is software that analyzes a user's text and voice data to recognize their emotional state.

[0308] A "Web questionnaire" is a questionnaire format for assessing mental health status by having users enter answers using an online form.

[0309] "Psychoanalysis" is the process of analyzing collected data and interview results to assess the user's mental state.

[0310] "Pre-mental illness" refers to users who are beginning to show mild symptoms of a mental illness, but have not yet been diagnosed.

[0311] "AI counseling" is the process of using artificial intelligence to provide psychological support and advice to users.

[0312] A "chatbot" is a program, usually an automated response system, that interacts with users in the form of messages.

[0313] This invention configures a system that includes an IoT device, a user terminal, and a server, and by combining these systems with an emotion engine, it monitors the user's daily rhythm and activity data, and also manages their mental health with high accuracy.

[0314] Specifically, the following steps are carried out.

[0315] Data collection

[0316] The terminal collects the user's biometric data from IoT devices. Specifically, smartwatches and fitness trackers record data such as heart rate, steps, and sleep time in real time. As users wear these devices on a daily basis, data is collected periodically.

[0317] Data transmission

[0318] The terminal (smartphone) periodically transmits data collected from IoT devices to a server. This data transmission is performed using wireless communication methods such as Bluetooth or Wi-Fi, and is set so that data is automatically uploaded from the smartphone to the server at specific intervals.

[0319] Data analysis

[0320] The server receives the transmitted data and uses a generative AI model to analyze the rhythm and activity data, comparing it with past data to detect sudden declines in activity or changes in sleep patterns.

[0321] Anomaly detection notification

[0322] If the server detects an abnormality, it includes a means for sending a notification to the user based on the results. The notification is sent to the smartphone via push notification or email, and displays a message such as "Your activity level has decreased recently. Please answer the online questionnaire to confirm."

[0323] Conducting online questionnaires

[0324] The user receives a notification and answers the online questionnaire. The user clicks on the link in the notification on their smartphone to access the questionnaire form in a web browser. The questionnaire form contains a wide range of questions, such as current mood, stress level, and sleep quality.

[0325] psychoanalysis

[0326] The server receives the user's web questionnaire results and performs a psychological analysis using the generative AI model and emotion engine. Based on the analysis results, it determines whether the user is at risk of developing a mental illness. This emotion engine analyzes the text data and voice data extracted from the user's questionnaire responses to recognize the user's emotional state. This improves the accuracy of the analysis results.

[0327] Providing AI counseling

[0328] If the server determines that the user is at risk of developing a mental illness, it provides AI counseling. The server activates a chatbot and provides counseling to the user. The chatbot converses with the user based on the emotional state recognized by the emotion engine, and provides advice on stress factors and how to manage mood. It provides appropriate counseling according to the user's current situation, such as asking questions like, "Is there anything that's been bothering you recently at work or in your personal life?"

[0329] Specific examples

[0330] Scenario: A user

[0331] 1. Data Collection

[0332] The device (smartwatch) records the user's heart rate, sleep time, and exercise volume throughout the day.

[0333] 2. Data Transmission

[0334] The device (smartphone) automatically collects data at night and sends it to the server.

[0335] 3. Data Analysis

[0336] The server analyzes the received data and detects a significant decrease in exercise volume from the data from the past week.

[0337] 4. Anomaly detection notification

[0338] The server detects the abnormality and sends the user a notification saying, "Your activity has decreased recently. Please answer the online questionnaire to confirm."

[0339] 5. Conducting an online questionnaire

[0340] The user checks the notification, clicks the link to the online medical questionnaire, and answers the medical questionnaire form.

[0341] 6. Psychoanalysis

[0342] The server analyzes the results of the interview, evaluates the user's mental health, and determines whether they are at risk of developing a mental illness. The emotion engine recognizes the user's emotional state from their responses and reflects this in the analysis results.

[0343] 7. Providing AI counseling

[0344] The server starts counseling the user through a chatbot, offering advice such as, "Is there anything that's been bothering you lately, either at work or in your personal life?" The server then provides more appropriate counseling based on the emotional state recognized by the emotion engine.

[0345] This allows users to have a more accurate understanding of their own mental health and take appropriate and prompt action.

[0346] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0347] Step 1: Data collection

[0348] The device (smartwatch or fitness tracker) collects the user's biometric data. The specific operation is as follows: the input is the user's activity and state, and the output is the collected data.

[0349] The smartwatch measures the user's heart rate in real time.

[0350] A fitness tracker counts the user's steps and calculates the distance walked and the calories burned.

[0351] These devices record sleep duration and analyze whether the user spends more time in light or deep sleep.

[0352] Step 2: Send data

[0353] The terminal (smartphone) sends the collected data to the server. The specific operation is as follows: the input is the collected biometric data, and the output is the data sent to the server.

[0354] Smartwatches and fitness trackers use Bluetooth to transmit data to your smartphone.

[0355] The smartphone uses Wi-Fi or 4G / 5G to upload data collected from IoT devices to a server at regular intervals.

[0356] This happens automatically in the background, without any user intervention.

[0357] Step 3: Data analysis

[0358] The server analyzes the received data using a generative AI model. The specific operation is as follows: The input is the biometric data received by the server, and the output is the analysis result.

[0359] The server stores the received lifestyle and activity data in a database.

[0360] By comparing with past data, it detects sudden changes in the user's activity level and sleep patterns.

[0361] Statistical analysis is performed using programming languages ​​such as Python and R to extract anomalies.

[0362] Step 4: Anomaly detection notification

[0363] If the server detects an anomaly, it sends a notification to the user based on the result. The specific operation is as follows: The input is the analysis result, and the output is an anomaly notification to the user.

[0364] If there is a sudden decrease in activity or a significant change in sleep patterns, the information is sent to a notification service on the server.

[0365] The server sends a push notification or email to the user saying, "Your activity level has decreased recently. Please answer the web questionnaire to confirm."

[0366] Step 5: Conducting an online questionnaire

[0367] The user checks the notification and answers the web questionnaire. The specific operation is as follows: The input is the notification received by the user, and the output is the questionnaire result.

[0368] Check your smartphone for the notification and tap the link to access the medical questionnaire form in your web browser.

[0369] The form asks questions about your current mood, stress level, appetite, sleep quality, and more.

[0370] The user answers each question and clicks a "Submit" button to send the data to the server.

[0371] Step 6: Psychoanalysis

[0372] The server receives the user's web questionnaire results and performs psychological analysis using the generative AI model and emotion engine. The specific operation is as follows: The input is the web questionnaire results, and the output is the analysis result of the mental state.

[0373] The interview results are analyzed in text format, and an emotion engine models the user's emotional state.

[0374] The analysis model uses natural language processing (NLP) technology to extract keywords and emotions from user responses. For example, if a response contains negative words such as "sad" or "tired," this will be reflected in the analysis results.

[0375] The final analysis results will determine whether or not the individual is at risk of developing a mental illness.

[0376] Step 7: Providing AI counseling

[0377] If the server determines that the user is at risk of developing a mental illness, it will provide AI counseling. The specific operation is as follows: the input is the results of psychoanalysis, and the output is the implementation of counseling.

[0378] The server starts the chatbot and starts counseling the user.

[0379] Using a generative AI model, the system engages in dialogue based on the user's current emotional state. For example, it generates questions such as, "Is there anything that's been bothering you lately, either at work or in your personal life?"

[0380] An emotion engine analyzes the user's responses and provides advice on stress management and mood improvement.

[0381] (Application example 2)

[0382] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0383] Conventional health management systems can collect data on a user's daily rhythm and activity and manage some aspects of their mental state, but they lack the ability to recommend personalized content based on this data. They also face challenges in accurately assessing mental state and providing prompt counseling. In particular, more advanced analysis and personalization are needed to provide appropriate relaxation content and entertainment during times of high stress.

[0384] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 2 is realized by the following means.

[0385] In this invention, the server includes means for collecting user's life rhythm and activity data from IoT devices, means for transmitting the data to the server via the user's information terminal, means for analyzing the data in the server and detecting disruption of the user's life rhythm or decline in activity, means for the server to send a link to an online questionnaire to the user based on the detection results, means for receiving the web questionnaire results from the user, performing psychoanalysis, and determining whether the user is at risk of a mental disorder, means for providing AI counseling based on the determination results, and means for the server to recommend personalized content based on the user's mental state. This makes it possible to accurately manage the user's mental state, provide appropriate and prompt counseling, and recommend personalized content according to the user's mental state.

[0386] An "IoT device" is a device that collects data on a user's daily rhythm and activity, and includes wearable devices such as smartwatches and fitness trackers.

[0387] "User's information terminal" refers to a device, such as a smartphone or tablet, that transmits data collected from IoT devices to a server.

[0388] The "server" is a computer system that analyzes the user's daily rhythm and activity data, evaluates their mental state, and provides counseling.

[0389] "Disruption of daily rhythm" refers to a significant change in normal life patterns, including lack of sleep and abnormal increases or decreases in the amount of exercise.

[0390] "Decreased activity" refers to a significant decrease in a user's normal activity level, including a decrease in the number of steps taken or a sudden decrease in the amount of exercise.

[0391] "Web questionnaire" refers to a series of questions or surveys administered via the Internet to understand the user's mental state.

[0392] "Psychoanalysis" refers to the process of evaluating and analyzing a user's mental state using a generative AI model based on the results of a user's online questionnaire and collected data.

[0393] "Pre-mental illness" refers to a state in which a user is judged to be at high risk of developing mental problems.

[0394] "AI counseling" is a counseling service provided to users through a chatbot, and is provided using a generative AI model.

[0395] "Personalized content" refers to content that is individually recommended based on the user's mental state, and includes, in particular, relaxation music and entertainment content for relaxation.

[0396] A "generative AI model" is an artificial intelligence model trained on large amounts of data and used to analyze the user's mental state.

[0397] A "prompt" refers to an instruction sentence that recommends content appropriate to the user based on the mental state assessment results of the generative AI model.

[0398] The present invention configures a system that includes an IoT device, a user's information terminal, and a server, and by combining these systems with a generative AI model and an emotion engine, it monitors the user's daily rhythm and activity data and manages their mental health with high accuracy.

[0399] Data collection

[0400] The server collects data on users' daily rhythms and activities from IoT devices. Specifically, wearable devices such as smartwatches and fitness trackers record data such as heart rate, number of steps, and sleep time in real time. As users wear these devices on a daily basis, data is collected periodically.

[0401] Data transmission

[0402] The user's information terminal (such as a smartphone) periodically transmits data collected from IoT devices to a server. Data transmission is performed using wireless communication methods such as Bluetooth or Wi-Fi, and the data is automatically uploaded from the smartphone to the server at specific intervals.

[0403] Data analysis

[0404] The server receives the transmitted data and uses the generative AI model to analyze the lifestyle and activity data. This analysis compares it with past data to detect, for example, a sudden decrease in activity or a change in sleep pattern. Specifically, it analyzes data from the past week for a decrease in activity or abnormal sleep patterns to detect disruptions to lifestyle and a decline in activity.

[0405] Anomaly detection notification

[0406] If the server detects an abnormality, it will send a notification to the user based on the results. The notification will be sent to the user's information device via push notification or email, and will display a message such as, "Your activity level has decreased recently. Please answer the online questionnaire to confirm."

[0407] Conducting online questionnaires

[0408] Users receive a notification and answer the online questionnaire. They click the link in the notification on their information device to access the questionnaire form in a web browser. The questionnaire form contains a wide range of questions, including about their current mood, stress level, and sleep quality.

[0409] psychoanalysis

[0410] The server receives the user's web questionnaire results and performs a psychoanalysis using the generative AI model and emotion engine. Based on the analysis results, it determines whether the user is at risk of developing a mental illness. This emotion engine analyzes the text data and voice data extracted from the user's questionnaire responses to recognize the user's emotional state. This improves the accuracy of the analysis results.

[0411] Personalized content recommendations

[0412] The server recommends personalized content based on the user's mental state. Depending on the user's mental state, content such as relaxing music, relaxing movies, and podcasts is presented. For example, if the user's stress level is determined to be high, relaxing music or a relaxing movie is recommended. An example of a prompt provided by the generative AI model is, "Please rate the user's mental state based on the following data: heart rate: 60-70 BPM, steps: 5000-6000 steps, sleep time: 6.5 hours, and online questionnaire response: I've been feeling stressed recently."

[0413] Providing AI counseling

[0414] If the server determines that the user is at risk of developing a mental illness, it provides AI counseling. The server then activates a chatbot and provides counseling to the user. The chatbot converses with the user based on the emotional state recognized by the emotion engine, and provides advice on stress factors and how to manage mood. It provides appropriate counseling based on the user's current situation, such as asking questions like, "Is there anything that's been bothering you recently at work or in your personal life?"

[0415] In this way, by implementing the system of the present invention, which combines a generative AI model and an emotion engine, users can more accurately understand their own mental state and take appropriate and prompt action. Furthermore, by recommending personalized content according to their mental state, it is expected that the quality of their daily lives will be improved.

[0416] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0417] Step 1:

[0418] The server collects data on users' daily rhythms and activities from IoT devices. Specifically, it receives data such as heart rate, number of steps, and sleep time obtained from smartwatches and fitness trackers via the Internet. This data is sent to the server in CSV or JSON format. Because the collected data also includes date and time information, it is generally stored in log format.

[0419] Input: Life rhythm and activity data from IoT devices (heart rate, steps, sleep time, etc.)

[0420] Output: Raw data recorded in a database on the server

[0421] Step 2:

[0422] The terminal periodically transmits data collected from IoT devices to the server. Data transmission is performed using wireless communication such as Bluetooth or Wi-Fi. The transmission interval can be changed by setting, and data is automatically uploaded once a day or every hour.

[0423] Input: Raw data collected from IoT devices

[0424] Output: Life rhythm and activity data uploaded to the server

[0425] Step 3:

[0426] The server analyzes the received data. Specifically, it uses a generative AI model to compare it with past data and analyze daily rhythms and activity patterns. For example, it applies an anomaly detection algorithm to detect decreased activity or abnormal sleep patterns from data from the past week. It also uses statistical methods to identify outliers.

[0427] Input: Life rhythm and activity data uploaded to the server

[0428] Output: Abnormal pattern detection results (decreased activity, abnormal sleep patterns, etc.)

[0429] Step 4:

[0430] If the server detects an abnormality based on the analysis results, it will send a notification to the user. This notification will be sent to the user's device, such as a smartphone or tablet, as a push notification or email. The notification content will include a message such as, "Your activity level has decreased recently. Please answer the online questionnaire to confirm."

[0431] Input: Abnormal pattern detection results

[0432] Output: An error notification message sent to the user's device

[0433] Step 5:

[0434] The user receives a notification on their device and answers the web questionnaire. They click on the link in the notification to access the questionnaire form in their web browser. The questionnaire form contains a wide range of questions, such as their current mood, stress level, and sleep quality. The user's answers are sent to the server in real time.

[0435] Input: Notification message, user's answers to the medical questionnaire

[0436] Output: Web questionnaire results saved on the server

[0437] Step 6:

[0438] The server receives the results of the web questionnaire and performs a psychological analysis using the generative AI model and emotion engine. Based on the analysis results, it determines whether the user is at risk of developing a mental illness. At this time, the text data and voice data from the questionnaire form are analyzed. An example of a prompt provided by the generative AI model is, "Please assess the user's mental state based on the following data: heart rate: 60-70 BPM, steps: 5000-6000, sleep time: 6.5 hours, web questionnaire response: I've been feeling stressed recently."

[0439] Input: Web questionnaire results (text data, voice data)

[0440] Output: Psychoanalysis result (whether or not the individual is at risk of developing a mental illness)

[0441] Step 7:

[0442] The server then recommends optimal content to the user based on the results of the mental state assessment. In particular, it presents relaxation music, distracting movies, podcasts, etc., depending on the user's emotional state. For example, if the stress level is determined to be high, it will recommend relaxation music. In addition, personalized content is recommended based on prompts provided by the generative AI model.

[0443] Input: Psychoanalysis result

[0444] Output: Personalized content recommendations displayed on the user's device

[0445] Step 8:

[0446] If the server determines that the user is at risk of developing a mental illness, it provides AI counseling. The server then activates a chatbot and provides counseling to the user. Based on the emotional state recognized by the emotion engine, the chatbot engages in a dialogue with the user and provides advice on stress factors and how to manage mood. An example of counseling is an exchange such as, "Is there anything that's been bothering you lately, at work or in your personal life?"

[0447] Input: Psychoanalysis result

[0448] Output: Counseling content provided by AI chatbot

[0449] 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 a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the 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.

[0450] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search engine).<URL: https: / / openai.com / blog / chatgpt> ), Gemini (registered trademark) (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0451] In the above embodiment, an example in which the specific process is performed by the data processing device 12 has been given, but the technology of the present disclosure is not limited to this, and the specific process may be performed by the smart device 14.

[0452] [Second embodiment]

[0453] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0454] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0455] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0456] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, and the camera 42 are also connected to the bus 52.

[0457] The microphone 238 receives instructions and the like from the user 20 by receiving voice uttered by the user 20. The microphone 238 captures the voice uttered by the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio in accordance with instructions from the processor 46.

[0458] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the surroundings of user 20 (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0459] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 control the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0460] Fig. 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Fig. 4, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0461] The specific processing program 56 is an example of a "program" according to the technology of the present 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.

[0462] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0463] In the smart glasses 214, the reception output process is performed by the processor 46. A reception output program 60 is stored in the storage 50. 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 process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[0464] Next, a description will be given of the identification process performed by the identification processing unit 290 of the data processing device 12. 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."

[0465] This invention configures a system including an IoT device, a user terminal, and a server, and by implementing the means described below, monitors the user's daily rhythm and activity data and effectively manages their mental health.

[0466] Data collection

[0467] The terminal collects the user's daily rhythm and activity data from IoT devices. Specifically, IoT devices such as smartwatches and fitness trackers record data such as heart rate, number of steps, and sleep time in real time. As users wear these devices on a daily basis, data is collected periodically.

[0468] Data transmission

[0469] A terminal (e.g., a smartphone) periodically transmits data collected from IoT devices to a server. Data transmission is performed using wireless communication methods such as Bluetooth or Wi-Fi, and is set so that data is automatically uploaded from the user's smartphone to the server at specific intervals.

[0470] Data analysis

[0471] The server receives the transmitted data and uses AI models to analyze the rhythm and activity data. As part of the analysis, anomaly detection algorithms are applied to detect unusual patterns or disruptions to rhythm, such as a sudden drop in activity compared to normal activity or a significant change in sleep duration.

[0472] Anomaly detection notification

[0473] If the server detects an abnormality, it includes a means to send a notification to the user based on the results. Specifically, the server sends a push notification or email to the user's smartphone to inform them that an abnormality has been detected in their daily rhythm. A message such as "Your activity level has decreased recently. Please answer the online questionnaire to confirm this" is sent.

[0474] Conducting online questionnaires

[0475] The user receives a notification and answers the online questionnaire. The user clicks on the link in the notification using their smartphone to access the questionnaire form in a web browser. The questionnaire form contains a wide range of questions, and the user reports their current mental health status by answering them.

[0476] psychoanalysis

[0477] The server receives the user's responses to the online questionnaire and performs a psychological analysis. This involves using an AI model to analyze the questionnaire results and determine whether the user is at risk of depression. For example, the responses can be used to detect signs of depression or loss of interest.

[0478] Providing AI counseling

[0479] If the server determines that the user is at risk of developing depression, it provides AI counseling. The server then activates a chatbot and provides counseling to the user. Through dialogue with the user, the chatbot provides advice on stress factors and how to manage mood. It asks questions such as, "Is there anything that's been bothering you lately at work or in your personal life?" and provides counseling tailored to the user's situation.

[0480] Specific examples

[0481] Scenario: A user

[0482] 1. Data Collection

[0483] The device (smartwatch) records the user's heart rate, sleep time, and exercise volume throughout the day.

[0484] 2. Data Transmission

[0485] The device (smartphone) automatically collects data at night and sends it to the server.

[0486] 3. Data Analysis

[0487] The server analyzes the received data and detects a significant decrease in exercise volume from the data from the past week.

[0488] 4. Anomaly detection notification

[0489] The server detects the abnormality and sends the user a notification saying, "Your activity has decreased recently. Please answer the online questionnaire to confirm."

[0490] 5. Conducting an online questionnaire

[0491] The user checks the notification, clicks the link to the online medical questionnaire, and answers the medical questionnaire form.

[0492] 6. Psychoanalysis

[0493] The server analyzes the results of the interview, evaluates the user's mental health status, and determines whether the user is at risk of developing depression.

[0494] 7. Providing AI counseling

[0495] The server begins counseling the user through a chatbot, offering advice such as, "Is there anything that's been bothering you lately, either at work or in your personal life?"

[0496] In this way, by implementing the present invention, users can recognize mental health problems early on and take appropriate measures.

[0497] The processing flow will be explained below.

[0498] Step 1:

[0499] The device (smartwatch) records the user's activity data, such as heart rate, sleep time, and exercise volume, in real time. This data is temporarily stored in the internal memory.

[0500] Step 2:

[0501] The device (smartwatch) transmits data to the smartphone via Bluetooth, including heart rate, sleep time, exercise volume, as well as date and time information.

[0502] Step 3:

[0503] The device (smartphone) automatically sends this data to the server at regular intervals via Wi-Fi or mobile data communication.

[0504] Step 4:

[0505] The server receives the data sent from the user's smartphone, stores it in a database, and passes it to an AI model for analysis.

[0506] Step 5:

[0507] The server analyzes the received data and uses an AI model to detect disruptions to daily rhythms and declines in activity, for example, by assessing the degree to which a person's daily rhythm has deviated from normal based on the amount of exercise, heart rate, and sleep patterns over the past week.

[0508] Step 6:

[0509] If the server detects an abnormality, it sends a notification to the user's smartphone. The notification contains a message stating that a disruption in daily rhythm or a decrease in activity level has been detected, along with a request to "answer a web questionnaire to confirm the status."

[0510] Step 7:

[0511] The user receives a notification on their smartphone and clicks on a link to access a web questionnaire that includes questions about their current mood, stress level, sleep quality, and more.

[0512] Step 8:

[0513] The user answers the questionnaire form and presses the send button. The user's answers are sent to the server via the Internet.

[0514] Step 9:

[0515] The server receives the results of the medical interview sent by the user and performs a psychological analysis using an AI model. Based on the analysis results, it determines whether the user is at risk of developing depression.

[0516] Step 10:

[0517] If the server determines that the individual is at risk of developing depression, it will launch a chatbot as a means of providing AI counseling.

[0518] Step 11:

[0519] The server (chatbot) begins counseling by asking the user questions such as, "Is there anything that's been bothering you lately, either at work or in your personal life?" The chatbot then provides appropriate advice and support based on the user's answers.

[0520] Through this series of steps, users can understand their own mental health at an early stage and take the necessary measures quickly.

[0521] Example 1

[0522] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0523] In modern society, mental health problems caused by stress and poor lifestyle habits are on the rise. In response to this, there is a need to manage users' mental states by detecting abnormalities early and providing appropriate counseling, thereby preventing and improving mental illnesses such as depression. However, conventional systems have been unable to provide sufficiently effective measures because the processes of data collection, analysis, notification, and counseling are fragmented and manual. The objective of this invention is to provide a system that utilizes IoT devices and AI technology to automatically and continuously monitor users' mental health states and take appropriate measures early.

[0524] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[0525] In this invention, the server includes means for collecting user life rhythm and activity data from IoT devices, means for transmitting the data to the server via the user's terminal, means for analyzing the data in the server and detecting disruption of the user's life rhythm or decline in activity using an anomaly detection algorithm, means for the server to send a link to an online questionnaire to the user based on the detection results, means for receiving the web questionnaire results from the user and performing psychoanalysis using an AI model to determine whether the user is at risk of depression, and means for providing counseling to the user using an AI counseling function based on the determination results. This makes it possible to detect abnormalities in the user's life rhythm and activity early and provide professional counseling in a timely manner.

[0526] An "IoT device" is a device that has the ability to collect and transmit data via the Internet, and specifically includes smartwatches and fitness trackers.

[0527] A "user device" is an electronic device that a user uses on a daily basis and has functions such as collecting and sending data and receiving notifications. Specifically, this applies to smartphones and tablets.

[0528] A "server" is a computer system that provides services to multiple terminals via a network, and receives, analyzes, stores, and manages notifications of data.

[0529] "Life rhythm" refers to the activity patterns in the user's daily life, such as sleep time, meal time, and exercise time.

[0530] "Activity data" refers to data related to the user's physical activity, including the number of steps taken, heart rate, and exercise intensity.

[0531] An "anomaly detection algorithm" is a computational method for detecting abnormal data that deviates from normal data patterns, and uses machine learning and statistical methods.

[0532] "Web questionnaire" refers to a user answering questions about their mental and physical state via a web browser.

[0533] An "AI model" is a mathematical model that uses artificial intelligence technology to analyze data and make predictions.

[0534] "Psychoanalysis" is the process of analyzing the results of a user's medical interview and assessing their mental state.

[0535] "Pre-depression" refers to people who show signs of depression but have not yet been definitively diagnosed.

[0536] The "AI counseling function" uses artificial intelligence to provide counseling to users, offering advice on stress management and mood improvement through a chatbot.

[0537] A "chatbot" is a software program that uses natural language processing to interact with users and provide counseling and support.

[0538] A "notification" is an information transmission message sent by a server to a user's device, and can take the form of a push notification or email.

[0539] The system according to the present invention is composed of a combination of IoT devices, a user's terminal, and a server. The main purpose of this system is to monitor the user's daily rhythm and activity data and effectively manage their mental health.

[0540] Data collection

[0541] The devices used are IoT devices such as smartwatches and fitness trackers. These devices record daily life data such as the user's heart rate, number of steps, and sleep time in real time and store the data in their internal memory. When users wear these devices on a daily basis, data is collected periodically.

[0542] Data transmission

[0543] The device (e.g., a smartphone) periodically sends the collected data to a server. Data is transferred from the smartwatch to the smartphone using Bluetooth, and then uploaded to the server using Wi-Fi or mobile data. This process is performed automatically at specific intervals (e.g., every day at 10 p.m.).

[0544] Data analysis

[0545] The server then analyzes the received data using AI models. During this analysis, anomaly detection algorithms are applied to the circadian rhythm and activity data to detect unusual patterns or disruptions to circadian rhythms, such as a sudden drop in activity compared to normal activity levels or significant fluctuations in sleep duration.

[0546] Anomaly detection notification

[0547] When the server detects an abnormality, it sends a notification to the user based on the results. This notification is sent to the user's smartphone as a push notification or email. The content of the notification might be, for example, a message such as, "Your activity level has decreased recently. Please answer the online questionnaire to confirm."

[0548] Conducting online questionnaires

[0549] The user receives the notification and clicks on the link to the online questionnaire, which opens a web browser and accesses the questionnaire form. The questionnaire form contains several questions about the user's physical and mental condition. The user answers the questions and submits the form.

[0550] psychoanalysis

[0551] The server receives the user's responses to the online questionnaire and performs a psychological analysis using an AI model. The analysis uses an algorithm to detect signs of depression from the responses, such as low mood or a lack of interest in daily activities.

[0552] Providing AI counseling

[0553] If the server determines that the user is at risk of depression, it activates an AI counseling function. A chatbot within the server is activated and begins counseling the user. The chatbot uses natural language processing technology to converse with the user and provide advice on stress management and mood improvement.

[0554] (Example)

[0555] Scenario: A user

[0556] 1. Data Collection

[0557] The device (smartwatch) records the user's heart rate, sleep time, and exercise volume throughout the day.

[0558] 2. Data Transmission

[0559] The device (smartphone) automatically collects data at night and sends it to the server.

[0560] 3. Data Analysis

[0561] The server analyzes the received data and detects a significant decrease in exercise volume from the data from the past week.

[0562] 4. Anomaly detection notification

[0563] The server detects the abnormality and sends the user a notification saying, "Your activity has decreased recently. Please answer the online questionnaire to confirm."

[0564] 5. Conducting an online questionnaire

[0565] The user checks the notification, clicks the link to the online medical questionnaire, and answers the medical questionnaire form.

[0566] 6. Psychoanalysis

[0567] The server analyzes the results of the interview, evaluates the user's mental health status, and determines whether the user is at risk of developing depression.

[0568] 7. Providing AI counseling

[0569] The server begins counseling the user through a chatbot, offering advice such as, "Is there anything that's been bothering you lately, either at work or in your personal life?"

[0570] Prompt Sentence Examples

[0571] "Have you been worried about anything recently at work or in your personal life? Please answer a few questions so we can provide you with counseling tailored to your situation."

[0572] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0573] Step 1: Data collection

[0574] The device (smartwatch or fitness tracker) records the user's daily life data, such as heart rate, steps, and sleep time, in real time. Sensors within the device detect physical activity and store it in internal memory. Specifically, when the user wakes up in the morning, the smartwatch records the amount of sleep the previous day and continuously monitors heart rate and steps throughout the day. Input: Raw data from the sensor. Output: Life rhythm and activity data stored in internal memory.

[0575] Step 2: Send data

[0576] The device (smartphone) receives the data collected from the smartwatch via Bluetooth. It then uploads it to the server using Wi-Fi or mobile data communication. This transmission process is carried out automatically at specific intervals (e.g., 10 p.m. every day). Specifically, at 10 p.m., the smartphone app automatically collects data from the smartwatch and sends it to the server. Input: Data transferred from the smartwatch. Output: Data uploaded to the server.

[0577] Step 3: Data analysis

[0578] The server analyzes the received data using an AI model. During this analysis process, an anomaly detection algorithm is applied to the lifestyle and activity data to detect unusual patterns or disruptions to lifestyle rhythms. For example, a week's worth of data can be compiled to identify sudden drops in activity levels or significant fluctuations in sleep duration. Input: Data uploaded to the server. Output: Analysis results (anomaly detection results).

[0579] Step 4: Anomaly detection notification

[0580] If the server detects an abnormality based on the analysis results, it will send a notification to the user. The notification is sent to the smartphone as a push notification or email, and includes a message saying, "Your activity level has decreased recently. Please answer the online questionnaire to confirm." Specifically, if a significant decrease in activity level is detected, the server will automatically generate and send a notification. Input: Data analysis results. Output: Notification sent to the user's smartphone.

[0581] Step 5: Conducting an online questionnaire

[0582] The user receives the notification, clicks the web questionnaire link to open a web browser, and accesses the questionnaire form. The questionnaire form contains multiple questions about the user's physical and mental condition, and the user answers each question and submits the form. Specifically, the user checks the notification on their smartphone, clicks the link to access the questionnaire form in a web browser, and answers the questions. Input: The questionnaire form displayed in the web browser. Output: The submitted questionnaire answers.

[0583] Step 6: Psychoanalysis

[0584] The server receives the user's responses to the web questionnaire and performs a psychoanalysis using an AI model. Signals such as depressed mood and lack of interest in daily activities are analyzed to detect signs of depression from the responses. Specifically, the server collects the questionnaire responses, analyzes them using an AI model, and checks for signs of pre-depression. Input: Questionnaire results. Output: Psychoanalysis results.

[0585] Step 7: Providing AI counseling

[0586] The server provides AI counseling functions to users who are determined to be at risk of depression. A chatbot within the server is activated and begins counseling the user. The chatbot uses natural language processing technology to converse with the user and provide advice on stress management and mood improvement. Specifically, the chatbot asks the user, "Is there anything at work that's been bothering you lately?" and provides counseling appropriate to the situation. Input: Psychoanalysis results. Output: Contents of the counseling conversation.

[0587] (Application example 1)

[0588] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0589] In recent years, with the spread of IoT devices and smartphones, it has become possible to collect data on users' daily rhythms and activities and manage their mental health. However, conventional systems only monitor users' mental health status and often do not suggest specific actions for lifestyle improvement. In particular, they lack the functionality to suggest meal menus based on the user's health status, and more comprehensive health management is required. Therefore, a system that can improve not only a user's mental health but also their overall health is needed.

[0590] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[0591] In this invention, the server includes means for collecting user's life rhythm and activity data from IoT devices, means for transmitting the data to the server via the user's terminal, means for analyzing the data and detecting disruption of the user's life rhythm or decrease in activity, means for transmitting a link to an online questionnaire to the user based on the detection results, means for receiving the web questionnaire results from the user and performing a psychoanalysis to determine whether the user is at risk of depression, means for providing AI counseling based on the determination results, and means for suggesting an appropriate meal menu based on the user's health condition. This enables specific measures to improve the user's overall health condition as well as their mental health condition.

[0592] An "IoT device" is a physical device that is connected to the Internet and has the ability to collect and communicate data.

[0593] "Life rhythm" refers to the time patterns of various activities in the user's daily life.

[0594] "Activity data" refers to data related to the user's physical activity, such as the amount of exercise, heart rate, and sleep time.

[0595] A "user's terminal" is a device that receives data from an IoT device and sends it to a server, and generally refers to a smartphone.

[0596] A "server" is a computer system for storing, managing, and analyzing data.

[0597] "Data analysis" is the process of detecting specific patterns or anomalies based on collected data.

[0598] "Disruption of daily rhythm" refers to an abnormality in the pattern of normal daily activities.

[0599] "Decreased activity" refers to a state in which the user's physical activity is less than usual.

[0600] "Web medical questionnaire" refers to a medical questionnaire conducted online, and is used to obtain a detailed understanding of a user's health condition.

[0601] "Psychoanalysis" is the assessment of a user's mental health based on collected data and the results of a web questionnaire.

[0602] "Pre-depression" refers to a condition in which symptoms of depression are present but the diagnostic criteria are not yet met.

[0603] "AI counseling" refers to providing counseling services to users using artificial intelligence.

[0604] A "meal menu" is a combination of meals suggested based on your health condition.

[0605] This invention configures a system including an IoT device, a user terminal, and a server, and by implementing the following means, monitors the user's daily rhythm and activity data, effectively managing their mental and overall health.

[0606] 1. Data Collection

[0607] The device collects data on the user's daily rhythm and activity from IoT devices such as smartwatches and fitness trackers, which record data such as heart rate, steps taken, and sleep time in real time. Data is collected periodically as users wear these devices daily.

[0608] 2. Data Transmission

[0609] A terminal (e.g., a smartphone) periodically transmits data collected from IoT devices to a server. Data transmission is performed using wireless communication methods such as Bluetooth or Wi-Fi, and data is uploaded from the user's smartphone to the server at regular intervals.

[0610] 3. Data Analysis

[0611] The server receives the data and uses AI models to analyze the rhythm and activity data. This analysis detects unusual patterns and disruptions to rhythm, such as a sudden drop in activity compared to normal activity levels or significant fluctuations in sleep duration.

[0612] 4. Anomaly detection notification

[0613] If the server detects an abnormality, it will send a notification to the user based on the results. Specifically, the server will send a push notification or email to the user's smartphone to inform them that an abnormality has been detected in their daily rhythm. A message such as "Your activity level has decreased recently. Please answer the online questionnaire to confirm this" will be sent.

[0614] 5. Conducting an online questionnaire

[0615] The user receives a notification and answers the online questionnaire. The user clicks on the link in the notification using their smartphone to access the questionnaire form in a web browser. The questionnaire form contains a wide range of questions, and by answering these questions, the user reports their current mental health status.

[0616] 6. Psychoanalysis

[0617] The server receives the user's responses to the online questionnaire and performs a psychological analysis. This involves using an AI model to analyze the questionnaire results and determine whether the user is at risk of depression. For example, the responses can be used to detect signs of depression or loss of interest.

[0618] 7. Providing AI counseling

[0619] If the server determines that the user is at risk of developing depression, it provides AI counseling. The server then activates a chatbot and provides counseling to the user. Through dialogue with the user, the chatbot provides advice on stress factors and how to manage mood. It asks questions such as, "Is there anything that's been bothering you lately at work or in your personal life?" and provides counseling tailored to the user's situation.

[0620] 8. Meal suggestions based on health status

[0621] The server proposes an appropriate meal menu based on the user's health condition and lifestyle. Specifically, it identifies nutritional deficiencies based on activity data and the results of an online questionnaire, and proposes meal menus to compensate for these. For example, it suggests a menu with a relaxing effect to a user with a high heart rate and lack of sleep.

[0622] Specific examples

[0623] For example, if a user's recent activity data shows that their physical activity has decreased significantly and their sleep schedule has become irregular, the server will make the following food delivery suggestions to the user:

[0624] "Recently, you've been showing signs of less exercise and sleep deprivation. To boost your immune system, I recommend a salad rich in vitamin C or a smoothie made with antioxidant-rich berries."

[0625] Example prompts to input to a generative AI model:

[0626] "Please provide appropriate advice to suggest meal menus to users who have recently decreased their exercise and have irregular sleep schedules."

[0627] The hardware used is a smartwatch (Apple Watch, Fitbit) or smartphone (iPhone, Android device), and the server uses a cloud service such as AWS or GCP. AI models such as TensorFlow or PyTorch are used for analysis, and MySQL or PostgreSQL is suitable for the database.

[0628] By taking these measures, users can receive comprehensive health management.

[0629] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0630] Step 1:

[0631] The device collects user's daily rhythm and activity data (heart rate, number of steps, sleep time, etc.) from IoT devices such as smartwatches and fitness trackers. This data is acquired in real time and stored in a local database.

[0632] Input: Sensor data from IoT devices.

[0633] Output: User's daily rhythm and activity data stored in a local database.

[0634] Step 2:

[0635] The device (smartphone) periodically sends collected data to the server using Bluetooth or Wi-Fi. The timing of the transmission is set for each interval.

[0636] Input: Circadian rhythm and activity data stored in a local database.

[0637] Output: Data uploaded to the server.

[0638] Step 3:

[0639] The server analyzes the received data and uses AI models to detect disruptions in daily rhythms and declines in activity, thereby identifying abnormal patterns.

[0640] Input: Life rhythm and activity data uploaded to the server.

[0641] Output: Anomaly detection results.

[0642] Step 4:

[0643] The server sends a push notification to the user's device based on the anomaly detection result, which includes a link to the online questionnaire.

[0644] Input: Anomaly detection results.

[0645] Output: The push notification sent to the user's device.

[0646] Step 5:

[0647] The user receives the notification and clicks on the link to the online questionnaire, which opens a web browser and allows the user to access the questionnaire form and answer each question.

[0648] Input: Web questionnaire link in the push notification.

[0649] Output: User's questionnaire response data.

[0650] Step 6:

[0651] The server receives the results of the web questionnaire from the user and performs a psychological analysis using an AI model to determine whether the user is at risk of developing depression.

[0652] Input: Web questionnaire result data.

[0653] Output: psychoanalysis results.

[0654] Step 7:

[0655] If the server determines that the user is at risk of developing depression, it will launch a chatbot to provide AI counseling. The chatbot will then provide advice in a dialogue format with the user.

[0656] Input: Psychoanalysis results.

[0657] Output: Counseling content via chatbot.

[0658] Step 8:

[0659] The server then proposes an appropriate meal plan based on the user's health status, including nutritional analysis based on activity data and online questionnaire results, and notifies the user's food delivery app of the proposed plan.

[0660] Input: Activity data and web questionnaire results.

[0661] Output: Meal suggestions notified to the user.

[0662] Through these steps, the entire system functions to comprehensively manage and improve the user's lifestyle and health.

[0663] Furthermore, an emotion engine that estimates the user's emotion may be further combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59, and perform identification processing using the user's emotion.

[0664] This invention configures a system that includes an IoT device, a user terminal, and a server, and by combining these systems with an emotion engine, it monitors the user's daily rhythm and activity data, and also manages their mental health with high accuracy.

[0665] Data collection

[0666] The terminal collects data on the user's daily rhythm and activity from IoT devices. Specifically, smartwatches and fitness trackers record data such as heart rate, number of steps, and sleep time in real time. As users wear these devices on a daily basis, data is collected periodically.

[0667] Data transmission

[0668] The terminal (smartphone) periodically sends data collected from IoT devices to a server. Data transmission is performed using wireless communication methods such as Bluetooth or Wi-Fi, and is set to automatically upload data from the smartphone to the server at specific intervals.

[0669] Data analysis

[0670] The server receives the transmitted data and uses AI models to analyze the rhythm and activity data, comparing it with past data to detect, for example, a sudden drop in activity or a change in sleep patterns.

[0671] Anomaly detection notification

[0672] If the server detects an abnormality, it includes a means for sending a notification to the user based on the results. The notification is sent to the smartphone via push notification or email, and displays a message such as "Your activity level has decreased recently. Please answer the online questionnaire to confirm."

[0673] Conducting online questionnaires

[0674] The user receives a notification and answers the online questionnaire. The user clicks on the link in the notification on their smartphone to access the questionnaire form in a web browser. The questionnaire form contains a wide range of questions, such as current mood, stress level, and sleep quality.

[0675] psychoanalysis

[0676] The server receives the user's web questionnaire results and performs a psychological analysis using an AI model and emotion engine. Based on the analysis results, it determines whether the user is at risk of developing depression. This emotion engine analyzes the text data and voice data extracted from the user's questionnaire responses to recognize the user's emotional state. This improves the accuracy of the analysis results.

[0677] Providing AI counseling

[0678] If the server determines that the user is at risk of developing depression, it provides AI counseling. The server then activates a chatbot and provides counseling to the user. The chatbot converses with the user based on the emotional state recognized by the emotion engine, and provides advice on stress factors and how to manage mood. It provides appropriate counseling according to the user's current situation, such as asking questions like, "Is there anything that's bothering you recently at work or in your personal life?"

[0679] Specific examples

[0680] Scenario: A user

[0681] 1. Data Collection

[0682] The device (smartwatch) records the user's heart rate, sleep time, and exercise volume throughout the day.

[0683] 2. Data Transmission

[0684] The device (smartphone) automatically collects data at night and sends it to the server.

[0685] 3. Data Analysis

[0686] The server analyzes the received data and detects a significant decrease in exercise volume from the data from the past week.

[0687] 4. Anomaly detection notification

[0688] The server detects the abnormality and sends the user a notification saying, "Your activity has decreased recently. Please answer the online questionnaire to confirm."

[0689] 5. Conducting an online questionnaire

[0690] The user checks the notification, clicks the link to the online medical questionnaire, and answers the medical questionnaire form.

[0691] 6. Psychoanalysis

[0692] The server analyzes the results of the interview, evaluates the user's mental health, and determines whether they are at risk of developing depression. The emotion engine recognizes the user's emotional state from their responses and reflects this in the analysis results.

[0693] 7. Providing AI counseling

[0694] The server starts counseling the user through a chatbot, offering advice such as, "Is there anything that's been bothering you lately, either at work or in your personal life?" The server then provides more appropriate counseling based on the emotional state recognized by the emotion engine.

[0695] In this way, by implementing the system of the present invention in combination with an emotion engine, users can more accurately understand their own mental health and take appropriate and prompt measures.

[0696] The processing flow will be explained below.

[0697] Step 1:

[0698] The device (smartwatch) records the user's activity data, such as heart rate, sleep time, and exercise volume, in real time. This data is stored in the internal memory.

[0699] Step 2:

[0700] The device (smartwatch) transmits the collected data via Bluetooth to a smartphone at regular intervals, including heart rate, sleep time, exercise volume, date, and time.

[0701] Step 3:

[0702] The device (smartphone) transmits the data received via Bluetooth to the server via Wi-Fi or mobile data communication at regular intervals.

[0703] Step 4:

[0704] The server receives the data sent from the user's smartphone and stores it in a database, which is then passed to the AI ​​model.

[0705] Step 5:

[0706] The server uses AI models to analyze the incoming data and compare it with past data to detect changes or anomalies in daily rhythms, such as a sudden drop in normal activity or a significant deviation from normal sleep patterns.

[0707] Step 6:

[0708] If the server detects an abnormality, such as a disruption in daily rhythm or a decrease in activity, it will send a notification to the user. The notification will be sent via push notification or email to the smartphone and will include a message such as, "Your activity level has decreased recently. Please answer the online questionnaire to confirm."

[0709] Step 7:

[0710] The user sees the notification on their smartphone and clicks on a link to access a web questionnaire, which includes questions about their current mood, stress level, sleep quality, loss of interest, etc.

[0711] Step 8:

[0712] The user fills in the medical interview form and presses the send button to send the results of the medical interview to the server. The results of the medical interview are sent to the server via the Internet.

[0713] Step 9:

[0714] The server receives the user's web questionnaire results and performs psychological analysis using an AI model and emotion engine. The emotion engine analyzes the user's text and voice data to recognize their emotional state, resulting in a more accurate mental health assessment.

[0715] Step 10:

[0716] The server determines whether the user is at risk of depression based on the psychoanalysis results. If the user is at risk of depression, the AI ​​counseling process begins.

[0717] Step 11:

[0718] The server launches a chatbot to provide AI counseling. The chatbot provides counseling based on the user's emotional state. For example, it asks questions such as, "Is there anything bothering you recently at work or in your personal life?"

[0719] Step 12:

[0720] The server (chatbot) provides appropriate advice and support based on the user's responses. For example, it provides advice on stress management and relaxation. Furthermore, if the user's emotional state changes due to the emotion engine, the content of the counseling will be adjusted accordingly.

[0721] Through this series of steps, the system can continuously monitor the user's daily rhythm and emotional state, detect mental health problems early, and take appropriate measures.

[0722] Example 2

[0723] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0724] In recent years, the risk of mental illness has increased due to disruptions in daily rhythms and reduced activity levels. However, there are limited systems that can detect these fluctuations early and provide appropriate responses. Conventional technologies have not only been able to collect and analyze users' biometric data, but also have struggled to comprehensively manage their mental health. In other words, there is a need for the development of a system that can quickly and accurately assess a user's mental state and provide appropriate counseling.

[0725] The identification process by the identification processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means. In this invention, the server includes means for collecting biometric data of a user from an IoT device, means for transmitting the data to the server via the user's terminal, and means for analyzing the data in the server and detecting a disturbance in biorhythm or a decrease in activity level. This makes it possible to quickly and accurately detect changes in the user's mental state through the collection and analysis of the user's biometric data and take appropriate measures.

[0726] Furthermore, the server includes a means for using a generative AI model to analyze the data and a means for using an emotion engine to analyze the emotional state from the results of the web questionnaire, which enables detailed analysis of the user's lifestyle, activity fluctuations, and emotional state, realizing highly accurate psychoanalysis and early detection of pre-depression.

[0727] The server also includes a means for providing AI counseling based on the results of the assessment, which enables counseling using an emotion engine to generate dialogue suited to the user, thereby effectively supporting the user's mental health.

[0728] An "IoT device" is a physical device that is connected to the Internet and is capable of collecting and transmitting data.

[0729] "Biometric data" refers to data related to the user's daily rhythm and activity, such as heart rate, number of steps, and sleep time.

[0730] A "terminal" is a device used by a user to send data collected from an IoT device to a server, and specifically refers to a smartphone or tablet.

[0731] A "server" is a computer system that receives, analyzes, and stores collected data.

[0732] A "generative AI model" is an artificial intelligence model for analyzing data and recognizing patterns.

[0733] An "emotion engine" is software that analyzes a user's text and voice data to recognize their emotional state.

[0734] A "Web questionnaire" is a questionnaire format for assessing mental health status by having users enter answers using an online form.

[0735] "Psychoanalysis" is the process of analyzing collected data and interview results to assess the user's mental state.

[0736] "Pre-mental illness" refers to users who are beginning to show mild symptoms of a mental illness, but have not yet been diagnosed.

[0737] "AI counseling" is the process of using artificial intelligence to provide psychological support and advice to users.

[0738] A "chatbot" is a program, usually an automated response system, that interacts with users in the form of messages.

[0739] This invention configures a system that includes an IoT device, a user terminal, and a server, and by combining these systems with an emotion engine, it monitors the user's daily rhythm and activity data, and also manages their mental health with high accuracy.

[0740] Specifically, the following steps are carried out.

[0741] Data collection

[0742] The terminal collects the user's biometric data from IoT devices. Specifically, smartwatches and fitness trackers record data such as heart rate, steps, and sleep time in real time. As users wear these devices on a daily basis, data is collected periodically.

[0743] Data transmission

[0744] The terminal (smartphone) periodically transmits data collected from IoT devices to a server. This data transmission is performed using wireless communication methods such as Bluetooth or Wi-Fi, and is set so that data is automatically uploaded from the smartphone to the server at specific intervals.

[0745] Data analysis

[0746] The server receives the transmitted data and uses a generative AI model to analyze the rhythm and activity data, comparing it with past data to detect sudden declines in activity or changes in sleep patterns.

[0747] Anomaly detection notification

[0748] If the server detects an abnormality, it includes a means for sending a notification to the user based on the results. The notification is sent to the smartphone via push notification or email, and displays a message such as "Your activity level has decreased recently. Please answer the online questionnaire to confirm."

[0749] Conducting online questionnaires

[0750] The user receives a notification and answers the online questionnaire. The user clicks on the link in the notification on their smartphone to access the questionnaire form in a web browser. The questionnaire form contains a wide range of questions, such as current mood, stress level, and sleep quality.

[0751] psychoanalysis

[0752] The server receives the user's web questionnaire results and performs a psychological analysis using the generative AI model and emotion engine. Based on the analysis results, it determines whether the user is at risk of developing a mental illness. This emotion engine analyzes the text data and voice data extracted from the user's questionnaire responses to recognize the user's emotional state. This improves the accuracy of the analysis results.

[0753] Providing AI counseling

[0754] If the server determines that the user is at risk of developing a mental illness, it provides AI counseling. The server activates a chatbot and provides counseling to the user. The chatbot converses with the user based on the emotional state recognized by the emotion engine, and provides advice on stress factors and how to manage mood. It provides appropriate counseling according to the user's current situation, such as asking questions like, "Is there anything that's been bothering you recently at work or in your personal life?"

[0755] Specific examples

[0756] Scenario: A user

[0757] 1. Data Collection

[0758] The device (smartwatch) records the user's heart rate, sleep time, and exercise volume throughout the day.

[0759] 2. Data Transmission

[0760] The device (smartphone) automatically collects data at night and sends it to the server.

[0761] 3. Data Analysis

[0762] The server analyzes the received data and detects a significant decrease in exercise volume from the data from the past week.

[0763] 4. Anomaly detection notification

[0764] The server detects the abnormality and sends the user a notification saying, "Your activity has decreased recently. Please answer the online questionnaire to confirm."

[0765] 5. Conducting an online questionnaire

[0766] The user checks the notification, clicks the link to the online medical questionnaire, and answers the medical questionnaire form.

[0767] 6. Psychoanalysis

[0768] The server analyzes the results of the interview, evaluates the user's mental health, and determines whether they are at risk of developing a mental illness. The emotion engine recognizes the user's emotional state from their responses and reflects this in the analysis results.

[0769] 7. Providing AI counseling

[0770] The server starts counseling the user through a chatbot, offering advice such as, "Is there anything that's been bothering you lately, either at work or in your personal life?" The server then provides more appropriate counseling based on the emotional state recognized by the emotion engine.

[0771] This allows users to have a more accurate understanding of their own mental health and take appropriate and prompt action.

[0772] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0773] Step 1: Data collection

[0774] The device (smartwatch or fitness tracker) collects the user's biometric data. The specific operation is as follows: the input is the user's activity and state, and the output is the collected data.

[0775] The smartwatch measures the user's heart rate in real time.

[0776] A fitness tracker counts the user's steps and calculates the distance walked and the calories burned.

[0777] These devices record sleep duration and analyze whether the user spends more time in light or deep sleep.

[0778] Step 2: Send data

[0779] The terminal (smartphone) sends the collected data to the server. The specific operation is as follows: the input is the collected biometric data, and the output is the data sent to the server.

[0780] Smartwatches and fitness trackers use Bluetooth to transmit data to your smartphone.

[0781] The smartphone uses Wi-Fi or 4G / 5G to upload data collected from IoT devices to a server at regular intervals.

[0782] This happens automatically in the background, without any user intervention.

[0783] Step 3: Data analysis

[0784] The server analyzes the received data using a generative AI model. The specific operation is as follows: The input is the biometric data received by the server, and the output is the analysis result.

[0785] The server stores the received lifestyle and activity data in a database.

[0786] By comparing with past data, it detects sudden changes in the user's activity level and sleep patterns.

[0787] Statistical analysis is performed using programming languages ​​such as Python and R to extract anomalies.

[0788] Step 4: Anomaly detection notification

[0789] If the server detects an anomaly, it sends a notification to the user based on the result. The specific operation is as follows: The input is the analysis result, and the output is an anomaly notification to the user.

[0790] If there is a sudden decrease in activity or a significant change in sleep patterns, the information is sent to a notification service on the server.

[0791] The server sends a push notification or email to the user saying, "Your activity level has decreased recently. Please answer the web questionnaire to confirm."

[0792] Step 5: Conducting an online questionnaire

[0793] The user checks the notification and answers the web questionnaire. The specific operation is as follows: The input is the notification received by the user, and the output is the questionnaire result.

[0794] Check your smartphone for the notification and tap the link to access the medical questionnaire form in your web browser.

[0795] The form asks questions about your current mood, stress level, appetite, sleep quality, and more.

[0796] The user answers each question and clicks a "Submit" button to send the data to the server.

[0797] Step 6: Psychoanalysis

[0798] The server receives the user's web questionnaire results and performs psychological analysis using the generative AI model and emotion engine. The specific operation is as follows: The input is the web questionnaire results, and the output is the analysis result of the mental state.

[0799] The interview results are analyzed in text format, and an emotion engine models the user's emotional state.

[0800] The analysis model uses natural language processing (NLP) technology to extract keywords and emotions from user responses. For example, if a response contains negative words such as "sad" or "tired," this will be reflected in the analysis results.

[0801] The final analysis results will determine whether or not the individual is at risk of developing a mental illness.

[0802] Step 7: Providing AI counseling

[0803] If the server determines that the user is at risk of developing a mental illness, it will provide AI counseling. The specific operation is as follows: the input is the results of psychoanalysis, and the output is the implementation of counseling.

[0804] The server starts the chatbot and starts counseling the user.

[0805] Using a generative AI model, the system engages in dialogue based on the user's current emotional state. For example, it generates questions such as, "Is there anything that's been bothering you lately, either at work or in your personal life?"

[0806] An emotion engine analyzes the user's responses and provides advice on stress management and mood improvement.

[0807] (Application example 2)

[0808] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0809] Conventional health management systems can collect data on a user's daily rhythm and activity and manage some aspects of their mental state, but they lack the ability to recommend personalized content based on this data. They also face challenges in accurately assessing mental state and providing prompt counseling. In particular, more advanced analysis and personalization are needed to provide appropriate relaxation content and entertainment during times of high stress.

[0810] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 2 is realized by the following means.

[0811] In this invention, the server includes means for collecting user's life rhythm and activity data from IoT devices, means for transmitting the data to the server via the user's information terminal, means for analyzing the data in the server and detecting disruption of the user's life rhythm or decline in activity, means for the server to send a link to an online questionnaire to the user based on the detection results, means for receiving the web questionnaire results from the user, performing psychoanalysis, and determining whether the user is at risk of a mental disorder, means for providing AI counseling based on the determination results, and means for the server to recommend personalized content based on the user's mental state. This makes it possible to accurately manage the user's mental state, provide appropriate and prompt counseling, and recommend personalized content according to the user's mental state.

[0812] An "IoT device" is a device that collects data on a user's daily rhythm and activity, and includes wearable devices such as smartwatches and fitness trackers.

[0813] "User's information terminal" refers to a device, such as a smartphone or tablet, that transmits data collected from IoT devices to a server.

[0814] The "server" is a computer system that analyzes the user's daily rhythm and activity data, evaluates their mental state, and provides counseling.

[0815] "Disruption of daily rhythm" refers to a significant change in normal life patterns, including lack of sleep and abnormal increases or decreases in the amount of exercise.

[0816] "Decreased activity" refers to a significant decrease in a user's normal activity level, including a decrease in the number of steps taken or a sudden decrease in the amount of exercise.

[0817] "Web questionnaire" refers to a series of questions or surveys administered via the Internet to understand the user's mental state.

[0818] "Psychoanalysis" refers to the process of evaluating and analyzing a user's mental state using a generative AI model based on the results of a user's online questionnaire and collected data.

[0819] "Pre-mental illness" refers to a state in which a user is judged to be at high risk of developing mental problems.

[0820] "AI counseling" is a counseling service provided to users through a chatbot, and is provided using a generative AI model.

[0821] "Personalized content" refers to content that is individually recommended based on the user's mental state, and includes, in particular, relaxation music and entertainment content for relaxation.

[0822] A "generative AI model" is an artificial intelligence model trained on large amounts of data and used to analyze the user's mental state.

[0823] A "prompt" refers to an instruction sentence that recommends content appropriate to the user based on the mental state assessment results of the generative AI model.

[0824] The present invention configures a system that includes an IoT device, a user's information terminal, and a server, and by combining these systems with a generative AI model and an emotion engine, it monitors the user's daily rhythm and activity data and manages their mental health with high accuracy.

[0825] Data collection

[0826] The server collects data on users' daily rhythms and activities from IoT devices. Specifically, wearable devices such as smartwatches and fitness trackers record data such as heart rate, number of steps, and sleep time in real time. As users wear these devices on a daily basis, data is collected periodically.

[0827] Data transmission

[0828] The user's information terminal (such as a smartphone) periodically transmits data collected from IoT devices to a server. Data transmission is performed using wireless communication methods such as Bluetooth or Wi-Fi, and the data is automatically uploaded from the smartphone to the server at specific intervals.

[0829] Data analysis

[0830] The server receives the transmitted data and uses the generative AI model to analyze the lifestyle and activity data. This analysis compares it with past data to detect, for example, a sudden decrease in activity or a change in sleep pattern. Specifically, it analyzes data from the past week for a decrease in activity or abnormal sleep patterns to detect disruptions to lifestyle and a decline in activity.

[0831] Anomaly detection notification

[0832] If the server detects an abnormality, it will send a notification to the user based on the results. The notification will be sent to the user's information device via push notification or email, and will display a message such as, "Your activity level has decreased recently. Please answer the online questionnaire to confirm."

[0833] Conducting online questionnaires

[0834] Users receive a notification and answer the online questionnaire. They click the link in the notification on their information device to access the questionnaire form in a web browser. The questionnaire form contains a wide range of questions, including about their current mood, stress level, and sleep quality.

[0835] psychoanalysis

[0836] The server receives the user's web questionnaire results and performs a psychoanalysis using the generative AI model and emotion engine. Based on the analysis results, it determines whether the user is at risk of developing a mental illness. This emotion engine analyzes the text data and voice data extracted from the user's questionnaire responses to recognize the user's emotional state. This improves the accuracy of the analysis results.

[0837] Personalized content recommendations

[0838] The server recommends personalized content based on the user's mental state. Depending on the user's mental state, content such as relaxing music, relaxing movies, and podcasts is presented. For example, if the user's stress level is determined to be high, relaxing music or a relaxing movie is recommended. An example of a prompt provided by the generative AI model is, "Please rate the user's mental state based on the following data: heart rate: 60-70 BPM, steps: 5000-6000 steps, sleep time: 6.5 hours, and online questionnaire response: I've been feeling stressed recently."

[0839] Providing AI counseling

[0840] If the server determines that the user is at risk of developing a mental illness, it provides AI counseling. The server then activates a chatbot and provides counseling to the user. The chatbot converses with the user based on the emotional state recognized by the emotion engine, and provides advice on stress factors and how to manage mood. It provides appropriate counseling based on the user's current situation, such as asking questions like, "Is there anything that's been bothering you recently at work or in your personal life?"

[0841] In this way, by implementing the system of the present invention, which combines a generative AI model and an emotion engine, users can more accurately understand their own mental state and take appropriate and prompt action. Furthermore, by recommending personalized content according to their mental state, it is expected that the quality of their daily lives will be improved.

[0842] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0843] Step 1:

[0844] The server collects data on users' daily rhythms and activities from IoT devices. Specifically, it receives data such as heart rate, number of steps, and sleep time obtained from smartwatches and fitness trackers via the Internet. This data is sent to the server in CSV or JSON format. Because the collected data also includes date and time information, it is generally stored in log format.

[0845] Input: Life rhythm and activity data from IoT devices (heart rate, steps, sleep time, etc.)

[0846] Output: Raw data recorded in a database on the server

[0847] Step 2:

[0848] The terminal periodically transmits data collected from IoT devices to the server. Data transmission is performed using wireless communication such as Bluetooth or Wi-Fi. The transmission interval can be changed by setting, and data is automatically uploaded once a day or every hour.

[0849] Input: Raw data collected from IoT devices

[0850] Output: Life rhythm and activity data uploaded to the server

[0851] Step 3:

[0852] The server analyzes the received data. Specifically, it uses a generative AI model to compare it with past data and analyze daily rhythms and activity patterns. For example, it applies an anomaly detection algorithm to detect decreased activity or abnormal sleep patterns from data from the past week. It also uses statistical methods to identify outliers.

[0853] Input: Life rhythm and activity data uploaded to the server

[0854] Output: Abnormal pattern detection results (decreased activity, abnormal sleep patterns, etc.)

[0855] Step 4:

[0856] If the server detects an abnormality based on the analysis results, it will send a notification to the user. This notification will be sent to the user's device, such as a smartphone or tablet, as a push notification or email. The notification content will include a message such as, "Your activity level has decreased recently. Please answer the online questionnaire to confirm."

[0857] Input: Abnormal pattern detection results

[0858] Output: An error notification message sent to the user's device

[0859] Step 5:

[0860] The user receives a notification on their device and answers the web questionnaire. They click on the link in the notification to access the questionnaire form in their web browser. The questionnaire form contains a wide range of questions, such as their current mood, stress level, and sleep quality. The user's answers are sent to the server in real time.

[0861] Input: Notification message, user's answers to the medical questionnaire

[0862] Output: Web questionnaire results saved on the server

[0863] Step 6:

[0864] The server receives the results of the web questionnaire and performs a psychological analysis using the generative AI model and emotion engine. Based on the analysis results, it determines whether the user is at risk of developing a mental illness. At this time, the text data and voice data from the questionnaire form are analyzed. An example of a prompt provided by the generative AI model is, "Please assess the user's mental state based on the following data: heart rate: 60-70 BPM, steps: 5000-6000, sleep time: 6.5 hours, web questionnaire response: I've been feeling stressed recently."

[0865] Input: Web questionnaire results (text data, voice data)

[0866] Output: Psychoanalysis result (whether or not the individual is at risk of developing a mental illness)

[0867] Step 7:

[0868] The server then recommends optimal content to the user based on the results of the mental state assessment. In particular, it presents relaxation music, distracting movies, podcasts, etc., depending on the user's emotional state. For example, if the stress level is determined to be high, it will recommend relaxation music. In addition, personalized content is recommended based on prompts provided by the generative AI model.

[0869] Input: Psychoanalysis result

[0870] Output: Personalized content recommendations displayed on the user's device

[0871] Step 8:

[0872] If the server determines that the user is at risk of developing a mental illness, it provides AI counseling. The server then activates a chatbot and provides counseling to the user. Based on the emotional state recognized by the emotion engine, the chatbot engages in a dialogue with the user and provides advice on stress factors and how to manage mood. An example of counseling is an exchange such as, "Is there anything that's been bothering you lately, at work or in your personal life?"

[0873] Input: Psychoanalysis result

[0874] Output: Counseling content provided by AI chatbot

[0875] 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 a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0876] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0877] In the above embodiment, an example in which the specific processing is performed by the data processing device 12 has been given, but the technology of the present disclosure is not limited to this, and the specific processing may be performed by the smart glasses 214.

[0878] [Third embodiment]

[0879] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0880] 5, the data processing system 310 includes the data processing device 12 and a headset type terminal 314. An example of the data processing device 12 is a server.

[0881] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0882] The headset type terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a display 343. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the display 343 are also connected to the bus 52.

[0883] The microphone 238 receives instructions and the like from the user 20 by receiving voice uttered by the user 20. The microphone 238 captures the voice uttered by the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio in accordance with instructions from the processor 46.

[0884] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the surroundings of user 20 (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0885] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 control the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0886] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset type terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0887] The specific processing program 56 is an example of a "program" according to the technology of the present 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.

[0888] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0889] In the headset type terminal 314, a reception output process is performed by the processor 46. A reception output program 60 is stored in the storage 50. 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 process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[0890] Next, a description will be given of the identification process performed by the identification processing unit 290 of the data processing device 12. 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."

[0891] This invention configures a system including an IoT device, a user terminal, and a server, and by implementing the means described below, monitors the user's daily rhythm and activity data and effectively manages their mental health.

[0892] Data collection

[0893] The terminal collects the user's daily rhythm and activity data from IoT devices. Specifically, IoT devices such as smartwatches and fitness trackers record data such as heart rate, number of steps, and sleep time in real time. As users wear these devices on a daily basis, data is collected periodically.

[0894] Data transmission

[0895] A terminal (e.g., a smartphone) periodically transmits data collected from IoT devices to a server. Data transmission is performed using wireless communication methods such as Bluetooth or Wi-Fi, and is set so that data is automatically uploaded from the user's smartphone to the server at specific intervals.

[0896] Data analysis

[0897] The server receives the transmitted data and uses AI models to analyze the rhythm and activity data. As part of the analysis, anomaly detection algorithms are applied to detect unusual patterns or disruptions to rhythm, such as a sudden drop in activity compared to normal activity or a significant change in sleep duration.

[0898] Anomaly detection notification

[0899] If the server detects an abnormality, it includes a means to send a notification to the user based on the results. Specifically, the server sends a push notification or email to the user's smartphone to inform them that an abnormality has been detected in their daily rhythm. A message such as "Your activity level has decreased recently. Please answer the online questionnaire to confirm this" is sent.

[0900] Conducting online questionnaires

[0901] The user receives a notification and answers the online questionnaire. The user clicks on the link in the notification using their smartphone to access the questionnaire form in a web browser. The questionnaire form contains a wide range of questions, and the user reports their current mental health status by answering them.

[0902] psychoanalysis

[0903] The server receives the user's responses to the online questionnaire and performs a psychological analysis. This involves using an AI model to analyze the questionnaire results and determine whether the user is at risk of depression. For example, the responses can be used to detect signs of depression or loss of interest.

[0904] Providing AI counseling

[0905] If the server determines that the user is at risk of developing depression, it provides AI counseling. The server then activates a chatbot and provides counseling to the user. Through dialogue with the user, the chatbot provides advice on stress factors and how to manage mood. It asks questions such as, "Is there anything that's been bothering you lately at work or in your personal life?" and provides counseling tailored to the user's situation.

[0906] Specific examples

[0907] Scenario: A user

[0908] 1. Data Collection

[0909] The device (smartwatch) records the user's heart rate, sleep time, and exercise volume throughout the day.

[0910] 2. Data Transmission

[0911] The device (smartphone) automatically collects data at night and sends it to the server.

[0912] 3. Data Analysis

[0913] The server analyzes the received data and detects a significant decrease in exercise volume from the data from the past week.

[0914] 4. Anomaly detection notification

[0915] The server detects the abnormality and sends the user a notification saying, "Your activity has decreased recently. Please answer the online questionnaire to confirm."

[0916] 5. Conducting an online questionnaire

[0917] The user checks the notification, clicks the link to the online medical questionnaire, and answers the medical questionnaire form.

[0918] 6. Psychoanalysis

[0919] The server analyzes the results of the interview, evaluates the user's mental health status, and determines whether the user is at risk of developing depression.

[0920] 7. Providing AI counseling

[0921] The server begins counseling the user through a chatbot, offering advice such as, "Is there anything that's been bothering you lately, either at work or in your personal life?"

[0922] In this way, by implementing the present invention, users can recognize mental health problems early on and take appropriate measures.

[0923] The processing flow will be explained below.

[0924] Step 1:

[0925] The device (smartwatch) records the user's activity data, such as heart rate, sleep time, and exercise volume, in real time. This data is temporarily stored in the internal memory.

[0926] Step 2:

[0927] The device (smartwatch) transmits data to the smartphone via Bluetooth, including heart rate, sleep time, exercise volume, as well as date and time information.

[0928] Step 3:

[0929] The device (smartphone) automatically sends this data to the server at regular intervals via Wi-Fi or mobile data communication.

[0930] Step 4:

[0931] The server receives the data sent from the user's smartphone, stores it in a database, and passes it to an AI model for analysis.

[0932] Step 5:

[0933] The server analyzes the received data and uses an AI model to detect disruptions to daily rhythms and declines in activity, for example, by assessing the degree to which a person's daily rhythm has deviated from normal based on the amount of exercise, heart rate, and sleep patterns over the past week.

[0934] Step 6:

[0935] If the server detects an abnormality, it sends a notification to the user's smartphone. The notification contains a message stating that a disruption in daily rhythm or a decrease in activity level has been detected, along with a request to "answer a web questionnaire to confirm the status."

[0936] Step 7:

[0937] The user receives a notification on their smartphone and clicks on a link to access a web questionnaire that includes questions about their current mood, stress level, sleep quality, and more.

[0938] Step 8:

[0939] The user answers the questionnaire form and presses the send button. The user's answers are sent to the server via the Internet.

[0940] Step 9:

[0941] The server receives the results of the medical interview sent by the user and performs a psychological analysis using an AI model. Based on the analysis results, it determines whether the user is at risk of developing depression.

[0942] Step 10:

[0943] If the server determines that the individual is at risk of developing depression, it will launch a chatbot as a means of providing AI counseling.

[0944] Step 11:

[0945] The server (chatbot) begins counseling by asking the user questions such as, "Is there anything that's been bothering you lately, either at work or in your personal life?" The chatbot then provides appropriate advice and support based on the user's answers.

[0946] Through this series of steps, users can understand their own mental health at an early stage and take the necessary measures quickly.

[0947] Example 1

[0948] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[0949] In modern society, mental health problems caused by stress and poor lifestyle habits are on the rise. In response to this, there is a need to manage users' mental states by detecting abnormalities early and providing appropriate counseling, thereby preventing and improving mental illnesses such as depression. However, conventional systems have been unable to provide sufficiently effective measures because the processes of data collection, analysis, notification, and counseling are fragmented and manual. The objective of this invention is to provide a system that utilizes IoT devices and AI technology to automatically and continuously monitor users' mental health states and take appropriate measures early.

[0950] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[0951] In this invention, the server includes means for collecting user life rhythm and activity data from IoT devices, means for transmitting the data to the server via the user's terminal, means for analyzing the data in the server and detecting disruption of the user's life rhythm or decline in activity using an anomaly detection algorithm, means for the server to send a link to an online questionnaire to the user based on the detection results, means for receiving the web questionnaire results from the user and performing psychoanalysis using an AI model to determine whether the user is at risk of depression, and means for providing counseling to the user using an AI counseling function based on the determination results. This makes it possible to detect abnormalities in the user's life rhythm and activity early and provide professional counseling in a timely manner.

[0952] An "IoT device" is a device that has the ability to collect and transmit data via the Internet, and specifically includes smartwatches and fitness trackers.

[0953] A "user device" is an electronic device that a user uses on a daily basis and has functions such as collecting and sending data and receiving notifications. Specifically, this applies to smartphones and tablets.

[0954] A "server" is a computer system that provides services to multiple terminals via a network, and receives, analyzes, stores, and manages notifications of data.

[0955] "Life rhythm" refers to the activity patterns in the user's daily life, such as sleep time, meal time, and exercise time.

[0956] "Activity data" refers to data related to the user's physical activity, including the number of steps taken, heart rate, and exercise intensity.

[0957] An "anomaly detection algorithm" is a computational method for detecting abnormal data that deviates from normal data patterns, and uses machine learning and statistical methods.

[0958] "Web questionnaire" refers to a user answering questions about their mental and physical state via a web browser.

[0959] An "AI model" is a mathematical model that uses artificial intelligence technology to analyze data and make predictions.

[0960] "Psychoanalysis" is the process of analyzing the results of a user's medical interview and assessing their mental state.

[0961] "Pre-depression" refers to people who show signs of depression but have not yet been definitively diagnosed.

[0962] The "AI counseling function" uses artificial intelligence to provide counseling to users, offering advice on stress management and mood improvement through a chatbot.

[0963] A "chatbot" is a software program that uses natural language processing to interact with users and provide counseling and support.

[0964] A "notification" is an information transmission message sent by a server to a user's device, and can take the form of a push notification or email.

[0965] The system according to the present invention is composed of a combination of IoT devices, a user's terminal, and a server. The main purpose of this system is to monitor the user's daily rhythm and activity data and effectively manage their mental health.

[0966] Data collection

[0967] The devices used are IoT devices such as smartwatches and fitness trackers. These devices record daily life data such as the user's heart rate, number of steps, and sleep time in real time and store the data in their internal memory. When users wear these devices on a daily basis, data is collected periodically.

[0968] Data transmission

[0969] The device (e.g., a smartphone) periodically sends the collected data to a server. Data is transferred from the smartwatch to the smartphone using Bluetooth, and then uploaded to the server using Wi-Fi or mobile data. This process is performed automatically at specific intervals (e.g., every day at 10 p.m.).

[0970] Data analysis

[0971] The server then analyzes the received data using AI models. During this analysis, anomaly detection algorithms are applied to the circadian rhythm and activity data to detect unusual patterns or disruptions to circadian rhythms, such as a sudden drop in activity compared to normal activity levels or significant fluctuations in sleep duration.

[0972] Anomaly detection notification

[0973] When the server detects an abnormality, it sends a notification to the user based on the results. This notification is sent to the user's smartphone as a push notification or email. The content of the notification might be, for example, a message such as, "Your activity level has decreased recently. Please answer the online questionnaire to confirm."

[0974] Conducting online questionnaires

[0975] The user receives the notification and clicks on the link to the online questionnaire, which opens a web browser and accesses the questionnaire form. The questionnaire form contains several questions about the user's physical and mental condition. The user answers the questions and submits the form.

[0976] psychoanalysis

[0977] The server receives the user's responses to the online questionnaire and performs a psychological analysis using an AI model. The analysis uses an algorithm to detect signs of depression from the responses, such as low mood or a lack of interest in daily activities.

[0978] Providing AI counseling

[0979] If the server determines that the user is at risk of depression, it activates an AI counseling function. A chatbot within the server is activated and begins counseling the user. The chatbot uses natural language processing technology to converse with the user and provide advice on stress management and mood improvement.

[0980] (Example)

[0981] Scenario: A user

[0982] 1. Data Collection

[0983] The device (smartwatch) records the user's heart rate, sleep time, and exercise volume throughout the day.

[0984] 2. Data Transmission

[0985] The device (smartphone) automatically collects data at night and sends it to the server.

[0986] 3. Data Analysis

[0987] The server analyzes the received data and detects a significant decrease in exercise volume from the data from the past week.

[0988] 4. Anomaly detection notification

[0989] The server detects the abnormality and sends the user a notification saying, "Your activity has decreased recently. Please answer the online questionnaire to confirm."

[0990] 5. Conducting an online questionnaire

[0991] The user checks the notification, clicks the link to the online medical questionnaire, and answers the medical questionnaire form.

[0992] 6. Psychoanalysis

[0993] The server analyzes the results of the interview, evaluates the user's mental health status, and determines whether the user is at risk of developing depression.

[0994] 7. Providing AI counseling

[0995] The server begins counseling the user through a chatbot, offering advice such as, "Is there anything that's been bothering you lately, either at work or in your personal life?"

[0996] Prompt Sentence Examples

[0997] "Have you been worried about anything recently at work or in your personal life? Please answer a few questions so we can provide you with counseling tailored to your situation."

[0998] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0999] Step 1: Data collection

[1000] The device (smartwatch or fitness tracker) records the user's daily life data, such as heart rate, steps, and sleep time, in real time. Sensors within the device detect physical activity and store it in internal memory. Specifically, when the user wakes up in the morning, the smartwatch records the amount of sleep the previous day and continuously monitors heart rate and steps throughout the day. Input: Raw data from the sensor. Output: Life rhythm and activity data stored in internal memory.

[1001] Step 2: Send data

[1002] The device (smartphone) receives the data collected from the smartwatch via Bluetooth. It then uploads it to the server using Wi-Fi or mobile data communication. This transmission process is carried out automatically at specific intervals (e.g., 10 p.m. every day). Specifically, at 10 p.m., the smartphone app automatically collects data from the smartwatch and sends it to the server. Input: Data transferred from the smartwatch. Output: Data uploaded to the server.

[1003] Step 3: Data analysis

[1004] The server analyzes the received data using an AI model. During this analysis process, an anomaly detection algorithm is applied to the lifestyle and activity data to detect unusual patterns or disruptions to lifestyle rhythms. For example, a week's worth of data can be compiled to identify sudden drops in activity levels or significant fluctuations in sleep duration. Input: Data uploaded to the server. Output: Analysis results (anomaly detection results).

[1005] Step 4: Anomaly detection notification

[1006] If the server detects an abnormality based on the analysis results, it will send a notification to the user. The notification is sent to the smartphone as a push notification or email, and includes a message saying, "Your activity level has decreased recently. Please answer the online questionnaire to confirm." Specifically, if a significant decrease in activity level is detected, the server will automatically generate and send a notification. Input: Data analysis results. Output: Notification sent to the user's smartphone.

[1007] Step 5: Conducting an online questionnaire

[1008] The user receives the notification, clicks the web questionnaire link to open a web browser, and accesses the questionnaire form. The questionnaire form contains multiple questions about the user's physical and mental condition, and the user answers each question and submits the form. Specifically, the user checks the notification on their smartphone, clicks the link to access the questionnaire form in a web browser, and answers the questions. Input: The questionnaire form displayed in the web browser. Output: The submitted questionnaire answers.

[1009] Step 6: Psychoanalysis

[1010] The server receives the user's responses to the web questionnaire and performs a psychoanalysis using an AI model. Signals such as depressed mood and lack of interest in daily activities are analyzed to detect signs of depression from the responses. Specifically, the server collects the questionnaire responses, analyzes them using an AI model, and checks for signs of pre-depression. Input: Questionnaire results. Output: Psychoanalysis results.

[1011] Step 7: Providing AI counseling

[1012] The server provides AI counseling functions to users who are determined to be at risk of depression. A chatbot within the server is activated and begins counseling the user. The chatbot uses natural language processing technology to converse with the user and provide advice on stress management and mood improvement. Specifically, the chatbot asks the user, "Is there anything at work that's been bothering you lately?" and provides counseling appropriate to the situation. Input: Psychoanalysis results. Output: Contents of the counseling conversation.

[1013] (Application example 1)

[1014] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[1015] In recent years, with the spread of IoT devices and smartphones, it has become possible to collect data on users' daily rhythms and activities and manage their mental health. However, conventional systems only monitor users' mental health status and often do not suggest specific actions for lifestyle improvement. In particular, they lack the functionality to suggest meal menus based on the user's health status, and more comprehensive health management is required. Therefore, a system that can improve not only a user's mental health but also their overall health is needed.

[1016] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[1017] In this invention, the server includes means for collecting user's life rhythm and activity data from IoT devices, means for transmitting the data to the server via the user's terminal, means for analyzing the data and detecting disruption of the user's life rhythm or decrease in activity, means for transmitting a link to an online questionnaire to the user based on the detection results, means for receiving the web questionnaire results from the user and performing a psychoanalysis to determine whether the user is at risk of depression, means for providing AI counseling based on the determination results, and means for suggesting an appropriate meal menu based on the user's health condition. This enables specific measures to improve the user's overall health condition as well as their mental health condition.

[1018] An "IoT device" is a physical device that is connected to the Internet and has the ability to collect and communicate data.

[1019] "Life rhythm" refers to the time patterns of various activities in the user's daily life.

[1020] "Activity data" refers to data related to the user's physical activity, such as the amount of exercise, heart rate, and sleep time.

[1021] A "user's terminal" is a device that receives data from an IoT device and sends it to a server, and generally refers to a smartphone.

[1022] A "server" is a computer system for storing, managing, and analyzing data.

[1023] "Data analysis" is the process of detecting specific patterns or anomalies based on collected data.

[1024] "Disruption of daily rhythm" refers to an abnormality in the pattern of normal daily activities.

[1025] "Decreased activity" refers to a state in which the user's physical activity is less than usual.

[1026] "Web medical questionnaire" refers to a medical questionnaire conducted online, and is used to obtain a detailed understanding of a user's health condition.

[1027] "Psychoanalysis" is the assessment of a user's mental health based on collected data and the results of a web questionnaire.

[1028] "Pre-depression" refers to a condition in which symptoms of depression are present but the diagnostic criteria are not yet met.

[1029] "AI counseling" refers to providing counseling services to users using artificial intelligence.

[1030] A "meal menu" is a combination of meals suggested based on your health condition.

[1031] This invention configures a system including an IoT device, a user terminal, and a server, and by implementing the following means, monitors the user's daily rhythm and activity data, effectively managing their mental and overall health.

[1032] 1. Data Collection

[1033] The device collects data on the user's daily rhythm and activity from IoT devices such as smartwatches and fitness trackers, which record data such as heart rate, steps taken, and sleep time in real time. Data is collected periodically as users wear these devices daily.

[1034] 2. Data Transmission

[1035] A terminal (e.g., a smartphone) periodically transmits data collected from IoT devices to a server. Data transmission is performed using wireless communication methods such as Bluetooth or Wi-Fi, and data is uploaded from the user's smartphone to the server at regular intervals.

[1036] 3. Data Analysis

[1037] The server receives the data and uses AI models to analyze the rhythm and activity data. This analysis detects unusual patterns and disruptions to rhythm, such as a sudden drop in activity compared to normal activity levels or significant fluctuations in sleep duration.

[1038] 4. Anomaly detection notification

[1039] If the server detects an abnormality, it will send a notification to the user based on the results. Specifically, the server will send a push notification or email to the user's smartphone to inform them that an abnormality has been detected in their daily rhythm. A message such as "Your activity level has decreased recently. Please answer the online questionnaire to confirm this" will be sent.

[1040] 5. Conducting an online questionnaire

[1041] The user receives a notification and answers the online questionnaire. The user clicks on the link in the notification using their smartphone to access the questionnaire form in a web browser. The questionnaire form contains a wide range of questions, and by answering these questions, the user reports their current mental health status.

[1042] 6. Psychoanalysis

[1043] The server receives the user's responses to the online questionnaire and performs a psychological analysis. This involves using an AI model to analyze the questionnaire results and determine whether the user is at risk of depression. For example, the responses can be used to detect signs of depression or loss of interest.

[1044] 7. Providing AI counseling

[1045] If the server determines that the user is at risk of developing depression, it provides AI counseling. The server then activates a chatbot and provides counseling to the user. Through dialogue with the user, the chatbot provides advice on stress factors and how to manage mood. It asks questions such as, "Is there anything that's been bothering you lately at work or in your personal life?" and provides counseling tailored to the user's situation.

[1046] 8. Meal suggestions based on health status

[1047] The server proposes an appropriate meal menu based on the user's health condition and lifestyle. Specifically, it identifies nutritional deficiencies based on activity data and the results of an online questionnaire, and proposes meal menus to compensate for these. For example, it suggests a menu with a relaxing effect to a user with a high heart rate and lack of sleep.

[1048] Specific examples

[1049] For example, if a user's recent activity data shows that their physical activity has decreased significantly and their sleep schedule has become irregular, the server will make the following food delivery suggestions to the user:

[1050] "Recently, you've been showing signs of less exercise and sleep deprivation. To boost your immune system, I recommend a salad rich in vitamin C or a smoothie made with antioxidant-rich berries."

[1051] Example prompts to input to a generative AI model:

[1052] "Please provide appropriate advice to suggest meal menus to users who have recently decreased their exercise and have irregular sleep schedules."

[1053] The hardware used is a smartwatch (Apple Watch, Fitbit) or smartphone (iPhone, Android device), and the server uses a cloud service such as AWS or GCP. AI models such as TensorFlow or PyTorch are used for analysis, and MySQL or PostgreSQL is suitable for the database.

[1054] By taking these measures, users can receive comprehensive health management.

[1055] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1056] Step 1:

[1057] The device collects user's daily rhythm and activity data (heart rate, number of steps, sleep time, etc.) from IoT devices such as smartwatches and fitness trackers. This data is acquired in real time and stored in a local database.

[1058] Input: Sensor data from IoT devices.

[1059] Output: User's daily rhythm and activity data stored in a local database.

[1060] Step 2:

[1061] The device (smartphone) periodically sends collected data to the server using Bluetooth or Wi-Fi. The timing of the transmission is set for each interval.

[1062] Input: Circadian rhythm and activity data stored in a local database.

[1063] Output: Data uploaded to the server.

[1064] Step 3:

[1065] The server analyzes the received data and uses AI models to detect disruptions in daily rhythms and declines in activity, thereby identifying abnormal patterns.

[1066] Input: Life rhythm and activity data uploaded to the server.

[1067] Output: Anomaly detection results.

[1068] Step 4:

[1069] The server sends a push notification to the user's device based on the anomaly detection result, which includes a link to the online questionnaire.

[1070] Input: Anomaly detection results.

[1071] Output: The push notification sent to the user's device.

[1072] Step 5:

[1073] The user receives the notification and clicks on the link to the online questionnaire, which opens a web browser and allows the user to access the questionnaire form and answer each question.

[1074] Input: Web questionnaire link in the push notification.

[1075] Output: User's questionnaire response data.

[1076] Step 6:

[1077] The server receives the results of the web questionnaire from the user and performs a psychological analysis using an AI model to determine whether the user is at risk of developing depression.

[1078] Input: Web questionnaire result data.

[1079] Output: psychoanalysis results.

[1080] Step 7:

[1081] If the server determines that the user is at risk of developing depression, it will launch a chatbot to provide AI counseling. The chatbot will then provide advice in a dialogue format with the user.

[1082] Input: Psychoanalysis results.

[1083] Output: Counseling content via chatbot.

[1084] Step 8:

[1085] The server then proposes an appropriate meal plan based on the user's health status, including nutritional analysis based on activity data and online questionnaire results, and notifies the user's food delivery app of the proposed plan.

[1086] Input: Activity data and web questionnaire results.

[1087] Output: Meal suggestions notified to the user.

[1088] Through these steps, the entire system functions to comprehensively manage and improve the user's lifestyle and health.

[1089] Furthermore, an emotion engine that estimates the user's emotion may be further combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59, and perform identification processing using the user's emotion.

[1090] This invention configures a system that includes an IoT device, a user terminal, and a server, and by combining these systems with an emotion engine, it monitors the user's daily rhythm and activity data, and also manages their mental health with high accuracy.

[1091] Data collection

[1092] The terminal collects data on the user's daily rhythm and activity from IoT devices. Specifically, smartwatches and fitness trackers record data such as heart rate, number of steps, and sleep time in real time. As users wear these devices on a daily basis, data is collected periodically.

[1093] Data transmission

[1094] The terminal (smartphone) periodically sends data collected from IoT devices to a server. Data transmission is performed using wireless communication methods such as Bluetooth or Wi-Fi, and is set to automatically upload data from the smartphone to the server at specific intervals.

[1095] Data analysis

[1096] The server receives the transmitted data and uses AI models to analyze the rhythm and activity data, comparing it with past data to detect, for example, a sudden drop in activity or a change in sleep patterns.

[1097] Anomaly detection notification

[1098] If the server detects an abnormality, it includes a means for sending a notification to the user based on the results. The notification is sent to the smartphone via push notification or email, and displays a message such as "Your activity level has decreased recently. Please answer the online questionnaire to confirm."

[1099] Conducting online questionnaires

[1100] The user receives a notification and answers the online questionnaire. The user clicks on the link in the notification on their smartphone to access the questionnaire form in a web browser. The questionnaire form contains a wide range of questions, such as current mood, stress level, and sleep quality.

[1101] psychoanalysis

[1102] The server receives the user's web questionnaire results and performs a psychological analysis using an AI model and emotion engine. Based on the analysis results, it determines whether the user is at risk of developing depression. This emotion engine analyzes the text data and voice data extracted from the user's questionnaire responses to recognize the user's emotional state. This improves the accuracy of the analysis results.

[1103] Providing AI counseling

[1104] If the server determines that the user is at risk of developing depression, it provides AI counseling. The server then activates a chatbot and provides counseling to the user. The chatbot converses with the user based on the emotional state recognized by the emotion engine, and provides advice on stress factors and how to manage mood. It provides appropriate counseling according to the user's current situation, such as asking questions like, "Is there anything that's bothering you recently at work or in your personal life?"

[1105] Specific examples

[1106] Scenario: A user

[1107] 1. Data Collection

[1108] The device (smartwatch) records the user's heart rate, sleep time, and exercise volume throughout the day.

[1109] 2. Data Transmission

[1110] The device (smartphone) automatically collects data at night and sends it to the server.

[1111] 3. Data Analysis

[1112] The server analyzes the received data and detects a significant decrease in exercise volume from the data from the past week.

[1113] 4. Anomaly detection notification

[1114] The server detects the abnormality and sends the user a notification saying, "Your activity has decreased recently. Please answer the online questionnaire to confirm."

[1115] 5. Conducting an online questionnaire

[1116] The user checks the notification, clicks the link to the online medical questionnaire, and answers the medical questionnaire form.

[1117] 6. Psychoanalysis

[1118] The server analyzes the results of the interview, evaluates the user's mental health, and determines whether they are at risk of developing depression. The emotion engine recognizes the user's emotional state from their responses and reflects this in the analysis results.

[1119] 7. Providing AI counseling

[1120] The server starts counseling the user through a chatbot, offering advice such as, "Is there anything that's been bothering you lately, either at work or in your personal life?" The server then provides more appropriate counseling based on the emotional state recognized by the emotion engine.

[1121] In this way, by implementing the system of the present invention in combination with an emotion engine, users can more accurately understand their own mental health and take appropriate and prompt measures.

[1122] The processing flow will be explained below.

[1123] Step 1:

[1124] The device (smartwatch) records the user's activity data, such as heart rate, sleep time, and exercise volume, in real time. This data is stored in the internal memory.

[1125] Step 2:

[1126] The device (smartwatch) transmits the collected data via Bluetooth to a smartphone at regular intervals, including heart rate, sleep time, exercise volume, date, and time.

[1127] Step 3:

[1128] The device (smartphone) transmits the data received via Bluetooth to the server via Wi-Fi or mobile data communication at regular intervals.

[1129] Step 4:

[1130] The server receives the data sent from the user's smartphone and stores it in a database, which is then passed to the AI ​​model.

[1131] Step 5:

[1132] The server uses AI models to analyze the incoming data and compare it with past data to detect changes or anomalies in daily rhythms, such as a sudden drop in normal activity or a significant deviation from normal sleep patterns.

[1133] Step 6:

[1134] If the server detects an abnormality, such as a disruption in daily rhythm or a decrease in activity, it will send a notification to the user. The notification will be sent via push notification or email to the smartphone and will include a message such as, "Your activity level has decreased recently. Please answer the online questionnaire to confirm."

[1135] Step 7:

[1136] The user sees the notification on their smartphone and clicks on a link to access a web questionnaire, which includes questions about their current mood, stress level, sleep quality, loss of interest, etc.

[1137] Step 8:

[1138] The user fills in the medical interview form and presses the send button to send the results of the medical interview to the server. The results of the medical interview are sent to the server via the Internet.

[1139] Step 9:

[1140] The server receives the user's web questionnaire results and performs psychological analysis using an AI model and emotion engine. The emotion engine analyzes the user's text and voice data to recognize their emotional state, resulting in a more accurate mental health assessment.

[1141] Step 10:

[1142] The server determines whether the user is at risk of depression based on the psychoanalysis results. If the user is at risk of depression, the AI ​​counseling process begins.

[1143] Step 11:

[1144] The server launches a chatbot to provide AI counseling. The chatbot provides counseling based on the user's emotional state. For example, it asks questions such as, "Is there anything bothering you recently at work or in your personal life?"

[1145] Step 12:

[1146] The server (chatbot) provides appropriate advice and support based on the user's responses. For example, it provides advice on stress management and relaxation. Furthermore, if the user's emotional state changes due to the emotion engine, the content of the counseling will be adjusted accordingly.

[1147] Through this series of steps, the system can continuously monitor the user's daily rhythm and emotional state, detect mental health problems early, and take appropriate measures.

[1148] Example 2

[1149] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[1150] In recent years, the risk of mental illness has increased due to disruptions in daily rhythms and reduced activity levels. However, there are limited systems that can detect these fluctuations early and provide appropriate responses. Conventional technologies have not only been able to collect and analyze users' biometric data, but also have struggled to comprehensively manage their mental health. In other words, there is a need for the development of a system that can quickly and accurately assess a user's mental state and provide appropriate counseling.

[1151] The identification process by the identification processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means. In this invention, the server includes means for collecting biometric data of a user from an IoT device, means for transmitting the data to the server via the user's terminal, and means for analyzing the data in the server and detecting a disturbance in biorhythm or a decrease in activity level. This makes it possible to quickly and accurately detect changes in the user's mental state through the collection and analysis of the user's biometric data and take appropriate measures.

[1152] Furthermore, the server includes a means for using a generative AI model to analyze the data and a means for using an emotion engine to analyze the emotional state from the results of the web questionnaire, which enables detailed analysis of the user's lifestyle, activity fluctuations, and emotional state, realizing highly accurate psychoanalysis and early detection of pre-depression.

[1153] The server also includes a means for providing AI counseling based on the results of the assessment, which enables counseling using an emotion engine to generate dialogue suited to the user, thereby effectively supporting the user's mental health.

[1154] An "IoT device" is a physical device that is connected to the Internet and is capable of collecting and transmitting data.

[1155] "Biometric data" refers to data related to the user's daily rhythm and activity, such as heart rate, number of steps, and sleep time.

[1156] A "terminal" is a device used by a user to send data collected from an IoT device to a server, and specifically refers to a smartphone or tablet.

[1157] A "server" is a computer system that receives, analyzes, and stores collected data.

[1158] A "generative AI model" is an artificial intelligence model for analyzing data and recognizing patterns.

[1159] An "emotion engine" is software that analyzes a user's text and voice data to recognize their emotional state.

[1160] A "Web questionnaire" is a questionnaire format for assessing mental health status by having users enter answers using an online form.

[1161] "Psychoanalysis" is the process of analyzing collected data and interview results to assess the user's mental state.

[1162] "Pre-mental illness" refers to users who are beginning to show mild symptoms of a mental illness, but have not yet been diagnosed.

[1163] "AI counseling" is the process of using artificial intelligence to provide psychological support and advice to users.

[1164] A "chatbot" is a program, usually an automated response system, that interacts with users in the form of messages.

[1165] This invention configures a system that includes an IoT device, a user terminal, and a server, and by combining these systems with an emotion engine, it monitors the user's daily rhythm and activity data, and also manages their mental health with high accuracy.

[1166] Specifically, the following steps are carried out.

[1167] Data collection

[1168] The terminal collects the user's biometric data from IoT devices. Specifically, smartwatches and fitness trackers record data such as heart rate, steps, and sleep time in real time. As users wear these devices on a daily basis, data is collected periodically.

[1169] Data transmission

[1170] The terminal (smartphone) periodically transmits data collected from IoT devices to a server. This data transmission is performed using wireless communication methods such as Bluetooth or Wi-Fi, and is set so that data is automatically uploaded from the smartphone to the server at specific intervals.

[1171] Data analysis

[1172] The server receives the transmitted data and uses a generative AI model to analyze the rhythm and activity data, comparing it with past data to detect sudden declines in activity or changes in sleep patterns.

[1173] Anomaly detection notification

[1174] If the server detects an abnormality, it includes a means for sending a notification to the user based on the results. The notification is sent to the smartphone via push notification or email, and displays a message such as "Your activity level has decreased recently. Please answer the online questionnaire to confirm."

[1175] Conducting online questionnaires

[1176] The user receives a notification and answers the online questionnaire. The user clicks on the link in the notification on their smartphone to access the questionnaire form in a web browser. The questionnaire form contains a wide range of questions, such as current mood, stress level, and sleep quality.

[1177] psychoanalysis

[1178] The server receives the user's web questionnaire results and performs a psychological analysis using the generative AI model and emotion engine. Based on the analysis results, it determines whether the user is at risk of developing a mental illness. This emotion engine analyzes the text data and voice data extracted from the user's questionnaire responses to recognize the user's emotional state. This improves the accuracy of the analysis results.

[1179] Providing AI counseling

[1180] If the server determines that the user is at risk of developing a mental illness, it provides AI counseling. The server activates a chatbot and provides counseling to the user. The chatbot converses with the user based on the emotional state recognized by the emotion engine, and provides advice on stress factors and how to manage mood. It provides appropriate counseling according to the user's current situation, such as asking questions like, "Is there anything that's been bothering you recently at work or in your personal life?"

[1181] Specific examples

[1182] Scenario: A user

[1183] 1. Data Collection

[1184] The device (smartwatch) records the user's heart rate, sleep time, and exercise volume throughout the day.

[1185] 2. Data Transmission

[1186] The device (smartphone) automatically collects data at night and sends it to the server.

[1187] 3. Data Analysis

[1188] The server analyzes the received data and detects a significant decrease in exercise volume from the data from the past week.

[1189] 4. Anomaly detection notification

[1190] The server detects the abnormality and sends the user a notification saying, "Your activity has decreased recently. Please answer the online questionnaire to confirm."

[1191] 5. Conducting an online questionnaire

[1192] The user checks the notification, clicks the link to the online medical questionnaire, and answers the medical questionnaire form.

[1193] 6. Psychoanalysis

[1194] The server analyzes the results of the interview, evaluates the user's mental health, and determines whether they are at risk of developing a mental illness. The emotion engine recognizes the user's emotional state from their responses and reflects this in the analysis results.

[1195] 7. Providing AI counseling

[1196] The server starts counseling the user through a chatbot, offering advice such as, "Is there anything that's been bothering you lately, either at work or in your personal life?" The server then provides more appropriate counseling based on the emotional state recognized by the emotion engine.

[1197] This allows users to have a more accurate understanding of their own mental health and take appropriate and prompt action.

[1198] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1199] Step 1: Data collection

[1200] The device (smartwatch or fitness tracker) collects the user's biometric data. The specific operation is as follows: the input is the user's activity and state, and the output is the collected data.

[1201] The smartwatch measures the user's heart rate in real time.

[1202] A fitness tracker counts the user's steps and calculates the distance walked and the calories burned.

[1203] These devices record sleep duration and analyze whether the user spends more time in light or deep sleep.

[1204] Step 2: Send data

[1205] The terminal (smartphone) sends the collected data to the server. The specific operation is as follows: the input is the collected biometric data, and the output is the data sent to the server.

[1206] Smartwatches and fitness trackers use Bluetooth to transmit data to your smartphone.

[1207] The smartphone uses Wi-Fi or 4G / 5G to upload data collected from IoT devices to a server at regular intervals.

[1208] This happens automatically in the background, without any user intervention.

[1209] Step 3: Data analysis

[1210] The server analyzes the received data using a generative AI model. The specific operation is as follows: The input is the biometric data received by the server, and the output is the analysis result.

[1211] The server stores the received lifestyle and activity data in a database.

[1212] By comparing with past data, it detects sudden changes in the user's activity level and sleep patterns.

[1213] Statistical analysis is performed using programming languages ​​such as Python and R to extract anomalies.

[1214] Step 4: Anomaly detection notification

[1215] If the server detects an anomaly, it sends a notification to the user based on the result. The specific operation is as follows: The input is the analysis result, and the output is an anomaly notification to the user.

[1216] If there is a sudden decrease in activity or a significant change in sleep patterns, the information is sent to a notification service on the server.

[1217] The server sends a push notification or email to the user saying, "Your activity level has decreased recently. Please answer the web questionnaire to confirm."

[1218] Step 5: Conducting an online questionnaire

[1219] The user checks the notification and answers the web questionnaire. The specific operation is as follows: The input is the notification received by the user, and the output is the questionnaire result.

[1220] Check your smartphone for the notification and tap the link to access the medical questionnaire form in your web browser.

[1221] The form asks questions about your current mood, stress level, appetite, sleep quality, and more.

[1222] The user answers each question and clicks a "Submit" button to send the data to the server.

[1223] Step 6: Psychoanalysis

[1224] The server receives the user's web questionnaire results and performs psychological analysis using the generative AI model and emotion engine. The specific operation is as follows: The input is the web questionnaire results, and the output is the analysis result of the mental state.

[1225] The interview results are analyzed in text format, and an emotion engine models the user's emotional state.

[1226] The analysis model uses natural language processing (NLP) technology to extract keywords and emotions from user responses. For example, if a response contains negative words such as "sad" or "tired," this will be reflected in the analysis results.

[1227] The final analysis results will determine whether or not the individual is at risk of developing a mental illness.

[1228] Step 7: Providing AI counseling

[1229] If the server determines that the user is at risk of developing a mental illness, it will provide AI counseling. The specific operation is as follows: the input is the results of psychoanalysis, and the output is the implementation of counseling.

[1230] The server starts the chatbot and starts counseling the user.

[1231] Using a generative AI model, the system engages in dialogue based on the user's current emotional state. For example, it generates questions such as, "Is there anything that's been bothering you lately, either at work or in your personal life?"

[1232] An emotion engine analyzes the user's responses and provides advice on stress management and mood improvement.

[1233] (Application example 2)

[1234] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[1235] Conventional health management systems can collect data on a user's daily rhythm and activity and manage some aspects of their mental state, but they lack the ability to recommend personalized content based on this data. They also face challenges in accurately assessing mental state and providing prompt counseling. In particular, more advanced analysis and personalization are needed to provide appropriate relaxation content and entertainment during times of high stress.

[1236] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 2 is realized by the following means.

[1237] In this invention, the server includes means for collecting user's life rhythm and activity data from IoT devices, means for transmitting the data to the server via the user's information terminal, means for analyzing the data in the server and detecting disruption of the user's life rhythm or decline in activity, means for the server to send a link to an online questionnaire to the user based on the detection results, means for receiving the web questionnaire results from the user, performing psychoanalysis, and determining whether the user is at risk of a mental disorder, means for providing AI counseling based on the determination results, and means for the server to recommend personalized content based on the user's mental state. This makes it possible to accurately manage the user's mental state, provide appropriate and prompt counseling, and recommend personalized content according to the user's mental state.

[1238] An "IoT device" is a device that collects data on a user's daily rhythm and activity, and includes wearable devices such as smartwatches and fitness trackers.

[1239] "User's information terminal" refers to a device, such as a smartphone or tablet, that transmits data collected from IoT devices to a server.

[1240] The "server" is a computer system that analyzes the user's daily rhythm and activity data, evaluates their mental state, and provides counseling.

[1241] "Disruption of daily rhythm" refers to a significant change in normal life patterns, including lack of sleep and abnormal increases or decreases in the amount of exercise.

[1242] "Decreased activity" refers to a significant decrease in a user's normal activity level, including a decrease in the number of steps taken or a sudden decrease in the amount of exercise.

[1243] "Web questionnaire" refers to a series of questions or surveys administered via the Internet to understand the user's mental state.

[1244] "Psychoanalysis" refers to the process of evaluating and analyzing a user's mental state using a generative AI model based on the results of a user's online questionnaire and collected data.

[1245] "Pre-mental illness" refers to a state in which a user is judged to be at high risk of developing mental problems.

[1246] "AI counseling" is a counseling service provided to users through a chatbot, and is provided using a generative AI model.

[1247] "Personalized content" refers to content that is individually recommended based on the user's mental state, and includes, in particular, relaxation music and entertainment content for relaxation.

[1248] A "generative AI model" is an artificial intelligence model trained on large amounts of data and used to analyze the user's mental state.

[1249] A "prompt" refers to an instruction sentence that recommends content appropriate to the user based on the mental state assessment results of the generative AI model.

[1250] The present invention configures a system that includes an IoT device, a user's information terminal, and a server, and by combining these systems with a generative AI model and an emotion engine, it monitors the user's daily rhythm and activity data and manages their mental health with high accuracy.

[1251] Data collection

[1252] The server collects data on users' daily rhythms and activities from IoT devices. Specifically, wearable devices such as smartwatches and fitness trackers record data such as heart rate, number of steps, and sleep time in real time. As users wear these devices on a daily basis, data is collected periodically.

[1253] Data transmission

[1254] The user's information terminal (such as a smartphone) periodically transmits data collected from IoT devices to a server. Data transmission is performed using wireless communication methods such as Bluetooth or Wi-Fi, and the data is automatically uploaded from the smartphone to the server at specific intervals.

[1255] Data analysis

[1256] The server receives the transmitted data and uses the generative AI model to analyze the lifestyle and activity data. This analysis compares it with past data to detect, for example, a sudden decrease in activity or a change in sleep pattern. Specifically, it analyzes data from the past week for a decrease in activity or abnormal sleep patterns to detect disruptions to lifestyle and a decline in activity.

[1257] Anomaly detection notification

[1258] If the server detects an abnormality, it will send a notification to the user based on the results. The notification will be sent to the user's information device via push notification or email, and will display a message such as, "Your activity level has decreased recently. Please answer the online questionnaire to confirm."

[1259] Conducting online questionnaires

[1260] Users receive a notification and answer the online questionnaire. They click the link in the notification on their information device to access the questionnaire form in a web browser. The questionnaire form contains a wide range of questions, including about their current mood, stress level, and sleep quality.

[1261] psychoanalysis

[1262] The server receives the user's web questionnaire results and performs a psychoanalysis using the generative AI model and emotion engine. Based on the analysis results, it determines whether the user is at risk of developing a mental illness. This emotion engine analyzes the text data and voice data extracted from the user's questionnaire responses to recognize the user's emotional state. This improves the accuracy of the analysis results.

[1263] Personalized content recommendations

[1264] The server recommends personalized content based on the user's mental state. Depending on the user's mental state, content such as relaxing music, relaxing movies, and podcasts is presented. For example, if the user's stress level is determined to be high, relaxing music or a relaxing movie is recommended. An example of a prompt provided by the generative AI model is, "Please rate the user's mental state based on the following data: heart rate: 60-70 BPM, steps: 5000-6000 steps, sleep time: 6.5 hours, and online questionnaire response: I've been feeling stressed recently."

[1265] Providing AI counseling

[1266] If the server determines that the user is at risk of developing a mental illness, it provides AI counseling. The server then activates a chatbot and provides counseling to the user. The chatbot converses with the user based on the emotional state recognized by the emotion engine, and provides advice on stress factors and how to manage mood. It provides appropriate counseling based on the user's current situation, such as asking questions like, "Is there anything that's been bothering you recently at work or in your personal life?"

[1267] In this way, by implementing the system of the present invention, which combines a generative AI model and an emotion engine, users can more accurately understand their own mental state and take appropriate and prompt action. Furthermore, by recommending personalized content according to their mental state, it is expected that the quality of their daily lives will be improved.

[1268] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1269] Step 1:

[1270] The server collects data on users' daily rhythms and activities from IoT devices. Specifically, it receives data such as heart rate, number of steps, and sleep time obtained from smartwatches and fitness trackers via the Internet. This data is sent to the server in CSV or JSON format. Because the collected data also includes date and time information, it is generally stored in log format.

[1271] Input: Life rhythm and activity data from IoT devices (heart rate, steps, sleep time, etc.)

[1272] Output: Raw data recorded in a database on the server

[1273] Step 2:

[1274] The terminal periodically transmits data collected from IoT devices to the server. Data transmission is performed using wireless communication such as Bluetooth or Wi-Fi. The transmission interval can be changed by setting, and data is automatically uploaded once a day or every hour.

[1275] Input: Raw data collected from IoT devices

[1276] Output: Life rhythm and activity data uploaded to the server

[1277] Step 3:

[1278] The server analyzes the received data. Specifically, it uses a generative AI model to compare it with past data and analyze daily rhythms and activity patterns. For example, it applies an anomaly detection algorithm to detect decreased activity or abnormal sleep patterns from data from the past week. It also uses statistical methods to identify outliers.

[1279] Input: Life rhythm and activity data uploaded to the server

[1280] Output: Abnormal pattern detection results (decreased activity, abnormal sleep patterns, etc.)

[1281] Step 4:

[1282] If the server detects an abnormality based on the analysis results, it will send a notification to the user. This notification will be sent to the user's device, such as a smartphone or tablet, as a push notification or email. The notification content will include a message such as, "Your activity level has decreased recently. Please answer the online questionnaire to confirm."

[1283] Input: Abnormal pattern detection results

[1284] Output: An error notification message sent to the user's device

[1285] Step 5:

[1286] The user receives a notification on their device and answers the web questionnaire. They click on the link in the notification to access the questionnaire form in their web browser. The questionnaire form contains a wide range of questions, such as their current mood, stress level, and sleep quality. The user's answers are sent to the server in real time.

[1287] Input: Notification message, user's answers to the medical questionnaire

[1288] Output: Web questionnaire results saved on the server

[1289] Step 6:

[1290] The server receives the results of the web questionnaire and performs a psychological analysis using the generative AI model and emotion engine. Based on the analysis results, it determines whether the user is at risk of developing a mental illness. At this time, the text data and voice data from the questionnaire form are analyzed. An example of a prompt provided by the generative AI model is, "Please assess the user's mental state based on the following data: heart rate: 60-70 BPM, steps: 5000-6000, sleep time: 6.5 hours, web questionnaire response: I've been feeling stressed recently."

[1291] Input: Web questionnaire results (text data, voice data)

[1292] Output: Psychoanalysis result (whether or not the individual is at risk of developing a mental illness)

[1293] Step 7:

[1294] The server then recommends optimal content to the user based on the results of the mental state assessment. In particular, it presents relaxation music, distracting movies, podcasts, etc., depending on the user's emotional state. For example, if the stress level is determined to be high, it will recommend relaxation music. In addition, personalized content is recommended based on prompts provided by the generative AI model.

[1295] Input: Psychoanalysis result

[1296] Output: Personalized content recommendations displayed on the user's device

[1297] Step 8:

[1298] If the server determines that the user is at risk of developing a mental illness, it provides AI counseling. The server then activates a chatbot and provides counseling to the user. Based on the emotional state recognized by the emotion engine, the chatbot engages in a dialogue with the user and provides advice on stress factors and how to manage mood. An example of counseling is an exchange such as, "Is there anything that's been bothering you lately, at work or in your personal life?"

[1299] Input: Psychoanalysis result

[1300] Output: Counseling content provided by AI chatbot

[1301] The specific processing unit 290 transmits the result of the specific processing to the headset type terminal 314. In the headset type terminal 314, the control unit 46A causes the speaker 240 and the display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[1302] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[1303] 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 the present disclosure is not limited to this, and the specific processing may be performed by the headset type terminal 314.

[1304] [Fourth embodiment]

[1305] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[1306] 7, a 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.

[1307] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[1308] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a control target 443. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the control target 443 are also connected to the bus 52.

[1309] The microphone 238 receives instructions and the like from the user 20 by receiving voice uttered by the user 20. The microphone 238 captures the voice uttered by the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio in accordance with instructions from the processor 46.

[1310] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the surroundings of user 20 (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[1311] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 control the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[1312] The control object 443 includes a display device, LEDs in the eyes, and motors for driving 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 emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

[1313] Fig. 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Fig. 8, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[1314] The specific processing program 56 is an example of a "program" according to the technology of the present 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.

[1315] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[1316] In the robot 414, the processor 46 performs the reception output process. A reception output program 60 is stored in the storage 50. 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 process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[1317] Next, a description will be given of the specific processing performed by the specific processing unit 290 of the data processing device 12. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1318] This invention configures a system including an IoT device, a user terminal, and a server, and by implementing the means described below, monitors the user's daily rhythm and activity data and effectively manages their mental health.

[1319] Data collection

[1320] The terminal collects the user's daily rhythm and activity data from IoT devices. Specifically, IoT devices such as smartwatches and fitness trackers record data such as heart rate, number of steps, and sleep time in real time. As users wear these devices on a daily basis, data is collected periodically.

[1321] Data transmission

[1322] A terminal (e.g., a smartphone) periodically transmits data collected from IoT devices to a server. Data transmission is performed using wireless communication methods such as Bluetooth or Wi-Fi, and is set so that data is automatically uploaded from the user's smartphone to the server at specific intervals.

[1323] Data analysis

[1324] The server receives the transmitted data and uses AI models to analyze the rhythm and activity data. As part of the analysis, anomaly detection algorithms are applied to detect unusual patterns or disruptions to rhythm, such as a sudden drop in activity compared to normal activity or a significant change in sleep duration.

[1325] Anomaly detection notification

[1326] If the server detects an abnormality, it includes a means to send a notification to the user based on the results. Specifically, the server sends a push notification or email to the user's smartphone to inform them that an abnormality has been detected in their daily rhythm. A message such as "Your activity level has decreased recently. Please answer the online questionnaire to confirm this" is sent.

[1327] Conducting online questionnaires

[1328] The user receives a notification and answers the online questionnaire. The user clicks on the link in the notification using their smartphone to access the questionnaire form in a web browser. The questionnaire form contains a wide range of questions, and the user reports their current mental health status by answering them.

[1329] psychoanalysis

[1330] The server receives the user's responses to the online questionnaire and performs a psychological analysis. This involves using an AI model to analyze the questionnaire results and determine whether the user is at risk of depression. For example, the responses can be used to detect signs of depression or loss of interest.

[1331] Providing AI counseling

[1332] If the server determines that the user is at risk of developing depression, it provides AI counseling. The server then activates a chatbot and provides counseling to the user. Through dialogue with the user, the chatbot provides advice on stress factors and how to manage mood. It asks questions such as, "Is there anything that's been bothering you lately at work or in your personal life?" and provides counseling tailored to the user's situation.

[1333] Specific examples

[1334] Scenario: A user

[1335] 1. Data Collection

[1336] The device (smartwatch) records the user's heart rate, sleep time, and exercise volume throughout the day.

[1337] 2. Data Transmission

[1338] The device (smartphone) automatically collects data at night and sends it to the server.

[1339] 3. Data Analysis

[1340] The server analyzes the received data and detects a significant decrease in exercise volume from the data from the past week.

[1341] 4. Anomaly detection notification

[1342] The server detects the abnormality and sends the user a notification saying, "Your activity has decreased recently. Please answer the online questionnaire to confirm."

[1343] 5. Conducting an online questionnaire

[1344] The user checks the notification, clicks the link to the online medical questionnaire, and answers the medical questionnaire form.

[1345] 6. Psychoanalysis

[1346] The server analyzes the results of the interview, evaluates the user's mental health status, and determines whether the user is at risk of developing depression.

[1347] 7. Providing AI counseling

[1348] The server begins counseling the user through a chatbot, offering advice such as, "Is there anything that's been bothering you lately, either at work or in your personal life?"

[1349] In this way, by implementing the present invention, users can recognize mental health problems early on and take appropriate measures.

[1350] The processing flow will be explained below.

[1351] Step 1:

[1352] The device (smartwatch) records the user's activity data, such as heart rate, sleep time, and exercise volume, in real time. This data is temporarily stored in the internal memory.

[1353] Step 2:

[1354] The device (smartwatch) transmits data to the smartphone via Bluetooth, including heart rate, sleep time, exercise volume, as well as date and time information.

[1355] Step 3:

[1356] The device (smartphone) automatically sends this data to the server at regular intervals via Wi-Fi or mobile data communication.

[1357] Step 4:

[1358] The server receives the data sent from the user's smartphone, stores it in a database, and passes it to an AI model for analysis.

[1359] Step 5:

[1360] The server analyzes the received data and uses an AI model to detect disruptions to daily rhythms and declines in activity, for example, by assessing the degree to which a person's daily rhythm has deviated from normal based on the amount of exercise, heart rate, and sleep patterns over the past week.

[1361] Step 6:

[1362] If the server detects an abnormality, it sends a notification to the user's smartphone. The notification contains a message stating that a disruption in daily rhythm or a decrease in activity level has been detected, along with a request to "answer a web questionnaire to confirm the status."

[1363] Step 7:

[1364] The user receives a notification on their smartphone and clicks on a link to access a web questionnaire that includes questions about their current mood, stress level, sleep quality, and more.

[1365] Step 8:

[1366] The user answers the questionnaire form and presses the send button. The user's answers are sent to the server via the Internet.

[1367] Step 9:

[1368] The server receives the results of the medical interview sent by the user and performs a psychological analysis using an AI model. Based on the analysis results, it determines whether the user is at risk of developing depression.

[1369] Step 10:

[1370] If the server determines that the individual is at risk of developing depression, it will launch a chatbot as a means of providing AI counseling.

[1371] Step 11:

[1372] The server (chatbot) begins counseling by asking the user questions such as, "Is there anything that's been bothering you lately, either at work or in your personal life?" The chatbot then provides appropriate advice and support based on the user's answers.

[1373] Through this series of steps, users can understand their own mental health at an early stage and take the necessary measures quickly.

[1374] Example 1

[1375] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1376] In modern society, mental health problems caused by stress and poor lifestyle habits are on the rise. In response to this, there is a need to manage users' mental states by detecting abnormalities early and providing appropriate counseling, thereby preventing and improving mental illnesses such as depression. However, conventional systems have been unable to provide sufficiently effective measures because the processes of data collection, analysis, notification, and counseling are fragmented and manual. The objective of this invention is to provide a system that utilizes IoT devices and AI technology to automatically and continuously monitor users' mental health states and take appropriate measures early.

[1377] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[1378] In this invention, the server includes means for collecting user life rhythm and activity data from IoT devices, means for transmitting the data to the server via the user's terminal, means for analyzing the data in the server and detecting disruption of the user's life rhythm or decline in activity using an anomaly detection algorithm, means for the server to send a link to an online questionnaire to the user based on the detection results, means for receiving the web questionnaire results from the user and performing psychoanalysis using an AI model to determine whether the user is at risk of depression, and means for providing counseling to the user using an AI counseling function based on the determination results. This makes it possible to detect abnormalities in the user's life rhythm and activity early and provide professional counseling in a timely manner.

[1379] An "IoT device" is a device that has the ability to collect and transmit data via the Internet, and specifically includes smartwatches and fitness trackers.

[1380] A "user device" is an electronic device that a user uses on a daily basis and has functions such as collecting and sending data and receiving notifications. Specifically, this applies to smartphones and tablets.

[1381] A "server" is a computer system that provides services to multiple terminals via a network, and receives, analyzes, stores, and manages notifications of data.

[1382] "Life rhythm" refers to the activity patterns in the user's daily life, such as sleep time, meal time, and exercise time.

[1383] "Activity data" refers to data related to the user's physical activity, including the number of steps taken, heart rate, and exercise intensity.

[1384] An "anomaly detection algorithm" is a computational method for detecting abnormal data that deviates from normal data patterns, and uses machine learning and statistical methods.

[1385] "Web questionnaire" refers to a user answering questions about their mental and physical state via a web browser.

[1386] An "AI model" is a mathematical model that uses artificial intelligence technology to analyze data and make predictions.

[1387] "Psychoanalysis" is the process of analyzing the results of a user's medical interview and assessing their mental state.

[1388] "Pre-depression" refers to people who show signs of depression but have not yet been definitively diagnosed.

[1389] The "AI counseling function" uses artificial intelligence to provide counseling to users, offering advice on stress management and mood improvement through a chatbot.

[1390] A "chatbot" is a software program that uses natural language processing to interact with users and provide counseling and support.

[1391] A "notification" is an information transmission message sent by a server to a user's device, and can take the form of a push notification or email.

[1392] The system according to the present invention is composed of a combination of IoT devices, a user's terminal, and a server. The main purpose of this system is to monitor the user's daily rhythm and activity data and effectively manage their mental health.

[1393] Data collection

[1394] The devices used are IoT devices such as smartwatches and fitness trackers. These devices record daily life data such as the user's heart rate, number of steps, and sleep time in real time and store the data in their internal memory. When users wear these devices on a daily basis, data is collected periodically.

[1395] Data transmission

[1396] The device (e.g., a smartphone) periodically sends the collected data to a server. Data is transferred from the smartwatch to the smartphone using Bluetooth, and then uploaded to the server using Wi-Fi or mobile data. This process is performed automatically at specific intervals (e.g., every day at 10 p.m.).

[1397] Data analysis

[1398] The server then analyzes the received data using AI models. During this analysis, anomaly detection algorithms are applied to the circadian rhythm and activity data to detect unusual patterns or disruptions to circadian rhythms, such as a sudden drop in activity compared to normal activity levels or significant fluctuations in sleep duration.

[1399] Anomaly detection notification

[1400] When the server detects an abnormality, it sends a notification to the user based on the results. This notification is sent to the user's smartphone as a push notification or email. The content of the notification might be, for example, a message such as, "Your activity level has decreased recently. Please answer the online questionnaire to confirm."

[1401] Conducting online questionnaires

[1402] The user receives the notification and clicks on the link to the online questionnaire, which opens a web browser and accesses the questionnaire form. The questionnaire form contains several questions about the user's physical and mental condition. The user answers the questions and submits the form.

[1403] psychoanalysis

[1404] The server receives the user's responses to the online questionnaire and performs a psychological analysis using an AI model. The analysis uses an algorithm to detect signs of depression from the responses, such as low mood or a lack of interest in daily activities.

[1405] Providing AI counseling

[1406] If the server determines that the user is at risk of depression, it activates an AI counseling function. A chatbot within the server is activated and begins counseling the user. The chatbot uses natural language processing technology to converse with the user and provide advice on stress management and mood improvement.

[1407] (Example)

[1408] Scenario: A user

[1409] 1. Data Collection

[1410] The device (smartwatch) records the user's heart rate, sleep time, and exercise volume throughout the day.

[1411] 2. Data Transmission

[1412] The device (smartphone) automatically collects data at night and sends it to the server.

[1413] 3. Data Analysis

[1414] The server analyzes the received data and detects a significant decrease in exercise volume from the data from the past week.

[1415] 4. Anomaly detection notification

[1416] The server detects the abnormality and sends the user a notification saying, "Your activity has decreased recently. Please answer the online questionnaire to confirm."

[1417] 5. Conducting an online questionnaire

[1418] The user checks the notification, clicks the link to the online medical questionnaire, and answers the medical questionnaire form.

[1419] 6. Psychoanalysis

[1420] The server analyzes the results of the interview, evaluates the user's mental health status, and determines whether the user is at risk of developing depression.

[1421] 7. Providing AI counseling

[1422] The server begins counseling the user through a chatbot, offering advice such as, "Is there anything that's been bothering you lately, either at work or in your personal life?"

[1423] Prompt Sentence Examples

[1424] "Have you been worried about anything recently at work or in your personal life? Please answer a few questions so we can provide you with counseling tailored to your situation."

[1425] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1426] Step 1: Data collection

[1427] The device (smartwatch or fitness tracker) records the user's daily life data, such as heart rate, steps, and sleep time, in real time. Sensors within the device detect physical activity and store it in internal memory. Specifically, when the user wakes up in the morning, the smartwatch records the amount of sleep the previous day and continuously monitors heart rate and steps throughout the day. Input: Raw data from the sensor. Output: Life rhythm and activity data stored in internal memory.

[1428] Step 2: Send data

[1429] The device (smartphone) receives the data collected from the smartwatch via Bluetooth. It then uploads it to the server using Wi-Fi or mobile data communication. This transmission process is carried out automatically at specific intervals (e.g., 10 p.m. every day). Specifically, at 10 p.m., the smartphone app automatically collects data from the smartwatch and sends it to the server. Input: Data transferred from the smartwatch. Output: Data uploaded to the server.

[1430] Step 3: Data analysis

[1431] The server analyzes the received data using an AI model. During this analysis process, an anomaly detection algorithm is applied to the lifestyle and activity data to detect unusual patterns or disruptions to lifestyle rhythms. For example, a week's worth of data can be compiled to identify sudden drops in activity levels or significant fluctuations in sleep duration. Input: Data uploaded to the server. Output: Analysis results (anomaly detection results).

[1432] Step 4: Anomaly detection notification

[1433] If the server detects an abnormality based on the analysis results, it will send a notification to the user. The notification is sent to the smartphone as a push notification or email, and includes a message saying, "Your activity level has decreased recently. Please answer the online questionnaire to confirm." Specifically, if a significant decrease in activity level is detected, the server will automatically generate and send a notification. Input: Data analysis results. Output: Notification sent to the user's smartphone.

[1434] Step 5: Conducting an online questionnaire

[1435] The user receives the notification, clicks the web questionnaire link to open a web browser, and accesses the questionnaire form. The questionnaire form contains multiple questions about the user's physical and mental condition, and the user answers each question and submits the form. Specifically, the user checks the notification on their smartphone, clicks the link to access the questionnaire form in a web browser, and answers the questions. Input: The questionnaire form displayed in the web browser. Output: The submitted questionnaire answers.

[1436] Step 6: Psychoanalysis

[1437] The server receives the user's responses to the web questionnaire and performs a psychoanalysis using an AI model. Signals such as depressed mood and lack of interest in daily activities are analyzed to detect signs of depression from the responses. Specifically, the server collects the questionnaire responses, analyzes them using an AI model, and checks for signs of pre-depression. Input: Questionnaire results. Output: Psychoanalysis results.

[1438] Step 7: Providing AI counseling

[1439] The server provides AI counseling functions to users who are determined to be at risk of depression. A chatbot within the server is activated and begins counseling the user. The chatbot uses natural language processing technology to converse with the user and provide advice on stress management and mood improvement. Specifically, the chatbot asks the user, "Is there anything at work that's been bothering you lately?" and provides counseling appropriate to the situation. Input: Psychoanalysis results. Output: Contents of the counseling conversation.

[1440] (Application example 1)

[1441] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1442] In recent years, with the spread of IoT devices and smartphones, it has become possible to collect data on users' daily rhythms and activities and manage their mental health. However, conventional systems only monitor users' mental health status and often do not suggest specific actions for lifestyle improvement. In particular, they lack the functionality to suggest meal menus based on the user's health status, and more comprehensive health management is required. Therefore, a system that can improve not only a user's mental health but also their overall health is needed.

[1443] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[1444] In this invention, the server includes means for collecting user's life rhythm and activity data from IoT devices, means for transmitting the data to the server via the user's terminal, means for analyzing the data and detecting disruption of the user's life rhythm or decrease in activity, means for transmitting a link to an online questionnaire to the user based on the detection results, means for receiving the web questionnaire results from the user and performing a psychoanalysis to determine whether the user is at risk of depression, means for providing AI counseling based on the determination results, and means for suggesting an appropriate meal menu based on the user's health condition. This enables specific measures to improve the user's overall health condition as well as their mental health condition.

[1445] An "IoT device" is a physical device that is connected to the Internet and has the ability to collect and communicate data.

[1446] "Life rhythm" refers to the time patterns of various activities in the user's daily life.

[1447] "Activity data" refers to data related to the user's physical activity, such as the amount of exercise, heart rate, and sleep time.

[1448] A "user's terminal" is a device that receives data from an IoT device and sends it to a server, and generally refers to a smartphone.

[1449] A "server" is a computer system for storing, managing, and analyzing data.

[1450] "Data analysis" is the process of detecting specific patterns or anomalies based on collected data.

[1451] "Disruption of daily rhythm" refers to an abnormality in the pattern of normal daily activities.

[1452] "Decreased activity" refers to a state in which the user's physical activity is less than usual.

[1453] "Web medical questionnaire" refers to a medical questionnaire conducted online, and is used to obtain a detailed understanding of a user's health condition.

[1454] "Psychoanalysis" is the assessment of a user's mental health based on collected data and the results of a web questionnaire.

[1455] "Pre-depression" refers to a condition in which symptoms of depression are present but the diagnostic criteria are not yet met.

[1456] "AI counseling" refers to providing counseling services to users using artificial intelligence.

[1457] A "meal menu" is a combination of meals suggested based on your health condition.

[1458] This invention configures a system including an IoT device, a user terminal, and a server, and by implementing the following means, monitors the user's daily rhythm and activity data, effectively managing their mental and overall health.

[1459] 1. Data Collection

[1460] The device collects data on the user's daily rhythm and activity from IoT devices such as smartwatches and fitness trackers, which record data such as heart rate, steps taken, and sleep time in real time. Data is collected periodically as users wear these devices daily.

[1461] 2. Data Transmission

[1462] A terminal (e.g., a smartphone) periodically transmits data collected from IoT devices to a server. Data transmission is performed using wireless communication methods such as Bluetooth or Wi-Fi, and data is uploaded from the user's smartphone to the server at regular intervals.

[1463] 3. Data Analysis

[1464] The server receives the data and uses AI models to analyze the rhythm and activity data. This analysis detects unusual patterns and disruptions to rhythm, such as a sudden drop in activity compared to normal activity levels or significant fluctuations in sleep duration.

[1465] 4. Anomaly detection notification

[1466] If the server detects an abnormality, it will send a notification to the user based on the results. Specifically, the server will send a push notification or email to the user's smartphone to inform them that an abnormality has been detected in their daily rhythm. A message such as "Your activity level has decreased recently. Please answer the online questionnaire to confirm this" will be sent.

[1467] 5. Conducting an online questionnaire

[1468] The user receives a notification and answers the online questionnaire. The user clicks on the link in the notification using their smartphone to access the questionnaire form in a web browser. The questionnaire form contains a wide range of questions, and by answering these questions, the user reports their current mental health status.

[1469] 6. Psychoanalysis

[1470] The server receives the user's responses to the online questionnaire and performs a psychological analysis. This involves using an AI model to analyze the questionnaire results and determine whether the user is at risk of depression. For example, the responses can be used to detect signs of depression or loss of interest.

[1471] 7. Providing AI counseling

[1472] If the server determines that the user is at risk of developing depression, it provides AI counseling. The server then activates a chatbot and provides counseling to the user. Through dialogue with the user, the chatbot provides advice on stress factors and how to manage mood. It asks questions such as, "Is there anything that's been bothering you lately at work or in your personal life?" and provides counseling tailored to the user's situation.

[1473] 8. Meal suggestions based on health status

[1474] The server proposes an appropriate meal menu based on the user's health condition and lifestyle. Specifically, it identifies nutritional deficiencies based on activity data and the results of an online questionnaire, and proposes meal menus to compensate for these. For example, it suggests a menu with a relaxing effect to a user with a high heart rate and lack of sleep.

[1475] Specific examples

[1476] For example, if a user's recent activity data shows that their physical activity has decreased significantly and their sleep schedule has become irregular, the server will make the following food delivery suggestions to the user:

[1477] "Recently, you've been showing signs of less exercise and sleep deprivation. To boost your immune system, I recommend a salad rich in vitamin C or a smoothie made with antioxidant-rich berries."

[1478] Example prompts to input to a generative AI model:

[1479] "Please provide appropriate advice to suggest meal menus to users who have recently decreased their exercise and have irregular sleep schedules."

[1480] The hardware used is a smartwatch (Apple Watch, Fitbit) or smartphone (iPhone, Android device), and the server uses a cloud service such as AWS or GCP. AI models such as TensorFlow or PyTorch are used for analysis, and MySQL or PostgreSQL is suitable for the database.

[1481] By taking these measures, users can receive comprehensive health management.

[1482] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1483] Step 1:

[1484] The device collects user's daily rhythm and activity data (heart rate, number of steps, sleep time, etc.) from IoT devices such as smartwatches and fitness trackers. This data is acquired in real time and stored in a local database.

[1485] Input: Sensor data from IoT devices.

[1486] Output: User's daily rhythm and activity data stored in a local database.

[1487] Step 2:

[1488] The device (smartphone) periodically sends collected data to the server using Bluetooth or Wi-Fi. The timing of the transmission is set for each interval.

[1489] Input: Circadian rhythm and activity data stored in a local database.

[1490] Output: Data uploaded to the server.

[1491] Step 3:

[1492] The server analyzes the received data and uses AI models to detect disruptions in daily rhythms and declines in activity, thereby identifying abnormal patterns.

[1493] Input: Life rhythm and activity data uploaded to the server.

[1494] Output: Anomaly detection results.

[1495] Step 4:

[1496] The server sends a push notification to the user's device based on the anomaly detection result, which includes a link to the online questionnaire.

[1497] Input: Anomaly detection results.

[1498] Output: The push notification sent to the user's device.

[1499] Step 5:

[1500] The user receives the notification and clicks on the link to the online questionnaire, which opens a web browser and allows the user to access the questionnaire form and answer each question.

[1501] Input: Web questionnaire link in the push notification.

[1502] Output: User's questionnaire response data.

[1503] Step 6:

[1504] The server receives the results of the web questionnaire from the user and performs a psychological analysis using an AI model to determine whether the user is at risk of developing depression.

[1505] Input: Web questionnaire result data.

[1506] Output: psychoanalysis results.

[1507] Step 7:

[1508] If the server determines that the user is at risk of developing depression, it will launch a chatbot to provide AI counseling. The chatbot will then provide advice in a dialogue format with the user.

[1509] Input: Psychoanalysis results.

[1510] Output: Counseling content via chatbot.

[1511] Step 8:

[1512] The server then proposes an appropriate meal plan based on the user's health status, including nutritional analysis based on activity data and online questionnaire results, and notifies the user's food delivery app of the proposed plan.

[1513] Input: Activity data and web questionnaire results.

[1514] Output: Meal suggestions notified to the user.

[1515] Through these steps, the entire system functions to comprehensively manage and improve the user's lifestyle and health.

[1516] Furthermore, an emotion engine that estimates the user's emotion may be further combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59, and perform identification processing using the user's emotion.

[1517] This invention configures a system that includes an IoT device, a user terminal, and a server, and by combining these systems with an emotion engine, it monitors the user's daily rhythm and activity data, and also manages their mental health with high accuracy.

[1518] Data collection

[1519] The terminal collects data on the user's daily rhythm and activity from IoT devices. Specifically, smartwatches and fitness trackers record data such as heart rate, number of steps, and sleep time in real time. As users wear these devices on a daily basis, data is collected periodically.

[1520] Data transmission

[1521] The terminal (smartphone) periodically sends data collected from IoT devices to a server. Data transmission is performed using wireless communication methods such as Bluetooth or Wi-Fi, and is set to automatically upload data from the smartphone to the server at specific intervals.

[1522] Data analysis

[1523] The server receives the transmitted data and uses AI models to analyze the rhythm and activity data, comparing it with past data to detect, for example, a sudden drop in activity or a change in sleep patterns.

[1524] Anomaly detection notification

[1525] If the server detects an abnormality, it includes a means for sending a notification to the user based on the results. The notification is sent to the smartphone via push notification or email, and displays a message such as "Your activity level has decreased recently. Please answer the online questionnaire to confirm."

[1526] Conducting online questionnaires

[1527] The user receives a notification and answers the online questionnaire. The user clicks on the link in the notification on their smartphone to access the questionnaire form in a web browser. The questionnaire form contains a wide range of questions, such as current mood, stress level, and sleep quality.

[1528] psychoanalysis

[1529] The server receives the user's web questionnaire results and performs a psychological analysis using an AI model and emotion engine. Based on the analysis results, it determines whether the user is at risk of developing depression. This emotion engine analyzes the text data and voice data extracted from the user's questionnaire responses to recognize the user's emotional state. This improves the accuracy of the analysis results.

[1530] Providing AI counseling

[1531] If the server determines that the user is at risk of developing depression, it provides AI counseling. The server then activates a chatbot and provides counseling to the user. The chatbot converses with the user based on the emotional state recognized by the emotion engine, and provides advice on stress factors and how to manage mood. It provides appropriate counseling according to the user's current situation, such as asking questions like, "Is there anything that's bothering you recently at work or in your personal life?"

[1532] Specific examples

[1533] Scenario: A user

[1534] 1. Data Collection

[1535] The device (smartwatch) records the user's heart rate, sleep time, and exercise volume throughout the day.

[1536] 2. Data Transmission

[1537] The device (smartphone) automatically collects data at night and sends it to the server.

[1538] 3. Data Analysis

[1539] The server analyzes the received data and detects a significant decrease in exercise volume from the data from the past week.

[1540] 4. Anomaly detection notification

[1541] The server detects the abnormality and sends the user a notification saying, "Your activity has decreased recently. Please answer the online questionnaire to confirm."

[1542] 5. Conducting an online questionnaire

[1543] The user checks the notification, clicks the link to the online medical questionnaire, and answers the medical questionnaire form.

[1544] 6. Psychoanalysis

[1545] The server analyzes the results of the interview, evaluates the user's mental health, and determines whether they are at risk of developing depression. The emotion engine recognizes the user's emotional state from their responses and reflects this in the analysis results.

[1546] 7. Providing AI counseling

[1547] The server starts counseling the user through a chatbot, offering advice such as, "Is there anything that's been bothering you lately, either at work or in your personal life?" The server then provides more appropriate counseling based on the emotional state recognized by the emotion engine.

[1548] In this way, by implementing the system of the present invention in combination with an emotion engine, users can more accurately understand their own mental health and take appropriate and prompt measures.

[1549] The processing flow will be explained below.

[1550] Step 1:

[1551] The device (smartwatch) records the user's activity data, such as heart rate, sleep time, and exercise volume, in real time. This data is stored in the internal memory.

[1552] Step 2:

[1553] The device (smartwatch) transmits the collected data via Bluetooth to a smartphone at regular intervals, including heart rate, sleep time, exercise volume, date, and time.

[1554] Step 3:

[1555] The device (smartphone) transmits the data received via Bluetooth to the server via Wi-Fi or mobile data communication at regular intervals.

[1556] Step 4:

[1557] The server receives the data sent from the user's smartphone and stores it in a database, which is then passed to the AI ​​model.

[1558] Step 5:

[1559] The server uses AI models to analyze the incoming data and compare it with past data to detect changes or anomalies in daily rhythms, such as a sudden drop in normal activity or a significant deviation from normal sleep patterns.

[1560] Step 6:

[1561] If the server detects an abnormality, such as a disruption in daily rhythm or a decrease in activity, it will send a notification to the user. The notification will be sent via push notification or email to the smartphone and will include a message such as, "Your activity level has decreased recently. Please answer the online questionnaire to confirm."

[1562] Step 7:

[1563] The user sees the notification on their smartphone and clicks on a link to access a web questionnaire, which includes questions about their current mood, stress level, sleep quality, loss of interest, etc.

[1564] Step 8:

[1565] The user fills in the medical interview form and presses the send button to send the results of the medical interview to the server. The results of the medical interview are sent to the server via the Internet.

[1566] Step 9:

[1567] The server receives the user's web questionnaire results and performs psychological analysis using an AI model and emotion engine. The emotion engine analyzes the user's text and voice data to recognize their emotional state, resulting in a more accurate mental health assessment.

[1568] Step 10:

[1569] The server determines whether the user is at risk of depression based on the psychoanalysis results. If the user is at risk of depression, the AI ​​counseling process begins.

[1570] Step 11:

[1571] The server launches a chatbot to provide AI counseling. The chatbot provides counseling based on the user's emotional state. For example, it asks questions such as, "Is there anything bothering you recently at work or in your personal life?"

[1572] Step 12:

[1573] The server (chatbot) provides appropriate advice and support based on the user's responses. For example, it provides advice on stress management and relaxation. Furthermore, if the user's emotional state changes due to the emotion engine, the content of the counseling will be adjusted accordingly.

[1574] Through this series of steps, the system can continuously monitor the user's daily rhythm and emotional state, detect mental health problems early, and take appropriate measures.

[1575] Example 2

[1576] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1577] In recent years, the risk of mental illness has increased due to disruptions in daily rhythms and reduced activity levels. However, there are limited systems that can detect these fluctuations early and provide appropriate responses. Conventional technologies have not only been able to collect and analyze users' biometric data, but also have struggled to comprehensively manage their mental health. In other words, there is a need for the development of a system that can quickly and accurately assess a user's mental state and provide appropriate counseling.

[1578] The identification process by the identification processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means. In this invention, the server includes means for collecting biometric data of a user from an IoT device, means for transmitting the data to the server via the user's terminal, and means for analyzing the data in the server and detecting a disturbance in biorhythm or a decrease in activity level. This makes it possible to quickly and accurately detect changes in the user's mental state through the collection and analysis of the user's biometric data and take appropriate measures.

[1579] Furthermore, the server includes a means for using a generative AI model to analyze the data and a means for using an emotion engine to analyze the emotional state from the results of the web questionnaire, which enables detailed analysis of the user's lifestyle, activity fluctuations, and emotional state, realizing highly accurate psychoanalysis and early detection of pre-depression.

[1580] The server also includes a means for providing AI counseling based on the results of the assessment, which enables counseling using an emotion engine to generate dialogue suited to the user, thereby effectively supporting the user's mental health.

[1581] An "IoT device" is a physical device that is connected to the Internet and is capable of collecting and transmitting data.

[1582] "Biometric data" refers to data related to the user's daily rhythm and activity, such as heart rate, number of steps, and sleep time.

[1583] A "terminal" is a device used by a user to send data collected from an IoT device to a server, and specifically refers to a smartphone or tablet.

[1584] A "server" is a computer system that receives, analyzes, and stores collected data.

[1585] A "generative AI model" is an artificial intelligence model for analyzing data and recognizing patterns.

[1586] An "emotion engine" is software that analyzes a user's text and voice data to recognize their emotional state.

[1587] A "Web questionnaire" is a questionnaire format for assessing mental health status by having users enter answers using an online form.

[1588] "Psychoanalysis" is the process of analyzing collected data and interview results to assess the user's mental state.

[1589] "Pre-mental illness" refers to users who are beginning to show mild symptoms of a mental illness, but have not yet been diagnosed.

[1590] "AI counseling" is the process of using artificial intelligence to provide psychological support and advice to users.

[1591] A "chatbot" is a program, usually an automated response system, that interacts with users in the form of messages.

[1592] This invention configures a system that includes an IoT device, a user terminal, and a server, and by combining these systems with an emotion engine, it monitors the user's daily rhythm and activity data, and also manages their mental health with high accuracy.

[1593] Specifically, the following steps are carried out.

[1594] Data collection

[1595] The terminal collects the user's biometric data from IoT devices. Specifically, smartwatches and fitness trackers record data such as heart rate, steps, and sleep time in real time. As users wear these devices on a daily basis, data is collected periodically.

[1596] Data transmission

[1597] The terminal (smartphone) periodically transmits data collected from IoT devices to a server. This data transmission is performed using wireless communication methods such as Bluetooth or Wi-Fi, and is set so that data is automatically uploaded from the smartphone to the server at specific intervals.

[1598] Data analysis

[1599] The server receives the transmitted data and uses a generative AI model to analyze the rhythm and activity data, comparing it with past data to detect sudden declines in activity or changes in sleep patterns.

[1600] Anomaly detection notification

[1601] If the server detects an abnormality, it includes a means for sending a notification to the user based on the results. The notification is sent to the smartphone via push notification or email, and displays a message such as "Your activity level has decreased recently. Please answer the online questionnaire to confirm."

[1602] Conducting online questionnaires

[1603] The user receives a notification and answers the online questionnaire. The user clicks on the link in the notification on their smartphone to access the questionnaire form in a web browser. The questionnaire form contains a wide range of questions, such as current mood, stress level, and sleep quality.

[1604] psychoanalysis

[1605] The server receives the user's web questionnaire results and performs a psychological analysis using the generative AI model and emotion engine. Based on the analysis results, it determines whether the user is at risk of developing a mental illness. This emotion engine analyzes the text data and voice data extracted from the user's questionnaire responses to recognize the user's emotional state. This improves the accuracy of the analysis results.

[1606] Providing AI counseling

[1607] If the server determines that the user is at risk of developing a mental illness, it provides AI counseling. The server activates a chatbot and provides counseling to the user. The chatbot converses with the user based on the emotional state recognized by the emotion engine, and provides advice on stress factors and how to manage mood. It provides appropriate counseling according to the user's current situation, such as asking questions like, "Is there anything that's been bothering you recently at work or in your personal life?"

[1608] Specific examples

[1609] Scenario: A user

[1610] 1. Data Collection

[1611] The device (smartwatch) records the user's heart rate, sleep time, and exercise volume throughout the day.

[1612] 2. Data Transmission

[1613] The device (smartphone) automatically collects data at night and sends it to the server.

[1614] 3. Data Analysis

[1615] The server analyzes the received data and detects a significant decrease in exercise volume from the data from the past week.

[1616] 4. Anomaly detection notification

[1617] The server detects the abnormality and sends the user a notification saying, "Your activity has decreased recently. Please answer the online questionnaire to confirm."

[1618] 5. Conducting an online questionnaire

[1619] The user checks the notification, clicks the link to the online medical questionnaire, and answers the medical questionnaire form.

[1620] 6. Psychoanalysis

[1621] The server analyzes the results of the interview, evaluates the user's mental health, and determines whether they are at risk of developing a mental illness. The emotion engine recognizes the user's emotional state from their responses and reflects this in the analysis results.

[1622] 7. Providing AI counseling

[1623] The server starts counseling the user through a chatbot, offering advice such as, "Is there anything that's been bothering you lately, either at work or in your personal life?" The server then provides more appropriate counseling based on the emotional state recognized by the emotion engine.

[1624] This allows users to have a more accurate understanding of their own mental health and take appropriate and prompt action.

[1625] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1626] Step 1: Data collection

[1627] The device (smartwatch or fitness tracker) collects the user's biometric data. The specific operation is as follows: the input is the user's activity and state, and the output is the collected data.

[1628] The smartwatch measures the user's heart rate in real time.

[1629] A fitness tracker counts the user's steps and calculates the distance walked and the calories burned.

[1630] These devices record sleep duration and analyze whether the user spends more time in light or deep sleep.

[1631] Step 2: Send data

[1632] The terminal (smartphone) sends the collected data to the server. The specific operation is as follows: the input is the collected biometric data, and the output is the data sent to the server.

[1633] Smartwatches and fitness trackers use Bluetooth to transmit data to your smartphone.

[1634] The smartphone uses Wi-Fi or 4G / 5G to upload data collected from IoT devices to a server at regular intervals.

[1635] This happens automatically in the background, without any user intervention.

[1636] Step 3: Data analysis

[1637] The server analyzes the received data using a generative AI model. The specific operation is as follows: The input is the biometric data received by the server, and the output is the analysis result.

[1638] The server stores the received lifestyle and activity data in a database.

[1639] By comparing with past data, it detects sudden changes in the user's activity level and sleep patterns.

[1640] Statistical analysis is performed using programming languages ​​such as Python and R to extract anomalies.

[1641] Step 4: Anomaly detection notification

[1642] If the server detects an anomaly, it sends a notification to the user based on the result. The specific operation is as follows: The input is the analysis result, and the output is an anomaly notification to the user.

[1643] If there is a sudden decrease in activity or a significant change in sleep patterns, the information is sent to a notification service on the server.

[1644] The server sends a push notification or email to the user saying, "Your activity level has decreased recently. Please answer the web questionnaire to confirm."

[1645] Step 5: Conducting an online questionnaire

[1646] The user checks the notification and answers the web questionnaire. The specific operation is as follows: The input is the notification received by the user, and the output is the questionnaire result.

[1647] Check your smartphone for the notification and tap the link to access the medical questionnaire form in your web browser.

[1648] The form asks questions about your current mood, stress level, appetite, sleep quality, and more.

[1649] The user answers each question and clicks a "Submit" button to send the data to the server.

[1650] Step 6: Psychoanalysis

[1651] The server receives the user's web questionnaire results and performs psychological analysis using the generative AI model and emotion engine. The specific operation is as follows: The input is the web questionnaire results, and the output is the analysis result of the mental state.

[1652] The interview results are analyzed in text format, and an emotion engine models the user's emotional state.

[1653] The analysis model uses natural language processing (NLP) technology to extract keywords and emotions from user responses. For example, if a response contains negative words such as "sad" or "tired," this will be reflected in the analysis results.

[1654] The final analysis results will determine whether or not the individual is at risk of developing a mental illness.

[1655] Step 7: Providing AI counseling

[1656] If the server determines that the user is at risk of developing a mental illness, it will provide AI counseling. The specific operation is as follows: the input is the results of psychoanalysis, and the output is the implementation of counseling.

[1657] The server starts the chatbot and starts counseling the user.

[1658] Using a generative AI model, the system engages in dialogue based on the user's current emotional state. For example, it generates questions such as, "Is there anything that's been bothering you lately, either at work or in your personal life?"

[1659] An emotion engine analyzes the user's responses and provides advice on stress management and mood improvement.

[1660] (Application example 2)

[1661] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1662] Conventional health management systems can collect data on a user's daily rhythm and activity and manage some aspects of their mental state, but they lack the ability to recommend personalized content based on this data. They also face challenges in accurately assessing mental state and providing prompt counseling. In particular, more advanced analysis and personalization are needed to provide appropriate relaxation content and entertainment during times of high stress.

[1663] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 2 is realized by the following means.

[1664] In this invention, the server includes means for collecting user's life rhythm and activity data from IoT devices, means for transmitting the data to the server via the user's information terminal, means for analyzing the data in the server and detecting disruption of the user's life rhythm or decline in activity, means for the server to send a link to an online questionnaire to the user based on the detection results, means for receiving the web questionnaire results from the user, performing psychoanalysis, and determining whether the user is at risk of a mental disorder, means for providing AI counseling based on the determination results, and means for the server to recommend personalized content based on the user's mental state. This makes it possible to accurately manage the user's mental state, provide appropriate and prompt counseling, and recommend personalized content according to the user's mental state.

[1665] An "IoT device" is a device that collects data on a user's daily rhythm and activity, and includes wearable devices such as smartwatches and fitness trackers.

[1666] "User's information terminal" refers to a device, such as a smartphone or tablet, that transmits data collected from IoT devices to a server.

[1667] The "server" is a computer system that analyzes the user's daily rhythm and activity data, evaluates their mental state, and provides counseling.

[1668] "Disruption of daily rhythm" refers to a significant change in normal life patterns, including lack of sleep and abnormal increases or decreases in the amount of exercise.

[1669] "Decreased activity" refers to a significant decrease in a user's normal activity level, including a decrease in the number of steps taken or a sudden decrease in the amount of exercise.

[1670] "Web questionnaire" refers to a series of questions or surveys administered via the Internet to understand the user's mental state.

[1671] "Psychoanalysis" refers to the process of evaluating and analyzing a user's mental state using a generative AI model based on the results of a user's online questionnaire and collected data.

[1672] "Pre-mental illness" refers to a state in which a user is judged to be at high risk of developing mental problems.

[1673] "AI counseling" is a counseling service provided to users through a chatbot, and is provided using a generative AI model.

[1674] "Personalized content" refers to content that is individually recommended based on the user's mental state, and includes, in particular, relaxation music and entertainment content for relaxation.

[1675] A "generative AI model" is an artificial intelligence model trained on large amounts of data and used to analyze the user's mental state.

[1676] A "prompt" refers to an instruction sentence that recommends content appropriate to the user based on the mental state assessment results of the generative AI model.

[1677] The present invention configures a system that includes an IoT device, a user's information terminal, and a server, and by combining these systems with a generative AI model and an emotion engine, it monitors the user's daily rhythm and activity data and manages their mental health with high accuracy.

[1678] Data collection

[1679] The server collects data on users' daily rhythms and activities from IoT devices. Specifically, wearable devices such as smartwatches and fitness trackers record data such as heart rate, number of steps, and sleep time in real time. As users wear these devices on a daily basis, data is collected periodically.

[1680] Data transmission

[1681] The user's information terminal (such as a smartphone) periodically transmits data collected from IoT devices to a server. Data transmission is performed using wireless communication methods such as Bluetooth or Wi-Fi, and the data is automatically uploaded from the smartphone to the server at specific intervals.

[1682] Data analysis

[1683] The server receives the transmitted data and uses the generative AI model to analyze the lifestyle and activity data. This analysis compares it with past data to detect, for example, a sudden decrease in activity or a change in sleep pattern. Specifically, it analyzes data from the past week for a decrease in activity or abnormal sleep patterns to detect disruptions to lifestyle and a decline in activity.

[1684] Anomaly detection notification

[1685] If the server detects an abnormality, it will send a notification to the user based on the results. The notification will be sent to the user's information device via push notification or email, and will display a message such as, "Your activity level has decreased recently. Please answer the online questionnaire to confirm."

[1686] Conducting online questionnaires

[1687] Users receive a notification and answer the online questionnaire. They click the link in the notification on their information device to access the questionnaire form in a web browser. The questionnaire form contains a wide range of questions, including about their current mood, stress level, and sleep quality.

[1688] psychoanalysis

[1689] The server receives the user's web questionnaire results and performs a psychoanalysis using the generative AI model and emotion engine. Based on the analysis results, it determines whether the user is at risk of developing a mental illness. This emotion engine analyzes the text data and voice data extracted from the user's questionnaire responses to recognize the user's emotional state. This improves the accuracy of the analysis results.

[1690] Personalized content recommendations

[1691] The server recommends personalized content based on the user's mental state. Depending on the user's mental state, content such as relaxing music, relaxing movies, and podcasts is presented. For example, if the user's stress level is determined to be high, relaxing music or a relaxing movie is recommended. An example of a prompt provided by the generative AI model is, "Please rate the user's mental state based on the following data: heart rate: 60-70 BPM, steps: 5000-6000 steps, sleep time: 6.5 hours, and online questionnaire response: I've been feeling stressed recently."

[1692] Providing AI counseling

[1693] If the server determines that the user is at risk of developing a mental illness, it provides AI counseling. The server then activates a chatbot and provides counseling to the user. The chatbot converses with the user based on the emotional state recognized by the emotion engine, and provides advice on stress factors and how to manage mood. It provides appropriate counseling based on the user's current situation, such as asking questions like, "Is there anything that's been bothering you recently at work or in your personal life?"

[1694] In this way, by implementing the system of the present invention, which combines a generative AI model and an emotion engine, users can more accurately understand their own mental state and take appropriate and prompt action. Furthermore, by recommending personalized content according to their mental state, it is expected that the quality of their daily lives will be improved.

[1695] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1696] Step 1:

[1697] The server collects data on users' daily rhythms and activities from IoT devices. Specifically, it receives data such as heart rate, number of steps, and sleep time obtained from smartwatches and fitness trackers via the Internet. This data is sent to the server in CSV or JSON format. Because the collected data also includes date and time information, it is generally stored in log format.

[1698] Input: Life rhythm and activity data from IoT devices (heart rate, steps, sleep time, etc.)

[1699] Output: Raw data recorded in a database on the server

[1700] Step 2:

[1701] The terminal periodically transmits data collected from IoT devices to the server. Data transmission is performed using wireless communication such as Bluetooth or Wi-Fi. The transmission interval can be changed by setting, and data is automatically uploaded once a day or every hour.

[1702] Input: Raw data collected from IoT devices

[1703] Output: Life rhythm and activity data uploaded to the server

[1704] Step 3:

[1705] The server analyzes the received data. Specifically, it uses a generative AI model to compare it with past data and analyze daily rhythms and activity patterns. For example, it applies an anomaly detection algorithm to detect decreased activity or abnormal sleep patterns from data from the past week. It also uses statistical methods to identify outliers.

[1706] Input: Life rhythm and activity data uploaded to the server

[1707] Output: Abnormal pattern detection results (decreased activity, abnormal sleep patterns, etc.)

[1708] Step 4:

[1709] If the server detects an abnormality based on the analysis results, it will send a notification to the user. This notification will be sent to the user's device, such as a smartphone or tablet, as a push notification or email. The notification content will include a message such as, "Your activity level has decreased recently. Please answer the online questionnaire to confirm."

[1710] Input: Abnormal pattern detection results

[1711] Output: An error notification message sent to the user's device

[1712] Step 5:

[1713] The user receives a notification on their device and answers the web questionnaire. They click on the link in the notification to access the questionnaire form in their web browser. The questionnaire form contains a wide range of questions, such as their current mood, stress level, and sleep quality. The user's answers are sent to the server in real time.

[1714] Input: Notification message, user's answers to the medical questionnaire

[1715] Output: Web questionnaire results saved on the server

[1716] Step 6:

[1717] The server receives the results of the web questionnaire and performs a psychological analysis using the generative AI model and emotion engine. Based on the analysis results, it determines whether the user is at risk of developing a mental illness. At this time, the text data and voice data from the questionnaire form are analyzed. An example of a prompt provided by the generative AI model is, "Please assess the user's mental state based on the following data: heart rate: 60-70 BPM, steps: 5000-6000, sleep time: 6.5 hours, web questionnaire response: I've been feeling stressed recently."

[1718] Input: Web questionnaire results (text data, voice data)

[1719] Output: Psychoanalysis result (whether or not the individual is at risk of developing a mental illness)

[1720] Step 7:

[1721] The server then recommends optimal content to the user based on the results of the mental state assessment. In particular, it presents relaxation music, distracting movies, podcasts, etc., depending on the user's emotional state. For example, if the stress level is determined to be high, it will recommend relaxation music. In addition, personalized content is recommended based on prompts provided by the generative AI model.

[1722] Input: Psychoanalysis result

[1723] Output: Personalized content recommendations displayed on the user's device

[1724] Step 8:

[1725] If the server determines that the user is at risk of developing a mental illness, it provides AI counseling. The server then activates a chatbot and provides counseling to the user. Based on the emotional state recognized by the emotion engine, the chatbot engages in a dialogue with the user and provides advice on stress factors and how to manage mood. An example of counseling is an exchange such as, "Is there anything that's been bothering you lately, at work or in your personal life?"

[1726] Input: Psychoanalysis result

[1727] Output: Counseling content provided by AI chatbot

[1728] 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 control target 443 to output the result of the specific processing. The microphone 238 acquires voice indicating a user input regarding the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[1729] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[1730] 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 the present disclosure is not limited to this, and the specific processing may be performed by the robot 414.

[1731] The emotion identification model 59 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 an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[1732] FIG. 9 is a diagram illustrating an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. Emotions closer to the center of the concentric circles are more primitive. Emotions representing states and actions arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion includes both affect and mental states. Emotions generally generated from reactions occurring in the brain are arranged on the left side of the concentric circles. Emotions generally induced by situational judgment are arranged on the right side of the concentric circles. Emotions generally generated from reactions occurring in the brain and induced by situational judgment are arranged on the upper and lower sides of the concentric circles. Furthermore, the emotion of "pleasure" is arranged on the upper side of the concentric circles, and the emotion of "discomfort" is arranged on the lower side. In this way, in the emotion map 400, multiple emotions are mapped based on the structure by which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[1733] These emotions are distributed in the 3 o'clock direction on emotion map 400, and typically fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

[1734] The inside of emotion map 400 represents what is going on in the mind, and the outside of emotion map 400 represents behavior, so the further you go outside emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[1735] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is indicated, and when they approach the ideal, a state of pleasure is indicated. Emotions can also be created for robots, automobiles, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is indicated, and when they approach the ideal, a state of pleasure is indicated. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on Voice Emotion Recognition and Emotional Brain Physiological Signal Analysis Systems, Tokushima University, Doctoral Dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the "reaction" domain, where sensation is dominant. The right half of the emotion map lists emotions belonging to the "situation" domain, where situational awareness is dominant.

[1736] The emotion map defines two emotions that promote learning. One is a negative emotion on the situation side, around the middle of "repentance" or "reflection." In other words, this occurs 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 a positive emotion on the response side, around "desire." In other words, this ...

Claims

1. A means for collecting user lifestyle and activity data from IoT devices; means for transmitting the data to a server via the user's terminal; means for analyzing the data in the server and detecting a disruption of life rhythm or a decrease in activity; a means for the server to send a link to a web questionnaire to the user based on the detection result; a means for receiving the results of the web questionnaire from the user, conducting a psychoanalysis, and determining whether the user is at risk of depression; A means for providing AI counseling based on the determination result; A system including:

2. The system according to claim 1, wherein the psychoanalysis means uses an AI model to analyze the interview results and determine whether or not the subject is at risk of depression.

3. The system according to claim 1 , wherein the AI ​​counseling means provides counseling to the user through a chatbot.

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