System

Abstract

A system is provided.SOLUTION: A system including a means for collecting body temperature, activity amount, excretion state, and meal data of a baby by a sensor module, a means for transmitting the collected data to a server in real time, a means for storing the data received by the server in a database, a means for analyzing the stored data by using an AI algorithm, a means for generating a warning or an advice based on a result of the analysis, a means for notifying a terminal of a user of the generated warning or advice, and a means for displaying the notification on the terminal of the user.SELECTED DRAWING: Figure 1

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] Babies cannot speak and cannot communicate to their parents what they want to say or changes in their health. As a result, parents, especially those raising a child for the first time, often find it difficult to grasp their baby's condition, which can cause great anxiety. There is a need for a system that can detect subtle changes in a baby's health early and take appropriate measures. [Means for solving the problem]

[0005] The present invention provides a system that includes a means for collecting data on a baby's body temperature, activity level, excretion status, and dietary diet using a sensor module, a means for transmitting the collected data to a server in real time, a means for storing the data received by the server in a database, a means for analyzing the stored data using an AI algorithm, a means for generating warnings and advice based on the analysis results, a means for notifying the user of the generated warnings and advice on their device, and a means for displaying the notifications on their device, thereby automatically detecting subtle changes in a baby's physical condition and enabling parents to take appropriate action quickly.

[0006] The "sensor module" is a set of sensor devices that collects data on a baby's temperature, activity, bowel movements, and diet.

[0007] "Body temperature" refers to data measuring the baby's body temperature.

[0008] "Activity level" refers to data that measures the amount of movement or exercise a baby undergoes.

[0009] "Excretion status" refers to data that detects the condition of a baby's urine and feces.

[0010] "Dietary Data" refers to information about the amount and time of milk or meals consumed by the baby.

[0011] "Real-time" means that data is processed as soon as it is collected, with little to no delay.

[0012] "Server" means a centralized computer system that receives, stores, and analyzes collected data.

[0013] A "database" is a system that efficiently organizes and manages data stored on a server and makes it searchable.

[0014] An "AI algorithm" is an artificial intelligence calculation procedure that analyzes large amounts of data, recognizes patterns, and makes judgments.

[0015] "Analysis" is the process of analyzing collected data and extracting meaningful information.

[0016] A "warning" is a notification sent to alert parents when an abnormal condition or risk is detected.

[0017] "Advice" is specific instructions for parents to take appropriate action based on the detected condition.

[0018] "User device" refers to a device such as a smartphone or tablet used by a parent or guardian.

[0019] A "notification" is a message sent from the server to the user's terminal.

[0020] "Display" refers to the process of visually presenting information on a user's terminal screen. [Brief explanation of the drawings]

[0021] [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

[0022] 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.

[0023] First, the terms used in the following description will be explained.

[0024] 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).

[0025] 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.

[0026] 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.

[0027] 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.

[0028] 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."

[0029] [First embodiment]

[0030] FIG. 1 shows an example of the configuration of a data processing system 10 according to the first embodiment.

[0031] 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.

[0032] 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).

[0033] 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.

[0034] 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.

[0035] 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.

[0036] 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.

[0037] FIG. 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0038] 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.

[0039] 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.

[0040] 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.

[0041] 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."

[0042] This invention is a system that collects and analyzes data on a baby's body temperature, activity level, excretion status, and dietary intake, and provides warnings and advice to parents as needed. This system is composed of a sensor module, a server, and a user's device.

[0043] Data collection (terminal)

[0044] The device consists of a sensor module attached to the baby, which contains the following sensors:

[0045] Body temperature sensor: Measures baby's skin temperature.

[0046] Activity sensor: Uses a camera and accelerometer to record baby's movements.

[0047] Excretion sensor: Detects changes in humidity and temperature inside the diaper.

[0048] Meal data entry: Parents manually enter the amount and time their baby eats into the app.

[0049] The data obtained from these sensors is transmitted by the terminal to a server in real time.

[0050] Data reception and storage (server)

[0051] The server receives data sent from the device in real time. After receiving the data, it is stored in a database. The data is organized chronologically, and the time and date of collection of the data are clearly managed.

[0052] Data analysis (server)

[0053] The server is equipped with an AI algorithm that analyzes the data stored in the database, detecting changes in physical condition such as:

[0054] Abnormal body temperature: Detects hyperthermia or hypothermia when the body temperature is outside the normal range.

[0055] Activity anomalies: Detect increases or decreases in movement that differ from normal activity patterns.

[0056] Excretory abnormalities: Detect frequent or absent bowel movements.

[0057] The AI ​​algorithm evaluates the baby's condition by comparing it with previously learned health data, allowing it to detect even subtle changes that would normally go unnoticed.

[0058] Generating Warnings and Advice (Server)

[0059] Based on the analysis results, the server will generate necessary warnings and advice, for example, if the baby's temperature is abnormally high, it will generate a warning message saying "Baby's temperature is too high. Please check with a thermometer and consult a doctor."

[0060] Notifications and displays (user device)

[0061] The generated warnings and advice are sent as push notifications to the user's device. The user's device (smartphone, etc.) displays the received warnings and advice as visual notifications. Tapping the notification launches a dedicated app, which displays detailed recommended actions.

[0062] Specific examples

[0063] For example, if a baby's temperature is 37.8 degrees Celsius at 9:00 a.m. one day, exceeding the normal range, the system will operate as follows:

[0064] 1. Data collection: The device's body temperature sensor measures 37.8 degrees and sends the data to the server.

[0065] 2. Data reception and storage: The server receives the data and stores it in the database.

[0066] 3. Data analysis: AI algorithms analyze this temperature data and detect anything outside the normal range.

[0067] 4. Generate Alert: The server generates an alert saying "May have a fever, recheck with a thermometer and consult a doctor."

[0068] 5. Notification and display: A warning is pushed to the user's smartphone and the user can check it.

[0069] In this way, the system of the present invention can quickly detect subtle changes in a baby's physical condition and provide specific advice for appropriate responses, thereby reducing the anxiety of parents, especially those raising children for the first time, and supporting the health management of their babies.

[0070] The processing flow will be explained below.

[0071] Step 1:

[0072] The device collects data from a sensor module attached to the baby: a temperature sensor measures the baby's temperature, an activity sensor detects the baby's movements, a toilet sensor detects the baby's bowel movements, and dietary data is manually entered by the parent.

[0073] Step 2:

[0074] The data collected by the device is sent to the server in real time. Specifically, the device collects various data acquired from sensors and sends them to the server via Wi-Fi or mobile networks.

[0075] Step 3:

[0076] The server stores the data received from the terminal in a database. Specifically, the server inserts the received data into the database using SQL queries and organizes it in chronological order.

[0077] Step 4:

[0078] The server periodically retrieves the data stored in the database and analyzes it using an AI algorithm. Specifically, the server retrieves the latest data from the database and inputs it into the AI ​​model to detect outliers and patterns.

[0079] Step 5:

[0080] The server generates warnings and advice based on the analysis results of the AI ​​algorithm. Specifically, if the temperature is abnormally high, it generates a message saying, "Your baby's temperature is too high. Please check with a thermometer and consult a doctor."

[0081] Step 6:

[0082] The server notifies the user of the warnings and advice it generates by sending messages to the user's smartphone using a push notification service.

[0083] Step 7:

[0084] The user's device receives the notification and displays it visually. Specifically, a message appears in the smartphone's notification bar, and tapping the notification opens a dedicated app with detailed information and advice on how to respond.

[0085] Step 8:

[0086] The user checks the notification and takes the necessary action, such as rechecking the baby's temperature and consulting a doctor.

[0087] Example 1

[0088] 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."

[0089] In childcare, it is important to accurately and quickly understand a baby's health status, including body temperature, activity level, excretion status, and feeding status. However, manually managing this data is time-consuming and prone to errors. It is also difficult to respond immediately if an abnormality is detected. Therefore, there is a need for a system that can solve these issues, automatically monitor a baby's health status, and provide necessary advice.

[0090] 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.

[0091] In this invention, the server includes means for collecting data on the baby's body temperature, activity level, excretion status, and dietary intake using a sensor module, means for transmitting the collected data in real time to a central processing unit, means for storing the data received by the central processing unit in a data storage device, means for analyzing the stored data using an AI algorithm, means for generating countermeasures and advice based on the analysis results, means for notifying the user's communication terminal of the generated countermeasures and advice, and means for displaying the notification on the user's communication terminal. This makes it possible to accurately and quickly grasp the baby's health condition and take necessary measures in a timely manner.

[0092] A "sensor module" refers to a device that combines multiple sensors to measure a baby's health.

[0093] "Body temperature" refers to data measuring the temperature of a baby's skin and internal body.

[0094] "Activity level" refers to data that records the baby's movements and amount of exercise.

[0095] "Excretion status" refers to data regarding the baby's urination and defecation.

[0096] "Dietary data" refers to information about the amount and time of meals a baby eats.

[0097] "Real-time" means that data is processed and transmitted immediately without delay.

[0098] "Central Processing Unit" refers to a central computer system for receiving, analyzing, and otherwise processing data.

[0099] "Data storage device" refers to a storage device for saving received data.

[0100] "AI algorithm" refers to a computational method that uses artificial intelligence technology to analyze data and detect anomalies or specific patterns.

[0101] "Countermeasures" refer to appropriate ways of dealing with specific situations and guidelines for action that are generated based on the results of data analysis.

[0102] "Advice" refers to suggestions or recommendations regarding health care provided based on the results of the analysis.

[0103] "Communication terminal" refers to electronic devices such as smartphones and tablets used by users.

[0104] "Notification" refers to an alert or message that notifies the user of a generated remedy or advice.

[0105] "Display" refers to providing the notified content in a visually visible form on the user's communication terminal.

[0106] "Thermometry device" refers to a sensor device for measuring a baby's body temperature.

[0107] "Video capture device" refers to a camera or video recording device used to record a baby's activity.

[0108] To implement the present invention, the following system configuration and specific means are used to monitor the baby's health condition in real time and provide warnings and advice to parents as necessary.

[0109] This system consists of the following main hardware and software:

[0110] Sensor Module

[0111] Central Processing Unit (Server)

[0112] Data storage device (database)

[0113] Communication device (user's smartphone or tablet)

[0114] Sensor Module

[0115] The sensor module is worn on the baby and contains the following sensors:

[0116] Body temperature sensor: Measures the baby's skin temperature. Example: If the body temperature is 37.8 degrees.

[0117] Activity sensors: Use cameras and accelerometers to record your baby's movements, for example if your baby is moving less than usual.

[0118] Excretion sensor: Detects changes in humidity and temperature inside the diaper. For example, it detects whether the diaper is wet.

[0119] Meal data entry: Parents manually enter data through the app.

[0120] Central Processing Unit and Data Storage Unit

[0121] The data obtained from the sensor module is sent in real time via the user's device to a central processing unit (server). The server receives this data and stores it in a database. The data is organized in chronological order and can be easily referenced.

[0122] Data analysis and result generation

[0123] The server is equipped with an AI algorithm that analyzes the data stored in the database, detecting anomalies such as:

[0124] Abnormal body temperature: Detects hyperthermia or hypothermia when the body temperature deviates from the normal range (36.5-37.5°C).

[0125] Activity anomalies: Detect increases or decreases in movement that differ from normal activity patterns.

[0126] Excretory abnormalities: Detect frequent or absent bowel movements.

[0127] The AI ​​algorithm detects subtle changes in your physical condition by comparing them with previously learned health data.

[0128] Generating warnings and advice

[0129] Based on the analysis results, the server generates necessary warnings and advice, for example, if the temperature reaches 37.8 degrees, it generates a warning saying "Possible fever, recheck with a thermometer and consult a doctor."

[0130] Notifications and Displays

[0131] The generated warnings and advice are pushed to the user's communication device, where they are visually displayed and, when tapped, launch a dedicated app that displays detailed instructions on how to respond.

[0132] Specific examples

[0133] For example, if a baby's temperature reaches 37.8°C at 9:00 AM on a certain day, the following process will occur:

[0134] 1. Data collection: The body temperature sensor measures 37.8 degrees and sends the data to the server.

[0135] 2. Data reception and storage: The server receives the body temperature data and stores it in the database.

[0136] 3. Data analysis: AI algorithms analyze this temperature data and detect temperatures outside the normal range.

[0137] 4. Alert Generation: The server generates an alert saying "Possible fever, recheck with thermometer and consult doctor."

[0138] 5. Notification and display: A warning is pushed to the user's smartphone and the user can check it.

[0139] Prompt Sentence Examples

[0140] The input prompts for the generative AI model are:

[0141] "Generate an alert if the baby's temperature exceeds the normal range."

[0142] "Detect abnormalities when your baby's activity level falls outside the normal range."

[0143] "Generate advice if your baby's bowel frequency is abnormal."

[0144] The system of the present invention makes it possible to accurately and quickly grasp the baby's health condition and provide appropriate advice to parents, thereby reducing the anxiety of parents, especially those raising a child for the first time, and supporting the health management of their baby.

[0145] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0146] Step 1: Data collection (user device)

[0147] The user's device collects body temperature, activity levels, and excretion status in real time from a sensor module attached to the baby. The device receives various data from the sensor module as input and temporarily stores it. Specifically, the body temperature sensor measures the baby's skin temperature, the activity sensor records the baby's movements, and the excretion sensor detects humidity and temperature changes inside the diaper. Each collected data is converted into a standard format for transmission to the server in the next step.

[0148] Step 2: Send data (user's device)

[0149] The user's device transmits the data collected and temporarily stored in step 1 to the server in real time. It receives the temporarily stored data in the device as input, converts it into packet format, and transmits it. Specifically, body temperature data, movement patterns, and logs of humidity and temperature changes are transmitted to the server via a secure communication channel.

[0150] Step 3: Data reception and storage (server)

[0151] The server receives data sent from the user's device in real time. It takes the received data as input and stores it in a database. The data is organized in chronological order, and each piece of data is stored with a timestamp. Specifically, the time at which data such as a body temperature of 37.8 degrees and activity records were collected is clearly managed.

[0152] Step 4: Data analysis (server)

[0153] The server uses an AI algorithm to analyze the data stored in the database. It receives stored body temperature data, activity data, and excretion data as input and analyzes them. Specifically, if the body temperature exceeds the normal range (36.5-37.5°C), it detects high or low fever. If the activity pattern differs from the normal, it is also detected as an abnormality. The output of the analysis is notification data or advice data if an abnormality is detected.

[0154] Step 5: Generating Warnings and Advice (Server)

[0155] The server generates warnings and advice based on the analysis results of step 4. It receives the abnormal data from the analysis results as input and creates appropriate warning messages and advice based on that. Specifically, if the body temperature reaches 37.8 degrees, a warning is generated stating, "Possible fever. Check with a thermometer and consult a doctor." The generated message is sent to the user's device in the next step.

[0156] Step 6: Notifications and Display (User's Device)

[0157] The user's device receives warnings and advice sent from the server. It receives notification data from the server as input and displays it visually. Specifically, a warning message appears on the smartphone saying, "Your temperature is too high. Please check with a thermometer and consult a doctor." Tapping the notification launches a dedicated app, which displays detailed instructions on how to respond.

[0158] Through the above steps, the system can accurately and quickly monitor the baby's health status and provide necessary advice to parents.

[0159] (Application example 1)

[0160] 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."

[0161] There is a need for real-time monitoring of baby's health and for parents to receive appropriate warnings and advice. However, conventional systems simply collect data and do not provide support for parents in selecting the most appropriate childcare products. This often makes it difficult for parents to respond appropriately and select the right products.

[0162] 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.

[0163] In this invention, the server includes means for suggesting childcare products suitable for the baby's health condition based on the analysis results, and means for allowing the user to view detailed information on the suggested childcare products in the virtual store. This allows parents to easily select appropriate childcare products according to the baby's health condition, reducing their anxiety about childcare.

[0164] A "sensor module" is a device used to collect data on a baby's temperature, activity, bowel movements, and diet.

[0165] A "thermosensor" is a sensor used to measure the temperature of a baby's skin.

[0166] "Photography equipment" refers to cameras or video equipment used to record the baby's activity.

[0167] A "server" is a computer system whose role is to receive collected data, store it in a database, and analyze it using AI algorithms.

[0168] A "database" is an information system for organizing, storing, and managing collected data in chronological order.

[0169] An "AI algorithm" is an artificial intelligence technology that analyzes collected data and performs anomaly detection and prediction.

[0170] "Warnings and advice" are notifications and recommended actions to parents that are generated based on the analysis results.

[0171] A "user device" is a parent's device (e.g., a smartphone or tablet) that can receive warnings and advice.

[0172] "Notification" refers to the sending of a message to a user's terminal in real time.

[0173] "Childcare products" refers to items used depending on the baby's health condition (e.g., thermometers, cooling sheets, etc.).

[0174] A "virtual store" is a virtual shopping space where you can purchase childcare products online.

[0175] MODE FOR CARRYING OUT THE INVENTION

[0176] This invention is a system that collects and analyzes a baby's body temperature, activity level, excretion status, and dietary data, and provides warnings and advice to parents as needed. It also has a function to suggest appropriate childcare products in a virtual store. The system consists of a sensor module, a server, and a user's terminal.

[0177] Data collection (terminal)

[0178] The sensor module includes the following sensors:

[0179] Thermosensor: Measures baby's skin temperature.

[0180] Camera: Records the baby's movements.

[0181] Excretion sensor: Detects changes in humidity and temperature inside the diaper.

[0182] Meal data entry: Parents manually enter the amount and time their baby eats into the app.

[0183] The data obtained from these sensors is transmitted in real time by the sensor modules to a server.

[0184] Data reception and storage (server)

[0185] The server receives data sent from the device in real time and stores it in a database. The data is organized chronologically, and the time and date of collection are clearly managed.

[0186] Data analysis (server)

[0187] The server is equipped with an AI algorithm that analyzes the data stored in the database, detecting changes in physical condition such as:

[0188] Abnormal body temperature: Detects hyperthermia or hypothermia when the body temperature is outside the normal range.

[0189] Activity anomalies: Detect increases or decreases in movement that differ from normal activity patterns.

[0190] Excretory abnormalities: Detect frequent or absent bowel movements.

[0191] The AI ​​algorithm evaluates the baby's condition by comparing it with previously learned health data, allowing it to detect even subtle changes that would normally go unnoticed.

[0192] Generating Warnings and Advice (Server)

[0193] Based on the analysis results, the server will generate necessary warnings and advice, for example, if the baby's temperature is abnormally high, it will generate a warning message saying "Baby's temperature is too high. Please check with a thermometer and consult a doctor."

[0194] Notifications and displays (user device)

[0195] The generated warnings and advice are sent to the user's device via push notification. The user's device (smartphone, etc.) displays the received warnings and advice as a visual notification. Tapping the notification launches a dedicated app, which displays detailed recommended actions.

[0196] Proposing childcare products in a virtual store

[0197] Furthermore, the system has the function of suggesting baby products suitable for the baby's health condition based on the analysis results.In the virtual store, users can view detailed information about the suggested baby products and easily purchase the necessary items.

[0198] Example

[0199] For example, if a baby's temperature is 37.8 degrees Celsius at 9:00 a.m. one day, exceeding the normal range, the system will operate as follows:

[0200] 1. Data collection: The device's thermosensor measures the temperature of 37.8 degrees and sends it to the server.

[0201] 2. Data reception and storage: The server receives the data and stores it in the database.

[0202] 3. Data analysis: AI algorithms analyze this temperature data and detect anything outside the normal range.

[0203] 4. Generate Alert: The server generates an alert saying "May have a fever, recheck with a thermometer and consult a doctor."

[0204] 5. Notification and display: A warning is pushed to the user's smartphone and the user can check it.

[0205] 6. Baby Product Suggestions: The virtual store will suggest suitable baby products, such as cooling pads or a new thermometer.

[0206] Examples of prompt statements

[0207] "Generate a message recommending a cooling sheet or thermometer if your baby has a high temperature."

[0208] In this way, the system can quickly detect subtle changes in a baby's physical condition and provide specific advice on how to respond appropriately. Furthermore, by suggesting baby care products in the virtual store, parents can easily obtain the necessary items, making childcare even smoother.

[0209] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0210] Step 1:

[0211] The sensor module collects data on the baby's body temperature, activity level, excretion status, and dietary intake. The collected data is temporarily stored within the sensor module. The input is biological and dietary information obtained from the sensor, and the output is sent to a terminal as organized data.

[0212] Step 2:

[0213] The terminal receives the organized data from the sensor module and transmits it to the server in real time. The input is the data received by the terminal, and the output is the raw data transmitted to the server.

[0214] Step 3:

[0215] The server receives the data sent from the terminal and stores it in a database. The input is the raw data received by the server, and the output is the stored data in the database organized in chronological order.

[0216] Step 4:

[0217] The server analyzes the stored data using an AI algorithm. The AI ​​algorithm detects abnormalities in body temperature, activity level, and excretion status, and outputs the results. The input is the data stored in the database, and the output is the abnormality detection result. For example, if body temperature exceeds 37.8 degrees, it will be detected as an abnormality.

[0218] Step 5:

[0219] The server generates warnings and advice based on the analysis results. The input is the anomaly detection result, and the output is the generated warning message or advice. Specifically, if an abnormal temperature is detected, a message is generated saying, "Your baby's temperature is too high. Check again with a thermometer and consult a doctor."

[0220] Step 6:

[0221] The server sends the generated warnings and advice to the user's device via push notifications. The input is the generated warning message, and the output is the notification sent to the user's device.

[0222] Step 7:

[0223] It visually displays notifications received by the user's device, allowing the user to check the notification and view more information if necessary. The input is the received alert message, and the output is the displayed notification and more information.

[0224] Step 8:

[0225] The server then recommends appropriate childcare products based on the analysis results. The input is the analysis results, and the output is a list of suggested childcare products. For example, if the child has a high temperature, it might suggest a cooling sheet or a new thermometer.

[0226] Step 9:

[0227] The user's device displays detailed information about the proposed childcare products in the virtual store. The input is a list of the proposed childcare products, and the output is the display of detailed information. The user can then select and purchase the products they need.

[0228] In this way, the system can monitor the baby's health in real time and provide appropriate treatment and support.

[0229] 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.

[0230] This invention combines a system that collects and analyzes a baby's body temperature, activity level, excretion status, and dietary data, and provides warnings and advice to parents as needed, with an emotion engine that recognizes the user's emotions. This system consists of a sensor module, a server, an emotion engine, and a user's device.

[0231] Data collection (terminal)

[0232] The device consists of a sensor module attached to the baby, which contains the following sensors:

[0233] Body temperature sensor: Measures baby's skin temperature.

[0234] Activity sensor: Uses a camera and accelerometer to record baby's movements.

[0235] Excretion sensor: Detects changes in humidity and temperature inside the diaper.

[0236] Meal data entry: Parents manually enter the amount and time their baby eats into the app.

[0237] The data obtained from these sensors is transmitted by the terminal to a server in real time.

[0238] Data reception and storage (server)

[0239] The server receives data sent from the device in real time. After receiving the data, it is stored in a database. The data is organized chronologically, and the time and date of collection of the data are clearly managed.

[0240] Data analysis (server)

[0241] The server is equipped with an AI algorithm that analyzes the data stored in the database, detecting changes in physical condition such as:

[0242] Abnormal body temperature: Detects hyperthermia or hypothermia when the body temperature is outside the normal range.

[0243] Activity anomalies: Detect increases or decreases in movement that differ from normal activity patterns.

[0244] Excretory abnormalities: Detect frequent or absent bowel movements.

[0245] The AI ​​algorithm evaluates the baby's condition by comparing it with previously learned health data, allowing it to detect even subtle changes that would normally go unnoticed.

[0246] Generating Warnings and Advice (Server)

[0247] Based on the analysis results, the server will generate necessary warnings and advice, for example, if the baby's temperature is abnormally high, it will generate a warning message saying "Baby's temperature is too high. Please check with a thermometer and consult a doctor."

[0248] Notifications and displays (user device)

[0249] The generated warnings and advice are sent as push notifications to the user's device. The user's device (smartphone, etc.) notifies and displays the received warnings and advice. Tapping the notification launches a dedicated app, which displays detailed recommended actions.

[0250] Introduction of emotion engine (server and user terminal)

[0251] The system also incorporates an emotion engine that recognizes the user's emotions. Specifically, it operates as follows:

[0252] The server collects the user's voice and facial recognition data and analyzes it using an emotion engine to detect when the user is in a particular emotional state, such as stress, worry, or anxiety.

[0253] Based on the emotion engine's analysis, the server adjusts the tone and content of warnings and advice. For example, if the user is feeling very stressed, a more friendly and reassuring message will be generated.

[0254] Specific examples

[0255] For example, if a baby has a fever and the user (mother) is very anxious, the system operates as follows:

[0256] 1. Data collection: The device's body temperature sensor measures 37.8 degrees and sends the data to the server.

[0257] 2. Data reception and storage: The server receives the data and stores it in the database.

[0258] 3. Data analysis: AI algorithms analyze body temperature data and detect when it is outside the normal range.

[0259] 4. Emotion data collection and analysis: When a user opens the app, the emotion engine detects the user's anxiety through voice and facial recognition.

[0260] 5. Alert Generation: The server generates a friendly tone alert saying "Your baby's temperature is too high. Please calm down, recheck with a thermometer and consult a doctor."

[0261] 6. Notification and display: A warning is pushed to the user's smartphone and the user can check it.

[0262] In this way, the system of the present invention can quickly detect subtle changes in a baby's physical condition and provide appropriate responses according to the user's emotional state, thereby reducing anxiety, especially for parents raising a child for the first time.

[0263] The processing flow will be explained below.

[0264] Step 1:

[0265] The device collects data from sensor modules attached to the baby: a temperature sensor measures the baby's temperature, an activity sensor records the baby's movements, a toilet sensor detects the baby's bowel movements, and dietary data is manually entered into the app by the parent.

[0266] Step 2:

[0267] The data collected by the device is sent to the server in real time. Specifically, the device collects various data acquired from sensors and sends them to the server via Wi-Fi or mobile networks.

[0268] Step 3:

[0269] The server receives the data sent from the device. Specifically, the server's API receives the HTTP request and retrieves the data.

[0270] Step 4:

[0271] The server stores the received data in a database. Specifically, it issues an SQL query to insert the data into the database and organizes it in chronological order.

[0272] Step 5:

[0273] The server periodically retrieves the data stored in the database and analyzes it using an AI algorithm. Specifically, the server retrieves the latest data from the database and inputs it into the AI ​​model to detect outliers and patterns.

[0274] Step 6:

[0275] The server generates warnings and advice based on the analysis results of the AI ​​algorithm. Specifically, if the temperature is abnormally high, it generates a message saying, "Your baby's temperature is too high. Please check with a thermometer and consult a doctor."

[0276] Step 7:

[0277] When a user opens the app, the device collects emotion data, specifically voice data and facial recognition data, and sends it to the emotion engine.

[0278] Step 8:

[0279] The emotion engine installed on the server analyzes the user's emotion data, specifically using voice and facial recognition algorithms to detect the user's emotional state (e.g., stress or anxiety).

[0280] Step 9:

[0281] The server adjusts the content and tone of warnings and advice based on the analysis results of the emotion engine. Specifically, if the user is feeling anxious, it generates a friendly and reassuring message such as, "Your baby's temperature is too high. Please calm down, check again with a thermometer, and consult a doctor."

[0282] Step 10:

[0283] The server notifies the user of the warnings and advice it generates by sending messages to the user's smartphone using a push notification service.

[0284] Step 11:

[0285] The user's device receives the notification and displays it visually. Specifically, a message appears in the smartphone's notification bar, and tapping the notification opens a dedicated app with detailed information and advice on how to respond.

[0286] Step 12:

[0287] The user checks the notification and takes the necessary action, such as rechecking the baby's temperature and consulting a doctor.

[0288] Example 2

[0289] 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."

[0290] Monitoring a baby's physical condition and health is extremely important in childcare. However, it is difficult to quickly detect subtle changes or abnormalities in a baby's physical condition, which can be very worrying for first-time parents. Furthermore, there is a lack of technological means to provide appropriate advice based on parents' emotions and stress levels. Under these circumstances, there is a need for a system that can monitor a baby's health in real time and provide parents with peace of mind.

[0291] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[0292] In this invention, the server includes means for collecting data on the baby's body temperature, activity level, excretion status, and dietary intake using a sensor module, means for transmitting the collected data in real time to a network device, means for storing data received by the network device in a storage device, means for analyzing the stored data using a machine learning algorithm, means for generating a warning or advice based on the analysis results, means for notifying the generated warning or advice on a user's display device, means for displaying the warning or advice on the user's display device, means for collecting user voice data and facial recognition data and analyzing it using an emotion recognition engine, means for adjusting the content of the warning or advice based on the analyzed emotion data, and means for notifying the user's display device of the warning or advice according to the emotion. This makes it possible to quickly detect subtle changes in the baby's physical condition and provide appropriate responses and advice according to the user's emotional state.

[0293] The "sensor module" is a device that collects data on a baby's body temperature, activity level, excretion status, and dietary intake.

[0294] A "body temperature sensor" is a temperature sensing device used to measure a baby's skin temperature.

[0295] An "activity sensor" is a device that uses a camera and an accelerometer to record a baby's movements.

[0296] An "excretion sensor" is a sensing device that detects changes in humidity and temperature inside a diaper.

[0297] "Collected data" is a general term for the baby's body temperature, activity level, excretion status, and dietary data obtained by the sensor module.

[0298] A "network device" is a communication device for transmitting collected data to a server in real time.

[0299] A "memory device" is a data storage device that stores received data and allows it to be accessed at a later time.

[0300] A "machine learning algorithm" is an artificial intelligence technique used for data analysis, which identifies patterns in stored data.

[0301] A "warning" is a message generated based on the results of data analysis to alert the user.

[0302] "Advice" is a recommended instruction for action provided to a user based on the results of data analysis.

[0303] A "display device" is a device for notifying and displaying generated warnings and advice to a user, and includes smartphones and tablets.

[0304] "Voice data" is information collected from the user's voice.

[0305] "Facial recognition data" refers to image or video information obtained by recognizing the user's face.

[0306] An "emotion recognition engine" is a system that analyzes collected voice data and facial recognition data to identify the user's emotional state.

[0307] This system collects and analyzes data on a baby's body temperature, activity level, excretion status, and dietary intake, and provides warnings and advice to parents based on the analysis results, while also incorporating an emotion engine that recognizes the user's emotions. This system is composed of a sensor module, a network device, a storage device, a machine learning algorithm, an emotion recognition engine, and a user's display device.

[0308] (Data collection (terminal))

[0309] The device collects data through a sensor module attached to the baby, which contains the following sensors:

[0310] Body temperature sensor: A temperature sensing device that measures the baby's skin temperature.

[0311] Activity sensor: An image capture device that uses a camera and accelerometer to record the baby's movements.

[0312] Excretion sensor: A sensing device that detects changes in humidity and temperature inside the diaper.

[0313] Meal data entry: Parents manually enter their baby's meal amounts and times using the app.

[0314] These data are transmitted in real time from the terminal to the network device.

[0315] (Data reception and storage (server))

[0316] The server receives data sent from the network device and stores it in a storage device. Specifically, the server receives data using a communication protocol such as HTTP or MQTT. The received data is organized by time, and the data collected at what point in time is clearly managed.

[0317] (Data analysis (server))

[0318] Machine learning algorithms in the server analyze the data stored in the storage device, specifically detecting anomalies such as:

[0319] Body temperature abnormality: Detects hyperthermia or hypothermia when the body temperature data exceeds a certain threshold.

[0320] Activity Anomalies: Detect abnormal increases or decreases in activity compared to normal activity patterns.

[0321] Excretory abnormalities: Detect frequent or absent bowel movements compared to normal bowel patterns.

[0322] (Emotion data collection and analysis (server and user terminal))

[0323] The server and the user's device recognize emotions using the user's voice data and facial recognition data. The specific operation is as follows:

[0324] The user launches the app and collects voice and facial information via voice input and the camera.

[0325] The server analyzes the collected information and detects whether the user is experiencing emotional states such as stress, anxiety, or worry.

[0326] (Generating Warnings and Advice (Server))

[0327] The server generates necessary warnings and advice based on the results of data analysis and the evaluation of emotional data. For example, if the baby's temperature is high, a warning such as "Your baby's temperature is too high. Please check it again with a thermometer and consult a doctor" can be generated. If the user is feeling stressed, a message in a reassuring tone can be generated, such as "You may be worried about your baby. Please check it again with a thermometer and consult a doctor if necessary."

[0328] (Notification and Display (User's Device))

[0329] The generated warnings and advice are pushed to the user's display device. When the user opens the notification on their smartphone or tablet, the app launches and displays detailed information.

[0330] Examples:

[0331] For example, if a baby has a fever and the user (mother) is in a very anxious state, the system will operate as follows:

[0332] 1. Data collection: The device's body temperature sensor measures 37.8 degrees and transmits the data to the server via the network device.

[0333] 2. Data reception and storage: The server receives the data and stores it in a storage device.

[0334] 3. Data analysis: Machine learning algorithms analyze body temperature data and detect when it is outside the normal range.

[0335] 4. Emotion data collection and analysis: When a user opens the app, the emotion recognition engine detects the user's anxiety through voice and facial recognition.

[0336] 5. Alert Generation: The server generates a friendly tone alert saying "Your baby's temperature is too high. Please calm down, recheck with a thermometer and consult a doctor."

[0337] 6. Notification and display: A warning is pushed to the user's smartphone and the user can check it.

[0338] Examples of prompts:

[0339] An example of a prompt to be input to the generative AI model is as follows:

[0340] "My baby has a fever of 37.8 degrees. Please generate a warning message that takes into account the user's emotional state and makes them feel at ease."

[0341] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0342] Step 1: Data collection (device)

[0343] The device collects data from the sensor module attached to the baby. The specific operations are as follows:

[0344] The temperature sensor continuously measures the baby's skin temperature and sends the measurements as input to the terminal. The output data is the baby's temperature data.

[0345] The activity sensors (camera and accelerometer) record the baby's movements every second and send the video and acceleration data to the terminal as input. The output data is the baby's activity data.

[0346] The excretion sensor detects changes in humidity and temperature inside the diaper and sends the change data to the terminal as input. The output data is excretion data.

[0347] For meal data input, the user manually inputs the amount and time of the baby's meal using the app, and the output data is the meal data.

[0348] Step 2: Send data (terminal)

[0349] The terminal transmits the collected data to the network device. The specific operation is as follows:

[0350] The terminal receives temperature data, activity data, excretion data, and dietary data as input and transmits them to the network device in real time. The output is data sent to the server via the network.

[0351] Step 3: Data reception and storage (server)

[0352] The server receives the data sent from the network device and stores it in the storage device. The specific operations are as follows:

[0353] The server receives input data such as body temperature, activity, excretion, and dietary data using communication protocols such as HTTP and MQTT. The output data is stored in a storage device.

[0354] The received data is organized by time, and it is clearly managed which data was collected at what point in time.

[0355] Step 4: Data analysis (server)

[0356] The machine learning algorithms in the server analyze the data stored in the storage device. Specifically, they do the following:

[0357] The server receives body temperature data, activity data, excretion data, and dietary data as input from the storage device.

[0358] Body temperature analysis: Checks whether the body temperature data exceeds a certain threshold, and if there is an abnormality, outputs the abnormal data.

[0359] Activity analysis: Activity data is compared with normal patterns to detect abnormal increases or decreases in activity and output the abnormal data.

[0360] Excretion analysis: Excretion data is compared with normal patterns to detect abnormalities and output the abnormal data.

[0361] Step 5: Emotion data collection and analysis (server and user device)

[0362] The server and the user's device collect the user's voice data and facial recognition data, and analyze their emotions. The specific operations are as follows:

[0363] The user launches the app and collects voice and facial information via voice input or camera. The output data is voice data and facial recognition data.

[0364] The server receives collected voice data and facial recognition data as input, analyzes them, and infers the user's emotional state. The output is the user's emotional state data.

[0365] Step 6: Generating Warnings and Advice (Server)

[0366] The server generates necessary warnings and advice based on the results of data analysis and the evaluation of the emotion data. Specific operations are as follows:

[0367] The server receives anomaly data and user emotional state data as input.

[0368] Based on the analysis results, a warning message such as "Your baby's temperature is too high. Please double-check with a thermometer and consult a doctor" is generated. If the user is feeling stressed, a reassuring message such as "We understand that you may be worried about your baby, but please double-check with a thermometer first and consult a doctor if necessary" is generated. The output is a warning or advice message.

[0369] Step 7: Notifications and Display (User's Device)

[0370] The generated warnings and advice are pushed to the user's display device and displayed. Specifically, the behavior is as follows:

[0371] The server sends warning and advice messages to the user terminal as input.

[0372] The user's device displays the received message in the notification bar, and when the user taps the notification, the app launches and displays more information. The output is a warning or advice message that is displayed to the user.

[0373] (Application example 2)

[0374] 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."

[0375] In modern homes, security measures that provide safety and peace of mind go beyond mere mechanical security. A responsive approach is needed, especially when household members are feeling anxious or stressed. However, current home security systems do not generate warnings or advice that take into account the user's emotional state. Therefore, more considerate and effective security measures are needed.

[0376] The identification process by the identification processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes means for collecting voice data and image data within the home, analyzing them using an emotion engine, and recognizing the user's emotional state, means for adjusting the tone and content of warnings and advice generated according to the user's emotional state, and means for analyzing the various collected home data using an AI algorithm and detecting abnormalities. This makes it possible to provide appropriate security measures that give peace of mind to members of the home who are feeling anxious or stressed.

[0377] A "sensor module" is hardware that integrates various sensors used to detect the environment and the status of members in the home.

[0378] A "thermal sensor" is a device for measuring the temperature of an object or living thing, especially when used to detect skin temperature.

[0379] An "activity sensor" is a device that detects changes in acceleration or movement. This includes motion sensors and cameras.

[0380] An "excretion sensor" is a sensor that detects changes in humidity and temperature to grasp the excretion status.

[0381] "Dietary data" refers to information such as the amount, type, and time of food consumed by household members.

[0382] A "server" is a centralized computer system that receives, stores, and analyzes data over a network.

[0383] A "database" is a collection of data that is systematically stored and constructed to allow easy searching and analysis of collected data.

[0384] "AI algorithms" are machine learning and artificial intelligence techniques used to analyze collected data and detect patterns and anomalies.

[0385] A "warning" is a notification that notifies the user when an abnormality or risk is detected in the home.

[0386] "Advice" refers to the appropriate response or recommendation provided along with the warning.

[0387] A "home terminal" is a device, such as a mobile device or computer, that a user uses to interact with or receive notifications.

[0388] An "emotion engine" is an artificial intelligence system that analyzes voice and image data to recognize the user's emotional state.

[0389] "Analysis" is the process of examining collected data in detail to discover specific patterns or anomalies.

[0390] "Real-time" means that data is collected, transmitted, and processed immediately.

[0391] A "push notification" is a highly instantaneous message sent directly from a server to a user's device.

[0392] "Speech recognition" is a technology for analyzing voice data to understand meaning and emotions.

[0393] "Facial recognition" is a technology that uses a camera to identify a person's face and determine their emotion or state.

[0394] The present invention is a security system for providing safety and a sense of security within the home, and is composed of a sensor module, a server, an emotion engine, and a user terminal.

[0395] Data collection (terminal)

[0396] The device consists of the following sensor modules installed in the home:

[0397] Motion Sensor: Detects movement within the home and records any unusual activity.

[0398] Door and window sensors: Detects when doors and windows are opened and closed, and records any unauthorized openings and closings.

[0399] Camera: Collects image data and can identify suspicious individuals.

[0400] Audio sensor: Detects abnormal sounds (e.g., the sound of glass breaking).

[0401] Emotional Data Input: An interface is used for household members to input their emotional states.

[0402] The data obtained from these sensor modules is transmitted in real time by the terminal to a server.

[0403] Data reception and storage (server)

[0404] The server receives data sent from the device in real time. After receiving the data, it is stored in a database. The stored data is organized in chronological order, and the time and date of collection of the data are clearly managed.

[0405] Data analysis (server)

[0406] The server is equipped with an AI algorithm that analyzes the data stored in the database, detecting anomalies such as the following:

[0407] Motion abnormalities: movements that differ from normal activity patterns.

[0408] Unauthorized opening and closing of doors and windows: Unauthorized opening and closing at specific times.

[0409] Recognizing suspicious people: Image analysis using a camera.

[0410] Abnormal sound detection: AI-based voice data analysis.

[0411] Emotion data collection and analysis (server and user terminal)

[0412] It is equipped with an emotion engine that analyzes voice and facial recognition data to recognize the user's emotional state, and can detect when a household member is feeling anxious or stressed.

[0413] Generating Warnings and Advice (Server)

[0414] Based on the analysis results, the server generates necessary warnings and advice. For example, if a motion sensor detects abnormal activity and household members are feeling anxious, a more friendly and reassuring message will be generated.

[0415] Notifications and displays (user device)

[0416] The generated warnings and advice are sent as push notifications to the user's device. The user's device (smartphone, etc.) then displays the received warnings and advice. Tapping the notification launches a dedicated app, which displays detailed information about recommended actions.

[0417] Specific examples

[0418] For example, if a motion sensor detects abnormal activity in the middle of the night and a household member (e.g., mother) is in a very anxious state, the system operates as follows.

[0419] 1. Data collection: Motion sensors detect abnormal movements and send the data to the server.

[0420] 2. Data reception and storage: The server receives the data and stores it in the database.

[0421] 3. Data analysis: AI algorithms detect abnormal behavior.

[0422] 4. Emotion data collection and analysis: When a user opens the app, the emotion engine detects the user's anxiety through voice and facial recognition.

[0423] 5. Alert Generation: The server generates a friendly tone of warning saying "Unusual activity detected. Please remain calm and make sure all doors and windows are closed."

[0424] 6. Notification and display: A warning is pushed to the user's smartphone and the user can check it.

[0425] Prompt Sentence Examples

[0426] "When a home security system detects abnormal motion, generate advice to reduce anxiety and give the user peace of mind."

[0427] The security system of the present invention configured as described above can provide appropriate security measures that give peace of mind to members of the household who feel anxious or stressed.

[0428] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0429] Step 1: Data collection (device)

[0430] Sensor modules installed in the home (motion sensors, door / window sensors, cameras, audio sensors, and emotion data input interfaces) collect various types of data. The collected data includes motion data, opening / closing data, image data, audio data, and members' emotion data. This data is input into the terminal.

[0431] Step 2: Send data (terminal)

[0432] The collected data is sent in real time from the device to a server, where it is uploaded via an internet connection and includes a timestamp.

[0433] Step 3: Receiving data (server)

[0434] The server receives data sent from the device in real time, including motion data, opening / closing data, image data, audio data, and emotion data.

[0435] Step 4: Data storage (server)

[0436] The received data is stored in a database on the server. When stored, it is organized by data type and recorded in the database in chronological order by timestamp.

[0437] Step 5: Data analysis (server)

[0438] The server's AI algorithms analyze the stored data, which includes the following steps:

[0439] Analyzes motion data and detects abnormal movements.

[0440] Analyzes opening and closing data to detect unauthorized opening and closing of doors and windows.

[0441] Analyze image data and recognize suspicious individuals.

[0442] Analyzes audio data to detect abnormal sounds (e.g., the sound of glass breaking).

[0443] Step 6: Emotion data collection and analysis (server)

[0444] The server uses the emotion data, voice data, and facial recognition data entered by the user to analyze the user's emotional state using an emotion engine, which determines whether the user is feeling anxious or stressed.

[0445] Step 7: Generating Warnings and Advice (Server)

[0446] Based on the analysis results, the server generates necessary warnings and advice, such as abnormal activity, unauthorized opening and closing, and a friendly tone of voice depending on the user's emotional state. For example, a message might be generated such as, "Abnormal activity has been detected. Please remain calm and make sure all doors and windows are closed."

[0447] Step 8: Notification (Server)

[0448] The generated warnings and advice are pushed to the user's device, and the notification includes a message along with a timestamp.

[0449] Step 9: Receive and display notifications (on the user's device)

[0450] The user's device receives a push notification from the server. The received notification is displayed in the device's notification area or in a dedicated app. When the user taps the notification, detailed instructions on how to respond are displayed.

[0451] The above are the specific processing steps for implementing the present invention. By clarifying how input data is processed and analyzed at each step and how the final output is obtained, it is possible to provide users with appropriate security measures.

[0452] 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.

[0453] 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.

[0454] 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.

[0455] [Second embodiment]

[0456] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0457] 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.

[0458] 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).

[0459] 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.

[0460] 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.

[0461] 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).

[0462] 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.

[0463] 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.

[0464] 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.

[0465] 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.

[0466] 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.

[0467] 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."

[0468] This invention is a system that collects and analyzes data on a baby's body temperature, activity level, excretion status, and dietary intake, and provides warnings and advice to parents as needed. This system is composed of a sensor module, a server, and a user's device.

[0469] Data collection (terminal)

[0470] The device consists of a sensor module attached to the baby, which contains the following sensors:

[0471] Body temperature sensor: Measures baby's skin temperature.

[0472] Activity sensor: Uses a camera and accelerometer to record baby's movements.

[0473] Excretion sensor: Detects changes in humidity and temperature inside the diaper.

[0474] Meal data entry: Parents manually enter the amount and time their baby eats into the app.

[0475] The data obtained from these sensors is transmitted by the terminal to a server in real time.

[0476] Data reception and storage (server)

[0477] The server receives data sent from the device in real time. After receiving the data, it is stored in a database. The data is organized chronologically, and the time and date of collection of the data are clearly managed.

[0478] Data analysis (server)

[0479] The server is equipped with an AI algorithm that analyzes the data stored in the database, detecting changes in physical condition such as:

[0480] Abnormal body temperature: Detects hyperthermia or hypothermia when the body temperature is outside the normal range.

[0481] Activity anomalies: Detect increases or decreases in movement that differ from normal activity patterns.

[0482] Excretory abnormalities: Detect frequent or absent bowel movements.

[0483] The AI ​​algorithm evaluates the baby's condition by comparing it with previously learned health data, allowing it to detect even subtle changes that would normally go unnoticed.

[0484] Generating Warnings and Advice (Server)

[0485] Based on the analysis results, the server will generate necessary warnings and advice, for example, if the baby's temperature is abnormally high, it will generate a warning message saying "Baby's temperature is too high. Please check with a thermometer and consult a doctor."

[0486] Notifications and displays (user device)

[0487] The generated warnings and advice are sent as push notifications to the user's device. The user's device (smartphone, etc.) displays the received warnings and advice as visual notifications. Tapping the notification launches a dedicated app, which displays detailed recommended actions.

[0488] Specific examples

[0489] For example, if a baby's temperature is 37.8 degrees Celsius at 9:00 a.m. one day, exceeding the normal range, the system will operate as follows:

[0490] 1. Data collection: The device's body temperature sensor measures 37.8 degrees and sends the data to the server.

[0491] 2. Data reception and storage: The server receives the data and stores it in the database.

[0492] 3. Data analysis: AI algorithms analyze this temperature data and detect anything outside the normal range.

[0493] 4. Generate Alert: The server generates an alert saying "May have a fever, recheck with a thermometer and consult a doctor."

[0494] 5. Notification and display: A warning is pushed to the user's smartphone and the user can check it.

[0495] In this way, the system of the present invention can quickly detect subtle changes in a baby's physical condition and provide specific advice for appropriate responses, thereby reducing the anxiety of parents, especially those raising children for the first time, and supporting the health management of their babies.

[0496] The processing flow will be explained below.

[0497] Step 1:

[0498] The device collects data from a sensor module attached to the baby: a temperature sensor measures the baby's temperature, an activity sensor detects the baby's movements, a toilet sensor detects the baby's bowel movements, and dietary data is manually entered by the parent.

[0499] Step 2:

[0500] The data collected by the device is sent to the server in real time. Specifically, the device collects various data acquired from sensors and sends them to the server via Wi-Fi or mobile networks.

[0501] Step 3:

[0502] The server stores the data received from the terminal in a database. Specifically, the server inserts the received data into the database using SQL queries and organizes it in chronological order.

[0503] Step 4:

[0504] The server periodically retrieves the data stored in the database and analyzes it using an AI algorithm. Specifically, the server retrieves the latest data from the database and inputs it into the AI ​​model to detect outliers and patterns.

[0505] Step 5:

[0506] The server generates warnings and advice based on the analysis results of the AI ​​algorithm. Specifically, if the temperature is abnormally high, it generates a message saying, "Your baby's temperature is too high. Please check with a thermometer and consult a doctor."

[0507] Step 6:

[0508] The server notifies the user of the warnings and advice it generates by sending messages to the user's smartphone using a push notification service.

[0509] Step 7:

[0510] The user's device receives the notification and displays it visually. Specifically, a message appears in the smartphone's notification bar, and tapping the notification opens a dedicated app with detailed information and advice on how to respond.

[0511] Step 8:

[0512] The user checks the notification and takes the necessary action, such as rechecking the baby's temperature and consulting a doctor.

[0513] Example 1

[0514] 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."

[0515] In childcare, it is important to accurately and quickly understand a baby's health status, including body temperature, activity level, excretion status, and feeding status. However, manually managing this data is time-consuming and prone to errors. It is also difficult to respond immediately if an abnormality is detected. Therefore, there is a need for a system that can solve these issues, automatically monitor a baby's health status, and provide necessary advice.

[0516] 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.

[0517] In this invention, the server includes means for collecting data on the baby's body temperature, activity level, excretion status, and dietary intake using a sensor module, means for transmitting the collected data in real time to a central processing unit, means for storing the data received by the central processing unit in a data storage device, means for analyzing the stored data using an AI algorithm, means for generating countermeasures and advice based on the analysis results, means for notifying the user's communication terminal of the generated countermeasures and advice, and means for displaying the notification on the user's communication terminal. This makes it possible to accurately and quickly grasp the baby's health condition and take necessary measures in a timely manner.

[0518] A "sensor module" refers to a device that combines multiple sensors to measure a baby's health.

[0519] "Body temperature" refers to data measuring the temperature of a baby's skin and internal body.

[0520] "Activity level" refers to data that records the baby's movements and amount of exercise.

[0521] "Excretion status" refers to data regarding the baby's urination and defecation.

[0522] "Dietary data" refers to information about the amount and time of meals a baby eats.

[0523] "Real-time" means that data is processed and transmitted immediately without delay.

[0524] "Central Processing Unit" refers to a central computer system for receiving, analyzing, and otherwise processing data.

[0525] "Data storage device" refers to a storage device for saving received data.

[0526] "AI algorithm" refers to a computational method that uses artificial intelligence technology to analyze data and detect anomalies or specific patterns.

[0527] "Countermeasures" refer to appropriate ways of dealing with specific situations and guidelines for action that are generated based on the results of data analysis.

[0528] "Advice" refers to suggestions or recommendations regarding health care provided based on the results of the analysis.

[0529] "Communication terminal" refers to electronic devices such as smartphones and tablets used by users.

[0530] "Notification" refers to an alert or message that notifies the user of a generated remedy or advice.

[0531] "Display" refers to providing the notified content in a visually visible form on the user's communication terminal.

[0532] "Thermometry device" refers to a sensor device for measuring a baby's body temperature.

[0533] "Video capture device" refers to a camera or video recording device used to record a baby's activity.

[0534] To implement the present invention, the following system configuration and specific means are used to monitor the baby's health condition in real time and provide warnings and advice to parents as necessary.

[0535] This system consists of the following main hardware and software:

[0536] Sensor Module

[0537] Central Processing Unit (Server)

[0538] Data storage device (database)

[0539] Communication device (user's smartphone or tablet)

[0540] Sensor Module

[0541] The sensor module is worn on the baby and contains the following sensors:

[0542] Body temperature sensor: Measures the baby's skin temperature. Example: If the body temperature is 37.8 degrees.

[0543] Activity sensors: Use cameras and accelerometers to record your baby's movements, for example if your baby is moving less than usual.

[0544] Excretion sensor: Detects changes in humidity and temperature inside the diaper. For example, it detects whether the diaper is wet.

[0545] Meal data entry: Parents manually enter data through the app.

[0546] Central Processing Unit and Data Storage Unit

[0547] The data obtained from the sensor module is sent in real time via the user's device to a central processing unit (server). The server receives this data and stores it in a database. The data is organized in chronological order and can be easily referenced.

[0548] Data analysis and result generation

[0549] The server is equipped with an AI algorithm that analyzes the data stored in the database, detecting anomalies such as:

[0550] Abnormal body temperature: Detects hyperthermia or hypothermia when the body temperature deviates from the normal range (36.5-37.5°C).

[0551] Activity anomalies: Detect increases or decreases in movement that differ from normal activity patterns.

[0552] Excretory abnormalities: Detect frequent or absent bowel movements.

[0553] The AI ​​algorithm detects subtle changes in your physical condition by comparing them with previously learned health data.

[0554] Generating warnings and advice

[0555] Based on the analysis results, the server generates necessary warnings and advice, for example, if the temperature reaches 37.8 degrees, it generates a warning saying "Possible fever, recheck with a thermometer and consult a doctor."

[0556] Notifications and Displays

[0557] The generated warnings and advice are pushed to the user's communication device, where they are visually displayed and, when tapped, launch a dedicated app that displays detailed instructions on how to respond.

[0558] Specific examples

[0559] For example, if a baby's temperature reaches 37.8°C at 9:00 AM on a certain day, the following process will occur:

[0560] 1. Data collection: The body temperature sensor measures 37.8 degrees and sends the data to the server.

[0561] 2. Data reception and storage: The server receives the body temperature data and stores it in the database.

[0562] 3. Data analysis: AI algorithms analyze this temperature data and detect temperatures outside the normal range.

[0563] 4. Alert Generation: The server generates an alert saying "Possible fever, recheck with thermometer and consult doctor."

[0564] 5. Notification and display: A warning is pushed to the user's smartphone and the user can check it.

[0565] Prompt Sentence Examples

[0566] The input prompts for the generative AI model are:

[0567] "Generate an alert if the baby's temperature exceeds the normal range."

[0568] "Detect abnormalities when your baby's activity level falls outside the normal range."

[0569] "Generate advice if your baby's bowel frequency is abnormal."

[0570] The system of the present invention makes it possible to accurately and quickly grasp the baby's health condition and provide appropriate advice to parents, thereby reducing the anxiety of parents, especially those raising a child for the first time, and supporting the health management of their baby.

[0571] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0572] Step 1: Data collection (user device)

[0573] The user's device collects body temperature, activity levels, and excretion status in real time from a sensor module attached to the baby. The device receives various data from the sensor module as input and temporarily stores it. Specifically, the body temperature sensor measures the baby's skin temperature, the activity sensor records the baby's movements, and the excretion sensor detects humidity and temperature changes inside the diaper. Each collected data is converted into a standard format for transmission to the server in the next step.

[0574] Step 2: Send data (user's device)

[0575] The user's device transmits the data collected and temporarily stored in step 1 to the server in real time. It receives the temporarily stored data in the device as input, converts it into packet format, and transmits it. Specifically, body temperature data, movement patterns, and logs of humidity and temperature changes are transmitted to the server via a secure communication channel.

[0576] Step 3: Data reception and storage (server)

[0577] The server receives data sent from the user's device in real time. It takes the received data as input and stores it in a database. The data is organized in chronological order, and each piece of data is stored with a timestamp. Specifically, the time at which data such as a body temperature of 37.8 degrees and activity records were collected is clearly managed.

[0578] Step 4: Data analysis (server)

[0579] The server uses an AI algorithm to analyze the data stored in the database. It receives stored body temperature data, activity data, and excretion data as input and analyzes them. Specifically, if the body temperature exceeds the normal range (36.5-37.5°C), it detects high or low fever. If the activity pattern differs from the normal, it is also detected as an abnormality. The output of the analysis is notification data or advice data if an abnormality is detected.

[0580] Step 5: Generating Warnings and Advice (Server)

[0581] The server generates warnings and advice based on the analysis results of step 4. It receives the abnormal data from the analysis results as input and creates appropriate warning messages and advice based on that. Specifically, if the body temperature reaches 37.8 degrees, a warning is generated stating, "Possible fever. Check with a thermometer and consult a doctor." The generated message is sent to the user's device in the next step.

[0582] Step 6: Notifications and Display (User's Device)

[0583] The user's device receives warnings and advice sent from the server. It receives notification data from the server as input and displays it visually. Specifically, a warning message appears on the smartphone saying, "Your temperature is too high. Please check with a thermometer and consult a doctor." Tapping the notification launches a dedicated app, which displays detailed instructions on how to respond.

[0584] Through the above steps, the system can accurately and quickly monitor the baby's health status and provide necessary advice to parents.

[0585] (Application example 1)

[0586] 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."

[0587] There is a need for real-time monitoring of baby's health and for parents to receive appropriate warnings and advice. However, conventional systems simply collect data and do not provide support for parents in selecting the most appropriate childcare products. This often makes it difficult for parents to respond appropriately and select the right products.

[0588] 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.

[0589] In this invention, the server includes means for suggesting childcare products suitable for the baby's health condition based on the analysis results, and means for allowing the user to view detailed information on the suggested childcare products in the virtual store. This allows parents to easily select appropriate childcare products according to the baby's health condition, reducing their anxiety about childcare.

[0590] A "sensor module" is a device used to collect data on a baby's temperature, activity, bowel movements, and diet.

[0591] A "thermosensor" is a sensor used to measure the temperature of a baby's skin.

[0592] "Photography equipment" refers to cameras or video equipment used to record the baby's activity.

[0593] A "server" is a computer system whose role is to receive collected data, store it in a database, and analyze it using AI algorithms.

[0594] A "database" is an information system for organizing, storing, and managing collected data in chronological order.

[0595] An "AI algorithm" is an artificial intelligence technology that analyzes collected data and performs anomaly detection and prediction.

[0596] "Warnings and advice" are notifications and recommended actions to parents that are generated based on the analysis results.

[0597] A "user device" is a parent's device (e.g., a smartphone or tablet) that can receive warnings and advice.

[0598] "Notification" refers to the sending of a message to a user's terminal in real time.

[0599] "Childcare products" refers to items used depending on the baby's health condition (e.g., thermometers, cooling sheets, etc.).

[0600] A "virtual store" is a virtual shopping space where you can purchase childcare products online.

[0601] MODE FOR CARRYING OUT THE INVENTION

[0602] This invention is a system that collects and analyzes a baby's body temperature, activity level, excretion status, and dietary data, and provides warnings and advice to parents as needed. It also has a function to suggest appropriate childcare products in a virtual store. The system consists of a sensor module, a server, and a user's terminal.

[0603] Data collection (terminal)

[0604] The sensor module includes the following sensors:

[0605] Thermosensor: Measures baby's skin temperature.

[0606] Camera: Records the baby's movements.

[0607] Excretion sensor: Detects changes in humidity and temperature inside the diaper.

[0608] Meal data entry: Parents manually enter the amount and time their baby eats into the app.

[0609] The data obtained from these sensors is transmitted in real time by the sensor modules to a server.

[0610] Data reception and storage (server)

[0611] The server receives data sent from the device in real time and stores it in a database. The data is organized chronologically, and the time and date of collection are clearly managed.

[0612] Data analysis (server)

[0613] The server is equipped with an AI algorithm that analyzes the data stored in the database, detecting changes in physical condition such as:

[0614] Abnormal body temperature: Detects hyperthermia or hypothermia when the body temperature is outside the normal range.

[0615] Activity anomalies: Detect increases or decreases in movement that differ from normal activity patterns.

[0616] Excretory abnormalities: Detect frequent or absent bowel movements.

[0617] The AI ​​algorithm evaluates the baby's condition by comparing it with previously learned health data, allowing it to detect even subtle changes that would normally go unnoticed.

[0618] Generating Warnings and Advice (Server)

[0619] Based on the analysis results, the server will generate necessary warnings and advice, for example, if the baby's temperature is abnormally high, it will generate a warning message saying "Baby's temperature is too high. Please check with a thermometer and consult a doctor."

[0620] Notifications and displays (user device)

[0621] The generated warnings and advice are sent to the user's device via push notification. The user's device (smartphone, etc.) displays the received warnings and advice as a visual notification. Tapping the notification launches a dedicated app, which displays detailed recommended actions.

[0622] Proposing childcare products in a virtual store

[0623] Furthermore, the system has the function of suggesting baby products suitable for the baby's health condition based on the analysis results.In the virtual store, users can view detailed information about the suggested baby products and easily purchase the necessary items.

[0624] Example

[0625] For example, if a baby's temperature is 37.8 degrees Celsius at 9:00 a.m. one day, exceeding the normal range, the system will operate as follows:

[0626] 1. Data collection: The device's thermosensor measures the temperature of 37.8 degrees and sends it to the server.

[0627] 2. Data reception and storage: The server receives the data and stores it in the database.

[0628] 3. Data analysis: AI algorithms analyze this temperature data and detect anything outside the normal range.

[0629] 4. Generate Alert: The server generates an alert saying "May have a fever, recheck with a thermometer and consult a doctor."

[0630] 5. Notification and display: A warning is pushed to the user's smartphone and the user can check it.

[0631] 6. Baby Product Suggestions: The virtual store will suggest suitable baby products, such as cooling pads or a new thermometer.

[0632] Examples of prompt statements

[0633] "Generate a message recommending a cooling sheet or thermometer if your baby has a high temperature."

[0634] In this way, the system can quickly detect subtle changes in a baby's physical condition and provide specific advice on how to respond appropriately. Furthermore, by suggesting baby care products in the virtual store, parents can easily obtain the necessary items, making childcare even smoother.

[0635] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0636] Step 1:

[0637] The sensor module collects data on the baby's body temperature, activity level, excretion status, and dietary intake. The collected data is temporarily stored within the sensor module. The input is biological and dietary information obtained from the sensor, and the output is sent to a terminal as organized data.

[0638] Step 2:

[0639] The terminal receives the organized data from the sensor module and transmits it to the server in real time. The input is the data received by the terminal, and the output is the raw data transmitted to the server.

[0640] Step 3:

[0641] The server receives the data sent from the terminal and stores it in a database. The input is the raw data received by the server, and the output is the stored data in the database organized in chronological order.

[0642] Step 4:

[0643] The server analyzes the stored data using an AI algorithm. The AI ​​algorithm detects abnormalities in body temperature, activity level, and excretion status, and outputs the results. The input is the data stored in the database, and the output is the abnormality detection result. For example, if body temperature exceeds 37.8 degrees, it will be detected as an abnormality.

[0644] Step 5:

[0645] The server generates warnings and advice based on the analysis results. The input is the anomaly detection result, and the output is the generated warning message or advice. Specifically, if an abnormal temperature is detected, a message is generated saying, "Your baby's temperature is too high. Check again with a thermometer and consult a doctor."

[0646] Step 6:

[0647] The server sends the generated warnings and advice to the user's device via push notifications. The input is the generated warning message, and the output is the notification sent to the user's device.

[0648] Step 7:

[0649] It visually displays notifications received by the user's device, allowing the user to check the notification and view more information if necessary. The input is the received alert message, and the output is the displayed notification and more information.

[0650] Step 8:

[0651] The server then recommends appropriate childcare products based on the analysis results. The input is the analysis results, and the output is a list of suggested childcare products. For example, if the child has a high temperature, it might suggest a cooling sheet or a new thermometer.

[0652] Step 9:

[0653] The user's device displays detailed information about the proposed childcare products in the virtual store. The input is a list of the proposed childcare products, and the output is the display of detailed information. The user can then select and purchase the products they need.

[0654] In this way, the system can monitor the baby's health in real time and provide appropriate treatment and support.

[0655] 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.

[0656] This invention combines a system that collects and analyzes a baby's body temperature, activity level, excretion status, and dietary data, and provides warnings and advice to parents as needed, with an emotion engine that recognizes the user's emotions. This system consists of a sensor module, a server, an emotion engine, and a user's device.

[0657] Data collection (terminal)

[0658] The device consists of a sensor module attached to the baby, which contains the following sensors:

[0659] Body temperature sensor: Measures baby's skin temperature.

[0660] Activity sensor: Uses a camera and accelerometer to record baby's movements.

[0661] Excretion sensor: Detects changes in humidity and temperature inside the diaper.

[0662] Meal data entry: Parents manually enter the amount and time their baby eats into the app.

[0663] The data obtained from these sensors is transmitted by the terminal to a server in real time.

[0664] Data reception and storage (server)

[0665] The server receives data sent from the device in real time. After receiving the data, it is stored in a database. The data is organized chronologically, and the time and date of collection of the data are clearly managed.

[0666] Data analysis (server)

[0667] The server is equipped with an AI algorithm that analyzes the data stored in the database, detecting changes in physical condition such as:

[0668] Abnormal body temperature: Detects hyperthermia or hypothermia when the body temperature is outside the normal range.

[0669] Activity anomalies: Detect increases or decreases in movement that differ from normal activity patterns.

[0670] Excretory abnormalities: Detect frequent or absent bowel movements.

[0671] The AI ​​algorithm evaluates the baby's condition by comparing it with previously learned health data, allowing it to detect even subtle changes that would normally go unnoticed.

[0672] Generating Warnings and Advice (Server)

[0673] Based on the analysis results, the server will generate necessary warnings and advice, for example, if the baby's temperature is abnormally high, it will generate a warning message saying "Baby's temperature is too high. Please check with a thermometer and consult a doctor."

[0674] Notifications and displays (user device)

[0675] The generated warnings and advice are sent as push notifications to the user's device. The user's device (smartphone, etc.) notifies and displays the received warnings and advice. Tapping the notification launches a dedicated app, which displays detailed recommended actions.

[0676] Introduction of emotion engine (server and user terminal)

[0677] The system also incorporates an emotion engine that recognizes the user's emotions. Specifically, it operates as follows:

[0678] The server collects the user's voice and facial recognition data and analyzes it using an emotion engine to detect when the user is in a particular emotional state, such as stress, worry, or anxiety.

[0679] Based on the emotion engine's analysis, the server adjusts the tone and content of warnings and advice. For example, if the user is feeling very stressed, a more friendly and reassuring message will be generated.

[0680] Specific examples

[0681] For example, if a baby has a fever and the user (mother) is very anxious, the system operates as follows:

[0682] 1. Data collection: The device's body temperature sensor measures 37.8 degrees and sends the data to the server.

[0683] 2. Data reception and storage: The server receives the data and stores it in the database.

[0684] 3. Data analysis: AI algorithms analyze body temperature data and detect when it is outside the normal range.

[0685] 4. Emotion data collection and analysis: When a user opens the app, the emotion engine detects the user's anxiety through voice and facial recognition.

[0686] 5. Alert Generation: The server generates a friendly tone alert saying "Your baby's temperature is too high. Please calm down, recheck with a thermometer and consult a doctor."

[0687] 6. Notification and display: A warning is pushed to the user's smartphone and the user can check it.

[0688] In this way, the system of the present invention can quickly detect subtle changes in a baby's physical condition and provide appropriate responses according to the user's emotional state, thereby reducing anxiety, especially for parents raising a child for the first time.

[0689] The processing flow will be explained below.

[0690] Step 1:

[0691] The device collects data from sensor modules attached to the baby: a temperature sensor measures the baby's temperature, an activity sensor records the baby's movements, a toilet sensor detects the baby's bowel movements, and dietary data is manually entered into the app by the parent.

[0692] Step 2:

[0693] The data collected by the device is sent to the server in real time. Specifically, the device collects various data acquired from sensors and sends them to the server via Wi-Fi or mobile networks.

[0694] Step 3:

[0695] The server receives the data sent from the device. Specifically, the server's API receives the HTTP request and retrieves the data.

[0696] Step 4:

[0697] The server stores the received data in a database. Specifically, it issues an SQL query to insert the data into the database and organizes it in chronological order.

[0698] Step 5:

[0699] The server periodically retrieves the data stored in the database and analyzes it using an AI algorithm. Specifically, the server retrieves the latest data from the database and inputs it into the AI ​​model to detect outliers and patterns.

[0700] Step 6:

[0701] The server generates warnings and advice based on the analysis results of the AI ​​algorithm. Specifically, if the temperature is abnormally high, it generates a message saying, "Your baby's temperature is too high. Please check with a thermometer and consult a doctor."

[0702] Step 7:

[0703] When a user opens the app, the device collects emotion data, specifically voice data and facial recognition data, and sends it to the emotion engine.

[0704] Step 8:

[0705] The emotion engine installed on the server analyzes the user's emotion data, specifically using voice and facial recognition algorithms to detect the user's emotional state (e.g., stress or anxiety).

[0706] Step 9:

[0707] The server adjusts the content and tone of warnings and advice based on the analysis results of the emotion engine. Specifically, if the user is feeling anxious, it generates a friendly and reassuring message such as, "Your baby's temperature is too high. Please calm down, check again with a thermometer, and consult a doctor."

[0708] Step 10:

[0709] The server notifies the user of the warnings and advice it generates by sending messages to the user's smartphone using a push notification service.

[0710] Step 11:

[0711] The user's device receives the notification and displays it visually. Specifically, a message appears in the smartphone's notification bar, and tapping the notification opens a dedicated app with detailed information and advice on how to respond.

[0712] Step 12:

[0713] The user checks the notification and takes the necessary action, such as rechecking the baby's temperature and consulting a doctor.

[0714] Example 2

[0715] 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."

[0716] Monitoring a baby's physical condition and health is extremely important in childcare. However, it is difficult to quickly detect subtle changes or abnormalities in a baby's physical condition, which can be very worrying for first-time parents. Furthermore, there is a lack of technological means to provide appropriate advice based on parents' emotions and stress levels. Under these circumstances, there is a need for a system that can monitor a baby's health in real time and provide parents with peace of mind.

[0717] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[0718] In this invention, the server includes means for collecting data on the baby's body temperature, activity level, excretion status, and dietary intake using a sensor module, means for transmitting the collected data in real time to a network device, means for storing data received by the network device in a storage device, means for analyzing the stored data using a machine learning algorithm, means for generating a warning or advice based on the analysis results, means for notifying the generated warning or advice on a user's display device, means for displaying the warning or advice on the user's display device, means for collecting user voice data and facial recognition data and analyzing it using an emotion recognition engine, means for adjusting the content of the warning or advice based on the analyzed emotion data, and means for notifying the user's display device of the warning or advice according to the emotion. This makes it possible to quickly detect subtle changes in the baby's physical condition and provide appropriate responses and advice according to the user's emotional state.

[0719] The "sensor module" is a device that collects data on a baby's body temperature, activity level, excretion status, and dietary intake.

[0720] A "body temperature sensor" is a temperature sensing device used to measure a baby's skin temperature.

[0721] An "activity sensor" is a device that uses a camera and an accelerometer to record a baby's movements.

[0722] An "excretion sensor" is a sensing device that detects changes in humidity and temperature inside a diaper.

[0723] "Collected data" is a general term for the baby's body temperature, activity level, excretion status, and dietary data obtained by the sensor module.

[0724] A "network device" is a communication device for transmitting collected data to a server in real time.

[0725] A "memory device" is a data storage device that stores received data and allows it to be accessed at a later time.

[0726] A "machine learning algorithm" is an artificial intelligence technique used for data analysis, which identifies patterns in stored data.

[0727] A "warning" is a message generated based on the results of data analysis to alert the user.

[0728] "Advice" is a recommended instruction for action provided to a user based on the results of data analysis.

[0729] A "display device" is a device for notifying and displaying generated warnings and advice to a user, and includes smartphones and tablets.

[0730] "Voice data" is information collected from the user's voice.

[0731] "Facial recognition data" refers to image or video information obtained by recognizing the user's face.

[0732] An "emotion recognition engine" is a system that analyzes collected voice data and facial recognition data to identify the user's emotional state.

[0733] This system collects and analyzes data on a baby's body temperature, activity level, excretion status, and dietary intake, and provides warnings and advice to parents based on the analysis results, while also incorporating an emotion engine that recognizes the user's emotions. This system is composed of a sensor module, a network device, a storage device, a machine learning algorithm, an emotion recognition engine, and a user's display device.

[0734] (Data collection (terminal))

[0735] The device collects data through a sensor module attached to the baby, which contains the following sensors:

[0736] Body temperature sensor: A temperature sensing device that measures the baby's skin temperature.

[0737] Activity sensor: An image capture device that uses a camera and accelerometer to record the baby's movements.

[0738] Excretion sensor: A sensing device that detects changes in humidity and temperature inside the diaper.

[0739] Meal data entry: Parents manually enter their baby's meal amounts and times using the app.

[0740] These data are transmitted in real time from the terminal to the network device.

[0741] (Data reception and storage (server))

[0742] The server receives data sent from the network device and stores it in a storage device. Specifically, the server receives data using a communication protocol such as HTTP or MQTT. The received data is organized by time, and the data collected at what point in time is clearly managed.

[0743] (Data analysis (server))

[0744] Machine learning algorithms in the server analyze the data stored in the storage device, specifically detecting anomalies such as:

[0745] Body temperature abnormality: Detects hyperthermia or hypothermia when the body temperature data exceeds a certain threshold.

[0746] Activity Anomalies: Detect abnormal increases or decreases in activity compared to normal activity patterns.

[0747] Excretory abnormalities: Detect frequent or absent bowel movements compared to normal bowel patterns.

[0748] (Emotion data collection and analysis (server and user terminal))

[0749] The server and the user's device recognize emotions using the user's voice data and facial recognition data. The specific operation is as follows:

[0750] The user launches the app and collects voice and facial information via voice input and the camera.

[0751] The server analyzes the collected information and detects whether the user is experiencing emotional states such as stress, anxiety, or worry.

[0752] (Generating Warnings and Advice (Server))

[0753] The server generates necessary warnings and advice based on the results of data analysis and the evaluation of emotional data. For example, if the baby's temperature is high, a warning such as "Your baby's temperature is too high. Please check it again with a thermometer and consult a doctor" can be generated. If the user is feeling stressed, a message in a reassuring tone can be generated, such as "You may be worried about your baby. Please check it again with a thermometer and consult a doctor if necessary."

[0754] (Notification and Display (User's Device))

[0755] The generated warnings and advice are pushed to the user's display device. When the user opens the notification on their smartphone or tablet, the app launches and displays detailed information.

[0756] Examples:

[0757] For example, if a baby has a fever and the user (mother) is in a very anxious state, the system will operate as follows:

[0758] 1. Data collection: The device's body temperature sensor measures 37.8 degrees and transmits the data to the server via the network device.

[0759] 2. Data reception and storage: The server receives the data and stores it in a storage device.

[0760] 3. Data analysis: Machine learning algorithms analyze body temperature data and detect when it is outside the normal range.

[0761] 4. Emotion data collection and analysis: When a user opens the app, the emotion recognition engine detects the user's anxiety through voice and facial recognition.

[0762] 5. Alert Generation: The server generates a friendly tone alert saying "Your baby's temperature is too high. Please calm down, recheck with a thermometer and consult a doctor."

[0763] 6. Notification and display: A warning is pushed to the user's smartphone and the user can check it.

[0764] Examples of prompts:

[0765] An example of a prompt to be input to the generative AI model is as follows:

[0766] "My baby has a fever of 37.8 degrees. Please generate a warning message that takes into account the user's emotional state and makes them feel at ease."

[0767] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0768] Step 1: Data collection (device)

[0769] The device collects data from the sensor module attached to the baby. The specific operations are as follows:

[0770] The temperature sensor continuously measures the baby's skin temperature and sends the measurements as input to the terminal. The output data is the baby's temperature data.

[0771] The activity sensors (camera and accelerometer) record the baby's movements every second and send the video and acceleration data to the terminal as input. The output data is the baby's activity data.

[0772] The excretion sensor detects changes in humidity and temperature inside the diaper and sends the change data to the terminal as input. The output data is excretion data.

[0773] For meal data input, the user manually inputs the amount and time of the baby's meal using the app, and the output data is the meal data.

[0774] Step 2: Send data (terminal)

[0775] The terminal transmits the collected data to the network device. The specific operation is as follows:

[0776] The terminal receives temperature data, activity data, excretion data, and dietary data as input and transmits them to the network device in real time. The output is data sent to the server via the network.

[0777] Step 3: Data reception and storage (server)

[0778] The server receives the data sent from the network device and stores it in the storage device. The specific operations are as follows:

[0779] The server receives input data such as body temperature, activity, excretion, and dietary data using communication protocols such as HTTP and MQTT. The output data is stored in a storage device.

[0780] The received data is organized by time, and it is clearly managed which data was collected at what point in time.

[0781] Step 4: Data analysis (server)

[0782] The machine learning algorithms in the server analyze the data stored in the storage device. Specifically, they do the following:

[0783] The server receives body temperature data, activity data, excretion data, and dietary data as input from the storage device.

[0784] Body temperature analysis: Checks whether the body temperature data exceeds a certain threshold, and if there is an abnormality, outputs the abnormal data.

[0785] Activity analysis: Activity data is compared with normal patterns to detect abnormal increases or decreases in activity and output the abnormal data.

[0786] Excretion analysis: Excretion data is compared with normal patterns to detect abnormalities and output the abnormal data.

[0787] Step 5: Emotion data collection and analysis (server and user device)

[0788] The server and the user's device collect the user's voice data and facial recognition data, and analyze their emotions. The specific operations are as follows:

[0789] The user launches the app and collects voice and facial information via voice input or camera. The output data is voice data and facial recognition data.

[0790] The server receives collected voice data and facial recognition data as input, analyzes them, and infers the user's emotional state. The output is the user's emotional state data.

[0791] Step 6: Generating Warnings and Advice (Server)

[0792] The server generates necessary warnings and advice based on the results of data analysis and the evaluation of the emotion data. Specific operations are as follows:

[0793] The server receives anomaly data and user emotional state data as input.

[0794] Based on the analysis results, a warning message such as "Your baby's temperature is too high. Please double-check with a thermometer and consult a doctor" is generated. If the user is feeling stressed, a reassuring message such as "We understand that you may be worried about your baby, but please double-check with a thermometer first and consult a doctor if necessary" is generated. The output is a warning or advice message.

[0795] Step 7: Notifications and Display (User's Device)

[0796] The generated warnings and advice are pushed to the user's display device and displayed. Specifically, the behavior is as follows:

[0797] The server sends warning and advice messages to the user terminal as input.

[0798] The user's device displays the received message in the notification bar, and when the user taps the notification, the app launches and displays more information. The output is a warning or advice message that is displayed to the user.

[0799] (Application example 2)

[0800] 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."

[0801] In modern homes, security measures that provide safety and peace of mind go beyond mere mechanical security. A responsive approach is needed, especially when household members are feeling anxious or stressed. However, current home security systems do not generate warnings or advice that take into account the user's emotional state. Therefore, more considerate and effective security measures are needed.

[0802] The identification process by the identification processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes means for collecting voice data and image data within the home, analyzing them using an emotion engine, and recognizing the user's emotional state, means for adjusting the tone and content of warnings and advice generated according to the user's emotional state, and means for analyzing the various collected home data using an AI algorithm and detecting abnormalities. This makes it possible to provide appropriate security measures that give peace of mind to members of the home who are feeling anxious or stressed.

[0803] A "sensor module" is hardware that integrates various sensors used to detect the environment and the status of members in the home.

[0804] A "thermal sensor" is a device for measuring the temperature of an object or living thing, especially when used to detect skin temperature.

[0805] An "activity sensor" is a device that detects changes in acceleration or movement. This includes motion sensors and cameras.

[0806] An "excretion sensor" is a sensor that detects changes in humidity and temperature to grasp the excretion status.

[0807] "Dietary data" refers to information such as the amount, type, and time of food consumed by household members.

[0808] A "server" is a centralized computer system that receives, stores, and analyzes data over a network.

[0809] A "database" is a collection of data that is systematically stored and constructed to allow easy searching and analysis of collected data.

[0810] "AI algorithms" are machine learning and artificial intelligence techniques used to analyze collected data and detect patterns and anomalies.

[0811] A "warning" is a notification that notifies the user when an abnormality or risk is detected in the home.

[0812] "Advice" refers to the appropriate response or recommendation provided along with the warning.

[0813] A "home terminal" is a device, such as a mobile device or computer, that a user uses to interact with or receive notifications.

[0814] An "emotion engine" is an artificial intelligence system that analyzes voice and image data to recognize the user's emotional state.

[0815] "Analysis" is the process of examining collected data in detail to discover specific patterns or anomalies.

[0816] "Real-time" means that data is collected, transmitted, and processed immediately.

[0817] A "push notification" is a highly instantaneous message sent directly from a server to a user's device.

[0818] "Speech recognition" is a technology for analyzing voice data to understand meaning and emotions.

[0819] "Facial recognition" is a technology that uses a camera to identify a person's face and determine their emotion or state.

[0820] The present invention is a security system for providing safety and a sense of security within the home, and is composed of a sensor module, a server, an emotion engine, and a user terminal.

[0821] Data collection (terminal)

[0822] The device consists of the following sensor modules installed in the home:

[0823] Motion Sensor: Detects movement within the home and records any unusual activity.

[0824] Door and window sensors: Detects when doors and windows are opened and closed, and records any unauthorized openings and closings.

[0825] Camera: Collects image data and can identify suspicious individuals.

[0826] Audio sensor: Detects abnormal sounds (e.g., the sound of glass breaking).

[0827] Emotional Data Input: An interface is used for household members to input their emotional states.

[0828] The data obtained from these sensor modules is transmitted in real time by the terminal to a server.

[0829] Data reception and storage (server)

[0830] The server receives data sent from the device in real time. After receiving the data, it is stored in a database. The stored data is organized in chronological order, and the time and date of collection of the data are clearly managed.

[0831] Data analysis (server)

[0832] The server is equipped with an AI algorithm that analyzes the data stored in the database, detecting anomalies such as the following:

[0833] Motion abnormalities: movements that differ from normal activity patterns.

[0834] Unauthorized opening and closing of doors and windows: Unauthorized opening and closing at specific times.

[0835] Recognizing suspicious people: Image analysis using a camera.

[0836] Abnormal sound detection: AI-based voice data analysis.

[0837] Emotion data collection and analysis (server and user terminal)

[0838] It is equipped with an emotion engine that analyzes voice and facial recognition data to recognize the user's emotional state, and can detect when a household member is feeling anxious or stressed.

[0839] Generating Warnings and Advice (Server)

[0840] Based on the analysis results, the server generates necessary warnings and advice. For example, if a motion sensor detects abnormal activity and household members are feeling anxious, a more friendly and reassuring message will be generated.

[0841] Notifications and displays (user device)

[0842] The generated warnings and advice are sent as push notifications to the user's device. The user's device (smartphone, etc.) notifies and displays the received warnings and advice. Tapping the notification launches a dedicated app, which displays detailed recommended actions.

[0843] Specific examples

[0844] For example, if a motion sensor detects abnormal activity in the middle of the night and a household member (e.g., mother) is in a very anxious state, the system operates as follows.

[0845] 1. Data collection: Motion sensors detect abnormal movements and send the data to the server.

[0846] 2. Data reception and storage: The server receives the data and stores it in the database.

[0847] 3. Data analysis: AI algorithms detect abnormal behavior.

[0848] 4. Emotion data collection and analysis: When a user opens the app, the emotion engine detects the user's anxiety through voice and facial recognition.

[0849] 5. Alert Generation: The server generates a friendly tone of warning saying "Unusual activity detected. Please remain calm and make sure all doors and windows are closed."

[0850] 6. Notification and display: A warning is pushed to the user's smartphone and the user can check it.

[0851] Prompt Sentence Examples

[0852] "When a home security system detects abnormal motion, generate advice to reduce anxiety and give the user peace of mind."

[0853] The security system of the present invention configured as described above can provide appropriate security measures that give peace of mind to members of the household who feel anxious or stressed.

[0854] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0855] Step 1: Data collection (device)

[0856] Sensor modules installed in the home (motion sensors, door / window sensors, cameras, audio sensors, and emotion data input interfaces) collect various types of data. The collected data includes motion data, opening / closing data, image data, audio data, and members' emotion data. This data is input into the terminal.

[0857] Step 2: Send data (terminal)

[0858] The collected data is sent in real time from the device to a server, where it is uploaded via an internet connection and includes a timestamp.

[0859] Step 3: Receiving data (server)

[0860] The server receives data sent from the device in real time, including motion data, opening / closing data, image data, audio data, and emotion data.

[0861] Step 4: Data storage (server)

[0862] The received data is stored in a database on the server. When stored, it is organized by data type and recorded in the database in chronological order by timestamp.

[0863] Step 5: Data analysis (server)

[0864] The server's AI algorithms analyze the stored data, which includes the following steps:

[0865] Analyzes motion data and detects abnormal movements.

[0866] Analyzes opening and closing data to detect unauthorized opening and closing of doors and windows.

[0867] Analyze image data and recognize suspicious individuals.

[0868] Analyzes audio data to detect abnormal sounds (e.g., the sound of glass breaking).

[0869] Step 6: Emotion data collection and analysis (server)

[0870] The server uses the emotion data, voice data, and facial recognition data entered by the user to analyze the user's emotional state using an emotion engine, which determines whether the user is feeling anxious or stressed.

[0871] Step 7: Generating Warnings and Advice (Server)

[0872] Based on the analysis results, the server generates necessary warnings and advice, such as abnormal activity, unauthorized opening and closing, and a friendly tone of voice depending on the user's emotional state. For example, a message might be generated such as, "Abnormal activity has been detected. Please remain calm and make sure all doors and windows are closed."

[0873] Step 8: Notification (Server)

[0874] The generated warnings and advice are pushed to the user's device, and the notification includes a message along with a timestamp.

[0875] Step 9: Receive and display notifications (on the user's device)

[0876] The user's device receives a push notification from the server. The received notification is displayed in the device's notification area or in a dedicated app. When the user taps the notification, detailed instructions on how to respond are displayed.

[0877] The above are the specific processing steps for implementing the present invention. By clarifying how input data is processed and analyzed at each step and how the final output is obtained, it is possible to provide users with appropriate security measures.

[0878] 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.

[0879] 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.

[0880] 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.

[0881] [Third embodiment]

[0882] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0883] 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.

[0884] 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).

[0885] 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.

[0886] 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.

[0887] 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).

[0888] 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.

[0889] 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.

[0890] 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.

[0891] 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.

[0892] 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.

[0893] 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."

[0894] This invention is a system that collects and analyzes data on a baby's body temperature, activity level, excretion status, and dietary intake, and provides warnings and advice to parents as needed. This system is composed of a sensor module, a server, and a user's device.

[0895] Data collection (terminal)

[0896] The device consists of a sensor module attached to the baby, which contains the following sensors:

[0897] Body temperature sensor: Measures baby's skin temperature.

[0898] Activity sensor: Uses a camera and accelerometer to record baby's movements.

[0899] Excretion sensor: Detects changes in humidity and temperature inside the diaper.

[0900] Meal data entry: Parents manually enter the amount and time their baby eats into the app.

[0901] The data obtained from these sensors is transmitted by the terminal to a server in real time.

[0902] Data reception and storage (server)

[0903] The server receives data sent from the device in real time. After receiving the data, it is stored in a database. The data is organized chronologically, and the time and date of collection of the data are clearly managed.

[0904] Data analysis (server)

[0905] The server is equipped with an AI algorithm that analyzes the data stored in the database, detecting changes in physical condition such as:

[0906] Abnormal body temperature: Detects hyperthermia or hypothermia when the body temperature is outside the normal range.

[0907] Activity anomalies: Detect increases or decreases in movement that differ from normal activity patterns.

[0908] Excretory abnormalities: Detect frequent or absent bowel movements.

[0909] The AI ​​algorithm evaluates the baby's condition by comparing it with previously learned health data, allowing it to detect even subtle changes that would normally go unnoticed.

[0910] Generating Warnings and Advice (Server)

[0911] Based on the analysis results, the server will generate necessary warnings and advice, for example, if the baby's temperature is abnormally high, it will generate a warning message saying "Baby's temperature is too high. Please check with a thermometer and consult a doctor."

[0912] Notifications and displays (user device)

[0913] The generated warnings and advice are sent as push notifications to the user's device. The user's device (smartphone, etc.) displays the received warnings and advice as visual notifications. Tapping the notification launches a dedicated app, which displays detailed recommended actions.

[0914] Specific examples

[0915] For example, if a baby's temperature is 37.8 degrees Celsius at 9:00 a.m. one day, exceeding the normal range, the system will operate as follows:

[0916] 1. Data collection: The device's body temperature sensor measures 37.8 degrees and sends the data to the server.

[0917] 2. Data reception and storage: The server receives the data and stores it in the database.

[0918] 3. Data analysis: AI algorithms analyze this temperature data and detect anything outside the normal range.

[0919] 4. Generate Alert: The server generates an alert saying "May have a fever, recheck with a thermometer and consult a doctor."

[0920] 5. Notification and display: A warning is pushed to the user's smartphone and the user can check it.

[0921] In this way, the system of the present invention can quickly detect subtle changes in a baby's physical condition and provide specific advice for appropriate responses, thereby reducing the anxiety of parents, especially those raising children for the first time, and supporting the health management of their babies.

[0922] The processing flow will be explained below.

[0923] Step 1:

[0924] The device collects data from a sensor module attached to the baby: a temperature sensor measures the baby's temperature, an activity sensor detects the baby's movements, a toilet sensor detects the baby's bowel movements, and dietary data is manually entered by the parent.

[0925] Step 2:

[0926] The data collected by the device is sent to the server in real time. Specifically, the device collects various data acquired from sensors and sends them to the server via Wi-Fi or mobile networks.

[0927] Step 3:

[0928] The server stores the data received from the terminal in a database. Specifically, the server inserts the received data into the database using SQL queries and organizes it in chronological order.

[0929] Step 4:

[0930] The server periodically retrieves the data stored in the database and analyzes it using an AI algorithm. Specifically, the server retrieves the latest data from the database and inputs it into the AI ​​model to detect outliers and patterns.

[0931] Step 5:

[0932] The server generates warnings and advice based on the analysis results of the AI ​​algorithm. Specifically, if the temperature is abnormally high, it generates a message saying, "Your baby's temperature is too high. Please check with a thermometer and consult a doctor."

[0933] Step 6:

[0934] The server notifies the user of the warnings and advice it generates by sending messages to the user's smartphone using a push notification service.

[0935] Step 7:

[0936] The user's device receives the notification and displays it visually. Specifically, a message appears in the smartphone's notification bar, and tapping the notification opens a dedicated app with detailed information and advice on how to respond.

[0937] Step 8:

[0938] The user checks the notification and takes the necessary action, such as rechecking the baby's temperature and consulting a doctor.

[0939] Example 1

[0940] 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."

[0941] In childcare, it is important to accurately and quickly understand a baby's health status, including body temperature, activity level, excretion status, and feeding status. However, manually managing this data is time-consuming and prone to errors. It is also difficult to respond immediately if an abnormality is detected. Therefore, there is a need for a system that can solve these issues, automatically monitor a baby's health status, and provide necessary advice.

[0942] 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.

[0943] In this invention, the server includes means for collecting data on the baby's body temperature, activity level, excretion status, and dietary intake using a sensor module, means for transmitting the collected data in real time to a central processing unit, means for storing the data received by the central processing unit in a data storage device, means for analyzing the stored data using an AI algorithm, means for generating countermeasures and advice based on the analysis results, means for notifying the user's communication terminal of the generated countermeasures and advice, and means for displaying the notification on the user's communication terminal. This makes it possible to accurately and quickly grasp the baby's health condition and take necessary measures in a timely manner.

[0944] A "sensor module" refers to a device that combines multiple sensors to measure a baby's health.

[0945] "Body temperature" refers to data measuring the temperature of a baby's skin and internal body.

[0946] "Activity level" refers to data that records the baby's movements and amount of exercise.

[0947] "Excretion status" refers to data regarding the baby's urination and defecation.

[0948] "Dietary data" refers to information about the amount and time of meals a baby eats.

[0949] "Real-time" means that data is processed and transmitted immediately without delay.

[0950] "Central Processing Unit" refers to a central computer system for receiving, analyzing, and otherwise processing data.

[0951] "Data storage device" refers to a storage device for saving received data.

[0952] "AI algorithm" refers to a computational method that uses artificial intelligence technology to analyze data and detect anomalies or specific patterns.

[0953] "Countermeasures" refer to appropriate ways of dealing with specific situations and guidelines for action that are generated based on the results of data analysis.

[0954] "Advice" refers to suggestions or recommendations regarding health care provided based on the results of the analysis.

[0955] "Communication terminal" refers to electronic devices such as smartphones and tablets used by users.

[0956] "Notification" refers to an alert or message that notifies the user of a generated remedy or advice.

[0957] "Display" refers to providing the notified content in a visually visible form on the user's communication terminal.

[0958] "Thermometry device" refers to a sensor device for measuring a baby's body temperature.

[0959] "Video capture device" refers to a camera or video recording device used to record a baby's activity.

[0960] To implement the present invention, the following system configuration and specific means are used to monitor the baby's health condition in real time and provide warnings and advice to parents as necessary.

[0961] This system consists of the following main hardware and software:

[0962] Sensor Module

[0963] Central Processing Unit (Server)

[0964] Data storage device (database)

[0965] Communication device (user's smartphone or tablet)

[0966] Sensor Module

[0967] The sensor module is worn on the baby and contains the following sensors:

[0968] Body temperature sensor: Measures the baby's skin temperature. Example: If the body temperature is 37.8 degrees.

[0969] Activity sensors: Use cameras and accelerometers to record your baby's movements, for example if your baby is moving less than usual.

[0970] Excretion sensor: Detects changes in humidity and temperature inside the diaper. For example, it detects whether the diaper is wet.

[0971] Meal data entry: Parents manually enter data through the app.

[0972] Central Processing Unit and Data Storage Unit

[0973] The data obtained from the sensor module is sent in real time via the user's device to a central processing unit (server). The server receives this data and stores it in a database. The data is organized in chronological order and can be easily referenced.

[0974] Data analysis and result generation

[0975] The server is equipped with an AI algorithm that analyzes the data stored in the database, detecting anomalies such as:

[0976] Abnormal body temperature: Detects hyperthermia or hypothermia when the body temperature deviates from the normal range (36.5-37.5°C).

[0977] Activity anomalies: Detect increases or decreases in movement that differ from normal activity patterns.

[0978] Excretory abnormalities: Detect frequent or absent bowel movements.

[0979] The AI ​​algorithm detects subtle changes in your physical condition by comparing them with previously learned health data.

[0980] Generating warnings and advice

[0981] Based on the analysis results, the server generates necessary warnings and advice, for example, if the temperature reaches 37.8 degrees, it generates a warning saying "Possible fever, recheck with a thermometer and consult a doctor."

[0982] Notifications and Displays

[0983] The generated warnings and advice are pushed to the user's communication device, where they are visually displayed and, when tapped, launch a dedicated app that displays detailed instructions on how to respond.

[0984] Specific examples

[0985] For example, if a baby's temperature reaches 37.8°C at 9:00 AM on a certain day, the following process will occur:

[0986] 1. Data collection: The body temperature sensor measures 37.8 degrees and sends the data to the server.

[0987] 2. Data reception and storage: The server receives the body temperature data and stores it in the database.

[0988] 3. Data analysis: AI algorithms analyze this temperature data and detect temperatures outside the normal range.

[0989] 4. Alert Generation: The server generates an alert saying "Possible fever, recheck with thermometer and consult doctor."

[0990] 5. Notification and display: A warning is pushed to the user's smartphone and the user can check it.

[0991] Prompt Sentence Examples

[0992] The input prompts for the generative AI model are:

[0993] "Generate an alert if the baby's temperature exceeds the normal range."

[0994] "Detect abnormalities when your baby's activity level falls outside the normal range."

[0995] "Generate advice if your baby's bowel frequency is abnormal."

[0996] The system of the present invention makes it possible to accurately and quickly grasp the baby's health condition and provide appropriate advice to parents, thereby reducing the anxiety of parents, especially those raising a child for the first time, and supporting the health management of their baby.

[0997] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0998] Step 1: Data collection (user device)

[0999] The user's device collects body temperature, activity levels, and excretion status in real time from a sensor module attached to the baby. The device receives various data from the sensor module as input and temporarily stores it. Specifically, the body temperature sensor measures the baby's skin temperature, the activity sensor records the baby's movements, and the excretion sensor detects humidity and temperature changes inside the diaper. Each collected data is converted into a standard format for transmission to the server in the next step.

[1000] Step 2: Send data (user's device)

[1001] The user's device transmits the data collected and temporarily stored in step 1 to the server in real time. It receives the temporarily stored data in the device as input, converts it into packet format, and transmits it. Specifically, body temperature data, movement patterns, and logs of humidity and temperature changes are transmitted to the server via a secure communication channel.

[1002] Step 3: Data reception and storage (server)

[1003] The server receives data sent from the user's device in real time. It takes the received data as input and stores it in a database. The data is organized in chronological order, and each piece of data is stored with a timestamp. Specifically, the time at which data such as a body temperature of 37.8 degrees and activity records were collected is clearly managed.

[1004] Step 4: Data analysis (server)

[1005] The server uses an AI algorithm to analyze the data stored in the database. It receives stored body temperature data, activity data, and excretion data as input and analyzes them. Specifically, if the body temperature exceeds the normal range (36.5-37.5°C), it detects high or low fever. If the activity pattern differs from the normal, it is also detected as an abnormality. The output of the analysis is notification data or advice data if an abnormality is detected.

[1006] Step 5: Generating Warnings and Advice (Server)

[1007] The server generates warnings and advice based on the analysis results of step 4. It receives the abnormal data from the analysis results as input and creates appropriate warning messages and advice based on that. Specifically, if the body temperature reaches 37.8 degrees, a warning is generated stating, "Possible fever. Check with a thermometer and consult a doctor." The generated message is sent to the user's device in the next step.

[1008] Step 6: Notifications and Display (User's Device)

[1009] The user's device receives warnings and advice sent from the server. It receives notification data from the server as input and displays it visually. Specifically, a warning message appears on the smartphone saying, "Your temperature is too high. Please check with a thermometer and consult a doctor." Tapping the notification launches a dedicated app, which displays detailed instructions on how to respond.

[1010] Through the above steps, the system can accurately and quickly monitor the baby's health status and provide necessary advice to parents.

[1011] (Application example 1)

[1012] 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."

[1013] There is a need for real-time monitoring of baby's health and for parents to receive appropriate warnings and advice. However, conventional systems simply collect data and do not provide support for parents in selecting the most appropriate childcare products. This often makes it difficult for parents to respond appropriately and select the right products.

[1014] 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.

[1015] In this invention, the server includes means for suggesting childcare products suitable for the baby's health condition based on the analysis results, and means for allowing the user to view detailed information on the suggested childcare products in the virtual store. This allows parents to easily select appropriate childcare products according to the baby's health condition, reducing their anxiety about childcare.

[1016] A "sensor module" is a device used to collect data on a baby's temperature, activity, bowel movements, and diet.

[1017] A "thermosensor" is a sensor used to measure the temperature of a baby's skin.

[1018] "Photography equipment" refers to cameras or video equipment used to record the baby's activity.

[1019] A "server" is a computer system whose role is to receive collected data, store it in a database, and analyze it using AI algorithms.

[1020] A "database" is an information system for organizing, storing, and managing collected data in chronological order.

[1021] An "AI algorithm" is an artificial intelligence technology that analyzes collected data and performs anomaly detection and prediction.

[1022] "Warnings and advice" are notifications and recommended actions to parents that are generated based on the analysis results.

[1023] A "user device" is a parent's device (e.g., a smartphone or tablet) that can receive warnings and advice.

[1024] "Notification" refers to the sending of a message to a user's terminal in real time.

[1025] "Childcare products" refers to items used depending on the baby's health condition (e.g., thermometers, cooling sheets, etc.).

[1026] A "virtual store" is a virtual shopping space where you can purchase childcare products online.

[1027] MODE FOR CARRYING OUT THE INVENTION

[1028] This invention is a system that collects and analyzes a baby's body temperature, activity level, excretion status, and dietary data, and provides warnings and advice to parents as needed. It also has a function to suggest appropriate childcare products in a virtual store. The system consists of a sensor module, a server, and a user's terminal.

[1029] Data collection (terminal)

[1030] The sensor module includes the following sensors:

[1031] Thermosensor: Measures baby's skin temperature.

[1032] Camera: Records the baby's movements.

[1033] Excretion sensor: Detects changes in humidity and temperature inside the diaper.

[1034] Meal data entry: Parents manually enter the amount and time their baby eats into the app.

[1035] The data obtained from these sensors is transmitted in real time by the sensor modules to a server.

[1036] Data reception and storage (server)

[1037] The server receives data sent from the device in real time and stores it in a database. The data is organized chronologically, and the time and date of collection are clearly managed.

[1038] Data analysis (server)

[1039] The server is equipped with an AI algorithm that analyzes the data stored in the database, detecting changes in physical condition such as:

[1040] Abnormal body temperature: Detects hyperthermia or hypothermia when the body temperature is outside the normal range.

[1041] Activity anomalies: Detect increases or decreases in movement that differ from normal activity patterns.

[1042] Excretory abnormalities: Detect frequent or absent bowel movements.

[1043] The AI ​​algorithm evaluates the baby's condition by comparing it with previously learned health data, allowing it to detect even subtle changes that would normally go unnoticed.

[1044] Generating Warnings and Advice (Server)

[1045] Based on the analysis results, the server will generate necessary warnings and advice, for example, if the baby's temperature is abnormally high, it will generate a warning message saying "Baby's temperature is too high. Please check with a thermometer and consult a doctor."

[1046] Notifications and displays (user device)

[1047] The generated warnings and advice are sent to the user's device via push notification. The user's device (smartphone, etc.) displays the received warnings and advice as a visual notification. Tapping the notification launches a dedicated app, which displays detailed recommended actions.

[1048] Proposing childcare products in a virtual store

[1049] Furthermore, the system has the function of suggesting baby products suitable for the baby's health condition based on the analysis results.In the virtual store, users can view detailed information about the suggested baby products and easily purchase the necessary items.

[1050] Example

[1051] For example, if a baby's temperature is 37.8 degrees Celsius at 9:00 a.m. one day, exceeding the normal range, the system will operate as follows:

[1052] 1. Data collection: The device's thermosensor measures the temperature of 37.8 degrees and sends it to the server.

[1053] 2. Data reception and storage: The server receives the data and stores it in the database.

[1054] 3. Data analysis: AI algorithms analyze this temperature data and detect anything outside the normal range.

[1055] 4. Generate Alert: The server generates an alert saying "May have a fever, recheck with a thermometer and consult a doctor."

[1056] 5. Notification and display: A warning is pushed to the user's smartphone and the user can check it.

[1057] 6. Baby Product Suggestions: The virtual store will suggest suitable baby products, such as cooling pads or a new thermometer.

[1058] Examples of prompt statements

[1059] "Generate a message recommending a cooling sheet or thermometer if your baby has a high temperature."

[1060] In this way, the system can quickly detect subtle changes in a baby's physical condition and provide specific advice on how to respond appropriately. Furthermore, by suggesting baby care products in the virtual store, parents can easily obtain the necessary items, making childcare even smoother.

[1061] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1062] Step 1:

[1063] The sensor module collects data on the baby's body temperature, activity level, excretion status, and dietary intake. The collected data is temporarily stored within the sensor module. The input is biological and dietary information obtained from the sensor, and the output is sent to a terminal as organized data.

[1064] Step 2:

[1065] The terminal receives the organized data from the sensor module and transmits it to the server in real time. The input is the data received by the terminal, and the output is the raw data transmitted to the server.

[1066] Step 3:

[1067] The server receives the data sent from the terminal and stores it in a database. The input is the raw data received by the server, and the output is the stored data in the database organized in chronological order.

[1068] Step 4:

[1069] The server analyzes the stored data using an AI algorithm. The AI ​​algorithm detects abnormalities in body temperature, activity level, and excretion status, and outputs the results. The input is the data stored in the database, and the output is the abnormality detection result. For example, if body temperature exceeds 37.8 degrees, it will be detected as an abnormality.

[1070] Step 5:

[1071] The server generates warnings and advice based on the analysis results. The input is the anomaly detection result, and the output is the generated warning message or advice. Specifically, if an abnormal temperature is detected, a message is generated saying, "Your baby's temperature is too high. Check again with a thermometer and consult a doctor."

[1072] Step 6:

[1073] The server sends the generated warnings and advice to the user's device via push notifications. The input is the generated warning message, and the output is the notification sent to the user's device.

[1074] Step 7:

[1075] It visually displays notifications received by the user's device, allowing the user to check the notification and view more information if necessary. The input is the received alert message, and the output is the displayed notification and more information.

[1076] Step 8:

[1077] The server then recommends appropriate childcare products based on the analysis results. The input is the analysis results, and the output is a list of suggested childcare products. For example, if the child has a high temperature, it might suggest a cooling sheet or a new thermometer.

[1078] Step 9:

[1079] The user's device displays detailed information about the proposed childcare products in the virtual store. The input is a list of the proposed childcare products, and the output is the display of detailed information. The user can then select and purchase the products they need.

[1080] In this way, the system can monitor the baby's health in real time and provide appropriate treatment and support.

[1081] 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.

[1082] This invention combines a system that collects and analyzes a baby's body temperature, activity level, excretion status, and dietary data, and provides warnings and advice to parents as needed, with an emotion engine that recognizes the user's emotions. This system consists of a sensor module, a server, an emotion engine, and a user's device.

[1083] Data collection (terminal)

[1084] The device consists of a sensor module attached to the baby, which contains the following sensors:

[1085] Body temperature sensor: Measures baby's skin temperature.

[1086] Activity sensor: Uses a camera and accelerometer to record baby's movements.

[1087] Excretion sensor: Detects changes in humidity and temperature inside the diaper.

[1088] Meal data entry: Parents manually enter the amount and time their baby eats into the app.

[1089] The data obtained from these sensors is transmitted by the terminal to a server in real time.

[1090] Data reception and storage (server)

[1091] The server receives data sent from the device in real time. After receiving the data, it is stored in a database. The data is organized chronologically, and the time and date of collection of the data are clearly managed.

[1092] Data analysis (server)

[1093] The server is equipped with an AI algorithm that analyzes the data stored in the database, detecting changes in physical condition such as:

[1094] Abnormal body temperature: Detects hyperthermia or hypothermia when the body temperature is outside the normal range.

[1095] Activity anomalies: Detect increases or decreases in movement that differ from normal activity patterns.

[1096] Excretory abnormalities: Detect frequent or absent bowel movements.

[1097] The AI ​​algorithm evaluates the baby's condition by comparing it with previously learned health data, allowing it to detect even subtle changes that would normally go unnoticed.

[1098] Generating Warnings and Advice (Server)

[1099] Based on the analysis results, the server will generate necessary warnings and advice, for example, if the baby's temperature is abnormally high, it will generate a warning message saying "Baby's temperature is too high. Please check with a thermometer and consult a doctor."

[1100] Notifications and displays (user device)

[1101] The generated warnings and advice are sent as push notifications to the user's device. The user's device (smartphone, etc.) notifies and displays the received warnings and advice. Tapping the notification launches a dedicated app, which displays detailed recommended actions.

[1102] Introduction of emotion engine (server and user terminal)

[1103] The system also incorporates an emotion engine that recognizes the user's emotions. Specifically, it operates as follows:

[1104] The server collects the user's voice and facial recognition data and analyzes it using an emotion engine to detect when the user is in a particular emotional state, such as stress, worry, or anxiety.

[1105] Based on the emotion engine's analysis, the server adjusts the tone and content of warnings and advice. For example, if the user is feeling very stressed, a more friendly and reassuring message will be generated.

[1106] Specific examples

[1107] For example, if a baby has a fever and the user (mother) is very anxious, the system operates as follows:

[1108] 1. Data collection: The device's body temperature sensor measures 37.8 degrees and sends the data to the server.

[1109] 2. Data reception and storage: The server receives the data and stores it in the database.

[1110] 3. Data analysis: AI algorithms analyze body temperature data and detect when it is outside the normal range.

[1111] 4. Emotion data collection and analysis: When a user opens the app, the emotion engine detects the user's anxiety through voice and facial recognition.

[1112] 5. Alert Generation: The server generates a friendly tone alert saying "Your baby's temperature is too high. Please calm down, recheck with a thermometer and consult a doctor."

[1113] 6. Notification and display: A warning is pushed to the user's smartphone and the user can check it.

[1114] In this way, the system of the present invention can quickly detect subtle changes in a baby's physical condition and provide appropriate responses according to the user's emotional state, thereby reducing anxiety, especially for parents raising a child for the first time.

[1115] The processing flow will be explained below.

[1116] Step 1:

[1117] The device collects data from sensor modules attached to the baby: a temperature sensor measures the baby's temperature, an activity sensor records the baby's movements, a toilet sensor detects the baby's bowel movements, and dietary data is manually entered into the app by the parent.

[1118] Step 2:

[1119] The data collected by the device is sent to the server in real time. Specifically, the device collects various data acquired from sensors and sends them to the server via Wi-Fi or mobile networks.

[1120] Step 3:

[1121] The server receives the data sent from the device. Specifically, the server's API receives the HTTP request and retrieves the data.

[1122] Step 4:

[1123] The server stores the received data in a database. Specifically, it issues an SQL query to insert the data into the database and organizes it in chronological order.

[1124] Step 5:

[1125] The server periodically retrieves the data stored in the database and analyzes it using an AI algorithm. Specifically, the server retrieves the latest data from the database and inputs it into the AI ​​model to detect outliers and patterns.

[1126] Step 6:

[1127] The server generates warnings and advice based on the analysis results of the AI ​​algorithm. Specifically, if the temperature is abnormally high, it generates a message saying, "Your baby's temperature is too high. Please check with a thermometer and consult a doctor."

[1128] Step 7:

[1129] When a user opens the app, the device collects emotion data, specifically voice data and facial recognition data, and sends it to the emotion engine.

[1130] Step 8:

[1131] The emotion engine installed on the server analyzes the user's emotion data, specifically using voice and facial recognition algorithms to detect the user's emotional state (e.g., stress or anxiety).

[1132] Step 9:

[1133] The server adjusts the content and tone of warnings and advice based on the analysis results of the emotion engine. Specifically, if the user is feeling anxious, it generates a friendly and reassuring message such as, "Your baby's temperature is too high. Please calm down, check again with a thermometer, and consult a doctor."

[1134] Step 10:

[1135] The server notifies the user of the warnings and advice it generates by sending messages to the user's smartphone using a push notification service.

[1136] Step 11:

[1137] The user's device receives the notification and displays it visually. Specifically, a message appears in the smartphone's notification bar, and tapping the notification opens a dedicated app with detailed information and advice on how to respond.

[1138] Step 12:

[1139] The user checks the notification and takes the necessary action, such as rechecking the baby's temperature and consulting a doctor.

[1140] Example 2

[1141] 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."

[1142] Monitoring a baby's physical condition and health is extremely important in childcare. However, it is difficult to quickly detect subtle changes or abnormalities in a baby's physical condition, which can be very worrying for first-time parents. Furthermore, there is a lack of technological means to provide appropriate advice based on parents' emotions and stress levels. Under these circumstances, there is a need for a system that can monitor a baby's health in real time and provide parents with peace of mind.

[1143] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[1144] In this invention, the server includes means for collecting data on the baby's body temperature, activity level, excretion status, and dietary intake using a sensor module, means for transmitting the collected data in real time to a network device, means for storing data received by the network device in a storage device, means for analyzing the stored data using a machine learning algorithm, means for generating a warning or advice based on the analysis results, means for notifying the generated warning or advice on a user's display device, means for displaying the warning or advice on the user's display device, means for collecting user voice data and facial recognition data and analyzing it using an emotion recognition engine, means for adjusting the content of the warning or advice based on the analyzed emotion data, and means for notifying the user's display device of the warning or advice according to the emotion. This makes it possible to quickly detect subtle changes in the baby's physical condition and provide appropriate responses and advice according to the user's emotional state.

[1145] The "sensor module" is a device that collects data on a baby's body temperature, activity level, excretion status, and dietary intake.

[1146] A "body temperature sensor" is a temperature sensing device used to measure a baby's skin temperature.

[1147] An "activity sensor" is a device that uses a camera and an accelerometer to record a baby's movements.

[1148] An "excretion sensor" is a sensing device that detects changes in humidity and temperature inside a diaper.

[1149] "Collected data" is a general term for the baby's body temperature, activity level, excretion status, and dietary data obtained by the sensor module.

[1150] A "network device" is a communication device for transmitting collected data to a server in real time.

[1151] A "memory device" is a data storage device that stores received data and allows it to be accessed at a later time.

[1152] A "machine learning algorithm" is an artificial intelligence technique used for data analysis, which identifies patterns in stored data.

[1153] A "warning" is a message generated based on the results of data analysis to alert the user.

[1154] "Advice" is a recommended instruction for action provided to a user based on the results of data analysis.

[1155] A "display device" is a device for notifying and displaying generated warnings and advice to a user, and includes smartphones and tablets.

[1156] "Voice data" is information collected from the user's voice.

[1157] "Facial recognition data" refers to image or video information obtained by recognizing the user's face.

[1158] An "emotion recognition engine" is a system that analyzes collected voice data and facial recognition data to identify the user's emotional state.

[1159] This system collects and analyzes data on a baby's body temperature, activity level, excretion status, and dietary intake, and provides warnings and advice to parents based on the analysis results, while also incorporating an emotion engine that recognizes the user's emotions. This system is composed of a sensor module, a network device, a storage device, a machine learning algorithm, an emotion recognition engine, and a user's display device.

[1160] (Data collection (terminal))

[1161] The device collects data through a sensor module attached to the baby, which contains the following sensors:

[1162] Body temperature sensor: A temperature sensing device that measures the baby's skin temperature.

[1163] Activity sensor: An image capture device that uses a camera and accelerometer to record the baby's movements.

[1164] Excretion sensor: A sensing device that detects changes in humidity and temperature inside the diaper.

[1165] Meal data entry: Parents manually enter their baby's meal amounts and times using the app.

[1166] These data are transmitted in real time from the terminal to the network device.

[1167] (Data reception and storage (server))

[1168] The server receives data sent from the network device and stores it in a storage device. Specifically, the server receives data using a communication protocol such as HTTP or MQTT. The received data is organized by time, and the data collected at what point in time is clearly managed.

[1169] (Data analysis (server))

[1170] Machine learning algorithms in the server analyze the data stored in the storage device, specifically detecting anomalies such as:

[1171] Body temperature abnormality: Detects hyperthermia or hypothermia when the body temperature data exceeds a certain threshold.

[1172] Activity Anomalies: Detect abnormal increases or decreases in activity compared to normal activity patterns.

[1173] Excretory abnormalities: Detect frequent or absent bowel movements compared to normal bowel patterns.

[1174] (Emotion data collection and analysis (server and user terminal))

[1175] The server and the user's device recognize emotions using the user's voice data and facial recognition data. The specific operation is as follows:

[1176] The user launches the app and collects voice and facial information via voice input and the camera.

[1177] The server analyzes the collected information and detects whether the user is experiencing emotional states such as stress, anxiety, or worry.

[1178] (Generating Warnings and Advice (Server))

[1179] The server generates necessary warnings and advice based on the results of data analysis and the evaluation of emotional data. For example, if the baby's temperature is high, a warning such as "Your baby's temperature is too high. Please check it again with a thermometer and consult a doctor" can be generated. If the user is feeling stressed, a message in a reassuring tone can be generated, such as "You may be worried about your baby. Please check it again with a thermometer and consult a doctor if necessary."

[1180] (Notification and Display (User's Device))

[1181] The generated warnings and advice are pushed to the user's display device. When the user opens the notification on their smartphone or tablet, the app launches and displays detailed information.

[1182] Examples:

[1183] For example, if a baby has a fever and the user (mother) is in a very anxious state, the system will operate as follows:

[1184] 1. Data collection: The device's body temperature sensor measures 37.8 degrees and transmits the data to the server via the network device.

[1185] 2. Data reception and storage: The server receives the data and stores it in a storage device.

[1186] 3. Data analysis: Machine learning algorithms analyze body temperature data and detect when it is outside the normal range.

[1187] 4. Emotion data collection and analysis: When a user opens the app, the emotion recognition engine detects the user's anxiety through voice and facial recognition.

[1188] 5. Alert Generation: The server generates a friendly tone alert saying "Your baby's temperature is too high. Please calm down, recheck with a thermometer and consult a doctor."

[1189] 6. Notification and display: A warning is pushed to the user's smartphone and the user can check it.

[1190] Examples of prompts:

[1191] An example of a prompt to be input to the generative AI model is as follows:

[1192] "My baby has a fever of 37.8 degrees. Please generate a warning message that takes into account the user's emotional state and makes them feel at ease."

[1193] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1194] Step 1: Data collection (device)

[1195] The device collects data from the sensor module attached to the baby. The specific operations are as follows:

[1196] The temperature sensor continuously measures the baby's skin temperature and sends the measurements as input to the terminal. The output data is the baby's temperature data.

[1197] The activity sensors (camera and accelerometer) record the baby's movements every second and send the video and acceleration data to the terminal as input. The output data is the baby's activity data.

[1198] The excretion sensor detects changes in humidity and temperature inside the diaper and sends the change data to the terminal as input. The output data is excretion data.

[1199] For meal data input, the user manually inputs the amount and time of the baby's meal using the app, and the output data is the meal data.

[1200] Step 2: Send data (terminal)

[1201] The terminal transmits the collected data to the network device. The specific operation is as follows:

[1202] The terminal receives temperature data, activity data, excretion data, and dietary data as input and transmits them to the network device in real time. The output is data sent to the server via the network.

[1203] Step 3: Data reception and storage (server)

[1204] The server receives the data sent from the network device and stores it in the storage device. The specific operations are as follows:

[1205] The server receives input data such as body temperature, activity, excretion, and dietary data using communication protocols such as HTTP and MQTT. The output data is stored in a storage device.

[1206] The received data is organized by time, and it is clearly managed which data was collected at what point in time.

[1207] Step 4: Data analysis (server)

[1208] The machine learning algorithms in the server analyze the data stored in the storage device. Specifically, they do the following:

[1209] The server receives body temperature data, activity data, excretion data, and dietary data as input from the storage device.

[1210] Body temperature analysis: Checks whether the body temperature data exceeds a certain threshold, and if there is an abnormality, outputs the abnormal data.

[1211] Activity analysis: Activity data is compared with normal patterns to detect abnormal increases or decreases in activity and output the abnormal data.

[1212] Excretion analysis: Excretion data is compared with normal patterns to detect abnormalities and output the abnormal data.

[1213] Step 5: Emotion data collection and analysis (server and user device)

[1214] The server and the user's device collect the user's voice data and facial recognition data, and analyze their emotions. The specific operations are as follows:

[1215] The user launches the app and collects voice and facial information via voice input or camera. The output data is voice data and facial recognition data.

[1216] The server receives collected voice data and facial recognition data as input, analyzes them, and infers the user's emotional state. The output is the user's emotional state data.

[1217] Step 6: Generating Warnings and Advice (Server)

[1218] The server generates necessary warnings and advice based on the results of data analysis and the evaluation of the emotion data. Specific operations are as follows:

[1219] The server receives anomaly data and user emotional state data as input.

[1220] Based on the analysis results, a warning message such as "Your baby's temperature is too high. Please double-check with a thermometer and consult a doctor" is generated. If the user is feeling stressed, a reassuring message such as "We understand that you may be worried about your baby, but please double-check with a thermometer first and consult a doctor if necessary" is generated. The output is a warning or advice message.

[1221] Step 7: Notifications and Display (User's Device)

[1222] The generated warnings and advice are pushed to the user's display device and displayed. Specifically, the behavior is as follows:

[1223] The server sends warning and advice messages to the user terminal as input.

[1224] The user's device displays the received message in the notification bar, and when the user taps the notification, the app launches and displays more information. The output is a warning or advice message that is displayed to the user.

[1225] (Application example 2)

[1226] 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."

[1227] In modern homes, security measures that provide safety and peace of mind go beyond mere mechanical security. A responsive approach is needed, especially when household members are feeling anxious or stressed. However, current home security systems do not generate warnings or advice that take into account the user's emotional state. Therefore, more considerate and effective security measures are needed.

[1228] The identification process by the identification processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes means for collecting voice data and image data within the home, analyzing them using an emotion engine, and recognizing the user's emotional state, means for adjusting the tone and content of warnings and advice generated according to the user's emotional state, and means for analyzing the various collected home data using an AI algorithm and detecting abnormalities. This makes it possible to provide appropriate security measures that give peace of mind to members of the home who are feeling anxious or stressed.

[1229] A "sensor module" is hardware that integrates various sensors used to detect the environment and the status of members in the home.

[1230] A "thermal sensor" is a device for measuring the temperature of an object or living thing, especially when used to detect skin temperature.

[1231] An "activity sensor" is a device that detects changes in acceleration or movement. This includes motion sensors and cameras.

[1232] An "excretion sensor" is a sensor that detects changes in humidity and temperature to grasp the excretion status.

[1233] "Dietary data" refers to information such as the amount, type, and time of food consumed by household members.

[1234] A "server" is a centralized computer system that receives, stores, and analyzes data over a network.

[1235] A "database" is a collection of data that is systematically stored and constructed to allow easy searching and analysis of collected data.

[1236] "AI algorithms" are machine learning and artificial intelligence techniques used to analyze collected data and detect patterns and anomalies.

[1237] A "warning" is a notification that notifies the user when an abnormality or risk is detected in the home.

[1238] "Advice" refers to the appropriate response or recommendation provided along with the warning.

[1239] A "home terminal" is a device, such as a mobile device or computer, that a user uses to interact with or receive notifications.

[1240] An "emotion engine" is an artificial intelligence system that analyzes voice and image data to recognize the user's emotional state.

[1241] "Analysis" is the process of examining collected data in detail to discover specific patterns or anomalies.

[1242] "Real-time" means that data is collected, transmitted, and processed immediately.

[1243] A "push notification" is a highly instantaneous message sent directly from a server to a user's device.

[1244] "Speech recognition" is a technology for analyzing voice data to understand meaning and emotions.

[1245] "Facial recognition" is a technology that uses a camera to identify a person's face and determine their emotion or state.

[1246] The present invention is a security system for providing safety and a sense of security within the home, and is composed of a sensor module, a server, an emotion engine, and a user terminal.

[1247] Data collection (terminal)

[1248] The device consists of the following sensor modules installed in the home:

[1249] Motion Sensor: Detects movement within the home and records any unusual activity.

[1250] Door and window sensors: Detects when doors and windows are opened and closed, and records any unauthorized openings and closings.

[1251] Camera: Collects image data and can identify suspicious individuals.

[1252] Audio sensor: Detects abnormal sounds (e.g., the sound of glass breaking).

[1253] Emotional Data Input: An interface is used for household members to input their emotional states.

[1254] The data obtained from these sensor modules is transmitted in real time by the terminal to a server.

[1255] Data reception and storage (server)

[1256] The server receives data sent from the device in real time. After receiving the data, it is stored in a database. The stored data is organized in chronological order, and the time and date of collection of the data are clearly managed.

[1257] Data analysis (server)

[1258] The server is equipped with an AI algorithm that analyzes the data stored in the database, detecting anomalies such as the following:

[1259] Motion abnormalities: movements that differ from normal activity patterns.

[1260] Unauthorized opening and closing of doors and windows: Unauthorized opening and closing at specific times.

[1261] Recognizing suspicious people: Image analysis using a camera.

[1262] Abnormal sound detection: AI-based voice data analysis.

[1263] Emotion data collection and analysis (server and user terminal)

[1264] It is equipped with an emotion engine that analyzes voice and facial recognition data to recognize the user's emotional state, and can detect when a household member is feeling anxious or stressed.

[1265] Generating Warnings and Advice (Server)

[1266] Based on the analysis results, the server generates necessary warnings and advice. For example, if a motion sensor detects abnormal activity and household members are feeling anxious, a more friendly and reassuring message will be generated.

[1267] Notifications and displays (user device)

[1268] The generated warnings and advice are sent as push notifications to the user's device. The user's device (smartphone, etc.) notifies and displays the received warnings and advice. Tapping the notification launches a dedicated app, which displays detailed recommended actions.

[1269] Specific examples

[1270] For example, if a motion sensor detects abnormal activity in the middle of the night and a household member (e.g., mother) is in a very anxious state, the system operates as follows.

[1271] 1. Data collection: Motion sensors detect abnormal movements and send the data to the server.

[1272] 2. Data reception and storage: The server receives the data and stores it in the database.

[1273] 3. Data analysis: AI algorithms detect abnormal behavior.

[1274] 4. Emotion data collection and analysis: When a user opens the app, the emotion engine detects the user's anxiety through voice and facial recognition.

[1275] 5. Alert Generation: The server generates a friendly tone of warning saying "Unusual activity detected. Please remain calm and make sure all doors and windows are closed."

[1276] 6. Notification and display: A warning is pushed to the user's smartphone and the user can check it.

[1277] Prompt Sentence Examples

[1278] "When a home security system detects abnormal motion, generate advice to reduce anxiety and give the user peace of mind."

[1279] The security system of the present invention configured as described above can provide appropriate security measures that give peace of mind to members of the household who feel anxious or stressed.

[1280] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1281] Step 1: Data collection (device)

[1282] Sensor modules installed in the home (motion sensors, door / window sensors, cameras, audio sensors, and emotion data input interfaces) collect various types of data. The collected data includes motion data, opening / closing data, image data, audio data, and members' emotion data. This data is input into the terminal.

[1283] Step 2: Send data (terminal)

[1284] The collected data is sent in real time from the device to a server, where it is uploaded via an internet connection and includes a timestamp.

[1285] Step 3: Receiving data (server)

[1286] The server receives data sent from the device in real time, including motion data, opening / closing data, image data, audio data, and emotion data.

[1287] Step 4: Data storage (server)

[1288] The received data is stored in a database on the server. When stored, it is organized by data type and recorded in the database in chronological order by timestamp.

[1289] Step 5: Data analysis (server)

[1290] The server's AI algorithms analyze the stored data, which includes the following steps:

[1291] Analyzes motion data and detects abnormal movements.

[1292] Analyzes opening and closing data to detect unauthorized opening and closing of doors and windows.

[1293] Analyze image data and recognize suspicious individuals.

[1294] Analyzes audio data to detect abnormal sounds (e.g., the sound of glass breaking).

[1295] Step 6: Emotion data collection and analysis (server)

[1296] The server uses the emotion data, voice data, and facial recognition data entered by the user to analyze the user's emotional state using an emotion engine, which determines whether the user is feeling anxious or stressed.

[1297] Step 7: Generating Warnings and Advice (Server)

[1298] Based on the analysis results, the server generates necessary warnings and advice, such as abnormal activity, unauthorized opening and closing, and a friendly tone of voice depending on the user's emotional state. For example, a message might be generated such as, "Abnormal activity has been detected. Please remain calm and make sure all doors and windows are closed."

[1299] Step 8: Notification (Server)

[1300] The generated warnings and advice are pushed to the user's device, and the notification includes a message along with a timestamp.

[1301] Step 9: Receive and display notifications (on the user's device)

[1302] The user's device receives a push notification from the server. The received notification is displayed in the device's notification area or in a dedicated app. When the user taps the notification, detailed instructions on how to respond are displayed.

[1303] The above are the specific processing steps for implementing the present invention. By clarifying how input data is processed and analyzed at each step and how the final output is obtained, it is possible to provide users with appropriate security measures.

[1304] 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.

[1305] 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.

[1306] 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.

[1307] [Fourth embodiment]

[1308] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[1309] 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.

[1310] 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).

[1311] 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.

[1312] 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.

[1313] 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).

[1314] 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.

[1315] 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.

[1316] 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.

[1317] 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.

[1318] 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.

[1319] 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.

[1320] 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."

[1321] This invention is a system that collects and analyzes data on a baby's body temperature, activity level, excretion status, and dietary intake, and provides warnings and advice to parents as needed. This system is composed of a sensor module, a server, and a user's device.

[1322] Data collection (terminal)

[1323] The device consists of a sensor module attached to the baby, which contains the following sensors:

[1324] Body temperature sensor: Measures baby's skin temperature.

[1325] Activity sensor: Uses a camera and accelerometer to record baby's movements.

[1326] Excretion sensor: Detects changes in humidity and temperature inside the diaper.

[1327] Meal data entry: Parents manually enter the amount and time their baby eats into the app.

[1328] The data obtained from these sensors is transmitted by the terminal to a server in real time.

[1329] Data reception and storage (server)

[1330] The server receives data sent from the device in real time. After receiving the data, it is stored in a database. The data is organized chronologically, and the time and date of collection of the data are clearly managed.

[1331] Data analysis (server)

[1332] The server is equipped with an AI algorithm that analyzes the data stored in the database, detecting changes in physical condition such as:

[1333] Abnormal body temperature: Detects hyperthermia or hypothermia when the body temperature is outside the normal range.

[1334] Activity anomalies: Detect increases or decreases in movement that differ from normal activity patterns.

[1335] Excretory abnormalities: Detect frequent or absent bowel movements.

[1336] The AI ​​algorithm evaluates the baby's condition by comparing it with previously learned health data, allowing it to detect even subtle changes that would normally go unnoticed.

[1337] Generating Warnings and Advice (Server)

[1338] Based on the analysis results, the server will generate necessary warnings and advice, for example, if the baby's temperature is abnormally high, it will generate a warning message saying "Baby's temperature is too high. Please check with a thermometer and consult a doctor."

[1339] Notifications and displays (user device)

[1340] The generated warnings and advice are sent as push notifications to the user's device. The user's device (smartphone, etc.) displays the received warnings and advice as visual notifications. Tapping the notification launches a dedicated app, which displays detailed recommended actions.

[1341] Specific examples

[1342] For example, if a baby's temperature is 37.8 degrees Celsius at 9:00 a.m. one day, exceeding the normal range, the system will operate as follows:

[1343] 1. Data collection: The device's body temperature sensor measures 37.8 degrees and sends the data to the server.

[1344] 2. Data reception and storage: The server receives the data and stores it in the database.

[1345] 3. Data analysis: AI algorithms analyze this temperature data and detect anything outside the normal range.

[1346] 4. Generate Alert: The server generates an alert saying "May have a fever, recheck with a thermometer and consult a doctor."

[1347] 5. Notification and display: A warning is pushed to the user's smartphone and the user can check it.

[1348] In this way, the system of the present invention can quickly detect subtle changes in a baby's physical condition and provide specific advice for appropriate responses, thereby reducing the anxiety of parents, especially those raising children for the first time, and supporting the health management of their babies.

[1349] The processing flow will be explained below.

[1350] Step 1:

[1351] The device collects data from a sensor module attached to the baby: a temperature sensor measures the baby's temperature, an activity sensor detects the baby's movements, a toilet sensor detects the baby's bowel movements, and dietary data is manually entered by the parent.

[1352] Step 2:

[1353] The data collected by the device is sent to the server in real time. Specifically, the device collects various data acquired from sensors and sends them to the server via Wi-Fi or mobile networks.

[1354] Step 3:

[1355] The server stores the data received from the terminal in a database. Specifically, the server inserts the received data into the database using SQL queries and organizes it in chronological order.

[1356] Step 4:

[1357] The server periodically retrieves the data stored in the database and analyzes it using an AI algorithm. Specifically, the server retrieves the latest data from the database and inputs it into the AI ​​model to detect outliers and patterns.

[1358] Step 5:

[1359] The server generates warnings and advice based on the analysis results of the AI ​​algorithm. Specifically, if the temperature is abnormally high, it generates a message saying, "Your baby's temperature is too high. Please check with a thermometer and consult a doctor."

[1360] Step 6:

[1361] The server notifies the user of the warnings and advice it generates by sending messages to the user's smartphone using a push notification service.

[1362] Step 7:

[1363] The user's device receives the notification and displays it visually. Specifically, a message appears in the smartphone's notification bar, and tapping the notification opens a dedicated app with detailed information and advice on how to respond.

[1364] Step 8:

[1365] The user checks the notification and takes the necessary action, such as rechecking the baby's temperature and consulting a doctor.

[1366] Example 1

[1367] 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."

[1368] In childcare, it is important to accurately and quickly understand a baby's health status, including body temperature, activity level, excretion status, and feeding status. However, manually managing this data is time-consuming and prone to errors. It is also difficult to respond immediately if an abnormality is detected. Therefore, there is a need for a system that can solve these issues, automatically monitor a baby's health status, and provide necessary advice.

[1369] 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.

[1370] In this invention, the server includes means for collecting data on the baby's body temperature, activity level, excretion status, and dietary intake using a sensor module, means for transmitting the collected data in real time to a central processing unit, means for storing the data received by the central processing unit in a data storage device, means for analyzing the stored data using an AI algorithm, means for generating countermeasures and advice based on the analysis results, means for notifying the user's communication terminal of the generated countermeasures and advice, and means for displaying the notification on the user's communication terminal. This makes it possible to accurately and quickly grasp the baby's health condition and take necessary measures in a timely manner.

[1371] A "sensor module" refers to a device that combines multiple sensors to measure a baby's health.

[1372] "Body temperature" refers to data measuring the temperature of a baby's skin and internal body.

[1373] "Activity level" refers to data that records the baby's movements and amount of exercise.

[1374] "Excretion status" refers to data regarding the baby's urination and defecation.

[1375] "Dietary data" refers to information about the amount and time of meals a baby eats.

[1376] "Real-time" means that data is processed and transmitted immediately without delay.

[1377] "Central Processing Unit" refers to a central computer system for receiving, analyzing, and otherwise processing data.

[1378] "Data storage device" refers to a storage device for saving received data.

[1379] "AI algorithm" refers to a computational method that uses artificial intelligence technology to analyze data and detect anomalies or specific patterns.

[1380] "Countermeasures" refer to appropriate ways of dealing with specific situations and guidelines for action that are generated based on the results of data analysis.

[1381] "Advice" refers to suggestions or recommendations regarding health care provided based on the results of the analysis.

[1382] "Communication terminal" refers to electronic devices such as smartphones and tablets used by users.

[1383] "Notification" refers to an alert or message that notifies the user of a generated remedy or advice.

[1384] "Display" refers to providing the notified content in a visually visible form on the user's communication terminal.

[1385] "Thermometry device" refers to a sensor device for measuring a baby's body temperature.

[1386] "Video capture device" refers to a camera or video recording device used to record a baby's activity.

[1387] To implement the present invention, the following system configuration and specific means are used to monitor the baby's health condition in real time and provide warnings and advice to parents as necessary.

[1388] This system consists of the following main hardware and software:

[1389] Sensor Module

[1390] Central Processing Unit (Server)

[1391] Data storage device (database)

[1392] Communication device (user's smartphone or tablet)

[1393] Sensor Module

[1394] The sensor module is worn on the baby and contains the following sensors:

[1395] Body temperature sensor: Measures the baby's skin temperature. Example: If the body temperature is 37.8 degrees.

[1396] Activity sensors: Use cameras and accelerometers to record your baby's movements, for example if your baby is moving less than usual.

[1397] Excretion sensor: Detects changes in humidity and temperature inside the diaper. For example, it detects whether the diaper is wet.

[1398] Meal data entry: Parents manually enter data through the app.

[1399] Central Processing Unit and Data Storage Unit

[1400] The data obtained from the sensor module is sent in real time via the user's device to a central processing unit (server). The server receives this data and stores it in a database. The data is organized in chronological order and can be easily referenced.

[1401] Data analysis and result generation

[1402] The server is equipped with an AI algorithm that analyzes the data stored in the database, detecting anomalies such as:

[1403] Abnormal body temperature: Detects hyperthermia or hypothermia when the body temperature deviates from the normal range (36.5-37.5°C).

[1404] Activity anomalies: Detect increases or decreases in movement that differ from normal activity patterns.

[1405] Excretory abnormalities: Detect frequent or absent bowel movements.

[1406] The AI ​​algorithm detects subtle changes in your physical condition by comparing them with previously learned health data.

[1407] Generating warnings and advice

[1408] Based on the analysis results, the server generates necessary warnings and advice, for example, if the temperature reaches 37.8 degrees, it generates a warning saying "Possible fever, recheck with a thermometer and consult a doctor."

[1409] Notifications and Displays

[1410] The generated warnings and advice are pushed to the user's communication device, where they are visually displayed and, when tapped, launch a dedicated app that displays detailed instructions on how to respond.

[1411] Specific examples

[1412] For example, if a baby's temperature reaches 37.8°C at 9:00 AM on a certain day, the following process will occur:

[1413] 1. Data collection: The body temperature sensor measures 37.8 degrees and sends the data to the server.

[1414] 2. Data reception and storage: The server receives the body temperature data and stores it in the database.

[1415] 3. Data analysis: AI algorithms analyze this temperature data and detect temperatures outside the normal range.

[1416] 4. Alert Generation: The server generates an alert saying "Possible fever, recheck with thermometer and consult doctor."

[1417] 5. Notification and display: A warning is pushed to the user's smartphone and the user can check it.

[1418] Prompt Sentence Examples

[1419] The input prompts for the generative AI model are:

[1420] "Generate an alert if the baby's temperature exceeds the normal range."

[1421] "Detect abnormalities when your baby's activity level falls outside the normal range."

[1422] "Generate advice if your baby's bowel frequency is abnormal."

[1423] The system of the present invention makes it possible to accurately and quickly grasp the baby's health condition and provide appropriate advice to parents, thereby reducing the anxiety of parents, especially those raising a child for the first time, and supporting the health management of their baby.

[1424] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1425] Step 1: Data collection (user device)

[1426] The user's device collects body temperature, activity levels, and excretion status in real time from a sensor module attached to the baby. The device receives various data from the sensor module as input and temporarily stores it. Specifically, the body temperature sensor measures the baby's skin temperature, the activity sensor records the baby's movements, and the excretion sensor detects humidity and temperature changes inside the diaper. Each collected data is converted into a standard format for transmission to the server in the next step.

[1427] Step 2: Send data (user's device)

[1428] The user's device transmits the data collected and temporarily stored in step 1 to the server in real time. It receives the temporarily stored data in the device as input, converts it into packet format, and transmits it. Specifically, body temperature data, movement patterns, and logs of humidity and temperature changes are transmitted to the server via a secure communication channel.

[1429] Step 3: Data reception and storage (server)

[1430] The server receives data sent from the user's device in real time. It takes the received data as input and stores it in a database. The data is organized in chronological order, and each piece of data is stored with a timestamp. Specifically, the time at which data such as a body temperature of 37.8 degrees and activity records were collected is clearly managed.

[1431] Step 4: Data analysis (server)

[1432] The server uses an AI algorithm to analyze the data stored in the database. It receives stored body temperature data, activity data, and excretion data as input and analyzes them. Specifically, if the body temperature exceeds the normal range (36.5-37.5°C), it detects high or low fever. If the activity pattern differs from the normal, it is also detected as an abnormality. The output of the analysis is notification data or advice data if an abnormality is detected.

[1433] Step 5: Generating Warnings and Advice (Server)

[1434] The server generates warnings and advice based on the analysis results of step 4. It receives the abnormal data from the analysis results as input and creates appropriate warning messages and advice based on that. Specifically, if the body temperature reaches 37.8 degrees, a warning is generated stating, "Possible fever. Check with a thermometer and consult a doctor." The generated message is sent to the user's device in the next step.

[1435] Step 6: Notifications and Display (User's Device)

[1436] The user's device receives warnings and advice sent from the server. It receives notification data from the server as input and displays it visually. Specifically, a warning message appears on the smartphone saying, "Your temperature is too high. Please check with a thermometer and consult a doctor." Tapping the notification launches a dedicated app, which displays detailed instructions on how to respond.

[1437] Through the above steps, the system can accurately and quickly monitor the baby's health status and provide necessary advice to parents.

[1438] (Application example 1)

[1439] 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."

[1440] There is a need for real-time monitoring of baby's health and for parents to receive appropriate warnings and advice. However, conventional systems simply collect data and do not provide support for parents in selecting the most appropriate childcare products. This often makes it difficult for parents to respond appropriately and select the right products.

[1441] 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.

[1442] In this invention, the server includes means for suggesting childcare products suitable for the baby's health condition based on the analysis results, and means for allowing the user to view detailed information on the suggested childcare products in the virtual store. This allows parents to easily select appropriate childcare products according to the baby's health condition, reducing their anxiety about childcare.

[1443] A "sensor module" is a device used to collect data on a baby's temperature, activity, bowel movements, and diet.

[1444] A "thermosensor" is a sensor used to measure the temperature of a baby's skin.

[1445] "Photography equipment" refers to cameras or video equipment used to record the baby's activity.

[1446] A "server" is a computer system whose role is to receive collected data, store it in a database, and analyze it using AI algorithms.

[1447] A "database" is an information system for organizing, storing, and managing collected data in chronological order.

[1448] An "AI algorithm" is an artificial intelligence technology that analyzes collected data and performs anomaly detection and prediction.

[1449] "Warnings and advice" are notifications and recommended actions to parents that are generated based on the analysis results.

[1450] A "user device" is a parent's device (e.g., a smartphone or tablet) that can receive warnings and advice.

[1451] "Notification" refers to the sending of a message to a user's terminal in real time.

[1452] "Childcare products" refers to items used depending on the baby's health condition (e.g., thermometers, cooling sheets, etc.).

[1453] A "virtual store" is a virtual shopping space where you can purchase childcare products online.

[1454] MODE FOR CARRYING OUT THE INVENTION

[1455] This invention is a system that collects and analyzes a baby's body temperature, activity level, excretion status, and dietary data, and provides warnings and advice to parents as needed. It also has a function to suggest appropriate childcare products in a virtual store. The system consists of a sensor module, a server, and a user's terminal.

[1456] Data collection (terminal)

[1457] The sensor module includes the following sensors:

[1458] Thermosensor: Measures baby's skin temperature.

[1459] Camera: Records the baby's movements.

[1460] Excretion sensor: Detects changes in humidity and temperature inside the diaper.

[1461] Meal data entry: Parents manually enter the amount and time their baby eats into the app.

[1462] The data obtained from these sensors is transmitted in real time by the sensor modules to a server.

[1463] Data reception and storage (server)

[1464] The server receives data sent from the device in real time and stores it in a database. The data is organized chronologically, and the time and date of collection are clearly managed.

[1465] Data analysis (server)

[1466] The server is equipped with an AI algorithm that analyzes the data stored in the database, detecting changes in physical condition such as:

[1467] Abnormal body temperature: Detects hyperthermia or hypothermia when the body temperature is outside the normal range.

[1468] Activity anomalies: Detect increases or decreases in movement that differ from normal activity patterns.

[1469] Excretory abnormalities: Detect frequent or absent bowel movements.

[1470] The AI ​​algorithm evaluates the baby's condition by comparing it with previously learned health data, allowing it to detect even subtle changes that would normally go unnoticed.

[1471] Generating Warnings and Advice (Server)

[1472] Based on the analysis results, the server will generate necessary warnings and advice, for example, if the baby's temperature is abnormally high, it will generate a warning message saying "Baby's temperature is too high. Please check with a thermometer and consult a doctor."

[1473] Notifications and displays (user device)

[1474] The generated warnings and advice are sent to the user's device via push notification. The user's device (smartphone, etc.) displays the received warnings and advice as a visual notification. Tapping the notification launches a dedicated app, which displays detailed recommended actions.

[1475] Proposing childcare products in a virtual store

[1476] Furthermore, the system has the function of suggesting baby products suitable for the baby's health condition based on the analysis results.In the virtual store, users can view detailed information about the suggested baby products and easily purchase the necessary items.

[1477] Example

[1478] For example, if a baby's temperature is 37.8 degrees Celsius at 9:00 a.m. one day, exceeding the normal range, the system will operate as follows:

[1479] 1. Data collection: The device's thermosensor measures the temperature of 37.8 degrees and sends it to the server.

[1480] 2. Data reception and storage: The server receives the data and stores it in the database.

[1481] 3. Data analysis: AI algorithms analyze this temperature data and detect anything outside the normal range.

[1482] 4. Generate Alert: The server generates an alert saying "May have a fever, recheck with a thermometer and consult a doctor."

[1483] 5. Notification and display: A warning is pushed to the user's smartphone and the user can check it.

[1484] 6. Baby Product Suggestions: The virtual store will suggest suitable baby products, such as cooling pads or a new thermometer.

[1485] Examples of prompt statements

[1486] "Generate a message recommending a cooling sheet or thermometer if your baby has a high temperature."

[1487] In this way, the system can quickly detect subtle changes in a baby's physical condition and provide specific advice on how to respond appropriately. Furthermore, by suggesting baby care products in the virtual store, parents can easily obtain the necessary items, making childcare even smoother.

[1488] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1489] Step 1:

[1490] The sensor module collects data on the baby's body temperature, activity level, excretion status, and dietary intake. The collected data is temporarily stored within the sensor module. The input is biological and dietary information obtained from the sensor, and the output is sent to a terminal as organized data.

[1491] Step 2:

[1492] The terminal receives the organized data from the sensor module and transmits it to the server in real time. The input is the data received by the terminal, and the output is the raw data transmitted to the server.

[1493] Step 3:

[1494] The server receives the data sent from the terminal and stores it in a database. The input is the raw data received by the server, and the output is the stored data in the database organized in chronological order.

[1495] Step 4:

[1496] The server analyzes the stored data using an AI algorithm. The AI ​​algorithm detects abnormalities in body temperature, activity level, and excretion status, and outputs the results. The input is the data stored in the database, and the output is the abnormality detection result. For example, if body temperature exceeds 37.8 degrees, it will be detected as an abnormality.

[1497] Step 5:

[1498] The server generates warnings and advice based on the analysis results. The input is the anomaly detection result, and the output is the generated warning message or advice. Specifically, if an abnormal temperature is detected, a message is generated saying, "Your baby's temperature is too high. Check again with a thermometer and consult a doctor."

[1499] Step 6:

[1500] The server sends the generated warnings and advice to the user's device via push notifications. The input is the generated warning message, and the output is the notification sent to the user's device.

[1501] Step 7:

[1502] It visually displays notifications received by the user's device, allowing the user to check the notification and view more information if necessary. The input is the received alert message, and the output is the displayed notification and more information.

[1503] Step 8:

[1504] The server then recommends appropriate childcare products based on the analysis results. The input is the analysis results, and the output is a list of suggested childcare products. For example, if the child has a high temperature, it might suggest a cooling sheet or a new thermometer.

[1505] Step 9:

[1506] The user's device displays detailed information about the proposed childcare products in the virtual store. The input is a list of the proposed childcare products, and the output is the display of detailed information. The user can then select and purchase the products they need.

[1507] In this way, the system can monitor the baby's health in real time and provide appropriate treatment and support.

[1508] 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.

[1509] This invention combines a system that collects and analyzes a baby's body temperature, activity level, excretion status, and dietary data, and provides warnings and advice to parents as needed, with an emotion engine that recognizes the user's emotions. This system consists of a sensor module, a server, an emotion engine, and a user's device.

[1510] Data collection (terminal)

[1511] The device consists of a sensor module attached to the baby, which contains the following sensors:

[1512] Body temperature sensor: Measures baby's skin temperature.

[1513] Activity sensor: Uses a camera and accelerometer to record baby's movements.

[1514] Excretion sensor: Detects changes in humidity and temperature inside the diaper.

[1515] Meal data entry: Parents manually enter the amount and time their baby eats into the app.

[1516] The data obtained from these sensors is transmitted by the terminal to a server in real time.

[1517] Data reception and storage (server)

[1518] The server receives data sent from the device in real time. After receiving the data, it is stored in a database. The data is organized chronologically, and the time and date of collection of the data are clearly managed.

[1519] Data analysis (server)

[1520] The server is equipped with an AI algorithm that analyzes the data stored in the database, detecting changes in physical condition such as:

[1521] Abnormal body temperature: Detects hyperthermia or hypothermia when the body temperature is outside the normal range.

[1522] Activity anomalies: Detect increases or decreases in movement that differ from normal activity patterns.

[1523] Excretory abnormalities: Detect frequent or absent bowel movements.

[1524] The AI ​​algorithm evaluates the baby's condition by comparing it with previously learned health data, allowing it to detect even subtle changes that would normally go unnoticed.

[1525] Generating Warnings and Advice (Server)

[1526] Based on the analysis results, the server will generate necessary warnings and advice, for example, if the baby's temperature is abnormally high, it will generate a warning message saying "Baby's temperature is too high. Please check with a thermometer and consult a doctor."

[1527] Notifications and displays (user device)

[1528] The generated warnings and advice are sent as push notifications to the user's device. The user's device (smartphone, etc.) notifies and displays the received warnings and advice. Tapping the notification launches a dedicated app, which displays detailed recommended actions.

[1529] Introduction of emotion engine (server and user terminal)

[1530] The system also incorporates an emotion engine that recognizes the user's emotions. Specifically, it operates as follows:

[1531] The server collects the user's voice and facial recognition data and analyzes it using an emotion engine to detect when the user is in a particular emotional state, such as stress, worry, or anxiety.

[1532] Based on the emotion engine's analysis, the server adjusts the tone and content of warnings and advice. For example, if the user is feeling very stressed, a more friendly and reassuring message will be generated.

[1533] Specific examples

[1534] For example, if a baby has a fever and the user (mother) is very anxious, the system operates as follows:

[1535] 1. Data collection: The device's body temperature sensor measures 37.8 degrees and sends the data to the server.

[1536] 2. Data reception and storage: The server receives the data and stores it in the database.

[1537] 3. Data analysis: AI algorithms analyze body temperature data and detect when it is outside the normal range.

[1538] 4. Emotion data collection and analysis: When a user opens the app, the emotion engine detects the user's anxiety through voice and facial recognition.

[1539] 5. Alert Generation: The server generates a friendly tone alert saying "Your baby's temperature is too high. Please calm down, recheck with a thermometer and consult a doctor."

[1540] 6. Notification and display: A warning is pushed to the user's smartphone and the user can check it.

[1541] In this way, the system of the present invention can quickly detect subtle changes in a baby's physical condition and provide appropriate responses according to the user's emotional state, thereby reducing anxiety, especially for parents raising a child for the first time.

[1542] The processing flow will be explained below.

[1543] Step 1:

[1544] The device collects data from sensor modules attached to the baby: a temperature sensor measures the baby's temperature, an activity sensor records the baby's movements, a toilet sensor detects the baby's bowel movements, and dietary data is manually entered into the app by the parent.

[1545] Step 2:

[1546] The data collected by the device is sent to the server in real time. Specifically, the device collects various data acquired from sensors and sends them to the server via Wi-Fi or mobile networks.

[1547] Step 3:

[1548] The server receives the data sent from the device. Specifically, the server's API receives the HTTP request and retrieves the data.

[1549] Step 4:

[1550] The server stores the received data in a database. Specifically, it issues an SQL query to insert the data into the database and organizes it in chronological order.

[1551] Step 5:

[1552] The server periodically retrieves the data stored in the database and analyzes it using an AI algorithm. Specifically, the server retrieves the latest data from the database and inputs it into the AI ​​model to detect outliers and patterns.

[1553] Step 6:

[1554] The server generates warnings and advice based on the analysis results of the AI ​​algorithm. Specifically, if the temperature is abnormally high, it generates a message saying, "Your baby's temperature is too high. Please check with a thermometer and consult a doctor."

[1555] Step 7:

[1556] When a user opens the app, the device collects emotion data, specifically voice data and facial recognition data, and sends it to the emotion engine.

[1557] Step 8:

[1558] The emotion engine installed on the server analyzes the user's emotion data, specifically using voice and facial recognition algorithms to detect the user's emotional state (e.g., stress or anxiety).

[1559] Step 9:

[1560] The server adjusts the content and tone of warnings and advice based on the analysis results of the emotion engine. Specifically, if the user is feeling anxious, it generates a friendly and reassuring message such as, "Your baby's temperature is too high. Please calm down, check again with a thermometer, and consult a doctor."

[1561] Step 10:

[1562] The server notifies the user of the warnings and advice it generates by sending messages to the user's smartphone using a push notification service.

[1563] Step 11:

[1564] The user's device receives the notification and displays it visually. Specifically, a message appears in the smartphone's notification bar, and tapping the notification opens a dedicated app with detailed information and advice on how to respond.

[1565] Step 12:

[1566] The user checks the notification and takes the necessary action, such as rechecking the baby's temperature and consulting a doctor.

[1567] Example 2

[1568] 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."

[1569] Monitoring a baby's physical condition and health is extremely important in childcare. However, it is difficult to quickly detect subtle changes or abnormalities in a baby's physical condition, which can be very worrying for first-time parents. Furthermore, there is a lack of technological means to provide appropriate advice based on parents' emotions and stress levels. Under these circumstances, there is a need for a system that can monitor a baby's health in real time and provide parents with peace of mind.

[1570] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[1571] In this invention, the server includes means for collecting data on the baby's body temperature, activity level, excretion status, and dietary intake using a sensor module, means for transmitting the collected data in real time to a network device, means for storing data received by the network device in a storage device, means for analyzing the stored data using a machine learning algorithm, means for generating a warning or advice based on the analysis results, means for notifying the generated warning or advice on a user's display device, means for displaying the warning or advice on the user's display device, means for collecting user voice data and facial recognition data and analyzing it using an emotion recognition engine, means for adjusting the content of the warning or advice based on the analyzed emotion data, and means for notifying the user's display device of the warning or advice according to the emotion. This makes it possible to quickly detect subtle changes in the baby's physical condition and provide appropriate responses and advice according to the user's emotional state.

[1572] The "sensor module" is a device that collects data on a baby's body temperature, activity level, excretion status, and dietary intake.

[1573] A "body temperature sensor" is a temperature sensing device used to measure a baby's skin temperature.

[1574] An "activity sensor" is a device that uses a camera and an accelerometer to record a baby's movements.

[1575] An "excretion sensor" is a sensing device that detects changes in humidity and temperature inside a diaper.

[1576] "Collected data" is a general term for the baby's body temperature, activity level, excretion status, and dietary data obtained by the sensor module.

[1577] A "network device" is a communication device for transmitting collected data to a server in real time.

[1578] A "memory device" is a data storage device that stores received data and allows it to be accessed at a later time.

[1579] A "machine learning algorithm" is an artificial intelligence technique used for data analysis, which identifies patterns in stored data.

[1580] A "warning" is a message generated based on the results of data analysis to alert the user.

[1581] "Advice" is a recommended instruction for action provided to a user based on the results of data analysis.

[1582] A "display device" is a device for notifying and displaying generated warnings and advice to a user, and includes smartphones and tablets.

[1583] "Voice data" is information collected from the user's voice.

[1584] "Facial recognition data" refers to image or video information obtained by recognizing the user's face.

[1585] An "emotion recognition engine" is a system that analyzes collected voice data and facial recognition data to identify the user's emotional state.

[1586] This system collects and analyzes data on a baby's body temperature, activity level, excretion status, and dietary intake, and provides warnings and advice to parents based on the analysis results, while also incorporating an emotion engine that recognizes the user's emotions. This system is composed of a sensor module, a network device, a storage device, a machine learning algorithm, an emotion recognition engine, and a user's display device.

[1587] (Data collection (terminal))

[1588] The device collects data through a sensor module attached to the baby, which contains the following sensors:

[1589] Body temperature sensor: A temperature sensing device that measures the baby's skin temperature.

[1590] Activity sensor: An image capture device that uses a camera and accelerometer to record the baby's movements.

[1591] Excretion sensor: A sensing device that detects changes in humidity and temperature inside the diaper.

[1592] Meal data entry: Parents manually enter their baby's meal amounts and times using the app.

[1593] These data are transmitted in real time from the terminal to the network device.

[1594] (Data reception and storage (server))

[1595] The server receives data sent from the network device and stores it in a storage device. Specifically, the server receives data using a communication protocol such as HTTP or MQTT. The received data is organized by time, and the data collected at what point in time is clearly managed.

[1596] (Data analysis (server))

[1597] Machine learning algorithms in the server analyze the data stored in the storage device, specifically detecting anomalies such as:

[1598] Body temperature abnormality: Detects hyperthermia or hypothermia when the body temperature data exceeds a certain threshold.

[1599] Activity Anomalies: Detect abnormal increases or decreases in activity compared to normal activity patterns.

[1600] Excretory abnormalities: Detect frequent or absent bowel movements compared to normal bowel patterns.

[1601] (Emotion data collection and analysis (server and user terminal))

[1602] The server and the user's device recognize emotions using the user's voice data and facial recognition data. The specific operation is as follows:

[1603] The user launches the app and collects voice and facial information via voice input and the camera.

[1604] The server analyzes the collected information and detects whether the user is experiencing emotional states such as stress, anxiety, or worry.

[1605] (Generating Warnings and Advice (Server))

[1606] The server generates necessary warnings and advice based on the results of data analysis and the evaluation of emotional data. For example, if the baby's temperature is high, a warning such as "Your baby's temperature is too high. Please check it again with a thermometer and consult a doctor" can be generated. If the user is feeling stressed, a message in a reassuring tone can be generated, such as "You may be worried about your baby. Please check it again with a thermometer and consult a doctor if necessary."

[1607] (Notification and Display (User's Device))

[1608] The generated warnings and advice are pushed to the user's display device. When the user opens the notification on their smartphone or tablet, the app launches and displays detailed information.

[1609] Examples:

[1610] For example, if a baby has a fever and the user (mother) is in a very anxious state, the system will operate as follows:

[1611] 1. Data collection: The device's body temperature sensor measures 37.8 degrees and transmits the data to the server via the network device.

[1612] 2. Data reception and storage: The server receives the data and stores it in a storage device.

[1613] 3. Data analysis: Machine learning algorithms analyze body temperature data and detect when it is outside the normal range.

[1614] 4. Emotion data collection and analysis: When a user opens the app, the emotion recognition engine detects the user's anxiety through voice and facial recognition.

[1615] 5. Alert Generation: The server generates a friendly tone alert saying "Your baby's temperature is too high. Please calm down, recheck with a thermometer and consult a doctor."

[1616] 6. Notification and display: A warning is pushed to the user's smartphone and the user can check it.

[1617] Examples of prompts:

[1618] An example of a prompt to be input to the generative AI model is as follows:

[1619] "My baby has a fever of 37.8 degrees. Please generate a warning message that takes into account the user's emotional state and makes them feel at ease."

[1620] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1621] Step 1: Data collection (device)

[1622] The device collects data from the sensor module attached to the baby. The specific operations are as follows:

[1623] The temperature sensor continuously measures the baby's skin temperature and sends the measurements as input to the terminal. The output data is the baby's temperature data.

[1624] The activity sensors (camera and accelerometer) record the baby's movements every second and send the video and acceleration data to the terminal as input. The output data is the baby's activity data.

[1625] The excretion sensor detects changes in humidity and temperature inside the diaper and sends the change data to the terminal as input. The output data is excretion data.

[1626] For meal data input, the user manually inputs the amount and time of the baby's meal using the app, and the output data is the meal data.

[1627] Step 2: Send data (terminal)

[1628] The terminal transmits the collected data to the network device. The specific operation is as follows:

[1629] The terminal receives temperature data, activity data, excretion data, and dietary data as input and transmits them to the network device in real time. The output is data sent to the server via the network.

[1630] Step 3: Data reception and storage (server)

[1631] The server receives the data sent from the network device and stores it in the storage device. The specific operations are as follows:

[1632] The server receives input data such as body temperature, activity, excretion, and dietary data using communication protocols such as HTTP and MQTT. The output data is stored in a storage device.

[1633] The received data is organized by time, and it is clearly managed which data was collected at what point in time.

[1634] Step 4: Data analysis (server)

[1635] The machine learning algorithms in the server analyze the data stored in the storage device. Specifically, they do the following:

[1636] The server receives body temperature data, activity data, excretion data, and dietary data as input from the storage device.

[1637] Body temperature analysis: Checks whether the body temperature data exceeds a certain threshold, and if there is an abnormality, outputs the abnormal data.

[1638] Activity analysis: Activity data is compared with normal patterns to detect abnormal increases or decreases in activity and output the abnormal data.

[1639] Excretion analysis: Excretion data is compared with normal patterns to detect abnormalities and output the abnormal data.

[1640] Step 5: Emotion data collection and analysis (server and user device)

[1641] The server and the user's device collect the user's voice data and facial recognition data, and analyze their emotions. The specific operations are as follows:

[1642] The user launches the app and collects voice and facial information via voice input or camera. The output data is voice data and facial recognition data.

[1643] The server receives collected voice data and facial recognition data as input, analyzes them, and infers the user's emotional state. The output is the user's emotional state data.

[1644] Step 6: Generating Warnings and Advice (Server)

[1645] The server generates necessary warnings and advice based on the results of data analysis and the evaluation of the emotion data. Specific operations are as follows:

[1646] The server receives anomaly data and user emotional state data as input.

[1647] Based on the analysis results, a warning message such as "Your baby's temperature is too high. Please double-check with a thermometer and consult a doctor" is generated. If the user is feeling stressed, a reassuring message such as "We understand that you may be worried about your baby, but please double-check with a thermometer first and consult a doctor if necessary" is generated. The output is a warning or advice message.

[1648] Step 7: Notifications and Display (User's Device)

[1649] The generated warnings and advice are pushed to the user's display device and displayed. Specifically, the behavior is as follows:

[1650] The server sends warning and advice messages to the user terminal as input.

[1651] The user's device displays the received message in the notification bar, and when the user taps the notification, the app launches and displays more information. The output is a warning or advice message that is displayed to the user.

[1652] (Application example 2)

[1653] 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."

[1654] In modern homes, security measures that provide safety and peace of mind go beyond mere mechanical security. A responsive approach is needed, especially when household members are feeling anxious or stressed. However, current home security systems do not generate warnings or advice that take into account the user's emotional state. Therefore, more considerate and effective security measures are needed.

[1655] The identification process by the identification processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes means for collecting voice data and image data within the home, analyzing them using an emotion engine, and recognizing the user's emotional state, means for adjusting the tone and content of warnings and advice generated according to the user's emotional state, and means for analyzing the various collected home data using an AI algorithm and detecting abnormalities. This makes it possible to provide appropriate security measures that give peace of mind to members of the home who are feeling anxious or stressed.

[1656] A "sensor module" is hardware that integrates various sensors used to detect the environment and the status of members in the home.

[1657] A "thermal sensor" is a device for measuring the temperature of an object or living thing, especially when used to detect skin temperature.

[1658] An "activity sensor" is a device that detects changes in acceleration or movement. This includes motion sensors and cameras.

[1659] An "excretion sensor" is a sensor that detects changes in humidity and temperature to grasp the excretion status.

[1660] "Dietary data" refers to information such as the amount, type, and time of food consumed by household members.

[1661] A "server" is a centralized computer system that receives, stores, and analyzes data over a network.

[1662] A "database" is a collection of data that is systematically stored and constructed to allow easy searching and analysis of collected data.

[1663] "AI algorithms" are machine learning and artificial intelligence techniques used to analyze collected data and detect patterns and anomalies.

[1664] A "warning" is a notification that notifies the user when an abnormality or risk is detected in the home.

[1665] "Advice" refers to the appropriate response or recommendation provided along with the warning.

[1666] A "home terminal" is a device, such as a mobile device or computer, that a user uses to interact with or receive notifications.

[1667] An "emotion engine" is an artificial intelligence system that analyzes voice and image data to recognize the user's emotional state.

[1668] "Analysis" is the process of examining collected data in detail to discover specific patterns or anomalies.

[1669] "Real-time" means that data is collected, transmitted, and processed immediately.

[1670] A "push notification" is a highly instantaneous message sent directly from a server to a user's device.

[1671] "Speech recognition" is a technology for analyzing voice data to understand meaning and emotions.

[1672] "Facial recognition" is a technology that uses a camera to identify a person's face and determine their emotion or state.

[1673] The present invention is a security system for providing safety and a sense of security within the home, and is composed of a sensor module, a server, an emotion engine, and a user terminal.

[1674] Data collection (terminal)

[1675] The device consists of the following sensor modules installed in the home:

[1676] Motion Sensor: Detects movement within the home and records any unusual activity.

[1677] Door and window sensors: Detects when doors and windows are opened and closed, and records any unauthorized openings and closings.

[1678] Camera: Collects image data and can identify suspicious individuals.

[1679] Audio sensor: Detects abnormal sounds (e.g., the sound of glass breaking).

[1680] Emotional Data Input: An interface is used for household members to input their emotional states.

[1681] The data obtained from these sensor modules is transmitted in real time by the terminal to a server.

[1682] Data reception and storage (server)

[1683] The server receives data sent from the device in real time. After receiving the data, it is stored in a database. The stored data is organized in chronological order, and the time and date of collection of the data are clearly managed.

[1684] Data analysis (server)

[1685] The server is equipped with an AI algorithm that analyzes the data stored in the database, detecting anomalies such as the following:

[1686] Motion abnormalities: movements that differ from normal activity patterns.

[1687] Unauthorized opening and closing of doors and windows: Unauthorized opening and closing at specific times.

[1688] Recognizing suspicious people: Image analysis using a camera.

[1689] Abnormal sound detection: AI-based voice data analysis.

[1690] Emotion data collection and analysis (server and user terminal)

[1691] It is equipped with an emotion engine that analyzes voice and facial recognition data to recognize the user's emotional state, and can detect when a household member is feeling anxious or stressed.

[1692] Generating Warnings and Advice (Server)

[1693] Based on the analysis results, the server generates necessary warnings and advice. For example, if a motion sensor detects abnormal activity and household members are feeling anxious, a more friendly and reassuring message will be generated.

[1694] Notifications and displays (user device)

[1695] The generated warnings and advice are sent as push notifications to the user's device. The user's device (smartphone, etc.) notifies and displays the received warnings and advice. Tapping the notification launches a dedicated app, which displays detailed recommended actions.

[1696] Specific examples

[1697] For example, if a motion sensor detects abnormal activity in the middle of the night and a household member (e.g., mother) is in a very anxious state, the system operates as follows.

[1698] 1. Data collection: Motion sensors detect abnormal movements and send the data to the server.

[1699] 2. Data reception and storage: The server receives the data and stores it in the database.

[1700] 3. Data analysis: AI algorithms detect abnormal behavior.

[1701] 4. Emotion data collection and analysis: When a user opens the app, the emotion engine detects the user's anxiety through voice and facial recognition.

[1702] 5. Alert Generation: The server generates a friendly tone of warning saying "Unusual activity detected. Please remain calm and make sure all doors and windows are closed."

[1703] 6. Notification and display: A warning is pushed to the user's smartphone and the user can check it.

[1704] Prompt Sentence Examples

[1705] "When a home security system detects abnormal motion, generate advice to reduce anxiety and give the user peace of mind."

[1706] The security system of the present invention configured as described above can provide appropriate security measures that give peace of mind to members of the household who feel anxious or stressed.

[1707] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1708] Step 1: Data collection (device)

[1709] Sensor modules installed in the home (motion sensors, door / window sensors, cameras, audio sensors, and emotion data input interfaces) collect various types of data. The collected data includes motion data, opening / closing data, image data, audio data, and members' emotion data. This data is input into the terminal.

[1710] Step 2: Send data (terminal)

[1711] The collected data is sent in real time from the device to a server, where it is uploaded via an internet connection and includes a timestamp.

[1712] Step 3: Receiving data (server)

[1713] The server receives data sent from the device in real time, including motion data, opening / closing data, image data, audio data, and emotion data.

[1714] Step 4: Data storage (server)

[1715] The received data is stored in a database on the server. When stored, it is organized by data type and recorded in the database in chronological order by timestamp.

[1716] Step 5: Data analysis (server)

[1717] The server's AI algorithms analyze the stored data, which includes the following steps:

[1718] Analyzes motion data and detects abnormal movements.

[1719] Analyzes opening and closing data to detect unauthorized opening and closing of doors and windows.

[1720] Analyze image data and recognize suspicious individuals.

[1721] Analyzes audio data to detect abnormal sounds (e.g., the sound of glass breaking).

[1722] Step 6: Emotion data collection and analysis (server)

[1723] The server uses the emotion data, voice data, and facial recognition data entered by the user to analyze the user's emotional state using an emotion engine, which determines whether the user is feeling anxious or stressed.

[1724] Step 7: Generating Warnings and Advice (Server)

[1725] Based on the analysis results, the server generates necessary warnings and advice, such as abnormal activity, unauthorized opening and closing, and a friendly tone of voice depending on the user's emotional state. For example, a message might be generated such as, "Abnormal activity has been detected. Please remain calm and make sure all doors and windows are closed."

[1726] Step 8: Notification (Server)

[1727] The generated warnings and advice are pushed to the user's device, and the notification includes a message along with a timestamp.

[1728] Step 9: Receive and display notifications (on the user's device)

[1729] The user's device receives a push notification from the server. The received notification is displayed in the device's notification area or in a dedicated app. When the user taps the notification, detailed instructions on how to respond are displayed.

[1730] The above are the specific processing steps for implementing the present invention. By clarifying how input data is processed and analyzed at each step and how the final output is obtained, it is possible to provide users with appropriate security measures.

[1731] 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.

[1732] 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.

[1733] 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.

[1734] 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.

[1735] 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.

[1736] 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.

[1737] 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).

[1738] 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.

[1739] 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 occurs when the robot experiences positive feelings such as "I want more" or "I want to know more."

[1740] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple pieces of training data that are combinations of user input and emotion values ​​indicating each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions that are located close to each other have similar values, as in the emotion map 900 shown in FIG. 10. FIG. 10 shows an example in which multiple emotions, "relieved," "calm," and "reassuring," have similar emotion values.

[1741] The system according to the present disclosure has been described above mainly with respect to the functions of the data processing device 12, but the system according to the present disclosure is not necessarily implemented on a server. The system according to the present disclosure may be implemented as a general information processing system. The present disclosure may be implemented, for example, as a software program running on a personal computer or an application running on a smartphone, etc. The method according to the present disclosure may be provided to users in the form of SaaS (Software as a Service).

[1742] In the above embodiment, an example was given in which the specific processing is performed by one computer 22, but the technology of the present disclosure is not limited to this, and the specific processing may be distributed and performed by a plurality of computers including the computer 22. For example, the data generation model 58 may be provided in an external device of the data processing device 12, and data may be generated in the external device in accordance with input data.

[1743] In the above embodiment, an example in which the specific processing program 56 is stored in the storage 32 has been described, but the technology of the present disclosure is not limited to this. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-transitory storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-transitory storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes the specific processing in accordance with the specific processing program 56.

[1744] Alternatively, the specific processing program 56 may be stored in a storage device such as a server connected to the data processing device 12 via the network 54, and the specific processing program 56 may be downloaded and installed on the computer 22 in response to a request from the data processing device 12.

[1745] It is not necessary to store all of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store all of the specific processing program 56 in the storage 32; only a portion of the specific processing program 56 may be stored.

[1746] The hardware resource for executing a specific process can be any of the following processors: An example of a processor is a CPU, which is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. Another example of a processor is a dedicated electrical circuit, such as an FPGA (Field-Programmable Gate Array), a PLD (Programmable Logic Device), or an ASIC (Application Specific Integrated Circuit), which is a processor with a circuit configuration designed specifically for executing a specific process. Each processor has built-in or connected memory, and each processor uses the memory to execute the specific process.

[1747] The hardware resource that executes the specific processing may be configured with one of these various processors, or may be configured with a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Also, the hardware resource that executes the specific processing may be a single processor.

[1748] As an example of a system configured with a single processor, first, one processor is configured by combining one or more CPUs and software, and this processor functions as a hardware resource that executes a specific process. Second, there is a system that uses a processor that realizes the functions of an entire system including multiple hardware resources that execute a specific process on a single IC chip, as typified by SoC (System-on-a-chip). In this way, a specific process is realized using one or more of the above-mentioned various processors as hardware resources.

[1749] Furthermore, the hardware structure of these various processors can be, more specifically, an electric circuit that combines circuit elements such as semiconductor devices. The specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps may be deleted, new steps may be added, or the processing order may be rearranged, without departing from the spirit of the invention.

[1750] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, to avoid confusion and facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[1751] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference.

[1752] The following is further disclosed regarding the above embodiment.

[1753] (Claim 1)

[1754] A means for collecting data on the baby's body temperature, activity, excretion status, and dietary intake using a sensor module;

[1755] means for transmitting the collected data to a server in real time;

[1756] means for storing the received data in a database in the server;

[1757] A means of analyzing the stored data using AI algorithms;

[1758] a means of generating warnings and advice based on the analysis results;

[1759] a means for notifying a user of the generated warning or advice on the user's terminal;

[1760] The system includes means for displaying said notification at a user's terminal.

[1761] (Claim 2)

[1762] 10. The system of claim 1, wherein the sensor module includes a thermosensor for measuring the baby's temperature.

[1763] (Claim 3)

[1764] 10. The system of claim 1, wherein the sensor module includes a camera that records the baby's activity.

[1765] "Example 1"

[1766] (Claim 1)

[1767] A means for collecting data on the baby's body temperature, activity, excretion status, and dietary intake using a sensor module;

[1768] means for transmitting the collected data to a central processing unit in real time;

[1769] means for storing data received by said central processing unit in a data storage device;

[1770] A means of analyzing the stored data using AI algorithms;

[1771] A means for generating countermeasures and advice based on the analysis results;

[1772] means for notifying the generated countermeasures and advice to the user's communication terminal;

[1773] The system includes means for displaying said notification on a user's communication terminal.

[1774] (Claim 2)

[1775] 10. The system of claim 1, wherein the sensor module includes a temperature measuring device for measuring the temperature of the baby.

[1776] (Claim 3)

[1777] 10. The system of claim 1, wherein the sensor module includes a video capture device that records the baby's activity.

[1778] "Application Example 1"

[1779] (Claim 1)

[1780] A means for collecting data on the baby's body temperature, activity, excretion status, and dietary intake using a sensor module;

[1781] means for transmitting the collected data to a server in real time;

[1782] means for storing the received data in a database in the server;

[1783] A means of analyzing the stored data using AI algorithms;

[1784] a means of generating warnings and advice based on the analysis results;

[1785] a means for notifying a user of the generated warning or advice on the user's terminal;

[1786] means for displaying said notification on the user's terminal;

[1787] A means for suggesting childcare products suitable for the baby's health condition based on the analysis results;

[1788] means for allowing a user to view detailed information about the proposed childcare products in the virtual store;

[1789] A system including:

[1790] (Claim 2)

[1791] 10. The system of claim 1, wherein the sensor module includes a thermosensor for measuring the baby's temperature.

[1792] (Claim 3)

[1793] 10. The system of claim 1, wherein the sensor module includes a camera that records the baby's activity.

[1794] "Example 2: Combining Emotion Engines"

[1795] (Claim 1)

[1796] A means for collecting data on the baby's body temperature, activity, excretion status, and dietary intake using a sensor module;

[1797] means for transmitting the collected data to a network device in real time;

[1798] means for storing data received by the network device in a storage device;

[1799] means for analyzing the stored data using machine learning algorithms;

[1800] a means of generating warnings and advice based on the analysis results;

[1801] means for displaying the generated warnings and advice on a user's display device;

[1802] means for displaying said notification on a user's display device;

[1803] A means for collecting user voice data and facial recognition data and analyzing them using an emotion recognition engine;

[1804] means for adjusting the content of warnings and advice based on the analyzed emotion data;

[1805] The system includes a means for displaying a warning or advice corresponding to the emotion on a display device of the user.

[1806] (Claim 2)

[1807] 10. The system of claim 1, wherein the sensor module includes a temperature sensor for measuring the baby's temperature.

[1808] (Claim 3)

[1809] 10. The system of claim 1, wherein the sensor module includes an image capture device that records the baby's activity.

[1810] "Application example 2 when combining emotion engines"

[1811] (Claim 1)

[1812] A means for collecting body temperature, activity, excretion status, and dietary data using a sensor module;

[1813] means for transmitting the collected data to a server in real time;

[1814] means for storing the received data in a database in the server;

[1815] A means of analyzing the stored data using AI algorithms;

[1816] a means of generating warnings and advice based on the analysis results;

[1817] means for notifying a home terminal of the warning or advice;

[1818] means for displaying a notification on the home terminal;

[1819] A means for collecting voice data and image data in the home, analyzing the data using an emotion engine, and recognizing the user's emotional state;

[1820] A system including means for adjusting the tone and content of generated warnings and advice depending on the user's emotional state.

[1821] (Claim 2)

[1822] The system of claim 1 , wherein the sensor module includes a thermosensor for measuring body temperature.

[1823] (Claim 3)

[1824] The system of claim 1 , wherein the sensor module includes a motion sensor that records an amount of activity. [Explanation of symbols]

[1825] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot< / url:> < / url:> < / url:> < / url:>

Claims

1. A means for collecting data on the baby's body temperature, activity, excretion status, and dietary intake using a sensor module; means for transmitting the collected data to a server in real time; means for storing the received data in a database in the server; A means of analyzing the stored data using AI algorithms; a means of generating warnings and advice based on the analysis results; a means for notifying a user of the generated warning or advice on the user's terminal; The system includes means for displaying said notification at a user's terminal.

2. The system of claim 1 , wherein the sensor module includes a thermosensor for measuring the temperature of the baby.

3. The system of claim 1 , wherein the sensor module includes a camera that records the baby's activity.

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