system

Abstract

Provide a system. 【Solution means】 Means for a user to input basic information related to child-rearing, Means for sending this basic information to a server and storing it in a database, Means for regularly inputting the baby's behavior data, Means for sending the input behavior data to a server and storing it in a database, Means for extracting behavior data from a database and analyzing it by an AI model, Means for generating advice on child-rearing based on this analysis result and notifying the user, Means for collecting feedback from the user and using it to improve the AI model, A system including the above.

Description

Technical Field

[0004] , , , ,

[0005] , , , ,

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

Background Art

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

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] Currently, there is a lack of an environment in which first-time parents and men taking parental leave can easily obtain knowledge and advice on child-rearing. As a result, the anxiety and burden associated with first-time parenting can increase, and the quality of parenting may decline. In addition, such parenting burden is considered to be one of the factors contributing to the declining birthrate, and it is necessary for the whole society to address this issue. Furthermore, since the information on child-rearing is not tailored to individual situations, there is also a problem that it is difficult to find specific solutions.

Means for Solving the Problems

[0005] This invention is a system in which a user inputs basic information about childcare and behavioral data of their baby, sends this data to a server and stores it in a database, and an AI model performs appropriate analysis. Based on the results, it generates specific advice about childcare and notifies the user. This system also includes means for collecting feedback from users and using it to improve the AI ​​model. Specifically, the system aims to reduce the burden of childcare and improve the quality of childcare by having users regularly input behavioral data such as the amount of milk consumed, sleep time, and bowel movements of their baby, and providing personalized advice based on the analysis results. Furthermore, the advice is sent as push notifications and in-app messages, making it easy for users to receive, thereby providing support for childcare.

[0006] A "user" refers to a person who uses this system to receive support related to childcare.

[0007] "Childcare" refers to the daily activities involved in caring for and supporting the growth of a baby.

[0008] "Basic information" refers to initial data related to childcare, such as the baby's date of birth, gender, and health information.

[0009] "Behavioral data" refers to data related to a baby's daily life, such as the amount of milk they consume, their sleep duration, and their bowel movements.

[0010] A "server" refers to a computer system that receives, stores, and analyzes data sent by users.

[0011] A "database" refers to an electronic data management system used to store and manage basic information and behavioral data collected from users.

[0012] An "AI model" refers to an artificial intelligence algorithm that analyzes user behavior data and generates specific advice regarding childcare.

[0013] "Advice" refers to specific instructions and suggestions regarding childcare that are generated by the AI ​​model based on its analysis results.

[0014] "Push notifications" refer to a technology that automatically displays notifications on the user's device.

[0015] "In-app messages" refer to messages displayed to the user within an application.

[0016] "Feedback" refers to opinions and evaluations from users regarding the effectiveness and practicality of the advice they receive. [Brief explanation of the drawing]

[0017] [Figure 1] This is a conceptual diagram showing an example of the configuration of a data processing system according to the first embodiment. [Figure 2] This is a conceptual diagram showing an example of the essential functions of a data processing device and a smart device according to the first embodiment. [Figure 3] This is a conceptual diagram showing an example of the configuration of a data processing system according to the second embodiment. [Figure 4] This is a conceptual diagram showing an example of the main functions of a data processing device and smart glasses according to the second embodiment. [Figure 5] This is a conceptual diagram showing an example of the configuration of a data processing system according to the third embodiment. [Figure 6] This is a conceptual diagram showing an example of the main functions of a data processing device and a headset-type terminal according to the third embodiment. [Figure 7] This is a conceptual diagram showing an example of the configuration of a data processing system according to the fourth embodiment. [Figure 8] This is a conceptual diagram showing an example of the main functions of a data processing device and a robot according to the fourth embodiment. [Figure 9] This shows an emotion map where multiple emotions are mapped. [Figure 10] This shows an emotion map where multiple emotions are mapped. [Figure 11] It is a sequence diagram showing the processing flow of the data processing system in Embodiment 1. [Figure 12] It is a sequence diagram showing the processing flow of the data processing system in Application Example 1. [Figure 13] It is a sequence diagram showing the processing flow of the data processing system in Embodiment 2 when combined with an emotion engine. [Figure 14] It is a sequence diagram showing the processing flow of the data processing system in Application Example 2 when combined with an emotion engine.

Mode for Carrying Out the Invention

[0018] Hereinafter, an example of an embodiment of a system according to the technology of the present disclosure will be described with reference to the accompanying drawings.

[0019] First, the terms used in the following description will be explained.

[0020] In the following embodiments, a processor with a reference numeral (hereinafter simply referred to as "processor") may be a single arithmetic unit or a combination of multiple arithmetic units. Also, the processor may be a single type of arithmetic unit or a combination of multiple types of arithmetic units. Examples of arithmetic units include a CPU (Central Processing Unit), a GPU (Graphics Processing Unit), a GPGPU (General-Purpose computing on Graphics Processing Units), an APU (Accelerated Processing Unit), and the like.

[0021] In the following embodiments, a RAM (Random Access Memory) with a reference numeral is a memory in which information is temporarily stored and is used as a work memory by the processor. <​In the following embodiments, the signed storage is one or more non-volatile storage devices that store various programs and various parameters. Examples of non-volatile storage devices include flash memory (SSD (Solid State Drive)), magnetic disks (e.g., hard disks), or magnetic tapes.

[0023] In the following embodiments, the signed communication interface (I / F) is an interface that includes a communication processor and an antenna, etc. The communication interface manages communication between multiple computers. Examples of communication standards applicable to the communication interface include wireless communication standards such as 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), or Bluetooth (registered trademark).

[0024] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B." That is, "A and / or B" means that it may be A alone, or B alone, or a combination of A and B. Furthermore, in this specification, the same concept as "A and / or B" applies when expressing three or more things linked by "and / or."

[0025] [First Embodiment]

[0026] Figure 1 shows an example of the configuration of the data processing system 10 according to the first embodiment.

[0027] As shown in Figure 1, the data processing system 10 includes a data processing device 12 and a smart device 14. An example of the data processing device 12 is a server.

[0028] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0029] The smart device 14 comprises a computer 36, a reception device 38, an output device 40, a camera 42, and a communication interface 44. The computer 36 comprises a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The reception device 38, output device 40, and camera 42 are also connected to the bus 52.

[0030] The reception device 38 is equipped with a touch panel 38A and a microphone 38B, etc., and receives user input. The touch panel 38A receives user input by detecting contact with an object (e.g., a pen or finger). The microphone 38B receives user input by detecting the user's voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the data indicating the user input.

[0031] The output device 40 includes a display 40A and a speaker 40B, and presents data to the user 20 by outputting the data in a form perceptible to the user 20 (e.g., audio and / or text). The display 40A displays visible information such as text and images according to instructions from the processor 46. The speaker 40B outputs audio according to instructions from the processor 46. The camera 42 is a small digital camera equipped with an optical system such as a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0032] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various types of information between processor 46 and processor 28 via network 54.

[0033] Figure 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0034] As shown in Figure 2, in the data processing device 12, a specific processing is performed by the processor 28. A specific processing program 56 is stored in the storage 32. The specific processing program 56 is an example of a "program" related to the technology of this disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0035] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0036] In the smart device 14, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The reception output program 60 is used in conjunction with a specific processing program 56 by the data processing system 10. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0037] Next, the specific processing performed by the specific processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the smart device 14 as the "terminal".

[0038] This invention is an AI advisor system that is helpful for first-time mothers and men taking paternity leave when it comes to childcare, and it is implemented as follows.

[0039] User data entry

[0040] The user downloads and installs the application and creates an account. During account creation, the user enters basic information such as their name, email address, and password, and this information is sent from the device to the server. The server stores the received data in a database and generates the account.

[0041] Next, the user enters the baby's basic information (date of birth, gender, health information, etc.). This basic information is also sent from the device to the server, which stores it in a database to create the baby's profile.

[0042] Behavioral tracking

[0043] In the course of daily childcare, users input data about their baby's activities into the app. For example, they input information such as milk intake, sleep duration, and bowel movements. The device sends this activity data to a server, which stores the received data in a database.

[0044] Furthermore, the device displays reminders at regular intervals prompting the user to input behavioral data, providing an environment that makes it easy for users to enter the latest behavioral data.

[0045] AI analysis

[0046] The server extracts the latest behavioral data from the database and analyzes it using an AI model. Specifically, the server preprocesses the data, including past behavioral data, and inputs it into the AI ​​model. This AI model analyzes, for example, the baby's sleep patterns and milk intake to assess the baby's condition.

[0047] Advice generation

[0048] Based on the analysis results of the AI ​​model, the server generates specific advice regarding childcare. The generated advice is then formatted by the server into a format that is easy for the user to understand and sent to the device.

[0049] For example, if a baby's nighttime crying increases, the server generates specific advice based on the AI ​​model's analysis, such as "It would be good to let the baby take a longer nap during the day," and displays it on the device.

[0050] User notifications

[0051] The device notifies the user of the advice it has received. The notification appears as a push notification or in-app message, and the user acts on the suggested advice.

[0052] Feedback Collection

[0053] Users can provide feedback on the effectiveness of the advice. For example, they can send evaluations within the app such as, "Extending the nap was effective." The device sends this feedback to the server, which stores the received feedback in a database. The collected feedback is then incorporated into the new advice generation process to improve the AI ​​model.

[0054] Specific example

[0055] Dealing with nighttime crying for the first time:

[0056] The user enters that they are having trouble with their baby crying at night.

[0057] The server analyzes the cause of nighttime crying based on past behavioral data and sends advice to the device, such as "extend daytime naps."

[0058] The device notifies the user of advice, and the user acts upon it.

[0059] First fever:

[0060] The user enters that the baby's body temperature is high.

[0061] The server analyzes general fever information and the baby's health information, and sends advice such as "Please ensure adequate hydration" to the device.

[0062] The device notifies the user of advice, and the user acts upon it.

[0063] In this way, the AI ​​advisor system provides an environment where users can receive appropriate childcare support, thereby reducing the burden of childcare and improving the quality of childcare.

[0064] The following describes the processing flow.

[0065] Step 1:

[0066] The user downloads and launches the application. They then enter basic information such as their name, email address, and password into the account creation form.

[0067] Step 2:

[0068] The terminal sends the entered basic information to the server. The server stores the received data in a database and creates an account.

[0069] Step 3:

[0070] The user enters the baby's basic information (date of birth, gender, health information, etc.) into the application. This information is also sent from the device to the server.

[0071] Step 4:

[0072] The server stores the baby's basic information in a database and creates a profile of the baby.

[0073] Step 5:

[0074] In the course of daily childcare, users input data about their baby's activities (such as milk intake, sleep duration, and bowel movements) into the app.

[0075] Step 6:

[0076] The terminal sends the entered behavioral data to the server. The server stores the received behavioral data in a database.

[0077] Step 7:

[0078] The device displays reminders at regular intervals prompting the user to enter behavioral data. This ensures the user enters the latest behavioral data.

[0079] Step 8:

[0080] The server extracts historical and recent behavioral data from the database. It then performs data preprocessing (data cleaning, supplementing missing data, etc.).

[0081] Step 9:

[0082] The server inputs the pre-processed data into the AI ​​model and performs the analysis. The AI ​​model evaluates the baby's current condition and performs the necessary analysis.

[0083] Step 10:

[0084] The server generates specific childcare advice based on the analysis results of the AI ​​model. The generated advice is then formatted in a way that is easy for the user to understand.

[0085] Step 11:

[0086] The server sends formatted advice to the device. The device then displays this advice to the user as a push notification or in-app message.

[0087] Step 12:

[0088] The user reviews the advice received and takes the necessary actions. For example, they might take specific measures such as extending the baby's nap time.

[0089] Step 13:

[0090] Users provide feedback on the effectiveness of the advice within the app. For example, they can input whether the advice was useful and what effect it had.

[0091] Step 14:

[0092] The device collects feedback and sends it to the server. The server stores the received feedback in a database.

[0093] Step 15:

[0094] The server improves the AI ​​model based on the newly collected feedback. This will improve the accuracy of future advice.

[0095] In this way, this system provides users with an environment where they can easily receive childcare support, reducing the burden of childcare and improving the quality of childcare.

[0096] (Example 1)

[0097] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server," and the smart device 14 will be referred to as the "terminal."

[0098] In modern childcare, it is crucial for first-time parents and men taking paternity leave to receive effective and appropriate childcare support. However, a lack of knowledge and experience regarding childcare makes it difficult to understand a baby's behavior and respond appropriately. In particular, a swift and accurate response is required for abnormal behaviors such as nighttime crying and fever. Furthermore, there is a lack of systems to appropriately provide feedback on the effectiveness of measures taken by users and to continuously improve them.

[0099] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0100] In this invention, the server includes means for the user to input basic information about childcare, means for transmitting this basic information to a communication device and storing it in a data bank, means for periodically inputting baby behavior data, means for transmitting the input behavior data to a communication device and storing it in a data bank, means for extracting behavior data from the data bank and analyzing it using a generative AI model, means for generating childcare advice based on the analysis results and notifying the user, means for collecting user feedback and using it to improve the generative AI model, means for displaying a reminder to prompt the user to input behavior data at regular intervals, and means for generating childcare advice in text format and formatting it into an easy-to-understand format. As a result, the user can receive appropriate childcare support, improve the quality of childcare, and reduce the burden of childcare.

[0101] A "user" is someone who inputs information about childcare and receives support.

[0102] "Basic information" refers to data such as the user's and the baby's name, email address, date of birth, gender, and health information.

[0103] A "communication device" is a device that includes hardware and software for sending and receiving data between a user's terminal and a server.

[0104] A "data bank" refers to a database system that stores and manages collected basic information and behavioral data.

[0105] "Behavioral data" refers to data about a baby's daily behavior, such as milk intake, sleep duration, and bowel movements.

[0106] A "generative AI model" refers to an artificial intelligence model that analyzes collected data to generate advice related to childcare.

[0107] "Analysis" refers to the process by which a generative AI model evaluates specific patterns and trends based on behavioral data and creates advice.

[0108] "Advice" refers to specific instructions and suggestions regarding childcare provided to the user based on the analysis results of the generated AI model.

[0109] A "reminder" refers to a notification or alert that prompts the user to input behavioral data at regular intervals.

[0110] "Text format" refers to a method of presenting generated advice in text or sentences that are easy for the user to understand.

[0111] "Feedback" refers to evaluations and comments that users provide regarding the effectiveness of advice.

[0112] This invention is an AI advisor system aimed at providing first-time mothers and men taking paternity leave with effective childcare support by offering information related to childcare. This system includes the following means and processes.

[0113] User data entry

[0114] Users download and install a dedicated application and create an account. Account creation requires entering basic information such as name, email address, and password. This basic information is sent from the device to the server, which stores the received data in a database and generates the account.

[0115] Furthermore, users enter basic information such as the baby's name, date of birth, gender, and health information. This information is also sent from the device to the server, stored in a database, and used to create the baby's profile.

[0116] Input of behavioral data

[0117] During daily childcare activities, users input data about their baby's behavior, such as milk intake, sleep duration, and bowel movements, into the app. The device then sends this data to a server, where it is stored in a database.

[0118] Furthermore, the device will display reminder notifications prompting the user to input behavioral data at regular intervals, making it easy to enter the latest behavioral data.

[0119] AI analysis

[0120] The server extracts the latest behavioral data from the database and analyzes it using a generative AI model. This process is carried out using a dedicated AI framework (e.g., TENSORFLOW®, PyTorch). After preprocessing, including past behavioral data, the data is input into the AI ​​model to evaluate the baby's behavioral patterns and state.

[0121] Advice generation

[0122] Based on the analysis results of the AI ​​model, the server generates specific advice regarding childcare. The generated advice is formatted in text and summarized for easy understanding. The server sends this advice to the terminal and notifies the user.

[0123] For example, if a baby starts crying frequently at night, the AI ​​model will analyze the cause and provide specific advice such as, "It would be good to let the baby take a longer nap during the day."

[0124] User notifications

[0125] The device displays received advice to the user as a push notification or in-app message. This allows the user to receive advice at the appropriate time.

[0126] Feedback Collection

[0127] Users can provide feedback on the effectiveness of the advice. For example, they can send ratings and comments within the app, such as "Extending the nap was effective." This feedback is sent from the device to the server and stored in a database. It is also used as data to improve the AI ​​model.

[0128] Specific example

[0129] Dealing with nighttime crying for the first time:

[0130] The user enters that they are having trouble with their baby crying at night.

[0131] The server analyzes the cause of nighttime crying based on past behavioral data and sends advice to the device, such as "extend daytime naps."

[0132] The device notifies the user of advice, and the user acts upon it.

[0133] First fever:

[0134] The user enters that the baby's body temperature is high.

[0135] The server analyzes general fever information and the baby's health information, and sends advice such as "Please ensure adequate hydration" to the device.

[0136] The device notifies the user of advice, and the user acts upon it.

[0137] In this way, the AI ​​advisor system provides users with an environment to receive effective childcare support, improving the quality of childcare and reducing the burden of childcare.

[0138] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0139] Step 1:

[0140] Create an account

[0141] The user downloads and installs the application. After installation, they enter basic information such as their name, email address, and password on the account creation screen. This information is sent from the device to the server, which stores the received data in its database and generates a new account.

[0142] Specific actions:

[0143] The user enters their name, email address, and password into the input form and clicks the "Create Account" button.

[0144] The device sends the entered data to the server via an API request.

[0145] The server validates the received data, stores it in the database, and generates and returns a new user ID.

[0146] Input: Basic information such as name, email address, and password.

[0147] Output: New User ID

[0148] Step 2:

[0149] Enter your baby's basic information

[0150] Users enter their baby's name, date of birth, gender, health information, etc., within the app. This information is sent from the device to the server, which stores it in a database to create a profile of the baby.

[0151] Specific actions:

[0152] The user enters the baby's information and presses the "Save" button.

[0153] The device sends the entered baby information to the server.

[0154] The server validates the information and stores it in the database.

[0155] Input: Baby's name, date of birth, gender, health information

[0156] Output: Baby profile

[0157] Step 3:

[0158] Input of behavioral data

[0159] Users input daily childcare data, such as milk intake, sleep duration, and bowel movements, into the app. This data is sent from the device to a server, which stores it in a database. The device also displays reminder notifications at regular intervals to prompt users to input behavioral data.

[0160] Specific actions:

[0161] The user launches the app, enters information such as milk intake, sleep duration, and bowel movements, and then presses the "Save" button.

[0162] The device sends this data to the server.

[0163] The server validates the data and stores it in the database.

[0164] The device displays reminder notifications at regular intervals.

[0165] Input: Milk intake, sleep duration, bowel movements

[0166] Output: Behavioral data stored in the database

[0167] Step 4:

[0168] AI analysis

[0169] The server extracts the latest behavioral data from the database and analyzes it using a generative AI model. This analysis involves preprocessing, including past behavioral data, and then inputting this data into the AI ​​model to evaluate behavioral patterns.

[0170] Specific actions:

[0171] The server extracts the latest behavioral data from the database.

[0172] The server preprocesses the data, inputs it into the AI ​​model, and performs the analysis.

[0173] Example: Analyzing sleep data and other information to identify the cause of nighttime crying.

[0174] Input: Behavioral data extracted from the database

[0175] Output: Evaluation results of behavioral patterns by AI model

[0176] Step 5:

[0177] Advice generation

[0178] The server generates specific childcare advice based on the AI ​​analysis results. The generated advice is formatted as text and sent to the terminal.

[0179] Specific actions:

[0180] The server generates parenting advice as text based on the AI ​​analysis results.

[0181] Example: Specific advice such as, "It would be good to let your child take a slightly longer nap during the day."

[0182] The server sends the generated advice to the terminal.

[0183] Input: AI analysis results

[0184] Output: Parenting advice in text format

[0185] Step 6:

[0186] Advice notification

[0187] The device notifies the user of advice received from the server. These notifications are delivered via push notifications or in-app messages.

[0188] Specific actions:

[0189] The terminal receives advice from the server.

[0190] The device will display this to the user via push notifications or in-app messages.

[0191] Input: Advice from the server

[0192] Output: Notification to the user

[0193] Step 7:

[0194] Feedback Collection

[0195] Users can provide feedback on the effectiveness of the advice. For example, they can send ratings and comments within the app, such as "Extending the nap was effective." This feedback is sent from the device to the server and stored in a database. The collected feedback is used to improve the generative AI model.

[0196] Specific actions:

[0197] The user fills out a feedback form within the app and presses the "Submit" button.

[0198] The device sends feedback to the server.

[0199] The server stores the feedback in a database and uses it to update the AI ​​model.

[0200] Input: User feedback

[0201] Output: Feedback stored in the data bank and improvements to the AI ​​model.

[0202] (Application Example 1)

[0203] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server," and the smart device 14 will be referred to as the "terminal."

[0204] The challenge lies in reducing the burden on new parents and those who support them, and in providing appropriate childcare support. In particular, it is necessary to reduce the burden of preparing and choosing meals during childcare, creating an environment where parents can focus on childcare. Furthermore, providing timely and appropriate advice based on the childcare situation is also crucial.

[0205] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0206] In this invention, the server includes means for the user to input basic information about childcare, means for transmitting this basic information to the server and storing it in a database, means for periodically inputting baby behavior data, means for transmitting the input behavior data to the server and storing it in a database, means for extracting behavior data from the database and analyzing it using an AI model, means for generating childcare advice based on the analysis results and notifying the user, means for collecting user feedback and using it to improve the AI ​​model, a food delivery function that suggests meal options according to the childcare situation, and means for notifying the user of advice and suggestions via push notifications at the necessary time. This makes it possible to reduce the burden of selecting and preparing meals during childcare and to provide appropriate childcare advice in a timely manner.

[0207] A "user" refers to a person who inputs information related to childcare and uses the childcare support system.

[0208] A "server" refers to a computer system that receives data sent by users, stores it in a database, and runs AI models.

[0209] A "database" refers to digital storage used to store and manage basic information and behavioral data related to childcare.

[0210] "Baby behavioral data" refers to information related to childcare, such as milk intake, sleep duration, and bowel movements.

[0211] An "AI model" refers to a machine learning or deep learning algorithm used to analyze childcare-related data and generate appropriate advice.

[0212] "Childcare advice" refers to specific childcare-related advice provided to users based on the results of analysis by an AI model.

[0213] "Feedback" refers to evaluations and opinions regarding the effectiveness and application of advice provided by users.

[0214] The "food delivery function" refers to a feature that suggests appropriate meal options based on childcare circumstances and assists with meal preparation.

[0215] "Push notifications" refer to a function that allows a system to send information and advice to users in real time.

[0216] "Basic information" refers to initial setup information about the user and the baby.

[0217] This invention relates to a system in which a user inputs basic information about childcare, and an AI model provides appropriate childcare advice based on that information. It also includes a food delivery function that suggests meal options according to the childcare situation.

[0218] System Overview

[0219] The childcare support system consists of user terminals and a server. Users input basic information and baby behavior data using a smartphone or other device, and this data is sent to the server and stored in a database. The server uses an AI model to analyze the data, generate childcare advice, and notify the user. In addition, a food delivery function suggests meal options tailored to the childcare situation.

[0220] Hardware and software to be used

[0221] User devices: such as smartphones and tablets.

[0222] Server: A cloud server with high-performance computing resources.

[0223] Database: A relational database used to store basic information and behavioral data related to childcare.

[0224] AI model: A machine learning or deep learning model used to analyze childcare data and generate advice.

[0225] Food delivery API: An API for an external service used to suggest meal options.

[0226] Explanation of the program's processing

[0227] The server receives basic information and baby behavior data sent from the user's terminal and stores it in a database. The stored data is periodically analyzed by an AI model. Based on past data, the AI ​​model evaluates the baby's condition and generates appropriate childcare advice. The generated advice is then notified to the user's terminal from the server.

[0228] Furthermore, the server uses a food delivery function to suggest appropriate meal options based on the childcare situation. This information is also sent to the user's device via push notification.

[0229] Specific example

[0230] For first-time nighttime crying:

[0231] The user enters that they are having trouble with their baby crying at night.

[0232] The server analyzes the cause of nighttime crying based on past behavioral data and generates advice such as "extend daytime naps."

[0233] This advice will be sent to the user's device.

[0234] The food delivery feature suggests easy meal options tailored to the user's needs.

[0235] Example of a prompt

[0236] (Example of a prompt message)

[0237] I've entered my baby's sleep and milk intake data (as they're experiencing persistent nighttime crying) into the parenting app. Please provide optimal parenting advice and recommended food delivery menus.

[0238] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0239] Step 1:

[0240] Users enter basic childcare information into a smartphone app. This information includes their name, email address, baby's date of birth, gender, and health information. This basic information is sent from the device to a server, which stores the information in a database.

[0241] Step 2:

[0242] Users input daily activity data about their baby into the app. This data includes milk intake, sleep duration, and bowel movements. This data is also sent from the device to the server, which stores it in a database. Upon receiving the data, the server prepares to provide parenting advice based on that data.

[0243] Step 3:

[0244] The server extracts the latest behavioral data from the database at regular intervals and inputs it into the AI ​​model. The AI ​​model performs behavioral analysis, including past data, to evaluate the baby's condition and patterns. In this evaluation process, data preprocessing such as normalization and feature extraction is performed. The input is behavioral data from the database, and the output is an evaluation of the baby's condition.

[0245] Step 4:

[0246] The server generates specific childcare advice based on the analysis results of the AI ​​model. For example, if a baby's nighttime crying increases, the AI ​​model might suggest, "It would be good to let the baby take a longer nap during the day." This advice generation uses both past and current behavioral data.

[0247] Step 5:

[0248] The server notifies the user's device of the generated advice. The device displays the advice to the user as a push notification or in-app message. The user can then act according to the received advice. The input is the advice from the server, and the output is the notification to the user.

[0249] Step 6:

[0250] The server collects feedback from users. For example, a user might input a rating such as, "Extending my nap was effective." This feedback is sent from the terminal to the server, which stores the information in a database. The feedback is used to improve the AI ​​model and is reflected in the new advice generation process. The input is user feedback, and the output is the storage in the database and the improvement of the AI ​​model.

[0251] Step 7:

[0252] The server utilizes a food delivery function that suggests meal options tailored to the childcare situation based on the analysis results of an AI model. The server calls an external food delivery API to retrieve appropriate menus and notifies the user's terminal. The input consists of the analysis results of the AI ​​model and menu information from the food delivery API, and the output is a notification of meal options to the user.

[0253] Step 8:

[0254] Users receive notifications of childcare advice and meal options, and then act on them. When users input the results and feedback of implementing the advice within the app, this data is also sent to the server and used to generate future advice. The input is the user's actions and feedback, and the output is the transmission of data to the server and improvement of the accuracy of the AI ​​model.

[0255] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0256] This invention combines an AI advisor system that is helpful for first-time mothers and men taking paternity leave when it comes to childcare with an emotion engine that recognizes the user's emotions, and is implemented as follows.

[0257] User data entry

[0258] The user downloads and installs the application and creates an account. During account creation, the user enters basic information such as their name, email address, and password, and this information is sent from the device to the server. The server stores the received basic information in a database and generates the account.

[0259] Next, the user enters the baby's basic information (date of birth, gender, health information, etc.). This basic information is also sent from the device to the server, which stores it in a database to create the baby's profile.

[0260] Behavioral tracking

[0261] In the course of daily childcare, users input data about their baby's activities into the app. For example, they input information such as milk intake, sleep duration, and bowel movements. The device sends this activity data to a server, which stores the received data in a database.

[0262] Furthermore, the device displays reminders at regular intervals prompting the user to input behavioral data, and the user then enters the latest behavioral data.

[0263] Emotion recognition by an emotion engine

[0264] The device is equipped with an emotion engine that recognizes the user's emotions. The emotion engine analyzes the user's emotions from their input, speech, and facial expressions. The results of this analysis are sent to a server and stored in a database.

[0265] AI analysis

[0266] The server extracts the latest behavioral and emotional data from the database and analyzes it using an AI model. Specifically, the server preprocesses this data and inputs it into the AI ​​model. The AI ​​model analyzes, for example, the baby's sleep patterns, milk intake, and the user's emotional state, and evaluates the state of both the baby and the user.

[0267] Advice generation

[0268] Based on the analysis results of the AI ​​model, the server generates specific advice regarding childcare. The generated advice is formatted to take the user's emotional state into consideration and then sent to the device.

[0269] For example, if the baby's nighttime crying increases and the user is feeling exhausted, the AI ​​model will analyze the situation and generate specific advice such as, "It would be good to let the baby take a longer nap during the day. Also, it would be good to set aside some time to relax in between childcare duties."

[0270] User notifications

[0271] The device notifies the user of the advice it has received. The notification appears as a push notification or in-app message, and the user acts on the suggested advice.

[0272] Feedback Collection

[0273] Users can provide feedback on the effectiveness of the advice. For example, they can send evaluations within the app such as, "Extending the nap was effective." The device sends this feedback to the server, which stores the received feedback in a database. The collected feedback and sentiment data are used to improve the AI ​​model and are incorporated into the new advice generation process.

[0274] Specific example

[0275] Dealing with nighttime crying for the first time: User fatigue

[0276] The user inputs that they are troubled by their baby's nighttime crying, and the emotion engine detects fatigue.

[0277] The server analyzes the cause of nighttime crying based on past behavioral and emotional data, and generates and sends advice to the device, such as "extend daytime naps" and "take time to relax in between childcare."

[0278] The device notifies the user of advice, and the user acts upon it.

[0279] First fever and anxiety

[0280] The user inputs that the baby's body temperature is high, and the emotion engine detects anxiety.

[0281] The server analyzes information based on general fever and anxiety, generates advice such as, "Stay hydrated, and stay calm," and sends it to the device.

[0282] The device notifies the user of advice, and the user acts upon it.

[0283] In this way, by taking into account the user's emotional state, this system provides more individualized and appropriate childcare support, reducing the burden of childcare and improving its quality.

[0284] The following describes the process flow.

[0285] Step 1:

[0286] The user downloads and launches the application. The user enters basic information such as name, email address, and password into the account creation form.

[0287] Step 2:

[0288] The terminal sends the entered basic information to the server. The server saves the received data in the database and generates an account.

[0289] Step 3:

[0290] The user enters the baby's basic information (date of birth, gender, health information, etc.) into the application. This information is also sent from the terminal to the server.

[0291] Step 4:

[0292] The server saves the received baby's basic information in the database and creates a baby profile.

[0293] Step 5:

[0294] In daily childcare, the user enters the baby's behavioral data (milk intake, sleep time, excretion status, etc.) into the application.

[0295] Step 6:

[0296] The terminal sends the entered behavioral data to the server. The server saves the received behavioral data in the database.

[0297] Step 7:

[0298] The terminal displays a reminder to prompt the user to input behavioral data at regular intervals. As a result, the user inputs the latest behavioral data.

[0299] Step 8:

[0300] The terminal generates the user's emotional data using an emotion engine that analyzes emotions from the user's input content, speech, and facial expressions.

[0301] Step 9:

[0302] The terminal sends the generated emotional data to the server. The server stores the received emotional data in the database.

[0303] Step 10:

[0304] The server extracts the latest behavioral data and emotional data from the database. The extracted data is preprocessed (such as data cleaning and complementing missing data).

[0305] Step 11:

[0306] The server inputs the preprocessed data into the AI model and performs analysis. The AI model evaluates the current situation of the baby and the user's emotional state.

[0307] Step 12:

[0308] Based on the analysis results of the AI model, the server generates specific advice on childcare. In doing so, the content takes into account the user's emotional state. [[ID=4२]]

[0309] Step 13:

[0310] The server formats the generated advice into a user-friendly form and sends it to the terminal.

[0311] Step 14:

[0312] The device displays the received advice to the user as a push notification or in-app message.

[0313] Step 15:

[0314] The user reviews the suggested advice and takes specific actions according to it.

[0315] Step 16:

[0316] Users provide feedback within the app regarding the effectiveness of the suggested advice.

[0317] Step 17:

[0318] The device sends the provided feedback to the server. The server stores the received feedback in its database.

[0319] Step 18:

[0320] The server improves the AI ​​model based on newly collected feedback and sentiment data, thereby improving the accuracy of future advice.

[0321] This series of steps allows users to receive appropriate childcare support that takes their emotional state into consideration, thereby reducing the burden of childcare and improving its quality.

[0322] (Example 2)

[0323] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server" and the smart device 14 as the "terminal".

[0324] Childcare involves a vast amount of information and emotional exchange, making it particularly burdensome for new parents and men on paternity leave. Traditional childcare support systems only provide general advice on childcare, lacking support that takes into account the individual emotional state and specific childcare situations of users. As a result, it is difficult for users to take appropriate immediate action, limiting the improvement of the quality of childcare. Therefore, there is a need for a system that combines and analyzes user behavioral data and emotional data to provide individualized and specific advice.

[0325] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0326] In this invention, the server includes means for the user to input basic information about childcare, means for periodically inputting data on the baby's behavior, and means for recognizing the user's emotions using an emotion engine. This makes it possible to periodically input basic information and behavioral data about childcare and analyze the user's emotions using an emotion engine.

[0327] A "user" is the entity that creates an account to use the childcare support system and inputs basic information and behavioral data.

[0328] "Basic information" refers to data such as the user's and baby's name, email address, date of birth, and health information.

[0329] A "server" is a device or system that receives basic information, behavioral data, and emotional data sent by users, stores them in a database, and analyzes them using an AI model.

[0330] A "database" is a system that stores and manages basic information, behavioral data, and emotional data received by a server, and allows for the extraction and manipulation of data as needed.

[0331] "Behavioral data" refers to detailed data related to childcare, such as the baby's daily milk intake, sleep duration, and bowel movements.

[0332] The "emotion engine" is a technology that analyzes emotions from user input, speech, and facial expressions, and uses the results to improve the quality of childcare support.

[0333] An "AI model" is an algorithm that uses machine learning and deep learning to analyze behavioral and emotional data and generate advice related to childcare.

[0334] "Advice" refers to information generated by the server based on analysis by an AI model, providing users with helpful information for childcare.

[0335] "Feedback" refers to data that users input about the effectiveness and areas for improvement of the advice they receive, and send to the server.

[0336] This invention is an AI advisor system intended to support childcare, and it incorporates an emotion engine that recognizes the user's emotions. Specific embodiments of the system will be described below.

[0337] User data entry

[0338] The user downloads and installs the childcare support application. After installation, the user opens the account creation screen and enters basic information such as name, email address, and password. This basic information is sent from the device to the server. The server stores the received information in a database and creates the account.

[0339] Next, the user enters basic information such as the baby's date of birth and health information on the account settings screen. This information is also sent from the device to the server, which stores it in a database to create the baby's profile.

[0340] Behavioral tracking

[0341] Users input data about their baby's daily activities (e.g., milk intake, sleep duration, bowel movements, etc.) using the app. This data is sent from the device to a server, which stores it in a database. The device also periodically displays reminders to prompt users to input the latest data.

[0342] Emotion recognition by an emotion engine

[0343] The device uses an emotion engine to analyze the user's emotions from their input, speech, and facial expressions. The analyzed emotion data is sent to a server and stored in a database. The emotion engine utilizes functions such as facial recognition, speech analysis, and text analysis.

[0344] AI analysis

[0345] The server extracts the latest behavioral and emotional data from the database. The extracted data is preprocessed and input into an AI model. The AI ​​model uses a common machine learning framework (e.g., TensorFlow or PyTorch). The AI ​​model analyzes the data and evaluates the state of the baby and the user.

[0346] Advice generation

[0347] Based on the analysis results of the AI ​​model, the server generates specific advice regarding childcare. The generated advice is formatted to take into account the user's emotional state. The advice is sent to the device and notified to the user.

[0348] User notifications

[0349] The device notifies the user of received advice via push notifications or in-app messages. By acting on the notified advice, the user can improve the quality of their parenting.

[0350] Feedback Collection

[0351] Users can provide feedback on the effectiveness of the advice they receive. This feedback is sent from the device to the server and stored in a database. The collected feedback and sentiment data are used to improve the AI ​​model.

[0352] Specific example

[0353] For example, if a user inputs that they are troubled by their baby's nighttime crying, and the emotion engine detects fatigue, the server analyzes the cause of the crying based on past behavioral and emotional data, generates advice such as "extend daytime naps" and "take time to relax between childcare duties," and sends it to the device. The device then notifies the user of this advice, and the user acts on it.

[0354] Furthermore, if the user inputs that the baby has a high temperature and the emotion engine detects anxiety, the server analyzes information based on general fever and anxiety data and generates and sends advice such as "Ensure adequate hydration" and "Stay calm." The device then notifies the user of this advice, and the user acts accordingly.

[0355] Example of a prompt

[0356] "My baby cries at night. What should I do?"

[0357] "My baby's temperature is high. What should I do?"

[0358] As described above, the system of the present invention can provide specific and individually optimized childcare support while also taking into account the user's emotional state.

[0359] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0360] Step 1:

[0361] The user downloads and installs the application, then enters basic information such as their name, email address, and password on the account creation screen. The entered basic information is sent from the device to the server. The server stores the received basic information in a database and creates the account.

[0362] Input: Name, email address, password

[0363] Data processing: None

[0364] Output: Account information is saved to the database.

[0365] Specific operation: Executes an SQL statement that sends information using HTTPS communication and saves it to the database.

[0366] Step 2:

[0367] The user enters basic information such as the baby's date of birth and health information, and sends it from their device to the server. The server saves the received information in a database and creates a profile of the baby.

[0368] Input: Baby's date of birth, gender, health information

[0369] Data processing: None

[0370] Output: The baby's profile is saved to the database.

[0371] Specific action: Execute an SQL statement to save the baby's information to the database.

[0372] Step 3:

[0373] During childcare, users input data about their baby's behavior (milk intake, sleep duration, bowel movements, etc.) into the app. The device sends this data to a server, which stores it in a database. The device also periodically displays reminders to prompt the user to input the latest behavior data.

[0374] Input: Milk intake, sleep duration, bowel movements

[0375] Data processing: None

[0376] Output: Behavioral data is saved to the database.

[0377] Specific actions: Display a reminder notification and send activity data.

[0378] Step 4:

[0379] The device uses an emotion engine to analyze the user's emotions from their input, speech, and facial expressions. The analysis results are sent to a server and stored in a database.

[0380] Input: User input text, speech, facial expressions

[0381] Data processing: Sentiment analysis

[0382] Output: The analyzed emotion data is saved to the database.

[0383] Specific operations: Uses facial recognition, speech analysis, and text analysis technologies.

[0384] Step 5:

[0385] The server extracts the latest behavioral and emotional data from the database. The extracted data is preprocessed and input into the AI ​​model. The AI ​​model analyzes the data and evaluates the state of the baby and the user.

[0386] Input: Latest behavioral data, emotional data

[0387] Data processing: Data cleaning, standardization, and feature engineering.

[0388] Output: Evaluation results

[0389] Specific operation: Extract data using SQL queries and input it into a model using a machine learning framework.

[0390] Step 6:

[0391] Based on the analysis results of the AI ​​model, the server generates specific advice regarding childcare. The generated advice is formatted and sent to the terminal, where it is notified to the user.

[0392] Input: Analysis results of the AI ​​model

[0393] Data processing: Advice generation, formatting.

[0394] Output: Advice is sent to the terminal.

[0395] Specific operation: Use a natural language generation model to perform formatting.

[0396] Step 7:

[0397] The device notifies the user of the generated advice via push notifications or in-app messages. The user then acts on the suggested advice.

[0398] Input: Advice

[0399] Data processing: None

[0400] Output: Sending push notifications or in-app messages

[0401] Specific action: Use the mobile platform's notification system.

[0402] Step 8:

[0403] Users provide feedback on the effectiveness of the advice. The device sends the feedback to the server, which stores the received feedback in a database. The feedback is used to improve the AI ​​model.

[0404] Input: Feedback

[0405] Data processing: Feedback analysis

[0406] Output: Feedback is saved to the database.

[0407] Specific actions: Enter data into the feedback form and execute the SQL statement to send the data.

[0408] (Application Example 2)

[0409] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as a "server" and the smart device 14 as a "terminal".

[0410] Conventional childcare support systems allowed users to input and analyze childcare data, but they lacked a function to consider the user's emotional state, which sometimes resulted in inappropriate advice. This invention aims to reduce stress during childcare and provide more effective support by recognizing the user's emotions and providing childcare advice that takes them into account.

[0411] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0412] In this invention, the server includes means for the user to input basic information about childcare, means for transmitting this basic information to the server and storing it in a database, means for periodically inputting baby behavior data, means for transmitting the input behavior data to the server and storing it in a database, means for extracting behavior data from the database and analyzing it using an AI model, means for generating childcare advice based on the analysis results and notifying the user, means for collecting user feedback and using it to improve the AI ​​model, means for incorporating an emotion recognition engine that analyzes emotions from the user's input, speech, and facial expressions and transmits the emotion data to the server, and means for analyzing the emotion data and generating childcare advice that takes into account the user's emotional state. This makes it possible to provide individualized and specific childcare advice according to the user's emotional state.

[0413] A "user" refers to a family member who is raising children and uses this system.

[0414] "Basic information regarding childcare" refers to all information necessary for childcare, such as the baby's date of birth, gender, and health information.

[0415] A "server" refers to a computer system that stores and analyzes information entered by users.

[0416] A "database" refers to a system on a server used to store basic user information, baby behavior data, analysis results, and other data.

[0417] "Baby behavioral data" refers to data on the baby's daily life, such as milk intake, sleep duration, and bowel movements.

[0418] An "AI model" refers to an artificial intelligence algorithm that analyzes baby behavior data and user emotional data to generate childcare advice.

[0419] An "emotion recognition engine" refers to a system that has the function of analyzing emotions from the user's input, speech, facial expressions, etc.

[0420] "Emotional data" refers to data that indicates the user's emotional state, as analyzed by an emotion recognition engine.

[0421] "Feedback" refers to the evaluation of the effectiveness and satisfaction level of advice provided by users.

[0422] System Overview

[0423] This invention is a system for childcare support in which the user inputs basic information about childcare and data on the baby's behavior. This data is then analyzed by AI to provide personalized advice. In particular, by also considering the user's emotional state, more appropriate support can be provided.

[0424] Hardware and software configuration

[0425] Device: The smartphone used by the user. It has an interface for inputting basic information and behavioral data. It also uses the camera and microphone as an emotion recognition engine.

[0426] Server: Uses a database and AI models to analyze received data and generate advice.

[0427] Database: Stores basic user information, baby behavior data, emotional data, etc.

[0428] Emotion recognition engine: A software module that performs speech recognition and facial expression recognition.

[0429] AI model: An artificial intelligence algorithm used for analysis.

[0430] Data entry and transmission

[0431] Users input basic childcare information and baby behavior data using their smartphones. This data is transmitted to a server via the internet and stored in a database. Additionally, an emotion recognition engine generates emotional data from the user's facial expressions and speech.

[0432] AI analysis and advice generation

[0433] The server extracts baby behavior data and user emotional data from the database and analyzes them using an AI model. Based on the analysis results, it generates specific childcare advice. The advice takes the user's emotional state into consideration, resulting in more individualized, specific, and appropriate content.

[0434] User notifications and feedback collection

[0435] The device notifies the user of the generated advice. Notification methods include push notifications and in-app messages. Users can also provide feedback on the effectiveness of the advice. This feedback is also sent to the server, stored in a database, and used to improve the AI ​​model.

[0436] Usage examples and prompts

[0437] Usage example

[0438] For example, if the user is feeling exhausted because their baby is crying more at night, the AI ​​model will analyze the situation and generate specific advice such as, "It would be good to let your baby take a longer nap during the day. Also, it would be good to set aside some time to relax in between childcare duties."

[0439] Example of a prompt

[0440] Recent behavioral data of the baby:

[0441] Milk intake: 120ml / session, 6 times a day

[0442] Sleep duration: 8 hours (nighttime), 3 hours of napping

[0443] User's emotional state:

[0444] Fatigue: 7 / 10

[0445] Anxiety: 5 / 10

[0446] Trends over the past 3 days:

[0447] The frequency of babies crying at night is increasing.

[0448] User fatigue is on the rise.

[0449] Advice to consider:

[0450] Extend nap time

[0451] An approach to ensure users have time to relax.

[0452] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0453] Step 1:

[0454] Users input basic information about childcare and data on their baby's behavior.

[0455] Users use their smartphones to enter basic information such as their name, email address, baby's date of birth, gender, and health information. They also enter daily behavioral data such as milk intake, sleep duration, and bowel movements. This data is entered manually by the user through the device's interface.

[0456] Input: Basic information about childcare, baby's behavioral data

[0457] Output: Input basic information and behavioral data

[0458] Step 2:

[0459] The entered basic information and behavioral data are sent to the server and stored in the database.

[0460] The terminal transmits user-entered information to a server via the internet. The server analyzes the received information and stores it in a database. The data is organized by category and used for subsequent analysis.

[0461] Input: Basic information about childcare, baby's behavioral data

[0462] Data processing: Organize by category

[0463] Output: Basic information and behavioral data stored in the database

[0464] Step 3:

[0465] Analysis of the user's emotional state using an emotion recognition engine.

[0466] The device uses a camera and microphone to capture the user's facial expressions and voice, and an emotion recognition engine analyzes this data. The resulting emotion data, which indicates the user's emotional state, is sent to the server.

[0467] Input: User's facial expressions, voice

[0468] Data processing: Analysis using an emotion recognition engine.

[0469] Output: Sentiment data

[0470] Step 4:

[0471] The server extracts behavioral and emotional data from the database and analyzes it using an AI model.

[0472] The server extracts the latest behavioral and emotional data stored in the database. This data is preprocessed and input into an AI model. The AI ​​model analyzes the data to provide insights for generating parenting advice.

[0473] Input: Behavioral data, emotional data

[0474] Data processing: Preprocessing, analysis using AI models.

[0475] Output: Analysis results from the AI ​​model

[0476] Step 5:

[0477] Based on the analysis results, the server generates childcare advice and notifies the user.

[0478] The server generates appropriate parenting advice based on the analysis results of the AI ​​model. The generated advice takes into account the user's emotional state and is sent to the user's device as a push notification or in-app message.

[0479] Input: Analysis results

[0480] Data processing: Generating childcare advice

[0481] Output: Childcare advice notification

[0482] Step 6:

[0483] Users provide feedback on the effectiveness of the advice.

[0484] Users input feedback on the advice they receive, including its effectiveness and satisfaction level, via their device and send it to the server. The server stores the received feedback in a database and uses it to improve the AI ​​model.

[0485] Input: Advice Feedback

[0486] Data processing: Saving feedback

[0487] Output: Saved feedback, AI model improvements

[0488] In this way, the system efficiently provides support related to childcare through a series of processes, from data input from users to feedback.

[0489] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0490] Data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of data generation model 58 is ChatGPT (registered trademark) (Internet search).<URL: https: / / openai.com / blog / chatgpt> ), Gemini (registered trademark) (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0491] In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and the specific processing may also be performed by the smart device 14.

[0492] [Second Embodiment]

[0493] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0494] As shown in Figure 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0495] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0496] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication interface 44. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, and camera 42 are also connected to the bus 52.

[0497] The microphone 238 receives voice signals from the user 20 and receives instructions from the user 20. The microphone 238 captures the voice signals from the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0498] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the area around the user 20 (for example, an imaging range defined by a field of view equivalent to the width of a typical healthy person's field of vision).

[0499] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0500] Figure 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Figure 4, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0501] The specific processing program 56 is an example of a "program" relating to the technology of this disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0502] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0503] In the smart glasses 214, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0504] Next, the identification processing performed by the identification processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the smart glasses 214 will be referred to as the "terminal".

[0505] This invention is an AI advisor system that is helpful for first-time mothers and men taking paternity leave when it comes to childcare, and it is implemented as follows.

[0506] User data entry

[0507] The user downloads and installs the application and creates an account. During account creation, the user enters basic information such as their name, email address, and password, and this information is sent from the device to the server. The server stores the received data in a database and generates the account.

[0508] Next, the user enters the baby's basic information (date of birth, gender, health information, etc.). This basic information is also sent from the device to the server, which stores it in a database to create the baby's profile.

[0509] Behavioral tracking

[0510] In the course of daily childcare, users input data about their baby's activities into the app. For example, they input information such as milk intake, sleep duration, and bowel movements. The device sends this activity data to a server, which stores the received data in a database.

[0511] Furthermore, the device displays reminders at regular intervals prompting the user to input behavioral data, providing an environment that makes it easy for users to enter the latest behavioral data.

[0512] AI analysis

[0513] The server extracts the latest behavioral data from the database and analyzes it using an AI model. Specifically, the server preprocesses the data, including past behavioral data, and inputs it into the AI ​​model. This AI model analyzes, for example, the baby's sleep patterns and milk intake to assess the baby's condition.

[0514] Advice generation

[0515] Based on the analysis results of the AI ​​model, the server generates specific advice regarding childcare. The generated advice is then formatted by the server into a format that is easy for the user to understand and sent to the device.

[0516] For example, if a baby's nighttime crying increases, the server generates specific advice based on the AI ​​model's analysis, such as "It would be good to let the baby take a longer nap during the day," and displays it on the device.

[0517] User notifications

[0518] The device notifies the user of the advice it has received. The notification appears as a push notification or in-app message, and the user acts on the suggested advice.

[0519] Feedback Collection

[0520] Users can provide feedback on the effectiveness of the advice. For example, they can send evaluations within the app such as, "Extending the nap was effective." The device sends this feedback to the server, which stores the received feedback in a database. The collected feedback is then incorporated into the new advice generation process to improve the AI ​​model.

[0521] Specific example

[0522] Dealing with nighttime crying for the first time:

[0523] The user enters that they are having trouble with their baby crying at night.

[0524] The server analyzes the cause of nighttime crying based on past behavioral data and sends advice to the device, such as "extend daytime naps."

[0525] The device notifies the user of advice, and the user acts upon it.

[0526] First fever:

[0527] The user enters that the baby's body temperature is high.

[0528] The server analyzes general fever information and the baby's health information, and sends advice such as "Please ensure adequate hydration" to the device.

[0529] The device notifies the user of advice, and the user acts upon it.

[0530] In this way, the AI ​​advisor system provides an environment where users can receive appropriate childcare support, thereby reducing the burden of childcare and improving the quality of childcare.

[0531] The following describes the processing flow.

[0532] Step 1:

[0533] The user downloads and launches the application. They then enter basic information such as their name, email address, and password into the account creation form.

[0534] Step 2:

[0535] The terminal sends the entered basic information to the server. The server stores the received data in a database and creates an account.

[0536] Step 3:

[0537] The user enters the baby's basic information (date of birth, gender, health information, etc.) into the application. This information is also sent from the device to the server.

[0538] Step 4:

[0539] The server stores the baby's basic information in a database and creates a profile of the baby.

[0540] Step 5:

[0541] In the course of daily childcare, users input data about their baby's activities (such as milk intake, sleep duration, and bowel movements) into the app.

[0542] Step 6:

[0543] The terminal sends the entered behavioral data to the server. The server stores the received behavioral data in a database.

[0544] Step 7:

[0545] The device displays reminders at regular intervals prompting the user to enter behavioral data. This ensures the user enters the latest behavioral data.

[0546] Step 8:

[0547] The server extracts historical and recent behavioral data from the database. It then performs data preprocessing (data cleaning, supplementing missing data, etc.).

[0548] Step 9:

[0549] The server inputs the pre-processed data into the AI ​​model and performs the analysis. The AI ​​model evaluates the baby's current condition and performs the necessary analysis.

[0550] Step 10:

[0551] The server generates specific childcare advice based on the analysis results of the AI ​​model. The generated advice is then formatted in a way that is easy for the user to understand.

[0552] Step 11:

[0553] The server sends formatted advice to the device. The device then displays this advice to the user as a push notification or in-app message.

[0554] Step 12:

[0555] The user reviews the advice received and takes the necessary actions. For example, they might take specific measures such as extending the baby's nap time.

[0556] Step 13:

[0557] Users provide feedback on the effectiveness of the advice within the app. For example, they can input whether the advice was useful and what effect it had.

[0558] Step 14:

[0559] The device collects feedback and sends it to the server. The server stores the received feedback in a database.

[0560] Step 15:

[0561] The server improves the AI ​​model based on the newly collected feedback. This will improve the accuracy of future advice.

[0562] In this way, this system provides users with an environment where they can easily receive childcare support, reducing the burden of childcare and improving the quality of childcare.

[0563] (Example 1)

[0564] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server," and the smart glasses 214 will be referred to as the "terminal."

[0565] In modern childcare, it is crucial for first-time parents and men taking paternity leave to receive effective and appropriate childcare support. However, a lack of knowledge and experience regarding childcare makes it difficult to understand a baby's behavior and respond appropriately. In particular, a swift and accurate response is required for abnormal behaviors such as nighttime crying and fever. Furthermore, there is a lack of systems to appropriately provide feedback on the effectiveness of measures taken by users and to continuously improve them.

[0566] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0567] In this invention, the server includes means for the user to input basic information about childcare, means for transmitting this basic information to a communication device and storing it in a data bank, means for periodically inputting baby behavior data, means for transmitting the input behavior data to a communication device and storing it in a data bank, means for extracting behavior data from the data bank and analyzing it using a generative AI model, means for generating childcare advice based on the analysis results and notifying the user, means for collecting user feedback and using it to improve the generative AI model, means for displaying a reminder to prompt the user to input behavior data at regular intervals, and means for generating childcare advice in text format and formatting it into an easy-to-understand format. As a result, the user can receive appropriate childcare support, improve the quality of childcare, and reduce the burden of childcare.

[0568] A "user" is someone who inputs information about childcare and receives support.

[0569] "Basic information" refers to data such as the user's and the baby's name, email address, date of birth, gender, and health information.

[0570] A "communication device" is a device that includes hardware and software for sending and receiving data between a user's terminal and a server.

[0571] A "data bank" refers to a database system that stores and manages collected basic information and behavioral data.

[0572] "Behavioral data" refers to data about a baby's daily behavior, such as milk intake, sleep duration, and bowel movements.

[0573] A "generative AI model" refers to an artificial intelligence model that analyzes collected data to generate advice related to childcare.

[0574] "Analysis" refers to the process by which a generative AI model evaluates specific patterns and trends based on behavioral data and creates advice.

[0575] "Advice" refers to specific instructions and suggestions regarding childcare provided to the user based on the analysis results of the generated AI model.

[0576] A "reminder" refers to a notification or alert that prompts the user to input behavioral data at regular intervals.

[0577] "Text format" refers to a method of presenting generated advice in text or sentences that are easy for the user to understand.

[0578] "Feedback" refers to evaluations and comments that users provide regarding the effectiveness of advice.

[0579] This invention is an AI advisor system aimed at providing first-time mothers and men taking paternity leave with effective childcare support by offering information related to childcare. This system includes the following means and processes.

[0580] User data entry

[0581] Users download and install a dedicated application and create an account. Account creation requires entering basic information such as name, email address, and password. This basic information is sent from the device to the server, which stores the received data in a database and generates the account.

[0582] Furthermore, users enter basic information such as the baby's name, date of birth, gender, and health information. This information is also sent from the device to the server, stored in a database, and used to create the baby's profile.

[0583] Input of behavioral data

[0584] During daily childcare activities, users input data about their baby's behavior, such as milk intake, sleep duration, and bowel movements, into the app. The device then sends this data to a server, where it is stored in a database.

[0585] Furthermore, the device will display reminder notifications prompting the user to input behavioral data at regular intervals, making it easy to enter the latest behavioral data.

[0586] AI analysis

[0587] The server extracts the latest behavioral data from the database and analyzes it using a generative AI model. This process is carried out using a dedicated AI framework (e.g., TensorFlow, PyTorch). After preprocessing, which includes past behavioral data, the data is input into the AI ​​model to evaluate the baby's behavioral patterns and state.

[0588] Advice generation

[0589] Based on the analysis results of the AI ​​model, the server generates specific advice regarding childcare. The generated advice is formatted in text and summarized for easy understanding. The server sends this advice to the terminal and notifies the user.

[0590] For example, if a baby starts crying frequently at night, the AI ​​model will analyze the cause and provide specific advice such as, "It would be good to let the baby take a longer nap during the day."

[0591] User notifications

[0592] The device displays received advice to the user as a push notification or in-app message. This allows the user to receive advice at the appropriate time.

[0593] Feedback Collection

[0594] Users can provide feedback on the effectiveness of the advice. For example, they can send ratings and comments within the app, such as "Extending the nap was effective." This feedback is sent from the device to the server and stored in a database. It is also used as data to improve the AI ​​model.

[0595] Specific example

[0596] Dealing with nighttime crying for the first time:

[0597] The user enters that they are having trouble with their baby crying at night.

[0598] The server analyzes the cause of nighttime crying based on past behavioral data and sends advice to the device, such as "extend daytime naps."

[0599] The device notifies the user of advice, and the user acts upon it.

[0600] First fever:

[0601] The user enters that the baby's body temperature is high.

[0602] The server analyzes general fever information and the baby's health information, and sends advice such as "Please ensure adequate hydration" to the device.

[0603] The device notifies the user of advice, and the user acts upon it.

[0604] In this way, the AI ​​advisor system provides users with an environment to receive effective childcare support, improving the quality of childcare and reducing the burden of childcare.

[0605] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0606] Step 1:

[0607] Create an account

[0608] The user downloads and installs the application. After installation, they enter basic information such as their name, email address, and password on the account creation screen. This information is sent from the device to the server, which stores the received data in its database and generates a new account.

[0609] Specific actions:

[0610] The user enters their name, email address, and password into the input form and clicks the "Create Account" button.

[0611] The device sends the entered data to the server via an API request.

[0612] The server validates the received data, stores it in the database, and generates and returns a new user ID.

[0613] Input: Basic information such as name, email address, and password.

[0614] Output: New User ID

[0615] Step 2:

[0616] Enter your baby's basic information

[0617] Users enter their baby's name, date of birth, gender, health information, etc., within the app. This information is sent from the device to the server, which stores it in a database to create a profile of the baby.

[0618] Specific actions:

[0619] The user enters the baby's information and presses the "Save" button.

[0620] The device sends the entered baby information to the server.

[0621] The server validates the information and stores it in the database.

[0622] Input: Baby's name, date of birth, gender, health information

[0623] Output: Baby profile

[0624] Step 3:

[0625] Input of behavioral data

[0626] Users input daily childcare data, such as milk intake, sleep duration, and bowel movements, into the app. This data is sent from the device to a server, which stores it in a database. The device also displays reminder notifications at regular intervals to prompt users to input behavioral data.

[0627] Specific actions:

[0628] The user launches the app, enters information such as milk intake, sleep duration, and bowel movements, and then presses the "Save" button.

[0629] The device sends this data to the server.

[0630] The server validates the data and stores it in the database.

[0631] The device displays reminder notifications at regular intervals.

[0632] Input: Milk intake, sleep duration, bowel movements

[0633] Output: Behavioral data stored in the database

[0634] Step 4:

[0635] AI analysis

[0636] The server extracts the latest behavioral data from the database and analyzes it using a generative AI model. This analysis involves preprocessing, including past behavioral data, and then inputting this data into the AI ​​model to evaluate behavioral patterns.

[0637] Specific actions:

[0638] The server extracts the latest behavioral data from the database.

[0639] The server preprocesses the data, inputs it into the AI ​​model, and performs the analysis.

[0640] Example: Analyzing sleep data and other information to identify the cause of nighttime crying.

[0641] Input: Behavioral data extracted from the database

[0642] Output: Evaluation results of behavioral patterns by AI model

[0643] Step 5:

[0644] Advice generation

[0645] The server generates specific childcare advice based on the AI ​​analysis results. The generated advice is formatted as text and sent to the terminal.

[0646] Specific actions:

[0647] The server generates parenting advice as text based on the AI ​​analysis results.

[0648] Example: Specific advice such as, "It would be good to let your child take a slightly longer nap during the day."

[0649] The server sends the generated advice to the terminal.

[0650] Input: AI analysis results

[0651] Output: Parenting advice in text format

[0652] Step 6:

[0653] Advice notification

[0654] The device notifies the user of advice received from the server. These notifications are delivered via push notifications or in-app messages.

[0655] Specific actions:

[0656] The terminal receives advice from the server.

[0657] The device will display this to the user via push notifications or in-app messages.

[0658] Input: Advice from the server

[0659] Output: Notification to the user

[0660] Step 7:

[0661] Feedback Collection

[0662] Users can provide feedback on the effectiveness of the advice. For example, they can send ratings and comments within the app, such as "Extending the nap was effective." This feedback is sent from the device to the server and stored in a database. The collected feedback is used to improve the generative AI model.

[0663] Specific actions:

[0664] The user fills out a feedback form within the app and presses the "Submit" button.

[0665] The device sends feedback to the server.

[0666] The server stores the feedback in a database and uses it to update the AI ​​model.

[0667] Input: User feedback

[0668] Output: Feedback stored in the data bank and improvements to the AI ​​model.

[0669] (Application Example 1)

[0670] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server," and the smart glasses 214 will be referred to as the "terminal."

[0671] The challenge lies in reducing the burden on new parents and those who support them, and in providing appropriate childcare support. In particular, it is necessary to reduce the burden of preparing and choosing meals during childcare, creating an environment where parents can focus on childcare. Furthermore, providing timely and appropriate advice based on the childcare situation is also crucial.

[0672] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0673] In this invention, the server includes means for the user to input basic information about childcare, means for transmitting this basic information to the server and storing it in a database, means for periodically inputting baby behavior data, means for transmitting the input behavior data to the server and storing it in a database, means for extracting behavior data from the database and analyzing it using an AI model, means for generating childcare advice based on the analysis results and notifying the user, means for collecting user feedback and using it to improve the AI ​​model, a food delivery function that suggests meal options according to the childcare situation, and means for notifying the user of advice and suggestions via push notifications at the necessary time. This makes it possible to reduce the burden of selecting and preparing meals during childcare and to provide appropriate childcare advice in a timely manner.

[0674] A "user" refers to a person who inputs information related to childcare and uses the childcare support system.

[0675] A "server" refers to a computer system that receives data sent by users, stores it in a database, and runs AI models.

[0676] A "database" refers to digital storage used to store and manage basic information and behavioral data related to childcare.

[0677] "Baby behavioral data" refers to information related to childcare, such as milk intake, sleep duration, and bowel movements.

[0678] An "AI model" refers to a machine learning or deep learning algorithm used to analyze childcare-related data and generate appropriate advice.

[0679] "Childcare advice" refers to specific childcare-related advice provided to users based on the results of analysis by an AI model.

[0680] "Feedback" refers to evaluations and opinions regarding the effectiveness and application of advice provided by users.

[0681] The "food delivery function" refers to a feature that suggests appropriate meal options based on childcare circumstances and assists with meal preparation.

[0682] "Push notifications" refer to a function that allows a system to send information and advice to users in real time.

[0683] "Basic information" refers to initial setup information about the user and the baby.

[0684] This invention relates to a system in which a user inputs basic information about childcare, and an AI model provides appropriate childcare advice based on that information. It also includes a food delivery function that suggests meal options according to the childcare situation.

[0685] System Overview

[0686] The childcare support system consists of user terminals and a server. Users input basic information and baby behavior data using a smartphone or other device, and this data is sent to the server and stored in a database. The server uses an AI model to analyze the data, generate childcare advice, and notify the user. In addition, a food delivery function suggests meal options tailored to the childcare situation.

[0687] Hardware and software to be used

[0688] User devices: such as smartphones and tablets.

[0689] Server: A cloud server with high-performance computing resources.

[0690] Database: A relational database used to store basic information and behavioral data related to childcare.

[0691] AI model: A machine learning or deep learning model used to analyze childcare data and generate advice.

[0692] Food delivery API: An API for an external service used to suggest meal options.

[0693] Explanation of the program's processing

[0694] The server receives basic information and baby behavior data sent from the user's terminal and stores it in a database. The stored data is periodically analyzed by an AI model. Based on past data, the AI ​​model evaluates the baby's condition and generates appropriate childcare advice. The generated advice is then notified to the user's terminal from the server.

[0695] Furthermore, the server uses a food delivery function to suggest appropriate meal options based on the childcare situation. This information is also sent to the user's device via push notification.

[0696] Specific example

[0697] For first-time nighttime crying:

[0698] The user enters that they are having trouble with their baby crying at night.

[0699] The server analyzes the cause of nighttime crying based on past behavioral data and generates advice such as "extend daytime naps."

[0700] This advice will be sent to the user's device.

[0701] The food delivery feature suggests easy meal options tailored to the user's needs.

[0702] Example of a prompt

[0703] (Example of a prompt message)

[0704] I've entered my baby's sleep and milk intake data (as they're experiencing persistent nighttime crying) into the parenting app. Please provide optimal parenting advice and recommended food delivery menus.

[0705] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0706] Step 1:

[0707] Users enter basic childcare information into a smartphone app. This information includes their name, email address, baby's date of birth, gender, and health information. This basic information is sent from the device to a server, which stores the information in a database.

[0708] Step 2:

[0709] Users input daily activity data about their baby into the app. This data includes milk intake, sleep duration, and bowel movements. This data is also sent from the device to the server, which stores it in a database. Upon receiving the data, the server prepares to provide parenting advice based on that data.

[0710] Step 3:

[0711] The server extracts the latest behavioral data from the database at regular intervals and inputs it into the AI ​​model. The AI ​​model performs behavioral analysis, including past data, to evaluate the baby's condition and patterns. In this evaluation process, data preprocessing such as normalization and feature extraction is performed. The input is behavioral data from the database, and the output is an evaluation of the baby's condition.

[0712] Step 4:

[0713] The server generates specific childcare advice based on the analysis results of the AI ​​model. For example, if a baby's nighttime crying increases, the AI ​​model might suggest, "It would be good to let the baby take a longer nap during the day." This advice generation uses both past and current behavioral data.

[0714] Step 5:

[0715] The server notifies the user's device of the generated advice. The device displays the advice to the user as a push notification or in-app message. The user can then act according to the received advice. The input is the advice from the server, and the output is the notification to the user.

[0716] Step 6:

[0717] The server collects feedback from users. For example, a user might input a rating such as, "Extending my nap was effective." This feedback is sent from the terminal to the server, which stores the information in a database. The feedback is used to improve the AI ​​model and is reflected in the new advice generation process. The input is user feedback, and the output is the storage in the database and the improvement of the AI ​​model.

[0718] Step 7:

[0719] The server utilizes a food delivery function that suggests meal options tailored to the childcare situation based on the analysis results of an AI model. The server calls an external food delivery API to retrieve appropriate menus and notifies the user's terminal. The input consists of the analysis results of the AI ​​model and menu information from the food delivery API, and the output is a notification of meal options to the user.

[0720] Step 8:

[0721] Users receive notifications of childcare advice and meal options, and then act on them. When users input the results and feedback of implementing the advice within the app, this data is also sent to the server and used to generate future advice. The input is the user's actions and feedback, and the output is the transmission of data to the server and improvement of the accuracy of the AI ​​model.

[0722] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0723] This invention combines an AI advisor system that is helpful for first-time mothers and men taking paternity leave when it comes to childcare with an emotion engine that recognizes the user's emotions, and is implemented as follows.

[0724] User data entry

[0725] The user downloads and installs the application and creates an account. During account creation, the user enters basic information such as their name, email address, and password, and this information is sent from the device to the server. The server stores the received basic information in a database and generates the account.

[0726] Next, the user enters the baby's basic information (date of birth, gender, health information, etc.). This basic information is also sent from the device to the server, which stores it in a database to create the baby's profile.

[0727] Behavioral tracking

[0728] In the course of daily childcare, users input data about their baby's activities into the app. For example, they input information such as milk intake, sleep duration, and bowel movements. The device sends this activity data to a server, which stores the received data in a database.

[0729] Furthermore, the device displays reminders at regular intervals prompting the user to input behavioral data, and the user then enters the latest behavioral data.

[0730] Emotion recognition by an emotion engine

[0731] The device is equipped with an emotion engine that recognizes the user's emotions. The emotion engine analyzes the user's emotions from their input, speech, and facial expressions. The results of this analysis are sent to a server and stored in a database.

[0732] AI analysis

[0733] The server extracts the latest behavioral and emotional data from the database and analyzes it using an AI model. Specifically, the server preprocesses this data and inputs it into the AI ​​model. The AI ​​model analyzes, for example, the baby's sleep patterns, milk intake, and the user's emotional state, and evaluates the state of both the baby and the user.

[0734] Advice generation

[0735] Based on the analysis results of the AI ​​model, the server generates specific advice regarding childcare. The generated advice is formatted to take the user's emotional state into consideration and then sent to the device.

[0736] For example, if the baby's nighttime crying increases and the user is feeling exhausted, the AI ​​model will analyze the situation and generate specific advice such as, "It would be good to let the baby take a longer nap during the day. Also, it would be good to set aside some time to relax in between childcare duties."

[0737] User notifications

[0738] The device notifies the user of the advice it has received. The notification appears as a push notification or in-app message, and the user acts on the suggested advice.

[0739] Feedback Collection

[0740] Users can provide feedback on the effectiveness of the advice. For example, they can send evaluations within the app such as, "Extending the nap was effective." The device sends this feedback to the server, which stores the received feedback in a database. The collected feedback and sentiment data are used to improve the AI ​​model and are incorporated into the new advice generation process.

[0741] Specific example

[0742] Dealing with nighttime crying for the first time: User fatigue

[0743] The user inputs that they are troubled by their baby's nighttime crying, and the emotion engine detects fatigue.

[0744] The server analyzes the cause of nighttime crying based on past behavioral and emotional data, and generates and sends advice to the device, such as "extend daytime naps" and "take time to relax in between childcare."

[0745] The device notifies the user of advice, and the user acts upon it.

[0746] First fever and anxiety

[0747] The user inputs that the baby's body temperature is high, and the emotion engine detects anxiety.

[0748] The server analyzes information based on general fever and anxiety, generates advice such as, "Stay hydrated, and stay calm," and sends it to the device.

[0749] The device notifies the user of advice, and the user acts upon it.

[0750] In this way, by taking into account the user's emotional state, this system provides more individualized and appropriate childcare support, reducing the burden of childcare and improving its quality.

[0751] The following describes the processing flow.

[0752] Step 1:

[0753] The user downloads and launches the application. They then enter basic information such as their name, email address, and password into the account creation form.

[0754] Step 2:

[0755] The terminal sends the entered basic information to the server. The server stores the received data in a database and creates an account.

[0756] Step 3:

[0757] The user enters the baby's basic information (date of birth, gender, health information, etc.) into the application. This information is also sent from the device to the server.

[0758] Step 4:

[0759] The server stores the baby's basic information in a database and creates a profile of the baby.

[0760] Step 5:

[0761] In the course of daily childcare, users input data about their baby's activities (such as milk intake, sleep duration, and bowel movements) into the app.

[0762] Step 6:

[0763] The terminal sends the entered behavioral data to the server. The server stores the received behavioral data in a database.

[0764] Step 7:

[0765] The device displays reminders at regular intervals prompting the user to enter behavioral data. This ensures the user enters the latest behavioral data.

[0766] Step 8:

[0767] The device generates user emotion data using an emotion engine that analyzes user input, speech, and facial expressions.

[0768] Step 9:

[0769] The device sends the generated emotion data to the server. The server stores the received emotion data in a database.

[0770] Step 10:

[0771] The server extracts the latest behavioral and sentiment data from the database. The extracted data is preprocessed (data cleaning, supplementing missing data, etc.).

[0772] Step 11:

[0773] The server inputs the pre-processed data into the AI ​​model and performs the analysis. The AI ​​model evaluates the baby's current condition and the user's emotional state.

[0774] Step 12:

[0775] The server generates specific childcare advice based on the analysis results of the AI ​​model, taking into account the user's emotional state.

[0776] Step 13:

[0777] The server formats the generated advice into a format that is easy for the user to understand and sends it to the terminal.

[0778] Step 14:

[0779] The device displays the received advice to the user as a push notification or in-app message.

[0780] Step 15:

[0781] The user reviews the suggested advice and takes specific actions according to it.

[0782] Step 16:

[0783] Users provide feedback within the app regarding the effectiveness of the suggested advice.

[0784] Step 17:

[0785] The device sends the provided feedback to the server. The server stores the received feedback in its database.

[0786] Step 18:

[0787] The server improves the AI ​​model based on newly collected feedback and sentiment data, thereby improving the accuracy of future advice.

[0788] This series of steps allows users to receive appropriate childcare support that takes their emotional state into consideration, thereby reducing the burden of childcare and improving its quality.

[0789] (Example 2)

[0790] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server" and the smart glasses 214 will be referred to as the "terminal".

[0791] Childcare involves a vast amount of information and emotional exchange, making it particularly burdensome for new parents and men on paternity leave. Traditional childcare support systems only provide general advice on childcare, lacking support that takes into account the individual emotional state and specific childcare situations of users. As a result, it is difficult for users to take appropriate immediate action, limiting the improvement of the quality of childcare. Therefore, there is a need for a system that combines and analyzes user behavioral data and emotional data to provide individualized and specific advice.

[0792] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0793] In this invention, the server includes means for the user to input basic information about childcare, means for periodically inputting data on the baby's behavior, and means for recognizing the user's emotions using an emotion engine. This makes it possible to periodically input basic information and behavioral data about childcare and analyze the user's emotions using an emotion engine.

[0794] A "user" is the entity that creates an account to use the childcare support system and inputs basic information and behavioral data.

[0795] "Basic information" refers to data such as the user's and baby's name, email address, date of birth, and health information.

[0796] A "server" is a device or system that receives basic information, behavioral data, and emotional data sent by users, stores them in a database, and analyzes them using an AI model.

[0797] A "database" is a system that stores and manages basic information, behavioral data, and emotional data received by a server, and allows for the extraction and manipulation of data as needed.

[0798] "Behavioral data" refers to detailed data related to childcare, such as the baby's daily milk intake, sleep duration, and bowel movements.

[0799] The "emotion engine" is a technology that analyzes emotions from user input, speech, and facial expressions, and uses the results to improve the quality of childcare support.

[0800] An "AI model" is an algorithm that uses machine learning and deep learning to analyze behavioral and emotional data and generate advice related to childcare.

[0801] "Advice" refers to information generated by the server based on analysis by an AI model, providing users with helpful information for childcare.

[0802] "Feedback" refers to data that users input about the effectiveness and areas for improvement of the advice they receive, and send to the server.

[0803] This invention is an AI advisor system intended to support childcare, and it incorporates an emotion engine that recognizes the user's emotions. Specific embodiments of the system will be described below.

[0804] User data entry

[0805] The user downloads and installs the childcare support application. After installation, the user opens the account creation screen and enters basic information such as name, email address, and password. This basic information is sent from the device to the server. The server stores the received information in a database and creates the account.

[0806] Next, the user enters basic information such as the baby's date of birth and health information on the account settings screen. This information is also sent from the device to the server, which stores it in a database to create the baby's profile.

[0807] Behavioral tracking

[0808] Users input data about their baby's daily activities (e.g., milk intake, sleep duration, bowel movements, etc.) using the app. This data is sent from the device to a server, which stores it in a database. The device also periodically displays reminders to prompt users to input the latest data.

[0809] Emotion recognition by an emotion engine

[0810] The device uses an emotion engine to analyze the user's emotions from their input, speech, and facial expressions. The analyzed emotion data is sent to a server and stored in a database. The emotion engine utilizes functions such as facial recognition, speech analysis, and text analysis.

[0811] AI analysis

[0812] The server extracts the latest behavioral and emotional data from the database. The extracted data is preprocessed and input into an AI model. The AI ​​model uses a common machine learning framework (e.g., TensorFlow or PyTorch). The AI ​​model analyzes the data and evaluates the state of the baby and the user.

[0813] Advice generation

[0814] Based on the analysis results of the AI ​​model, the server generates specific advice regarding childcare. The generated advice is formatted to take into account the user's emotional state. The advice is sent to the device and notified to the user.

[0815] User notifications

[0816] The device notifies the user of received advice via push notifications or in-app messages. By acting on the notified advice, the user can improve the quality of their parenting.

[0817] Feedback Collection

[0818] Users can provide feedback on the effectiveness of the advice they receive. This feedback is sent from the device to the server and stored in a database. The collected feedback and sentiment data are used to improve the AI ​​model.

[0819] Specific example

[0820] For example, if a user inputs that they are troubled by their baby's nighttime crying, and the emotion engine detects fatigue, the server analyzes the cause of the crying based on past behavioral and emotional data, generates advice such as "extend daytime naps" and "take time to relax between childcare duties," and sends it to the device. The device then notifies the user of this advice, and the user acts on it.

[0821] Furthermore, if the user inputs that the baby has a high temperature and the emotion engine detects anxiety, the server analyzes information based on general fever and anxiety data and generates and sends advice such as "Ensure adequate hydration" and "Stay calm." The device then notifies the user of this advice, and the user acts accordingly.

[0822] Example of a prompt

[0823] "My baby cries at night. What should I do?"

[0824] "My baby's temperature is high. What should I do?"

[0825] As described above, the system of the present invention can provide specific and individually optimized childcare support while also taking into account the user's emotional state.

[0826] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0827] Step 1:

[0828] The user downloads and installs the application, then enters basic information such as their name, email address, and password on the account creation screen. The entered basic information is sent from the device to the server. The server stores the received basic information in a database and creates the account.

[0829] Input: Name, email address, password

[0830] Data processing: None

[0831] Output: Account information is saved to the database.

[0832] Specific operation: Executes an SQL statement that sends information using HTTPS communication and saves it to the database.

[0833] Step 2:

[0834] The user enters basic information such as the baby's date of birth and health information, and sends it from their device to the server. The server saves the received information in a database and creates a profile of the baby.

[0835] Input: Baby's date of birth, gender, health information

[0836] Data processing: None

[0837] Output: The baby's profile is saved to the database.

[0838] Specific action: Execute an SQL statement to save the baby's information to the database.

[0839] Step 3:

[0840] During childcare, users input data about their baby's behavior (milk intake, sleep duration, bowel movements, etc.) into the app. The device sends this data to a server, which stores it in a database. The device also periodically displays reminders to prompt the user to input the latest behavior data.

[0841] Input: Milk intake, sleep duration, bowel movements

[0842] Data processing: None

[0843] Output: Behavioral data is saved to the database.

[0844] Specific actions: Display a reminder notification and send activity data.

[0845] Step 4:

[0846] The device uses an emotion engine to analyze the user's emotions from their input, speech, and facial expressions. The analysis results are sent to a server and stored in a database.

[0847] Input: User input text, speech, facial expressions

[0848] Data processing: Sentiment analysis

[0849] Output: The analyzed emotion data is saved to the database.

[0850] Specific operations: Uses facial recognition, speech analysis, and text analysis technologies.

[0851] Step 5:

[0852] The server extracts the latest behavioral and emotional data from the database. The extracted data is preprocessed and input into the AI ​​model. The AI ​​model analyzes the data and evaluates the state of the baby and the user.

[0853] Input: Latest behavioral data, emotional data

[0854] Data processing: Data cleaning, standardization, and feature engineering.

[0855] Output: Evaluation results

[0856] Specific operation: Extract data using SQL queries and input it into a model using a machine learning framework.

[0857] Step 6:

[0858] Based on the analysis results of the AI ​​model, the server generates specific advice regarding childcare. The generated advice is formatted and sent to the terminal, where it is notified to the user.

[0859] Input: Analysis results of the AI ​​model

[0860] Data processing: Advice generation, formatting.

[0861] Output: Advice is sent to the terminal.

[0862] Specific operation: Use a natural language generation model to perform formatting.

[0863] Step 7:

[0864] The device notifies the user of the generated advice via push notifications or in-app messages. The user then acts on the suggested advice.

[0865] Input: Advice

[0866] Data processing: None

[0867] Output: Sending push notifications or in-app messages

[0868] Specific action: Use the mobile platform's notification system.

[0869] Step 8:

[0870] Users provide feedback on the effectiveness of the advice. The device sends the feedback to the server, which stores the received feedback in a database. The feedback is used to improve the AI ​​model.

[0871] Input: Feedback

[0872] Data processing: Feedback analysis

[0873] Output: Feedback is saved to the database.

[0874] Specific actions: Enter data into the feedback form and execute the SQL statement to send the data.

[0875] (Application Example 2)

[0876] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as the "server," and the smart glasses 214 will be referred to as the "terminal."

[0877] Conventional childcare support systems allowed users to input and analyze childcare data, but they lacked a function to consider the user's emotional state, which sometimes resulted in inappropriate advice. This invention aims to reduce stress during childcare and provide more effective support by recognizing the user's emotions and providing childcare advice that takes them into account.

[0878] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0879] In this invention, the server includes means for the user to input basic information about childcare, means for transmitting this basic information to the server and storing it in a database, means for periodically inputting baby behavior data, means for transmitting the input behavior data to the server and storing it in a database, means for extracting behavior data from the database and analyzing it using an AI model, means for generating childcare advice based on the analysis results and notifying the user, means for collecting user feedback and using it to improve the AI ​​model, means for incorporating an emotion recognition engine that analyzes emotions from the user's input, speech, and facial expressions and transmits the emotion data to the server, and means for analyzing the emotion data and generating childcare advice that takes into account the user's emotional state. This makes it possible to provide individualized and specific childcare advice according to the user's emotional state.

[0880] A "user" refers to a family member who is raising children and uses this system.

[0881] "Basic information regarding childcare" refers to all information necessary for childcare, such as the baby's date of birth, gender, and health information.

[0882] A "server" refers to a computer system that stores and analyzes information entered by users.

[0883] A "database" refers to a system on a server used to store basic user information, baby behavior data, analysis results, and other data.

[0884] "Baby behavioral data" refers to data on the baby's daily life, such as milk intake, sleep duration, and bowel movements.

[0885] An "AI model" refers to an artificial intelligence algorithm that analyzes baby behavior data and user emotional data to generate childcare advice.

[0886] An "emotion recognition engine" refers to a system that has the function of analyzing emotions from the user's input, speech, facial expressions, etc.

[0887] "Emotional data" refers to data that indicates the user's emotional state, as analyzed by an emotion recognition engine.

[0888] "Feedback" refers to the evaluation of the effectiveness and satisfaction level of advice provided by users.

[0889] System Overview

[0890] This invention is a system for childcare support in which the user inputs basic information about childcare and data on the baby's behavior. This data is then analyzed by AI to provide personalized advice. In particular, by also considering the user's emotional state, more appropriate support can be provided.

[0891] Hardware and software configuration

[0892] Device: The smartphone used by the user. It has an interface for inputting basic information and behavioral data. It also uses the camera and microphone as an emotion recognition engine.

[0893] Server: Uses a database and AI models to analyze received data and generate advice.

[0894] Database: Stores basic user information, baby behavior data, emotional data, etc.

[0895] Emotion recognition engine: A software module that performs speech recognition and facial expression recognition.

[0896] AI model: An artificial intelligence algorithm used for analysis.

[0897] Data entry and transmission

[0898] Users input basic childcare information and baby behavior data using their smartphones. This data is transmitted to a server via the internet and stored in a database. Additionally, an emotion recognition engine generates emotional data from the user's facial expressions and speech.

[0899] AI analysis and advice generation

[0900] The server extracts baby behavior data and user emotional data from the database and analyzes them using an AI model. Based on the analysis results, it generates specific childcare advice. The advice takes the user's emotional state into consideration, resulting in more individualized, specific, and appropriate content.

[0901] User notifications and feedback collection

[0902] The device notifies the user of the generated advice. Notification methods include push notifications and in-app messages. Users can also provide feedback on the effectiveness of the advice. This feedback is also sent to the server, stored in a database, and used to improve the AI ​​model.

[0903] Usage examples and prompts

[0904] Usage example

[0905] For example, if the user is feeling exhausted because their baby is crying more at night, the AI ​​model will analyze the situation and generate specific advice such as, "It would be good to let your baby take a longer nap during the day. Also, it would be good to set aside some time to relax in between childcare duties."

[0906] Example of a prompt

[0907] Recent behavioral data of the baby:

[0908] Milk intake: 120ml / session, 6 times a day

[0909] Sleep duration: 8 hours (nighttime), 3 hours of napping

[0910] User's emotional state:

[0911] Fatigue: 7 / 10

[0912] Anxiety: 5 / 10

[0913] Trends over the past 3 days:

[0914] The frequency of babies crying at night is increasing.

[0915] User fatigue is on the rise.

[0916] Advice to consider:

[0917] Extend nap time

[0918] An approach to ensure users have time to relax.

[0919] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0920] Step 1:

[0921] Users input basic information about childcare and data on their baby's behavior.

[0922] Users use their smartphones to enter basic information such as their name, email address, baby's date of birth, gender, and health information. They also enter daily behavioral data such as milk intake, sleep duration, and bowel movements. This data is entered manually by the user through the device's interface.

[0923] Input: Basic information about childcare, baby's behavioral data

[0924] Output: Input basic information and behavioral data

[0925] Step 2:

[0926] The entered basic information and behavioral data are sent to the server and stored in the database.

[0927] The terminal transmits user-entered information to a server via the internet. The server analyzes the received information and stores it in a database. The data is organized by category and used for subsequent analysis.

[0928] Input: Basic information about childcare, baby's behavioral data

[0929] Data processing: Organize by category

[0930] Output: Basic information and behavioral data stored in the database

[0931] Step 3:

[0932] Analysis of the user's emotional state using an emotion recognition engine.

[0933] The device uses a camera and microphone to capture the user's facial expressions and voice, and an emotion recognition engine analyzes this data. The resulting emotion data, which indicates the user's emotional state, is sent to the server.

[0934] Input: User's facial expressions, voice

[0935] Data processing: Analysis using an emotion recognition engine.

[0936] Output: Sentiment data

[0937] Step 4:

[0938] The server extracts behavioral and emotional data from the database and analyzes it using an AI model.

[0939] The server extracts the latest behavioral and emotional data stored in the database. This data is preprocessed and input into an AI model. The AI ​​model analyzes the data to provide insights for generating parenting advice.

[0940] Input: Behavioral data, emotional data

[0941] Data processing: Preprocessing, analysis using AI models.

[0942] Output: Analysis results from the AI ​​model

[0943] Step 5:

[0944] Based on the analysis results, the server generates childcare advice and notifies the user.

[0945] The server generates appropriate parenting advice based on the analysis results of the AI ​​model. The generated advice takes into account the user's emotional state and is sent to the user's device as a push notification or in-app message.

[0946] Input: Analysis results

[0947] Data processing: Generating childcare advice

[0948] Output: Childcare advice notification

[0949] Step 6:

[0950] Users provide feedback on the effectiveness of the advice.

[0951] Users input feedback on the advice they receive, including its effectiveness and satisfaction level, via their device and send it to the server. The server stores the received feedback in a database and uses it to improve the AI ​​model.

[0952] Input: Advice Feedback

[0953] Data processing: Saving feedback

[0954] Output: Saved feedback, AI model improvements

[0955] In this way, the system efficiently provides support related to childcare through a series of processes, from data input from users to feedback.

[0956] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0957] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). One example of data generation model 58 is ChatGPT (Internet search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0958] In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and the specific processing may also be performed by the smart glasses 214.

[0959] [Third Embodiment]

[0960] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0961] As shown in Figure 5, the data processing system 310 includes a data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0962] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0963] The headset terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a display 343. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and display 343 are also connected to the bus 52.

[0964] The microphone 238 receives voice signals from the user 20 and receives instructions from the user 20. The microphone 238 captures the voice signals from the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0965] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the area around the user 20 (for example, an imaging range defined by a field of view equivalent to the width of a typical healthy person's field of vision).

[0966] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0967] Figure 6 shows an example of the main functions of the data processing device 12 and the headset terminal 314. As shown in Figure 6, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0968] The specific processing program 56 is an example of a "program" relating to the technology of this disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0969] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0970] In the headset terminal 314, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0971] Next, the specific processing performed by the specific processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the headset terminal 314 will be referred to as the "terminal".

[0972] This invention is an AI advisor system that is helpful for first-time mothers and men taking paternity leave when it comes to childcare, and it is implemented as follows.

[0973] User data entry

[0974] The user downloads and installs the application and creates an account. During account creation, the user enters basic information such as their name, email address, and password, and this information is sent from the device to the server. The server stores the received data in a database and generates the account.

[0975] Next, the user enters the baby's basic information (date of birth, gender, health information, etc.). This basic information is also sent from the device to the server, which stores it in a database to create the baby's profile.

[0976] Behavioral tracking

[0977] In the course of daily childcare, users input data about their baby's activities into the app. For example, they input information such as milk intake, sleep duration, and bowel movements. The device sends this activity data to a server, which stores the received data in a database.

[0978] Furthermore, the device displays reminders at regular intervals prompting the user to input behavioral data, providing an environment that makes it easy for users to enter the latest behavioral data.

[0979] AI analysis

[0980] The server extracts the latest behavioral data from the database and analyzes it using an AI model. Specifically, the server preprocesses the data, including past behavioral data, and inputs it into the AI ​​model. This AI model analyzes, for example, the baby's sleep patterns and milk intake to assess the baby's condition.

[0981] Advice generation

[0982] Based on the analysis results of the AI ​​model, the server generates specific advice regarding childcare. The generated advice is then formatted by the server into a format that is easy for the user to understand and sent to the device.

[0983] For example, if a baby's nighttime crying increases, the server generates specific advice based on the AI ​​model's analysis, such as "It would be good to let the baby take a longer nap during the day," and displays it on the device.

[0984] User notifications

[0985] The device notifies the user of the advice it has received. The notification appears as a push notification or in-app message, and the user acts on the suggested advice.

[0986] Feedback Collection

[0987] Users can provide feedback on the effectiveness of the advice. For example, they can send evaluations within the app such as, "Extending the nap was effective." The device sends this feedback to the server, which stores the received feedback in a database. The collected feedback is then incorporated into the new advice generation process to improve the AI ​​model.

[0988] Specific example

[0989] Dealing with nighttime crying for the first time:

[0990] The user enters that they are having trouble with their baby crying at night.

[0991] The server analyzes the cause of nighttime crying based on past behavioral data and sends advice to the device, such as "extend daytime naps."

[0992] The device notifies the user of advice, and the user acts upon it.

[0993] First fever:

[0994] The user enters that the baby's body temperature is high.

[0995] The server analyzes general fever information and the baby's health information, and sends advice such as "Please ensure adequate hydration" to the device.

[0996] The device notifies the user of advice, and the user acts upon it.

[0997] In this way, the AI ​​advisor system provides an environment where users can receive appropriate childcare support, thereby reducing the burden of childcare and improving the quality of childcare.

[0998] The following describes the processing flow.

[0999] Step 1:

[1000] The user downloads and launches the application. They then enter basic information such as their name, email address, and password into the account creation form.

[1001] Step 2:

[1002] The terminal sends the entered basic information to the server. The server stores the received data in a database and creates an account.

[1003] Step 3:

[1004] The user enters the baby's basic information (date of birth, gender, health information, etc.) into the application. This information is also sent from the device to the server.

[1005] Step 4:

[1006] The server stores the baby's basic information in a database and creates a profile of the baby.

[1007] Step 5:

[1008] In the course of daily childcare, users input data about their baby's activities (such as milk intake, sleep duration, and bowel movements) into the app.

[1009] Step 6:

[1010] The terminal sends the entered behavioral data to the server. The server stores the received behavioral data in a database.

[1011] Step 7:

[1012] The device displays reminders at regular intervals prompting the user to enter behavioral data. This ensures the user enters the latest behavioral data.

[1013] Step 8:

[1014] The server extracts historical and recent behavioral data from the database. It then performs data preprocessing (data cleaning, supplementing missing data, etc.).

[1015] Step 9:

[1016] The server inputs the pre-processed data into the AI ​​model and performs the analysis. The AI ​​model evaluates the baby's current condition and performs the necessary analysis.

[1017] Step 10:

[1018] The server generates specific childcare advice based on the analysis results of the AI ​​model. The generated advice is then formatted in a way that is easy for the user to understand.

[1019] Step 11:

[1020] The server sends formatted advice to the device. The device then displays this advice to the user as a push notification or in-app message.

[1021] Step 12:

[1022] The user reviews the advice received and takes the necessary actions. For example, they might take specific measures such as extending the baby's nap time.

[1023] Step 13:

[1024] Users provide feedback on the effectiveness of the advice within the app. For example, they can input whether the advice was useful and what effect it had.

[1025] Step 14:

[1026] The device collects feedback and sends it to the server. The server stores the received feedback in a database.

[1027] Step 15:

[1028] The server improves the AI ​​model based on the newly collected feedback. This will improve the accuracy of future advice.

[1029] In this way, this system provides users with an environment where they can easily receive childcare support, reducing the burden of childcare and improving the quality of childcare.

[1030] (Example 1)

[1031] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server," and the headset-type terminal 314 will be referred to as the "terminal."

[1032] In modern childcare, it is crucial for first-time parents and men taking paternity leave to receive effective and appropriate childcare support. However, a lack of knowledge and experience regarding childcare makes it difficult to understand a baby's behavior and respond appropriately. In particular, a swift and accurate response is required for abnormal behaviors such as nighttime crying and fever. Furthermore, there is a lack of systems to appropriately provide feedback on the effectiveness of measures taken by users and to continuously improve them.

[1033] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[1034] In this invention, the server includes means for the user to input basic information about childcare, means for transmitting this basic information to a communication device and storing it in a data bank, means for periodically inputting baby behavior data, means for transmitting the input behavior data to a communication device and storing it in a data bank, means for extracting behavior data from the data bank and analyzing it using a generative AI model, means for generating childcare advice based on the analysis results and notifying the user, means for collecting user feedback and using it to improve the generative AI model, means for displaying a reminder to prompt the user to input behavior data at regular intervals, and means for generating childcare advice in text format and formatting it into an easy-to-understand format. As a result, the user can receive appropriate childcare support, improve the quality of childcare, and reduce the burden of childcare.

[1035] A "user" is someone who inputs information about childcare and receives support.

[1036] "Basic information" refers to data such as the user's and the baby's name, email address, date of birth, gender, and health information.

[1037] A "communication device" is a device that includes hardware and software for sending and receiving data between a user's terminal and a server.

[1038] A "data bank" refers to a database system that stores and manages collected basic information and behavioral data.

[1039] "Behavioral data" refers to data about a baby's daily behavior, such as milk intake, sleep duration, and bowel movements.

[1040] A "generative AI model" refers to an artificial intelligence model that analyzes collected data to generate advice related to childcare.

[1041] "Analysis" refers to the process by which a generative AI model evaluates specific patterns and trends based on behavioral data and creates advice.

[1042] "Advice" refers to specific instructions and suggestions regarding childcare provided to the user based on the analysis results of the generated AI model.

[1043] A "reminder" refers to a notification or alert that prompts the user to input behavioral data at regular intervals.

[1044] "Text format" refers to a method of presenting generated advice in text or sentences that are easy for the user to understand.

[1045] "Feedback" refers to evaluations and comments that users provide regarding the effectiveness of advice.

[1046] This invention is an AI advisor system aimed at providing first-time mothers and men taking paternity leave with effective childcare support by offering information related to childcare. This system includes the following means and processes.

[1047] User data entry

[1048] Users download and install a dedicated application and create an account. Account creation requires entering basic information such as name, email address, and password. This basic information is sent from the device to the server, which stores the received data in a database and generates the account.

[1049] Furthermore, users enter basic information such as the baby's name, date of birth, gender, and health information. This information is also sent from the device to the server, stored in a database, and used to create the baby's profile.

[1050] Input of behavioral data

[1051] During daily childcare activities, users input data about their baby's behavior, such as milk intake, sleep duration, and bowel movements, into the app. The device then sends this data to a server, where it is stored in a database.

[1052] Furthermore, the device will display reminder notifications prompting the user to input behavioral data at regular intervals, making it easy to enter the latest behavioral data.

[1053] AI analysis

[1054] The server extracts the latest behavioral data from the database and analyzes it using a generative AI model. This process is carried out using a dedicated AI framework (e.g., TensorFlow, PyTorch). After preprocessing, which includes past behavioral data, the data is input into the AI ​​model to evaluate the baby's behavioral patterns and state.

[1055] Advice generation

[1056] Based on the analysis results of the AI ​​model, the server generates specific advice regarding childcare. The generated advice is formatted in text and summarized for easy understanding. The server sends this advice to the terminal and notifies the user.

[1057] For example, if a baby starts crying frequently at night, the AI ​​model will analyze the cause and provide specific advice such as, "It would be good to let the baby take a longer nap during the day."

[1058] User notifications

[1059] The device displays received advice to the user as a push notification or in-app message. This allows the user to receive advice at the appropriate time.

[1060] Feedback Collection

[1061] Users can provide feedback on the effectiveness of the advice. For example, they can send ratings and comments within the app, such as "Extending the nap was effective." This feedback is sent from the device to the server and stored in a database. It is also used as data to improve the AI ​​model.

[1062] Specific example

[1063] Dealing with nighttime crying for the first time:

[1064] The user enters that they are having trouble with their baby crying at night.

[1065] The server analyzes the cause of nighttime crying based on past behavioral data and sends advice to the device, such as "extend daytime naps."

[1066] The device notifies the user of advice, and the user acts upon it.

[1067] First fever:

[1068] The user enters that the baby's body temperature is high.

[1069] The server analyzes general fever information and the baby's health information, and sends advice such as "Please ensure adequate hydration" to the device.

[1070] The device notifies the user of advice, and the user acts upon it.

[1071] In this way, the AI ​​advisor system provides users with an environment to receive effective childcare support, improving the quality of childcare and reducing the burden of childcare.

[1072] The flow of the specific processing in Example 1 will be explained using Figure 11.

[1073] Step 1:

[1074] Create an account

[1075] The user downloads and installs the application. After installation, they enter basic information such as their name, email address, and password on the account creation screen. This information is sent from the device to the server, which stores the received data in its database and generates a new account.

[1076] Specific actions:

[1077] The user enters their name, email address, and password into the input form and clicks the "Create Account" button.

[1078] The device sends the entered data to the server via an API request.

[1079] The server validates the received data, stores it in the database, and generates and returns a new user ID.

[1080] Input: Basic information such as name, email address, and password.

[1081] Output: New User ID

[1082] Step 2:

[1083] Enter your baby's basic information

[1084] Users enter their baby's name, date of birth, gender, health information, etc., within the app. This information is sent from the device to the server, which stores it in a database to create a profile of the baby.

[1085] Specific actions:

[1086] The user enters the baby's information and presses the "Save" button.

[1087] The device sends the entered baby information to the server.

[1088] The server validates the information and stores it in the database.

[1089] Input: Baby's name, date of birth, gender, health information

[1090] Output: Baby profile

[1091] Step 3:

[1092] Input of behavioral data

[1093] Users input daily childcare data, such as milk intake, sleep duration, and bowel movements, into the app. This data is sent from the device to a server, which stores it in a database. The device also displays reminder notifications at regular intervals to prompt users to input behavioral data.

[1094] Specific actions:

[1095] The user launches the app, enters information such as milk intake, sleep duration, and bowel movements, and then presses the "Save" button.

[1096] The device sends this data to the server.

[1097] The server validates the data and stores it in the database.

[1098] The device displays reminder notifications at regular intervals.

[1099] Input: Milk intake, sleep duration, bowel movements

[1100] Output: Behavioral data stored in the database

[1101] Step 4:

[1102] AI analysis

[1103] The server extracts the latest behavioral data from the database and analyzes it using a generative AI model. This analysis involves preprocessing, including past behavioral data, and then inputting this data into the AI ​​model to evaluate behavioral patterns.

[1104] Specific actions:

[1105] The server extracts the latest behavioral data from the database.

[1106] The server preprocesses the data, inputs it into the AI ​​model, and performs the analysis.

[1107] Example: Analyzing sleep data and other information to identify the cause of nighttime crying.

[1108] Input: Behavioral data extracted from the database

[1109] Output: Evaluation results of behavioral patterns by AI model

[1110] Step 5:

[1111] Advice generation

[1112] The server generates specific childcare advice based on the AI ​​analysis results. The generated advice is formatted as text and sent to the terminal.

[1113] Specific actions:

[1114] The server generates parenting advice as text based on the AI ​​analysis results.

[1115] Example: Specific advice such as, "It would be good to let your child take a slightly longer nap during the day."

[1116] The server sends the generated advice to the terminal.

[1117] Input: AI analysis results

[1118] Output: Parenting advice in text format

[1119] Step 6:

[1120] Advice notification

[1121] The device notifies the user of advice received from the server. These notifications are delivered via push notifications or in-app messages.

[1122] Specific actions:

[1123] The terminal receives advice from the server.

[1124] The device will display this to the user via push notifications or in-app messages.

[1125] Input: Advice from the server

[1126] Output: Notification to the user

[1127] Step 7:

[1128] Feedback Collection

[1129] Users can provide feedback on the effectiveness of the advice. For example, they can send ratings and comments within the app, such as "Extending the nap was effective." This feedback is sent from the device to the server and stored in a database. The collected feedback is used to improve the generative AI model.

[1130] Specific actions:

[1131] The user fills out a feedback form within the app and presses the "Submit" button.

[1132] The device sends feedback to the server.

[1133] The server stores the feedback in a database and uses it to update the AI ​​model.

[1134] Input: User feedback

[1135] Output: Feedback stored in the data bank and improvements to the AI ​​model.

[1136] (Application Example 1)

[1137] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server," and the headset-type terminal 314 will be referred to as the "terminal."

[1138] The challenge lies in reducing the burden on new parents and those who support them, and in providing appropriate childcare support. In particular, it is necessary to reduce the burden of preparing and choosing meals during childcare, creating an environment where parents can focus on childcare. Furthermore, providing timely and appropriate advice based on the childcare situation is also crucial.

[1139] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[1140] In this invention, the server includes means for the user to input basic information about childcare, means for transmitting this basic information to the server and storing it in a database, means for periodically inputting baby behavior data, means for transmitting the input behavior data to the server and storing it in a database, means for extracting behavior data from the database and analyzing it using an AI model, means for generating childcare advice based on the analysis results and notifying the user, means for collecting user feedback and using it to improve the AI ​​model, a food delivery function that suggests meal options according to the childcare situation, and means for notifying the user of advice and suggestions via push notifications at the necessary time. This makes it possible to reduce the burden of selecting and preparing meals during childcare and to provide appropriate childcare advice in a timely manner.

[1141] A "user" refers to a person who inputs information related to childcare and uses the childcare support system.

[1142] A "server" refers to a computer system that receives data sent by users, stores it in a database, and runs AI models.

[1143] A "database" refers to digital storage used to store and manage basic information and behavioral data related to childcare.

[1144] "Baby behavioral data" refers to information related to childcare, such as milk intake, sleep duration, and bowel movements.

[1145] An "AI model" refers to a machine learning or deep learning algorithm used to analyze childcare-related data and generate appropriate advice.

[1146] "Childcare advice" refers to specific childcare-related advice provided to users based on the results of analysis by an AI model.

[1147] "Feedback" refers to evaluations and opinions regarding the effectiveness and application of advice provided by users.

[1148] The "food delivery function" refers to a feature that suggests appropriate meal options based on childcare circumstances and assists with meal preparation.

[1149] "Push notifications" refer to a function that allows a system to send information and advice to users in real time.

[1150] "Basic information" refers to initial setup information about the user and the baby.

[1151] This invention relates to a system in which a user inputs basic information about childcare, and an AI model provides appropriate childcare advice based on that information. It also includes a food delivery function that suggests meal options according to the childcare situation.

[1152] System Overview

[1153] The childcare support system consists of user terminals and a server. Users input basic information and baby behavior data using a smartphone or other device, and this data is sent to the server and stored in a database. The server uses an AI model to analyze the data, generate childcare advice, and notify the user. In addition, a food delivery function suggests meal options tailored to the childcare situation.

[1154] Hardware and software to be used

[1155] User devices: such as smartphones and tablets.

[1156] Server: A cloud server with high-performance computing resources.

[1157] Database: A relational database used to store basic information and behavioral data related to childcare.

[1158] AI model: A machine learning or deep learning model used to analyze childcare data and generate advice.

[1159] Food delivery API: An API for an external service used to suggest meal options.

[1160] Explanation of the program's processing

[1161] The server receives basic information and baby behavior data sent from the user's terminal and stores it in a database. The stored data is periodically analyzed by an AI model. Based on past data, the AI ​​model evaluates the baby's condition and generates appropriate childcare advice. The generated advice is then notified to the user's terminal from the server.

[1162] Furthermore, the server uses a food delivery function to suggest appropriate meal options based on the childcare situation. This information is also sent to the user's device via push notification.

[1163] Specific example

[1164] For first-time nighttime crying:

[1165] The user enters that they are having trouble with their baby crying at night.

[1166] The server analyzes the cause of nighttime crying based on past behavioral data and generates advice such as "extend daytime naps."

[1167] This advice will be sent to the user's device.

[1168] The food delivery feature suggests easy meal options tailored to the user's needs.

[1169] Example of a prompt

[1170] (Example of a prompt message)

[1171] I've entered my baby's sleep and milk intake data (as they're experiencing persistent nighttime crying) into the parenting app. Please provide optimal parenting advice and recommended food delivery menus.

[1172] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[1173] Step 1:

[1174] Users enter basic childcare information into a smartphone app. This information includes their name, email address, baby's date of birth, gender, and health information. This basic information is sent from the device to a server, which stores the information in a database.

[1175] Step 2:

[1176] Users input daily activity data about their baby into the app. This data includes milk intake, sleep duration, and bowel movements. This data is also sent from the device to the server, which stores it in a database. Upon receiving the data, the server prepares to provide parenting advice based on that data.

[1177] Step 3:

[1178] The server extracts the latest behavioral data from the database at regular intervals and inputs it into the AI ​​model. The AI ​​model performs behavioral analysis, including past data, to evaluate the baby's condition and patterns. In this evaluation process, data preprocessing such as normalization and feature extraction is performed. The input is behavioral data from the database, and the output is an evaluation of the baby's condition.

[1179] Step 4:

[1180] The server generates specific childcare advice based on the analysis results of the AI ​​model. For example, if a baby's nighttime crying increases, the AI ​​model might suggest, "It would be good to let the baby take a longer nap during the day." This advice generation uses both past and current behavioral data.

[1181] Step 5:

[1182] The server notifies the user's device of the generated advice. The device displays the advice to the user as a push notification or in-app message. The user can then act according to the received advice. The input is the advice from the server, and the output is the notification to the user.

[1183] Step 6:

[1184] The server collects feedback from users. For example, a user might input a rating such as, "Extending my nap was effective." This feedback is sent from the terminal to the server, which stores the information in a database. The feedback is used to improve the AI ​​model and is reflected in the new advice generation process. The input is user feedback, and the output is the storage in the database and the improvement of the AI ​​model.

[1185] Step 7:

[1186] The server utilizes a food delivery function that suggests meal options tailored to the childcare situation based on the analysis results of an AI model. The server calls an external food delivery API to retrieve appropriate menus and notifies the user's terminal. The input consists of the analysis results of the AI ​​model and menu information from the food delivery API, and the output is a notification of meal options to the user.

[1187] Step 8:

[1188] Users receive notifications of childcare advice and meal options, and then act on them. When users input the results and feedback of implementing the advice within the app, this data is also sent to the server and used to generate future advice. The input is the user's actions and feedback, and the output is the transmission of data to the server and improvement of the accuracy of the AI ​​model.

[1189] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[1190] This invention combines an AI advisor system that is helpful for first-time mothers and men taking paternity leave when it comes to childcare with an emotion engine that recognizes the user's emotions, and is implemented as follows.

[1191] User data entry

[1192] The user downloads and installs the application and creates an account. During account creation, the user enters basic information such as their name, email address, and password, and this information is sent from the device to the server. The server stores the received basic information in a database and generates the account.

[1193] Next, the user enters the baby's basic information (date of birth, gender, health information, etc.). This basic information is also sent from the device to the server, which stores it in a database to create the baby's profile.

[1194] Behavioral tracking

[1195] In the course of daily childcare, users input data about their baby's activities into the app. For example, they input information such as milk intake, sleep duration, and bowel movements. The device sends this activity data to a server, which stores the received data in a database.

[1196] Furthermore, the device displays reminders at regular intervals prompting the user to input behavioral data, and the user then enters the latest behavioral data.

[1197] Emotion recognition by an emotion engine

[1198] The device is equipped with an emotion engine that recognizes the user's emotions. The emotion engine analyzes the user's emotions from their input, speech, and facial expressions. The results of this analysis are sent to a server and stored in a database.

[1199] AI analysis

[1200] The server extracts the latest behavioral and emotional data from the database and analyzes it using an AI model. Specifically, the server preprocesses this data and inputs it into the AI ​​model. The AI ​​model analyzes, for example, the baby's sleep patterns, milk intake, and the user's emotional state, and evaluates the state of both the baby and the user.

[1201] Advice generation

[1202] Based on the analysis results of the AI ​​model, the server generates specific advice regarding childcare. The generated advice is formatted to take the user's emotional state into consideration and then sent to the device.

[1203] For example, if the baby's nighttime crying increases and the user is feeling exhausted, the AI ​​model will analyze the situation and generate specific advice such as, "It would be good to let the baby take a longer nap during the day. Also, it would be good to set aside some time to relax in between childcare duties."

[1204] User notifications

[1205] The device notifies the user of the advice it has received. The notification appears as a push notification or in-app message, and the user acts on the suggested advice.

[1206] Feedback Collection

[1207] Users can provide feedback on the effectiveness of the advice. For example, they can send evaluations within the app such as, "Extending the nap was effective." The device sends this feedback to the server, which stores the received feedback in a database. The collected feedback and sentiment data are used to improve the AI ​​model and are incorporated into the new advice generation process.

[1208] Specific example

[1209] Dealing with nighttime crying for the first time: User fatigue

[1210] The user inputs that they are troubled by their baby's nighttime crying, and the emotion engine detects fatigue.

[1211] The server analyzes the cause of nighttime crying based on past behavioral and emotional data, and generates and sends advice to the device, such as "extend daytime naps" and "take time to relax in between childcare."

[1212] The device notifies the user of advice, and the user acts upon it.

[1213] First fever and anxiety

[1214] The user inputs that the baby's body temperature is high, and the emotion engine detects anxiety.

[1215] The server analyzes information based on general fever and anxiety, generates advice such as, "Stay hydrated, and stay calm," and sends it to the device.

[1216] The device notifies the user of advice, and the user acts upon it.

[1217] In this way, by taking into account the user's emotional state, this system provides more individualized and appropriate childcare support, reducing the burden of childcare and improving its quality.

[1218] The following describes the processing flow.

[1219] Step 1:

[1220] The user downloads and launches the application. They then enter basic information such as their name, email address, and password into the account creation form.

[1221] Step 2:

[1222] The terminal sends the entered basic information to the server. The server stores the received data in a database and creates an account.

[1223] Step 3:

[1224] The user enters the baby's basic information (date of birth, gender, health information, etc.) into the application. This information is also sent from the device to the server.

[1225] Step 4:

[1226] The server stores the baby's basic information in a database and creates a profile of the baby.

[1227] Step 5:

[1228] In the course of daily childcare, users input data about their baby's activities (such as milk intake, sleep duration, and bowel movements) into the app.

[1229] Step 6:

[1230] The terminal sends the entered behavioral data to the server. The server stores the received behavioral data in a database.

[1231] Step 7:

[1232] The device displays reminders at regular intervals prompting the user to enter behavioral data. This ensures the user enters the latest behavioral data.

[1233] Step 8:

[1234] The device generates user emotion data using an emotion engine that analyzes user input, speech, and facial expressions.

[1235] Step 9:

[1236] The device sends the generated emotion data to the server. The server stores the received emotion data in a database.

[1237] Step 10:

[1238] The server extracts the latest behavioral and sentiment data from the database. The extracted data is preprocessed (data cleaning, supplementing missing data, etc.).

[1239] Step 11:

[1240] The server inputs the pre-processed data into the AI ​​model and performs the analysis. The AI ​​model evaluates the baby's current condition and the user's emotional state.

[1241] Step 12:

[1242] The server generates specific childcare advice based on the analysis results of the AI ​​model, taking into account the user's emotional state.

[1243] Step 13:

[1244] The server formats the generated advice into a format that is easy for the user to understand and sends it to the terminal.

[1245] Step 14:

[1246] The device displays the received advice to the user as a push notification or in-app message.

[1247] Step 15:

[1248] The user reviews the suggested advice and takes specific actions according to it.

[1249] Step 16:

[1250] Users provide feedback within the app regarding the effectiveness of the suggested advice.

[1251] Step 17:

[1252] The device sends the provided feedback to the server. The server stores the received feedback in its database.

[1253] Step 18:

[1254] The server improves the AI ​​model based on newly collected feedback and sentiment data, thereby improving the accuracy of future advice.

[1255] This series of steps allows users to receive appropriate childcare support that takes their emotional state into consideration, thereby reducing the burden of childcare and improving its quality.

[1256] (Example 2)

[1257] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server," and the headset-type terminal 314 will be referred to as the "terminal."

[1258] Childcare involves a vast amount of information and emotional exchange, making it particularly burdensome for new parents and men on paternity leave. Traditional childcare support systems only provide general advice on childcare, lacking support that takes into account the individual emotional state and specific childcare situations of users. As a result, it is difficult for users to take appropriate immediate action, limiting the improvement of the quality of childcare. Therefore, there is a need for a system that combines and analyzes user behavioral data and emotional data to provide individualized and specific advice.

[1259] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[1260] In this invention, the server includes means for the user to input basic information about childcare, means for periodically inputting data on the baby's behavior, and means for recognizing the user's emotions using an emotion engine. This makes it possible to periodically input basic information and behavioral data about childcare and analyze the user's emotions using an emotion engine.

[1261] A "user" is the entity that creates an account to use the childcare support system and inputs basic information and behavioral data.

[1262] "Basic information" refers to data such as the user's and baby's name, email address, date of birth, and health information.

[1263] A "server" is a device or system that receives basic information, behavioral data, and emotional data sent by users, stores them in a database, and analyzes them using an AI model.

[1264] A "database" is a system that stores and manages basic information, behavioral data, and emotional data received by a server, and allows for the extraction and manipulation of data as needed.

[1265] "Behavioral data" refers to detailed data related to childcare, such as the baby's daily milk intake, sleep duration, and bowel movements.

[1266] The "emotion engine" is a technology that analyzes emotions from user input, speech, and facial expressions, and uses the results to improve the quality of childcare support.

[1267] An "AI model" is an algorithm that uses machine learning and deep learning to analyze behavioral and emotional data and generate advice related to childcare.

[1268] "Advice" refers to information generated by the server based on analysis by an AI model, providing users with helpful information for childcare.

[1269] "Feedback" refers to data that users input about the effectiveness and areas for improvement of the advice they receive, and send to the server.

[1270] This invention is an AI advisor system intended to support childcare, and it incorporates an emotion engine that recognizes the user's emotions. Specific embodiments of the system will be described below.

[1271] User data entry

[1272] The user downloads and installs the childcare support application. After installation, the user opens the account creation screen and enters basic information such as name, email address, and password. This basic information is sent from the device to the server. The server stores the received information in a database and creates the account.

[1273] Next, the user enters basic information such as the baby's date of birth and health information on the account settings screen. This information is also sent from the device to the server, which stores it in a database to create the baby's profile.

[1274] Behavioral tracking

[1275] Users input data about their baby's daily activities (e.g., milk intake, sleep duration, bowel movements, etc.) using the app. This data is sent from the device to a server, which stores it in a database. The device also periodically displays reminders to prompt users to input the latest data.

[1276] Emotion recognition by an emotion engine

[1277] The device uses an emotion engine to analyze the user's emotions from their input, speech, and facial expressions. The analyzed emotion data is sent to a server and stored in a database. The emotion engine utilizes functions such as facial recognition, speech analysis, and text analysis.

[1278] AI analysis

[1279] The server extracts the latest behavioral and emotional data from the database. The extracted data is preprocessed and input into an AI model. The AI ​​model uses a common machine learning framework (e.g., TensorFlow or PyTorch). The AI ​​model analyzes the data and evaluates the state of the baby and the user.

[1280] Advice generation

[1281] Based on the analysis results of the AI ​​model, the server generates specific advice regarding childcare. The generated advice is formatted to take into account the user's emotional state. The advice is sent to the device and notified to the user.

[1282] User notifications

[1283] The device notifies the user of received advice via push notifications or in-app messages. By acting on the notified advice, the user can improve the quality of their parenting.

[1284] Feedback Collection

[1285] Users can provide feedback on the effectiveness of the advice they receive. This feedback is sent from the device to the server and stored in a database. The collected feedback and sentiment data are used to improve the AI ​​model.

[1286] Specific example

[1287] For example, if a user inputs that they are troubled by their baby's nighttime crying, and the emotion engine detects fatigue, the server analyzes the cause of the crying based on past behavioral and emotional data, generates advice such as "extend daytime naps" and "take time to relax between childcare duties," and sends it to the device. The device then notifies the user of this advice, and the user acts on it.

[1288] Furthermore, if the user inputs that the baby has a high temperature and the emotion engine detects anxiety, the server analyzes information based on general fever and anxiety data and generates and sends advice such as "Ensure adequate hydration" and "Stay calm." The device then notifies the user of this advice, and the user acts accordingly.

[1289] Example of a prompt

[1290] "My baby cries at night. What should I do?"

[1291] "My baby's temperature is high. What should I do?"

[1292] As described above, the system of the present invention can provide specific and individually optimized childcare support while also taking into account the user's emotional state.

[1293] The flow of the specific processing in Example 2 will be explained using Figure 13.

[1294] Step 1:

[1295] The user downloads and installs the application, then enters basic information such as their name, email address, and password on the account creation screen. The entered basic information is sent from the device to the server. The server stores the received basic information in a database and creates the account.

[1296] Input: Name, email address, password

[1297] Data processing: None

[1298] Output: Account information is saved to the database.

[1299] Specific operation: Executes an SQL statement that sends information using HTTPS communication and saves it to the database.

[1300] Step 2:

[1301] The user enters basic information such as the baby's date of birth and health information, and sends it from their device to the server. The server saves the received information in a database and creates a profile of the baby.

[1302] Input: Baby's date of birth, gender, health information

[1303] Data processing: None

[1304] Output: The baby's profile is saved to the database.

[1305] Specific action: Execute an SQL statement to save the baby's information to the database.

[1306] Step 3:

[1307] During childcare, users input data about their baby's behavior (milk intake, sleep duration, bowel movements, etc.) into the app. The device sends this data to a server, which stores it in a database. The device also periodically displays reminders to prompt the user to input the latest behavior data.

[1308] Input: Milk intake, sleep duration, bowel movements

[1309] Data processing: None

[1310] Output: Behavioral data is saved to the database.

[1311] Specific actions: Display a reminder notification and send activity data.

[1312] Step 4:

[1313] The device uses an emotion engine to analyze the user's emotions from their input, speech, and facial expressions. The analysis results are sent to a server and stored in a database.

[1314] Input: User input text, speech, facial expressions

[1315] Data processing: Sentiment analysis

[1316] Output: The analyzed emotion data is saved to the database.

[1317] Specific operations: Uses facial recognition, speech analysis, and text analysis technologies.

[1318] Step 5:

[1319] The server extracts the latest behavioral and emotional data from the database. The extracted data is preprocessed and input into the AI ​​model. The AI ​​model analyzes the data and evaluates the state of the baby and the user.

[1320] Input: Latest behavioral data, emotional data

[1321] Data processing: Data cleaning, standardization, and feature engineering.

[1322] Output: Evaluation results

[1323] Specific operation: Extract data using SQL queries and input it into a model using a machine learning framework.

[1324] Step 6:

[1325] Based on the analysis results of the AI ​​model, the server generates specific advice regarding childcare. The generated advice is formatted and sent to the terminal, where it is notified to the user.

[1326] Input: Analysis results of the AI ​​model

[1327] Data processing: Advice generation, formatting.

[1328] Output: Advice is sent to the terminal.

[1329] Specific operation: Use a natural language generation model to perform formatting.

[1330] Step 7:

[1331] The device notifies the user of the generated advice via push notifications or in-app messages. The user then acts on the suggested advice.

[1332] Input: Advice

[1333] Data processing: None

[1334] Output: Sending push notifications or in-app messages

[1335] Specific action: Use the mobile platform's notification system.

[1336] Step 8:

[1337] Users provide feedback on the effectiveness of the advice. The device sends the feedback to the server, which stores the received feedback in a database. The feedback is used to improve the AI ​​model.

[1338] Input: Feedback

[1339] Data processing: Feedback analysis

[1340] Output: Feedback is saved to the database.

[1341] Specific actions: Enter data into the feedback form and execute the SQL statement to send the data.

[1342] (Application Example 2)

[1343] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as the "server," and the headset-type terminal 314 will be referred to as the "terminal."

[1344] Conventional childcare support systems allowed users to input and analyze childcare data, but they lacked a function to consider the user's emotional state, which sometimes resulted in inappropriate advice. This invention aims to reduce stress during childcare and provide more effective support by recognizing the user's emotions and providing childcare advice that takes them into account.

[1345] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[1346] In this invention, the server includes means for the user to input basic information about childcare, means for transmitting this basic information to the server and storing it in a database, means for periodically inputting baby behavior data, means for transmitting the input behavior data to the server and storing it in a database, means for extracting behavior data from the database and analyzing it using an AI model, means for generating childcare advice based on the analysis results and notifying the user, means for collecting user feedback and using it to improve the AI ​​model, means for incorporating an emotion recognition engine that analyzes emotions from the user's input, speech, and facial expressions and transmits the emotion data to the server, and means for analyzing the emotion data and generating childcare advice that takes into account the user's emotional state. This makes it possible to provide individualized and specific childcare advice according to the user's emotional state.

[1347] A "user" refers to a family member who is raising children and uses this system.

[1348] "Basic information regarding childcare" refers to all information necessary for childcare, such as the baby's date of birth, gender, and health information.

[1349] A "server" refers to a computer system that stores and analyzes information entered by users.

[1350] A "database" refers to a system on a server used to store basic user information, baby behavior data, analysis results, and other data.

[1351] "Baby behavioral data" refers to data on the baby's daily life, such as milk intake, sleep duration, and bowel movements.

[1352] An "AI model" refers to an artificial intelligence algorithm that analyzes baby behavior data and user emotional data to generate childcare advice.

[1353] An "emotion recognition engine" refers to a system that has the function of analyzing emotions from the user's input, speech, facial expressions, etc.

[1354] "Emotional data" refers to data that indicates the user's emotional state, as analyzed by an emotion recognition engine.

[1355] "Feedback" refers to the evaluation of the effectiveness and satisfaction level of advice provided by users.

[1356] System Overview

[1357] This invention is a system for childcare support in which the user inputs basic information about childcare and data on the baby's behavior. This data is then analyzed by AI to provide personalized advice. In particular, by also considering the user's emotional state, more appropriate support can be provided.

[1358] Hardware and software configuration

[1359] Device: The smartphone used by the user. It has an interface for inputting basic information and behavioral data. It also uses the camera and microphone as an emotion recognition engine.

[1360] Server: Uses a database and AI models to analyze received data and generate advice.

[1361] Database: Stores basic user information, baby behavior data, emotional data, etc.

[1362] Emotion recognition engine: A software module that performs speech recognition and facial expression recognition.

[1363] AI model: An artificial intelligence algorithm used for analysis.

[1364] Data entry and transmission

[1365] Users input basic childcare information and baby behavior data using their smartphones. This data is transmitted to a server via the internet and stored in a database. Additionally, an emotion recognition engine generates emotional data from the user's facial expressions and speech.

[1366] AI analysis and advice generation

[1367] The server extracts baby behavior data and user emotional data from the database and analyzes them using an AI model. Based on the analysis results, it generates specific childcare advice. The advice takes the user's emotional state into consideration, resulting in more individualized, specific, and appropriate content.

[1368] User notifications and feedback collection

[1369] The device notifies the user of the generated advice. Notification methods include push notifications and in-app messages. Users can also provide feedback on the effectiveness of the advice. This feedback is also sent to the server, stored in a database, and used to improve the AI ​​model.

[1370] Usage examples and prompts

[1371] Usage example

[1372] For example, if the user is feeling exhausted because their baby is crying more at night, the AI ​​model will analyze the situation and generate specific advice such as, "It would be good to let your baby take a longer nap during the day. Also, it would be good to set aside some time to relax in between childcare duties."

[1373] Example of a prompt

[1374] Recent behavioral data of the baby:

[1375] Milk intake: 120ml / session, 6 times a day

[1376] Sleep duration: 8 hours (nighttime), 3 hours of napping

[1377] User's emotional state:

[1378] Fatigue: 7 / 10

[1379] Anxiety: 5 / 10

[1380] Trends over the past 3 days:

[1381] The frequency of babies crying at night is increasing.

[1382] User fatigue is on the rise.

[1383] Advice to consider:

[1384] Extend nap time

[1385] An approach to ensure users have time to relax.

[1386] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[1387] Step 1:

[1388] Users input basic information about childcare and data on their baby's behavior.

[1389] Users use their smartphones to enter basic information such as their name, email address, baby's date of birth, gender, and health information. They also enter daily behavioral data such as milk intake, sleep duration, and bowel movements. This data is entered manually by the user through the device's interface.

[1390] Input: Basic information about childcare, baby's behavioral data

[1391] Output: Input basic information and behavioral data

[1392] Step 2:

[1393] The entered basic information and behavioral data are sent to the server and stored in the database.

[1394] The terminal transmits user-entered information to a server via the internet. The server analyzes the received information and stores it in a database. The data is organized by category and used for subsequent analysis.

[1395] Input: Basic information about childcare, baby's behavioral data

[1396] Data processing: Organize by category

[1397] Output: Basic information and behavioral data stored in the database

[1398] Step 3:

[1399] Analysis of the user's emotional state using an emotion recognition engine.

[1400] The device uses a camera and microphone to capture the user's facial expressions and voice, and an emotion recognition engine analyzes this data. The resulting emotion data, which indicates the user's emotional state, is sent to the server.

[1401] Input: User's facial expressions, voice

[1402] Data processing: Analysis using an emotion recognition engine.

[1403] Output: Sentiment data

[1404] Step 4:

[1405] The server extracts behavioral and emotional data from the database and analyzes it using an AI model.

[1406] The server extracts the latest behavioral and emotional data stored in the database. This data is preprocessed and input into an AI model. The AI ​​model analyzes the data to provide insights for generating parenting advice.

[1407] Input: Behavioral data, emotional data

[1408] Data processing: Preprocessing, analysis using AI models.

[1409] Output: Analysis results from the AI ​​model

[1410] Step 5:

[1411] Based on the analysis results, the server generates childcare advice and notifies the user.

[1412] The server generates appropriate parenting advice based on the analysis results of the AI ​​model. The generated advice takes into account the user's emotional state and is sent to the user's device as a push notification or in-app message.

[1413] Input: Analysis results

[1414] Data processing: Generating childcare advice

[1415] Output: Childcare advice notification

[1416] Step 6:

[1417] Users provide feedback on the effectiveness of the advice.

[1418] Users input feedback on the advice they receive, including its effectiveness and satisfaction level, via their device and send it to the server. The server stores the received feedback in a database and uses it to improve the AI ​​model.

[1419] Input: Advice Feedback

[1420] Data processing: Saving feedback

[1421] Output: Saved feedback, AI model improvements

[1422] In this way, the system efficiently provides support related to childcare through a series of processes, from data input from users to feedback.

[1423] The specific processing unit 290 transmits the result of the specific processing to the headset terminal 314. In the headset terminal 314, the control unit 46A causes the speaker 240 and display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[1424] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). One example of data generation model 58 is ChatGPT (Internet search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[1425] In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and specific processing may also be performed by the headset terminal 314.

[1426] [Fourth Embodiment]

[1427] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[1428] As shown in Figure 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[1429] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[1430] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a controlled object 443. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and controlled object 443 are also connected to the bus 52.

[1431] The microphone 238 receives voice signals from the user 20 and receives instructions from the user 20. The microphone 238 captures the voice signals from the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[1432] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the area around the user 20 (for example, an imaging range defined by a field of view equivalent to the width of a typical healthy person's field of vision).

[1433] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[1434] The controlled object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the robot 414's emotions can be expressed by controlling these motors. Furthermore, the robot 414's facial expressions can also be expressed by controlling the illumination state of the LEDs in its eyes.

[1435] Figure 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Figure 8, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[1436] The specific processing program 56 is an example of a "program" relating to the technology of this disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[1437] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[1438] In robot 414, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[1439] Next, the specific processing performed by the specific processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[1440] This invention is an AI advisor system that is helpful for first-time mothers and men taking paternity leave when it comes to childcare, and it is implemented as follows.

[1441] User data entry

[1442] The user downloads and installs the application and creates an account. During account creation, the user enters basic information such as their name, email address, and password, and this information is sent from the device to the server. The server stores the received data in a database and generates the account.

[1443] Next, the user enters the baby's basic information (date of birth, gender, health information, etc.). This basic information is also sent from the device to the server, which stores it in a database to create the baby's profile.

[1444] Behavioral tracking

[1445] In the course of daily childcare, users input data about their baby's activities into the app. For example, they input information such as milk intake, sleep duration, and bowel movements. The device sends this activity data to a server, which stores the received data in a database.

[1446] Furthermore, the device displays reminders at regular intervals prompting the user to input behavioral data, providing an environment that makes it easy for users to enter the latest behavioral data.

[1447] AI analysis

[1448] The server extracts the latest behavioral data from the database and analyzes it using an AI model. Specifically, the server preprocesses the data, including past behavioral data, and inputs it into the AI ​​model. This AI model analyzes, for example, the baby's sleep patterns and milk intake to assess the baby's condition.

[1449] Advice generation

[1450] Based on the analysis results of the AI ​​model, the server generates specific advice regarding childcare. The generated advice is then formatted by the server into a format that is easy for the user to understand and sent to the device.

[1451] For example, if a baby's nighttime crying increases, the server generates specific advice based on the AI ​​model's analysis, such as "It would be good to let the baby take a longer nap during the day," and displays it on the device.

[1452] User notifications

[1453] The device notifies the user of the advice it has received. The notification appears as a push notification or in-app message, and the user acts on the suggested advice.

[1454] Feedback Collection

[1455] Users can provide feedback on the effectiveness of the advice. For example, they can send evaluations within the app such as, "Extending the nap was effective." The device sends this feedback to the server, which stores the received feedback in a database. The collected feedback is then incorporated into the new advice generation process to improve the AI ​​model.

[1456] Specific example

[1457] Dealing with nighttime crying for the first time:

[1458] The user enters that they are having trouble with their baby crying at night.

[1459] The server analyzes the cause of nighttime crying based on past behavioral data and sends advice to the device, such as "extend daytime naps."

[1460] The device notifies the user of advice, and the user acts upon it.

[1461] First fever:

[1462] The user enters that the baby's body temperature is high.

[1463] The server analyzes general fever information and the baby's health information, and sends advice such as "Please ensure adequate hydration" to the device.

[1464] The device notifies the user of advice, and the user acts upon it.

[1465] In this way, the AI ​​advisor system provides an environment where users can receive appropriate childcare support, thereby reducing the burden of childcare and improving the quality of childcare.

[1466] The following describes the processing flow.

[1467] Step 1:

[1468] The user downloads and launches the application. They then enter basic information such as their name, email address, and password into the account creation form.

[1469] Step 2:

[1470] The terminal sends the entered basic information to the server. The server stores the received data in a database and creates an account.

[1471] Step 3:

[1472] The user enters the baby's basic information (date of birth, gender, health information, etc.) into the application. This information is also sent from the device to the server.

[1473] Step 4:

[1474] The server stores the baby's basic information in a database and creates a profile of the baby.

[1475] Step 5:

[1476] In the course of daily childcare, users input data about their baby's activities (such as milk intake, sleep duration, and bowel movements) into the app.

[1477] Step 6:

[1478] The terminal sends the entered behavioral data to the server. The server stores the received behavioral data in a database.

[1479] Step 7:

[1480] The device displays reminders at regular intervals prompting the user to enter behavioral data. This ensures the user enters the latest behavioral data.

[1481] Step 8:

[1482] The server extracts historical and recent behavioral data from the database. It then performs data preprocessing (data cleaning, supplementing missing data, etc.).

[1483] Step 9:

[1484] The server inputs the pre-processed data into the AI ​​model and performs the analysis. The AI ​​model evaluates the baby's current condition and performs the necessary analysis.

[1485] Step 10:

[1486] The server generates specific childcare advice based on the analysis results of the AI ​​model. The generated advice is then formatted in a way that is easy for the user to understand.

[1487] Step 11:

[1488] The server sends formatted advice to the device. The device then displays this advice to the user as a push notification or in-app message.

[1489] Step 12:

[1490] The user reviews the advice received and takes the necessary actions. For example, they might take specific measures such as extending the baby's nap time.

[1491] Step 13:

[1492] Users provide feedback on the effectiveness of the advice within the app. For example, they can input whether the advice was useful and what effect it had.

[1493] Step 14:

[1494] The device collects feedback and sends it to the server. The server stores the received feedback in a database.

[1495] Step 15:

[1496] The server improves the AI ​​model based on the newly collected feedback. This will improve the accuracy of future advice.

[1497] In this way, this system provides users with an environment where they can easily receive childcare support, reducing the burden of childcare and improving the quality of childcare.

[1498] (Example 1)

[1499] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[1500] In modern childcare, it is crucial for first-time parents and men taking paternity leave to receive effective and appropriate childcare support. However, a lack of knowledge and experience regarding childcare makes it difficult to understand a baby's behavior and respond appropriately. In particular, a swift and accurate response is required for abnormal behaviors such as nighttime crying and fever. Furthermore, there is a lack of systems to appropriately provide feedback on the effectiveness of measures taken by users and to continuously improve them.

[1501] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[1502] In this invention, the server includes means for the user to input basic information about childcare, means for transmitting this basic information to a communication device and storing it in a data bank, means for periodically inputting baby behavior data, means for transmitting the input behavior data to a communication device and storing it in a data bank, means for extracting behavior data from the data bank and analyzing it using a generative AI model, means for generating childcare advice based on the analysis results and notifying the user, means for collecting user feedback and using it to improve the generative AI model, means for displaying a reminder to prompt the user to input behavior data at regular intervals, and means for generating childcare advice in text format and formatting it into an easy-to-understand format. As a result, the user can receive appropriate childcare support, improve the quality of childcare, and reduce the burden of childcare.

[1503] A "user" is someone who inputs information about childcare and receives support.

[1504] "Basic information" refers to data such as the user's and the baby's name, email address, date of birth, gender, and health information.

[1505] A "communication device" is a device that includes hardware and software for sending and receiving data between a user's terminal and a server.

[1506] A "data bank" refers to a database system that stores and manages collected basic information and behavioral data.

[1507] "Behavioral data" refers to data about a baby's daily behavior, such as milk intake, sleep duration, and bowel movements.

[1508] A "generative AI model" refers to an artificial intelligence model that analyzes collected data to generate advice related to childcare.

[1509] "Analysis" refers to the process by which a generative AI model evaluates specific patterns and trends based on behavioral data and creates advice.

[1510] "Advice" refers to specific instructions and suggestions regarding childcare provided to the user based on the analysis results of the generated AI model.

[1511] A "reminder" refers to a notification or alert that prompts the user to input behavioral data at regular intervals.

[1512] "Text format" refers to a method of presenting generated advice in text or sentences that are easy for the user to understand.

[1513] "Feedback" refers to evaluations and comments that users provide regarding the effectiveness of advice.

[1514] This invention is an AI advisor system aimed at providing first-time mothers and men taking paternity leave with effective childcare support by offering information related to childcare. This system includes the following means and processes.

[1515] User data entry

[1516] Users download and install a dedicated application and create an account. Account creation requires entering basic information such as name, email address, and password. This basic information is sent from the device to the server, which stores the received data in a database and generates the account.

[1517] Furthermore, users enter basic information such as the baby's name, date of birth, gender, and health information. This information is also sent from the device to the server, stored in a database, and used to create the baby's profile.

[1518] Input of behavioral data

[1519] During daily childcare activities, users input data about their baby's behavior, such as milk intake, sleep duration, and bowel movements, into the app. The device then sends this data to a server, where it is stored in a database.

[1520] Furthermore, the device will display reminder notifications prompting the user to input behavioral data at regular intervals, making it easy to enter the latest behavioral data.

[1521] AI analysis

[1522] The server extracts the latest behavioral data from the database and analyzes it using a generative AI model. This process is carried out using a dedicated AI framework (e.g., TensorFlow, PyTorch). After preprocessing, which includes past behavioral data, the data is input into the AI ​​model to evaluate the baby's behavioral patterns and state.

[1523] Advice generation

[1524] Based on the analysis results of the AI ​​model, the server generates specific advice regarding childcare. The generated advice is formatted in text and summarized for easy understanding. The server sends this advice to the terminal and notifies the user.

[1525] For example, if a baby starts crying frequently at night, the AI ​​model will analyze the cause and provide specific advice such as, "It would be good to let the baby take a longer nap during the day."

[1526] User notifications

[1527] The device displays received advice to the user as a push notification or in-app message. This allows the user to receive advice at the appropriate time.

[1528] Feedback Collection

[1529] Users can provide feedback on the effectiveness of the advice. For example, they can send ratings and comments within the app, such as "Extending the nap was effective." This feedback is sent from the device to the server and stored in a database. It is also used as data to improve the AI ​​model.

[1530] Specific example

[1531] Dealing with nighttime crying for the first time:

[1532] The user enters that they are having trouble with their baby crying at night.

[1533] The server analyzes the cause of nighttime crying based on past behavioral data and sends advice to the device, such as "extend daytime naps."

[1534] The device notifies the user of advice, and the user acts upon it.

[1535] First fever:

[1536] The user enters that the baby's body temperature is high.

[1537] The server analyzes general fever information and the baby's health information, and sends advice such as "Please ensure adequate hydration" to the device.

[1538] The device notifies the user of advice, and the user acts upon it.

[1539] In this way, the AI ​​advisor system provides users with an environment to receive effective childcare support, improving the quality of childcare and reducing the burden of childcare.

[1540] The flow of the specific processing in Example 1 will be explained using Figure 11.

[1541] Step 1:

[1542] Create an account

[1543] The user downloads and installs the application. After installation, they enter basic information such as their name, email address, and password on the account creation screen. This information is sent from the device to the server, which stores the received data in its database and generates a new account.

[1544] Specific actions:

[1545] The user enters their name, email address, and password into the input form and clicks the "Create Account" button.

[1546] The device sends the entered data to the server via an API request.

[1547] The server validates the received data, stores it in the database, and generates and returns a new user ID.

[1548] Input: Basic information such as name, email address, and password.

[1549] Output: New User ID

[1550] Step 2:

[1551] Enter your baby's basic information

[1552] Users enter their baby's name, date of birth, gender, health information, etc., within the app. This information is sent from the device to the server, which stores it in a database to create a profile of the baby.

[1553] Specific actions:

[1554] The user enters the baby's information and presses the "Save" button.

[1555] The device sends the entered baby information to the server.

[1556] The server validates the information and stores it in the database.

[1557] Input: Baby's name, date of birth, gender, health information

[1558] Output: Baby profile

[1559] Step 3:

[1560] Input of behavioral data

[1561] Users input daily childcare data, such as milk intake, sleep duration, and bowel movements, into the app. This data is sent from the device to a server, which stores it in a database. The device also displays reminder notifications at regular intervals to prompt users to input behavioral data.

[1562] Specific actions:

[1563] The user launches the app, enters information such as milk intake, sleep duration, and bowel movements, and then presses the "Save" button.

[1564] The device sends this data to the server.

[1565] The server validates the data and stores it in the database.

[1566] The device displays reminder notifications at regular intervals.

[1567] Input: Milk intake, sleep duration, bowel movements

[1568] Output: Behavioral data stored in the database

[1569] Step 4:

[1570] AI analysis

[1571] The server extracts the latest behavioral data from the database and analyzes it using a generative AI model. This analysis involves preprocessing, including past behavioral data, and then inputting this data into the AI ​​model to evaluate behavioral patterns.

[1572] Specific actions:

[1573] The server extracts the latest behavioral data from the database.

[1574] The server preprocesses the data, inputs it into the AI ​​model, and performs the analysis.

[1575] Example: Analyzing sleep data and other information to identify the cause of nighttime crying.

[1576] Input: Behavioral data extracted from the database

[1577] Output: Evaluation results of behavioral patterns by AI model

[1578] Step 5:

[1579] Advice generation

[1580] The server generates specific childcare advice based on the AI ​​analysis results. The generated advice is formatted as text and sent to the terminal.

[1581] Specific actions:

[1582] The server generates parenting advice as text based on the AI ​​analysis results.

[1583] Example: Specific advice such as, "It would be good to let your child take a slightly longer nap during the day."

[1584] The server sends the generated advice to the terminal.

[1585] Input: AI analysis results

[1586] Output: Parenting advice in text format

[1587] Step 6:

[1588] Advice notification

[1589] The device notifies the user of advice received from the server. These notifications are delivered via push notifications or in-app messages.

[1590] Specific actions:

[1591] The terminal receives advice from the server.

[1592] The device will display this to the user via push notifications or in-app messages.

[1593] Input: Advice from the server

[1594] Output: Notification to the user

[1595] Step 7:

[1596] Feedback Collection

[1597] Users can provide feedback on the effectiveness of the advice. For example, they can send ratings and comments within the app, such as "Extending the nap was effective." This feedback is sent from the device to the server and stored in a database. The collected feedback is used to improve the generative AI model.

[1598] Specific actions:

[1599] The user fills out a feedback form within the app and presses the "Submit" button.

[1600] The device sends feedback to the server.

[1601] The server stores the feedback in a database and uses it to update the AI ​​model.

[1602] Input: User feedback

[1603] Output: Feedback stored in the data bank and improvements to the AI ​​model.

[1604] (Application Example 1)

[1605] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[1606] The challenge lies in reducing the burden on new parents and those who support them, and in providing appropriate childcare support. In particular, it is necessary to reduce the burden of preparing and choosing meals during childcare, creating an environment where parents can focus on childcare. Furthermore, providing timely and appropriate advice based on the childcare situation is also crucial.

[1607] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[1608] In this invention, the server includes means for the user to input basic information about childcare, means for transmitting this basic information to the server and storing it in a database, means for periodically inputting baby behavior data, means for transmitting the input behavior data to the server and storing it in a database, means for extracting behavior data from the database and analyzing it using an AI model, means for generating childcare advice based on the analysis results and notifying the user, means for collecting user feedback and using it to improve the AI ​​model, a food delivery function that suggests meal options according to the childcare situation, and means for notifying the user of advice and suggestions via push notifications at the necessary time. This makes it possible to reduce the burden of selecting and preparing meals during childcare and to provide appropriate childcare advice in a timely manner.

[1609] A "user" refers to a person who inputs information related to childcare and uses the childcare support system.

[1610] A "server" refers to a computer system that receives data sent by users, stores it in a database, and runs AI models.

[1611] A "database" refers to digital storage used to store and manage basic information and behavioral data related to childcare.

[1612] "Baby behavioral data" refers to information related to childcare, such as milk intake, sleep duration, and bowel movements.

[1613] An "AI model" refers to a machine learning or deep learning algorithm used to analyze childcare-related data and generate appropriate advice.

[1614] "Childcare advice" refers to specific childcare-related advice provided to users based on the results of analysis by an AI model.

[1615] "Feedback" refers to evaluations and opinions regarding the effectiveness and application of advice provided by users.

[1616] The "food delivery function" refers to a feature that suggests appropriate meal options based on childcare circumstances and assists with meal preparation.

[1617] "Push notifications" refer to a function that allows a system to send information and advice to users in real time.

[1618] "Basic information" refers to initial setup information about the user and the baby.

[1619] This invention relates to a system in which a user inputs basic information about childcare, and an AI model provides appropriate childcare advice based on that information. It also includes a food delivery function that suggests meal options according to the childcare situation.

[1620] System Overview

[1621] The childcare support system consists of user terminals and a server. Users input basic information and baby behavior data using a smartphone or other device, and this data is sent to the server and stored in a database. The server uses an AI model to analyze the data, generate childcare advice, and notify the user. In addition, a food delivery function suggests meal options tailored to the childcare situation.

[1622] Hardware and software to be used

[1623] User devices: such as smartphones and tablets.

[1624] Server: A cloud server with high-performance computing resources.

[1625] Database: A relational database used to store basic information and behavioral data related to childcare.

[1626] AI model: A machine learning or deep learning model used to analyze childcare data and generate advice.

[1627] Food delivery API: An API for an external service used to suggest meal options.

[1628] Explanation of the program's processing

[1629] The server receives basic information and baby behavior data sent from the user's terminal and stores it in a database. The stored data is periodically analyzed by an AI model. Based on past data, the AI ​​model evaluates the baby's condition and generates appropriate childcare advice. The generated advice is then notified to the user's terminal from the server.

[1630] Furthermore, the server uses a food delivery function to suggest appropriate meal options based on the childcare situation. This information is also sent to the user's device via push notification.

[1631] Specific example

[1632] For first-time nighttime crying:

[1633] The user enters that they are having trouble with their baby crying at night.

[1634] The server analyzes the cause of nighttime crying based on past behavioral data and generates advice such as "extend daytime naps."

[1635] This advice will be sent to the user's device.

[1636] The food delivery feature suggests easy meal options tailored to the user's needs.

[1637] Example of a prompt

[1638] (Example of a prompt message)

[1639] I've entered my baby's sleep and milk intake data (as they're experiencing persistent nighttime crying) into the parenting app. Please provide optimal parenting advice and recommended food delivery menus.

[1640] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[1641] Step 1:

[1642] Users enter basic childcare information into a smartphone app. This information includes their name, email address, baby's date of birth, gender, and health information. This basic information is sent from the device to a server, which stores the information in a database.

[1643] Step 2:

[1644] Users input daily activity data about their baby into the app. This data includes milk intake, sleep duration, and bowel movements. This data is also sent from the device to the server, which stores it in a database. Upon receiving the data, the server prepares to provide parenting advice based on that data.

[1645] Step 3:

[1646] The server extracts the latest behavioral data from the database at regular intervals and inputs it into the AI ​​model. The AI ​​model performs behavioral analysis, including past data, to evaluate the baby's condition and patterns. In this evaluation process, data preprocessing such as normalization and feature extraction is performed. The input is behavioral data from the database, and the output is an evaluation of the baby's condition.

[1647] Step 4:

[1648] The server generates specific childcare advice based on the analysis results of the AI ​​model. For example, if a baby's nighttime crying increases, the AI ​​model might suggest, "It would be good to let the baby take a longer nap during the day." This advice generation uses both past and current behavioral data.

[1649] Step 5:

[1650] The server notifies the user's device of the generated advice. The device displays the advice to the user as a push notification or in-app message. The user can then act according to the received advice. The input is the advice from the server, and the output is the notification to the user.

[1651] Step 6:

[1652] The server collects feedback from users. For example, a user might input a rating such as, "Extending my nap was effective." This feedback is sent from the terminal to the server, which stores the information in a database. The feedback is used to improve the AI ​​model and is reflected in the new advice generation process. The input is user feedback, and the output is the storage in the database and the improvement of the AI ​​model.

[1653] Step 7:

[1654] The server utilizes a food delivery function that suggests meal options tailored to the childcare situation based on the analysis results of an AI model. The server calls an external food delivery API to retrieve appropriate menus and notifies the user's terminal. The input consists of the analysis results of the AI ​​model and menu information from the food delivery API, and the output is a notification of meal options to the user.

[1655] Step 8:

[1656] Users receive notifications of childcare advice and meal options, and then act on them. When users input the results and feedback of implementing the advice within the app, this data is also sent to the server and used to generate future advice. The input is the user's actions and feedback, and the output is the transmission of data to the server and improvement of the accuracy of the AI ​​model.

[1657] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[1658] This invention combines an AI advisor system that is helpful for first-time mothers and men taking paternity leave when it comes to childcare with an emotion engine that recognizes the user's emotions, and is implemented as follows.

[1659] User data entry

[1660] The user downloads and installs the application and creates an account. During account creation, the user enters basic information such as their name, email address, and password, and this information is sent from the device to the server. The server stores the received basic information in a database and generates the account.

[1661] Next, the user enters the baby's basic information (date of birth, gender, health information, etc.). This basic information is also sent from the device to the server, which stores it in a database to create the baby's profile.

[1662] Behavioral tracking

[1663] In the course of daily childcare, users input data about their baby's activities into the app. For example, they input information such as milk intake, sleep duration, and bowel movements. The device sends this activity data to a server, which stores the received data in a database.

[1664] Furthermore, the device displays reminders at regular intervals prompting the user to input behavioral data, and the user then enters the latest behavioral data.

[1665] Emotion recognition by an emotion engine

[1666] The device is equipped with an emotion engine that recognizes the user's emotions. The emotion engine analyzes the user's emotions from their input, speech, and facial expressions. The results of this analysis are sent to a server and stored in a database.

[1667] AI analysis

[1668] The server extracts the latest behavioral and emotional data from the database and analyzes it using an AI model. Specifically, the server preprocesses this data and inputs it into the AI ​​model. The AI ​​model analyzes, for example, the baby's sleep patterns, milk intake, and the user's emotional state, and evaluates the state of both the baby and the user.

[1669] Advice generation

[1670] Based on the analysis results of the AI ​​model, the server generates specific advice regarding childcare. The generated advice is formatted to take the user's emotional state into consideration and then sent to the device.

[1671] For example, if the baby's nighttime crying increases and the user is feeling exhausted, the AI ​​model will analyze the situation and generate specific advice such as, "It would be good to let the baby take a longer nap during the day. Also, it would be good to set aside some time to relax in between childcare duties."

[1672] User notifications

[1673] The device notifies the user of the advice it has received. The notification appears as a push notification or in-app message, and the user acts on the suggested advice.

[1674] Feedback Collection

[1675] Users can provide feedback on the effectiveness of the advice. For example, they can send evaluations within the app such as, "Extending the nap was effective." The device sends this feedback to the server, which stores the received feedback in a database. The collected feedback and sentiment data are used to improve the AI ​​model and are incorporated into the new advice generation process.

[1676] Specific example

[1677] Dealing with nighttime crying for the first time: User fatigue

[1678] The user inputs that they are troubled by their baby's nighttime crying, and the emotion engine detects fatigue.

[1679] The server analyzes the cause of nighttime crying based on past behavioral and emotional data, and generates and sends advice to the device, such as "extend daytime naps" and "take time to relax in between childcare."

[1680] The device notifies the user of advice, and the user acts upon it.

[1681] First fever and anxiety

[1682] The user inputs that the baby's body temperature is high, and the emotion engine detects anxiety.

[1683] The server analyzes information based on general fever and anxiety, generates advice such as, "Stay hydrated, and stay calm," and sends it to the device.

[1684] The device notifies the user of advice, and the user acts upon it.

[1685] In this way, by taking into account the user's emotional state, this system provides more individualized and appropriate childcare support, reducing the burden of childcare and improving its quality.

[1686] The following describes the processing flow.

[1687] Step 1:

[1688] The user downloads and launches the application. They then enter basic information such as their name, email address, and password into the account creation form.

[1689] Step 2:

[1690] The terminal sends the entered basic information to the server. The server stores the received data in a database and creates an account.

[1691] Step 3:

[1692] The user enters the baby's basic information (date of birth, gender, health information, etc.) into the application. This information is also sent from the device to the server.

[1693] Step 4:

[1694] The server stores the baby's basic information in a database and creates a profile of the baby.

[1695] Step 5:

[1696] In the course of daily childcare, users input data about their baby's activities (such as milk intake, sleep duration, and bowel movements) into the app.

[1697] Step 6:

[1698] The terminal sends the entered behavioral data to the server. The server stores the received behavioral data in a database.

[1699] Step 7:

[1700] The device displays reminders at regular intervals prompting the user to enter behavioral data. This ensures the user enters the latest behavioral data.

[1701] Step 8:

[1702] The device generates user emotion data using an emotion engine that analyzes user input, speech, and facial expressions.

[1703] Step 9:

[1704] The device sends the generated emotion data to the server. The server stores the received emotion data in a database.

[1705] Step 10:

[1706] The server extracts the latest behavioral and sentiment data from the database. The extracted data is preprocessed (data cleaning, supplementing missing data, etc.).

[1707] Step 11:

[1708] The server inputs the pre-processed data into the AI ​​model and performs the analysis. The AI ​​model evaluates the baby's current condition and the user's emotional state.

[1709] Step 12:

[1710] The server generates specific childcare advice based on the analysis results of the AI ​​model, taking into account the user's emotional state.

[1711] Step 13:

[1712] The server formats the generated advice into a format that is easy for the user to understand and sends it to the terminal.

[1713] Step 14:

[1714] The device displays the received advice to the user as a push notification or in-app message.

[1715] Step 15:

[1716] The user reviews the suggested advice and takes specific actions according to it.

[1717] Step 16:

[1718] Users provide feedback within the app regarding the effectiveness of the suggested advice.

[1719] Step 17:

[1720] The device sends the provided feedback to the server. The server stores the received feedback in its database.

[1721] Step 18:

[1722] The server improves the AI ​​model based on newly collected feedback and sentiment data, thereby improving the accuracy of future advice.

[1723] This series of steps allows users to receive appropriate childcare support that takes their emotional state into consideration, thereby reducing the burden of childcare and improving its quality.

[1724] (Example 2)

[1725] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[1726] Childcare involves a vast amount of information and emotional exchange, making it particularly burdensome for new parents and men on paternity leave. Traditional childcare support systems only provide general advice on childcare, lacking support that takes into account the individual emotional state and specific childcare situations of users. As a result, it is difficult for users to take appropriate immediate action, limiting the improvement of the quality of childcare. Therefore, there is a need for a system that combines and analyzes user behavioral data and emotional data to provide individualized and specific advice.

[1727] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[1728] In this invention, the server includes means for the user to input basic information about childcare, means for periodically inputting data on the baby's behavior, and means for recognizing the user's emotions using an emotion engine. This makes it possible to periodically input basic information and behavioral data about childcare and analyze the user's emotions using an emotion engine.

[1729] A "user" is the entity that creates an account to use the childcare support system and inputs basic information and behavioral data.

[1730] "Basic information" refers to data such as the user's and baby's name, email address, date of birth, and health information.

[1731] A "server" is a device or system that receives basic information, behavioral data, and emotional data sent by users, stores them in a database, and analyzes them using an AI model.

[1732] A "database" is a system that stores and manages basic information, behavioral data, and emotional data received by a server, and allows for the extraction and manipulation of data as needed.

[1733] "Behavioral data" refers to detailed data related to childcare, such as the baby's daily milk intake, sleep duration, and bowel movements.

[1734] The "emotion engine" is a technology that analyzes emotions from user input, speech, and facial expressions, and uses the results to improve the quality of childcare support.

[1735] An "AI model" is an algorithm that uses machine learning and deep learning to analyze behavioral and emotional data and generate advice related to childcare.

[1736] "Advice" refers to information generated by the server based on analysis by an AI model, providing users with helpful information for childcare.

[1737] "Feedback" refers to data that users input about the effectiveness and areas for improvement of the advice they receive, and send to the server.

[1738] This invention is an AI advisor system intended to support childcare, and it incorporates an emotion engine that recognizes the user's emotions. Specific embodiments of the system will be described below.

[1739] User data entry

[1740] The user downloads and installs the childcare support application. After installation, the user opens the account creation screen and enters basic information such as name, email address, and password. This basic information is sent from the device to the server. The server stores the received information in a database and creates the account.

[1741] Next, the user enters basic information such as the baby's date of birth and health information on the account settings screen. This information is also sent from the device to the server, which stores it in a database to create the baby's profile.

[1742] Behavioral tracking

[1743] Users input data about their baby's daily activities (e.g., milk intake, sleep duration, bowel movements, etc.) using the app. This data is sent from the device to a server, which stores it in a database. The device also periodically displays reminders to prompt users to input the latest data.

[1744] Emotion recognition by an emotion engine

[1745] The device uses an emotion engine to analyze the user's emotions from their input, speech, and facial expressions. The analyzed emotion data is sent to a server and stored in a database. The emotion engine utilizes functions such as facial recognition, speech analysis, and text analysis.

[1746] AI analysis

[1747] The server extracts the latest behavioral and emotional data from the database. The extracted data is preprocessed and input into an AI model. The AI ​​model uses a common machine learning framework (e.g., TensorFlow or PyTorch). The AI ​​model analyzes the data and evaluates the state of the baby and the user.

[1748] Advice generation

[1749] Based on the analysis results of the AI ​​model, the server generates specific advice regarding childcare. The generated advice is formatted to take into account the user's emotional state. The advice is sent to the device and notified to the user.

[1750] User notifications

[1751] The device notifies the user of received advice via push notifications or in-app messages. By acting on the notified advice, the user can improve the quality of their parenting.

[1752] Feedback Collection

[1753] Users can provide feedback on the effectiveness of the advice they receive. This feedback is sent from the device to the server and stored in a database. The collected feedback and sentiment data are used to improve the AI ​​model.

[1754] Specific example

[1755] For example, if a user inputs that they are troubled by their baby's nighttime crying, and the emotion engine detects fatigue, the server analyzes the cause of the crying based on past behavioral and emotional data, generates advice such as "extend daytime naps" and "take time to relax between childcare duties," and sends it to the device. The device then notifies the user of this advice, and the user acts on it.

[1756] Furthermore, if the user inputs that the baby has a high temperature and the emotion engine detects anxiety, the server analyzes information based on general fever and anxiety data and generates and sends advice such as "Ensure adequate hydration" and "Stay calm." The device then notifies the user of this advice, and the user acts accordingly.

[1757] Example of a prompt

[1758] "My baby cries at night. What should I do?"

[1759] "My baby's temperature is high. What should I do?"

[1760] As described above, the system of the present invention can provide specific and individually optimized childcare support while also taking into account the user's emotional state.

[1761] The flow of the specific processing in Example 2 will be explained using Figure 13.

[1762] Step 1:

[1763] The user downloads and installs the application, then enters basic information such as their name, email address, and password on the account creation screen. The entered basic information is sent from the device to the server. The server stores the received basic information in a database and creates the account.

[1764] Input: Name, email address, password

[1765] Data processing: None

[1766] Output: Account information is saved to the database.

[1767] Specific operation: Executes an SQL statement that sends information using HTTPS communication and saves it to the database.

[1768] Step 2:

[1769] The user enters basic information such as the baby's date of birth and health information, and sends it from their device to the server. The server saves the received information in a database and creates a profile of the baby.

[1770] Input: Baby's date of birth, gender, health information

[1771] Data processing: None

[1772] Output: The baby's profile is saved to the database.

[1773] Specific action: Execute an SQL statement to save the baby's information to the database.

[1774] Step 3:

[1775] During childcare, users input data about their baby's behavior (milk intake, sleep duration, bowel movements, etc.) into the app. The device sends this data to a server, which stores it in a database. The device also periodically displays reminders to prompt the user to input the latest behavior data.

[1776] Input: Milk intake, sleep duration, bowel movements

[1777] Data processing: None

[1778] Output: Behavioral data is saved to the database.

[1779] Specific actions: Display a reminder notification and send activity data.

[1780] Step 4:

[1781] The device uses an emotion engine to analyze the user's emotions from their input, speech, and facial expressions. The analysis results are sent to a server and stored in a database.

[1782] Input: User input text, speech, facial expressions

[1783] Data processing: Sentiment analysis

[1784] Output: The analyzed emotion data is saved to the database.

[1785] Specific operations: Uses facial recognition, speech analysis, and text analysis technologies.

[1786] Step 5:

[1787] The server extracts the latest behavioral and emotional data from the database. The extracted data is preprocessed and input into the AI ​​model. The AI ​​model analyzes the data and evaluates the state of the baby and the user.

[1788] Input: Latest behavioral data, emotional data

[1789] Data processing: Data cleaning, standardization, and feature engineering.

[1790] Output: Evaluation results

[1791] Specific operation: Extract data using SQL queries and input it into a model using a machine learning framework.

[1792] Step 6:

[1793] Based on the analysis results of the AI ​​model, the server generates specific advice regarding childcare. The generated advice is formatted and sent to the terminal, where it is notified to the user.

[1794] Input: Analysis results of the AI ​​model

[1795] Data processing: Advice generation, formatting.

[1796] Output: Advice is sent to the terminal.

[1797] Specific operation: Use a natural language generation model to perform formatting.

[1798] Step 7:

[1799] The device notifies the user of the generated advice via push notifications or in-app messages. The user then acts on the suggested advice.

[1800] Input: Advice

[1801] Data processing: None

[1802] Output: Sending push notifications or in-app messages

[1803] Specific action: Use the mobile platform's notification system.

[1804] Step 8:

[1805] Users provide feedback on the effectiveness of the advice. The device sends the feedback to the server, which stores the received feedback in a database. The feedback is used to improve the AI ​​model.

[1806] Input: Feedback

[1807] Data processing: Feedback analysis

[1808] Output: Feedback is saved to the database.

[1809] Specific actions: Enter data into the feedback form and execute the SQL statement to send the data.

[1810] (Application Example 2)

[1811] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[1812] Conventional childcare support systems allowed users to input and analyze childcare data, but they lacked a function to consider the user's emotional state, which sometimes resulted in inappropriate advice. This invention aims to reduce stress during childcare and provide more effective support by recognizing the user's emotions and providing childcare advice that takes them into account.

[1813] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[1814] In this invention, the server includes means for the user to input basic information about childcare, means for transmitting this basic information to the server and storing it in a database, means for periodically inputting baby behavior data, means for transmitting the input behavior data to the server and storing it in a database, means for extracting behavior data from the database and analyzing it using an AI model, means for generating childcare advice based on the analysis results and notifying the user, means for collecting user feedback and using it to improve the AI ​​model, means for incorporating an emotion recognition engine that analyzes emotions from the user's input, speech, and facial expressions and transmits the emotion data to the server, and means for analyzing the emotion data and generating childcare advice that takes into account the user's emotional state. This makes it possible to provide individualized and specific childcare advice according to the user's emotional state.

[1815] A "user" refers to a family member who is raising children and uses this system.

[1816] "Basic information regarding childcare" refers to all information necessary for childcare, such as the baby's date of birth, gender, and health information.

[1817] A "server" refers to a computer system that stores and analyzes information entered by users.

[1818] A "database" refers to a system on a server used to store basic user information, baby behavior data, analysis results, and other data.

[1819] "Baby behavioral data" refers to data on the baby's daily life, such as milk intake, sleep duration, and bowel movements.

[1820] An "AI model" refers to an artificial intelligence algorithm that analyzes baby behavior data and user emotional data to generate childcare advice.

[1821] An "emotion recognition engine" refers to a system that has the function of analyzing emotions from the user's input, speech, facial expressions, etc.

[1822] "Emotional data" refers to data that indicates the user's emotional state, as analyzed by an emotion recognition engine.

[1823] "Feedback" refers to the evaluation of the effectiveness and satisfaction level of advice provided by users.

[1824] System Overview

[1825] This invention is a system for childcare support in which the user inputs basic information about childcare and data on the baby's behavior. This data is then analyzed by AI to provide personalized advice. In particular, by also considering the user's emotional state, more appropriate support can be provided.

[1826] Hardware and software configuration

[1827] Device: The smartphone used by the user. It has an interface for inputting basic information and behavioral data. It also uses the camera and microphone as an emotion recognition engine.

[1828] Server: Uses a database and AI models to analyze received data and generate advice.

[1829] Database: Stores basic user information, baby behavior data, emotional data, etc.

[1830] Emotion recognition engine: A software module that performs speech recognition and facial expression recognition.

[1831] AI model: An artificial intelligence algorithm used for analysis.

[1832] Data entry and transmission

[1833] Users input basic childcare information and baby behavior data using their smartphones. This data is transmitted to a server via the internet and stored in a database. Additionally, an emotion recognition engine generates emotional data from the user's facial expressions and speech.

[1834] AI analysis and advice generation

[1835] The server extracts baby behavior data and user emotional data from the database and analyzes them using an AI model. Based on the analysis results, it generates specific childcare advice. The advice takes the user's emotional state into consideration, resulting in more individualized, specific, and appropriate content.

[1836] User notifications and feedback collection

[1837] The device notifies the user of the generated advice. Notification methods include push notifications and in-app messages. Users can also provide feedback on the effectiveness of the advice. This feedback is also sent to the server, stored in a database, and used to improve the AI ​​model.

[1838] Usage examples and prompts

[1839] Usage example

[1840] For example, if the user is feeling exhausted because their baby is crying more at night, the AI ​​model will analyze the situation and generate specific advice such as, "It would be good to let your baby take a longer nap during the day. Also, it would be good to set aside some time to relax in between childcare duties."

[1841] Example of a prompt

[1842] Recent behavioral data of the baby:

[1843] Milk intake: 120ml / session, 6 times a day

[1844] Sleep duration: 8 hours (nighttime), 3 hours of napping

[1845] User's emotional state:

[1846] Fatigue: 7 / 10

[1847] Anxiety: 5 / 10

[1848] Trends over the past 3 days:

[1849] The frequency of babies crying at night is increasing.

[1850] User fatigue is on the rise.

[1851] Advice to consider:

[1852] Extend nap time

[1853] An approach to ensure users have time to relax.

[1854] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[1855] Step 1:

[1856] Users input basic information about childcare and data on their baby's behavior.

[1857] Users use their smartphones to enter basic information such as their name, email address, baby's date of birth, gender, and health information. They also enter daily behavioral data such as milk intake, sleep duration, and bowel movements. This data is entered manually by the user through the device's interface.

[1858] Input: Basic information about childcare, baby's behavioral data

[1859] Output: Input basic information and behavioral data

[1860] Step 2:

[1861] The entered basic information and behavioral data are sent to the server and stored in the database.

[1862] The terminal transmits user-entered information to a server via the internet. The server analyzes the received information and stores it in a database. The data is organized by category and used for subsequent analysis.

[1863] Input: Basic information about childcare, baby's behavioral data

[1864] Data processing: Organize by category

[1865] Output: Basic information and behavioral data stored in the database

[1866] Step 3:

[1867] Analysis of the user's emotional state using an emotion recognition engine.

[1868] The device uses a camera and microphone to capture the user's facial expressions and voice, and an emotion recognition engine analyzes this data. The resulting emotion data, which indicates the user's emotional state, is sent to the server.

[1869] Input: User's facial expressions, voice

[1870] Data processing: Analysis using an emotion recognition engine.

[1871] Output: Sentiment data

[1872] Step 4:

[1873] The server extracts behavioral and emotional data from the database and analyzes it using an AI model.

[1874] The server extracts the latest behavioral and emotional data stored in the database. This data is preprocessed and input into an AI model. The AI ​​model analyzes the data to provide insights for generating parenting advice.

[1875] Input: Behavioral data, emotional data

[1876] Data processing: Preprocessing, analysis using AI models.

[1877] Output: Analysis results from the AI ​​model

[1878] Step 5:

[1879] Based on the analysis results, the server generates childcare advice and notifies the user.

[1880] The server generates appropriate parenting advice based on the analysis results of the AI ​​model. The generated advice takes into account the user's emotional state and is sent to the user's device as a push notification or in-app message.

[1881] Input: Analysis results

[1882] Data processing: Generating childcare advice

[1883] Output: Childcare advice notification

[1884] Step 6:

[1885] Users provide feedback on the effectiveness of the advice.

[1886] Users input feedback on the advice they receive, including its effectiveness and satisfaction level, via their device and send it to the server. The server stores the received feedback in a database and uses it to improve the AI ​​model.

[1887] Input: Advice Feedback

[1888] Data processing: Saving feedback

[1889] Output: Saved feedback, AI model improvements

[1890] In this way, the system efficiently provides support related to childcare through a series of processes, from data input from users to feedback.

[1891] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the controlled object 443 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[1892] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). One example of data generation model 58 is ChatGPT (Internet search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[1893] In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and the specific processing may also be performed by the robot 414.

[1894] Furthermore, the emotion identification model 59, acting as an emotion engine, may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to a specific mapping, which is an emotion map (see Figure 9). Similarly, the emotion identification model 59 may also determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[1895] Figure 9 shows an emotion map 400 in which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. The closer to the center of the concentric circles, the more primitive the emotions are located. Further out of the concentric circles, emotions representing states and actions arising from mental states are located. Emotion is a concept that includes feelings and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions occurring in the brain are located. On the right side of the concentric circles, emotions that are generally induced by situational judgment are located. Above and below the concentric circles, emotions that are generally generated from reactions occurring in the brain and induced by situational judgment are located. In addition, the emotion of "pleasure" is located on the upper side of the concentric circles, and the emotion of "displeasure" is located on the lower side. Thus, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions arise, and emotions that are likely to occur simultaneously are mapped close together.

[1896] These emotions are distributed at the 3 o'clock position on the Emotion Map 400, and usually fluctuate between feelings of security and anxiety. In the right half of the Emotion Map 400, situational awareness takes precedence over internal feelings, resulting in a calm impression.

[1897] The inside of the Emotion Map 400 represents inner thoughts, while the outside represents actions. Therefore, the further you go from the outside of the Emotion Map 400, the more visible (expressed in actions) your emotions become.

[1898] Here, human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. Similarly, in robots, cars, motorcycles, etc., emotions can be created based on various balances, such as posture and battery level. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. The emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on a system for analyzing brain physiological signals of speech emotion recognition and emotion, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map contains emotions belonging to a region called "response," where sensation is dominant. The right half of the emotion map contains emotions belonging to a region called "situation," where situational awareness is dominant.

[1899] The emotion map defines two emotions that promote learning. One is the emotion around the middle of the negative "repentance" and "reflection" on the situation side. In other words, it is when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is the emotion around the positive "desire" on the reaction side. In other words, it is when the robot has positive feelings such as "I want more" or "I want to know more."

[1900] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​representing each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple training data sets, which are combinations of user input and emotion values ​​representing each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions located close together have similar values, as shown in the emotion map 900 in Figure 10. Figure 10 shows an example where multiple emotions such as "reassured," "calm," and "confident" have similar emotion values.

[1901] The above description primarily focuses on the functions of the data processing device 12 in relation to this disclosure. However, the system related to this disclosure is not necessarily implemented on a server. The system related to this disclosure may be implemented as a general information processing system. This disclosure may be implemented, for example, as a software program that runs on a personal computer or as an application that runs on a smartphone. The method related to this disclosure may be provided to users in SaaS (Software as a Service) format.

[1902] In the above embodiment, an example was given in which a specific process is performed by a single computer 22. However, the technology of this disclosure is not limited thereto, and a distributed processing of the specific process may be performed by multiple computers, including computer 22. For example, a data generation model 58 may be provided in an external device of the data processing device 12, and the external device may generate data according to the input data.

[1903] In the above embodiment, an example was given in which the specific processing program 56 is stored in the storage 32, but the technology of this disclosure is not limited thereto. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-temporary storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-temporary storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes specific processing according to the specific processing program 56.

[1904] Alternatively, the specific processing program 56 may be stored in a storage device such as a server connected to the data processing device 12 via the network 54, and the specific processing program 56 may be downloaded and installed on the computer 22 in response to a request from the data processing device 12.

[1905] Furthermore, it is not necessary to store the entirety of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store the entirety of the specific processing program 56 in the storage 32; it is acceptable to store only a portion of the specific processing program 56.

[1906] The following types of processors can be used as hardware resources to perform specific processing. Examples of processors include a CPU, a general-purpose processor that functions as a hardware resource to perform specific processing by executing software, i.e., a program. Other examples of processors include dedicated electrical circuits, such as FPGAs (Field-Programmable Gate Arrays), PLDs (Programmable Logic Devices), or ASICs (Application Specific Integrated Circuits), which have circuit configurations specifically designed to perform specific processing. All of these processors have built-in or connected memory, and all of them perform specific processing by using memory.

[1907] The hardware resource that performs a specific process may consist of one of these various processors, or it may consist of a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Alternatively, the hardware resource that performs a specific process may consist of a single processor.

[1908] Examples of configurations using a single processor include, firstly, a configuration in which one or more CPUs and software are combined to form a single processor, and this processor functions as a hardware resource that performs a specific process. Secondly, there is a configuration using a processor that realizes the functions of the entire system, including multiple hardware resources that perform a specific process, on a single IC chip, as exemplified by SoCs (System-on-a-chip). In this way, a specific process is realized using one or more of the above types of processors as hardware resources.

[1909] Furthermore, the hardware structure of these various processors can more specifically utilize electrical circuits that combine circuit elements such as semiconductor devices. Also, the specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps can be deleted, new steps added, or the processing order rearranged, as long as it does not deviate from the main purpose.

[1910] The descriptions and illustrations presented above are detailed explanations of the technical aspects of this disclosure and are merely examples of the technical aspects. For example, the above descriptions of the structure, function, operation, and effect are examples of the structure, function, operation, and effect of the technical aspects of this disclosure. Therefore, it goes without saying that you may delete unnecessary parts, add new elements, or replace elements in the descriptions and illustrations presented above, as long as you do not deviate from the essence of the technical aspects of this disclosure. Furthermore, in order to avoid confusion and facilitate understanding of the technical aspects of this disclosure, explanations of common technical knowledge and the like that do not require special explanation to enable the implementation of the technical aspects of this disclosure have been omitted from the descriptions and illustrations presented above.

[1911] All documents, patent applications, and technical standards described herein are incorporated by reference to the same extent as if each individual document, patent application, and technical standard were specifically and individually noted to be incorporated by reference.

[1912] The following is further disclosed regarding the embodiments described above.

[1913] (Claim 1)

[1914] A means for users to input basic information about childcare,

[1915] A means of sending this basic information to a server and storing it in a database,

[1916] A means of regularly inputting data on the baby's behavior,

[1917] A means of sending the entered behavioral data to a server and storing it in a database,

[1918] A method for extracting behavioral data from a database and analyzing it using an AI model,

[1919] Based on these analysis results, a means of generating and notifying users of childcare advice will be developed.

[1920] A means of collecting user feedback and using it to improve the AI ​​model,

[1921] A system that includes this.

[1922] (Claim 2)

[1923] The system according to claim 1, which includes, as behavioral data of the baby, milk intake, sleep duration, and excretion status.

[1924] (Claim 3)

[1925] The system according to claim 1, comprising means for sending advice to a user as a push notification or in-app message.

[1926] "Example 1"

[1927] (Claim 1)

[1928] A means for users to input basic information about childcare,

[1929] A means of transmitting this basic information to a communication device and storing it in a data bank,

[1930] A means of regularly inputting data on the baby's behavior,

[1931] A means for transmitting input behavioral data to a communication device and storing it in a data bank,

[1932] A method for extracting behavioral data from a database and analyzing it using a generative AI model,

[1933] Based on these analysis results, a means of generating and notifying users of childcare advice will be developed.

[1934] A means of collecting user feedback and using it to improve the generated AI model,

[1935] A means of displaying a reminder to prompt the user to input behavioral data at regular intervals,

[1936] A method for generating childcare advice in text format and formatting it into an easy-to-understand format,

[1937] A system that includes this.

[1938] (Claim 2)

[1939] The system according to claim 1, which includes infant behavioral data such as the amount of milk given, sleep duration, and bowel movements.

[1940] (Claim 3)

[1941] The system according to claim 1, comprising means for sending advice to a user as a push notification or in-app message.

[1942] "Application Example 1"

[1943] (Claim 1)

[1944] A means for users to input basic information about childcare,

[1945] A means of sending this basic information to a server and storing it in a database,

[1946] A means of regularly inputting data on the baby's behavior,

[1947] A means of sending the entered behavioral data to a server and storing it in a database,

[1948] A method for extracting behavioral data from a database and analyzing it using an AI model,

[1949] Based on these analysis results, a means of generating and notifying users of childcare advice will be developed.

[1950] A means of collecting user feedback and using it to improve the AI ​​model,

[1951] A food delivery function that suggests meal options tailored to childcare situations,

[1952] A means of notifying users of advice and suggestions via push notifications at the necessary time,

[1953] A system that includes this.

[1954] (Claim 2)

[1955] The system according to claim 1, which includes, as behavioral data of the baby, milk intake, sleep duration, and excretion status.

[1956] (Claim 3)

[1957] The system according to claim 1, comprising means of sending advice or meal options to a user as a push notification or in-app message.

[1958] "Example 2 of combining an emotion engine"

[1959] (Claim 1)

[1960] A means for users to input basic information about childcare,

[1961] A means of sending this basic information to a server and storing it in a database,

[1962] A means of regularly inputting data on the baby's behavior,

[1963] A means of sending the entered behavioral data to a server and storing it in a database,

[1964] A means of recognizing a user's emotions using an emotion engine, sending the results to a server, and storing them in a database,

[1965] A method for extracting behavioral and emotional data from a database and analyzing it using an AI model,

[1966] Based on these analysis results, a means of generating and notifying users of childcare advice that takes into account their emotional state is provided.

[1967] A means of collecting user feedback and using it to improve the AI ​​model,

[1968] A system that includes this.

[1969] (Claim 2)

[1970] The system according to claim 1, which includes, as behavioral data of the baby, milk intake, sleep duration, and excretion status.

[1971] (Claim 3)

[1972] The system according to claim 1, comprising means for sending advice to a user as a push notification or in-app message.

[1973] "Application example 2 when combining with an emotional engine"

[1974] (Claim 1)

[1975] A means for users to input basic information about childcare,

[1976] A means of sending this basic information to a server and storing it in a database,

[1977] A means of regularly inputting data on the baby's behavior,

[1978] A means of sending the entered behavioral data to a server and storing it in a database,

[1979] A method for extracting behavioral data from a database and analyzing it using an AI model,

[1980] Based on these analysis results, a means of generating and notifying users of childcare advice will be developed.

[1981] A means of collecting user feedback and using it to improve the AI ​​model,

[1982] It is equipped with an emotion recognition engine that analyzes emotions from user input, speech, and facial expressions, and has a means to send emotion data to a server.

[1983] A means of analyzing emotional data and generating parenting advice that takes into account the user's emotional state,

[1984] A system that includes this.

[1985] (Claim 2)

[1986] The system according to claim 1, which includes, as behavioral data of the baby, milk intake, sleep duration, and excretion status.

[1987] (Claim 3)

[1988] The system according to claim 1, comprising means for sending advice to a user as a push notification or in-app message. [Explanation of Symbols]

[1989] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots< / url:> < / url:> < / url:> < / url:>

Claims

1. A means for users to input basic information about childcare, A means of sending this basic information to a server and storing it in a database, A means of regularly inputting data on the baby's behavior, A means of sending the entered behavioral data to a server and storing it in a database, A method for extracting behavioral data from a database and analyzing it using an AI model, Based on these analysis results, a means of generating and notifying users of childcare advice will be developed. A means of collecting user feedback and using it to improve the AI ​​model, A system that includes this.

2. The system according to claim 1, which includes, as behavioral data of the baby, milk intake, sleep duration, and excretion status.

3. The system according to claim 1, comprising means for sending advice to a user as a push notification or in-app message.

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