System

Abstract

A system is provided.SOLUTION: A system comprising: means for a parent to input information related to an Children's Day daily life; means for receiving the information and storing the information in a database; means for analyzing the received information using a generative AI model; means for generating advice personalized to the parent based on the analysis result; and means for transmitting the generated advice to the parent.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] Among the challenges parents face while raising children are worries and questions about their children's development and health, the time and effort required to gather information, and the difficulty of meeting individual needs. These issues often place a heavy burden on parents. Furthermore, because it is difficult to provide individually tailored services, there are many cases where effective support is not available. [Means for solving the problem]

[0005] The present invention is a system that includes a means for parents to input data about their child's daily life, a means for receiving the data and storing it in a database, a means for analyzing the received data with a generative AI model, a means for generating personalized advice for the parent based on the analysis results, and a means for sending the generated advice to the parent's device. The generative AI model detects abnormalities in the child's physical condition and lifestyle patterns, and the system includes a means for generating a notification directing the parent to an online medical consultation app, enabling a prompt response. Furthermore, the system includes a means for receiving feedback from parents and using it as data to improve the generative AI model, enabling continuous improvement in the accuracy and reliability of the system.

[0006] A "guardian" is a person who is responsible for the care of a child.

[0007] "Child" means a minor, especially an infant or school-age child.

[0008] "Data about daily life" refers to information about a child's daily behavior and health status, such as their body temperature, diet, sleep time, and toilet habits.

[0009] A "smartphone app" is a software application that runs on a mobile device and allows a user to enter data about their child and receive advice.

[0010] A "database" is a collection of information that structures and stores received data and makes it available for analysis and reference.

[0011] A "generative AI model" is a mathematical or logical model that uses artificial intelligence techniques to analyze input data and detect specific patterns or anomalies.

[0012] "Analysis results" are the output of data processed by the generative AI model, and are information that includes the meaning of the data and abnormal patterns.

[0013] "Personalized advice" refers to customized advice and suggestions provided based on analysis results to meet the needs of individual children and parents.

[0014] A "terminal" is a device used by a user to input data or receive advice, and includes a smartphone, tablet, or the like.

[0015] An "online medical consultation app" is an application that allows you to have an online consultation or examination with a medical professional when an abnormality is detected.

[0016] "Feedback" is information that parents use to communicate to the system their evaluations and opinions of the advice provided.

[0017] "Data to improve generative AI models" is information used to improve the performance or accuracy of an AI model based on feedback or new data. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

[0026] [First embodiment]

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

[0028] 1, a data processing system 10 includes a data processing device 12 and a smart device 14. An example of the data processing device 12 is a server.

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

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

[0031] The reception device 38 includes a touch panel 38A, a microphone 38B, and the like, and receives user input. The touch panel 38A detects contact with an indicator (for example, a pen or a finger) to receive user input by the touch of the indicator. The microphone 38B detects the user's voice to receive user input by voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the data indicating the user input.

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

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

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

[0035] 2, in the data processing device 12, a specific process is performed by the processor 28. A specific processing program 56 is stored in the storage 32. The specific processing program 56 is an example of a "program" according to the technology of the present disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific process is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

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

[0037] In the smart device 14, the processor 46 performs the reception output process. The storage 50 stores a reception output program 60. The reception output program 60 is used in conjunction with the specific processing program 56 by the data processing system 10. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

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

[0039] The present invention is a system that includes a smartphone app that allows parents to input data about their children's daily lives, a server that receives and analyzes the data, and a system that provides personalized advice based on the analysis results. Specific embodiments of this system are described below.

[0040] Data collection and input

[0041] User

[0042] Parents enter data about their child's daily life into a smartphone app. For example, they fill out an input form with data such as their child's morning temperature, what they eat, how much sleep they get, and how many times they go to the toilet. This data is used to record their child's daily health and lifestyle.

[0043] Device (smartphone app)

[0044] The smartphone app has a function that temporarily stores the data entered by the parent or guardian in local storage and sends it to the server when the Internet connection is stable. When the data transmission is successful, the data is deleted from local storage or the transmission flag is updated.

[0045] Data reception and analysis

[0046] server

[0047] The server receives the data sent from the smartphone app and stores it in a temporary database. The stored data is then analyzed by a generative AI model, which recognizes patterns in the child's daily life and determines whether there are any abnormalities.

[0048] server

[0049] The analysis results are the basis for advice provided to parents. For example, if a child's temperature is above the normal range (36.1 to 37.2 degrees Celsius), the system will determine that the child has a high fever and notify the parent of the appropriate course of action.

[0050] Advice Generation and Notifications

[0051] server

[0052] Based on the analysis results of the generative AI model, personalized advice is generated for parents. For example, if the child's sleep time is short, specific advice such as "Sufficient sleep is essential for your child's development. Please try to get them to bed early tonight" is generated.

[0053] server

[0054] The generated advice is sent to the parent's device and added to the notification queue.

[0055] Device (smartphone app)

[0056] Advice received from the server is notified to parents and displayed within the app. Information is provided in real time using push notifications, etc.

[0057] Anomaly detection and medical collaboration

[0058] server

[0059] If the analysis detects abnormal data, a notification will be generated directing users to an online medical consultation app. For example, if an abnormally high temperature persists, a notification will be generated stating, "Your child appears to have a high fever. Please consult a doctor immediately."

[0060] server

[0061] A notification message containing a link to the online consultation app will be sent to the parent's device.

[0062] Device (smartphone app)

[0063] An abnormality notification will be displayed to parents, along with a link to an online medical consultation app, allowing them to quickly seek medical advice.

[0064] Gathering feedback and improving the system

[0065] User

[0066] Parents can enter their evaluations and opinions about the advice provided as feedback into a smartphone app, such as "The advice was helpful" or "I would like more specific instructions."

[0067] Device (smartphone app)

[0068] Send feedback data to the server. Feedback is a key element in improving user experience.

[0069] server

[0070] Feedback is received and stored in a database. This feedback is used to improve the generative AI model. Specifically, it adjusts the model's parameters to improve the accuracy of the next analysis and advice generation.

[0071] Example: What to do if a child has a high temperature

[0072] User

[0073] The parent enters "This morning's temperature was 38.5 degrees" into a smartphone app.

[0074] Terminal

[0075] Sends input data to the server.

[0076] server

[0077] The data is received and stored in a database. At the same time, the generative AI model analyzes the body temperature data and determines whether the person has a high fever.

[0078] server

[0079] The system generates advice such as, "Your child has a high fever. Drink plenty of fluids and check their temperature frequently. If symptoms persist, consult a doctor," and sends it to the parent's device.

[0080] Terminal

[0081] Display an advisory notice to parents.

[0082] User

[0083] Parents will follow advice and ensure their children are hydrated and have their temperatures checked.

[0084] As described above, the present invention realizes a system that can solve the problems faced by parents raising children and effectively support the health of their children.

[0085] The processing flow will be explained below.

[0086] Step 1:

[0087] User

[0088] Parents enter data about their child's daily life into a smartphone app. Examples of input include "This morning's body temperature was 38.5 degrees" and "I slept for five hours last night."

[0089] Step 2:

[0090] Terminal

[0091] The smartphone app temporarily saves the entered data in local storage, so that the data is saved even if the connection is unstable.

[0092] Step 3:

[0093] Terminal

[0094] Ensure that your internet connection is stable, then send the data stored on the server. If the transmission is successful, delete the data in local storage or set a sent flag.

[0095] Step 4:

[0096] server

[0097] The server receives the data sent from the smartphone app and stores it in a temporary database, where it is ready for analysis.

[0098] Step 5:

[0099] server

[0100] A generative AI model is invoked to analyze the incoming data, for example to determine whether the body temperature is above the normal range (36.1°C to 37.2°C).

[0101] Step 6:

[0102] server

[0103] Based on the analysis results, the system checks for abnormal data. For example, if a body temperature of 38.5 degrees is determined to be abnormal, it will be treated as a "high fever."

[0104] Step 7:

[0105] server

[0106] Based on the analysis results, the system generates personalized advice for parents, such as a message saying, "Your child has a high fever. Drink plenty of fluids and check their temperature frequently."

[0107] Step 8:

[0108] server

[0109] The generated advice is sent to the parent's smartphone. The advice is added to the notification queue and sent.

[0110] Step 9:

[0111] Terminal

[0112] Advice received from the server is notified to parents in real time via push notifications and displayed within the app.

[0113] Step 10:

[0114] server

[0115] If abnormal data is detected during the analysis process, a notification will be generated directing users to an online medical consultation app, with a message such as, "Your child appears to have a high fever. Please consult a doctor immediately."

[0116] Step 11:

[0117] server

[0118] A notification message will be sent to the device containing a link to an online consultation app, allowing parents to quickly contact a medical institution.

[0119] Step 12:

[0120] Terminal

[0121] An abnormality notification will be displayed to parents, along with a link to an online consultation app, allowing them to quickly contact a doctor.

[0122] Step 13:

[0123] User

[0124] Review any advice or guidance provided and act as necessary, for example, by drinking fluids or rechecking your temperature.

[0125] Step 14:

[0126] User

[0127] Users can enter feedback about the advice and notifications into the smartphone app, such as "The advice was helpful" or "I would like more detailed explanations."

[0128] Step 15:

[0129] Terminal

[0130] The feedback data is sent to the server. If the sending is successful, a notification of feedback completion will be displayed in the app.

[0131] Step 16:

[0132] server

[0133] Receive feedback and store it in a database. The collected feedback is used to improve the generative AI model.

[0134] Step 17:

[0135] server

[0136] The feedback data is analyzed and the parameters of the generative AI model are adjusted, thereby improving the accuracy of the next data analysis and advice generation.

[0137] Through these steps, the system can quickly and accurately analyze data on children's daily lives, provide optimal advice to parents, and continuously improve the system using feedback.

[0138] Example 1

[0139] 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."

[0140] Collecting data on children's daily lives and providing appropriate advice to parents regarding their health and daily rhythms based on that data is a crucial challenge. However, many parents are busy and find it difficult to continuously collect and analyze detailed data. Furthermore, when an abnormality occurs, they are required to make a prompt response, but they often lack the specialized knowledge to do so. To solve these challenges, there is a need to develop a system that allows parents to easily input daily data and automatically detects abnormalities and provides personalized advice based on that data.

[0141] 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.

[0142] In this invention, the server includes means for temporarily storing input data in local storage and transmitting it to the server when the internet connection is stable, means for the server to receive the transmitted data and store it in a database, and means for analyzing the stored data with a generative AI model. This allows parents to easily input daily data, and the generative AI model automatically analyzes the data based on the data, enabling anomaly detection and personalized advice to be provided in real time.

[0143] A "guardian" is an adult who is responsible for managing a child's daily life and health and entering data into the system.

[0144] "Children" are minors whose guardians aim to improve their health and lifestyle through the system.

[0145] "Data related to daily life" refers to data that reflects a child's daily health condition and lifestyle, and includes, for example, body temperature, dietary habits, sleep duration, and number of trips to the toilet.

[0146] "Input means" refers to the interface and functions that allow parents to input data about their daily lives into the system using a smartphone app or other device.

[0147] "Local storage" refers to a storage device or memory for temporarily storing data on a device.

[0148] "Server" refers to a computer system that receives, stores, and analyzes data sent from the device, and generates and provides advice and notifications to parents.

[0149] "Database" refers to a system or software for storing and managing received data in a structured manner.

[0150] "Generative AI models" refer to artificial intelligence algorithms and machine learning models that analyze collected data and recognize patterns and detect anomalies in children's daily lives.

[0151] "Analysis means" refers to the process or method by which the server uses the generated AI model to analyze data and determine whether there are any abnormalities.

[0152] "Personalized advice" refers to information that provides appropriate countermeasures and suggestions to specific parents based on the analysis results of the generative AI model, depending on the child's health condition and lifestyle.

[0153] "Transmission means" refers to the communication functions and methods for sending advice and notifications from the server to the parent's device.

[0154] "Notification means" refers to the interface or function for displaying received advice or notifications on the parent's device and conveying information to the parent.

[0155] An "online medical consultation app" refers to an application or service that provides medical consultations and examinations online.

[0156] "Anomaly detection" refers to the discovery of unusual data patterns or anomalies by a generative AI model from the results of its analysis.

[0157] "Feedback" refers to information that parents submit by entering their thoughts, evaluations, and suggestions for improvement regarding the advice provided by the system.

[0158] "Data improvement measures" refers to methods and processes for improving the performance and accuracy of generative AI models based on parental feedback.

[0159] This system allows parents to input and manage data about their children's daily lives using a smartphone app, and provides personalized advice based on the analysis results. The system is designed to allow parents to easily input data and respond quickly if an abnormality is detected.

[0160] Specifically, the system includes the following elements:

[0161] Data collection and input

[0162] User

[0163] Parents open a smartphone app and enter data about their child's daily life, such as their body temperature each morning, what they eat, how much sleep they get, and how many times they go to the toilet. This data is important for recording their child's health and daily rhythms.

[0164] Device (smartphone app)

[0165] The smartphone app first temporarily stores the data entered by the parent in local storage (for example, using SQLite or Realm Database). When the Internet connection is stable, the app sends this data to the server. After the data is successfully sent, the app either deletes the data from local storage or updates a flag indicating that it has been sent.

[0166] Data reception and analysis

[0167] server

[0168] The server receives the data sent from the smartphone app and stores it in a temporary database (e.g., MySQL or PostgreSQL). After the data is stored, the generative AI model analyzes it. The generative AI model recognizes patterns in the child's daily life and determines whether there are any abnormalities.

[0169] For example, if a parent or guardian inputs "My temperature this morning was 38.5 degrees," this data is sent to the server. The server receives the data and stores it in a temporary database. The generative AI model analyzes this input data and determines that it is a high fever.

[0170] Advice Generation and Notifications

[0171] server

[0172] Based on the analysis results of the generative AI model, the server generates personalized advice for parents. For example, if the child has a high temperature, the server will generate specific advice such as, "Your child has a high fever. Drink plenty of fluids and check their temperature frequently. If symptoms persist, consult a doctor."

[0173] server

[0174] The generated advice is sent to the parent's device. The advice is added to a notification queue so that the parent can immediately know how to respond. For example, advice can be provided in real time using push notifications.

[0175] Device (smartphone app)

[0176] The parent's device will receive advice from the server and display it in a notification and within the app. Parents can check the notification and take appropriate action.

[0177] Anomaly detection and medical collaboration

[0178] server

[0179] If the analysis detects abnormal data, the server generates a notification directing the user to the online medical examination app. For example, if an abnormally high temperature of 39 degrees continues, a specific notification will be created stating, "Your child appears to have a high fever. Please consult a doctor immediately."

[0180] server

[0181] A notification message containing a link to the online consultation app will be sent to the parent's device, allowing them to quickly arrange a medical consultation.

[0182] Device (smartphone app)

[0183] An abnormality notification will be displayed to parents, who will be provided with a link to an online consultation app, allowing them to quickly access medical services.

[0184] Gathering feedback and improving the system

[0185] User

[0186] Parents can enter their evaluations and opinions about the advice provided as feedback into a smartphone app, such as "The advice was helpful" or "I would like more specific instructions."

[0187] Device (smartphone app)

[0188] The smartphone app sends the input feedback data to the server. The feedback data is also sent securely and is important as a means of improving the entire system.

[0189] server

[0190] The server receives the feedback data and stores it in a database. This feedback is used to improve the performance of the generative AI model by adjusting the model's parameters and improving the accuracy of the next analysis and advice generation.

[0191] Through a series of processes from data collection and analysis to advice generation and feedback, this system can solve the challenges faced by parents raising children and effectively support their children's health.

[0192] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0193] Step 1:

[0194] User

[0195] Parents open the smartphone app and enter data about their child's daily life. The data includes the child's body temperature, what they eat, how much they sleep, and how many times they go to the toilet. For example, they might enter information such as "body temperature: 36.8 degrees" and "sleep time: 8 hours." The entered data is temporarily stored in the smartphone's local storage.

[0196] Input: Data about the child's daily life

[0197] Output: Data saved in local storage

[0198] Step 2:

[0199] Device (smartphone app)

[0200] Once the smartphone app confirms that the internet connection is stable, it sends the data stored in local storage to the server. At this time, it packages the data in JSON format and sends it via an HTTP POST request. If the transmission is successful, it deletes the data from local storage or updates the sent flag.

[0201] Input: Data stored in local storage

[0202] Output: Data sent to the server

[0203] Step 3:

[0204] server

[0205] The server receives the data sent from the smartphone app and stores it in a temporary database. The data is then saved in a database such as MySQL or PostgreSQL. At this point, validation is performed to ensure that the data format and content are accurate.

[0206] Input: Data sent from the smartphone app

[0207] Output: Data stored in a temporary database

[0208] Step 4:

[0209] server

[0210] Once the data is stored in the temporary database, the server passes it to the generative AI model for analysis. During analysis, the generative AI model uses a dataset of learned patterns from a child's daily life to detect any abnormalities. For example, if "body temperature: 38.5 degrees" is entered, the generative AI model will determine that it is a high fever.

[0211] Input: Data stored in a temporary database

[0212] Output: Analysis results

[0213] Step 5:

[0214] server

[0215] Based on the analysis results of the generative AI model, the server generates personalized advice for parents. For example, if the child has a high temperature, the server generates specific advice such as, "Your child has a high fever. Drink plenty of fluids and check their temperature frequently. If symptoms persist, consult a doctor."

[0216] Input: Analysis results

[0217] Output: Personalized advice

[0218] Step 6:

[0219] server

[0220] The generated advice is sent to the parent's device. The server sends the generated advice to the parent's device via push notification, etc. The advice is added to the notification queue so that the parent can immediately know what to do.

[0221] Enter: personalized advice

[0222] Output: Advice sent to parent's device

[0223] Step 7:

[0224] Device (smartphone app)

[0225] The parent's device will receive the advice from the server and display it within the app. Real-time information is provided to parents via push notifications. Parents can then check the notifications and take appropriate action.

[0226] Input: Advice sent to parent's device

[0227] Output: Advice displayed to parents

[0228] Step 8:

[0229] server

[0230] If the analysis detects abnormal data, the server generates a notification directing the user to the online medical examination app. For example, if an abnormally high temperature of 39 degrees continues, a specific notification will be created stating, "Your child appears to have a high fever. Please consult a doctor immediately."

[0231] Input: Analysis results (abnormal data)

[0232] Output: Notification directing to online medical consultation app

[0233] Step 9:

[0234] server

[0235] A notification message containing a link to the online consultation app will be sent to the parent's device. When the parent clicks on the link, they will be taken directly to the online consultation app.

[0236] Input: Notification to direct you to the online medical consultation app

[0237] Output: A notification message with a link to the online consultation app.

[0238] Step 10:

[0239] Device (smartphone app)

[0240] A notification of an abnormality will be displayed to parents, who will be provided with a link to an online medical consultation app that they can tap to quickly access medical services.

[0241] Input: Notification message with link to online consultation app

[0242] Output: Anomaly notification and link displayed to the parent

[0243] Step 11:

[0244] User

[0245] Parents can enter their evaluation and opinions of the advice provided as feedback into a smartphone app, such as "The advice was helpful" or "I would like more specific instructions."

[0246] Input: Feedback on the advice

[0247] Output: Feedback entered into the smartphone app

[0248] Step 12:

[0249] Device (smartphone app)

[0250] The smartphone app sends the entered feedback data to the server, where it is also securely transmitted and made available.

[0251] Input: Feedback entered into the smartphone app

[0252] Output: Feedback data sent to the server

[0253] Step 13:

[0254] server

[0255] The server receives the feedback data and stores it in a database. This feedback is used to improve the performance of the generative AI model. Specifically, it is used to adjust the parameters of the generative AI model and improve the accuracy of the next analysis and advice generation.

[0256] Input: Feedback data sent to the server

[0257] Output: Feedback data stored in a database

[0258] The above is a specific processing flow of the system.

[0259] (Application example 1)

[0260] 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."

[0261] Conventional child health management systems monitor health conditions based on data entered by parents on a daily basis and provide necessary advice. However, if the system goes beyond simply analyzing health data and takes into account a wide variety of purchasing activities and preference patterns, it becomes possible to make more extensive lifestyle suggestions and make appropriate product recommendations, further increasing user satisfaction. The objective of this invention is to support users' overall lifestyles by providing personalized product recommendations and shopping advice based on purchasing history and preferences, in addition to the conventional health management systems.

[0262] 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.

[0263] In this invention, the server includes: means for a guardian to input data about their child's daily life; means for receiving the data and storing it in a database; means for analyzing the received data with a generative AI model; means for generating personalized advice for the guardian based on the analysis results; means for transmitting the generated advice to the guardian's terminal; means for collecting user purchase data, browsing history, and reviews and transmitting them to a private server; means for analyzing the collected data with a generative AI model and generating personalized product recommendations and shopping advice; and means for notifying the user's terminal of the generated recommendations and advice. This makes it possible to provide not only health management for the user but also personalized product recommendations and shopping advice based on the user's purchase history.

[0264] A "guardian" is a parent or guardian in charge of the child's upbringing and health care.

[0265] "Data related to daily life" includes information such as a child's daily body temperature, diet, sleep time, and number of times they go to the toilet.

[0266] A "generative AI model" is an artificial intelligence system that uses machine learning algorithms to analyze data and recognize patterns and anomalies.

[0267] "Advice" refers to specific instructions or suggestions provided to parents based on the analysis results.

[0268] A "terminal" is an electronic device, such as a smartphone or tablet, through which a user enters data or receives advice.

[0269] "User" refers to the person who uses the system to input data and receive advice, primarily parents.

[0270] "Purchase data" refers to historical information about products purchased by a user.

[0271] "Browsing history" is a record of the products and pages a user has viewed online.

[0272] A "review" is information that describes the user's evaluation and impressions of a product they have purchased.

[0273] A "private server" is a computer system that is a dedicated server managed within a company or home and is used to collect and analyze data.

[0274] "Product recommendations" are new products suggested to users based on the analysis results.

[0275] "Shopping Advice" means specific shopping-related instructions or suggestions provided to a user based on their purchasing data and preferences.

[0276] "Push Notification API" is an application program interface for sending notifications from a server to a device in real time.

[0277] MODE FOR CARRYING OUT THE INVENTION

[0278] System configuration

[0279] The system of this invention includes a smartphone app where parents input data about their children's daily lives, a server that receives and analyzes the data, and a function that provides personalized advice based on the analysis results. It also collects users' purchasing data, browsing history, and reviews, and generates product recommendations and shopping advice based on these. The specific configuration and processing flow are described below.

[0280] 1. Data collection and input

[0281] User

[0282] Parents enter data about their children's daily lives into a smartphone app, such as their morning temperature, what they eat, how much sleep they get, and how many times they go to the toilet. Users also enter or allow shopping-related information, such as purchase data, browsing history, and reviews.

[0283] Device (smartphone app)

[0284] The smartphone app has a function to temporarily store data entered by parents or users in local storage and send it to the server when the Internet connection is stable. When the data transmission is successful, the data is deleted from local storage or the transmission flag is updated.

[0285] 2. Data Reception and Analysis

[0286] server

[0287] The server receives data sent from the smartphone app and stores it in a temporary database. The stored data is then analyzed by a generative AI model. The generative AI model recognizes patterns in the child's daily life and determines whether there are any abnormalities. Similarly, it analyzes shopping data to understand the user's preferences and purchasing patterns.

[0288] server

[0289] The analysis results are the basis for providing advice to parents and users. For example, if a child's temperature exceeds the normal range (36.1 to 37.2 degrees Celsius), the system will determine that the child has a high fever and notify the parent or guardian of the appropriate course of action. At the same time, it will also recommend new products based on the user's purchasing history.

[0290] 3. Advice Generation and Notification

[0291] server

[0292] Based on the analysis results of the generative AI model, personalized advice is generated for parents and users. For example, if a child's sleep time is short, specific advice such as "Sufficient sleep is essential for your child's development. Make sure your child goes to bed early tonight" is generated. It also analyzes the user's purchasing history to generate shopping advice such as "We recommend this conditioner as a related product to the shampoo you recently purchased."

[0293] server

[0294] The generated advice is sent to the parent or user's device and added to a notification queue.

[0295] Device (smartphone app)

[0296] Advice received from the server is notified to parents and users and displayed within the app. Push notifications are used to provide information in real time.

[0297] 4. Anomaly Detection and Medical Collaboration

[0298] server

[0299] If the analysis detects abnormal data, a notification will be generated directing users to an online medical consultation app. For example, if an abnormally high temperature persists, a notification will be generated stating, "Your child appears to have a high fever. Please consult a doctor immediately."

[0300] server

[0301] A notification message containing a link to the online consultation app will be sent to the parent's device.

[0302] Device (smartphone app)

[0303] An abnormality notification will be displayed to parents, along with a link to an online medical consultation app, allowing them to quickly seek medical advice.

[0304] 5. Gathering feedback and improving the system

[0305] User

[0306] Parents and users can enter their evaluations and opinions about the advice provided as feedback into the smartphone app, such as "The advice was helpful" or "I would like more specific instructions."

[0307] Device (smartphone app)

[0308] Send feedback data to the server. Feedback is a key element in improving user experience.

[0309] server

[0310] Feedback is received and stored in a database. This feedback is used to improve the generative AI model. Specifically, it adjusts the model's parameters to improve the accuracy of the next analysis and advice generation.

[0311] Example: What to do if a child has a high temperature

[0312] User

[0313] The parent enters "This morning's temperature was 38.5 degrees" into a smartphone app.

[0314] Terminal

[0315] Sends input data to the server.

[0316] server

[0317] The data is received and stored in a database. At the same time, the generative AI model analyzes the body temperature data and determines whether the person has a high fever.

[0318] server

[0319] The system generates advice such as, "Your child has a high fever. Drink plenty of fluids and check their temperature frequently. If symptoms persist, consult a doctor," and sends it to the parent's device.

[0320] Terminal

[0321] Display an advisory notice to parents.

[0322] User

[0323] Parents will follow advice and ensure their children are hydrated and have their temperatures checked.

[0324] Example: Providing shopping advice

[0325] User

[0326] A user is happy with the shampoo they recently purchased and posts a review within a smartphone app.

[0327] Terminal

[0328] Purchase history, browsing history, and reviews are sent to the server.

[0329] server

[0330] The data is received and stored in a database, while the generative AI model analyzes the purchasing data and recognizes user preferences.

[0331] server

[0332] A shopping advice such as "We recommend this conditioner as a related product to the shampoo you recently purchased" is generated and sent to the user's device.

[0333] Terminal

[0334] Display an advisory notice to the user.

[0335] Prompt Sentence Examples

[0336] Examples of prompts include:

[0337] "Generate product recommendations based on user purchasing history and preferences."

[0338] "Recommend items from this week's new products list that are similar to items the user has previously purchased."

[0339] In this way, the present invention realizes a system that not only manages the user's health but also supports their overall lifestyle by providing personalized product recommendations and shopping advice based on their purchasing history.

[0340] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0341] Specific processing steps of the system

[0342] Program processing steps

[0343] Step 1: Data entry

[0344] 1. Users

[0345] Parents enter data about their children's daily lives (body temperature, diet, sleep time, number of times they went to the toilet, etc.) into a smartphone app. Users also enter shopping-related information (purchase data, browsing history, reviews) into the app.

[0346] Input: Children's daily life data, shopping-related data

[0347] Output: Save data to local storage

[0348] Step 2: Send data

[0349] 2. Device (smartphone app)

[0350] The app temporarily stores the data entered by the parent or user in local storage and sends it to the server when the internet connection is stable. If the transmission is successful, the data in local storage is deleted or the transmission flag is updated.

[0351] Input: Data stored in local storage

[0352] Output: Send data to the server

[0353] Step 3: Receiving and storing data

[0354] 3. Server

[0355] The server receives the data sent from the smartphone app and stores it in a temporary database, where data from parents and user purchasing data are aggregated.

[0356] Input: Data sent from the terminal

[0357] Output: Data saved to a temporary database

[0358] Step 4: Data analysis

[0359] 4. Server

[0360] The incoming data is analyzed by a generative AI model that recognizes patterns and anomalies in the child's daily life and generates personalized recommendations based on the shopping data.

[0361] Input: Data in the temporary database

[0362] Output: Analysis results (anomaly detection, product recommendations)

[0363] Step 5: Advice Generation

[0364] 5. Server

[0365] The server generates advice to provide to parents and users based on the analysis results, such as notifying appropriate measures if the user has a high body temperature, or recommending related products based on the user's purchasing history.

[0366] Input: Analysis results

[0367] Output: Advice generation (health advice, product recommendations)

[0368] Step 6: Advice Notification

[0369] 6. Server

[0370] The generated advice is sent to the user's device and added to a notification queue.

[0371] Input: Generated advice

[0372] Output: Advice sent to terminal

[0373] Step 7: Notifications

[0374] 7. Device (smartphone app)

[0375] The device notifies the user of the advice received from the server and displays it within the app, providing real-time information using a push notification API.

[0376] Input: Advice sent by the server

[0377] Output: Notification displayed to the user

[0378] Step 8: Gather feedback

[0379] 8. Users

[0380] Parents and users enter feedback on the advice into the app and send the data to the server.

[0381] Input: Feedback data

[0382] Output: Send feedback to the server

[0383] Step 9: Model Improvement

[0384] 9. Server

[0385] The server receives the feedback and uses it as data to improve the generative AI model, adjusting the model's parameters to improve the accuracy of the next analysis and advice generation.

[0386] Input: Feedback data

[0387] Output: Parameter updates for the model

[0388] Processing flow based on a specific example

[0389] Example: If your child has a high temperature

[0390] User

[0391] The parent enters "This morning's temperature was 38.5 degrees" into a smartphone app.

[0392] Input: Body temperature data (38.5°C)

[0393] Output: Save to local storage

[0394] Terminal

[0395] Sends input data to the server.

[0396] Input: Stored body temperature data

[0397] Output: Send data to the server

[0398] server

[0399] The data is received and stored in a database. At the same time, the generative AI model analyzes the body temperature data and determines whether the person has a high fever.

[0400] Input: Received body temperature data

[0401] Output: Analysis results (high temperature)

[0402] server

[0403] Generates advice like, "Your child has a high fever. Drink plenty of fluids and check their temperature frequently. If symptoms persist, consult a doctor."

[0404] Input: High temperature analysis results

[0405] output: Advice generation

[0406] server

[0407] The generated advice is sent to the parent's device.

[0408] Input: Advice data

[0409] Output: sent to terminal

[0410] Terminal

[0411] Display an advisory notice to parents.

[0412] Input: Advice data

[0413] Output: Parental Notice

[0414] User

[0415] Parents will follow advice and ensure their children are hydrated and have their temperatures checked.

[0416] 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.

[0417] The present invention provides a smartphone app that allows parents to input data about their children's daily lives, a server that receives and analyzes the data, and a system that provides personalized advice based on the analysis results. Furthermore, by combining this with an emotion engine that recognizes the user's emotions, more appropriate advice can be provided. A specific embodiment of this system is described below.

[0418] Data collection and input

[0419] User

[0420] Parents enter data about their child's daily life into a smartphone app, such as their body temperature, what they eat, how much they sleep, and how often they go to the toilet. This data is used to record their child's daily health and lifestyle.

[0421] Device (smartphone app)

[0422] The smartphone app temporarily stores the data entered by the parent or guardian in local storage and sends it to the server when the internet connection is stable. If the data transmission is successful, the data in local storage is deleted or the transmission flag is updated.

[0423] Data reception and analysis

[0424] server

[0425] The server receives the data sent from the smartphone app and stores it in a temporary database, where it is ready for analysis.

[0426] server

[0427] A generative AI model is invoked to analyze the incoming data, for example to determine whether the body temperature is above the normal range (36.1°C to 37.2°C).

[0428] Use of emotion engine

[0429] Device (smartphone app)

[0430] Parents can input their own emotions or use emotion recognition to capture emotion data. For example, they can input "I feel tired today" or use the camera to automatically recognize emotions.

[0431] server

[0432] The emotion engine analyzes the received emotion data and classifies the parent's current emotional state, for example, as "high stress" or "relaxed."

[0433] Advice Generation and Notifications

[0434] server

[0435] The analysis results of the generative AI model are combined with the results of the emotion engine to generate personalized advice for parents. For example, in addition to the advice, "Your child has a high fever. Drink plenty of fluids and check their temperature frequently," a message that takes into consideration emotions such as, "The parent is currently tired, so please refrain from overexerting yourself and consult a doctor" is created.

[0436] server

[0437] The generated advice is sent to the parent's smartphone. The advice is added to the notification queue and sent.

[0438] Device (smartphone app)

[0439] Advice received from the server is notified to parents and displayed within the app. Advice is displayed in real time via push notifications.

[0440] Anomaly detection and medical collaboration

[0441] server

[0442] If the analysis detects abnormal data, a notification will be generated directing users to the online medical consultation app. For example, the notification may include the message, "Your child appears to have a high fever. Please consult a doctor immediately."

[0443] server

[0444] A notification message containing a link to the online consultation app will be sent to the parent's device.

[0445] Device (smartphone app)

[0446] An abnormality notification will be displayed to parents, along with a link to an online medical consultation app, allowing them to quickly seek medical advice.

[0447] Gathering feedback and improving the system

[0448] User

[0449] Parents can enter their evaluations and opinions about the advice provided as feedback into a smartphone app, such as "The advice was helpful" or "I would like more specific instructions."

[0450] Device (smartphone app)

[0451] Send the feedback data to the server.

[0452] server

[0453] Receive feedback and store it in a database. The collected feedback is used to improve the generative AI model and emotion engine.

[0454] server

[0455] The feedback data is analyzed and the parameters of the generative AI model and emotion engine are adjusted to improve the accuracy of the next data analysis and advice generation.

[0456] Example: What to do if a child has a high temperature

[0457] User

[0458] The parent enters "This morning's temperature was 38.5 degrees" into a smartphone app.

[0459] Terminal

[0460] Sends input data to the server.

[0461] server

[0462] The data is received and stored in a database. At the same time, the generative AI model analyzes the body temperature data and determines whether the person has a high fever.

[0463] Terminal

[0464] The parent enters emotional data such as "I'm tired today."

[0465] server

[0466] The emotion engine analyzes the emotional data and determines that the person is under high stress.

[0467] server

[0468] In addition to the advice "Your child has a high fever. Drink plenty of fluids and check their temperature frequently," the system also generates advice such as "Your parent is currently tired, so please refrain from pushing yourself and consult a doctor," and sends this to the parent's device.

[0469] Terminal

[0470] Display an advisory notice to parents.

[0471] User

[0472] Parents should follow advice to keep their child hydrated, recheck their temperature, and consult a doctor if necessary.

[0473] As described above, the present invention realizes a system that solves the problems faced by parents raising children, effectively supports the health of their children, and provides appropriate advice according to the emotional state of the parents.

[0474] The processing flow will be explained below.

[0475] Step 1:

[0476] User

[0477] Parents enter data about their child's daily life into a smartphone app, for example, filling out an input form with information such as "This morning's body temperature was 38.5 degrees" or "I slept for five hours last night."

[0478] Step 2:

[0479] Terminal

[0480] The smartphone app temporarily stores the entered data in local storage, which allows the data to be saved even if the internet connection is unstable.

[0481] Step 3:

[0482] Terminal

[0483] Ensure that your internet connection is stable, then send the data stored on the server. If the transmission is successful, delete the data in local storage or set a sent flag.

[0484] Step 4:

[0485] server

[0486] The server receives the data sent from the smartphone app and stores it in a temporary database, where it is ready for analysis.

[0487] Step 5:

[0488] server

[0489] A generative AI model is invoked to analyze the incoming data, for example to determine whether the body temperature is above the normal range (36.1°C to 37.2°C).

[0490] Step 6:

[0491] server

[0492] Based on the analysis results, the system checks for abnormal data. For example, if a body temperature of 38.5 degrees is determined to be abnormal, it will be treated as a "high fever."

[0493] Step 7:

[0494] Terminal

[0495] Parents can input their own emotions or use emotion recognition to capture emotion data. For example, they can input "I feel tired today" or use the camera to automatically recognize emotions.

[0496] Step 8:

[0497] server

[0498] The emotion engine receives the emotion data and classifies the parent's emotional state, for example, as "high stress" or "relaxed."

[0499] Step 9:

[0500] server

[0501] The analysis results of the generative AI model are combined with the results of the emotion engine to generate personalized advice for parents. For example, in addition to the advice, "Your child has a high fever. Drink plenty of fluids and check their temperature frequently," a message such as, "The parent is currently tired, so please refrain from overexerting yourself and consult a doctor" can be generated.

[0502] Step 10:

[0503] server

[0504] The generated advice is sent to the parent's smartphone. The advice is added to the notification queue and sent.

[0505] Step 11:

[0506] Terminal

[0507] Advice received from the server is notified to parents and displayed within the app. Advice is displayed in real time via push notifications.

[0508] Step 12:

[0509] server

[0510] If the analysis detects abnormal data, a notification will be generated directing users to the online medical consultation app. For example, the notification may include the message, "Your child appears to have a high fever. Please consult a doctor immediately."

[0511] Step 13:

[0512] server

[0513] A notification message containing a link to the online consultation app will be sent to the parent's device.

[0514] Step 14:

[0515] Terminal

[0516] An abnormality notification will be displayed to parents, along with a link to an online medical consultation app, allowing them to quickly seek medical advice.

[0517] Step 15:

[0518] User

[0519] Review any advice or guidance provided and act as necessary, for example, by drinking fluids or rechecking your temperature.

[0520] Step 16:

[0521] User

[0522] Users can enter feedback about the advice and notifications into the smartphone app, such as "The advice was helpful" or "I would like more detailed explanations."

[0523] Step 17:

[0524] Terminal

[0525] The feedback data is sent to the server. If the sending is successful, a notification of feedback completion will be displayed in the app.

[0526] Step 18:

[0527] server

[0528] Receive feedback and store it in a database. The collected feedback is used to improve the generative AI model and emotion engine.

[0529] Step 19:

[0530] server

[0531] The feedback data is analyzed and the parameters of the generative AI model and emotion engine are adjusted to improve the accuracy of the next data analysis and advice generation.

[0532] Through these steps, the system can quickly and accurately analyze data on children's daily lives and parents' emotions, provide optimal advice, and continuously improve the system using feedback.

[0533] Example 2

[0534] 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."

[0535] In modern child-rearing, parents must devote a great deal of time and effort to managing their children's daily health and keeping track of their lifestyle. Emotional care is also important, as the parents' own emotional state directly impacts the quality of their child-rearing. However, there is currently a lack of systems that can centrally manage these and provide appropriate advice. Furthermore, it is also necessary to respond to situations that require early detection of abnormal data and prompt medical consultation.

[0536] The identification process by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means. In this invention, the server includes: a means for a guardian to input data about their child's daily life; a means for temporarily saving the data in local storage and transmitting it to the server when the Internet connection is stable; a means for receiving the data and storing it in a temporary database; a means for analyzing the received data using a generative AI model; a means for the guardian to input emotional data or acquire it using an emotion recognition function; a means for analyzing the received emotional data using an emotion engine and classifying the guardian's emotional state; a means for combining the analysis results of the generative AI model and the results of the emotion engine to generate personalized advice; and a means for transmitting the generated advice to the guardian's terminal and notifying them. This enables integrated management of the child's daily life data and the guardian's emotional data, enabling appropriate and personalized advice to be provided, and enabling prompt medical cooperation when an abnormality is detected.

[0537] A "guardian" is a person who is responsible for managing a child's health and daily routine.

[0538] "Data on a child's daily life" is information that indicates a child's health condition and daily rhythm, such as body temperature, diet, sleep time, and number of times they go to the toilet.

[0539] "Local storage" is a storage device that temporarily stores data inside a device such as a smartphone or tablet.

[0540] "When the Internet connection is stable" refers to a state in which the communication line is secured and data can be transmitted smoothly.

[0541] A "server" is a computer system that receives, processes, and stores data over a network.

[0542] A "temporary database" is a database for temporarily storing received data before analyzing it.

[0543] A "generative AI model" is an artificial intelligence algorithm that analyzes input data and detects patterns and anomalies.

[0544] "Emotion data" is information that indicates the emotional state of the guardian, and includes emotions such as "tired" or "relaxed," for example.

[0545] The "emotion recognition function" is a technology that automatically recognizes the emotional state of parents, and uses cameras and voice analysis.

[0546] The "emotion engine" is a system that analyzes input emotion data and classifies the parent's emotional state.

[0547] "Personalized advice" refers to specific instructions or suggestions that are individually generated based on the analysis results and the parent's emotional state.

[0548] "Notification" is a message delivery method for informing parents of important information and advice on their devices.

[0549] "Abnormal data" is data that indicates abnormal physical conditions or lifestyle patterns that exceed the normal range.

[0550] An "online consultation app" is a software application that allows you to receive consultations with doctors over the internet.

[0551] "Feedback" refers to information such as parents' evaluations, opinions, and impressions of the advice provided.

[0552] This invention is a system that includes a smartphone app for parents to input data about their children's daily lives, a server that receives and analyzes the data, and a system that provides personalized advice based on the analysis results.Furthermore, by combining this with an emotion engine that recognizes the user's emotions, it is possible to provide more appropriate advice.

[0553] Data collection and input

[0554] User

[0555] Parents enter data about their child's daily life into a smartphone app, such as their body temperature, what they eat, how much they sleep, and how often they go to the toilet. This data is used to record their child's daily health and lifestyle.

[0556] Device (smartphone app)

[0557] The smartphone app temporarily saves the data entered by the parent or guardian in local storage. Specifically, it saves the data using an SQLite database. When the internet connection is stable, it sends the saved data to the server in JSON format. If the transmission is successful, the data in local storage is deleted or the "transmission completed" flag is updated.

[0558] Data reception and analysis

[0559] server

[0560] The server receives data sent from the smartphone app through a dedicated endpoint (API). For example, it uses the endpoint "POST / data / receive." The received data is stored in a temporary database. MongoDB or MySQL is used as this database.

[0561] server

[0562] The server checks the consistency of the data stored in the database and performs preprocessing for analysis. Data whose consistency has been confirmed is input into the generative AI model for analysis. As a specific example, the prompt sentence "Check body temperature" is input into the generative AI model, and it determines whether the body temperature is outside the normal range (36.1 to 37.2 degrees Celsius).

[0563] Use of emotion engine

[0564] Device (smartphone app)

[0565] Parents can input their emotions using a smartphone app, or use the emotion recognition function to capture emotion data through the camera. For example, parents can input "I'm tired today," or the camera can automatically recognize emotions.

[0566] server

[0567] The server analyzes the received emotional data using an emotion engine, which classifies the parent's current emotional state, for example, as "high stress" or "relaxed."

[0568] Advice Generation and Notifications

[0569] server

[0570] The server combines the analysis results of the generative AI model with the results of the emotion engine to generate personalized advice for parents. For example, in addition to the advice, "Your child has a high fever. Drink plenty of fluids and check their temperature frequently," it also generates a message that takes into account their emotions, such as, "You are currently tired, so please refrain from overexerting yourself and consult a doctor."

[0571] server

[0572] The generated advice is sent to the parent's smartphone. The advice is added to a notification queue and sent via push notification using Firebase Cloud Messaging (FCM).

[0573] Device (smartphone app)

[0574] Advice received from the server is notified to parents and displayed in a pop-up or dedicated page within the app, with real-time push notifications used to ensure advice is displayed promptly.

[0575] Anomaly detection and medical collaboration

[0576] server

[0577] The server detects abnormal data from the analysis results of the generative AI model and generates a notification to direct the user to the online medical consultation app. For example, it generates a notification containing the content, "Your child appears to have a high fever. Please consult a doctor immediately."

[0578] server

[0579] The generated guidance notification is sent to the parent's device, including a link to the online consultation app.

[0580] Device (smartphone app)

[0581] The parent's device will display an abnormality notification and provide a link to an online medical consultation app, allowing the parent to quickly seek medical advice.

[0582] Gathering feedback and improving the system

[0583] User

[0584] Parents can enter their evaluations and opinions about the advice provided as feedback into a smartphone app, such as "The advice was helpful" or "I would like more specific instructions."

[0585] Device (smartphone app)

[0586] The feedback data is sent to the server.

[0587] server

[0588] The server receives the feedback and stores it in a database. The collected feedback is analyzed and used to improve the generative AI model and emotion engine. For example, adjusting the parameters of the generative AI model and emotion engine can improve the accuracy of the next data analysis and advice generation.

[0589] As described above, the present invention realizes a system that allows parents to manage and analyze their children's daily life data, providing appropriate and personalized advice and enabling rapid medical cooperation in the event of an abnormality.

[0590] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0591] Step 1: Data entry

[0592] User

[0593] Parents enter data about their child's daily life into a smartphone app, such as body temperature (e.g., "38.5 degrees"), diet (e.g., "curry rice"), sleep time (e.g., "8 hours"), and number of times they go to the toilet (e.g., "3 times").

[0594] Input: Child's daily life data (body temperature, diet, sleep time, number of toilet visits, etc.)

[0595] Output: Data entered into the input form in the smartphone app

[0596] Step 2: Temporarily save data

[0597] Device (smartphone app)

[0598] The smartphone app temporarily stores the data entered by the parent or guardian in local storage (e.g., SQLite database). For example, if "body temperature: 38.5 degrees" is entered, it is saved in the SQLite database.

[0599] Input: Data entered into the smartphone app

[0600] Output: Data saved in local storage

[0601] Step 3: Prepare to send data

[0602] Device (smartphone app)

[0603] Make sure you have a stable internet connection and convert the saved data into JSON format. For example, convert the input data "Body temperature: 38.5 degrees, Meal content: Curry rice" into the JSON format "{"Body temperature": "38.5 degrees", "Meal content": "Curry rice"}".

[0604] Input: Data stored in local storage

[0605] Output: JSON format data

[0606] Step 4: Sending data

[0607] Device (smartphone app)

[0608] The data converted to JSON format is sent to the server using the HTTPS protocol. If the sending is successful, the "Sent" flag is updated and the data in the local storage is deleted.

[0609] Input: JSON format data

[0610] Output: Data sent to server, data deleted from local storage

[0611] Step 5: Receiving the data

[0612] server

[0613] The server receives the data sent from the smartphone app via a dedicated endpoint (e.g., "POST / data / receive"). For example, it parses the received JSON data and extracts each data item.

[0614] Input: JSON data sent to the server

[0615] Output: Extracted data items

[0616] Step 6: Temporarily save data

[0617] server

[0618] The received data is stored in a temporary database. Specifically, data such as "body temperature: 38.5 degrees, meal contents: curry rice" is stored in a MongoDB or MySQL database.

[0619] Input: Extracted data items

[0620] Output: Data stored in a temporary database

[0621] Step 7: Check data integrity before analysis

[0622] server

[0623] Perform consistency checks and pre-process the data for analysis, for example, verifying that required fields are present and that the data is in the correct format.

[0624] Input: Data stored in a temporary database

[0625] Output: Data with integrity checked

[0626] Step 8: Analyze the data with a generative AI model

[0627] server

[0628] The data whose consistency has been confirmed is input into the generative AI model and analyzed. For example, the prompt "Check body temperature" is input, and the model determines whether "Body temperature: 38.5°C" is outside the normal range (36.1°C to 37.2°C).

[0629] Input: Data that has been verified for integrity, and the prompt "Temperature check"

[0630] Output: Analysis results from the generative AI model

[0631] Step 9: Enter emotion data

[0632] User

[0633] Parents can input their emotional state into a smartphone app, for example, by typing "I'm tired today," or the camera can be used to automatically recognize emotions.

[0634] Input: Emotion data (e.g., "I'm tired today")

[0635] Output: Emotion data entered into the input form in the smartphone app

[0636] Step 10: Sending Emotion Data

[0637] Device (smartphone app)

[0638] The input emotion data is sent to the server. For example, the emotion data "I'm tired today" is converted to JSON format and sent to the server.

[0639] Input: Emotion data

[0640] Output: Emotion data sent to the server

[0641] Step 11: Emotion Engine Analysis

[0642] server

[0643] The received emotional data is analyzed by an emotion engine to classify the parent's emotional state. For example, based on the emotional data "I'm tired today," it can be classified as "high stress."

[0644] Input: Emotion data received by the server

[0645] Output: Emotional state classified by the emotion engine

[0646] Step 12: Generating Advice

[0647] server

[0648] The analysis results of the generative AI model are combined with the results of the emotion engine to generate personalized advice. For example, it provides advice such as "Your child has a high fever. Drink plenty of fluids and check their temperature frequently," along with an emotionally sensitive message such as "You are currently tired, so please refrain from overexerting yourself and consult a doctor."

[0649] Input: Analysis results of the generative AI model, classification results of the emotion engine

[0650] Output: Personalized advice

[0651] Step 13: Submitting Advice

[0652] server

[0653] The generated personalized advice is sent to the parent's smartphone device, where it is added to a notification queue and a push notification is sent using Firebase Cloud Messaging (FCM), for example.

[0654] Input: Personalized advice

[0655] Output: Advice sent to parent's smartphone

[0656] Step 14: Viewing Advice

[0657] Device (smartphone app)

[0658] The advice received from the server is notified to the parent and displayed within the app, for example, as a pop-up notification or on a dedicated page.

[0659] Input: Advice sent by the server

[0660] Output: Advice displayed in the app

[0661] Step 15: Detect and notify abnormal data

[0662] server

[0663] The generative AI model detects abnormal data from the analysis results and generates a notification to direct users to an online medical consultation app. For example, it generates a notification saying, "Your child appears to have a high fever. Please consult a doctor immediately."

[0664] Input: Analysis results of the generative AI model

[0665] Output: Notification directing you to the online medical consultation app

[0666] Step 16: Send notification to guide users to the online consultation app

[0667] server

[0668] The generated notification is sent to the parent's smartphone, and includes a link to the online consultation app.

[0669] Input: Notification to direct you to the online medical consultation app

[0670] Output: Guidance notification sent to the parent's smartphone

[0671] Step 17: Displaying abnormality notifications

[0672] Device (smartphone app)

[0673] The system displays an abnormality notification on the parent's device and provides a link to an online medical consultation app, allowing the parent to quickly seek medical advice.

[0674] Input: Abnormal notification sent from the server

[0675] Output: Anomaly notification and link displayed in the app

[0676] Step 18: Enter your feedback

[0677] User

[0678] Parents can enter their evaluations and opinions about the advice provided as feedback into a smartphone app, such as "The advice was helpful" or "I would like more specific instructions."

[0679] Input: Feedback data

[0680] Output: Feedback data entered into the smartphone app

[0681] Step 19: Submit your feedback

[0682] Device (smartphone app)

[0683] The input feedback data is sent to the server.

[0684] Input: Feedback data entered into the smartphone app

[0685] Output: Feedback data sent to the server

[0686] Step 20: Storing and analyzing feedback

[0687] server

[0688] The received feedback is stored in a database and analyzed. The generative AI model and emotion engine are adjusted based on the analysis results. This improves the accuracy of the next data analysis and advice generation.

[0689] Input: Received feedback data

[0690] Output: Feedback data stored in the database, and tuning of generative AI models and emotion engines based on the analysis results.

[0691] The detailed processing steps described above realize a system that allows parents to manage their children's daily life data and provide appropriate and personalized advice.

[0692] (Application example 2)

[0693] 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."

[0694] It is necessary to provide personalized advice that takes into account not only the child's daily life data but also the parent's emotional state. It is also necessary to ensure the safety of children by quickly detecting abnormalities in a child's behavior or environment and prompting parents to take appropriate action. However, current systems do not support emotional data analysis or real-time emergency notifications, making it difficult to reduce parents' stress and anxiety.

[0695] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 2 is realized by the following means.

[0696] In this invention, the server includes means for a guardian to input data about the child's daily life, means for receiving the data and storing it in a database, means for analyzing the received data with a generative AI model, means for generating personalized advice for the guardian based on the analysis results, means for acquiring and analyzing emotional data from the guardian, means for reinforcing the personalized advice for the guardian based on the analyzed emotional data, and means for transmitting the generated advice to the guardian's terminal. This makes it possible to provide more appropriate and personalized advice by analyzing the data about the child's daily life and the guardian's emotional data, further ensuring the safety of the child and reducing stress for the guardian.

[0697] "Guardian" refers to an adult who is responsible for protecting a child's life and safety.

[0698] "Daily life data" refers to information about a child's daily life, such as their body temperature, meals, sleep, number of trips to the toilet, location information, and environmental data.

[0699] A "generative AI model" refers to the machine learning algorithm used to analyze collected data and make decisions.

[0700] "Personalized advice" refers to specific instructions and advice that are customized to the individual user's (parent's) situation and emotional state.

[0701] "Emotional data" refers to information that indicates the emotional state of the guardian, including, for example, the degree of stress or fatigue that the guardian is feeling.

[0702] "Database" refers to a system for temporarily or permanently storing collected data.

[0703] "Analysis tools" refers to the methods and devices used to analyze collected data and extract meaningful information.

[0704] "Notification means" refers to a mechanism for sending the generated advice to the parent's device and notifying them in real time.

[0705] An "online consultation app" refers to an application that allows users to consult with a doctor remotely.

[0706] This system analyzes a child's daily life data and a parent's emotional data to provide personalized advice. This system is composed of a smartphone app, a server, a generative AI model, and an emotion engine.

[0707] Data collection and input

[0708] User:

[0709] Parents enter data about their child's daily life into a smartphone app, such as body temperature, diet, sleep time, number of toilet visits, location information, and environmental data.

[0710] Device (smartphone app):

[0711] The smartphone app temporarily stores the data entered by the parent or guardian in local storage and sends it to the server when the internet connection is stable. If the data transmission is successful, the data in local storage is cleared or the transmission flag is updated.

[0712] Data reception and analysis

[0713] server:

[0714] The server receives data sent from the smartphone app and stores it in a database, where it is ready for analysis. The server then invokes a generative AI model built with TensorFlow to analyze the data, for example, to determine whether a child's temperature is above or below the normal range.

[0715] Acquiring and analyzing emotion data

[0716] User:

[0717] Parents can input emotional data into a smartphone app or use the camera to automatically recognize it.

[0718] Device (smartphone app):

[0719] The smartphone app uses the camera to capture a facial image of the parent or guardian and sends the image to a server on the internet.

[0720] server:

[0721] The server uses the Google Cloud Vision API to analyze the parent's emotions from the images sent, and the emotion engine classifies their status, such as stressed or relaxed.

[0722] Advice Generation and Notifications

[0723] server:

[0724] The analysis results of the generated AI model are combined with the results of the emotion engine to generate personalized advice. For example, in addition to advice such as "Your child has a high fever. Drink plenty of fluids and check their temperature frequently," it also generates emotionally sensitive messages such as "The parent is currently tired, so please refrain from overexerting yourself and consult a doctor."

[0725] server:

[0726] The generated advice is pushed to the parent's smartphone in real time, and the advice is added to a notification queue and notified to the parent.

[0727] Device (smartphone app):

[0728] Advice received from the server is notified to parents and displayed within the app. Advice is displayed in real time via push notifications.

[0729] Anomaly detection and medical collaboration

[0730] server:

[0731] If the analysis detects abnormal data, a notification will be generated directing users to the online medical consultation app. For example, the notification may include the message, "Your child appears to have a high fever. Please consult a doctor immediately."

[0732] server:

[0733] A notification message containing a link to the online consultation app will be sent to the parent's device.

[0734] Device (smartphone app):

[0735] An abnormality notification will be displayed to parents, along with a link to an online medical consultation app, allowing them to quickly seek medical advice.

[0736] Gathering feedback and improving the system

[0737] User:

[0738] Parents can enter their evaluations and opinions about the advice provided as feedback into a smartphone app, such as "The advice was helpful" or "I would like more specific instructions."

[0739] Device (smartphone app):

[0740] Send the feedback data to the server.

[0741] server:

[0742] Feedback is received and stored in a database. The collected feedback is used to improve the generative AI model and emotion engine. The feedback data is analyzed and the parameters of the generative AI model and emotion engine are adjusted, which improves the accuracy of the next data analysis and advice generation.

[0743] Specific examples

[0744] scenario:

[0745] If a parent goes missing from a park while the child is there.

[0746] 1. User: Parent types into app: "I can't find my child."

[0747] 2. Device: Sends location data and environmental data.

[0748] 3. Server: Analyze the child's location information (predict the latest location using a TensorFlow model).

[0749] 4. Emotion recognition: Parents submit photos of "anxious expressions" as emotional data.

[0750] 5. Server: Analyzes emotions and determines that the parent's stress level is high.

[0751] 6. Server: Generates the advice, "Your child may be on the east side of the park. Remain calm and call for help."

[0752] 7. Device: Display advice notification to parents.

[0753] Example prompt sentence:

[0754] "A child has gone missing from the park. Please suggest the best course of action based on the latest location data and the anxious expression of the parent."

[0755] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0756] Step 1:

[0757] The user uses a smartphone app to input data about their child's daily life (body temperature, meals, sleep time, number of toilet visits, location information, environmental data, etc.) as well as emotional data from the parent.

[0758] Input: Body temperature, meals, sleep time, toilet visits, location information, environmental data, emotional data

[0759] Output: The input data

[0760] Step 2:

[0761] The device (smartphone app) temporarily stores the data entered by the user in local storage and sends it to the server when the Internet connection is stable.

[0762] Input: Data stored in local storage

[0763] Output: Data sent to the server

[0764] Step 3:

[0765] The server receives the data sent from the smartphone app and stores it in a database.

[0766] Input: Data sent

[0767] Output: Data stored in a database

[0768] Step 4:

[0769] The server invokes a generative AI model built with TensorFlow to analyze the incoming data, for example, to determine whether a child's temperature is above or below the normal range.

[0770] Input: Data stored in a database

[0771] Output: Analysis results (e.g., abnormal body temperature detection)

[0772] Step 5:

[0773] Users can input emotional data into a smartphone app or use a camera to automatically recognize it.

[0774] Input: Emotion data or camera images

[0775] Output: Emotion data

[0776] Step 6:

[0777] The device (smartphone app) uses the camera to capture a facial image of the parent or guardian and sends the image to the server.

[0778] Input: Captured face image

[0779] Output: Face image sent to the server

[0780] Step 7:

[0781] The server uses the Google Cloud Vision API to analyze the parent's emotions from the images sent, and the emotion engine classifies their status, such as stressed or relaxed.

[0782] Input: Parent's face image

[0783] Output: Parent's emotional state (e.g., stress level)

[0784] Step 8:

[0785] The server combines the analysis results of the generated AI model with the results of the emotion engine to generate personalized advice.

[0786] Input: Analysis results and emotion data

[0787] Output: Personalized advice

[0788] Step 9:

[0789] The server pushes the generated advice to the parent's smartphone app in real time.

[0790] Input: Generated advice

[0791] Output: Advice notice sent to parent

[0792] Step 10:

[0793] The device (smartphone app) will push the advice received from the server to the parent and display it within the app.

[0794] Input: Advice received from the server

[0795] Output: Advice notice displayed to parent

[0796] Step 11:

[0797] If the server detects any abnormal data as a result of its analysis, it generates a notification directing the parent to the online medical consultation app and sends it to the parent's device. For example, it may include a message such as, "Your child appears to have a high fever. Please consult a doctor immediately."

[0798] Input: Analysis results of abnormal data

[0799] Output: Notification directing to online medical consultation app

[0800] Step 12:

[0801] The device (smartphone app) will display an abnormality notification to the parent and provide a link to the online medical examination app.

[0802] Input: Notification received from the server

[0803] Output: Abnormality notification displayed to the parent with a link to the online medical examination app

[0804] Step 13:

[0805] Users input their evaluations and opinions on the advice provided as feedback into the smartphone app.

[0806] Input: Feedback (e.g., "The advice was helpful")

[0807] Output: Feedback data

[0808] Step 14:

[0809] The device (smartphone app) sends feedback data to the server.

[0810] Input: User-entered feedback data

[0811] Output: Feedback data sent to the server

[0812] Step 15:

[0813] The server receives the feedback and stores it in a database. The collected feedback is used to improve the generative AI model and emotion engine. By analyzing the feedback and adjusting the parameters of the generative AI model, the accuracy of the next data analysis and advice generation will be improved.

[0814] Input: Feedback data

[0815] Output: Improved generative AI models and emotion engines

[0816] 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.

[0817] 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.

[0818] 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.

[0819] [Second embodiment]

[0820] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0821] 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.

[0822] 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).

[0823] 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.

[0824] 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.

[0825] 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).

[0826] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for 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.

[0827] 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.

[0828] 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.

[0829] 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.

[0830] 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.

[0831] 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."

[0832] The present invention is a system that includes a smartphone app that allows parents to input data about their children's daily lives, a server that receives and analyzes the data, and a system that provides personalized advice based on the analysis results. Specific embodiments of this system are described below.

[0833] Data collection and input

[0834] User

[0835] Parents enter data about their child's daily life into a smartphone app. For example, they fill out an input form with data such as their child's morning temperature, what they eat, how much sleep they get, and how many times they go to the toilet. This data is used to record their child's daily health and lifestyle.

[0836] Device (smartphone app)

[0837] The smartphone app has a function that temporarily stores the data entered by the parent or guardian in local storage and sends it to the server when the Internet connection is stable. When the data transmission is successful, the data is deleted from local storage or the transmission flag is updated.

[0838] Data reception and analysis

[0839] server

[0840] The server receives the data sent from the smartphone app and stores it in a temporary database. The stored data is then analyzed by a generative AI model, which recognizes patterns in the child's daily life and determines whether there are any abnormalities.

[0841] server

[0842] The analysis results form the basis for advice provided to parents. For example, if a child's temperature exceeds the normal range (36.1 to 37.2 degrees Celsius), the system will determine that the child has a high fever and notify the parent of the appropriate course of action.

[0843] Advice Generation and Notifications

[0844] server

[0845] Based on the analysis results of the generative AI model, personalized advice is generated for parents. For example, if the child's sleep time is short, specific advice such as "Sufficient sleep is essential for your child's development. Please try to get them to bed early tonight" is generated.

[0846] server

[0847] The generated advice is sent to the parent's device and added to the notification queue.

[0848] Device (smartphone app)

[0849] Advice received from the server is notified to parents and displayed within the app. Information is provided in real time using push notifications, etc.

[0850] Anomaly detection and medical collaboration

[0851] server

[0852] If the analysis detects abnormal data, a notification will be generated directing users to an online medical consultation app. For example, if an abnormally high temperature persists, a notification will be generated stating, "Your child appears to have a high fever. Please consult a doctor immediately."

[0853] server

[0854] A notification message containing a link to the online consultation app will be sent to the parent's device.

[0855] Device (smartphone app)

[0856] An abnormality notification will be displayed to parents, along with a link to an online medical consultation app, allowing them to quickly seek medical advice.

[0857] Gathering feedback and improving the system

[0858] User

[0859] Parents can enter their evaluations and opinions about the advice provided as feedback into a smartphone app, such as "The advice was helpful" or "I would like more specific instructions."

[0860] Device (smartphone app)

[0861] Send feedback data to the server. Feedback is a key element in improving user experience.

[0862] server

[0863] Feedback is received and stored in a database. This feedback is used to improve the generative AI model. Specifically, it adjusts the model's parameters to improve the accuracy of the next analysis and advice generation.

[0864] Example: What to do if a child has a high temperature

[0865] User

[0866] The parent enters "This morning's temperature was 38.5 degrees" into a smartphone app.

[0867] Terminal

[0868] Sends input data to the server.

[0869] server

[0870] The data is received and stored in a database. At the same time, the generative AI model analyzes the body temperature data and determines whether the person has a high fever.

[0871] server

[0872] The system generates advice such as, "Your child has a high fever. Drink plenty of fluids and check their temperature frequently. If symptoms persist, consult a doctor," and sends it to the parent's device.

[0873] Terminal

[0874] Display an advisory notice to parents.

[0875] User

[0876] Parents will follow advice and ensure their children are hydrated and have their temperatures checked.

[0877] As described above, the present invention realizes a system that can solve the problems faced by parents raising children and effectively support the health of their children.

[0878] The processing flow will be explained below.

[0879] Step 1:

[0880] User

[0881] Parents enter data about their child's daily life into a smartphone app. Examples of input include "This morning's body temperature was 38.5 degrees" and "I slept for five hours last night."

[0882] Step 2:

[0883] Terminal

[0884] The smartphone app temporarily saves the entered data in local storage, so that the data is saved even if the connection is unstable.

[0885] Step 3:

[0886] Terminal

[0887] Ensure that your internet connection is stable, then send the data stored on the server. If the transmission is successful, delete the data in local storage or set a sent flag.

[0888] Step 4:

[0889] server

[0890] The server receives the data sent from the smartphone app and stores it in a temporary database, where it is ready for analysis.

[0891] Step 5:

[0892] server

[0893] A generative AI model is invoked to analyze the incoming data, for example to determine whether the body temperature is above the normal range (36.1°C to 37.2°C).

[0894] Step 6:

[0895] server

[0896] Based on the analysis results, the system checks for abnormal data. For example, if a body temperature of 38.5 degrees is determined to be abnormal, it will be treated as a "high fever."

[0897] Step 7:

[0898] server

[0899] Based on the analysis results, the system generates personalized advice for parents, such as a message saying, "Your child has a high fever. Drink plenty of fluids and check their temperature frequently."

[0900] Step 8:

[0901] server

[0902] The generated advice is sent to the parent's smartphone. The advice is added to the notification queue and sent.

[0903] Step 9:

[0904] Terminal

[0905] Advice received from the server is notified to parents in real time via push notifications and displayed within the app.

[0906] Step 10:

[0907] server

[0908] If abnormal data is detected during the analysis process, a notification will be generated directing users to an online medical consultation app, with a message such as, "Your child appears to have a high fever. Please consult a doctor immediately."

[0909] Step 11:

[0910] server

[0911] A notification message will be sent to the device containing a link to an online consultation app, allowing parents to quickly contact a medical institution.

[0912] Step 12:

[0913] Terminal

[0914] An abnormality notification will be displayed to parents, along with a link to an online consultation app, allowing them to quickly contact a doctor.

[0915] Step 13:

[0916] User

[0917] Review any advice or guidance provided and act as necessary, for example, by drinking fluids or rechecking your temperature.

[0918] Step 14:

[0919] User

[0920] Users can enter feedback about the advice and notifications into the smartphone app, such as "The advice was helpful" or "I would like more detailed explanations."

[0921] Step 15:

[0922] Terminal

[0923] The feedback data is sent to the server. If the sending is successful, a notification of feedback completion will be displayed in the app.

[0924] Step 16:

[0925] server

[0926] Receive feedback and store it in a database. The collected feedback is used to improve the generative AI model.

[0927] Step 17:

[0928] server

[0929] The feedback data is analyzed and the parameters of the generative AI model are adjusted to improve the accuracy of the next data analysis and advice generation.

[0930] Through these steps, the system can quickly and accurately analyze data on children's daily lives, provide optimal advice to parents, and continuously improve the system using feedback.

[0931] Example 1

[0932] 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."

[0933] Collecting data on children's daily lives and providing appropriate advice to parents regarding their health and daily rhythms based on that data is a crucial challenge. However, many parents are busy and find it difficult to continuously collect and analyze detailed data. Furthermore, when an abnormality occurs, they are required to make a prompt response, but they often lack the specialized knowledge to do so. To solve these challenges, there is a need to develop a system that allows parents to easily input daily data and automatically detects abnormalities and provides personalized advice based on that data.

[0934] 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.

[0935] In this invention, the server includes means for temporarily storing input data in local storage and transmitting it to the server when the internet connection is stable, means for the server to receive the transmitted data and store it in a database, and means for analyzing the stored data with a generative AI model. This allows parents to easily input daily data, and the generative AI model automatically analyzes the data based on the data, enabling anomaly detection and personalized advice to be provided in real time.

[0936] A "guardian" is an adult who is responsible for managing a child's daily life and health and entering data into the system.

[0937] "Children" are minors whose guardians aim to improve their health and lifestyle through the system.

[0938] "Data related to daily life" refers to data that reflects a child's daily health condition and lifestyle, and includes, for example, body temperature, dietary habits, sleep duration, and number of times they went to the toilet.

[0939] "Input means" refers to the interface and functions that allow parents to input data about their daily lives into the system using a smartphone app or other device.

[0940] "Local storage" refers to a storage device or memory for temporarily storing data on a device.

[0941] "Server" refers to a computer system that receives, stores, and analyzes data sent from the device, and generates and provides advice and notifications to parents.

[0942] "Database" refers to a system or software for storing and managing received data in a structured manner.

[0943] "Generative AI models" refer to artificial intelligence algorithms and machine learning models that analyze collected data and recognize patterns and detect anomalies in children's daily lives.

[0944] "Analysis means" refers to the process or method by which the server uses the generated AI model to analyze data and determine whether there are any abnormalities.

[0945] "Personalized advice" refers to information that provides appropriate countermeasures and suggestions to specific parents based on the analysis results of the generative AI model, depending on the child's health condition and lifestyle.

[0946] "Transmission means" refers to the communication functions and methods for sending advice and notifications from the server to the parent's device.

[0947] "Notification means" refers to the interface or function for displaying received advice or notifications on the parent's device and conveying information to the parent.

[0948] An "online medical consultation app" refers to an application or service that provides medical consultations and examinations online.

[0949] "Anomaly detection" refers to the discovery of unusual data patterns or anomalies by a generative AI model from the results of its analysis.

[0950] "Feedback" refers to information that parents submit by entering their thoughts, evaluations, and suggestions for improvement regarding the advice provided by the system.

[0951] "Data improvement measures" refers to methods and processes for improving the performance and accuracy of generative AI models based on parental feedback.

[0952] This system allows parents to input and manage data about their children's daily lives using a smartphone app, and provides personalized advice based on the analysis results. The system is designed to allow parents to easily input data and respond quickly if an abnormality is detected.

[0953] Specifically, the system includes the following elements:

[0954] Data collection and input

[0955] User

[0956] Parents open a smartphone app and enter data about their child's daily life, such as their body temperature each morning, what they eat, how much sleep they get, and how many times they go to the toilet. This data is important for recording their child's health and daily rhythms.

[0957] Device (smartphone app)

[0958] The smartphone app first temporarily stores the data entered by the parent in local storage (for example, using SQLite or Realm Database). When the Internet connection is stable, the app sends this data to the server. After the data is successfully sent, the app either deletes the data from local storage or updates a flag indicating that it has been sent.

[0959] Data reception and analysis

[0960] server

[0961] The server receives the data sent from the smartphone app and stores it in a temporary database (e.g., MySQL or PostgreSQL). After the data is stored, the generative AI model analyzes it. The generative AI model recognizes patterns in the child's daily life and determines whether there are any abnormalities.

[0962] For example, if a parent or guardian inputs "My temperature this morning was 38.5 degrees," this data is sent to the server. The server receives the data and stores it in a temporary database. The generative AI model analyzes this input data and determines that it is a high fever.

[0963] Advice Generation and Notifications

[0964] server

[0965] Based on the analysis results of the generative AI model, the server generates personalized advice for parents. For example, if the child has a high temperature, the server will generate specific advice such as, "Your child has a high fever. Drink plenty of fluids and check their temperature frequently. If symptoms persist, consult a doctor."

[0966] server

[0967] The generated advice is sent to the parent's device. The advice is added to a notification queue so that the parent can immediately know how to respond. For example, advice can be provided in real time using push notifications.

[0968] Device (smartphone app)

[0969] The parent's device will receive advice from the server and display it in a notification and within the app. Parents can check the notification and take appropriate action.

[0970] Anomaly detection and medical collaboration

[0971] server

[0972] If the analysis detects abnormal data, the server generates a notification directing the user to the online medical examination app. For example, if an abnormally high temperature of 39 degrees continues, a specific notification will be created stating, "Your child appears to have a high fever. Please consult a doctor immediately."

[0973] server

[0974] A notification message containing a link to the online consultation app will be sent to the parent's device, allowing them to quickly arrange a medical consultation.

[0975] Device (smartphone app)

[0976] An abnormality notification will be displayed to parents, who will be provided with a link to an online consultation app, allowing them to quickly access medical services.

[0977] Gathering feedback and improving the system

[0978] User

[0979] Parents can enter their evaluations and opinions about the advice provided as feedback into a smartphone app, such as "The advice was helpful" or "I would like more specific instructions."

[0980] Device (smartphone app)

[0981] The smartphone app sends the input feedback data to the server. The feedback data is also sent securely and is important as a means of improving the entire system.

[0982] server

[0983] The server receives the feedback data and stores it in a database. This feedback is used to improve the performance of the generative AI model by adjusting the model's parameters and improving the accuracy of the next analysis and advice generation.

[0984] Through a series of processes from data collection and analysis to advice generation and feedback, this system can solve the challenges faced by parents raising children and effectively support their children's health.

[0985] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0986] Step 1:

[0987] User

[0988] Parents open the smartphone app and enter data about their child's daily life. The data includes the child's body temperature, what they eat, how much they sleep, and how many times they go to the toilet. For example, they might enter information such as "body temperature: 36.8 degrees" and "sleep time: 8 hours." The entered data is temporarily stored in the smartphone's local storage.

[0989] Input: Data about the child's daily life

[0990] Output: Data saved in local storage

[0991] Step 2:

[0992] Device (smartphone app)

[0993] Once the smartphone app confirms that the internet connection is stable, it sends the data stored in local storage to the server. At this time, it packages the data in JSON format and sends it via an HTTP POST request. If the transmission is successful, it deletes the data from local storage or updates the sent flag.

[0994] Input: Data stored in local storage

[0995] Output: Data sent to the server

[0996] Step 3:

[0997] server

[0998] The server receives the data sent from the smartphone app and stores it in a temporary database. The data is then saved in a database such as MySQL or PostgreSQL. At this point, validation is performed to ensure that the data format and content are accurate.

[0999] Input: Data sent from the smartphone app

[1000] Output: Data stored in a temporary database

[1001] Step 4:

[1002] server

[1003] Once the data is stored in the temporary database, the server passes it to the generative AI model for analysis. During analysis, the generative AI model uses a dataset of learned patterns from a child's daily life to detect any abnormalities. For example, if "body temperature: 38.5 degrees" is entered, the generative AI model will determine that it is a high fever.

[1004] Input: Data stored in a temporary database

[1005] Output: Analysis results

[1006] Step 5:

[1007] server

[1008] Based on the analysis results of the generative AI model, the server generates personalized advice for parents. For example, if the child has a high temperature, the server generates specific advice such as, "Your child has a high fever. Drink plenty of fluids and check their temperature frequently. If symptoms persist, consult a doctor."

[1009] Input: Analysis results

[1010] Output: Personalized advice

[1011] Step 6:

[1012] server

[1013] The generated advice is sent to the parent's device. The server sends the generated advice to the parent's device via push notification, etc. The advice is added to the notification queue so that the parent can immediately know what to do.

[1014] Enter: personalized advice

[1015] Output: Advice sent to parent's device

[1016] Step 7:

[1017] Device (smartphone app)

[1018] The parent's device will receive the advice from the server and display it within the app. Real-time information is provided to parents via push notifications. Parents can then check the notifications and take appropriate action.

[1019] Input: Advice sent to parent's device

[1020] Output: Advice displayed to parents

[1021] Step 8:

[1022] server

[1023] If the analysis detects abnormal data, the server generates a notification directing the user to the online medical examination app. For example, if an abnormally high temperature of 39 degrees continues, a specific notification will be created stating, "Your child appears to have a high fever. Please consult a doctor immediately."

[1024] Input: Analysis results (abnormal data)

[1025] Output: Notification directing to online medical consultation app

[1026] Step 9:

[1027] server

[1028] A notification message containing a link to the online consultation app will be sent to the parent's device. When the parent clicks on the link, they will be taken directly to the online consultation app.

[1029] Input: Notification to direct you to the online medical consultation app

[1030] Output: A notification message with a link to the online consultation app.

[1031] Step 10:

[1032] Device (smartphone app)

[1033] A notification of an abnormality will be displayed to parents, who will be provided with a link to an online medical consultation app that they can tap to quickly access medical services.

[1034] Input: Notification message with link to online consultation app

[1035] Output: Anomaly notification and link displayed to the parent

[1036] Step 11:

[1037] User

[1038] Parents can enter their evaluation and opinions of the advice provided as feedback into a smartphone app, such as "The advice was helpful" or "I would like more specific instructions."

[1039] Input: Feedback on the advice

[1040] Output: Feedback entered into the smartphone app

[1041] Step 12:

[1042] Device (smartphone app)

[1043] The smartphone app sends the entered feedback data to the server, where it is also securely transmitted and made available.

[1044] Input: Feedback entered into the smartphone app

[1045] Output: Feedback data sent to the server

[1046] Step 13:

[1047] server

[1048] The server receives the feedback data and stores it in a database. This feedback is used to improve the performance of the generative AI model. Specifically, it is used to adjust the parameters of the generative AI model and improve the accuracy of the next analysis and advice generation.

[1049] Input: Feedback data sent to the server

[1050] Output: Feedback data stored in a database

[1051] The above is a specific processing flow of the system.

[1052] (Application example 1)

[1053] 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."

[1054] Conventional child health management systems monitor health conditions based on data entered by parents on a daily basis and provide necessary advice. However, if the system goes beyond simply analyzing health data and takes into account a wide variety of purchasing activities and preference patterns, it becomes possible to make more extensive lifestyle suggestions and make appropriate product recommendations, further increasing user satisfaction. The objective of this invention is to support users' overall lifestyles by providing personalized product recommendations and shopping advice based on purchasing history and preferences, in addition to the conventional health management systems.

[1055] 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.

[1056] In this invention, the server includes: means for a guardian to input data about their child's daily life; means for receiving the data and storing it in a database; means for analyzing the received data with a generative AI model; means for generating personalized advice for the guardian based on the analysis results; means for transmitting the generated advice to the guardian's terminal; means for collecting user purchase data, browsing history, and reviews and transmitting them to a private server; means for analyzing the collected data with a generative AI model and generating personalized product recommendations and shopping advice; and means for notifying the user's terminal of the generated recommendations and advice. This makes it possible to provide not only health management for the user but also personalized product recommendations and shopping advice based on the user's purchase history.

[1057] A "guardian" is a parent or guardian in charge of the child's upbringing and health care.

[1058] "Data related to daily life" includes information such as a child's daily body temperature, diet, sleep time, and number of times they go to the toilet.

[1059] A "generative AI model" is an artificial intelligence system that uses machine learning algorithms to analyze data and recognize patterns and anomalies.

[1060] "Advice" refers to specific instructions or suggestions provided to parents based on the analysis results.

[1061] A "terminal" is an electronic device, such as a smartphone or tablet, through which a user enters data or receives advice.

[1062] "User" refers to the person who uses the system to input data and receive advice, primarily parents.

[1063] "Purchase data" refers to historical information about products purchased by a user.

[1064] "Browsing history" is a record of the products and pages a user has viewed online.

[1065] A "review" is information that describes the user's evaluation and impressions of a product they have purchased.

[1066] A "private server" is a computer system that is a dedicated server managed within a company or home and is used to collect and analyze data.

[1067] "Product recommendations" are new products suggested to users based on the analysis results.

[1068] "Shopping Advice" means specific shopping-related instructions or suggestions provided to a user based on their purchasing data and preferences.

[1069] "Push Notification API" is an application program interface for sending notifications from a server to a device in real time.

[1070] MODE FOR CARRYING OUT THE INVENTION

[1071] System configuration

[1072] The system of this invention includes a smartphone app where parents input data about their children's daily lives, a server that receives and analyzes the data, and a function that provides personalized advice based on the analysis results. It also collects users' purchasing data, browsing history, and reviews, and generates product recommendations and shopping advice based on these. The specific configuration and processing flow are described below.

[1073] 1. Data collection and input

[1074] User

[1075] Parents enter data about their children's daily lives into a smartphone app, such as their morning temperature, what they eat, how much sleep they get, and how many times they go to the toilet. Users also enter or allow shopping-related information, such as purchase data, browsing history, and reviews.

[1076] Device (smartphone app)

[1077] The smartphone app has a function to temporarily store data entered by parents or users in local storage and send it to the server when the Internet connection is stable. When the data transmission is successful, the data is deleted from local storage or the transmission flag is updated.

[1078] 2. Data Reception and Analysis

[1079] server

[1080] The server receives data sent from the smartphone app and stores it in a temporary database. The stored data is then analyzed by a generative AI model. The generative AI model recognizes patterns in the child's daily life and determines whether there are any abnormalities. Similarly, it analyzes shopping data to understand the user's preferences and purchasing patterns.

[1081] server

[1082] The analysis results are the basis for providing advice to parents and users. For example, if a child's temperature exceeds the normal range (36.1 to 37.2 degrees Celsius), the system will determine that the child has a high fever and notify the parent or guardian of the appropriate course of action. At the same time, it will also recommend new products based on the user's purchasing history.

[1083] 3. Advice Generation and Notification

[1084] server

[1085] Based on the analysis results of the generative AI model, personalized advice is generated for parents and users. For example, if a child's sleep time is short, specific advice such as "Sufficient sleep is essential for your child's development. Make sure your child goes to bed early tonight" is generated. It also analyzes the user's purchasing history to generate shopping advice such as "We recommend this conditioner as a related product to the shampoo you recently purchased."

[1086] server

[1087] The generated advice is sent to the parent or user's device and added to a notification queue.

[1088] Device (smartphone app)

[1089] Advice received from the server is notified to parents and users and displayed within the app. Push notifications are used to provide information in real time.

[1090] 4. Anomaly Detection and Medical Collaboration

[1091] server

[1092] If the analysis detects abnormal data, a notification will be generated directing users to an online medical consultation app. For example, if an abnormally high temperature persists, a notification will be generated stating, "Your child appears to have a high fever. Please consult a doctor immediately."

[1093] server

[1094] A notification message containing a link to the online consultation app will be sent to the parent's device.

[1095] Device (smartphone app)

[1096] An abnormality notification will be displayed to parents, along with a link to an online medical consultation app, allowing them to quickly seek medical advice.

[1097] 5. Gathering feedback and improving the system

[1098] User

[1099] Parents and users can enter their evaluations and opinions about the advice provided as feedback into the smartphone app, such as "The advice was helpful" or "I would like more specific instructions."

[1100] Device (smartphone app)

[1101] Send feedback data to the server. Feedback is a key element in improving user experience.

[1102] server

[1103] Feedback is received and stored in a database. This feedback is used to improve the generative AI model. Specifically, it adjusts the model's parameters to improve the accuracy of the next analysis and advice generation.

[1104] Example: What to do if a child has a high temperature

[1105] User

[1106] The parent enters "This morning's temperature was 38.5 degrees" into a smartphone app.

[1107] Terminal

[1108] Sends input data to the server.

[1109] server

[1110] The data is received and stored in a database. At the same time, the generative AI model analyzes the body temperature data and determines whether the person has a high fever.

[1111] server

[1112] The system generates advice such as, "Your child has a high fever. Drink plenty of fluids and check their temperature frequently. If symptoms persist, consult a doctor," and sends it to the parent's device.

[1113] Terminal

[1114] Display an advisory notice to parents.

[1115] User

[1116] Parents will follow advice and ensure their children are hydrated and have their temperatures checked.

[1117] Example: Providing shopping advice

[1118] User

[1119] A user is happy with the shampoo they recently purchased and posts a review within a smartphone app.

[1120] Terminal

[1121] Purchase history, browsing history, and reviews are sent to the server.

[1122] server

[1123] The data is received and stored in a database, while the generative AI model analyzes the purchasing data and recognizes user preferences.

[1124] server

[1125] A shopping advice such as "We recommend this conditioner as a related product to the shampoo you recently purchased" is generated and sent to the user's device.

[1126] Terminal

[1127] Display an advisory notice to the user.

[1128] Prompt Sentence Examples

[1129] Examples of prompts include:

[1130] "Generate product recommendations based on user purchasing history and preferences."

[1131] "Recommend items from this week's new products list that are similar to items the user has previously purchased."

[1132] In this way, the present invention realizes a system that not only manages the user's health but also supports their overall lifestyle by providing personalized product recommendations and shopping advice based on their purchasing history.

[1133] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1134] Specific processing steps of the system

[1135] Program processing steps

[1136] Step 1: Data entry

[1137] 1. Users

[1138] Parents enter data about their children's daily lives (body temperature, diet, sleep time, number of times they went to the toilet, etc.) into a smartphone app. Users also enter shopping-related information (purchase data, browsing history, reviews) into the app.

[1139] Input: Children's daily life data, shopping-related data

[1140] Output: Save data to local storage

[1141] Step 2: Send data

[1142] 2. Device (smartphone app)

[1143] The app temporarily stores the data entered by the parent or user in local storage and sends it to the server when the internet connection is stable. If the transmission is successful, the data in local storage is deleted or the transmission flag is updated.

[1144] Input: Data stored in local storage

[1145] Output: Send data to the server

[1146] Step 3: Receiving and storing data

[1147] 3. Server

[1148] The server receives the data sent from the smartphone app and stores it in a temporary database, where data from parents and user purchasing data are aggregated.

[1149] Input: Data sent from the terminal

[1150] Output: Data saved to a temporary database

[1151] Step 4: Data analysis

[1152] 4. Server

[1153] The incoming data is analyzed by a generative AI model that recognizes patterns and anomalies in the child's daily life and generates personalized recommendations based on the shopping data.

[1154] Input: Data in the temporary database

[1155] Output: Analysis results (anomaly detection, product recommendations)

[1156] Step 5: Advice Generation

[1157] 5. Server

[1158] The server generates advice to provide to parents and users based on the analysis results, such as notifying appropriate measures if the user has a high body temperature, or recommending related products based on the user's purchasing history.

[1159] Input: Analysis results

[1160] Output: Advice generation (health advice, product recommendations)

[1161] Step 6: Advice Notification

[1162] 6. Server

[1163] The generated advice is sent to the user's device and added to a notification queue.

[1164] Input: Generated advice

[1165] Output: Advice sent to terminal

[1166] Step 7: Notifications

[1167] 7. Device (smartphone app)

[1168] The device notifies the user of the advice received from the server and displays it within the app, providing real-time information using a push notification API.

[1169] Input: Advice sent by the server

[1170] Output: Notification displayed to the user

[1171] Step 8: Gather feedback

[1172] 8. Users

[1173] Parents and users enter feedback on the advice into the app and send the data to the server.

[1174] Input: Feedback data

[1175] Output: Send feedback to the server

[1176] Step 9: Model Improvement

[1177] 9. Server

[1178] The server receives the feedback and uses it as data to improve the generative AI model, adjusting the model's parameters to improve the accuracy of the next analysis and advice generation.

[1179] Input: Feedback data

[1180] Output: Parameter updates for the model

[1181] Processing flow based on a specific example

[1182] Example: If your child has a high temperature

[1183] User

[1184] The parent enters "This morning's temperature was 38.5 degrees" into a smartphone app.

[1185] Input: Body temperature data (38.5°C)

[1186] Output: Save to local storage

[1187] Terminal

[1188] Sends input data to the server.

[1189] Input: Stored body temperature data

[1190] Output: Send data to the server

[1191] server

[1192] The data is received and stored in a database. At the same time, the generative AI model analyzes the body temperature data and determines whether the person has a high fever.

[1193] Input: Received body temperature data

[1194] Output: Analysis results (high temperature)

[1195] server

[1196] Generates advice like, "Your child has a high fever. Drink plenty of fluids and check their temperature frequently. If symptoms persist, consult a doctor."

[1197] Input: High temperature analysis results

[1198] output: Advice generation

[1199] server

[1200] The generated advice is sent to the parent's device.

[1201] Input: Advice data

[1202] Output: sent to terminal

[1203] Terminal

[1204] Display an advisory notice to parents.

[1205] Input: Advice data

[1206] Output: Parental Notice

[1207] User

[1208] Parents will follow advice and ensure their children are hydrated and have their temperatures checked.

[1209] 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.

[1210] The present invention provides a smartphone app that allows parents to input data about their children's daily lives, a server that receives and analyzes the data, and a system that provides personalized advice based on the analysis results. Furthermore, by combining this with an emotion engine that recognizes the user's emotions, more appropriate advice can be provided. A specific embodiment of this system is described below.

[1211] Data collection and input

[1212] User

[1213] Parents enter data about their child's daily life into a smartphone app, such as their body temperature, what they eat, how much they sleep, and how often they go to the toilet. This data is used to record their child's daily health and lifestyle.

[1214] Device (smartphone app)

[1215] The smartphone app temporarily stores the data entered by the parent or guardian in local storage and sends it to the server when the internet connection is stable. If the data transmission is successful, the data in local storage is deleted or the transmission flag is updated.

[1216] Data reception and analysis

[1217] server

[1218] The server receives the data sent from the smartphone app and stores it in a temporary database, where it is ready for analysis.

[1219] server

[1220] A generative AI model is invoked to analyze the incoming data, for example to determine whether the body temperature is above the normal range (36.1°C to 37.2°C).

[1221] Use of emotion engine

[1222] Device (smartphone app)

[1223] Parents can input their own emotions or use emotion recognition to capture emotion data. For example, they can input "I feel tired today" or use the camera to automatically recognize emotions.

[1224] server

[1225] The emotion engine analyzes the received emotion data and classifies the parent's current emotional state, for example, as "high stress" or "relaxed."

[1226] Advice Generation and Notifications

[1227] server

[1228] The analysis results of the generative AI model are combined with the results of the emotion engine to generate personalized advice for parents. For example, in addition to the advice, "Your child has a high fever. Drink plenty of fluids and check their temperature frequently," a message that takes into consideration emotions such as, "The parent is currently tired, so please refrain from overexerting yourself and consult a doctor" is created.

[1229] server

[1230] The generated advice is sent to the parent's smartphone. The advice is added to the notification queue and sent.

[1231] Device (smartphone app)

[1232] Advice received from the server is notified to parents and displayed within the app. Advice is displayed in real time via push notifications.

[1233] Anomaly detection and medical collaboration

[1234] server

[1235] If the analysis detects abnormal data, a notification will be generated directing users to the online medical consultation app. For example, the notification may include the message, "Your child appears to have a high fever. Please consult a doctor immediately."

[1236] server

[1237] A notification message containing a link to the online consultation app will be sent to the parent's device.

[1238] Device (smartphone app)

[1239] An abnormality notification will be displayed to parents, along with a link to an online medical consultation app, allowing them to quickly seek medical advice.

[1240] Gathering feedback and improving the system

[1241] User

[1242] Parents can enter their evaluations and opinions about the advice provided as feedback into a smartphone app, such as "The advice was helpful" or "I would like more specific instructions."

[1243] Device (smartphone app)

[1244] Send the feedback data to the server.

[1245] server

[1246] Receive feedback and store it in a database. The collected feedback is used to improve the generative AI model and emotion engine.

[1247] server

[1248] The feedback data is analyzed and the parameters of the generative AI model and emotion engine are adjusted to improve the accuracy of the next data analysis and advice generation.

[1249] Example: What to do if a child has a high temperature

[1250] User

[1251] The parent enters "This morning's temperature was 38.5 degrees" into a smartphone app.

[1252] Terminal

[1253] Sends input data to the server.

[1254] server

[1255] The data is received and stored in a database. At the same time, the generative AI model analyzes the body temperature data and determines whether the person has a high fever.

[1256] Terminal

[1257] The parent enters emotional data such as "I'm tired today."

[1258] server

[1259] The emotion engine analyzes the emotional data and determines that the person is under high stress.

[1260] server

[1261] In addition to the advice "Your child has a high fever. Drink plenty of fluids and check their temperature frequently," the system also generates advice such as "Your parent is currently tired, so please refrain from pushing yourself and consult a doctor," and sends this to the parent's device.

[1262] Terminal

[1263] Display an advisory notice to parents.

[1264] User

[1265] Parents should follow advice to keep their child hydrated, recheck their temperature, and consult a doctor if necessary.

[1266] As described above, the present invention realizes a system that solves the problems faced by parents raising children, effectively supports the health of their children, and provides appropriate advice according to the emotional state of the parents.

[1267] The processing flow will be explained below.

[1268] Step 1:

[1269] User

[1270] Parents enter data about their child's daily life into a smartphone app, for example, filling out an input form with information such as "This morning's body temperature was 38.5 degrees" or "I slept for five hours last night."

[1271] Step 2:

[1272] Terminal

[1273] The smartphone app temporarily stores the entered data in local storage, which allows the data to be saved even if the internet connection is unstable.

[1274] Step 3:

[1275] Terminal

[1276] Ensure that your internet connection is stable, then send the data stored on the server. If the transmission is successful, delete the data in local storage or set a sent flag.

[1277] Step 4:

[1278] server

[1279] The server receives the data sent from the smartphone app and stores it in a temporary database, where it is ready for analysis.

[1280] Step 5:

[1281] server

[1282] A generative AI model is invoked to analyze the incoming data, for example to determine whether the body temperature is above the normal range (36.1°C to 37.2°C).

[1283] Step 6:

[1284] server

[1285] Based on the analysis results, the system checks for abnormal data. For example, if a body temperature of 38.5 degrees is determined to be abnormal, it will be treated as a "high fever."

[1286] Step 7:

[1287] Terminal

[1288] Parents can input their own emotions or use emotion recognition to capture emotion data. For example, they can input "I feel tired today" or use the camera to automatically recognize emotions.

[1289] Step 8:

[1290] server

[1291] The emotion engine receives the emotion data and classifies the parent's emotional state, for example, as "high stress" or "relaxed."

[1292] Step 9:

[1293] server

[1294] The analysis results of the generative AI model are combined with the results of the emotion engine to generate personalized advice for parents. For example, in addition to the advice, "Your child has a high fever. Drink plenty of fluids and check their temperature frequently," a message such as, "The parent is currently tired, so please refrain from overexerting yourself and consult a doctor" can be generated.

[1295] Step 10:

[1296] server

[1297] The generated advice is sent to the parent's smartphone. The advice is added to the notification queue and sent.

[1298] Step 11:

[1299] Terminal

[1300] Advice received from the server is notified to parents and displayed within the app. Advice is displayed in real time via push notifications.

[1301] Step 12:

[1302] server

[1303] If the analysis detects abnormal data, a notification will be generated directing users to the online medical consultation app. For example, the notification may include the message, "Your child appears to have a high fever. Please consult a doctor immediately."

[1304] Step 13:

[1305] server

[1306] A notification message containing a link to the online consultation app will be sent to the parent's device.

[1307] Step 14:

[1308] Terminal

[1309] An abnormality notification will be displayed to parents, along with a link to an online medical consultation app, allowing them to quickly seek medical advice.

[1310] Step 15:

[1311] User

[1312] Review any advice or guidance provided and act as necessary, for example, by drinking fluids or rechecking your temperature.

[1313] Step 16:

[1314] User

[1315] Users can enter feedback about the advice and notifications into the smartphone app, such as "The advice was helpful" or "I would like more detailed explanations."

[1316] Step 17:

[1317] Terminal

[1318] The feedback data is sent to the server. If the sending is successful, a notification of feedback completion will be displayed in the app.

[1319] Step 18:

[1320] server

[1321] Receive feedback and store it in a database. The collected feedback is used to improve the generative AI model and emotion engine.

[1322] Step 19:

[1323] server

[1324] The feedback data is analyzed and the parameters of the generative AI model and emotion engine are adjusted to improve the accuracy of the next data analysis and advice generation.

[1325] Through these steps, the system can quickly and accurately analyze data on children's daily lives and parents' emotions, provide optimal advice, and continuously improve the system using feedback.

[1326] Example 2

[1327] 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."

[1328] In modern child-rearing, parents must devote a great deal of time and effort to managing their children's daily health and keeping track of their lifestyle. Emotional care is also important, as the parents' own emotional state directly impacts the quality of their child-rearing. However, there is currently a lack of systems that can centrally manage these and provide appropriate advice. Furthermore, it is also necessary to respond to situations that require early detection of abnormal data and prompt medical consultation.

[1329] The identification process by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means. In this invention, the server includes: a means for a guardian to input data about their child's daily life; a means for temporarily saving the data in local storage and transmitting it to the server when the Internet connection is stable; a means for receiving the data and storing it in a temporary database; a means for analyzing the received data using a generative AI model; a means for the guardian to input emotional data or acquire it using an emotion recognition function; a means for analyzing the received emotional data using an emotion engine and classifying the guardian's emotional state; a means for combining the analysis results of the generative AI model and the results of the emotion engine to generate personalized advice; and a means for transmitting the generated advice to the guardian's terminal and notifying them. This enables integrated management of the child's daily life data and the guardian's emotional data, enabling appropriate and personalized advice to be provided, and enabling prompt medical cooperation when an abnormality is detected.

[1330] A "guardian" is a person who is responsible for managing a child's health and daily routine.

[1331] "Data on a child's daily life" is information that indicates a child's health condition and daily rhythm, such as body temperature, diet, sleep time, and number of times they go to the toilet.

[1332] "Local storage" is a storage device that temporarily stores data inside a device such as a smartphone or tablet.

[1333] "When the Internet connection is stable" refers to a state in which the communication line is secured and data can be transmitted smoothly.

[1334] A "server" is a computer system that receives, processes, and stores data over a network.

[1335] A "temporary database" is a database for temporarily storing received data before analyzing it.

[1336] A "generative AI model" is an artificial intelligence algorithm that analyzes input data and detects patterns and anomalies.

[1337] "Emotion data" is information that indicates the emotional state of the guardian, and includes emotions such as "tired" or "relaxed," for example.

[1338] The "emotion recognition function" is a technology that automatically recognizes the emotional state of parents, and uses cameras and voice analysis.

[1339] The "emotion engine" is a system that analyzes input emotion data and classifies the parent's emotional state.

[1340] "Personalized advice" refers to specific instructions or suggestions that are individually generated based on the analysis results and the parent's emotional state.

[1341] "Notification" is a message delivery method for informing parents of important information and advice on their devices.

[1342] "Abnormal data" is data that indicates abnormal physical conditions or lifestyle patterns that exceed the normal range.

[1343] An "online consultation app" is a software application that allows you to receive consultations with doctors over the internet.

[1344] "Feedback" refers to information such as parents' evaluations, opinions, and impressions of the advice provided.

[1345] This invention is a system that includes a smartphone app for parents to input data about their children's daily lives, a server that receives and analyzes the data, and a system that provides personalized advice based on the analysis results.Furthermore, by combining this with an emotion engine that recognizes the user's emotions, it is possible to provide more appropriate advice.

[1346] Data collection and input

[1347] User

[1348] Parents enter data about their child's daily life into a smartphone app, such as their body temperature, what they eat, how much they sleep, and how often they go to the toilet. This data is used to record their child's daily health and lifestyle.

[1349] Device (smartphone app)

[1350] The smartphone app temporarily saves the data entered by the parent or guardian in local storage. Specifically, it saves the data using an SQLite database. When the internet connection is stable, it sends the saved data to the server in JSON format. If the transmission is successful, the data in local storage is deleted or the "transmission completed" flag is updated.

[1351] Data reception and analysis

[1352] server

[1353] The server receives data sent from the smartphone app through a dedicated endpoint (API). For example, it uses the endpoint "POST / data / receive." The received data is stored in a temporary database. MongoDB or MySQL is used as this database.

[1354] server

[1355] The server checks the consistency of the data stored in the database and performs preprocessing for analysis. Data whose consistency has been confirmed is input into the generative AI model for analysis. As a specific example, the prompt sentence "Check body temperature" is input into the generative AI model, and it determines whether the body temperature is outside the normal range (36.1 to 37.2 degrees Celsius).

[1356] Use of emotion engine

[1357] Device (smartphone app)

[1358] Parents can input their emotions using a smartphone app, or use the emotion recognition function to capture emotion data through the camera. For example, parents can input "I'm tired today," or the camera can automatically recognize emotions.

[1359] server

[1360] The server analyzes the received emotional data using an emotion engine, which classifies the parent's current emotional state, for example, as "high stress" or "relaxed."

[1361] Advice Generation and Notifications

[1362] server

[1363] The server combines the analysis results of the generative AI model with the results of the emotion engine to generate personalized advice for parents. For example, in addition to the advice, "Your child has a high fever. Drink plenty of fluids and check their temperature frequently," it also generates a message that takes into account their emotions, such as, "You are currently tired, so please refrain from overexerting yourself and consult a doctor."

[1364] server

[1365] The generated advice is sent to the parent's smartphone. The advice is added to a notification queue and sent via push notification using Firebase Cloud Messaging (FCM).

[1366] Device (smartphone app)

[1367] Advice received from the server is notified to parents and displayed in a pop-up or dedicated page within the app, with real-time push notifications used to ensure advice is displayed promptly.

[1368] Anomaly detection and medical collaboration

[1369] server

[1370] The server detects abnormal data from the analysis results of the generative AI model and generates a notification to direct the user to the online medical consultation app. For example, it generates a notification containing the content, "Your child appears to have a high fever. Please consult a doctor immediately."

[1371] server

[1372] The generated guidance notification is sent to the parent's device, including a link to the online consultation app.

[1373] Device (smartphone app)

[1374] The parent's device will display an abnormality notification and provide a link to an online medical consultation app, allowing the parent to quickly seek medical advice.

[1375] Gathering feedback and improving the system

[1376] User

[1377] Parents can enter their evaluations and opinions about the advice provided as feedback into a smartphone app, such as "The advice was helpful" or "I would like more specific instructions."

[1378] Device (smartphone app)

[1379] The feedback data is sent to the server.

[1380] server

[1381] The server receives the feedback and stores it in a database. The collected feedback is analyzed and used to improve the generative AI model and emotion engine. For example, adjusting the parameters of the generative AI model and emotion engine can improve the accuracy of the next data analysis and advice generation.

[1382] As described above, the present invention realizes a system that allows parents to manage and analyze their children's daily life data, providing appropriate and personalized advice and enabling rapid medical cooperation in the event of an abnormality.

[1383] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1384] Step 1: Data entry

[1385] User

[1386] Parents enter data about their child's daily life into a smartphone app, such as body temperature (e.g., "38.5 degrees"), diet (e.g., "curry rice"), sleep time (e.g., "8 hours"), and number of times they go to the toilet (e.g., "3 times").

[1387] Input: Child's daily life data (body temperature, diet, sleep time, number of toilet visits, etc.)

[1388] Output: Data entered into the input form in the smartphone app

[1389] Step 2: Temporarily save data

[1390] Device (smartphone app)

[1391] The smartphone app temporarily stores the data entered by the parent or guardian in local storage (e.g., SQLite database). For example, if "body temperature: 38.5 degrees" is entered, it is saved in the SQLite database.

[1392] Input: Data entered into the smartphone app

[1393] Output: Data saved in local storage

[1394] Step 3: Prepare to send data

[1395] Device (smartphone app)

[1396] Make sure you have a stable internet connection and convert the saved data into JSON format. For example, convert the input data "Body temperature: 38.5 degrees, Meal content: Curry rice" into the JSON format "{"Body temperature": "38.5 degrees", "Meal content": "Curry rice"}".

[1397] Input: Data stored in local storage

[1398] Output: JSON format data

[1399] Step 4: Sending data

[1400] Device (smartphone app)

[1401] The data converted to JSON format is sent to the server using the HTTPS protocol. If the sending is successful, the "Sent" flag is updated and the data in the local storage is deleted.

[1402] Input: JSON format data

[1403] Output: Data sent to server, data deleted from local storage

[1404] Step 5: Receiving the data

[1405] server

[1406] The server receives the data sent from the smartphone app via a dedicated endpoint (e.g., "POST / data / receive"). For example, it parses the received JSON data and extracts each data item.

[1407] Input: JSON data sent to the server

[1408] Output: Extracted data items

[1409] Step 6: Temporarily save data

[1410] server

[1411] The received data is stored in a temporary database. Specifically, data such as "body temperature: 38.5 degrees, meal contents: curry rice" is stored in a MongoDB or MySQL database.

[1412] Input: Extracted data items

[1413] Output: Data stored in a temporary database

[1414] Step 7: Check data integrity before analysis

[1415] server

[1416] Perform consistency checks and pre-process the data for analysis, for example, verifying that required fields are present and that the data is in the correct format.

[1417] Input: Data stored in a temporary database

[1418] Output: Data with integrity checked

[1419] Step 8: Analyze the data with a generative AI model

[1420] server

[1421] The data whose consistency has been confirmed is input into the generative AI model and analyzed. For example, the prompt "Check body temperature" is input, and the model determines whether "Body temperature: 38.5°C" is outside the normal range (36.1°C to 37.2°C).

[1422] Input: Data that has been verified for integrity, and the prompt "Temperature check"

[1423] Output: Analysis results from the generative AI model

[1424] Step 9: Enter emotion data

[1425] User

[1426] Parents can input their emotional state into a smartphone app, for example, by typing "I'm tired today," or the camera can be used to automatically recognize emotions.

[1427] Input: Emotion data (e.g., "I'm tired today")

[1428] Output: Emotion data entered into the input form in the smartphone app

[1429] Step 10: Sending Emotion Data

[1430] Device (smartphone app)

[1431] The input emotion data is sent to the server. For example, the emotion data "I'm tired today" is converted to JSON format and sent to the server.

[1432] Input: Emotion data

[1433] Output: Emotion data sent to the server

[1434] Step 11: Emotion Engine Analysis

[1435] server

[1436] The received emotional data is analyzed by an emotion engine to classify the parent's emotional state. For example, based on the emotional data "I'm tired today," it can be classified as "high stress."

[1437] Input: Emotion data received by the server

[1438] Output: Emotional state classified by the emotion engine

[1439] Step 12: Generating Advice

[1440] server

[1441] The analysis results of the generative AI model are combined with the results of the emotion engine to generate personalized advice. For example, it provides advice such as "Your child has a high fever. Drink plenty of fluids and check their temperature frequently," along with an emotionally sensitive message such as "You are currently tired, so please refrain from overexerting yourself and consult a doctor."

[1442] Input: Analysis results of the generative AI model, classification results of the emotion engine

[1443] Output: Personalized advice

[1444] Step 13: Submitting Advice

[1445] server

[1446] The generated personalized advice is sent to the parent's smartphone device, where it is added to a notification queue and a push notification is sent using Firebase Cloud Messaging (FCM), for example.

[1447] Input: Personalized advice

[1448] Output: Advice sent to parent's smartphone

[1449] Step 14: Viewing Advice

[1450] Device (smartphone app)

[1451] The advice received from the server is notified to the parent and displayed within the app, for example, as a pop-up notification or on a dedicated page.

[1452] Input: Advice sent by the server

[1453] Output: Advice displayed in the app

[1454] Step 15: Detect and notify abnormal data

[1455] server

[1456] The generative AI model detects abnormal data from the analysis results and generates a notification to direct users to an online medical consultation app. For example, it generates a notification saying, "Your child appears to have a high fever. Please consult a doctor immediately."

[1457] Input: Analysis results of the generative AI model

[1458] Output: Notification directing you to the online medical consultation app

[1459] Step 16: Send notification to guide users to the online consultation app

[1460] server

[1461] The generated notification is sent to the parent's smartphone, and includes a link to the online consultation app.

[1462] Input: Notification to direct you to the online medical consultation app

[1463] Output: Guidance notification sent to the parent's smartphone

[1464] Step 17: Displaying abnormality notifications

[1465] Device (smartphone app)

[1466] The system displays an abnormality notification on the parent's device and provides a link to an online medical consultation app, allowing the parent to quickly seek medical advice.

[1467] Input: Abnormal notification sent from the server

[1468] Output: Anomaly notification and link displayed in the app

[1469] Step 18: Enter your feedback

[1470] User

[1471] Parents can enter their evaluations and opinions about the advice provided as feedback into a smartphone app, such as "The advice was helpful" or "I would like more specific instructions."

[1472] Input: Feedback data

[1473] Output: Feedback data entered into the smartphone app

[1474] Step 19: Submit your feedback

[1475] Device (smartphone app)

[1476] The input feedback data is sent to the server.

[1477] Input: Feedback data entered into the smartphone app

[1478] Output: Feedback data sent to the server

[1479] Step 20: Storing and analyzing feedback

[1480] server

[1481] The received feedback is stored in a database and analyzed. The generative AI model and emotion engine are adjusted based on the analysis results. This improves the accuracy of the next data analysis and advice generation.

[1482] Input: Received feedback data

[1483] Output: Feedback data stored in the database, and tuning of generative AI models and emotion engines based on the analysis results.

[1484] The detailed processing steps described above realize a system that allows parents to manage their children's daily life data and provide appropriate and personalized advice.

[1485] (Application example 2)

[1486] 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."

[1487] It is necessary to provide personalized advice that takes into account not only the child's daily life data but also the parent's emotional state. It is also necessary to ensure the safety of children by quickly detecting abnormalities in a child's behavior or environment and prompting parents to take appropriate action. However, current systems do not support emotional data analysis or real-time emergency notifications, making it difficult to reduce parents' stress and anxiety.

[1488] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 2 is realized by the following means.

[1489] In this invention, the server includes means for a guardian to input data about the child's daily life, means for receiving the data and storing it in a database, means for analyzing the received data with a generative AI model, means for generating personalized advice for the guardian based on the analysis results, means for acquiring and analyzing emotional data from the guardian, means for reinforcing the personalized advice for the guardian based on the analyzed emotional data, and means for transmitting the generated advice to the guardian's terminal. This makes it possible to provide more appropriate and personalized advice by analyzing the data about the child's daily life and the guardian's emotional data, further ensuring the safety of the child and reducing stress for the guardian.

[1490] "Guardian" refers to an adult who is responsible for protecting a child's life and safety.

[1491] "Daily life data" refers to information about a child's daily life, such as their body temperature, meals, sleep, number of trips to the toilet, location information, and environmental data.

[1492] A "generative AI model" refers to the machine learning algorithm used to analyze collected data and make decisions.

[1493] "Personalized advice" refers to specific instructions and advice that are customized to the individual user's (parent's) situation and emotional state.

[1494] "Emotional data" refers to information that indicates the emotional state of the guardian, including, for example, the degree of stress or fatigue that the guardian is feeling.

[1495] "Database" refers to a system for temporarily or permanently storing collected data.

[1496] "Analysis tools" refers to the methods and devices used to analyze collected data and extract meaningful information.

[1497] "Notification means" refers to a mechanism for sending the generated advice to the parent's device and notifying them in real time.

[1498] An "online consultation app" refers to an application that allows users to consult with a doctor remotely.

[1499] This system analyzes a child's daily life data and a parent's emotional data to provide personalized advice. This system is composed of a smartphone app, a server, a generative AI model, and an emotion engine.

[1500] Data collection and input

[1501] User:

[1502] Parents enter data about their child's daily life into a smartphone app, such as body temperature, diet, sleep time, number of toilet visits, location information, and environmental data.

[1503] Device (smartphone app):

[1504] The smartphone app temporarily stores the data entered by the parent or guardian in local storage and sends it to the server when the internet connection is stable. If the data transmission is successful, the data in local storage is cleared or the transmission flag is updated.

[1505] Data reception and analysis

[1506] server:

[1507] The server receives data sent from the smartphone app and stores it in a database, where it is ready for analysis. The server then invokes a generative AI model built with TensorFlow to analyze the data, for example, to determine whether a child's temperature is above or below the normal range.

[1508] Acquiring and analyzing emotion data

[1509] User:

[1510] Parents can input emotional data into a smartphone app or use the camera to automatically recognize it.

[1511] Device (smartphone app):

[1512] The smartphone app uses the camera to capture a facial image of the parent or guardian and sends the image to a server on the internet.

[1513] server:

[1514] The server uses the Google Cloud Vision API to analyze the parent's emotions from the images sent, and the emotion engine classifies their status, such as stressed or relaxed.

[1515] Advice Generation and Notifications

[1516] server:

[1517] The analysis results of the generated AI model are combined with the results of the emotion engine to generate personalized advice. For example, in addition to advice such as "Your child has a high fever. Drink plenty of fluids and check their temperature frequently," it also generates emotionally sensitive messages such as "The parent is currently tired, so please refrain from overexerting yourself and consult a doctor."

[1518] server:

[1519] The generated advice is pushed to the parent's smartphone in real time, and the advice is added to a notification queue and notified to the parent.

[1520] Device (smartphone app):

[1521] Advice received from the server is notified to parents and displayed within the app. Advice is displayed in real time via push notifications.

[1522] Anomaly detection and medical collaboration

[1523] server:

[1524] If the analysis detects abnormal data, a notification will be generated directing users to the online medical consultation app. For example, the notification may include the message, "Your child appears to have a high fever. Please consult a doctor immediately."

[1525] server:

[1526] A notification message containing a link to the online consultation app will be sent to the parent's device.

[1527] Device (smartphone app):

[1528] An abnormality notification will be displayed to parents, along with a link to an online medical consultation app, allowing them to quickly seek medical advice.

[1529] Gathering feedback and improving the system

[1530] User:

[1531] Parents can enter their evaluations and opinions about the advice provided as feedback into a smartphone app, such as "The advice was helpful" or "I would like more specific instructions."

[1532] Device (smartphone app):

[1533] Send the feedback data to the server.

[1534] server:

[1535] Feedback is received and stored in a database. The collected feedback is used to improve the generative AI model and emotion engine. The feedback data is analyzed and the parameters of the generative AI model and emotion engine are adjusted, which improves the accuracy of the next data analysis and advice generation.

[1536] Specific examples

[1537] scenario:

[1538] If a parent goes missing from a park while the child is there.

[1539] 1. User: Parent types into app: "I can't find my child."

[1540] 2. Device: Sends location data and environmental data.

[1541] 3. Server: Analyze the child's location information (predict the latest location using a TensorFlow model).

[1542] 4. Emotion recognition: Parents submit photos of "anxious expressions" as emotional data.

[1543] 5. Server: Analyzes emotions and determines that the parent's stress level is high.

[1544] 6. Server: Generates the advice, "Your child may be on the east side of the park. Remain calm and call for help."

[1545] 7. Device: Display advice notification to parents.

[1546] Example prompt sentence:

[1547] "A child has gone missing from the park. Please suggest the best course of action based on the latest location data and the anxious expression of the parent."

[1548] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1549] Step 1:

[1550] The user uses a smartphone app to input data about their child's daily life (body temperature, meals, sleep time, number of toilet visits, location information, environmental data, etc.) as well as emotional data from the parent.

[1551] Input: Body temperature, meals, sleep time, toilet visits, location information, environmental data, emotional data

[1552] Output: The input data

[1553] Step 2:

[1554] The device (smartphone app) temporarily stores the data entered by the user in local storage and sends it to the server when the Internet connection is stable.

[1555] Input: Data stored in local storage

[1556] Output: Data sent to the server

[1557] Step 3:

[1558] The server receives the data sent from the smartphone app and stores it in a database.

[1559] Input: Data sent

[1560] Output: Data stored in a database

[1561] Step 4:

[1562] The server invokes a generative AI model built with TensorFlow to analyze the incoming data, for example, to determine whether a child's temperature is above or below the normal range.

[1563] Input: Data stored in a database

[1564] Output: Analysis results (e.g., abnormal body temperature detection)

[1565] Step 5:

[1566] Users can input emotional data into a smartphone app or use a camera to automatically recognize it.

[1567] Input: Emotion data or camera images

[1568] Output: Emotion data

[1569] Step 6:

[1570] The device (smartphone app) uses the camera to capture a facial image of the parent or guardian and sends the image to the server.

[1571] Input: Captured face image

[1572] Output: Face image sent to the server

[1573] Step 7:

[1574] The server uses the Google Cloud Vision API to analyze the parent's emotions from the images sent, and the emotion engine classifies their status, such as stressed or relaxed.

[1575] Input: Parent's face image

[1576] Output: Parent's emotional state (e.g., stress level)

[1577] Step 8:

[1578] The server combines the analysis results of the generated AI model with the results of the emotion engine to generate personalized advice.

[1579] Input: Analysis results and emotion data

[1580] Output: Personalized advice

[1581] Step 9:

[1582] The server pushes the generated advice to the parent's smartphone app in real time.

[1583] Input: Generated advice

[1584] Output: Advice notice sent to parent

[1585] Step 10:

[1586] The device (smartphone app) will push the advice received from the server to the parent and display it within the app.

[1587] Input: Advice received from the server

[1588] Output: Advice notice displayed to parent

[1589] Step 11:

[1590] If the server detects any abnormal data as a result of its analysis, it generates a notification directing the parent to the online medical consultation app and sends it to the parent's device. For example, it may include a message such as, "Your child appears to have a high fever. Please consult a doctor immediately."

[1591] Input: Analysis results of abnormal data

[1592] Output: Notification directing to online medical consultation app

[1593] Step 12:

[1594] The device (smartphone app) will display an abnormality notification to the parent and provide a link to the online medical examination app.

[1595] Input: Notification received from the server

[1596] Output: Abnormality notification displayed to the parent with a link to the online medical examination app

[1597] Step 13:

[1598] Users input their evaluations and opinions on the advice provided as feedback into the smartphone app.

[1599] Input: Feedback (e.g., "The advice was helpful")

[1600] Output: Feedback data

[1601] Step 14:

[1602] The device (smartphone app) sends feedback data to the server.

[1603] Input: User-entered feedback data

[1604] Output: Feedback data sent to the server

[1605] Step 15:

[1606] The server receives the feedback and stores it in a database. The collected feedback is used to improve the generative AI model and emotion engine. By analyzing the feedback and adjusting the parameters of the generative AI model, the accuracy of the next data analysis and advice generation will be improved.

[1607] Input: Feedback data

[1608] Output: Improved generative AI models and emotion engines

[1609] 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.

[1610] 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.

[1611] 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.

[1612] [Third embodiment]

[1613] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[1614] 5, the data processing system 310 includes the data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[1615] 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).

[1616] 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.

[1617] 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.

[1618] 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).

[1619] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for 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.

[1620] 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.

[1621] 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.

[1622] 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.

[1623] 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.

[1624] 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."

[1625] The present invention is a system that includes a smartphone app that allows parents to input data about their children's daily lives, a server that receives and analyzes the data, and a system that provides personalized advice based on the analysis results. Specific embodiments of this system are described below.

[1626] Data collection and input

[1627] User

[1628] Parents enter data about their child's daily life into a smartphone app. For example, they fill out an input form with data such as their child's morning temperature, what they eat, how much sleep they get, and how many times they go to the toilet. This data is used to record their child's daily health and lifestyle.

[1629] Device (smartphone app)

[1630] The smartphone app has a function that temporarily stores the data entered by the parent or guardian in local storage and sends it to the server when the Internet connection is stable. When the data transmission is successful, the data is deleted from local storage or the transmission flag is updated.

[1631] Data reception and analysis

[1632] server

[1633] The server receives the data sent from the smartphone app and stores it in a temporary database. The stored data is then analyzed by a generative AI model, which recognizes patterns in the child's daily life and determines whether there are any abnormalities.

[1634] server

[1635] The analysis results form the basis for advice provided to parents. For example, if a child's temperature exceeds the normal range (36.1 to 37.2 degrees Celsius), the system will determine that the child has a high fever and notify the parent of the appropriate course of action.

[1636] Advice Generation and Notifications

[1637] server

[1638] Based on the analysis results of the generative AI model, personalized advice is generated for parents. For example, if the child's sleep time is short, specific advice such as "Sufficient sleep is essential for your child's development. Please try to get them to bed early tonight" is generated.

[1639] server

[1640] The generated advice is sent to the parent's device and added to the notification queue.

[1641] Device (smartphone app)

[1642] Advice received from the server is notified to parents and displayed within the app. Information is provided in real time using push notifications, etc.

[1643] Anomaly detection and medical collaboration

[1644] server

[1645] If the analysis detects abnormal data, a notification will be generated directing users to an online medical consultation app. For example, if an abnormally high temperature persists, a notification will be generated stating, "Your child appears to have a high fever. Please consult a doctor immediately."

[1646] server

[1647] A notification message containing a link to the online consultation app will be sent to the parent's device.

[1648] Device (smartphone app)

[1649] An abnormality notification will be displayed to parents, along with a link to an online medical consultation app, allowing them to quickly seek medical advice.

[1650] Gathering feedback and improving the system

[1651] User

[1652] Parents can enter their evaluations and opinions about the advice provided as feedback into a smartphone app, such as "The advice was helpful" or "I would like more specific instructions."

[1653] Device (smartphone app)

[1654] Send feedback data to the server. Feedback is a key element in improving user experience.

[1655] server

[1656] Feedback is received and stored in a database. This feedback is used to improve the generative AI model. Specifically, it adjusts the model's parameters to improve the accuracy of the next analysis and advice generation.

[1657] Example: What to do if a child has a high temperature

[1658] User

[1659] The parent enters "This morning's temperature was 38.5 degrees" into a smartphone app.

[1660] Terminal

[1661] Sends input data to the server.

[1662] server

[1663] The data is received and stored in a database. At the same time, the generative AI model analyzes the body temperature data and determines whether the person has a high fever.

[1664] server

[1665] The system generates advice such as, "Your child has a high fever. Drink plenty of fluids and check their temperature frequently. If symptoms persist, consult a doctor," and sends it to the parent's device.

[1666] Terminal

[1667] Display an advisory notice to parents.

[1668] User

[1669] Parents will follow advice and ensure their children are hydrated and have their temperatures checked.

[1670] As described above, the present invention realizes a system that can solve the problems faced by parents raising children and effectively support the health of their children.

[1671] The processing flow will be explained below.

[1672] Step 1:

[1673] User

[1674] Parents enter data about their child's daily life into a smartphone app. Examples of input include "This morning's body temperature was 38.5 degrees" and "I slept for five hours last night."

[1675] Step 2:

[1676] Terminal

[1677] The smartphone app temporarily saves the entered data in local storage, so that the data is saved even if the connection is unstable.

[1678] Step 3:

[1679] Terminal

[1680] Ensure that your internet connection is stable, then send the data stored on the server. If the transmission is successful, delete the data in local storage or set a sent flag.

[1681] Step 4:

[1682] server

[1683] The server receives the data sent from the smartphone app and stores it in a temporary database, where it is ready for analysis.

[1684] Step 5:

[1685] server

[1686] A generative AI model is invoked to analyze the incoming data, for example to determine whether the body temperature is above the normal range (36.1°C to 37.2°C).

[1687] Step 6:

[1688] server

[1689] Based on the analysis results, the system checks for abnormal data. For example, if a body temperature of 38.5 degrees is determined to be abnormal, it will be treated as a "high fever."

[1690] Step 7:

[1691] server

[1692] Based on the analysis results, the system generates personalized advice for parents, such as a message saying, "Your child has a high fever. Drink plenty of fluids and check their temperature frequently."

[1693] Step 8:

[1694] server

[1695] The generated advice is sent to the parent's smartphone. The advice is added to the notification queue and sent.

[1696] Step 9:

[1697] Terminal

[1698] Advice received from the server is notified to parents in real time via push notifications and displayed within the app.

[1699] Step 10:

[1700] server

[1701] If abnormal data is detected during the analysis process, a notification will be generated directing users to an online medical consultation app, with a message such as, "Your child appears to have a high fever. Please consult a doctor immediately."

[1702] Step 11:

[1703] server

[1704] A notification message will be sent to the device containing a link to an online consultation app, allowing parents to quickly contact a medical institution.

[1705] Step 12:

[1706] Terminal

[1707] An abnormality notification will be displayed to parents, along with a link to an online consultation app, allowing them to quickly contact a doctor.

[1708] Step 13:

[1709] User

[1710] Review any advice or guidance provided and act as necessary, for example, by drinking fluids or rechecking your temperature.

[1711] Step 14:

[1712] User

[1713] Users can enter feedback about the advice and notifications into the smartphone app, such as "The advice was helpful" or "I would like more detailed explanations."

[1714] Step 15:

[1715] Terminal

[1716] The feedback data is sent to the server. If the sending is successful, a notification of feedback completion will be displayed in the app.

[1717] Step 16:

[1718] server

[1719] Receive feedback and store it in a database. The collected feedback is used to improve the generative AI model.

[1720] Step 17:

[1721] server

[1722] The feedback data is analyzed and the parameters of the generative AI model are adjusted to improve the accuracy of the next data analysis and advice generation.

[1723] Through these steps, the system can quickly and accurately analyze data on children's daily lives, provide optimal advice to parents, and continuously improve the system using feedback.

[1724] Example 1

[1725] 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."

[1726] Collecting data on children's daily lives and providing appropriate advice to parents regarding their health and daily rhythms based on that data is a crucial challenge. However, many parents are busy and find it difficult to continuously collect and analyze detailed data. Furthermore, when an abnormality occurs, they are required to make a prompt response, but they often lack the specialized knowledge to do so. To solve these challenges, there is a need to develop a system that allows parents to easily input daily data and automatically detects abnormalities and provides personalized advice based on that data.

[1727] 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.

[1728] In this invention, the server includes means for temporarily storing input data in local storage and transmitting it to the server when the internet connection is stable, means for the server to receive the transmitted data and store it in a database, and means for analyzing the stored data with a generative AI model. This allows parents to easily input daily data, and the generative AI model automatically analyzes the data based on the data, enabling anomaly detection and personalized advice to be provided in real time.

[1729] A "guardian" is an adult who is responsible for managing a child's daily life and health and entering data into the system.

[1730] "Children" are minors whose guardians aim to improve their health and lifestyle through the system.

[1731] "Data related to daily life" refers to data that reflects a child's daily health condition and lifestyle, and includes, for example, body temperature, dietary habits, sleep duration, and number of times they went to the toilet.

[1732] "Input means" refers to the interface and functions that allow parents to input data about their daily lives into the system using a smartphone app or other device.

[1733] "Local storage" refers to a storage device or memory for temporarily storing data on a device.

[1734] "Server" refers to a computer system that receives, stores, and analyzes data sent from the device, and generates and provides advice and notifications to parents.

[1735] "Database" refers to a system or software for storing and managing received data in a structured manner.

[1736] "Generative AI models" refer to artificial intelligence algorithms and machine learning models that analyze collected data and recognize patterns and detect anomalies in children's daily lives.

[1737] "Analysis means" refers to the process or method by which the server uses the generated AI model to analyze data and determine whether there are any abnormalities.

[1738] "Personalized advice" refers to information that provides appropriate countermeasures and suggestions to specific parents based on the analysis results of the generative AI model, depending on the child's health condition and lifestyle.

[1739] "Transmission means" refers to the communication functions and methods for sending advice and notifications from the server to the parent's device.

[1740] "Notification means" refers to the interface or function for displaying received advice or notifications on the parent's device and conveying information to the parent.

[1741] An "online medical consultation app" refers to an application or service that provides medical consultations and examinations online.

[1742] "Anomaly detection" refers to the discovery of unusual data patterns or anomalies by a generative AI model from the results of its analysis.

[1743] "Feedback" refers to information that parents submit by entering their thoughts, evaluations, and suggestions for improvement regarding the advice provided by the system.

[1744] "Data improvement measures" refers to methods and processes for improving the performance and accuracy of generative AI models based on parental feedback.

[1745] This system allows parents to input and manage data about their children's daily lives using a smartphone app, and provides personalized advice based on the analysis results. The system is designed to allow parents to easily input data and respond quickly if an abnormality is detected.

[1746] Specifically, the system includes the following elements:

[1747] Data collection and input

[1748] User

[1749] Parents open a smartphone app and enter data about their child's daily life, such as their body temperature each morning, what they eat, how much sleep they get, and how many times they go to the toilet. This data is important for recording their child's health and daily rhythms.

[1750] Device (smartphone app)

[1751] The smartphone app first temporarily stores the data entered by the parent in local storage (for example, using SQLite or Realm Database). When the Internet connection is stable, the app sends this data to the server. After the data is successfully sent, the app either deletes the data from local storage or updates a flag indicating that it has been sent.

[1752] Data reception and analysis

[1753] server

[1754] The server receives the data sent from the smartphone app and stores it in a temporary database (e.g., MySQL or PostgreSQL). After the data is stored, the generative AI model analyzes it. The generative AI model recognizes patterns in the child's daily life and determines whether there are any abnormalities.

[1755] For example, if a parent or guardian inputs "My temperature this morning was 38.5 degrees," this data is sent to the server. The server receives the data and stores it in a temporary database. The generative AI model analyzes this input data and determines that it is a high fever.

[1756] Advice Generation and Notifications

[1757] server

[1758] Based on the analysis results of the generative AI model, the server generates personalized advice for parents. For example, if the child has a high temperature, the server will generate specific advice such as, "Your child has a high fever. Drink plenty of fluids and check their temperature frequently. If symptoms persist, consult a doctor."

[1759] server

[1760] The generated advice is sent to the parent's device. The advice is added to a notification queue so that the parent can immediately know how to respond. For example, advice can be provided in real time using push notifications.

[1761] Device (smartphone app)

[1762] The parent's device will receive advice from the server and display it in a notification and within the app. Parents can check the notification and take appropriate action.

[1763] Anomaly detection and medical collaboration

[1764] server

[1765] If the analysis detects abnormal data, the server generates a notification directing the user to the online medical examination app. For example, if an abnormally high temperature of 39 degrees continues, a specific notification will be created stating, "Your child appears to have a high fever. Please consult a doctor immediately."

[1766] server

[1767] A notification message containing a link to the online consultation app will be sent to the parent's device, allowing them to quickly arrange a medical consultation.

[1768] Device (smartphone app)

[1769] An abnormality notification will be displayed to parents, who will be provided with a link to an online consultation app, allowing them to quickly access medical services.

[1770] Gathering feedback and improving the system

[1771] User

[1772] Parents can enter their evaluations and opinions about the advice provided as feedback into a smartphone app, such as "The advice was helpful" or "I would like more specific instructions."

[1773] Device (smartphone app)

[1774] The smartphone app sends the input feedback data to the server. The feedback data is also sent securely and is important as a means of improving the entire system.

[1775] server

[1776] The server receives the feedback data and stores it in a database. This feedback is used to improve the performance of the generative AI model by adjusting the model's parameters and improving the accuracy of the next analysis and advice generation.

[1777] Through a series of processes from data collection and analysis to advice generation and feedback, this system can solve the challenges faced by parents raising children and effectively support their children's health.

[1778] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1779] Step 1:

[1780] User

[1781] Parents open the smartphone app and enter data about their child's daily life. The data includes the child's body temperature, what they eat, how much they sleep, and how many times they go to the toilet. For example, they might enter information such as "body temperature: 36.8 degrees" and "sleep time: 8 hours." The entered data is temporarily stored in the smartphone's local storage.

[1782] Input: Data about the child's daily life

[1783] Output: Data saved in local storage

[1784] Step 2:

[1785] Device (smartphone app)

[1786] Once the smartphone app confirms that the internet connection is stable, it sends the data stored in local storage to the server. At this time, it packages the data in JSON format and sends it via an HTTP POST request. If the transmission is successful, it deletes the data from local storage or updates the sent flag.

[1787] Input: Data stored in local storage

[1788] Output: Data sent to the server

[1789] Step 3:

[1790] server

[1791] The server receives the data sent from the smartphone app and stores it in a temporary database. The data is then saved in a database such as MySQL or PostgreSQL. At this point, validation is performed to ensure that the data format and content are accurate.

[1792] Input: Data sent from the smartphone app

[1793] Output: Data stored in a temporary database

[1794] Step 4:

[1795] server

[1796] Once the data is stored in the temporary database, the server passes it to the generative AI model for analysis. During analysis, the generative AI model uses a dataset of learned patterns from a child's daily life to detect any abnormalities. For example, if "body temperature: 38.5 degrees" is entered, the generative AI model will determine that it is a high fever.

[1797] Input: Data stored in a temporary database

[1798] Output: Analysis results

[1799] Step 5:

[1800] server

[1801] Based on the analysis results of the generative AI model, the server generates personalized advice for parents. For example, if the child has a high temperature, the server generates specific advice such as, "Your child has a high fever. Drink plenty of fluids and check their temperature frequently. If symptoms persist, consult a doctor."

[1802] Input: Analysis results

[1803] Output: Personalized advice

[1804] Step 6:

[1805] server

[1806] The generated advice is sent to the parent's device. The server sends the generated advice to the parent's device via push notification, etc. The advice is added to the notification queue so that the parent can immediately know what to do.

[1807] Enter: personalized advice

[1808] Output: Advice sent to parent's device

[1809] Step 7:

[1810] Device (smartphone app)

[1811] The parent's device will receive the advice from the server and display it within the app. Real-time information is provided to parents via push notifications. Parents can then check the notifications and take appropriate action.

[1812] Input: Advice sent to parent's device

[1813] Output: Advice displayed to parents

[1814] Step 8:

[1815] server

[1816] If the analysis detects abnormal data, the server generates a notification directing the user to the online medical examination app. For example, if an abnormally high temperature of 39 degrees continues, a specific notification will be created stating, "Your child appears to have a high fever. Please consult a doctor immediately."

[1817] Input: Analysis results (abnormal data)

[1818] Output: Notification directing to online medical consultation app

[1819] Step 9:

[1820] server

[1821] A notification message containing a link to the online consultation app will be sent to the parent's device. When the parent clicks on the link, they will be taken directly to the online consultation app.

[1822] Input: Notification to direct you to the online medical consultation app

[1823] Output: A notification message with a link to the online consultation app.

[1824] Step 10:

[1825] Device (smartphone app)

[1826] A notification of an abnormality will be displayed to parents, who will be provided with a link to an online medical consultation app that they can tap to quickly access medical services.

[1827] Input: Notification message with link to online consultation app

[1828] Output: Anomaly notification and link displayed to the parent

[1829] Step 11:

[1830] User

[1831] Parents can enter their evaluation and opinions of the advice provided as feedback into a smartphone app, such as "The advice was helpful" or "I would like more specific instructions."

[1832] Input: Feedback on the advice

[1833] Output: Feedback entered into the smartphone app

[1834] Step 12:

[1835] Device (smartphone app)

[1836] The smartphone app sends the entered feedback data to the server, where it is also securely transmitted and made available.

[1837] Input: Feedback entered into the smartphone app

[1838] Output: Feedback data sent to the server

[1839] Step 13:

[1840] server

[1841] The server receives the feedback data and stores it in a database. This feedback is used to improve the performance of the generative AI model. Specifically, it is used to adjust the parameters of the generative AI model and improve the accuracy of the next analysis and advice generation.

[1842] Input: Feedback data sent to the server

[1843] Output: Feedback data stored in a database

[1844] The above is a specific processing flow of the system.

[1845] (Application example 1)

[1846] 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."

[1847] Conventional child health management systems monitor health conditions based on data entered by parents on a daily basis and provide necessary advice. However, if health data is not simply analyzed but a wide variety of purchasing activities and preference patterns are taken into account, it becomes possible to make more extensive lifestyle suggestions and appropriate product recommendations, further increasing user satisfaction. The objective of this invention is to support users' overall lifestyles by providing personalized product recommendations and shopping advice based on purchasing history and preferences, in addition to the conventional health management systems.

[1848] 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.

[1849] In this invention, the server includes: means for a guardian to input data about their child's daily life; means for receiving the data and storing it in a database; means for analyzing the received data with a generative AI model; means for generating personalized advice for the guardian based on the analysis results; means for transmitting the generated advice to the guardian's terminal; means for collecting user purchase data, browsing history, and reviews and transmitting them to a private server; means for analyzing the collected data with a generative AI model and generating personalized product recommendations and shopping advice; and means for notifying the user's terminal of the generated recommendations and advice. This makes it possible to provide not only health management for the user but also personalized product recommendations and shopping advice based on the user's purchase history.

[1850] A "guardian" is a parent or guardian in charge of the child's upbringing and health care.

[1851] "Data related to daily life" includes information such as a child's daily body temperature, diet, sleep time, and number of times they go to the toilet.

[1852] A "generative AI model" is an artificial intelligence system that uses machine learning algorithms to analyze data and recognize patterns and anomalies.

[1853] "Advice" refers to specific instructions or suggestions provided to parents based on the analysis results.

[1854] A "terminal" is an electronic device, such as a smartphone or tablet, through which a user enters data or receives advice.

[1855] "User" refers to the person who uses the system to input data and receive advice, primarily parents.

[1856] "Purchase data" refers to historical information about products purchased by a user.

[1857] "Browsing history" is a record of the products and pages a user has viewed online.

[1858] A "review" is information that describes the user's evaluation and impressions of a product they have purchased.

[1859] A "private server" is a computer system that is a dedicated server managed within a company or home and is used to collect and analyze data.

[1860] "Product recommendations" are new products suggested to users based on the analysis results.

[1861] "Shopping Advice" means specific shopping-related instructions or suggestions provided to a user based on their purchasing data and preferences.

[1862] "Push Notification API" is an application program interface for sending notifications from a server to a device in real time.

[1863] MODE FOR CARRYING OUT THE INVENTION

[1864] System configuration

[1865] The system of this invention includes a smartphone app where parents input data about their children's daily lives, a server that receives and analyzes the data, and a function that provides personalized advice based on the analysis results. It also collects users' purchasing data, browsing history, and reviews, and generates product recommendations and shopping advice based on these. The specific configuration and processing flow are described below.

[1866] 1. Data collection and input

[1867] User

[1868] Parents enter data about their children's daily lives into a smartphone app, such as their morning temperature, what they eat, how much sleep they get, and how many times they go to the toilet. Users also enter or allow shopping-related information, such as purchase data, browsing history, and reviews.

[1869] Device (smartphone app)

[1870] The smartphone app has a function to temporarily store data entered by parents or users in local storage and send it to the server when the Internet connection is stable. When the data transmission is successful, the data is deleted from local storage or the transmission flag is updated.

[1871] 2. Data Reception and Analysis

[1872] server

[1873] The server receives data sent from the smartphone app and stores it in a temporary database. The stored data is then analyzed by a generative AI model. The generative AI model recognizes patterns in the child's daily life and determines whether there are any abnormalities. Similarly, it analyzes shopping data to understand the user's preferences and purchasing patterns.

[1874] server

[1875] The analysis results are the basis for providing advice to parents and users. For example, if a child's temperature exceeds the normal range (36.1 to 37.2 degrees Celsius), the system will determine that the child has a high fever and notify the parent or guardian of the appropriate course of action. At the same time, it will also recommend new products based on the user's purchasing history.

[1876] 3. Advice Generation and Notification

[1877] server

[1878] Based on the analysis results of the generative AI model, personalized advice is generated for parents and users. For example, if a child's sleep time is short, specific advice such as "Sufficient sleep is essential for your child's development. Make sure your child goes to bed early tonight" is generated. It also analyzes the user's purchasing history to generate shopping advice such as "We recommend this conditioner as a related product to the shampoo you recently purchased."

[1879] server

[1880] The generated advice is sent to the parent or user's device and added to a notification queue.

[1881] Device (smartphone app)

[1882] Advice received from the server is notified to parents and users and displayed within the app. Push notifications are used to provide information in real time.

[1883] 4. Anomaly Detection and Medical Collaboration

[1884] server

[1885] If the analysis detects abnormal data, a notification will be generated directing users to an online medical consultation app. For example, if an abnormally high temperature persists, a notification will be generated stating, "Your child appears to have a high fever. Please consult a doctor immediately."

[1886] server

[1887] A notification message containing a link to the online consultation app will be sent to the parent's device.

[1888] Device (smartphone app)

[1889] An abnormality notification will be displayed to parents, along with a link to an online medical consultation app, allowing them to quickly seek medical advice.

[1890] 5. Gathering feedback and improving the system

[1891] User

[1892] Parents and users can enter their evaluations and opinions of the advice provided as feedback into the smartphone app, such as "The advice was helpful" or "I would like more specific instructions."

[1893] Device (smartphone app)

[1894] Send feedback data to the server. Feedback is a key element in improving user experience.

[1895] server

[1896] Feedback is received and stored in a database. This feedback is used to improve the generative AI model. Specifically, it adjusts the model's parameters to improve the accuracy of the next analysis and advice generation.

[1897] Example: What to do if a child has a high temperature

[1898] User

[1899] The parent enters "This morning's temperature was 38.5 degrees" into a smartphone app.

[1900] Terminal

[1901] Sends input data to the server.

[1902] server

[1903] The data is received and stored in a database. At the same time, the generative AI model analyzes the body temperature data and determines whether the person has a high fever.

[1904] server

[1905] The system generates advice such as, "Your child has a high fever. Drink plenty of fluids and check their temperature frequently. If symptoms persist, consult a doctor," and sends it to the parent's device.

[1906] Terminal

[1907] Display an advisory notice to parents.

[1908] User

[1909] Parents will follow advice and ensure their children are hydrated and have their temperatures checked.

[1910] Example: Providing shopping advice

[1911] User

[1912] A user is happy with the shampoo they recently purchased and posts a review within a smartphone app.

[1913] Terminal

[1914] Purchase history, browsing history, and reviews are sent to the server.

[1915] server

[1916] The data is received and stored in a database, while the generative AI model analyzes the purchasing data and recognizes user preferences.

[1917] server

[1918] A shopping advice such as "We recommend this conditioner as a related product to the shampoo you recently purchased" is generated and sent to the user's device.

[1919] Terminal

[1920] Display an advisory notice to the user.

[1921] Prompt Sentence Examples

[1922] Examples of prompts include:

[1923] "Generate product recommendations based on user purchasing history and preferences."

[1924] "Recommend items from this week's new products list that are similar to items the user has previously purchased."

[1925] In this way, the present invention realizes a system that not only manages the user's health but also supports their overall lifestyle by providing personalized product recommendations and shopping advice based on their purchasing history.

[1926] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1927] Specific processing steps of the system

[1928] Program processing steps

[1929] Step 1: Data entry

[1930] 1. Users

[1931] Parents enter data about their children's daily lives (body temperature, diet, sleep time, number of times they went to the toilet, etc.) into a smartphone app. Users also enter shopping-related information (purchase data, browsing history, reviews) into the app.

[1932] Input: Children's daily life data, shopping-related data

[1933] Output: Save data to local storage

[1934] Step 2: Send data

[1935] 2. Device (smartphone app)

[1936] The app temporarily stores the data entered by the parent or user in local storage and sends it to the server when the internet connection is stable. If the transmission is successful, the data in local storage is deleted or the transmission flag is updated.

[1937] Input: Data stored in local storage

[1938] Output: Send data to the server

[1939] Step 3: Receiving and storing data

[1940] 3. Server

[1941] The server receives the data sent from the smartphone app and stores it in a temporary database, where data from parents and user purchasing data are aggregated.

[1942] Input: Data sent from the terminal

[1943] Output: Data saved to a temporary database

[1944] Step 4: Data analysis

[1945] 4. Server

[1946] The incoming data is analyzed by a generative AI model that recognizes patterns and anomalies in the child's daily life and generates personalized recommendations based on the shopping data.

[1947] Input: Data in the temporary database

[1948] Output: Analysis results (anomaly detection, product recommendations)

[1949] Step 5: Advice Generation

[1950] 5. Server

[1951] The server generates advice to provide to parents and users based on the analysis results, such as notifying appropriate measures if the user has a high body temperature, or recommending related products based on the user's purchasing history.

[1952] Input: Analysis results

[1953] Output: Advice generation (health advice, product recommendations)

[1954] Step 6: Advice Notification

[1955] 6. Server

[1956] The generated advice is sent to the user's device and added to a notification queue.

[1957] Input: Generated advice

[1958] Output: Advice sent to terminal

[1959] Step 7: Notifications

[1960] 7. Device (smartphone app)

[1961] The device notifies the user of the advice received from the server and displays it within the app, providing real-time information using a push notification API.

[1962] Input: Advice sent by the server

[1963] Output: Notification displayed to the user

[1964] Step 8: Gather feedback

[1965] 8. Users

[1966] Parents and users enter feedback on the advice into the app and send the data to the server.

[1967] Input: Feedback data

[1968] Output: Send feedback to the server

[1969] Step 9: Model Improvement

[1970] 9. Server

[1971] The server receives the feedback and uses it as data to improve the generative AI model, adjusting the model's parameters to improve the accuracy of the next analysis and advice generation.

[1972] Input: Feedback data

[1973] Output: Parameter updates for the model

[1974] Processing flow based on a specific example

[1975] Example: If your child has a high temperature

[1976] User

[1977] The parent enters "This morning's temperature was 38.5 degrees" into a smartphone app.

[1978] Input: Body temperature data (38.5°C)

[1979] Output: Save to local storage

[1980] Terminal

[1981] Sends input data to the server.

[1982] Input: Stored body temperature data

[1983] Output: Send data to the server

[1984] server

[1985] The data is received and stored in a database. At the same time, the generative AI model analyzes the body temperature data and determines whether the person has a high fever.

[1986] Input: Received body temperature data

[1987] Output: Analysis results (high temperature)

[1988] server

[1989] Generates advice like, "Your child has a high fever. Drink plenty of fluids and check their temperature frequently. If symptoms persist, consult a doctor."

[1990] Input: High temperature analysis results

[1991] output: Advice generation

[1992] server

[1993] The generated advice is sent to the parent's device.

[1994] Input: Advice data

[1995] Output: sent to terminal

[1996] Terminal

[1997] Display an advisory notice to parents.

[1998] Input: Advice data

[1999] Output: Parental Notice

[2000] User

[2001] Parents will follow advice and ensure their children are hydrated and have their temperatures checked.

[2002] 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.

[2003] The present invention provides a smartphone app that allows parents to input data about their children's daily lives, a server that receives and analyzes the data, and a system that provides personalized advice based on the analysis results. Furthermore, by combining this system with an emotion engine that recognizes the user's emotions, more appropriate advice can be provided. A specific embodiment of this system is described below.

[2004] Data collection and input

[2005] User

[2006] Parents enter data about their child's daily life into a smartphone app, such as their body temperature, what they eat, how much they sleep, and how often they go to the toilet. This data is used to record their child's daily health and lifestyle.

[2007] Device (smartphone app)

[2008] The smartphone app temporarily stores the data entered by the parent or guardian in local storage and sends it to the server when the internet connection is stable. If the data transmission is successful, the data in local storage is deleted or the transmission flag is updated.

[2009] Data reception and analysis

[2010] server

[2011] The server receives the data sent from the smartphone app and stores it in a temporary database, where it is ready for analysis.

[2012] server

[2013] A generative AI model is invoked to analyze the incoming data, for example to determine whether the body temperature is above the normal range (36.1°C to 37.2°C).

[2014] Use of emotion engine

[2015] Device (smartphone app)

[2016] Parents can input their own emotions or use emotion recognition to capture emotion data. For example, they can input "I feel tired today" or use the camera to automatically recognize emotions.

[2017] server

[2018] The emotion engine analyzes the received emotion data and classifies the parent's current emotional state, for example, as "high stress" or "relaxed."

[2019] Advice Generation and Notifications

[2020] server

[2021] The analysis results of the generative AI model are combined with the results of the emotion engine to generate personalized advice for parents. For example, in addition to the advice, "Your child has a high fever. Drink plenty of fluids and check their temperature frequently," a message that takes into consideration emotions such as, "The parent is currently tired, so please refrain from overexerting yourself and consult a doctor" is created.

[2022] server

[2023] The generated advice is sent to the parent's smartphone. The advice is added to the notification queue and sent.

[2024] Device (smartphone app)

[2025] Advice received from the server is notified to parents and displayed within the app. Advice is displayed in real time using push notifications.

[2026] Anomaly detection and medical collaboration

[2027] server

[2028] If the analysis detects abnormal data, a notification will be generated directing the user to the online medical consultation app. For example, the notification may include the message, "Your child appears to have a high fever. Please consult a doctor immediately."

[2029] server

[2030] A notification message containing a link to the online consultation app will be sent to the parent's device.

[2031] Device (smartphone app)

[2032] An abnormality notification will be displayed to parents, along with a link to an online medical consultation app, allowing them to quickly seek medical advice.

[2033] Gathering feedback and improving the system

[2034] User

[2035] Parents can enter their evaluations and opinions about the advice provided as feedback into a smartphone app, such as "The advice was helpful" or "I would like more specific instructions."

[2036] Device (smartphone app)

[2037] Send the feedback data to the server.

[2038] server

[2039] Receive feedback and store it in a database. The collected feedback is used to improve the generative AI model and emotion engine.

[2040] server

[2041] The feedback data is analyzed and the parameters of the generative AI model and emotion engine are adjusted to improve the accuracy of the next data analysis and advice generation.

[2042] Example: What to do if a child has a high temperature

[2043] User

[2044] The parent enters "This morning's temperature was 38.5 degrees" into a smartphone app.

[2045] Terminal

[2046] Sends input data to the server.

[2047] server

[2048] The data is received and stored in a database. At the same time, the generative AI model analyzes the body temperature data and determines whether the person has a high fever.

[2049] Terminal

[2050] The parent enters emotional data such as "I'm tired today."

[2051] server

[2052] The emotion engine analyzes the emotional data and determines that the person is under high stress.

[2053] server

[2054] In addition to the advice "Your child has a high fever. Drink plenty of fluids and check their temperature frequently," the system also generates advice such as "Your parent is currently tired, so please refrain from pushing yourself and consult a doctor," and sends this to the parent's device.

[2055] Terminal

[2056] Display an advisory notice to parents.

[2057] User

[2058] Parents should follow advice to keep their child hydrated, recheck their temperature, and consult a doctor if necessary.

[2059] As described above, the present invention realizes a system that solves the problems faced by parents raising children, effectively supports the health of their children, and provides appropriate advice according to the emotional state of the parents.

[2060] The processing flow will be explained below.

[2061] Step 1:

[2062] User

[2063] Parents enter data about their child's daily life into a smartphone app, for example, filling out an input form with information such as "This morning's body temperature was 38.5 degrees" or "I slept for five hours last night."

[2064] Step 2:

[2065] Terminal

[2066] The smartphone app temporarily stores the entered data in local storage, which allows the data to be saved even if the internet connection is unstable.

[2067] Step 3:

[2068] Terminal

[2069] Ensure that your internet connection is stable, then send the data stored on the server. If the transmission is successful, delete the data in local storage or set a sent flag.

[2070] Step 4:

[2071] server

[2072] The server receives the data sent from the smartphone app and stores it in a temporary database, where it is ready for analysis.

[2073] Step 5:

[2074] server

[2075] A generative AI model is invoked to analyze the incoming data, for example to determine whether the body temperature is above the normal range (36.1°C to 37.2°C).

[2076] Step 6:

[2077] server

[2078] Based on the analysis results, the system checks for abnormal data. For example, if a body temperature of 38.5 degrees is determined to be abnormal, it will be treated as a "high fever."

[2079] Step 7:

[2080] Terminal

[2081] Parents can input their own emotions or use emotion recognition to capture emotion data. For example, they can input "I feel tired today" or use the camera to automatically recognize emotions.

[2082] Step 8:

[2083] server

[2084] The emotion engine receives the emotion data and classifies the parent's emotional state, for example, as "high stress" or "relaxed."

[2085] Step 9:

[2086] server

[2087] The analysis results of the generative AI model are combined with the results of the emotion engine to generate personalized advice for parents. For example, in addition to the advice, "Your child has a high fever. Drink plenty of fluids and check their temperature frequently," a message such as, "The parent is currently tired, so please refrain from overexerting yourself and consult a doctor" can be generated.

[2088] Step 10:

[2089] server

[2090] The generated advice is sent to the parent's smartphone. The advice is added to the notification queue and sent.

[2091] Step 11:

[2092] Terminal

[2093] Advice received from the server is notified to parents and displayed within the app. Advice is displayed in real time using push notifications.

[2094] Step 12:

[2095] server

[2096] If the analysis detects abnormal data, a notification will be generated directing the user to the online medical consultation app. For example, the notification may include the message, "Your child appears to have a high fever. Please consult a doctor immediately."

[2097] Step 13:

[2098] server

[2099] A notification message containing a link to the online consultation app will be sent to the parent's device.

[2100] Step 14:

[2101] Terminal

[2102] An abnormality notification will be displayed to parents, along with a link to an online medical consultation app, allowing them to quickly seek medical advice.

[2103] Step 15:

[2104] User

[2105] Review any advice or guidance provided and act as necessary, for example, by drinking fluids or rechecking your temperature.

[2106] Step 16:

[2107] User

[2108] Users can enter feedback about the advice and notifications into the smartphone app, such as "The advice was helpful" or "I would like more detailed explanations."

[2109] Step 17:

[2110] Terminal

[2111] The feedback data is sent to the server. If the sending is successful, a notification of feedback completion will be displayed in the app.

[2112] Step 18:

[2113] server

[2114] Receive feedback and store it in a database. The collected feedback is used to improve the generative AI model and emotion engine.

[2115] Step 19:

[2116] server

[2117] The feedback data is analyzed and the parameters of the generative AI model and emotion engine are adjusted to improve the accuracy of the next data analysis and advice generation.

[2118] Through these steps, the system can quickly and accurately analyze data on children's daily lives and parents' emotions, provide optimal advice, and continuously improve the system using feedback.

[2119] Example 2

[2120] 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."

[2121] In modern child-rearing, parents must devote a great deal of time and effort to managing their children's daily health and keeping track of their lifestyle. Emotional care is also important, as the parents' own emotional state directly impacts the quality of their child-rearing. However, there is currently a lack of systems that can centrally manage these and provide appropriate advice. Furthermore, it is also necessary to respond to situations that require early detection of abnormal data and prompt medical consultation.

[2122] The identification process by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means. In this invention, the server includes: a means for a guardian to input data about the child's daily life; a means for temporarily saving the data in local storage and transmitting it to the server when the Internet connection is stable; a means for receiving the data and storing it in a temporary database; a means for analyzing the received data using a generative AI model; a means for the guardian to input emotional data or acquire it using an emotion recognition function; a means for analyzing the received emotional data using an emotion engine and classifying the guardian's emotional state; a means for combining the analysis results of the generative AI model and the results of the emotion engine to generate personalized advice; and a means for transmitting the generated advice to the guardian's terminal and notifying the guardian. This enables integrated management of the child's daily life data and the guardian's emotional data, enabling appropriate and personalized advice to be provided, and enabling prompt medical cooperation when an abnormality is detected.

[2123] A "guardian" is a person who is responsible for managing a child's health and daily routine.

[2124] "Data on a child's daily life" is information that indicates a child's health condition and daily rhythm, such as body temperature, diet, sleep time, and number of times they go to the toilet.

[2125] "Local storage" is a storage device that temporarily stores data inside a device such as a smartphone or tablet.

[2126] "When the Internet connection is stable" refers to a state in which the communication line is secured and data can be transmitted smoothly.

[2127] A "server" is a computer system that receives, processes, and stores data over a network.

[2128] A "temporary database" is a database for temporarily storing received data before analyzing it.

[2129] A "generative AI model" is an artificial intelligence algorithm that analyzes input data and detects patterns and anomalies.

[2130] "Emotion data" is information that indicates the emotional state of the guardian, and includes emotions such as "tired" or "relaxed," for example.

[2131] The "emotion recognition function" is a technology that automatically recognizes the emotional state of parents, and uses cameras and voice analysis.

[2132] The "emotion engine" is a system that analyzes input emotion data and classifies the parent's emotional state.

[2133] "Personalized advice" refers to specific instructions or suggestions that are individually generated based on the analysis results and the parent's emotional state.

[2134] "Notification" is a message delivery method for informing parents of important information and advice on their devices.

[2135] "Abnormal data" is data that indicates abnormal physical conditions or lifestyle patterns that exceed the normal range.

[2136] An "online consultation app" is a software application that allows you to receive consultations with doctors over the internet.

[2137] "Feedback" refers to information such as parents' evaluations, opinions, and impressions of the advice provided.

[2138] This invention is a system that includes a smartphone app for parents to input data about their children's daily lives, a server that receives and analyzes the data, and a system that provides personalized advice based on the analysis results.Furthermore, by combining this with an emotion engine that recognizes the user's emotions, it is possible to provide more appropriate advice.

[2139] Data collection and input

[2140] User

[2141] Parents enter data about their child's daily life into a smartphone app, such as their body temperature, what they eat, how much they sleep, and how often they go to the toilet. This data is used to record their child's daily health and lifestyle.

[2142] Device (smartphone app)

[2143] The smartphone app temporarily saves the data entered by the parent or guardian in local storage. Specifically, it saves the data using an SQLite database. When the internet connection is stable, it sends the saved data to the server in JSON format. If the transmission is successful, the data in local storage is deleted or the "transmission completed" flag is updated.

[2144] Data reception and analysis

[2145] server

[2146] The server receives data sent from the smartphone app through a dedicated endpoint (API). For example, it uses the endpoint "POST / data / receive." The received data is stored in a temporary database. MongoDB or MySQL is used as this database.

[2147] server

[2148] The server checks the consistency of the data stored in the database and performs preprocessing for analysis. Data whose consistency has been confirmed is input into the generative AI model for analysis. As a specific example, the prompt sentence "Check body temperature" is input into the generative AI model, and it determines whether the body temperature is outside the normal range (36.1 to 37.2 degrees Celsius).

[2149] Use of emotion engine

[2150] Device (smartphone app)

[2151] Parents can input their emotions using a smartphone app, or use the emotion recognition function to capture emotion data through the camera. For example, parents can input "I'm tired today," or the camera can automatically recognize emotions.

[2152] server

[2153] The server analyzes the received emotional data using an emotion engine, which classifies the parent's current emotional state, for example, as "high stress" or "relaxed."

[2154] Advice Generation and Notifications

[2155] server

[2156] The server combines the analysis results of the generative AI model with those of the emotion engine to generate personalized advice for parents. For example, in addition to the advice, "Your child has a high fever. Drink plenty of fluids and check their temperature frequently," it also generates a message that takes into account their emotions, such as, "You are currently tired, so please refrain from overexerting yourself and consult a doctor."

[2157] server

[2158] The generated advice is sent to the parent's smartphone. The advice is added to a notification queue and sent via push notification using Firebase Cloud Messaging (FCM).

[2159] Device (smartphone app)

[2160] Advice received from the server is notified to parents and displayed in a pop-up or dedicated page within the app, with real-time push notifications used to ensure advice is displayed promptly.

[2161] Anomaly detection and medical collaboration

[2162] server

[2163] The server detects abnormal data from the analysis results of the generative AI model and generates a notification directing the user to the online medical examination app. For example, it generates a notification containing the content, "Your child appears to have a high fever. Please consult a doctor immediately."

[2164] server

[2165] The generated guidance notification is sent to the parent's device, including a link to the online consultation app.

[2166] Device (smartphone app)

[2167] The parent's device will display an abnormality notification and provide a link to an online medical consultation app, allowing the parent to quickly seek medical advice.

[2168] Gathering feedback and improving the system

[2169] User

[2170] Parents can enter their evaluations and opinions about the advice provided as feedback into a smartphone app, such as "The advice was helpful" or "I would like more specific instructions."

[2171] Device (smartphone app)

[2172] The feedback data is sent to the server.

[2173] server

[2174] The server receives the feedback and stores it in a database. The collected feedback is analyzed and used to improve the generative AI model and emotion engine. For example, adjusting the parameters of the generative AI model and emotion engine can improve the accuracy of the next data analysis and advice generation.

[2175] As described above, the present invention realizes a system that allows parents to manage and analyze their children's daily life data, providing appropriate and personalized advice and enabling rapid medical cooperation in the event of an abnormality.

[2176] The flow of the identification process in the second embodiment will be described with reference to FIG.

[2177] Step 1: Data entry

[2178] User

[2179] Parents enter data about their child's daily life into a smartphone app, such as body temperature (e.g., "38.5 degrees"), diet (e.g., "curry rice"), sleep time (e.g., "8 hours"), and number of times they go to the toilet (e.g., "3 times").

[2180] Input: Child's daily life data (body temperature, diet, sleep time, number of toilet visits, etc.)

[2181] Output: Data entered into the input form in the smartphone app

[2182] Step 2: Temporarily save data

[2183] Device (smartphone app)

[2184] The smartphone app temporarily stores the data entered by the parent or guardian in local storage (e.g., SQLite database). For example, if "body temperature: 38.5 degrees" is entered, it is saved in the SQLite database.

[2185] Input: Data entered into the smartphone app

[2186] Output: Data saved in local storage

[2187] Step 3: Prepare to send data

[2188] Device (smartphone app)

[2189] Make sure you have a stable internet connection and convert the saved data into JSON format. For example, convert the input data "Body temperature: 38.5 degrees, Meal content: Curry rice" into the JSON format "{"Body temperature": "38.5 degrees", "Meal content": "Curry rice"}".

[2190] Input: Data stored in local storage

[2191] Output: JSON format data

[2192] Step 4: Sending data

[2193] Device (smartphone app)

[2194] The data converted to JSON format is sent to the server using the HTTPS protocol. If the sending is successful, the "Sent" flag is updated and the data in the local storage is deleted.

[2195] Input: JSON format data

[2196] Output: Data sent to server, data deleted from local storage

[2197] Step 5: Receiving the data

[2198] server

[2199] The server receives the data sent from the smartphone app via a dedicated endpoint (e.g., "POST / data / receive"). For example, it parses the received JSON data and extracts each data item.

[2200] Input: JSON data sent to the server

[2201] Output: Extracted data items

[2202] Step 6: Temporarily save data

[2203] server

[2204] The received data is stored in a temporary database. Specifically, data such as "body temperature: 38.5 degrees, meal contents: curry rice" is stored in a MongoDB or MySQL database.

[2205] Input: Extracted data items

[2206] Output: Data stored in a temporary database

[2207] Step 7: Check data integrity before analysis

[2208] server

[2209] Perform consistency checks and pre-process the data for analysis, for example, verifying that required fields are present and that the data is in the correct format.

[2210] Input: Data stored in a temporary database

[2211] Output: Data with integrity checked

[2212] Step 8: Analyze the data with a generative AI model

[2213] server

[2214] The data whose consistency has been confirmed is input into the generative AI model and analyzed. For example, the prompt "Check body temperature" is input, and the model determines whether "Body temperature: 38.5°C" is outside the normal range (36.1°C to 37.2°C).

[2215] Input: Integrity-checked data and the prompt "Temperature check"

[2216] Output: Analysis results from the generative AI model

[2217] Step 9: Enter emotion data

[2218] User

[2219] Parents can input their emotional state into a smartphone app, for example, by typing "I'm tired today," or the camera can be used to automatically recognize emotions.

[2220] Input: Emotion data (e.g., "I'm tired today")

[2221] Output: Emotion data entered into the input form in the smartphone app

[2222] Step 10: Sending Emotion Data

[2223] Device (smartphone app)

[2224] The input emotion data is sent to the server. For example, the emotion data "I'm tired today" is converted to JSON format and sent to the server.

[2225] Input: Emotion data

[2226] Output: Emotion data sent to the server

[2227] Step 11: Emotion Engine Analysis

[2228] server

[2229] The received emotional data is analyzed by an emotion engine to classify the parent's emotional state. For example, the emotional data "I'm tired today" can be classified as "high stress."

[2230] Input: Emotion data received by the server

[2231] Output: Emotional state classified by the emotion engine

[2232] Step 12: Generating Advice

[2233] server

[2234] The analysis results of the generative AI model are combined with the results of the emotion engine to generate personalized advice. For example, it provides advice such as "Your child has a high fever. Drink plenty of fluids and check their temperature frequently," along with an emotionally sensitive message such as "You are currently tired, so please refrain from overexerting yourself and consult a doctor."

[2235] Input: Analysis results of the generative AI model, classification results of the emotion engine

[2236] Output: Personalized advice

[2237] Step 13: Submitting Advice

[2238] server

[2239] The generated personalized advice is sent to the parent's smartphone device, where it is added to a notification queue and a push notification is sent using Firebase Cloud Messaging (FCM), for example.

[2240] Input: Personalized advice

[2241] Output: Advice sent to parent's smartphone

[2242] Step 14: Viewing Advice

[2243] Device (smartphone app)

[2244] The advice received from the server is notified to the parent and displayed within the app, for example, as a pop-up notification or on a dedicated page.

[2245] Input: Advice sent by the server

[2246] Output: Advice displayed in the app

[2247] Step 15: Detect and notify abnormal data

[2248] server

[2249] The generative AI model detects abnormal data from the analysis results and generates a notification to direct users to an online medical consultation app. For example, it generates a notification saying, "Your child appears to have a high fever. Please consult a doctor immediately."

[2250] Input: Analysis results of the generative AI model

[2251] Output: Notification directing you to the online medical consultation app

[2252] Step 16: Send notification to guide users to the online consultation app

[2253] server

[2254] The generated notification is sent to the parent's smartphone, and includes a link to the online consultation app.

[2255] Input: Notification to direct you to the online medical consultation app

[2256] Output: Guidance notification sent to the parent's smartphone

[2257] Step 17: Displaying abnormality notifications

[2258] Device (smartphone app)

[2259] The system displays an abnormality notification on the parent's device and provides a link to an online medical consultation app, allowing the parent to quickly seek medical advice.

[2260] Input: Abnormal notification sent from the server

[2261] Output: Anomaly notification and link displayed in the app

[2262] Step 18: Enter your feedback

[2263] User

[2264] Parents can enter their evaluations and opinions about the advice provided as feedback into a smartphone app, such as "The advice was helpful" or "I would like more specific instructions."

[2265] Input: Feedback data

[2266] Output: Feedback data entered into the smartphone app

[2267] Step 19: Submit your feedback

[2268] Device (smartphone app)

[2269] The input feedback data is sent to the server.

[2270] Input: Feedback data entered into the smartphone app

[2271] Output: Feedback data sent to the server

[2272] Step 20: Storing and analyzing feedback

[2273] server

[2274] The received feedback is stored in a database and analyzed. The generative AI model and emotion engine are adjusted based on the analysis results. This improves the accuracy of the next data analysis and advice generation.

[2275] Input: Received feedback data

[2276] Output: Feedback data stored in the database, and tuning of generative AI models and emotion engines based on the analysis results.

[2277] The detailed processing steps described above realize a system that allows parents to manage their children's daily life data and provide appropriate and personalized advice.

[2278] (Application example 2)

[2279] 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."

[2280] It is necessary to provide personalized advice that takes into account not only the child's daily life data but also the parent's emotional state. It is also necessary to ensure the safety of children by quickly detecting abnormalities in a child's behavior or environment and prompting parents to take appropriate action. However, current systems do not support emotional data analysis or real-time emergency notifications, making it difficult to reduce parents' stress and anxiety.

[2281] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 2 is realized by the following means.

[2282] In this invention, the server includes means for a guardian to input data about the child's daily life, means for receiving the data and storing it in a database, means for analyzing the received data with a generative AI model, means for generating personalized advice for the guardian based on the analysis results, means for acquiring and analyzing emotional data from the guardian, means for reinforcing the personalized advice for the guardian based on the analyzed emotional data, and means for transmitting the generated advice to the guardian's terminal. This makes it possible to provide more appropriate and personalized advice by analyzing the data about the child's daily life and the guardian's emotional data, further ensuring the safety of the child and reducing stress for the guardian.

[2283] "Guardian" refers to an adult who is responsible for protecting a child's life and safety.

[2284] "Daily life data" refers to information about a child's daily life, such as their body temperature, meals, sleep, number of trips to the toilet, location information, and environmental data.

[2285] A "generative AI model" refers to the machine learning algorithm used to analyze collected data and make decisions.

[2286] "Personalized advice" refers to specific instructions and advice that are customized to the individual user's (parent's) situation and emotional state.

[2287] "Emotional data" refers to information that indicates the emotional state of the guardian, including, for example, the degree of stress or fatigue that the guardian is feeling.

[2288] "Database" refers to a system for temporarily or permanently storing collected data.

[2289] "Analysis tools" refers to the methods and devices used to analyze collected data and extract meaningful information.

[2290] "Notification means" refers to a mechanism for sending the generated advice to the parent's device and notifying them in real time.

[2291] An "online consultation app" refers to an application that allows users to consult with a doctor remotely.

[2292] This system analyzes a child's daily life data and a parent's emotional data to provide personalized advice. This system is composed of a smartphone app, a server, a generative AI model, and an emotion engine.

[2293] Data collection and input

[2294] User:

[2295] Parents enter data about their child's daily life into a smartphone app, such as body temperature, diet, sleep time, number of toilet visits, location information, and environmental data.

[2296] Device (smartphone app):

[2297] The smartphone app temporarily stores the data entered by the parent or guardian in local storage and sends it to the server when the internet connection is stable. If the data transmission is successful, the data in local storage is cleared or the transmission flag is updated.

[2298] Data reception and analysis

[2299] server:

[2300] The server receives data sent from the smartphone app and stores it in a database, where it is ready for analysis. The server then invokes a generative AI model built with TensorFlow to analyze the data, for example, to determine whether a child's temperature is above or below the normal range.

[2301] Acquiring and analyzing emotion data

[2302] User:

[2303] Parents can input emotional data into a smartphone app or use the camera to automatically recognize it.

[2304] Device (smartphone app):

[2305] The smartphone app uses the camera to capture a facial image of the parent or guardian and sends the image to a server on the internet.

[2306] server:

[2307] The server uses the Google Cloud Vision API to analyze the parent's emotions from the images sent, and the emotion engine classifies their status, such as stressed or relaxed.

[2308] Advice Generation and Notifications

[2309] server:

[2310] The analysis results of the generated AI model are combined with the results of the emotion engine to generate personalized advice. For example, in addition to advice such as "Your child has a high fever. Drink plenty of fluids and check their temperature frequently," it also generates emotionally sensitive messages such as "The parent is currently tired, so please refrain from overexerting yourself and consult a doctor."

[2311] server:

[2312] The generated advice is pushed to the parent's smartphone in real time, and the advice is added to a notification queue and notified to the parent.

[2313] Device (smartphone app):

[2314] Advice received from the server is notified to parents and displayed within the app. Advice is displayed in real time using push notifications.

[2315] Anomaly detection and medical collaboration

[2316] server:

[2317] If the analysis detects abnormal data, a notification will be generated directing the user to the online medical consultation app. For example, the notification may include the message, "Your child appears to have a high fever. Please consult a doctor immediately."

[2318] server:

[2319] A notification message containing a link to the online consultation app will be sent to the parent's device.

[2320] Device (smartphone app):

[2321] An abnormality notification will be displayed to parents, along with a link to an online medical consultation app, allowing them to quickly seek medical advice.

[2322] Gathering feedback and improving the system

[2323] User:

[2324] Parents can enter their evaluations and opinions about the advice provided as feedback into a smartphone app, such as "The advice was helpful" or "I would like more specific instructions."

[2325] Device (smartphone app):

[2326] Send the feedback data to the server.

[2327] server:

[2328] Feedback is received and stored in a database. The collected feedback is used to improve the generative AI model and emotion engine. The feedback data is analyzed and the parameters of the generative AI model and emotion engine are adjusted, which improves the accuracy of the next data analysis and advice generation.

[2329] Specific examples

[2330] scenario:

[2331] If a parent goes missing from a park while the child is there.

[2332] 1. User: Parent types into app: "I can't find my child."

[2333] 2. Device: Sends location data and environmental data.

[2334] 3. Server: Analyze the child's location information (predict the latest location using a TensorFlow model).

[2335] 4. Emotion recognition: Parents submit photos of "anxious expressions" as emotional data.

[2336] 5. Server: Analyzes emotions and determines that the parent's stress level is high.

[2337] 6. Server: Generates the advice, "Your child may be on the east side of the park. Remain calm and call for help."

[2338] 7. Device: Display advice notification to parents.

[2339] Example prompt sentence:

[2340] "A child has gone missing from the park. Please suggest the best course of action based on the latest location data and the anxious expression of the parent."

[2341] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[2342] Step 1:

[2343] The user uses a smartphone app to input data about their child's daily life (body temperature, meals, sleep time, number of toilet visits, location information, environmental data, etc.) as well as emotional data from the parent.

[2344] Input: Body temperature, meals, sleep time, toilet visits, location information, environmental data, emotional data

[2345] Output: The input data

[2346] Step 2:

[2347] The device (smartphone app) temporarily stores the data entered by the user in local storage and sends it to the server when the Internet connection is stable.

[2348] Input: Data stored in local storage

[2349] Output: Data sent to the server

[2350] Step 3:

[2351] The server receives the data sent from the smartphone app and stores it in a database.

[2352] Input: Data sent

[2353] Output: Data stored in a database

[2354] Step 4:

[2355] The server invokes a generative AI model built with TensorFlow to analyze the incoming data, for example, to determine whether a child's temperature is above or below the normal range.

[2356] Input: Data stored in a database

[2357] Output: Analysis results (e.g., abnormal body temperature detection)

[2358] Step 5:

[2359] Users can input emotional data into a smartphone app or use a camera to automatically recognize it.

[2360] Input: Emotion data or camera images

[2361] Output: Emotion data

[2362] Step 6:

[2363] The device (smartphone app) uses the camera to capture a facial image of the parent or guardian and sends the image to the server.

[2364] Input: Captured face image

[2365] Output: Face image sent to the server

[2366] Step 7:

[2367] The server uses the Google Cloud Vision API to analyze the parent's emotions from the images sent, and the emotion engine classifies their status, such as stressed or relaxed.

[2368] Input: Parent's face image

[2369] Output: Parent's emotional state (e.g., stress level)

[2370] Step 8:

[2371] The server combines the analysis results of the generated AI model with the results of the emotion engine to generate personalized advice.

[2372] Input: Analysis results and emotion data

[2373] Output: Personalized advice

[2374] Step 9:

[2375] The server pushes the generated advice to the parent's smartphone app in real time.

[2376] Input: Generated advice

[2377] Output: Advice notice sent to parent

[2378] Step 10:

[2379] The device (smartphone app) will push the advice received from the server to the parent and display it within the app.

[2380] Input: Advice received from the server

[2381] Output: Advice notice displayed to parent

[2382] Step 11:

[2383] If the server detects any abnormal data as a result of its analysis, it generates a notification directing the parent to the online medical consultation app and sends it to the parent's device. For example, it may include a message such as, "Your child appears to have a high fever. Please consult a doctor immediately."

[2384] Input: Analysis results of abnormal data

[2385] Output: Notification directing to online medical consultation app

[2386] Step 12:

[2387] The device (smartphone app) will display an abnormality notification to the parent and provide a link to the online medical examination app.

[2388] Input: Notification received from the server

[2389] Output: Abnormality notification displayed to the parent with a link to the online medical examination app

[2390] Step 13:

[2391] Users input their evaluations and opinions on the advice provided as feedback into the smartphone app.

[2392] Input: Feedback (e.g., "The advice was helpful")

[2393] Output: Feedback data

[2394] Step 14:

[2395] The device (smartphone app) sends feedback data to the server.

[2396] Input: User-entered feedback data

[2397] Output: Feedback data sent to the server

[2398] Step 15:

[2399] The server receives the feedback and stores it in a database. The collected feedback is used to improve the generative AI model and emotion engine. By analyzing the feedback and adjusting the parameters of the generative AI model, the accuracy of the next data analysis and advice generation will be improved.

[2400] Input: Feedback data

[2401] Output: Improved generative AI models and emotion engines

[2402] 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.

[2403] 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.

[2404] 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.

[2405] [Fourth embodiment]

[2406] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[2407] 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.

[2408] 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).

[2409] 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.

[2410] 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.

[2411] 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).

[2412] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for 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.

[2413] 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.

[2414] 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.

[2415] 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.

[2416] 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.

[2417] 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.

[2418] 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."

[2419] The present invention is a system that includes a smartphone app that allows parents to input data about their children's daily lives, a server that receives and analyzes the data, and a system that provides personalized advice based on the analysis results. Specific embodiments of this system are described below.

[2420] Data collection and input

[2421] User

[2422] Parents enter data about their child's daily life into a smartphone app. For example, they fill out an input form with data such as their child's morning temperature, what they eat, how much sleep they get, and how many times they go to the toilet. This data is used to record their child's daily health and lifestyle.

[2423] Device (smartphone app)

[2424] The smartphone app has a function that temporarily stores the data entered by the parent or guardian in local storage and sends it to the server when the Internet connection is stable. When the data transmission is successful, the data is deleted from local storage or the transmission flag is updated.

[2425] Data reception and analysis

[2426] server

[2427] The server receives the data sent from the smartphone app and stores it in a temporary database. The stored data is then analyzed by a generative AI model, which recognizes patterns in the child's daily life and determines whether there are any abnormalities.

[2428] server

[2429] The analysis results form the basis for advice provided to parents. For example, if a child's temperature exceeds the normal range (36.1 to 37.2 degrees Celsius), the system will determine that the child has a high fever and notify the parent of the appropriate course of action.

[2430] Advice Generation and Notifications

[2431] server

[2432] Based on the analysis results of the generative AI model, personalized advice is generated for parents. For example, if the child's sleep time is short, specific advice such as "Sufficient sleep is essential for your child's development. Please try to get them to bed early tonight" is generated.

[2433] server

[2434] The generated advice is sent to the parent's device and added to the notification queue.

[2435] Device (smartphone app)

[2436] Advice received from the server is notified to parents and displayed within the app. Information is provided in real time using push notifications, etc.

[2437] Anomaly detection and medical collaboration

[2438] server

[2439] If the analysis detects abnormal data, a notification will be generated directing users to an online medical consultation app. For example, if an abnormally high temperature persists, a notification will be generated stating, "Your child appears to have a high fever. Please consult a doctor immediately."

[2440] server

[2441] A notification message containing a link to the online consultation app will be sent to the parent's device.

[2442] Device (smartphone app)

[2443] An abnormality notification will be displayed to parents, along with a link to an online medical consultation app, allowing them to quickly seek medical advice.

[2444] Gathering feedback and improving the system

[2445] User

[2446] Parents can enter their evaluations and opinions about the advice provided as feedback into a smartphone app, such as "The advice was helpful" or "I would like more specific instructions."

[2447] Device (smartphone app)

[2448] Send feedback data to the server. Feedback is a key element in improving user experience.

[2449] server

[2450] Feedback is received and stored in a database. This feedback is used to improve the generative AI model. Specifically, it adjusts the model's parameters to improve the accuracy of the next analysis and advice generation.

[2451] Example: What to do if a child has a high temperature

[2452] User

[2453] The parent enters "This morning's temperature was 38.5 degrees" into a smartphone app.

[2454] Terminal

[2455] Sends input data to the server.

[2456] server

[2457] The data is received and stored in a database. At the same time, the generative AI model analyzes the body temperature data and determines whether the person has a high fever.

[2458] server

[2459] The system generates advice such as, "Your child has a high fever. Drink plenty of fluids and check their temperature frequently. If symptoms persist, consult a doctor," and sends it to the parent's device.

[2460] Terminal

[2461] Display an advisory notice to parents.

[2462] User

[2463] Parents will follow advice and ensure their children are hydrated and have their temperatures checked.

[2464] As described above, the present invention realizes a system that can solve the problems faced by parents raising children and effectively support the health of their children.

[2465] The processing flow will be explained below.

[2466] Step 1:

[2467] User

[2468] Parents enter data about their child's daily life into a smartphone app. Examples of input include "This morning's body temperature was 38.5 degrees" and "I slept for five hours last night."

[2469] Step 2:

[2470] Terminal

[2471] The smartphone app temporarily saves the entered data in local storage, so that the data is saved even if the connection is unstable.

[2472] Step 3:

[2473] Terminal

[2474] Ensure that your internet connection is stable, then send the data stored on the server. If the transmission is successful, delete the data in local storage or set a sent flag.

[2475] Step 4:

[2476] server

[2477] The server receives the data sent from the smartphone app and stores it in a temporary database, where it is ready for analysis.

[2478] Step 5:

[2479] server

[2480] A generative AI model is invoked to analyze the incoming data, for example to determine whether the body temperature is above the normal range (36.1°C to 37.2°C).

[2481] Step 6:

[2482] server

[2483] Based on the analysis results, the system checks for abnormal data. For example, if a body temperature of 38.5 degrees is determined to be abnormal, it will be treated as a "high fever."

[2484] Step 7:

[2485] server

[2486] Based on the analysis results, the system generates personalized advice for parents, such as a message saying, "Your child has a high fever. Drink plenty of fluids and check their temperature frequently."

[2487] Step 8:

[2488] server

[2489] The generated advice is sent to the parent's smartphone. The advice is added to the notification queue and sent.

[2490] Step 9:

[2491] Terminal

[2492] Advice received from the server is notified to parents in real time via push notifications and displayed within the app.

[2493] Step 10:

[2494] server

[2495] If abnormal data is detected during the analysis process, a notification will be generated directing users to an online medical consultation app, with a message such as, "Your child appears to have a high fever. Please consult a doctor immediately."

[2496] Step 11:

[2497] server

[2498] A notification message will be sent to the device containing a link to an online consultation app, allowing parents to quickly contact a medical institution.

[2499] Step 12:

[2500] Terminal

[2501] An abnormality notification will be displayed to parents, along with a link to an online consultation app, allowing them to quickly contact a doctor.

[2502] Step 13:

[2503] User

[2504] Review any advice or guidance provided and act as necessary, for example, by drinking fluids or rechecking your temperature.

[2505] Step 14:

[2506] User

[2507] Users can enter feedback about the advice and notifications into the smartphone app, such as "The advice was helpful" or "I would like more detailed explanations."

[2508] Step 15:

[2509] Terminal

[2510] The feedback data is sent to the server. If the sending is successful, a notification of feedback completion will be displayed in the app.

[2511] Step 16:

[2512] server

[2513] Receive feedback and store it in a database. The collected feedback is used to improve the generative AI model.

[2514] Step 17:

[2515] server

[2516] The feedback data is analyzed and the parameters of the generative AI model are adjusted to improve the accuracy of the next data analysis and advice generation.

[2517] Through these steps, the system can quickly and accurately analyze data on children's daily lives, provide optimal advice to parents, and continuously improve the system using feedback.

[2518] Example 1

[2519] 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."

[2520] Collecting data on children's daily lives and providing appropriate advice to parents regarding their health and daily rhythms based on that data is a crucial challenge. However, many parents are busy and find it difficult to continuously collect and analyze detailed data. Furthermore, when an abnormality occurs, they are required to make a prompt response, but they often lack the specialized knowledge to do so. To solve these challenges, there is a need to develop a system that allows parents to easily input daily data and automatically detects abnormalities and provides personalized advice based on that data.

[2521] 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.

[2522] In this invention, the server includes means for temporarily storing input data in local storage and transmitting it to the server when the internet connection is stable, means for the server to receive the transmitted data and store it in a database, and means for analyzing the stored data with a generative AI model. This allows parents to easily input daily data, and the generative AI model automatically analyzes the data based on the data, enabling anomaly detection and personalized advice to be provided in real time.

[2523] A "guardian" is an adult who is responsible for managing a child's daily life and health and entering data into the system.

[2524] "Children" are minors whose guardians aim to improve their health and lifestyle through the system.

[2525] "Data related to daily life" refers to data that reflects a child's daily health condition and lifestyle, and includes, for example, body temperature, dietary habits, sleep duration, and number of times they went to the toilet.

[2526] "Input means" refers to the interface and functions that allow parents to input data about their daily lives into the system using a smartphone app or other device.

[2527] "Local storage" refers to a storage device or memory for temporarily storing data on a device.

[2528] "Server" refers to a computer system that receives, stores, and analyzes data sent from the device, and generates and provides advice and notifications to parents.

[2529] "Database" refers to a system or software for storing and managing received data in a structured manner.

[2530] "Generative AI models" refer to artificial intelligence algorithms and machine learning models that analyze collected data and recognize patterns and detect anomalies in children's daily lives.

[2531] "Analysis means" refers to the process or method by which the server uses the generated AI model to analyze data and determine whether there are any abnormalities.

[2532] "Personalized advice" refers to information that provides appropriate countermeasures and suggestions to specific parents based on the analysis results of the generative AI model, depending on the child's health condition and lifestyle.

[2533] "Transmission means" refers to the communication functions and methods for sending advice and notifications from the server to the parent's device.

[2534] "Notification means" refers to the interface or function for displaying received advice or notifications on the parent's device and conveying information to the parent.

[2535] An "online medical consultation app" refers to an application or service that provides medical consultations and examinations online.

[2536] "Anomaly detection" refers to the discovery of unusual data patterns or anomalies by a generative AI model from the results of its analysis.

[2537] "Feedback" refers to information that parents submit by entering their thoughts, evaluations, and suggestions for improvement regarding the advice provided by the system.

[2538] "Data improvement measures" refers to methods and processes for improving the performance and accuracy of generative AI models based on parental feedback.

[2539] This system allows parents to input and manage data about their children's daily lives using a smartphone app, and provides personalized advice based on the analysis results. The system is designed to allow parents to easily input data and respond quickly if an abnormality is detected.

[2540] Specifically, the system includes the following elements:

[2541] Data collection and input

[2542] User

[2543] Parents open a smartphone app and enter data about their child's daily life, such as their body temperature each morning, what they eat, how much sleep they get, and how many times they go to the toilet. This data is important for recording their child's health and daily rhythms.

[2544] Device (smartphone app)

[2545] The smartphone app first temporarily stores the data entered by the parent in local storage (for example, using SQLite or Realm Database). When the Internet connection is stable, the app sends this data to the server. After the data is successfully sent, the app either deletes the data from local storage or updates a flag indicating that it has been sent.

[2546] Data reception and analysis

[2547] server

[2548] The server receives the data sent from the smartphone app and stores it in a temporary database (e.g., MySQL or PostgreSQL). After the data is stored, the generative AI model analyzes it. The generative AI model recognizes patterns in the child's daily life and determines whether there are any abnormalities.

[2549] For example, if a parent or guardian inputs "My temperature this morning was 38.5 degrees," this data is sent to the server. The server receives the data and stores it in a temporary database. The generative AI model analyzes this input data and determines that it is a high fever.

[2550] Advice Generation and Notifications

[2551] server

[2552] Based on the analysis results of the generative AI model, the server generates personalized advice for parents. For example, if the child has a high temperature, the server will generate specific advice such as, "Your child has a high fever. Drink plenty of fluids and check their temperature frequently. If symptoms persist, consult a doctor."

[2553] server

[2554] The generated advice is sent to the parent's device. The advice is added to a notification queue so that the parent can immediately know how to respond. For example, advice can be provided in real time using push notifications.

[2555] Device (smartphone app)

[2556] The parent's device will receive advice from the server and display it in a notification and within the app. Parents can check the notification and take appropriate action.

[2557] Anomaly detection and medical collaboration

[2558] server

[2559] If the analysis detects abnormal data, the server generates a notification directing the user to the online medical examination app. For example, if an abnormally high temperature of 39 degrees continues, a specific notification will be created stating, "Your child appears to have a high fever. Please consult a doctor immediately."

[2560] server

[2561] A notification message containing a link to the online consultation app will be sent to the parent's device, allowing them to quickly arrange a medical consultation.

[2562] Device (smartphone app)

[2563] An abnormality notification will be displayed to parents, who will be provided with a link to an online consultation app, allowing them to quickly access medical services.

[2564] Gathering feedback and improving the system

[2565] User

[2566] Parents can enter their evaluations and opinions about the advice provided as feedback into a smartphone app, such as "The advice was helpful" or "I would like more specific instructions."

[2567] Device (smartphone app)

[2568] The smartphone app sends the input feedback data to the server. The feedback data is also sent securely and is important as a means of improving the entire system.

[2569] server

[2570] The server receives the feedback data and stores it in a database. This feedback is used to improve the performance of the generative AI model by adjusting the model's parameters and improving the accuracy of the next analysis and advice generation.

[2571] Through a series of processes from data collection and analysis to advice generation and feedback, this system can solve the challenges faced by parents raising children and effectively support their children's health.

[2572] The flow of the identification process in the first embodiment will be described with reference to FIG.

[2573] Step 1:

[2574] User

[2575] Parents open the smartphone app and enter data about their child's daily life. The data includes the child's body temperature, what they eat, how much they sleep, and how many times they go to the toilet. For example, they might enter information such as "body temperature: 36.8 degrees" and "sleep time: 8 hours." The entered data is temporarily stored in the smartphone's local storage.

[2576] Input: Data about the child's daily life

[2577] Output: Data saved in local storage

[2578] Step 2:

[2579] Device (smartphone app)

[2580] Once the smartphone app confirms that the internet connection is stable, it sends the data stored in local storage to the server. At this time, it packages the data in JSON format and sends it via an HTTP POST request. If the transmission is successful, it deletes the data from local storage or updates the sent flag.

[2581] Input: Data stored in local storage

[2582] Output: Data sent to the server

[2583] Step 3:

[2584] server

[2585] The server receives the data sent from the smartphone app and stores it in a temporary database. The data is then saved in a database such as MySQL or PostgreSQL. At this point, validation is performed to ensure that the data format and content are accurate.

[2586] Input: Data sent from the smartphone app

[2587] Output: Data stored in a temporary database

[2588] Step 4:

[2589] server

[2590] Once the data is stored in the temporary database, the server passes it to the generative AI model for analysis. During analysis, the generative AI model uses a dataset of learned patterns from a child's daily life to detect any abnormalities. For example, if "body temperature: 38.5 degrees" is entered, the generative AI model will determine that it is a high fever.

[2591] Input: Data stored in a temporary database

[2592] Output: Analysis results

[2593] Step 5:

[2594] server

[2595] Based on the analysis results of the generative AI model, the server generates personalized advice for parents. For example, if the child has a high temperature, the server generates specific advice such as, "Your child has a high fever. Drink plenty of fluids and check their temperature frequently. If symptoms persist, consult a doctor."

[2596] Input: Analysis results

[2597] Output: Personalized advice

[2598] Step 6:

[2599] server

[2600] The generated advice is sent to the parent's device. The server sends the generated advice to the parent's device via push notification, etc. The advice is added to the notification queue so that the parent can immediately know what to do.

[2601] Enter: personalized advice

[2602] Output: Advice sent to parent's device

[2603] Step 7:

[2604] Device (smartphone app)

[2605] The parent's device will receive the advice from the server and display it within the app. Real-time information is provided to parents via push notifications. Parents can then check the notifications and take appropriate action.

[2606] Input: Advice sent to parent's device

[2607] Output: Advice displayed to parents

[2608] Step 8:

[2609] server

[2610] If the analysis detects abnormal data, the server generates a notification directing the user to the online medical examination app. For example, if an abnormally high temperature of 39 degrees continues, a specific notification will be created stating, "Your child appears to have a high fever. Please consult a doctor immediately."

[2611] Input: Analysis results (abnormal data)

[2612] Output: Notification directing to online medical consultation app

[2613] Step 9:

[2614] server

[2615] A notification message containing a link to the online consultation app will be sent to the parent's device. When the parent clicks on the link, they will be taken directly to the online consultation app.

[2616] Input: Notification to direct you to the online medical consultation app

[2617] Output: A notification message with a link to the online consultation app.

[2618] Step 10:

[2619] Device (smartphone app)

[2620] A notification of an abnormality will be displayed to parents, who will be provided with a link to an online medical consultation app that they can tap to quickly access medical services.

[2621] Input: Notification message with link to online consultation app

[2622] Output: Anomaly notification and link displayed to the parent

[2623] Step 11:

[2624] User

[2625] Parents can enter their evaluation and opinions of the advice provided as feedback into a smartphone app, such as "The advice was helpful" or "I would like more specific instructions."

[2626] Input: Feedback on the advice

[2627] Output: Feedback entered into the smartphone app

[2628] Step 12:

[2629] Device (smartphone app)

[2630] The smartphone app sends the entered feedback data to the server, where it is also securely transmitted and made available.

[2631] Input: Feedback entered into the smartphone app

[2632] Output: Feedback data sent to the server

[2633] Step 13:

[2634] server

[2635] The server receives the feedback data and stores it in a database. This feedback is used to improve the performance of the generative AI model. Specifically, it is used to adjust the parameters of the generative AI model and improve the accuracy of the next analysis and advice generation.

[2636] Input: Feedback data sent to the server

[2637] Output: Feedback data stored in a database

[2638] The above is a specific processing flow of the system.

[2639] (Application example 1)

[2640] 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."

[2641] Conventional child health management systems monitor health conditions based on data entered by parents on a daily basis and provide necessary advice. However, if health data is not simply analyzed but a wide variety of purchasing activities and preference patterns are taken into account, it becomes possible to make more extensive lifestyle suggestions and appropriate product recommendations, further increasing user satisfaction. The objective of this invention is to support users' overall lifestyles by providing personalized product recommendations and shopping advice based on purchasing history and preferences, in addition to the conventional health management systems.

[2642] 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.

[2643] In this invention, the server includes: means for a guardian to input data about their child's daily life; means for receiving the data and storing it in a database; means for analyzing the received data with a generative AI model; means for generating personalized advice for the guardian based on the analysis results; means for transmitting the generated advice to the guardian's terminal; means for collecting user purchase data, browsing history, and reviews and transmitting them to a private server; means for analyzing the collected data with a generative AI model and generating personalized ...

Claims

1. a means for parents to input data about their child's daily life; means for receiving said data and storing it in a database; A means for analyzing the received data with a generative AI model; A means for generating personalized advice to parents based on the analysis results; A means for transmitting the generated advice to a parent's device; A system including:

2. The system according to claim 1, further comprising a means for detecting abnormalities in a child's physical condition or lifestyle patterns using the generative AI model and generating a notification directing the child to an online medical examination app.

3. The system of claim 1 , further comprising: means for receiving feedback from the parent and using it as data to improve the generative AI model.

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