System

Abstract

A system is provided.SOLUTION: A system comprising: means for a user to input basic information of a child; means for a server to receive and store the inputted data; means for the server to cross-reference and analyze the collected external database information and user data; means for the server to propose an appropriate educational institution, training method, and health care method based on the analysis result; means for the user to receive the proposal; means for the user to receive feedback and store the feedback; and means for the server to analyze the feedback and make a proposal revision and model improvement.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] The declining birthrate and aging population are causing anxiety and stress about child-rearing, leading to a declining birthrate. In response to this issue, there is a growing need for technology that provides individually customized child-rearing methods and educational information to reduce child-rearing anxiety and maximize children's abilities. However, it is not easy for parents to select the optimal educational institution and child-rearing method based on their child's characteristics and interests. Furthermore, they are also required to receive advice on health management and discipline. Effective methods are needed to resolve these complex child-rearing challenges. [Means for solving the problem]

[0005] To solve the above problems, the present invention provides the following means. First, a means is provided for the user to input basic information about their child. Next, a means is provided for the server to receive and store the input data. Finally, a means is provided for the server to cross-reference and analyze collected external database information with user data. This provides a means for the server to suggest appropriate educational institutions, upbringing methods, and health management methods based on the analysis results. A means is also provided for the user to receive the suggestions and for receiving and storing feedback from the user regarding the suggestions. Finally, a means is provided for the server to analyze the feedback, revise the suggestions, and improve the model, thereby realizing continuous system improvement that reflects user needs. In this way, complex child-rearing challenges can be solved and an optimal upbringing plan based on the child's qualities can be provided.

[0006] "User" refers to an individual or parent who utilizes the system to input child development information and receive suggestions from the system.

[0007] "Server" refers to a computer system that has the functionality to receive input data, store it, analyze it, generate recommendations, and provide results to the user.

[0008] "Terminal" refers to the device (smartphone, tablet, PC, etc.) that a user uses to input information and receive the system's proposal results.

[0009] "External database information" refers to databases containing information on specific regions, existing educational institutions, training methods, health management, etc., that the system collects for reference.

[0010] "Cross-reference" refers to a method in which a server cross-references data collected from users with external database information to analyze the relevance.

[0011] "Analysis results" refers to the results obtained after cross-referencing, which derive the optimal training methods and educational institutions based on the user's characteristics.

[0012] "Suggestion" refers to specific recommendations that the system provides to the user, such as educational institutions for children, upbringing methods, health management methods, etc.

[0013] "Feedback" refers to opinions, evaluations, additional requested information, etc. that users input regarding the proposed results.

[0014] "Generative AI" refers to artificial intelligence that learns from large datasets and generates appropriate suggestions and recommendations based on user input data.

[0015] "Model improvement" refers to the process of improving the prediction accuracy and quality of suggestions of an AI model through feedback from users. [Brief explanation of the drawings]

[0016] [Figure 1] 1 is a conceptual diagram showing an example of the configuration of a data processing system according to a first embodiment. [Figure 2] 1 is a conceptual diagram showing an example of main functions of a data processing device and a smart device according to a first embodiment. [Figure 3] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a second embodiment. [Figure 4] FIG. 10 is a conceptual diagram showing an example of main functions of a data processing device and smart glasses according to a second embodiment. [Figure 5] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a third embodiment. [Figure 6] FIG. 11 is a conceptual diagram showing an example of main functions of a data processing device and a headset-type terminal according to a third embodiment. [Figure 7] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a fourth embodiment. [Figure 8]FIG. 10 is a conceptual diagram showing an example of main functions of a data processing device and a robot according to a fourth embodiment. [Figure 9] 1 shows an emotion map onto which multiple emotions are mapped. [Figure 10] 1 shows an emotion map onto which multiple emotions are mapped. [Figure 11] FIG. 3 is a sequence diagram showing a processing flow of the data processing system according to the first embodiment. [Figure 12] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system in Application Example 1. [Figure 13] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system according to the second embodiment when an emotion engine is combined. [Figure 14] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system in Application Example 2 when an emotion engine is combined. DETAILED DESCRIPTION OF THE INVENTION

[0017] An example of an embodiment of a system according to the technology of the present disclosure will be described below with reference to the accompanying drawings.

[0018] First, the terms used in the following description will be explained.

[0019] In the following embodiments, a coded processor (hereinafter simply referred to as a "processor") may be a single arithmetic device or a combination of multiple arithmetic devices. Furthermore, a processor may be a single type of arithmetic device or a combination of multiple types of arithmetic devices. Examples of arithmetic devices include a CPU (Central Processing Unit), a GPU (Graphics Processing Unit), a GPGPU (General-Purpose computing on Graphics Processing Units), and an APU (Accelerated Processing Unit).

[0020] In the following embodiments, a coded RAM (Random Access Memory) is a memory in which information is temporarily stored and is used as a working memory by a processor.

[0021] In the following embodiments, the coded storage is one or more non-volatile storage devices that store various programs, various parameters, etc. Examples of non-volatile storage devices include flash memory (SSD (Solid State Drive)), magnetic disks (e.g., hard disks), and magnetic tapes.

[0022] In the following embodiments, a communication I / F (Interface) with a symbol is an interface including a communication processor, an antenna, etc. The communication I / F controls communication between multiple computers. Examples of communication standards applied to the communication I / F include wireless communication standards including 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), Bluetooth (registered trademark), etc.

[0023] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B." In other words, "A and / or B" means that it may be only A, only B, or a combination of A and B. Furthermore, in this specification, the same concept as "A and / or B" is also applied when three or more things are expressed connected by "and / or."

[0024] [First embodiment]

[0025] FIG. 1 shows an example of the configuration of a data processing system 10 according to the first embodiment.

[0026] 1, a data processing system 10 includes a data processing device 12 and a smart device 14. An example of the data processing device 12 is a server.

[0027] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0028] The smart device 14 includes a computer 36, a reception device 38, an output device 40, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The reception device 38, the output device 40, and the camera 42 are also connected to the bus 52.

[0029] The reception device 38 includes a touch panel 38A, a microphone 38B, and the like, and receives user input. The touch panel 38A detects contact with an indicator (for example, a pen or a finger) to receive user input by the touch of the indicator. The microphone 38B detects the user's voice to receive user input by voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the data indicating the user input.

[0030] The output device 40 includes a display 40A and a speaker 40B, and presents data to the user 20 by outputting the data in a form of expression that the user 20 can perceive (for example, audio and / or text). The display 40A displays visible information such as text and images in accordance with instructions from the processor 46. The speaker 40B outputs audio in accordance with instructions from the processor 46. The camera 42 is a compact digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0031] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 control the exchange of various information between the processor 46 and the processor 28 via the network 54.

[0032] FIG. 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0033] 2, in the data processing device 12, a specific process is performed by the processor 28. A specific processing program 56 is stored in the storage 32. The specific processing program 56 is an example of a "program" according to the technology of the present disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific process is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0034] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0035] In the smart device 14, the processor 46 performs the reception output process. The storage 50 stores a reception output program 60. The reception output program 60 is used in conjunction with the specific processing program 56 by the data processing system 10. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[0036] Next, a description will be given of the specific processing performed by the specific processing unit 290 of the data processing device 12. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0037] This invention is a system that uses generative AI to individually suggest child-raising methods. Based on basic information about the child provided by the user, this system suggests educational institutions, extracurricular activities, discipline methods, health management, etc. Below, we will explain in detail how this system works.

[0038] 1. User registration and data entry procedures

[0039] Users: When they download the application for the first time, they are directed to a registration screen where they enter basic information about their child (such as name, age, gender, interests, and health status).

[0040] On your device: The information you enter is temporarily stored in local storage and prepared for transmission.

[0041] Server: Receives data sent from the device and stores it in a database, which creates individual user profiles.

[0042] 2. Data collection and cross-referencing procedures

[0043] Server: Periodically collects external databases such as information on educational institutions, training methods, health data, etc. in the test region (e.g., India). The collected data is cross-referenced with user data.

[0044] Example: Comparing data from educational institutions in urban India with user profiles in Japan to extract similarities.

[0045] 3. AI-based analysis and proposal generation procedure

[0046] Server: Generative AI analyzes the collected external data and user data. The AI ​​determines the child's interests and characteristics from the input information and determines the optimal development method based on that.

[0047] Example: If a user types "my child is interested in music," the AI ​​will generate suggestions for local music schools, online lessons, and even advice for home music education.

[0048] 4. Procedures for presenting proposals and gathering feedback

[0049] Device: The server generates suggestions and sends them to the device, where they are displayed to the user. The suggestions cover multiple topics, such as educational institutions, extracurricular activities, discipline methods, and health management methods.

[0050] User: Review the proposal and enter any questions or feedback. For example, provide specific feedback such as "the proposed school does not exist nearby."

[0051] Device: Sends feedback to the server.

[0052] 5. Feedback analysis and continuous improvement procedures

[0053] Server: Analyzes the received feedback and, if necessary, modifies the proposal content and improves the model. Based on the feedback analysis, the generative AI's learning data is updated, improving the accuracy of future proposals.

[0054] Example: If feedback is received that there are no music schools nearby, the AI ​​will re-suggest online lessons or other educational institutions in the area.

[0055] Through the above-mentioned series of steps, the present invention provides a child-rearing method that best suits the user's needs. This reduces anxiety about child-rearing and enables children to reach their full potential. The system will be tested in India, and data will be collected before being rolled out to Japan and other countries.

[0056] The above is the detailed description of the mode for carrying out the invention. This system flexibly and effectively supports child rearing and contributes to solving the problem of a declining birthrate and an aging population.

[0057] The processing flow will be explained below.

[0058] Step 1:

[0059] The user downloads and launches the application for the first time. They are directed to a new registration screen where they enter basic information about their child (such as name, age, gender, interests, and health status).

[0060] Step 2:

[0061] The device temporarily stores the information entered by the user in local storage and prepares it for transmission. Once the input is finalized, the data is sent to the server.

[0062] Step 3:

[0063] The server receives the user data sent from the terminal, stores the received data in a database, and generates an individual user profile.

[0064] Step 4:

[0065] The server periodically collects information from external databases, such as educational institution information, training methods, and health data from the test location (e.g., India). This data is stored in the database for later cross-referencing.

[0066] Step 5:

[0067] The server cross-references the user profile with the external data collected, and performs data analysis to identify suitable upbringing methods and educational institutions for the user's children.

[0068] Step 6:

[0069] The server's generative AI analyzes the cross-referenced data and identifies the best upbringing methods, educational institutions, and health care methods based on the child's characteristics and interests.

[0070] Step 7:

[0071] The server generates specific development proposals based on the analysis results, including educational institutions, extracurricular activities, discipline methods, and health management methods.

[0072] Step 8:

[0073] The server sends the proposed content to the user's terminal, where it is displayed on the user interface.

[0074] Step 9:

[0075] The user reviews the suggestions and enters feedback, for example, providing specific feedback such as "the suggested school is not nearby."

[0076] Step 10:

[0077] The terminal transmits the user's feedback to the server, which formats the transmitted data so that the feedback content is accurately reflected.

[0078] Step 11:

[0079] The server receives user feedback, stores it in a database, and prepares for reanalysis and model improvement based on the feedback.

[0080] Step 12:

[0081] The server analyzes the feedback and, if necessary, modifies the proposal and updates the training data for the generative AI model, thereby improving the accuracy of future proposals.

[0082] The above are the specific processing steps of this system. This series of processes allows users to easily find the best way to raise their children, thereby reducing anxiety about raising children.

[0083] Example 1

[0084] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0085] In today's world, child-raising methods are extremely diverse, making it difficult for parents to choose the most appropriate method for their children. Furthermore, collecting information on local educational institutions and child-raising methods and making recommendations tailored to individual children requires a great deal of time and effort. Therefore, there is a need for a system that can efficiently and accurately suggest individual child-raising methods.

[0086] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[0087] In this invention, the server includes: means for a user to input basic information about a child; means for the server to receive and store the input data; means for the server to cross-reference and analyze the collected external database information with the user data; means for the server to suggest appropriate educational institutions, upbringing methods, and health management methods based on the analysis results; means for the user to receive the suggestions; means for receiving and storing feedback from the user; means for the server to analyze the feedback, revise the suggestions, and improve the model; means for the server to periodically collect and analyze external databases; means for the terminal to send feedback data to the server; and means for the server to use prompt sentences to cause the generative AI model to perform analysis. This makes it possible to quickly and accurately suggest the optimal upbringing method for each individual child.

[0088] A "user" is an individual who uses the system to input basic information about their child and receive suggestions on how to raise them.

[0089] "Server" means a central processing unit that receives and stores input data, cross-references user data with collected external database information, analyzes it, and generates appropriate recommendations.

[0090] "Terminal" means a device on which a user inputs basic information and confirms and sends suggestions and feedback.

[0091] "Basic information" includes information such as the child's name, age, gender, interests, and health status.

[0092] "External database information" refers to information such as educational institution information, training methods, and health data for a specific region.

[0093] "Cross-referencing" is the process of comparing external database information with user data to find correlations.

[0094] "Analysis" is the process of evaluation and judgment to generate optimal proposals based on collected data and user data.

[0095] A "generative AI model" is an artificial intelligence that analyzes input information and suggests optimal training methods.

[0096] A "prompt statement" is an instruction statement entered into a generative AI model to cause it to perform analysis.

[0097] "Feedback" refers to information that a user inputs into the system, such as opinions or questions about the proposed content.

[0098] "Suggestions" are advice on selecting educational institutions, training methods, health management methods, etc. provided based on the analysis results.

[0099] The present invention is a system that utilizes a generative AI model to individually propose child-raising methods. This system generates and proposes optimal child-raising methods based on basic information about the child provided by the user. A specific embodiment of this system is described below.

[0100] Hardware and Software Configuration

[0101] User: Users access the system using a user device such as a smartphone or PC. The user device must be connected to the Internet, and operations are performed using a dedicated application or web browser.

[0102] Terminal: The terminal is a device where users enter basic information and review and enter suggestions and feedback. The terminal temporarily stores the input information in local storage and sends it to the server. The software used includes web technologies such as HTML, CSS, and JavaScript (registered trademark).

[0103] Server: The server is built using cloud services such as AWS (registered trademark). The server stores data received from users, collects external database information, and performs cross-referencing and analysis. Python is used for the server-side program, and MongoDB is used as the database.

[0104] Data collection and analysis

[0105] Server: The server periodically collects information from external databases, such as information on educational institutions in a specific region, training methods, and health data. This collection is performed using web scraping with Python libraries (e.g., BeautifulSoup). The collected data is stored in MongoDB.

[0106] Analysis: The server uses the collected external data and basic information entered by the user to perform analysis using a generative AI model (e.g., GPT-4 (registered trademark)). The AI ​​model operates based on the input prompt and generates appropriate suggestions.

[0107] Suggestions and Feedback

[0108] Terminal: The server generates suggestions, which are sent to the terminal and displayed to the user. The suggestions include educational institutions, training methods, and health management methods.

[0109] User: The user reviews the proposal and enters questions or feedback. The feedback is sent from the device to the server. The server analyzes the received feedback and modifies the proposal or improves the generative AI model.

[0110] Specific examples

[0111] 1. The user downloads and launches the app, entering basic information such as their child's name, age, gender, interests, and health status.

[0112] 2. The device temporarily stores the entered information in local storage and sends it to the server.

[0113] 3. The server receives the information and stores it securely in a database.

[0114] 4. The server periodically collects information on educational institutions and training methods from external databases and stores it in an analysis database.

[0115] 5. The server sends a prompt to the generative AI model and begins analysis. For example, the prompt might read, "The user's child is interested in music. Please suggest nearby music schools."

[0116] 6. The AI ​​model analyzes and generates recommendations, such as "ABC music school in X city, XYZ online piano lessons."

[0117] 7. The server sends the generated proposal to the terminal and displays it to the user.

[0118] 8. The user reviews the proposal and provides feedback, such as "the proposed school does not exist nearby."

[0119] 9. The device sends the feedback to the server, which receives it.

[0120] 10. The server analyzes the feedback and modifies the suggestions and improves the AI ​​model.

[0121] This series of operations allows the system to provide the best training method for the user's needs. The system will be tested in India, data will be collected, and the system will be rolled out to Japan and other countries.

[0122] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0123] Step 1:

[0124] User registration and data entry

[0125] User: The user downloads the application and is presented with a new registration screen when they launch it for the first time. Here, they enter their child's basic information (name, age, gender, interests, health status, etc.). The user presses the "Confirm Input" button to receive output based on the input (basic information).

[0126] Terminal: The input information is temporarily stored in local storage, converted to JSON format, and prepared for transmission. The data is saved in a local file as output based on the input (basic information).

[0127] Terminal: Once ready to send, it sends data to the server via API. The data is sent to the server as output based on the input (local data).

[0128] Step 2:

[0129] Data reception and storage

[0130] Server: Receives data sent from the terminal. Based on the input (user data), the server validates the data. If validation is successful, the data is securely stored in a database (e.g., AWS RDS). A save completion message is generated as output based on the input (sent data).

[0131] Step 3:

[0132] External Data Collection and Cross-referencing

[0133] Server: The server periodically collects information from external databases, such as information on educational institutions in a specific region, development methods, and health data, using Python libraries (e.g., BeautifulSoup). The collected data is converted into JSON format as output based on the input (web page URL).

[0134] Server: Collected data is stored in MongoDB and cross-referenced with user data. Highly relevant data is extracted as output based on the input (user data and collected data).

[0135] Step 4:

[0136] AI-based analysis and proposal generation

[0137] Server: Generates prompts for analysis and inputs them into a generative AI model (e.g., GPT-4). For example, a prompt containing the following content is used: "The user's child is interested in music. Please suggest nearby music schools." The optimal suggestion is generated as output based on the input (prompt and analysis data).

[0138] Example: If a user inputs "My child is interested in music," the AI ​​will generate suggestions for local music schools and online lessons. The output will be suggestions such as "Music School ABC in XYZ City" and "Online Piano Lessons XYZ."

[0139] Step 5:

[0140] Presentation of proposal content

[0141] Terminal: Receives the proposal content generated by the server and displays it on the user's terminal. The proposal content is displayed on the terminal screen as an output based on the input (proposal data).

[0142] User: The user reviews the proposal and considers the content and relevance of the information. As an output, the user presses the "Confirm" button.

[0143] Step 6:

[0144] Enter and submit feedback

[0145] User: Enters questions or feedback about the proposal. For example, provides feedback such as "the proposed school does not exist nearby." Based on the input (feedback), the user presses the "Submit" button.

[0146] Terminal: Converts feedback data into JSON format and sends it to the server. Data is sent to the server as output based on the input (feedback data).

[0147] Step 7:

[0148] Analyze feedback and improve

[0149] Server: Analyzes the received feedback data and, if necessary, modifies the proposal content and improves the AI ​​model. The analysis results are generated as output based on the input (feedback data), and the model is updated. The next time a proposal is made, a more accurate proposal will be made based on this.

[0150] The above steps realize a system that can quickly and accurately provide a training method that is optimal for the user's needs.

[0151] (Application example 1)

[0152] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0153] Traditional child development methods often rely on general guidelines and standard educational measures, lacking personalized suggestions tailored to each child's characteristics and interests. In particular, there are no effective systems for using regional educational institutions and development data, or handling user feedback. As a result, parents have to expend a great deal of effort selecting the appropriate educational institution and development method, making it difficult to find the optimal development environment.

[0154] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[0155] In this invention, the server includes: a means for a user to input basic information; a means for the server to receive and store the input data; a means for the server to cross-reference and analyze the collected external database information and user data; a means for the server to suggest appropriate educational institutions, development methods, and health management methods based on the analysis results; a means for the user to receive the suggestions; a means for receiving and storing feedback from the user; a means for the server to analyze the feedback and revise the suggestions and improve the model; a means for the user to provide questions or additional feedback based on the suggestions; and a means for continuously training the generative AI model using the collected feedback to improve the accuracy of the suggestions. This makes it possible to suggest the most appropriate development methods and educational institutions for each individual child, reducing the burden on parents and maximizing their children's abilities.

[0156] "Means for users to input basic information" refers to an input interface that allows users to input their child's name, age, gender, interests, health status, etc. through the application.

[0157] "Means for the server to receive and store the entered data" refers to the function by which the server receives the basic information entered by the user and stores it in a database.

[0158] "Means for the server to cross-reference and analyze collected external database information and user data" refers to the process by which the server compares and analyzes information collected from multiple external databases with data entered by the user.

[0159] "Means for the server to suggest appropriate educational institutions, upbringing methods, and health management methods based on the analysis results" refers to the function of suggesting the most suitable educational institutions, upbringing methods, and health management methods for children based on the data collected by the server and the analysis results.

[0160] "Means for users to receive suggestions" refers to a mechanism that allows users to view the suggestions sent from the server through the application.

[0161] "Means for receiving and storing feedback from users" refers to a function that allows users to send opinions and questions about the proposal content, and the server receives and stores them.

[0162] "Means for the server to analyze feedback and revise proposals and improve the model" refers to the process by which the server analyzes the feedback it receives and uses it to revise proposals and improve the AI ​​model.

[0163] "Means for users to ask questions or provide additional feedback based on the suggestions" refers to an interface that allows users to ask additional questions or provide feedback about the suggestions received from the server.

[0164] "Means of continuously training the generative AI model using collected feedback to improve the accuracy of suggestions" refers to the process of continuously training the generative AI model using collected user feedback to make the next suggestions more accurate.

[0165] This invention is a system that utilizes generative AI to individually suggest child-raising methods. Based on basic information about the child provided by the user, the system suggests educational institutions, extracurricular activities, discipline methods, health management, etc. Below, we will explain in detail how this system works.

[0166] User registration and data entry

[0167] When a user downloads a smartphone application for the first time, they are directed to a new registration screen where they enter their child's basic information (name, age, gender, interests, health status, etc.). This information is temporarily stored in the device's local storage and then sent to the server. The server stores the received data in a database and generates an individual user profile.

[0168] Data collection and cross-referencing

[0169] The server periodically collects external databases such as educational institution information, training methods, and health data, including data for specific regions (multi-regional). The server cross-references the collected external data with user data to extract and analyze similarities.

[0170] AI analysis and proposal generation

[0171] The server uses a generative AI model to analyze the collected external data and user data. The AI ​​determines the child's interests and characteristics from the information entered by the user and identifies the optimal development method based on that. For example, if a user enters that their child is interested in music, the AI ​​will suggest local music schools and online lessons. The AI ​​model used utilizes machine learning frameworks such as TENSORFLOW (registered trademark).

[0172] Presenting proposals and collecting feedback

[0173] The suggestions generated by the server are sent to the terminal and displayed to the user. The user can review the suggestions and enter questions or feedback. For example, they can enter feedback such as "the suggested school does not exist nearby," and this feedback is also sent to the server. The suggestions include educational institutions, extracurricular activities, discipline methods, health management methods, etc.

[0174] Feedback analysis and continuous improvement

[0175] The server analyzes the received feedback and, if necessary, modifies the suggestions and improves the model. The collected feedback is used as training data for the generative AI model, improving the accuracy of future suggestions. This allows the system to continuously provide the optimal upbringing method for each individual child.

[0176] Examples of prompt statements

[0177] "Generate the best musical education recommendations for your child with the EduCare app."

[0178] ---

[0179] The system consists of a backend server built using the Django framework and a smartphone application. A generative AI model using Python and TensorFlow is implemented on the server side to analyze data and provide generated suggestions. PostgreSQL is used as the database to store and manage user profiles and feedback information. Users can intuitively enter information through the interface and receive suggestions.

[0180] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0181] Step 1:

[0182] The user downloads the smartphone application and enters basic information about their child (such as name, age, gender, interests, and health status) on the new registration screen. The information entered here is temporarily saved in the device's local storage. The input data is formatted in a structured data format such as JSON, which makes it ready to be sent to the server.

[0183] Step 2:

[0184] The terminal sends the basic information entered by the user to the server. The sent data is received by the server and stored in a database. The data is stored in an SQL database (specifically, PostgreSQL), and a user profile is generated. Database operations are performed on the input data, and a query is executed to insert the data into a table.

[0185] Step 3:

[0186] The server periodically collects data from external databases (such as educational institution information, development methods, and health data). This data collection is done via APIs and web crawling. The collected data is temporarily stored on the server and later cross-referenced with user data. The data collection results are stored as structured data.

[0187] Step 4:

[0188] The server cross-references the collected external data with the user's basic information and performs analysis. This analysis utilizes a generative AI model (using TensorFlow). Specifically, the AI ​​model uses user data and external data as input to identify the optimal training method and educational institution and generate recommendations. This model is pre-trained using techniques such as supervised learning and reinforcement learning.

[0189] Step 5:

[0190] The server sends the generated suggestions to the device. The device then displays the received suggestions to the user. This display is done using the smartphone app's UI components (e.g., list view or card view) so that the user can easily check the content. The output suggestions consist of specific items, such as "nearby music schools."

[0191] Step 6:

[0192] The user checks the displayed suggestions and enters feedback if necessary. The feedback can be questions or opinions about the suggestions. The feedback entered by the user is saved on the device and later sent to the server. The feedback data is packaged in JSON format.

[0193] Step 7:

[0194] The server receives the feedback submitted by the user and stores it in a database, again using a SQL database and running queries to insert data into the feedback table.

[0195] Step 8:

[0196] The server analyzes the stored feedback and uses it as data to continuously train the generative AI model, thereby improving the accuracy of future suggestions. This process involves retraining and reevaluating the AI ​​model, and updating the suggestion algorithm.

[0197] Step 9:

[0198] Based on the updated AI model and newly collected data, the server generates recommendations for future users, and this process is repeated, continuously improving the accuracy and efficiency of the entire system.

[0199] Examples of prompt statements

[0200] "Generate the best musical education recommendations for your child with the EduCare app."

[0201] ---

[0202] The above are the specific processing steps for carrying out the present invention. We have clarified how data is input, processed, calculated, and output in each step, and shown an effective method for carrying out the present invention.

[0203] Furthermore, an emotion engine that estimates the user's emotion may be combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0204] This invention combines an emotion engine with a generative AI-based training method proposal system to achieve customization that closely reflects the user's emotions. This system operates in the following steps.

[0205] 1. User registration and data entry procedures

[0206] User: After downloading and launching the application, the user is directed to a new registration screen. The user enters basic information about the child (name, age, gender, interests, health status, etc.). There is also an emotion input field, where the user can enter their current state of mind or emotions.

[0207] 2. Procedure for emotion analysis using the emotion engine

[0208] Device: The basic information and emotion information entered by the user are temporarily stored in local storage and prepared for transmission. After final confirmation of the input, the data, including the emotion information, is sent to the server.

[0209] Server: The received data is stored in a database. The emotion engine analyzes the user's emotion data and reflects the emotional trends in the analysis results.

[0210] 3. Data collection and cross-referencing procedures

[0211] Server: Periodically collects external databases such as information on educational institutions, training methods, health data, etc. in the test region (e.g., India). The collected data is cross-referenced with user data.

[0212] Example: Comparing data from educational institutions in urban India with user profiles in Japan to extract similarities.

[0213] 4. AI-based analysis and proposal generation procedure

[0214] Server: Generative AI analyzes the collected external data and user data. The AI ​​determines the child's characteristics and interests from the user's input information and emotional data, and determines the optimal development method based on that.

[0215] Example: If a user types "my child is interested in music" and selects "anxiety" as the emotion, the AI ​​will suggest local music schools and online lessons, as well as ways to support the child in easing their anxiety.

[0216] 5. Procedure for presenting proposals and collecting emotional feedback

[0217] Device: The server generates suggestions and sends them to the device, where they are displayed to the user. These suggestions include educational institutions, extracurricular activities, discipline methods, and health management methods. A field for entering emotions about the suggestions is also displayed.

[0218] User: Checks the proposal and enters feedback. For emotional feedback, the user can select emotions such as "happiness" or "dissatisfaction" and enter specific opinions.

[0219] Device: Sends feedback and emotion data to the server.

[0220] 6. Feedback analysis and continuous improvement procedures

[0221] Server: Analyzes the received feedback and emotion data and stores it in a database. The emotion engine analyzes the emotion data of the feedback and prepares it to be reflected in the next proposal.

[0222] Example: If the AI ​​receives feedback such as "The proposed music school is good, but transportation is inconvenient" along with emotion data of "dissatisfied," it will generate new suggestions that also take transportation convenience into account.

[0223] 7. Continuous system improvement

[0224] Server: Continually trains the generative AI model based on feedback to improve the accuracy of suggestions, and updates the emotion engine to make suggestions more relevant to the user's emotions.

[0225] Through these steps, the system can propose training methods that take the user's emotions into consideration and provide the optimal training plan that meets their individual needs.The system will be tested in India and data will be collected, with plans to expand to Japan and other countries.

[0226] The above is a detailed description of the mode for carrying out the invention. The purpose of the present invention is to realize suggestions that take into consideration the user's feelings, thereby reducing anxiety about child-rearing and enabling children to reach their full potential.

[0227] The processing flow will be explained below.

[0228] Step 1:

[0229] The user downloads and launches the application. A new registration screen appears, and the user enters basic information about the child (name, age, gender, interests, and health status). An emotion input field also appears, allowing the user to select or enter their current state of mind or emotion.

[0230] Step 2:

[0231] The device temporarily stores the basic information and emotion information entered by the user in local storage. After the input contents are confirmed, the device prepares and sends the data to the server.

[0232] Step 3:

[0233] The server receives the user data and emotion data sent from the device, stores the received data in a database, and creates a user profile.

[0234] Step 4:

[0235] The server periodically collects external databases such as information on educational institutions in the test area (e.g., India), training methods, health data, etc. The collected external data is stored in the database.

[0236] Step 5:

[0237] The server cross-references the user profile with the collected external data, and AI analyzes both to find the right upbringing method and educational institution for the user's child.

[0238] Step 6:

[0239] The server's generative AI analyzes the cross-referenced data and the user's emotional data to identify the optimal upbringing method, educational institution, and health care method, taking into account the child's characteristics and interests, as well as the user's emotions.

[0240] Example: If a user types "my child is interested in science" and selects "confident" as the emotion, the AI ​​generates suggestions for local science clubs and online science courses. By taking the user's "confident" emotion into account and providing more detailed information, the AI ​​maintains a sense of security in parenting.

[0241] Step 7:

[0242] The server generates specific developmental suggestions based on the analysis results. These suggestions include educational institutions, extracurricular activities, discipline methods, and health management methods. The suggestions also reflect the emotional data selected by the user.

[0243] Step 8:

[0244] The server sends the suggestion to the user's device, and an emotional feedback field is displayed on the user interface along with the suggestion.

[0245] Step 9:

[0246] The user confirms the proposal, selects emotions such as "joy," "dissatisfaction," or "relief" as emotional feedback, and also enters specific opinions and impressions.

[0247] Example: If you are happy with the proposed science club, select "Delighted" and enter a comment such as "It fits perfectly with what I'm interested in and I'm very happy."

[0248] Step 10:

[0249] The device transmits the user's feedback and emotion data to the server, where the feedback data is ready to be analyzed.

[0250] Step 11:

[0251] The server analyzes the received feedback and emotional data. The analysis results are stored in a database, and the generative AI model is updated and improved based on the feedback analysis. This improves the accuracy of future suggestions.

[0252] Step 12:

[0253] The server uses the improved AI model to generate new suggestions as needed and sends them to the user, enabling the system to provide the optimal training method and personalized suggestions that are sensitive to the user's emotions.

[0254] The above are the specific processing steps of this system. Through this series of processes, the system aims to quickly and accurately propose child-rearing methods that take into consideration the user's feelings, reduce anxiety about child-rearing, and maximize the child's potential.

[0255] Example 2

[0256] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0257] There is a need to provide personalized training methods based on the characteristics, interests, and current emotions of the training recipient. However, conventional systems were unable to take the user's emotions into account, and their suggestions were general and insufficient for individualization. Furthermore, they lacked the functionality to analyze the quality of feedback and emotions and reflect them in future suggestions, making continuous improvement difficult.

[0258] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[0259] In this invention, the server includes means for the user to input basic information about the person being trained and their current emotional information, means for the terminal to temporarily store the input data and transmit it to the server, means for the server to store the received data and analyze the emotional information using an emotional engine, means for the server to cross-reference and analyze the collected external database information with the user data, means for the server to suggest appropriate educational institutions, training methods, and health management methods based on the analysis results, means for the user to receive the suggestions and input feedback, means for the terminal to transmit the user's feedback and emotional data to the server, and means for the server to analyze the feedback and generate next suggestions and improve the model. This enables customized training suggestions that are tailored to the emotions of each individual user.

[0260] "User" refers to an individual or organization that uses the system to input information about the person being trained and receive proposals.

[0261] "Training target" refers to the child or student who receives the proposed training method or educational plan.

[0262] The "emotion engine" is a software module that analyzes the emotional information entered by the user and determines the tendency of that emotion.

[0263] "External database information" refers to information obtained from external resources, such as data on educational institutions, data on development methods, and health data, which is collected for the system to reference.

[0264] "Cross-reference" is a process that compares user data with external database information and extracts commonalities and relationships.

[0265] "Generative AI" is artificial intelligence that generates optimal suggestions based on user input and collected data.

[0266] "Feedback" refers to the opinions and emotional information provided by a user after receiving a suggestion.

[0267] "Terminal" refers to an electronic device that a user uses to input information and communicate with a server.

[0268] A "server" is a central computer system that processes received data and generates offers.

[0269] "Suggestions" are recommendations on educational institutions, training methods, health management methods, etc. provided by the server based on the analysis results.

[0270] "Model improvement" is the process of incorporating user feedback to improve the accuracy of generative AI.

[0271] The present invention is a system that proposes an optimal training method based on the characteristics, interests, and emotions of a person to be trained. To implement this system, the following hardware and software are used.

[0272] Users download the application and enter basic information about the child they are raising, as well as their current emotional state. For example, they can enter the child's name, age, gender, interests, health condition, and current state of mind and emotions. The user's input is done on a device (electronic device such as a smartphone or tablet), which temporarily stores the input data in local storage. After confirmation, the data is sent to the server.

[0273] The server stores the received data in a database. The emotion information is analyzed using an emotion engine. For example, IBM Watson (registered trademark) or Microsoft (registered trademark) Azure (registered trademark) emotion analysis API can be used as the emotion engine. The analysis results are reflected in the database.

[0274] The server then collects education, upbringing, and health data from external databases, including government education databases and professional organization repositories. This data is then cross-referenced with the user data. For example, it collects information on educational institutions, upbringing, and health data for a specified region and extracts commonalities and associations with the user data.

[0275] A generative AI model (e.g., GPT-4) determines the optimal upbringing method based on the cross-referenced data, user input, and emotional data, and generates suggestions. These suggestions may include educational institutions, extracurricular activities, discipline methods, and health management methods. Support methods may also be included. For example, if a user inputs "my child is interested in music" and selects "anxiety" as the emotion, the generative AI may suggest local music schools and online lessons, as well as support methods to alleviate anxiety.

[0276] An example of a prompt is as follows:

[0277] "What music school would you recommend for a 3-year-old boy who is interested in music, and how can I ease his concerns?"

[0278] The suggestions are sent to the device and displayed to the user. The user checks the suggestions and enters feedback. The feedback can include specific opinions such as "transportation is inconvenient" and emotions such as "dissatisfaction." The device then sends this feedback to the server.

[0279] The server analyzes the feedback and reflects it in the next recommendation. This data is used to train the generative AI model, which continuously improves the model. This allows the system to provide customized training suggestions that are in tune with each individual user's emotions.

[0280] The system of the present invention promotes individualized education and training, reduces user anxiety, and maximizes the abilities of trainees. This system will be tested in a specific region, and data will be collected before it can be expanded to other regions.

[0281] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0282] Step 1:

[0283] The user enters basic information and emotional information about the person being trained.

[0284] Input: The user enters the name, age, gender, interests, health status, and current state and feelings of the person being fostered.

[0285] How it works: A user launches the application and enters information into each field on the registration screen, specifically using text boxes and drop-down menus.

[0286] Output: The input data is temporarily stored in the device's local storage.

[0287] Step 2:

[0288] The device temporarily stores the data and sends it to the server

[0289] Input: Basic information and emotional information entered by the user is stored in the device's local storage.

[0290] How it works: When the user clicks the "Send" button, the device converts this data into JSON format and sends it to the server.

[0291] Output: The server receives the data in JSON format.

[0292] Step 3:

[0293] The server stores the data and analyzes it with the emotion engine

[0294] Input: Basic information and emotion information received by the server from the device in JSON format.

[0295] How it works: The server stores the received data in a database and analyzes the emotional information using an emotion engine (e.g., IBM Watson or Microsoft Azure's emotion analysis API). The analysis results are added to the database.

[0296] Output: The sentiment analysis results are stored in a database.

[0297] Step 4:

[0298] Server collects external database information

[0299] Input: A trigger that requires external database information.

[0300] How it works: The server periodically uses API requests to collect information about educational institutions, development practices, and health data for a specified region, e.g., by retrieving data in JSON format from a government education database.

[0301] Output: Collected external data is stored in a database.

[0302] Step 5:

[0303] Server performs cross-referencing and analysis

[0304] Input: User data and external database information.

[0305] How it works: The server cross-references user data with information from external databases to extract commonalities and relationships. This is done using data analysis algorithms.

[0306] Output: Cross-reference results and analysis results are provided.

[0307] Step 6:

[0308] Generative AI generates suggestions

[0309] Input: User data, sentiment analysis results, cross-reference results.

[0310] How it works: Generative AI (e.g., GPT-4) uses these inputs to determine the optimal training method and generate specific suggestions. For example, if the interest is "music" and the emotion is "anxiety," the AI ​​will suggest local music schools and online lessons, along with support methods to alleviate anxiety.

[0311] Output: Customized development suggestions.

[0312] Step 7:

[0313] Users submit suggestions and feedback

[0314] Input: Development suggestions generated by generative AI.

[0315] How it works: The suggestions are displayed on the user's device. The user reviews the suggestions and enters their emotional feedback and specific opinions through a feedback form.

[0316] Output: User feedback data is sent from the device to the server.

[0317] Step 8:

[0318] The server analyzes the feedback and improves the model

[0319] Input: Feedback data submitted by the user.

[0320] How it works: The server stores the feedback in a database and analyzes the emotional information in the feedback using an emotion engine. The results of this analysis are used to train a generative AI model and are reflected in the next proposal.

[0321] Output: An improved generative AI model.

[0322] Through these specific processing steps, the system is able to continuously provide and improve customized development suggestions while being sensitive to the user's emotions.

[0323] (Application example 2)

[0324] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0325] Conventional autonomous vehicles have the problem of being unable to provide optimal routes and entertainment options that take into account the emotional state of passengers. This has resulted in cases where the riding experience is not necessarily comfortable for each individual user. Furthermore, there has been a problem with continuous improvement of the system due to insufficient collection and analysis of feedback.

[0326] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 2 is realized by the following means.

[0327] In this invention, the server includes: a means for a user to input basic information and emotional information of a target person; a means for the server to receive and store the input data; a means for the server to analyze the received emotional data and analyze emotional trends; a means for the server to cross-reference and analyze collected external database information with the user data; a means for the server to suggest optimal routes and entertainment options for the autonomous vehicle based on the analysis results; a means for the user to receive the suggestions; a means for the server to receive and store feedback from the user; and a means for the server to analyze the feedback, revise the suggestions, and improve the model. This makes it possible to provide optimal routes and entertainment options that take into account the emotional state of passengers, thereby improving the riding experience and achieving continuous improvement of the system.

[0328] "Means for users to input basic information and emotional information of a subject" refers to an interface that allows users to input basic information about themselves or others and their current emotional state into the system.

[0329] "Means for the server to receive and store input data" refers to the function of the server receiving data input by the user via the network and storing that data in a database.

[0330] "Means for analyzing the emotional data received by the server and analyzing the emotional trends" refers to the function of the server processing the emotional data received from the user using an analysis program and identifying emotional patterns and trends.

[0331] "Means for the server to cross-reference and analyze collected external database information with user data" refers to the function of comparing and analyzing information collected from external databases with data provided by users.

[0332] "Means for the server to suggest optimal routes and entertainment options for autonomous vehicles based on the analysis results" refers to a function that suggests optimal routes for autonomous vehicles and entertainment options suitable for users based on data analyzed by the server.

[0333] The "means for the user to receive the proposal" refers to an interface that allows the user to check and receive the proposal content generated by the server.

[0334] "Means for receiving and storing feedback from users" refers to a function in which users input their thoughts and opinions about the proposal content, and the server receives and stores them in a database.

[0335] "Means for the server to analyze feedback, revise proposals, and improve the model" refers to the function by which the server analyzes the user feedback collected, revises the next proposal based on the analysis results, and further improves the model of the entire system.

[0336] The present invention provides a system for analyzing a user's emotional state in real time and suggesting optimal routes and entertainment options for an autonomous vehicle.

[0337] System configuration and hardware / software overview

[0338] The system includes user devices such as smartphones and tablets, a communication network, and a server. Its main software components include an emotion engine, a generative AI model, a database, and an external data collection module.

[0339] User terminal: Provides an interface for users to input basic and emotional information, including touchscreen and voice input capabilities.

[0340] Emotion engine: Software that analyzes emotional information entered by users in real time and detects emotional trends.

[0341] Generative AI model: An AI that generates optimal routes and entertainment options based on user and external data.

[0342] Database: A storage system for storing user data, emotion data, external data, feedback, etc.

[0343] External data collection module: A module for periodically collecting external information such as traffic conditions and weather data.

[0344] Program processing overview

[0345] 1. User registration and data entry: A user launches the application and enters basic information such as name, age, gender, destination, current emotional state, etc. This data is temporarily stored in local storage and then sent to the server.

[0346] 2. Emotion data analysis: The server uses an emotion engine to analyze the received emotion data in real time, detect emotional trends, and then save the analysis results in a database.

[0347] 3. External data collection and cross-referencing: The external data collection module is used to periodically collect external information such as traffic and weather data and cross-reference it with user data.

[0348] 4. Analysis and recommendation generation: Generative AI models analyze the cross-referenced data to generate optimal routes and entertainment options for the user.

[0349] 5. Proposal presentation and feedback collection: The proposal is sent to the user's device, where the user can review it and provide feedback. Feedback about the proposed plan is sent to the server.

[0350] 6. Feedback analysis and system improvement: The server analyzes the received feedback and stores it in a database to continuously improve the quality of the emotion engine and generative AI model.

[0351] Specific examples

[0352] For example, if a user enters the following information:

[0353] Name: Yamada Taro

[0354] Age: 35

[0355] Gender: Male

[0356] Destination: Workplace

[0357] Current emotional state: Anxiety

[0358] The emotion engine analyzes the input "anxiety" and suggests routes, music, videos, and other entertainment that will help the user relax. The generative AI model combines real-time traffic and weather data to calculate the optimal route. The user then selects and uses the suggested route and entertainment options through the app.

[0359] Prompt Sentence Examples

[0360] Name: Yamada Taro

[0361] Age: 35

[0362] Gender: Male

[0363] Destination: Workplace

[0364] Current emotional state: Anxiety

[0365] In this way, the present invention is able to take into account the user's emotional state and provide a personally optimized self-driving vehicle experience.

[0366] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0367] Step 1:

[0368] A user launches the application and enters basic information such as name, age, gender, destination, and current emotional state.

[0369] Input: User's basic information and emotional information

[0370] Output: User data saved in local storage

[0371] How it works: The user enters the required information into the input form on their device, and the input is temporarily saved in local storage. The data is then ready to be sent to the server.

[0372] Step 2:

[0373] The server receives the user's input data and stores it in a database, including the emotion data.

[0374] Input: User data stored in local storage

[0375] Output: User data and emotion data stored in a database

[0376] Operation: The server receives data from the user terminal via the network and stores it in a database.

[0377] Step 3:

[0378] The server uses an emotion engine to analyze the received emotion data and detect the trend of emotions.

[0379] Input: Emotion data stored in a database

[0380] Output: Emotional tendency data as the analysis result

[0381] How it works: The emotion engine analyzes emotion data, identifies positive, negative, or specific emotional states (e.g., anxiety, joy), and stores the results in a database.

[0382] Step 4:

[0383] The external data collection module is used to periodically collect external information such as traffic conditions and weather.

[0384] Input: Traffic and weather data from external APIs and databases

[0385] Output: External data stored on the server

[0386] How it works: The server periodically calls an external API to get the latest traffic and weather information and stores it in a database.

[0387] Step 5:

[0388] The server cross-references and analyzes the collected external data with the user data.

[0389] Input: User data in the database, sentiment data, and external data

[0390] Output: Optimal routes and entertainment options as analysis results

[0391] How it works: A generative AI model uses this data to perform analysis and generate optimal routes and entertainment options.

[0392] Step 6:

[0393] The server transmits the generated proposal to the user terminal, and the user receives it.

[0394] Input: Suggested best route and entertainment options

[0395] Output: Proposal displayed on the user's device

[0396] Operation: The server sends the generated results to the user's terminal and displays them on the interface for the user to check.

[0397] Step 7:

[0398] The user inputs feedback about the proposal and sends it to the server.

[0399] Input: User feedback (e.g., satisfaction with the proposal, specific opinions)

[0400] Output: Feedback data stored on the server

[0401] How it works: Users enter their opinions through a feedback form, and the server receives the data and stores it in a database.

[0402] Step 8:

[0403] The server analyzes the received feedback and uses it to revise the proposals and improve the system for the next time.

[0404] Input: Feedback data

[0405] Output: Revised proposal and improved system model

[0406] How it works: The server analyzes the feedback data and uses it as training data for the generative AI model and emotion engine to improve the accuracy of the next suggestion.

[0407] The above are the specific processing steps for carrying out the present invention.

[0408] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0409] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search engine).<URL: https: / / openai.com / blog / chatgpt> ), Gemini (registered trademark) (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0410] In the above embodiment, an example in which the specific process is performed by the data processing device 12 has been given, but the technology of the present disclosure is not limited to this, and the specific process may be performed by the smart device 14.

[0411] [Second embodiment]

[0412] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0413] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0414] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0415] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, and the camera 42 are also connected to the bus 52.

[0416] The microphone 238 receives instructions and the like from the user 20 by receiving voice uttered by the user 20. The microphone 238 captures the voice uttered by the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio in accordance with instructions from the processor 46.

[0417] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the surroundings of user 20 (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0418] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 control the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0419] Fig. 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Fig. 4, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0420] The specific processing program 56 is an example of a "program" according to the technology of the present disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0421] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0422] In the smart glasses 214, the reception output process is performed by the processor 46. A reception output program 60 is stored in the storage 50. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[0423] Next, a description will be given of the identification process performed by the identification processing unit 290 of the data processing device 12. In the following description, the data processing device 12 will be referred to as the "server" and the smart glasses 214 will be referred to as the "terminal."

[0424] This invention is a system that uses generative AI to individually suggest child-raising methods. Based on basic information about the child provided by the user, this system suggests educational institutions, extracurricular activities, discipline methods, health management, etc. Below, we will explain in detail how this system works.

[0425] 1. User registration and data entry procedures

[0426] Users: When they download the application for the first time, they are directed to a registration screen where they enter basic information about their child (such as name, age, gender, interests, and health status).

[0427] On your device: The information you enter is temporarily stored in local storage and prepared for transmission.

[0428] Server: Receives data sent from the device and stores it in a database, which creates individual user profiles.

[0429] 2. Data collection and cross-referencing procedures

[0430] Server: Periodically collects external databases such as information on educational institutions, training methods, health data, etc. in the test region (e.g., India). The collected data is cross-referenced with user data.

[0431] Example: Comparing data from educational institutions in urban India with user profiles in Japan to extract similarities.

[0432] 3. AI-based analysis and proposal generation procedure

[0433] Server: Generative AI analyzes the collected external data and user data. The AI ​​determines the child's interests and characteristics from the input information and determines the optimal development method based on that.

[0434] Example: If a user types "my child is interested in music," the AI ​​will generate suggestions for local music schools, online lessons, and even advice for home music education.

[0435] 4. Procedures for presenting proposals and gathering feedback

[0436] Device: The server generates suggestions and sends them to the device, where they are displayed to the user. The suggestions cover multiple topics, such as educational institutions, extracurricular activities, discipline methods, and health management methods.

[0437] User: Review the proposal and enter any questions or feedback. For example, provide specific feedback such as "the proposed school does not exist nearby."

[0438] Device: Sends feedback to the server.

[0439] 5. Feedback analysis and continuous improvement procedures

[0440] Server: Analyzes the received feedback and, if necessary, modifies the proposal content and improves the model. Based on the feedback analysis, the generative AI's learning data is updated, improving the accuracy of future proposals.

[0441] Example: If feedback is received that there are no music schools nearby, the AI ​​will re-suggest online lessons or other educational institutions in the area.

[0442] Through the above-mentioned series of steps, the present invention provides a child-rearing method that best suits the user's needs. This reduces anxiety about child-rearing and enables children to reach their full potential. The system will be tested in India, and data will be collected before being rolled out to Japan and other countries.

[0443] The above is the detailed description of the mode for carrying out the invention. This system flexibly and effectively supports child rearing and contributes to solving the problem of a declining birthrate and an aging population.

[0444] The processing flow will be explained below.

[0445] Step 1:

[0446] The user downloads and launches the application for the first time. They are directed to a new registration screen where they enter basic information about their child (such as name, age, gender, interests, and health status).

[0447] Step 2:

[0448] The device temporarily stores the information entered by the user in local storage and prepares it for transmission. Once the input is finalized, the data is sent to the server.

[0449] Step 3:

[0450] The server receives the user data sent from the terminal, stores the received data in a database, and generates an individual user profile.

[0451] Step 4:

[0452] The server periodically collects information from external databases, such as educational institution information, training methods, and health data from the test location (e.g., India). This data is stored in the database for later cross-referencing.

[0453] Step 5:

[0454] The server cross-references the user profile with the external data collected, and performs data analysis to identify suitable upbringing methods and educational institutions for the user's children.

[0455] Step 6:

[0456] The server's generative AI analyzes the cross-referenced data and identifies the best upbringing methods, educational institutions, and health care methods based on the child's characteristics and interests.

[0457] Step 7:

[0458] The server generates specific development proposals based on the analysis results, including educational institutions, extracurricular activities, discipline methods, and health management methods.

[0459] Step 8:

[0460] The server sends the proposed content to the user's terminal, where it is displayed on the user interface.

[0461] Step 9:

[0462] The user reviews the suggestions and enters feedback, for example, providing specific feedback such as "the suggested school is not nearby."

[0463] Step 10:

[0464] The terminal transmits the user's feedback to the server, which formats the transmitted data so that the feedback content is accurately reflected.

[0465] Step 11:

[0466] The server receives user feedback, stores it in a database, and prepares for reanalysis and model improvement based on the feedback.

[0467] Step 12:

[0468] The server analyzes the feedback and, if necessary, modifies the proposal and updates the training data for the generative AI model, thereby improving the accuracy of future proposals.

[0469] The above are the specific processing steps of this system. This series of processes allows users to easily find the best way to raise their children, thereby reducing anxiety about raising children.

[0470] Example 1

[0471] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0472] In today's world, child-raising methods are extremely diverse, making it difficult for parents to choose the most appropriate method for their children. Furthermore, collecting information on local educational institutions and child-raising methods and making recommendations tailored to individual children requires a great deal of time and effort. Therefore, there is a need for a system that can efficiently and accurately suggest individual child-raising methods.

[0473] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[0474] In this invention, the server includes: means for a user to input basic information about a child; means for the server to receive and store the input data; means for the server to cross-reference and analyze the collected external database information with the user data; means for the server to suggest appropriate educational institutions, upbringing methods, and health management methods based on the analysis results; means for the user to receive the suggestions; means for receiving and storing feedback from the user; means for the server to analyze the feedback, revise the suggestions, and improve the model; means for the server to periodically collect and analyze external databases; means for the terminal to send feedback data to the server; and means for the server to use prompt sentences to cause the generative AI model to perform analysis. This makes it possible to quickly and accurately suggest the optimal upbringing method for each individual child.

[0475] A "user" is an individual who uses the system to input basic information about their child and receive suggestions on how to raise them.

[0476] "Server" means a central processing unit that receives and stores input data, cross-references user data with collected external database information, analyzes it, and generates appropriate recommendations.

[0477] "Terminal" means a device on which a user inputs basic information and confirms and sends suggestions and feedback.

[0478] "Basic information" includes information such as the child's name, age, gender, interests, and health status.

[0479] "External database information" refers to information such as educational institution information, training methods, and health data for a specific region.

[0480] "Cross-referencing" is the process of comparing external database information with user data to find correlations.

[0481] "Analysis" is the process of evaluation and judgment to generate optimal proposals based on collected data and user data.

[0482] A "generative AI model" is an artificial intelligence that analyzes input information and suggests optimal training methods.

[0483] A "prompt statement" is an instruction statement entered into a generative AI model to cause it to perform analysis.

[0484] "Feedback" refers to information that a user inputs into the system, such as opinions or questions about the proposed content.

[0485] "Suggestions" are advice on selecting educational institutions, training methods, health management methods, etc. provided based on the analysis results.

[0486] The present invention is a system that utilizes a generative AI model to individually propose child-raising methods. This system generates and proposes optimal child-raising methods based on basic information about the child provided by the user. A specific embodiment of this system is described below.

[0487] Hardware and Software Configuration

[0488] User: Users access the system using a user device such as a smartphone or PC. The user device must be connected to the Internet, and operations are performed using a dedicated application or web browser.

[0489] Device: The device is where users enter basic information and review and enter suggestions and feedback. The device temporarily stores the information in local storage and then sends it to the server. The software used includes web technologies such as HTML, CSS, and JavaScript.

[0490] Server: The server is built using cloud services such as AWS. The server stores data received from users, collects external database information, and performs cross-referencing and analysis. Python is used for server-side programming, and MongoDB is used as the database.

[0491] Data collection and analysis

[0492] Server: The server periodically collects information from external databases, such as information on educational institutions in a specific region, training methods, and health data. This collection is performed using web scraping with Python libraries (e.g., BeautifulSoup). The collected data is stored in MongoDB.

[0493] Analysis: The server uses the collected external data and basic information entered by the user to perform analysis using a generative AI model (e.g., GPT-4). The AI ​​model operates based on the prompt text and generates appropriate suggestions.

[0494] Suggestions and Feedback

[0495] Terminal: The server generates suggestions, which are sent to the terminal and displayed to the user. The suggestions include educational institutions, training methods, and health management methods.

[0496] User: The user reviews the proposal and enters questions or feedback. The feedback is sent from the device to the server. The server analyzes the received feedback and modifies the proposal or improves the generative AI model.

[0497] Specific examples

[0498] 1. The user downloads and launches the app, entering basic information such as their child's name, age, gender, interests, and health status.

[0499] 2. The device temporarily stores the entered information in local storage and sends it to the server.

[0500] 3. The server receives the information and stores it securely in a database.

[0501] 4. The server periodically collects information on educational institutions and training methods from external databases and stores it in an analysis database.

[0502] 5. The server sends a prompt to the generative AI model and begins analysis. For example, the prompt might read, "The user's child is interested in music. Please suggest nearby music schools."

[0503] 6. The AI ​​model analyzes and generates recommendations, such as "ABC music school in X city, XYZ online piano lessons."

[0504] 7. The server sends the generated proposal to the terminal and displays it to the user.

[0505] 8. The user reviews the proposal and provides feedback, such as "the proposed school does not exist nearby."

[0506] 9. The device sends the feedback to the server, which receives it.

[0507] 10. The server analyzes the feedback and modifies the suggestions and improves the AI ​​model.

[0508] This series of operations allows the system to provide the best training method for the user's needs. The system will be tested in India, data will be collected, and the system will be rolled out to Japan and other countries.

[0509] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0510] Step 1:

[0511] User registration and data entry

[0512] User: The user downloads the application and is presented with a new registration screen when they launch it for the first time. Here, they enter their child's basic information (name, age, gender, interests, health status, etc.). The user presses the "Confirm Input" button to receive output based on the input (basic information).

[0513] Terminal: The input information is temporarily stored in local storage, converted to JSON format, and prepared for transmission. The data is saved in a local file as output based on the input (basic information).

[0514] Terminal: Once ready to send, it sends data to the server via API. The data is sent to the server as output based on the input (local data).

[0515] Step 2:

[0516] Data reception and storage

[0517] Server: Receives data sent from the terminal. Based on the input (user data), the server validates the data. If validation is successful, the data is securely stored in a database (e.g., AWS RDS). A save completion message is generated as output based on the input (sent data).

[0518] Step 3:

[0519] External Data Collection and Cross-referencing

[0520] Server: The server periodically collects information from external databases, such as information on educational institutions in a specific region, development methods, and health data, using Python libraries (e.g., BeautifulSoup). The collected data is converted into JSON format as output based on the input (web page URL).

[0521] Server: Collected data is stored in MongoDB and cross-referenced with user data. Highly relevant data is extracted as output based on the input (user data and collected data).

[0522] Step 4:

[0523] AI-based analysis and proposal generation

[0524] Server: Generates prompts for analysis and inputs them into a generative AI model (e.g., GPT-4). For example, a prompt containing the following content is used: "The user's child is interested in music. Please suggest nearby music schools." The optimal suggestion is generated as output based on the input (prompt and analysis data).

[0525] Example: If a user inputs "My child is interested in music," the AI ​​will generate suggestions for local music schools and online lessons. The output will be suggestions such as "Music School ABC in XYZ City" and "Online Piano Lessons XYZ."

[0526] Step 5:

[0527] Presentation of proposal content

[0528] Terminal: Receives the proposal content generated by the server and displays it on the user's terminal. The proposal content is displayed on the terminal screen as an output based on the input (proposal data).

[0529] User: The user reviews the proposal and considers the content and relevance of the information. As an output, the user presses the "Confirm" button.

[0530] Step 6:

[0531] Enter and submit feedback

[0532] User: Enters questions or feedback about the proposal. For example, provides feedback such as "the proposed school does not exist nearby." Based on the input (feedback), the user presses the "Submit" button.

[0533] Terminal: Converts feedback data into JSON format and sends it to the server. Data is sent to the server as output based on the input (feedback data).

[0534] Step 7:

[0535] Analyze feedback and improve

[0536] Server: Analyzes the received feedback data and, if necessary, modifies the proposal content and improves the AI ​​model. The analysis results are generated as output based on the input (feedback data), and the model is updated. The next time a proposal is made, a more accurate proposal will be made based on this.

[0537] The above steps realize a system that can quickly and accurately provide a training method that is optimal for the user's needs.

[0538] (Application example 1)

[0539] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0540] Traditional child development methods often rely on general guidelines and standard educational measures, lacking personalized suggestions tailored to each child's characteristics and interests. In particular, there are no effective systems for using regional educational institutions and development data, or handling user feedback. As a result, parents have to expend a great deal of effort selecting the appropriate educational institution and development method, making it difficult to find the optimal development environment.

[0541] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[0542] In this invention, the server includes: a means for a user to input basic information; a means for the server to receive and store the input data; a means for the server to cross-reference and analyze the collected external database information and user data; a means for the server to suggest appropriate educational institutions, development methods, and health management methods based on the analysis results; a means for the user to receive the suggestions; a means for receiving and storing feedback from the user; a means for the server to analyze the feedback and revise the suggestions and improve the model; a means for the user to provide questions or additional feedback based on the suggestions; and a means for continuously training the generative AI model using the collected feedback to improve the accuracy of the suggestions. This makes it possible to suggest the most appropriate development methods and educational institutions for each individual child, reducing the burden on parents and maximizing their children's abilities.

[0543] "Means for users to input basic information" refers to an input interface that allows users to input their child's name, age, gender, interests, health status, etc. through the application.

[0544] "Means for the server to receive and store the entered data" refers to the function by which the server receives the basic information entered by the user and stores it in a database.

[0545] "Means for the server to cross-reference and analyze collected external database information and user data" refers to the process by which the server compares and analyzes information collected from multiple external databases with data entered by the user.

[0546] "Means for the server to suggest appropriate educational institutions, upbringing methods, and health management methods based on the analysis results" refers to the function of suggesting the most suitable educational institutions, upbringing methods, and health management methods for children based on the data collected by the server and the analysis results.

[0547] "Means for users to receive suggestions" refers to a mechanism that allows users to view the suggestions sent from the server through the application.

[0548] "Means for receiving and storing feedback from users" refers to a function that allows users to send opinions and questions about the proposal content, and the server receives and stores them.

[0549] "Means for the server to analyze feedback and revise proposals and improve the model" refers to the process by which the server analyzes the feedback it receives and uses it to revise proposals and improve the AI ​​model.

[0550] "Means for users to ask questions or provide additional feedback based on the suggestions" refers to an interface that allows users to ask additional questions or provide feedback about the suggestions received from the server.

[0551] "Means of continuously training the generative AI model using collected feedback to improve the accuracy of suggestions" refers to the process of continuously training the generative AI model using collected user feedback to make the next suggestions more accurate.

[0552] This invention is a system that utilizes generative AI to individually suggest child-raising methods. Based on basic information about the child provided by the user, the system suggests educational institutions, extracurricular activities, discipline methods, health management, etc. Below, we will explain in detail how this system works.

[0553] User registration and data entry

[0554] When a user downloads a smartphone application for the first time, they are directed to a new registration screen where they enter their child's basic information (name, age, gender, interests, health status, etc.). This information is temporarily stored in the device's local storage and then sent to the server. The server stores the received data in a database and generates an individual user profile.

[0555] Data collection and cross-referencing

[0556] The server periodically collects external databases such as educational institution information, training methods, and health data, including data for specific regions (multi-regional). The server cross-references the collected external data with user data to extract and analyze similarities.

[0557] AI analysis and proposal generation

[0558] The server uses a generative AI model to analyze the collected external data and user data. The AI ​​determines the child's interests and characteristics from the information entered by the user and identifies the optimal development method based on that. For example, if a user enters that their child is interested in music, the AI ​​will suggest local music schools and online lessons. The AI ​​model used utilizes machine learning frameworks such as TensorFlow.

[0559] Presenting proposals and collecting feedback

[0560] The suggestions generated by the server are sent to the terminal and displayed to the user. The user can review the suggestions and enter questions or feedback. For example, they can enter feedback such as "the suggested school does not exist nearby," and this feedback is also sent to the server. The suggestions include educational institutions, extracurricular activities, discipline methods, health management methods, etc.

[0561] Feedback analysis and continuous improvement

[0562] The server analyzes the received feedback and, if necessary, modifies the suggestions and improves the model. The collected feedback is used as training data for the generative AI model, improving the accuracy of future suggestions. This allows the system to continuously provide the optimal upbringing method for each individual child.

[0563] Examples of prompt statements

[0564] "Generate the best musical education recommendations for your child with the EduCare app."

[0565] ---

[0566] The system consists of a backend server built using the Django framework and a smartphone application. A generative AI model using Python and TensorFlow is implemented on the server side to analyze data and provide generated suggestions. PostgreSQL is used as the database to store and manage user profiles and feedback information. Users can intuitively enter information through the interface and receive suggestions.

[0567] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0568] Step 1:

[0569] The user downloads the smartphone application and enters basic information about their child (such as name, age, gender, interests, and health status) on the new registration screen. The information entered here is temporarily saved in the device's local storage. The input data is formatted in a structured data format such as JSON, which makes it ready to be sent to the server.

[0570] Step 2:

[0571] The terminal sends the basic information entered by the user to the server. The sent data is received by the server and stored in a database. The data is stored in an SQL database (specifically, PostgreSQL), and a user profile is generated. Database operations are performed on the input data, and a query is executed to insert the data into a table.

[0572] Step 3:

[0573] The server periodically collects data from external databases (such as educational institution information, development methods, and health data). This data collection is done via APIs and web crawling. The collected data is temporarily stored on the server and later cross-referenced with user data. The data collection results are stored as structured data.

[0574] Step 4:

[0575] The server cross-references the collected external data with the user's basic information and performs analysis. This analysis utilizes a generative AI model (using TensorFlow). Specifically, the AI ​​model uses user data and external data as input to identify the optimal training method and educational institution and generate recommendations. This model is pre-trained using techniques such as supervised learning and reinforcement learning.

[0576] Step 5:

[0577] The server sends the generated suggestions to the device. The device then displays the received suggestions to the user. This display is done using the smartphone app's UI components (e.g., list view or card view) so that the user can easily check the content. The output suggestions consist of specific items, such as "nearby music schools."

[0578] Step 6:

[0579] The user checks the displayed suggestions and enters feedback if necessary. The feedback can be questions or opinions about the suggestions. The feedback entered by the user is saved on the device and later sent to the server. The feedback data is packaged in JSON format.

[0580] Step 7:

[0581] The server receives the feedback submitted by the user and stores it in a database, again using a SQL database and running queries to insert data into the feedback table.

[0582] Step 8:

[0583] The server analyzes the stored feedback and uses it as data to continuously train the generative AI model, thereby improving the accuracy of future suggestions. This process involves retraining and reevaluating the AI ​​model, and updating the suggestion algorithm.

[0584] Step 9:

[0585] Based on the updated AI model and newly collected data, the server generates recommendations for future users, and this process is repeated, continuously improving the accuracy and efficiency of the entire system.

[0586] Examples of prompt statements

[0587] "Generate the best musical education recommendations for your child with the EduCare app."

[0588] ---

[0589] The above are the specific processing steps for carrying out the present invention. We have clarified how data is input, processed, calculated, and output in each step, and shown an effective method for carrying out the present invention.

[0590] Furthermore, an emotion engine that estimates the user's emotion may be further combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59, and perform identification processing using the user's emotion.

[0591] This invention combines an emotion engine with a generative AI-based training method proposal system to achieve customization that closely reflects the user's emotions. This system operates in the following steps.

[0592] 1. User registration and data entry procedures

[0593] User: After downloading and launching the application, the user is directed to a new registration screen. The user enters basic information about the child (name, age, gender, interests, health status, etc.). There is also an emotion input field, where the user can enter their current state of mind or emotions.

[0594] 2. Procedure for emotion analysis using the emotion engine

[0595] Device: The basic information and emotion information entered by the user are temporarily stored in local storage and prepared for transmission. After final confirmation of the input, the data, including the emotion information, is sent to the server.

[0596] Server: The received data is stored in a database. The emotion engine analyzes the user's emotion data and reflects the emotional trends in the analysis results.

[0597] 3. Data collection and cross-referencing procedures

[0598] Server: Periodically collects external databases such as information on educational institutions, training methods, health data, etc. in the test region (e.g., India). The collected data is cross-referenced with user data.

[0599] Example: Comparing data from educational institutions in urban India with user profiles in Japan to extract similarities.

[0600] 4. AI-based analysis and proposal generation procedure

[0601] Server: Generative AI analyzes the collected external data and user data. The AI ​​determines the child's characteristics and interests from the user's input information and emotional data, and determines the optimal development method based on that.

[0602] Example: If a user types "my child is interested in music" and selects "anxiety" as the emotion, the AI ​​will suggest local music schools and online lessons, as well as ways to support the child in easing their anxiety.

[0603] 5. Procedure for presenting proposals and collecting emotional feedback

[0604] Device: The server generates suggestions and sends them to the device, where they are displayed to the user. These suggestions include educational institutions, extracurricular activities, discipline methods, and health management methods. A field for entering emotions about the suggestions is also displayed.

[0605] User: Checks the proposal and enters feedback. For emotional feedback, the user can select emotions such as "happiness" or "dissatisfaction" and enter specific opinions.

[0606] Device: Sends feedback and emotion data to the server.

[0607] 6. Feedback analysis and continuous improvement procedures

[0608] Server: Analyzes the received feedback and emotion data and stores it in a database. The emotion engine analyzes the emotion data of the feedback and prepares it to be reflected in the next proposal.

[0609] Example: If the AI ​​receives feedback such as "The proposed music school is good, but transportation is inconvenient" along with emotion data of "dissatisfied," it will generate new suggestions that also take transportation convenience into account.

[0610] 7. Continuous system improvement

[0611] Server: Continually trains the generative AI model based on feedback to improve the accuracy of suggestions, and updates the emotion engine to make suggestions more relevant to the user's emotions.

[0612] Through these steps, the system can propose training methods that take the user's emotions into consideration and provide the optimal training plan that meets their individual needs.The system will be tested in India and data will be collected, with plans to expand to Japan and other countries.

[0613] The above is a detailed description of the mode for carrying out the invention. The purpose of the present invention is to realize suggestions that take into consideration the user's feelings, thereby reducing anxiety about child-rearing and enabling children to reach their full potential.

[0614] The processing flow will be explained below.

[0615] Step 1:

[0616] The user downloads and launches the application. A new registration screen appears, and the user enters basic information about the child (name, age, gender, interests, and health status). An emotion input field also appears, allowing the user to select or enter their current state of mind or emotion.

[0617] Step 2:

[0618] The device temporarily stores the basic information and emotion information entered by the user in local storage. After the input contents are confirmed, the device prepares and sends the data to the server.

[0619] Step 3:

[0620] The server receives the user data and emotion data sent from the device, stores the received data in a database, and creates a user profile.

[0621] Step 4:

[0622] The server periodically collects external databases such as information on educational institutions in the test area (e.g., India), training methods, health data, etc. The collected external data is stored in the database.

[0623] Step 5:

[0624] The server cross-references the user profile with the collected external data, and AI analyzes both to find the right upbringing method and educational institution for the user's child.

[0625] Step 6:

[0626] The server's generative AI analyzes the cross-referenced data and the user's emotional data to identify the optimal upbringing method, educational institution, and health care method, taking into account the child's characteristics and interests, as well as the user's emotions.

[0627] Example: If a user types "my child is interested in science" and selects "confident" as the emotion, the AI ​​generates suggestions for local science clubs and online science courses. By taking the user's "confident" emotion into account and providing more detailed information, the AI ​​maintains a sense of security in parenting.

[0628] Step 7:

[0629] The server generates specific developmental suggestions based on the analysis results. These suggestions include educational institutions, extracurricular activities, discipline methods, and health management methods. The suggestions also reflect the emotional data selected by the user.

[0630] Step 8:

[0631] The server sends the suggestion to the user's device, and an emotional feedback field is displayed on the user interface along with the suggestion.

[0632] Step 9:

[0633] The user confirms the proposal, selects emotions such as "joy," "dissatisfaction," or "relief" as emotional feedback, and also enters specific opinions and impressions.

[0634] Example: If you are happy with the proposed science club, select "Delighted" and enter a comment such as "It fits perfectly with what I'm interested in and I'm very happy."

[0635] Step 10:

[0636] The device transmits the user's feedback and emotion data to the server, where the feedback data is ready to be analyzed.

[0637] Step 11:

[0638] The server analyzes the received feedback and emotional data. The analysis results are stored in a database, and the generative AI model is updated and improved based on the feedback analysis. This improves the accuracy of future suggestions.

[0639] Step 12:

[0640] The server uses the improved AI model to generate new suggestions as needed and sends them to the user, enabling the system to provide the optimal training method and personalized suggestions that are sensitive to the user's emotions.

[0641] The above are the specific processing steps of this system. Through this series of processes, the system aims to quickly and accurately propose child-rearing methods that take into consideration the user's feelings, reduce anxiety about child-rearing, and maximize the child's potential.

[0642] Example 2

[0643] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0644] There is a need to provide personalized training methods based on the characteristics, interests, and current emotions of the training recipient. However, conventional systems were unable to take the user's emotions into account, and their suggestions were general and insufficient for individualization. Furthermore, they lacked the functionality to analyze the quality of feedback and emotions and reflect them in future suggestions, making continuous improvement difficult.

[0645] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[0646] In this invention, the server includes means for the user to input basic information about the person being trained and their current emotional information, means for the terminal to temporarily store the input data and transmit it to the server, means for the server to store the received data and analyze the emotional information using an emotional engine, means for the server to cross-reference and analyze the collected external database information with the user data, means for the server to suggest appropriate educational institutions, training methods, and health management methods based on the analysis results, means for the user to receive the suggestions and input feedback, means for the terminal to transmit the user's feedback and emotional data to the server, and means for the server to analyze the feedback and generate next suggestions and improve the model. This enables customized training suggestions that are tailored to the emotions of each individual user.

[0647] "User" refers to an individual or organization that uses the system to input information about the person being trained and receive proposals.

[0648] "Training target" refers to the child or student who receives the proposed training method or educational plan.

[0649] The "emotion engine" is a software module that analyzes the emotional information entered by the user and determines the tendency of that emotion.

[0650] "External database information" refers to information obtained from external resources, such as data on educational institutions, data on development methods, and health data, which is collected for the system to reference.

[0651] "Cross-reference" is a process that compares user data with external database information and extracts commonalities and relationships.

[0652] "Generative AI" is artificial intelligence that generates optimal suggestions based on user input and collected data.

[0653] "Feedback" refers to the opinions and emotional information provided by a user after receiving a suggestion.

[0654] "Terminal" refers to an electronic device that a user uses to input information and communicate with a server.

[0655] A "server" is a central computer system that processes received data and generates offers.

[0656] "Suggestions" are recommendations on educational institutions, training methods, health management methods, etc. provided by the server based on the analysis results.

[0657] "Model improvement" is the process of incorporating user feedback to improve the accuracy of generative AI.

[0658] The present invention is a system that proposes an optimal training method based on the characteristics, interests, and emotions of a person to be trained. To implement this system, the following hardware and software are used.

[0659] Users download the application and enter basic information about the child they are raising, as well as their current emotional state. For example, they can enter the child's name, age, gender, interests, health condition, and current state of mind and emotions. The user's input is done on a device (electronic device such as a smartphone or tablet), which temporarily stores the input data in local storage. After confirmation, the data is sent to the server.

[0660] The server stores the received data in a database. The emotional information is analyzed using an emotional engine. For example, IBM Watson or Microsoft Azure's emotional analysis API can be used as the emotional engine. The analysis results are reflected in the database.

[0661] The server then collects education, upbringing, and health data from external databases, including government education databases and professional organization repositories. This data is then cross-referenced with the user data. For example, it collects information on educational institutions, upbringing, and health data for a specified region and extracts commonalities and associations with the user data.

[0662] A generative AI model (e.g., GPT-4) determines the optimal upbringing method based on the cross-referenced data, user input, and emotional data, and generates suggestions. These suggestions may include educational institutions, extracurricular activities, discipline methods, and health management methods. Support methods may also be included. For example, if a user inputs "my child is interested in music" and selects "anxiety" as the emotion, the generative AI may suggest local music schools and online lessons, as well as support methods to alleviate anxiety.

[0663] An example of a prompt is as follows:

[0664] "What music school would you recommend for a 3-year-old boy who is interested in music, and how can I ease his concerns?"

[0665] The suggestions are sent to the device and displayed to the user. The user checks the suggestions and enters feedback. The feedback can include specific opinions such as "transportation is inconvenient" and emotions such as "dissatisfaction." The device then sends this feedback to the server.

[0666] The server analyzes the feedback and reflects it in the next recommendation. This data is used to train the generative AI model, which continuously improves the model. This allows the system to provide customized training suggestions that are in tune with each individual user's emotions.

[0667] The system of the present invention promotes individualized education and training, reduces user anxiety, and maximizes the abilities of trainees. This system will be tested in a specific region, and data will be collected before it can be expanded to other regions.

[0668] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0669] Step 1:

[0670] The user enters basic information and emotional information about the person being trained.

[0671] Input: The user enters the name, age, gender, interests, health status, and current state and feelings of the person being fostered.

[0672] How it works: A user launches the application and enters information into each field on the registration screen, specifically using text boxes and drop-down menus.

[0673] Output: The input data is temporarily stored in the device's local storage.

[0674] Step 2:

[0675] The device temporarily stores the data and sends it to the server

[0676] Input: Basic information and emotional information entered by the user is stored in the device's local storage.

[0677] How it works: When the user clicks the "Send" button, the device converts this data into JSON format and sends it to the server.

[0678] Output: The server receives the data in JSON format.

[0679] Step 3:

[0680] The server stores the data and analyzes it with the emotion engine

[0681] Input: Basic information and emotion information received by the server from the device in JSON format.

[0682] How it works: The server stores the received data in a database and analyzes the emotional information using an emotion engine (e.g., IBM Watson or Microsoft Azure's emotion analysis API). The analysis results are added to the database.

[0683] Output: The sentiment analysis results are stored in a database.

[0684] Step 4:

[0685] Server collects external database information

[0686] Input: A trigger that requires external database information.

[0687] How it works: The server periodically uses API requests to collect information about educational institutions, development practices, and health data for a specified region, e.g., by retrieving data in JSON format from a government education database.

[0688] Output: Collected external data is stored in a database.

[0689] Step 5:

[0690] Server performs cross-referencing and analysis

[0691] Input: User data and external database information.

[0692] How it works: The server cross-references user data with information from external databases to extract commonalities and relationships. This is done using data analysis algorithms.

[0693] Output: Cross-reference results and analysis results are provided.

[0694] Step 6:

[0695] Generative AI generates suggestions

[0696] Input: User data, sentiment analysis results, cross-reference results.

[0697] How it works: Generative AI (e.g., GPT-4) uses these inputs to determine the optimal training method and generate specific suggestions. For example, if the interest is "music" and the emotion is "anxiety," the AI ​​will suggest local music schools and online lessons, along with support methods to alleviate anxiety.

[0698] Output: Customized development suggestions.

[0699] Step 7:

[0700] Users submit suggestions and feedback

[0701] Input: Development suggestions generated by generative AI.

[0702] How it works: The suggestions are displayed on the user's device. The user reviews the suggestions and enters their emotional feedback and specific opinions through a feedback form.

[0703] Output: User feedback data is sent from the device to the server.

[0704] Step 8:

[0705] The server analyzes the feedback and improves the model

[0706] Input: Feedback data submitted by the user.

[0707] How it works: The server stores the feedback in a database and analyzes the emotional information in the feedback using an emotion engine. The results of this analysis are used to train a generative AI model and are reflected in the next proposal.

[0708] Output: An improved generative AI model.

[0709] Through these specific processing steps, the system is able to continuously provide and improve customized development suggestions while being sensitive to the user's emotions.

[0710] (Application example 2)

[0711] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0712] Conventional autonomous vehicles have the problem of being unable to provide optimal routes and entertainment options that take into account the emotional state of passengers. This has resulted in cases where the riding experience is not necessarily comfortable for each individual user. Furthermore, there has been a problem with continuous improvement of the system due to insufficient collection and analysis of feedback.

[0713] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 2 is realized by the following means.

[0714] In this invention, the server includes: a means for a user to input basic information and emotional information of a target person; a means for the server to receive and store the input data; a means for the server to analyze the received emotional data and analyze emotional trends; a means for the server to cross-reference and analyze collected external database information with the user data; a means for the server to suggest optimal routes and entertainment options for the autonomous vehicle based on the analysis results; a means for the user to receive the suggestions; a means for the server to receive and store feedback from the user; and a means for the server to analyze the feedback, revise the suggestions, and improve the model. This makes it possible to provide optimal routes and entertainment options that take into account the emotional state of passengers, thereby improving the riding experience and achieving continuous improvement of the system.

[0715] "Means for users to input basic information and emotional information of a subject" refers to an interface that allows users to input basic information about themselves or others and their current emotional state into the system.

[0716] "Means for the server to receive and store input data" refers to the function of the server receiving data input by the user via the network and storing that data in a database.

[0717] "Means for analyzing the emotional data received by the server and analyzing the emotional trends" refers to the function of the server processing the emotional data received from the user using an analysis program and identifying emotional patterns and trends.

[0718] "Means for the server to cross-reference and analyze collected external database information with user data" refers to the function of comparing and analyzing information collected from external databases with data provided by users.

[0719] "Means for the server to suggest optimal routes and entertainment options for autonomous vehicles based on the analysis results" refers to a function that suggests optimal routes for autonomous vehicles and entertainment options suitable for users based on data analyzed by the server.

[0720] The "means for the user to receive the proposal" refers to an interface that allows the user to check and receive the proposal content generated by the server.

[0721] "Means for receiving and storing feedback from users" refers to a function in which users input their thoughts and opinions about the proposal content, and the server receives and stores them in a database.

[0722] "Means for the server to analyze feedback, revise proposals, and improve the model" refers to the function by which the server analyzes the user feedback collected, revises the next proposal based on the analysis results, and further improves the model of the entire system.

[0723] The present invention provides a system for analyzing a user's emotional state in real time and suggesting optimal routes and entertainment options for an autonomous vehicle.

[0724] System configuration and hardware / software overview

[0725] The system includes user devices such as smartphones and tablets, a communication network, and a server. Its main software components include an emotion engine, a generative AI model, a database, and an external data collection module.

[0726] User terminal: Provides an interface for users to input basic and emotional information, including touchscreen and voice input capabilities.

[0727] Emotion engine: Software that analyzes emotional information entered by users in real time and detects emotional trends.

[0728] Generative AI model: An AI that generates optimal routes and entertainment options based on user and external data.

[0729] Database: A storage system for storing user data, emotion data, external data, feedback, etc.

[0730] External data collection module: A module for periodically collecting external information such as traffic conditions and weather data.

[0731] Program processing overview

[0732] 1. User registration and data entry: A user launches the application and enters basic information such as name, age, gender, destination, current emotional state, etc. This data is temporarily stored in local storage and then sent to the server.

[0733] 2. Emotion data analysis: The server uses an emotion engine to analyze the received emotion data in real time, detect emotional trends, and then save the analysis results in a database.

[0734] 3. External data collection and cross-referencing: The external data collection module is used to periodically collect external information such as traffic and weather data and cross-reference it with user data.

[0735] 4. Analysis and recommendation generation: Generative AI models analyze the cross-referenced data to generate optimal routes and entertainment options for the user.

[0736] 5. Proposal presentation and feedback collection: The proposal is sent to the user's device, where the user can review it and provide feedback. Feedback about the proposed plan is sent to the server.

[0737] 6. Feedback analysis and system improvement: The server analyzes the received feedback and stores it in a database to continuously improve the quality of the emotion engine and generative AI model.

[0738] Specific examples

[0739] For example, if a user enters the following information:

[0740] Name: Yamada Taro

[0741] Age: 35

[0742] Gender: Male

[0743] Destination: Workplace

[0744] Current emotional state: Anxiety

[0745] The emotion engine analyzes the input "anxiety" and suggests routes, music, videos, and other entertainment that will help the user relax. The generative AI model combines real-time traffic and weather data to calculate the optimal route. The user then selects and uses the suggested route and entertainment options through the app.

[0746] Prompt Sentence Examples

[0747] Name: Yamada Taro

[0748] Age: 35

[0749] Gender: Male

[0750] Destination: Workplace

[0751] Current emotional state: Anxiety

[0752] In this way, the present invention is able to take into account the user's emotional state and provide a personally optimized self-driving vehicle experience.

[0753] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0754] Step 1:

[0755] A user launches the application and enters basic information such as name, age, gender, destination, and current emotional state.

[0756] Input: User's basic information and emotional information

[0757] Output: User data saved in local storage

[0758] How it works: The user enters the required information into the input form on their device, and the input is temporarily saved in local storage. The data is then ready to be sent to the server.

[0759] Step 2:

[0760] The server receives the user's input data and stores it in a database, including the emotion data.

[0761] Input: User data stored in local storage

[0762] Output: User data and emotion data stored in a database

[0763] Operation: The server receives data from the user terminal via the network and stores it in a database.

[0764] Step 3:

[0765] The server uses an emotion engine to analyze the received emotion data and detect the trend of emotions.

[0766] Input: Emotion data stored in a database

[0767] Output: Emotional tendency data as the analysis result

[0768] How it works: The emotion engine analyzes emotion data, identifies positive, negative, or specific emotional states (e.g., anxiety, joy), and stores the results in a database.

[0769] Step 4:

[0770] The external data collection module is used to periodically collect external information such as traffic conditions and weather.

[0771] Input: Traffic and weather data from external APIs and databases

[0772] Output: External data stored on the server

[0773] How it works: The server periodically calls an external API to get the latest traffic and weather information and stores it in a database.

[0774] Step 5:

[0775] The server cross-references and analyzes the collected external data with the user data.

[0776] Input: User data in the database, sentiment data, and external data

[0777] Output: Optimal routes and entertainment options as analysis results

[0778] How it works: A generative AI model uses this data to perform analysis and generate optimal routes and entertainment options.

[0779] Step 6:

[0780] The server transmits the generated proposal to the user terminal, and the user receives it.

[0781] Input: Suggested best route and entertainment options

[0782] Output: Proposal displayed on the user's device

[0783] Operation: The server sends the generated results to the user's terminal and displays them on the interface for the user to check.

[0784] Step 7:

[0785] The user inputs feedback about the proposal and sends it to the server.

[0786] Input: User feedback (e.g., satisfaction with the proposal, specific opinions)

[0787] Output: Feedback data stored on the server

[0788] How it works: Users enter their opinions through a feedback form, and the server receives the data and stores it in a database.

[0789] Step 8:

[0790] The server analyzes the received feedback and uses it to revise the proposals and improve the system for the next time.

[0791] Input: Feedback data

[0792] Output: Revised proposal and improved system model

[0793] How it works: The server analyzes the feedback data and uses it as training data for the generative AI model and emotion engine to improve the accuracy of the next suggestion.

[0794] The above are the specific processing steps for carrying out the present invention.

[0795] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0796] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0797] In the above embodiment, an example in which the specific processing is performed by the data processing device 12 has been given, but the technology of the present disclosure is not limited to this, and the specific processing may be performed by the smart glasses 214.

[0798] [Third embodiment]

[0799] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0800] 5, the data processing system 310 includes the data processing device 12 and a headset type terminal 314. An example of the data processing device 12 is a server.

[0801] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0802] The headset type terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a display 343. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the display 343 are also connected to the bus 52.

[0803] The microphone 238 receives instructions and the like from the user 20 by receiving voice uttered by the user 20. The microphone 238 captures the voice uttered by the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio in accordance with instructions from the processor 46.

[0804] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the surroundings of user 20 (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0805] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 control the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0806] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset type terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0807] The specific processing program 56 is an example of a "program" according to the technology of the present disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0808] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0809] In the headset type terminal 314, a reception output process is performed by the processor 46. A reception output program 60 is stored in the storage 50. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[0810] Next, a description will be given of the identification process performed by the identification processing unit 290 of the data processing device 12. In the following description, the data processing device 12 will be referred to as the "server" and the headset type terminal 314 will be referred to as the "terminal."

[0811] This invention is a system that uses generative AI to individually suggest child-raising methods. Based on basic information about the child provided by the user, this system suggests educational institutions, extracurricular activities, discipline methods, health management, etc. Below, we will explain in detail how this system works.

[0812] 1. User registration and data entry procedures

[0813] Users: When they download the application for the first time, they are directed to a registration screen where they enter basic information about their child (such as name, age, gender, interests, and health status).

[0814] On your device: The information you enter is temporarily stored in local storage and prepared for transmission.

[0815] Server: Receives data sent from the device and stores it in a database, which creates individual user profiles.

[0816] 2. Data collection and cross-referencing procedures

[0817] Server: Periodically collects external databases such as information on educational institutions, training methods, health data, etc. in the test region (e.g., India). The collected data is cross-referenced with user data.

[0818] Example: Comparing data from educational institutions in urban India with user profiles in Japan to extract similarities.

[0819] 3. AI-based analysis and proposal generation procedure

[0820] Server: Generative AI analyzes the collected external data and user data. The AI ​​determines the child's interests and characteristics from the input information and determines the optimal development method based on that.

[0821] Example: If a user types "my child is interested in music," the AI ​​will generate suggestions for local music schools, online lessons, and even advice for home music education.

[0822] 4. Procedures for presenting proposals and gathering feedback

[0823] Device: The server generates suggestions and sends them to the device, where they are displayed to the user. The suggestions cover multiple topics, such as educational institutions, extracurricular activities, discipline methods, and health management methods.

[0824] User: Review the proposal and enter any questions or feedback. For example, provide specific feedback such as "the proposed school does not exist nearby."

[0825] Device: Sends feedback to the server.

[0826] 5. Feedback analysis and continuous improvement procedures

[0827] Server: Analyzes the received feedback and, if necessary, modifies the proposal content and improves the model. Based on the feedback analysis, the generative AI's learning data is updated, improving the accuracy of future proposals.

[0828] Example: If feedback is received that there are no music schools nearby, the AI ​​will re-suggest online lessons or other educational institutions in the area.

[0829] Through the above-mentioned series of steps, the present invention provides a child-rearing method that best suits the user's needs. This reduces anxiety about child-rearing and enables children to reach their full potential. The system will be tested in India, and data will be collected before being rolled out to Japan and other countries.

[0830] The above is the detailed description of the mode for carrying out the invention. This system flexibly and effectively supports child rearing and contributes to solving the problem of a declining birthrate and an aging population.

[0831] The processing flow will be explained below.

[0832] Step 1:

[0833] The user downloads and launches the application for the first time. They are directed to a new registration screen where they enter basic information about their child (such as name, age, gender, interests, and health status).

[0834] Step 2:

[0835] The device temporarily stores the information entered by the user in local storage and prepares it for transmission. Once the input is finalized, the data is sent to the server.

[0836] Step 3:

[0837] The server receives the user data sent from the terminal, stores the received data in a database, and generates an individual user profile.

[0838] Step 4:

[0839] The server periodically collects information from external databases, such as educational institution information, training methods, and health data from the test location (e.g., India). This data is stored in the database for later cross-referencing.

[0840] Step 5:

[0841] The server cross-references the user profile with the external data collected, and performs data analysis to identify suitable upbringing methods and educational institutions for the user's children.

[0842] Step 6:

[0843] The server's generative AI analyzes the cross-referenced data and identifies the best upbringing methods, educational institutions, and health care methods based on the child's characteristics and interests.

[0844] Step 7:

[0845] The server generates specific development proposals based on the analysis results, including educational institutions, extracurricular activities, discipline methods, and health management methods.

[0846] Step 8:

[0847] The server sends the proposed content to the user's terminal, where it is displayed on the user interface.

[0848] Step 9:

[0849] The user reviews the suggestions and enters feedback, for example, providing specific feedback such as "the suggested school is not nearby."

[0850] Step 10:

[0851] The terminal transmits the user's feedback to the server, which formats the transmitted data so that the feedback content is accurately reflected.

[0852] Step 11:

[0853] The server receives user feedback, stores it in a database, and prepares for reanalysis and model improvement based on the feedback.

[0854] Step 12:

[0855] The server analyzes the feedback and, if necessary, modifies the proposal and updates the training data for the generative AI model, thereby improving the accuracy of future proposals.

[0856] The above are the specific processing steps of this system. This series of processes allows users to easily find the best way to raise their children, thereby reducing anxiety about raising children.

[0857] Example 1

[0858] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[0859] In today's world, child-raising methods are extremely diverse, making it difficult for parents to choose the most appropriate method for their children. Furthermore, collecting information on local educational institutions and child-raising methods and making recommendations tailored to individual children requires a great deal of time and effort. Therefore, there is a need for a system that can efficiently and accurately suggest individual child-raising methods.

[0860] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[0861] In this invention, the server includes: means for a user to input basic information about a child; means for the server to receive and store the input data; means for the server to cross-reference and analyze the collected external database information with the user data; means for the server to suggest appropriate educational institutions, upbringing methods, and health management methods based on the analysis results; means for the user to receive the suggestions; means for receiving and storing feedback from the user; means for the server to analyze the feedback, revise the suggestions, and improve the model; means for the server to periodically collect and analyze external databases; means for the terminal to send feedback data to the server; and means for the server to use prompt sentences to cause the generative AI model to perform analysis. This makes it possible to quickly and accurately suggest the optimal upbringing method for each individual child.

[0862] A "user" is an individual who uses the system to input basic information about their child and receive suggestions on how to raise them.

[0863] "Server" means a central processing unit that receives and stores input data, cross-references user data with collected external database information, analyzes it, and generates appropriate recommendations.

[0864] "Terminal" means a device on which a user inputs basic information and confirms and sends suggestions and feedback.

[0865] "Basic information" includes information such as the child's name, age, gender, interests, and health status.

[0866] "External database information" refers to information such as educational institution information, training methods, and health data for a specific region.

[0867] "Cross-referencing" is the process of comparing external database information with user data to find correlations.

[0868] "Analysis" is the process of evaluation and judgment to generate optimal proposals based on collected data and user data.

[0869] A "generative AI model" is an artificial intelligence that analyzes input information and suggests optimal training methods.

[0870] A "prompt statement" is an instruction statement entered into a generative AI model to cause it to perform analysis.

[0871] "Feedback" refers to information that a user inputs into the system, such as opinions or questions about the proposed content.

[0872] "Suggestions" are advice on selecting educational institutions, training methods, health management methods, etc. provided based on the analysis results.

[0873] The present invention is a system that utilizes a generative AI model to individually propose child-raising methods. This system generates and proposes optimal child-raising methods based on basic information about the child provided by the user. A specific embodiment of this system is described below.

[0874] Hardware and Software Configuration

[0875] User: Users access the system using a user device such as a smartphone or PC. The user device must be connected to the Internet, and operations are performed using a dedicated application or web browser.

[0876] Device: The device is where users enter basic information and review and enter suggestions and feedback. The device temporarily stores the information in local storage and then sends it to the server. The software used includes web technologies such as HTML, CSS, and JavaScript.

[0877] Server: The server is built using cloud services such as AWS. The server stores data received from users, collects external database information, and performs cross-referencing and analysis. Python is used for server-side programming, and MongoDB is used as the database.

[0878] Data collection and analysis

[0879] Server: The server periodically collects information from external databases, such as information on educational institutions in a specific region, training methods, and health data. This collection is performed using web scraping with Python libraries (e.g., BeautifulSoup). The collected data is stored in MongoDB.

[0880] Analysis: The server uses the collected external data and basic information entered by the user to perform analysis using a generative AI model (e.g., GPT-4). The AI ​​model operates based on the prompt text and generates appropriate suggestions.

[0881] Suggestions and Feedback

[0882] Terminal: The server generates suggestions, which are sent to the terminal and displayed to the user. The suggestions include educational institutions, training methods, and health management methods.

[0883] User: The user reviews the proposal and enters questions or feedback. The feedback is sent from the device to the server. The server analyzes the received feedback and modifies the proposal or improves the generative AI model.

[0884] Specific examples

[0885] 1. The user downloads and launches the app, entering basic information such as their child's name, age, gender, interests, and health status.

[0886] 2. The device temporarily stores the entered information in local storage and sends it to the server.

[0887] 3. The server receives the information and stores it securely in a database.

[0888] 4. The server periodically collects information on educational institutions and training methods from external databases and stores it in an analysis database.

[0889] 5. The server sends a prompt to the generative AI model and begins analysis. For example, the prompt might read, "The user's child is interested in music. Please suggest nearby music schools."

[0890] 6. The AI ​​model analyzes and generates recommendations, such as "ABC music school in X city, XYZ online piano lessons."

[0891] 7. The server sends the generated proposal to the terminal and displays it to the user.

[0892] 8. The user reviews the proposal and provides feedback, such as "the proposed school does not exist nearby."

[0893] 9. The device sends the feedback to the server, which receives it.

[0894] 10. The server analyzes the feedback and modifies the suggestions and improves the AI ​​model.

[0895] This series of operations allows the system to provide the best training method for the user's needs. The system will be tested in India, data will be collected, and the system will be rolled out to Japan and other countries.

[0896] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0897] Step 1:

[0898] User registration and data entry

[0899] User: The user downloads the application and is presented with a new registration screen when they launch it for the first time. Here, they enter their child's basic information (name, age, gender, interests, health status, etc.). The user presses the "Confirm Input" button to receive output based on the input (basic information).

[0900] Terminal: The input information is temporarily stored in local storage, converted to JSON format, and prepared for transmission. The data is saved in a local file as output based on the input (basic information).

[0901] Terminal: Once ready to send, it sends data to the server via API. The data is sent to the server as output based on the input (local data).

[0902] Step 2:

[0903] Data reception and storage

[0904] Server: Receives data sent from the terminal. Based on the input (user data), the server validates the data. If validation is successful, the data is securely stored in a database (e.g., AWS RDS). A save completion message is generated as output based on the input (sent data).

[0905] Step 3:

[0906] External Data Collection and Cross-referencing

[0907] Server: The server periodically collects information from external databases, such as information on educational institutions in a specific region, development methods, and health data, using Python libraries (e.g., BeautifulSoup). The collected data is converted into JSON format as output based on the input (web page URL).

[0908] Server: Collected data is stored in MongoDB and cross-referenced with user data. Highly relevant data is extracted as output based on the input (user data and collected data).

[0909] Step 4:

[0910] AI-based analysis and proposal generation

[0911] Server: Generates prompts for analysis and inputs them into a generative AI model (e.g., GPT-4). For example, a prompt containing the following content is used: "The user's child is interested in music. Please suggest nearby music schools." The optimal suggestion is generated as output based on the input (prompt and analysis data).

[0912] Example: If a user inputs "My child is interested in music," the AI ​​will generate suggestions for local music schools and online lessons. The output will be suggestions such as "Music School ABC in XYZ City" and "Online Piano Lessons XYZ."

[0913] Step 5:

[0914] Presentation of proposal content

[0915] Terminal: Receives the proposal content generated by the server and displays it on the user's terminal. The proposal content is displayed on the terminal screen as an output based on the input (proposal data).

[0916] User: The user reviews the proposal and considers the content and relevance of the information. As an output, the user presses the "Confirm" button.

[0917] Step 6:

[0918] Enter and submit feedback

[0919] User: Enters questions or feedback about the proposal. For example, provides feedback such as "the proposed school does not exist nearby." Based on the input (feedback), the user presses the "Submit" button.

[0920] Terminal: Converts feedback data into JSON format and sends it to the server. Data is sent to the server as output based on the input (feedback data).

[0921] Step 7:

[0922] Analyze feedback and improve

[0923] Server: Analyzes the received feedback data and, if necessary, modifies the proposal content and improves the AI ​​model. The analysis results are generated as output based on the input (feedback data), and the model is updated. The next time a proposal is made, a more accurate proposal will be made based on this.

[0924] The above steps realize a system that can quickly and accurately provide a training method that is optimal for the user's needs.

[0925] (Application example 1)

[0926] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[0927] Traditional child development methods often rely on general guidelines and standard educational measures, lacking personalized suggestions tailored to each child's characteristics and interests. In particular, there are no effective systems for using regional educational institutions and development data, or handling user feedback. As a result, parents have to expend a great deal of effort selecting the appropriate educational institution and development method, making it difficult to find the optimal development environment.

[0928] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[0929] In this invention, the server includes: a means for a user to input basic information; a means for the server to receive and store the input data; a means for the server to cross-reference and analyze the collected external database information and user data; a means for the server to suggest appropriate educational institutions, development methods, and health management methods based on the analysis results; a means for the user to receive the suggestions; a means for receiving and storing feedback from the user; a means for the server to analyze the feedback and revise the suggestions and improve the model; a means for the user to provide questions or additional feedback based on the suggestions; and a means for continuously training the generative AI model using the collected feedback to improve the accuracy of the suggestions. This makes it possible to suggest the most appropriate development methods and educational institutions for each individual child, reducing the burden on parents and maximizing their children's abilities.

[0930] "Means for users to input basic information" refers to an input interface that allows users to input their child's name, age, gender, interests, health status, etc. through the application.

[0931] "Means for the server to receive and store the entered data" refers to the function by which the server receives the basic information entered by the user and stores it in a database.

[0932] "Means for the server to cross-reference and analyze collected external database information and user data" refers to the process by which the server compares and analyzes information collected from multiple external databases with data entered by the user.

[0933] "Means for the server to suggest appropriate educational institutions, upbringing methods, and health management methods based on the analysis results" refers to the function of suggesting the most suitable educational institutions, upbringing methods, and health management methods for children based on the data collected by the server and the analysis results.

[0934] "Means for users to receive suggestions" refers to a mechanism that allows users to view the suggestions sent from the server through the application.

[0935] "Means for receiving and storing feedback from users" refers to a function that allows users to send opinions and questions about the proposal content, and the server receives and stores them.

[0936] "Means for the server to analyze feedback and revise proposals and improve the model" refers to the process by which the server analyzes the feedback it receives and uses it to revise proposals and improve the AI ​​model.

[0937] "Means for users to ask questions or provide additional feedback based on the suggestions" refers to an interface that allows users to ask additional questions or provide feedback about the suggestions received from the server.

[0938] "Means of continuously training the generative AI model using collected feedback to improve the accuracy of suggestions" refers to the process of continuously training the generative AI model using collected user feedback to make the next suggestions more accurate.

[0939] This invention is a system that utilizes generative AI to individually suggest child-raising methods. Based on basic information about the child provided by the user, the system suggests educational institutions, extracurricular activities, discipline methods, health management, etc. Below, we will explain in detail how this system works.

[0940] User registration and data entry

[0941] When a user downloads a smartphone application for the first time, they are directed to a new registration screen where they enter their child's basic information (name, age, gender, interests, health status, etc.). This information is temporarily stored in the device's local storage and then sent to the server. The server stores the received data in a database and generates an individual user profile.

[0942] Data collection and cross-referencing

[0943] The server periodically collects external databases such as educational institution information, training methods, and health data, including data for specific regions (multi-regional). The server cross-references the collected external data with user data to extract and analyze similarities.

[0944] AI analysis and proposal generation

[0945] The server uses a generative AI model to analyze the collected external data and user data. The AI ​​determines the child's interests and characteristics from the information entered by the user and identifies the optimal development method based on that. For example, if a user enters that their child is interested in music, the AI ​​will suggest local music schools and online lessons. The AI ​​model used utilizes machine learning frameworks such as TensorFlow.

[0946] Presenting proposals and collecting feedback

[0947] The suggestions generated by the server are sent to the terminal and displayed to the user. The user can review the suggestions and enter questions or feedback. For example, they can enter feedback such as "the suggested school does not exist nearby," and this feedback is also sent to the server. The suggestions include educational institutions, extracurricular activities, discipline methods, health management methods, etc.

[0948] Feedback analysis and continuous improvement

[0949] The server analyzes the received feedback and, if necessary, modifies the suggestions and improves the model. The collected feedback is used as training data for the generative AI model, improving the accuracy of future suggestions. This allows the system to continuously provide the optimal upbringing method for each individual child.

[0950] Examples of prompt statements

[0951] "Generate the best musical education recommendations for your child with the EduCare app."

[0952] ---

[0953] The system consists of a backend server built using the Django framework and a smartphone application. A generative AI model using Python and TensorFlow is implemented on the server side to analyze data and provide generated suggestions. PostgreSQL is used as the database to store and manage user profiles and feedback information. Users can intuitively enter information through the interface and receive suggestions.

[0954] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0955] Step 1:

[0956] The user downloads the smartphone application and enters basic information about their child (such as name, age, gender, interests, and health status) on the new registration screen. The information entered here is temporarily saved in the device's local storage. The input data is formatted in a structured data format such as JSON, which makes it ready to be sent to the server.

[0957] Step 2:

[0958] The terminal sends the basic information entered by the user to the server. The sent data is received by the server and stored in a database. The data is stored in an SQL database (specifically, PostgreSQL), and a user profile is generated. Database operations are performed on the input data, and a query is executed to insert the data into a table.

[0959] Step 3:

[0960] The server periodically collects data from external databases (such as educational institution information, development methods, and health data). This data collection is done via APIs and web crawling. The collected data is temporarily stored on the server and later cross-referenced with user data. The data collection results are stored as structured data.

[0961] Step 4:

[0962] The server cross-references the collected external data with the user's basic information and performs analysis. This analysis utilizes a generative AI model (using TensorFlow). Specifically, the AI ​​model uses user data and external data as input to identify the optimal training method and educational institution and generate recommendations. This model is pre-trained using techniques such as supervised learning and reinforcement learning.

[0963] Step 5:

[0964] The server sends the generated suggestions to the device. The device then displays the received suggestions to the user. This display is done using the smartphone app's UI components (e.g., list view or card view) so that the user can easily check the content. The output suggestions consist of specific items, such as "nearby music schools."

[0965] Step 6:

[0966] The user checks the displayed suggestions and enters feedback if necessary. The feedback can be questions or opinions about the suggestions. The feedback entered by the user is saved on the device and later sent to the server. The feedback data is packaged in JSON format.

[0967] Step 7:

[0968] The server receives the feedback submitted by the user and stores it in a database, again using a SQL database and running queries to insert data into the feedback table.

[0969] Step 8:

[0970] The server analyzes the stored feedback and uses it as data to continuously train the generative AI model, thereby improving the accuracy of future suggestions. This process involves retraining and reevaluating the AI ​​model, and updating the suggestion algorithm.

[0971] Step 9:

[0972] Based on the updated AI model and newly collected data, the server generates recommendations for future users, and this process is repeated, continuously improving the accuracy and efficiency of the entire system.

[0973] Examples of prompt statements

[0974] "Generate the best musical education recommendations for your child with the EduCare app."

[0975] ---

[0976] The above are the specific processing steps for carrying out the present invention. We have clarified how data is input, processed, calculated, and output in each step, and shown an effective method for carrying out the present invention.

[0977] Furthermore, an emotion engine that estimates the user's emotion may be further combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59, and perform identification processing using the user's emotion.

[0978] This invention combines an emotion engine with a generative AI-based training method proposal system to achieve customization that closely reflects the user's emotions. This system operates in the following steps.

[0979] 1. User registration and data entry procedures

[0980] User: After downloading and launching the application, the user is directed to a new registration screen. The user enters basic information about the child (name, age, gender, interests, health status, etc.). There is also an emotion input field, where the user can enter their current state of mind or emotions.

[0981] 2. Procedure for emotion analysis using the emotion engine

[0982] Device: The basic information and emotion information entered by the user are temporarily stored in local storage and prepared for transmission. After final confirmation of the input, the data, including the emotion information, is sent to the server.

[0983] Server: The received data is stored in a database. The emotion engine analyzes the user's emotion data and reflects the emotional trends in the analysis results.

[0984] 3. Data collection and cross-referencing procedures

[0985] Server: Periodically collects external databases such as information on educational institutions, training methods, health data, etc. in the test region (e.g., India). The collected data is cross-referenced with user data.

[0986] Example: Comparing data from educational institutions in urban India with user profiles in Japan to extract similarities.

[0987] 4. AI-based analysis and proposal generation procedure

[0988] Server: Generative AI analyzes the collected external data and user data. The AI ​​determines the child's characteristics and interests from the user's input information and emotional data, and determines the optimal development method based on that.

[0989] Example: If a user types "my child is interested in music" and selects "anxiety" as the emotion, the AI ​​will suggest local music schools and online lessons, as well as ways to support the child in easing their anxiety.

[0990] 5. Procedure for presenting proposals and collecting emotional feedback

[0991] Device: The server generates suggestions and sends them to the device, where they are displayed to the user. These suggestions include educational institutions, extracurricular activities, discipline methods, and health management methods. A field for entering emotions about the suggestions is also displayed.

[0992] User: Checks the proposal and enters feedback. For emotional feedback, the user can select emotions such as "happiness" or "dissatisfaction" and enter specific opinions.

[0993] Device: Sends feedback and emotion data to the server.

[0994] 6. Feedback analysis and continuous improvement procedures

[0995] Server: Analyzes the received feedback and emotion data and stores it in a database. The emotion engine analyzes the emotion data of the feedback and prepares it to be reflected in the next proposal.

[0996] Example: If the AI ​​receives feedback such as "The proposed music school is good, but transportation is inconvenient" along with emotion data of "dissatisfied," it will generate new suggestions that also take transportation convenience into account.

[0997] 7. Continuous system improvement

[0998] Server: Continually trains the generative AI model based on feedback to improve the accuracy of suggestions, and updates the emotion engine to make suggestions more relevant to the user's emotions.

[0999] Through these steps, the system can propose training methods that take the user's emotions into consideration and provide the optimal training plan that meets their individual needs.The system will be tested in India and data will be collected, with plans to expand to Japan and other countries.

[1000] The above is a detailed description of the mode for carrying out the invention. The purpose of the present invention is to realize suggestions that take into consideration the user's feelings, thereby reducing anxiety about child-rearing and enabling children to reach their full potential.

[1001] The processing flow will be explained below.

[1002] Step 1:

[1003] The user downloads and launches the application. A new registration screen appears, and the user enters basic information about the child (name, age, gender, interests, and health status). An emotion input field also appears, allowing the user to select or enter their current state of mind or emotion.

[1004] Step 2:

[1005] The device temporarily stores the basic information and emotion information entered by the user in local storage. After the input contents are confirmed, the device prepares and sends the data to the server.

[1006] Step 3:

[1007] The server receives the user data and emotion data sent from the device, stores the received data in a database, and creates a user profile.

[1008] Step 4:

[1009] The server periodically collects external databases such as information on educational institutions in the test area (e.g., India), training methods, health data, etc. The collected external data is stored in the database.

[1010] Step 5:

[1011] The server cross-references the user profile with the collected external data, and AI analyzes both to find the right upbringing method and educational institution for the user's child.

[1012] Step 6:

[1013] The server's generative AI analyzes the cross-referenced data and the user's emotional data to identify the optimal upbringing method, educational institution, and health care method, taking into account the child's characteristics and interests, as well as the user's emotions.

[1014] Example: If a user types "my child is interested in science" and selects "confident" as the emotion, the AI ​​generates suggestions for local science clubs and online science courses. By taking the user's "confident" emotion into account and providing more detailed information, the AI ​​maintains a sense of security in parenting.

[1015] Step 7:

[1016] The server generates specific developmental suggestions based on the analysis results. These suggestions include educational institutions, extracurricular activities, discipline methods, and health management methods. The suggestions also reflect the emotional data selected by the user.

[1017] Step 8:

[1018] The server sends the suggestion to the user's device, and an emotional feedback field is displayed on the user interface along with the suggestion.

[1019] Step 9:

[1020] The user confirms the proposal, selects emotions such as "joy," "dissatisfaction," or "relief" as emotional feedback, and also enters specific opinions and impressions.

[1021] Example: If you are happy with the proposed science club, select "Delighted" and enter a comment such as "It fits perfectly with what I'm interested in and I'm very happy."

[1022] Step 10:

[1023] The device transmits the user's feedback and emotion data to the server, where the feedback data is ready to be analyzed.

[1024] Step 11:

[1025] The server analyzes the received feedback and emotional data. The analysis results are stored in a database, and the generative AI model is updated and improved based on the feedback analysis. This improves the accuracy of future suggestions.

[1026] Step 12:

[1027] The server uses the improved AI model to generate new suggestions as needed and sends them to the user, enabling the system to provide the optimal training method and personalized suggestions that are sensitive to the user's emotions.

[1028] The above are the specific processing steps of this system. Through this series of processes, the system aims to quickly and accurately propose child-rearing methods that take into consideration the user's feelings, reduce anxiety about child-rearing, and maximize the child's potential.

[1029] Example 2

[1030] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[1031] There is a need to provide personalized training methods based on the characteristics, interests, and current emotions of the training recipient. However, conventional systems were unable to take the user's emotions into account, and their suggestions were general and insufficient for individualization. Furthermore, they lacked the functionality to analyze the quality of feedback and emotions and reflect them in future suggestions, making continuous improvement difficult.

[1032] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[1033] In this invention, the server includes means for the user to input basic information about the person being trained and their current emotional information, means for the terminal to temporarily store the input data and transmit it to the server, means for the server to store the received data and analyze the emotional information using an emotional engine, means for the server to cross-reference and analyze the collected external database information with the user data, means for the server to suggest appropriate educational institutions, training methods, and health management methods based on the analysis results, means for the user to receive the suggestions and input feedback, means for the terminal to transmit the user's feedback and emotional data to the server, and means for the server to analyze the feedback and generate next suggestions and improve the model. This enables customized training suggestions that are tailored to the emotions of each individual user.

[1034] "User" refers to an individual or organization that uses the system to input information about the person being trained and receive proposals.

[1035] "Training target" refers to the child or student who receives the proposed training method or educational plan.

[1036] The "emotion engine" is a software module that analyzes the emotional information entered by the user and determines the tendency of that emotion.

[1037] "External database information" refers to information obtained from external resources, such as data on educational institutions, data on development methods, and health data, which is collected for the system to reference.

[1038] "Cross-reference" is a process that compares user data with external database information and extracts commonalities and relationships.

[1039] "Generative AI" is artificial intelligence that generates optimal suggestions based on user input and collected data.

[1040] "Feedback" refers to the opinions and emotional information provided by a user after receiving a suggestion.

[1041] "Terminal" refers to an electronic device that a user uses to input information and communicate with a server.

[1042] A "server" is a central computer system that processes received data and generates offers.

[1043] "Suggestions" are recommendations on educational institutions, training methods, health management methods, etc. provided by the server based on the analysis results.

[1044] "Model improvement" is the process of incorporating user feedback to improve the accuracy of generative AI.

[1045] The present invention is a system that proposes an optimal training method based on the characteristics, interests, and emotions of a person to be trained. To implement this system, the following hardware and software are used.

[1046] Users download the application and enter basic information about the child they are raising, as well as their current emotional state. For example, they can enter the child's name, age, gender, interests, health condition, and current state of mind and emotions. The user's input is done on a device (electronic device such as a smartphone or tablet), which temporarily stores the input data in local storage. After confirmation, the data is sent to the server.

[1047] The server stores the received data in a database. The emotional information is analyzed using an emotional engine. For example, IBM Watson or Microsoft Azure's emotional analysis API can be used as the emotional engine. The analysis results are reflected in the database.

[1048] The server then collects education, upbringing, and health data from external databases, including government education databases and professional organization repositories. This data is then cross-referenced with the user data. For example, it collects information on educational institutions, upbringing, and health data for a specified region and extracts commonalities and associations with the user data.

[1049] A generative AI model (e.g., GPT-4) determines the optimal upbringing method based on the cross-referenced data, user input, and emotional data, and generates suggestions. These suggestions may include educational institutions, extracurricular activities, discipline methods, and health management methods. Support methods may also be included. For example, if a user inputs "my child is interested in music" and selects "anxiety" as the emotion, the generative AI may suggest local music schools and online lessons, as well as support methods to alleviate anxiety.

[1050] An example of a prompt is as follows:

[1051] "What music school would you recommend for a 3-year-old boy who is interested in music, and how can I ease his concerns?"

[1052] The suggestions are sent to the device and displayed to the user. The user checks the suggestions and enters feedback. The feedback can include specific opinions such as "transportation is inconvenient" and emotions such as "dissatisfaction." The device then sends this feedback to the server.

[1053] The server analyzes the feedback and reflects it in the next recommendation. This data is used to train the generative AI model, which continuously improves the model. This allows the system to provide customized training suggestions that are in tune with each individual user's emotions.

[1054] The system of the present invention promotes individualized education and training, reduces user anxiety, and maximizes the abilities of trainees. This system will be tested in a specific region, and data will be collected before it can be expanded to other regions.

[1055] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1056] Step 1:

[1057] The user enters basic information and emotional information about the person being trained.

[1058] Input: The user enters the name, age, gender, interests, health status, and current state and feelings of the person being fostered.

[1059] How it works: A user launches the application and enters information into each field on the registration screen, specifically using text boxes and drop-down menus.

[1060] Output: The input data is temporarily stored in the device's local storage.

[1061] Step 2:

[1062] The device temporarily stores the data and sends it to the server

[1063] Input: Basic information and emotional information entered by the user is stored in the device's local storage.

[1064] How it works: When the user clicks the "Send" button, the device converts this data into JSON format and sends it to the server.

[1065] Output: The server receives the data in JSON format.

[1066] Step 3:

[1067] The server stores the data and analyzes it with the emotion engine

[1068] Input: Basic information and emotion information received by the server from the device in JSON format.

[1069] How it works: The server stores the received data in a database and analyzes the emotional information using an emotion engine (e.g., IBM Watson or Microsoft Azure's emotion analysis API). The analysis results are added to the database.

[1070] Output: The sentiment analysis results are stored in a database.

[1071] Step 4:

[1072] Server collects external database information

[1073] Input: A trigger that requires external database information.

[1074] How it works: The server periodically uses API requests to collect information about educational institutions, development practices, and health data for a specified region, e.g., by retrieving data in JSON format from a government education database.

[1075] Output: Collected external data is stored in a database.

[1076] Step 5:

[1077] Server performs cross-referencing and analysis

[1078] Input: User data and external database information.

[1079] How it works: The server cross-references user data with information from external databases to extract commonalities and relationships. This is done using data analysis algorithms.

[1080] Output: Cross-reference results and analysis results are provided.

[1081] Step 6:

[1082] Generative AI generates suggestions

[1083] Input: User data, sentiment analysis results, cross-reference results.

[1084] How it works: Generative AI (e.g., GPT-4) uses these inputs to determine the optimal training method and generate specific suggestions. For example, if the interest is "music" and the emotion is "anxiety," the AI ​​will suggest local music schools and online lessons, along with support methods to alleviate anxiety.

[1085] Output: Customized development suggestions.

[1086] Step 7:

[1087] Users submit suggestions and feedback

[1088] Input: Development suggestions generated by generative AI.

[1089] How it works: The suggestions are displayed on the user's device. The user reviews the suggestions and enters their emotional feedback and specific opinions through a feedback form.

[1090] Output: User feedback data is sent from the device to the server.

[1091] Step 8:

[1092] The server analyzes the feedback and improves the model

[1093] Input: Feedback data submitted by the user.

[1094] How it works: The server stores the feedback in a database and analyzes the emotional information in the feedback using an emotion engine. The results of this analysis are used to train a generative AI model and are reflected in the next proposal.

[1095] Output: An improved generative AI model.

[1096] Through these specific processing steps, the system is able to continuously provide and improve customized development suggestions while being sensitive to the user's emotions.

[1097] (Application example 2)

[1098] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[1099] Conventional autonomous vehicles have the problem of being unable to provide optimal routes and entertainment options that take into account the emotional state of passengers. This has resulted in cases where the riding experience is not necessarily comfortable for each individual user. Furthermore, there has been a problem with continuous improvement of the system due to insufficient collection and analysis of feedback.

[1100] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 2 is realized by the following means.

[1101] In this invention, the server includes: a means for a user to input basic information and emotional information of a target person; a means for the server to receive and store the input data; a means for the server to analyze the received emotional data and analyze emotional trends; a means for the server to cross-reference and analyze collected external database information with the user data; a means for the server to suggest optimal routes and entertainment options for the autonomous vehicle based on the analysis results; a means for the user to receive the suggestions; a means for the server to receive and store feedback from the user; and a means for the server to analyze the feedback, revise the suggestions, and improve the model. This makes it possible to provide optimal routes and entertainment options that take into account the emotional state of passengers, thereby improving the riding experience and achieving continuous improvement of the system.

[1102] "Means for users to input basic information and emotional information of a subject" refers to an interface that allows users to input basic information about themselves or others and their current emotional state into the system.

[1103] "Means for the server to receive and store input data" refers to the function of the server receiving data input by the user via the network and storing that data in a database.

[1104] "Means for analyzing the emotional data received by the server and analyzing the emotional trends" refers to the function of the server processing the emotional data received from the user using an analysis program and identifying emotional patterns and trends.

[1105] "Means for the server to cross-reference and analyze collected external database information with user data" refers to the function of comparing and analyzing information collected from external databases with data provided by users.

[1106] "Means for the server to suggest optimal routes and entertainment options for autonomous vehicles based on the analysis results" refers to a function that suggests optimal routes for autonomous vehicles and entertainment options suitable for users based on data analyzed by the server.

[1107] The "means for the user to receive the proposal" refers to an interface that allows the user to check and receive the proposal content generated by the server.

[1108] "Means for receiving and storing feedback from users" refers to a function in which users input their thoughts and opinions about the proposal content, and the server receives and stores them in a database.

[1109] "Means for the server to analyze feedback, revise proposals, and improve the model" refers to the function by which the server analyzes the user feedback collected, revises the next proposal based on the analysis results, and further improves the model of the entire system.

[1110] The present invention provides a system for analyzing a user's emotional state in real time and suggesting optimal routes and entertainment options for an autonomous vehicle.

[1111] System configuration and hardware / software overview

[1112] The system includes user devices such as smartphones and tablets, a communication network, and a server. Its main software components include an emotion engine, a generative AI model, a database, and an external data collection module.

[1113] User terminal: Provides an interface for users to input basic and emotional information, including touchscreen and voice input capabilities.

[1114] Emotion engine: Software that analyzes emotional information entered by users in real time and detects emotional trends.

[1115] Generative AI model: An AI that generates optimal routes and entertainment options based on user and external data.

[1116] Database: A storage system for storing user data, emotion data, external data, feedback, etc.

[1117] External data collection module: A module for periodically collecting external information such as traffic conditions and weather data.

[1118] Program processing overview

[1119] 1. User registration and data entry: A user launches the application and enters basic information such as name, age, gender, destination, current emotional state, etc. This data is temporarily stored in local storage and then sent to the server.

[1120] 2. Emotion data analysis: The server uses an emotion engine to analyze the received emotion data in real time, detect emotional trends, and then save the analysis results in a database.

[1121] 3. External data collection and cross-referencing: The external data collection module is used to periodically collect external information such as traffic and weather data and cross-reference it with user data.

[1122] 4. Analysis and recommendation generation: Generative AI models analyze the cross-referenced data to generate optimal routes and entertainment options for the user.

[1123] 5. Proposal presentation and feedback collection: The proposal is sent to the user's device, where the user can review it and provide feedback. Feedback about the proposed plan is sent to the server.

[1124] 6. Feedback analysis and system improvement: The server analyzes the received feedback and stores it in a database to continuously improve the quality of the emotion engine and generative AI model.

[1125] Specific examples

[1126] For example, if a user enters the following information:

[1127] Name: Yamada Taro

[1128] Age: 35

[1129] Gender: Male

[1130] Destination: Workplace

[1131] Current emotional state: Anxiety

[1132] The emotion engine analyzes the input "anxiety" and suggests routes, music, videos, and other entertainment that will help the user relax. The generative AI model combines real-time traffic and weather data to calculate the optimal route. The user then selects and uses the suggested route and entertainment options through the app.

[1133] Prompt Sentence Examples

[1134] Name: Yamada Taro

[1135] Age: 35

[1136] Gender: Male

[1137] Destination: Workplace

[1138] Current emotional state: Anxiety

[1139] In this way, the present invention is able to take into account the user's emotional state and provide a personally optimized self-driving vehicle experience.

[1140] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1141] Step 1:

[1142] A user launches the application and enters basic information such as name, age, gender, destination, and current emotional state.

[1143] Input: User's basic information and emotional information

[1144] Output: User data saved in local storage

[1145] How it works: The user enters the required information into the input form on their device, and the input is temporarily saved in local storage. The data is then ready to be sent to the server.

[1146] Step 2:

[1147] The server receives the user's input data and stores it in a database, including the emotion data.

[1148] Input: User data stored in local storage

[1149] Output: User data and emotion data stored in a database

[1150] Operation: The server receives data from the user terminal via the network and stores it in a database.

[1151] Step 3:

[1152] The server uses an emotion engine to analyze the received emotion data and detect the trend of emotions.

[1153] Input: Emotion data stored in a database

[1154] Output: Emotional tendency data as the analysis result

[1155] How it works: The emotion engine analyzes emotion data, identifies positive, negative, or specific emotional states (e.g., anxiety, joy), and stores the results in a database.

[1156] Step 4:

[1157] The external data collection module is used to periodically collect external information such as traffic conditions and weather.

[1158] Input: Traffic and weather data from external APIs and databases

[1159] Output: External data stored on the server

[1160] How it works: The server periodically calls an external API to get the latest traffic and weather information and stores it in a database.

[1161] Step 5:

[1162] The server cross-references and analyzes the collected external data with the user data.

[1163] Input: User data in the database, sentiment data, and external data

[1164] Output: Optimal routes and entertainment options as analysis results

[1165] How it works: A generative AI model uses this data to perform analysis and generate optimal routes and entertainment options.

[1166] Step 6:

[1167] The server transmits the generated proposal to the user terminal, and the user receives it.

[1168] Input: Suggested best route and entertainment options

[1169] Output: Proposal displayed on the user's device

[1170] Operation: The server sends the generated results to the user's terminal and displays them on the interface for the user to check.

[1171] Step 7:

[1172] The user inputs feedback about the proposal and sends it to the server.

[1173] Input: User feedback (e.g., satisfaction with the proposal, specific opinions)

[1174] Output: Feedback data stored on the server

[1175] How it works: Users enter their opinions through a feedback form, and the server receives the data and stores it in a database.

[1176] Step 8:

[1177] The server analyzes the received feedback and uses it to revise the proposals and improve the system for the next time.

[1178] Input: Feedback data

[1179] Output: Revised proposal and improved system model

[1180] How it works: The server analyzes the feedback data and uses it as training data for the generative AI model and emotion engine to improve the accuracy of the next suggestion.

[1181] The above are the specific processing steps for carrying out the present invention.

[1182] The specific processing unit 290 transmits the result of the specific processing to the headset type terminal 314. In the headset type terminal 314, the control unit 46A causes the speaker 240 and the display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[1183] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[1184] In the above embodiment, an example was given in which the specific processing is performed by the data processing device 12, but the technology of the present disclosure is not limited to this, and the specific processing may be performed by the headset type terminal 314.

[1185] [Fourth embodiment]

[1186] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[1187] 7, a data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[1188] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[1189] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a control target 443. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the control target 443 are also connected to the bus 52.

[1190] The microphone 238 receives instructions and the like from the user 20 by receiving voice uttered by the user 20. The microphone 238 captures the voice uttered by the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio in accordance with instructions from the processor 46.

[1191] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the surroundings of user 20 (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[1192] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 control the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[1193] The control object 443 includes a display device, LEDs in the eyes, and motors for driving the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

[1194] Fig. 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Fig. 8, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[1195] The specific processing program 56 is an example of a "program" according to the technology of the present disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[1196] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[1197] In the robot 414, the processor 46 performs the reception output process. A reception output program 60 is stored in the storage 50. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[1198] Next, a description will be given of the specific processing performed by the specific processing unit 290 of the data processing device 12. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1199] This invention is a system that uses generative AI to individually suggest child-raising methods. Based on basic information about the child provided by the user, this system suggests educational institutions, extracurricular activities, discipline methods, health management, etc. Below, we will explain in detail how this system works.

[1200] 1. User registration and data entry procedures

[1201] Users: When they download the application for the first time, they are directed to a registration screen where they enter basic information about their child (such as name, age, gender, interests, and health status).

[1202] On your device: The information you enter is temporarily stored in local storage and prepared for transmission.

[1203] Server: Receives data sent from the device and stores it in a database, which creates individual user profiles.

[1204] 2. Data collection and cross-referencing procedures

[1205] Server: Periodically collects external databases such as information on educational institutions, training methods, health data, etc. in the test region (e.g., India). The collected data is cross-referenced with user data.

[1206] Example: Comparing data from educational institutions in urban India with user profiles in Japan to extract similarities.

[1207] 3. AI-based analysis and proposal generation procedure

[1208] Server: Generative AI analyzes the collected external data and user data. The AI ​​determines the child's interests and characteristics from the input information and determines the optimal development method based on that.

[1209] Example: If a user types "my child is interested in music," the AI ​​will generate suggestions for local music schools, online lessons, and even advice for home music education.

[1210] 4. Procedures for presenting proposals and gathering feedback

[1211] Device: The server generates suggestions and sends them to the device, where they are displayed to the user. The suggestions cover multiple topics, such as educational institutions, extracurricular activities, discipline methods, and health management methods.

[1212] User: Review the proposal and enter any questions or feedback. For example, provide specific feedback such as "the proposed school does not exist nearby."

[1213] Device: Sends feedback to the server.

[1214] 5. Feedback analysis and continuous improvement procedures

[1215] Server: Analyzes the received feedback and, if necessary, modifies the proposal content and improves the model. Based on the feedback analysis, the generative AI's learning data is updated, improving the accuracy of future proposals.

[1216] Example: If feedback is received that there are no music schools nearby, the AI ​​will re-suggest online lessons or other educational institutions in the area.

[1217] Through the above-mentioned series of steps, the present invention provides a child-rearing method that best suits the user's needs. This reduces anxiety about child-rearing and enables children to reach their full potential. The system will be tested in India, and data will be collected before being rolled out to Japan and other countries.

[1218] The above is the detailed description of the mode for carrying out the invention. This system flexibly and effectively supports child rearing and contributes to solving the problem of a declining birthrate and an aging population.

[1219] The processing flow will be explained below.

[1220] Step 1:

[1221] The user downloads and launches the application for the first time. They are directed to a new registration screen where they enter basic information about their child (such as name, age, gender, interests, and health status).

[1222] Step 2:

[1223] The device temporarily stores the information entered by the user in local storage and prepares it for transmission. Once the input is finalized, the data is sent to the server.

[1224] Step 3:

[1225] The server receives the user data sent from the terminal, stores the received data in a database, and generates an individual user profile.

[1226] Step 4:

[1227] The server periodically collects information from external databases, such as educational institution information, training methods, and health data from the test location (e.g., India). This data is stored in the database for later cross-referencing.

[1228] Step 5:

[1229] The server cross-references the user profile with the external data collected, and performs data analysis to identify suitable upbringing methods and educational institutions for the user's children.

[1230] Step 6:

[1231] The server's generative AI analyzes the cross-referenced data and identifies the best upbringing methods, educational institutions, and health care methods based on the child's characteristics and interests.

[1232] Step 7:

[1233] The server generates specific development proposals based on the analysis results, including educational institutions, extracurricular activities, discipline methods, and health management methods.

[1234] Step 8:

[1235] The server sends the proposed content to the user's terminal, where it is displayed on the user interface.

[1236] Step 9:

[1237] The user reviews the suggestions and enters feedback, for example, providing specific feedback such as "the suggested school is not nearby."

[1238] Step 10:

[1239] The terminal transmits the user's feedback to the server, which formats the transmitted data so that the feedback content is accurately reflected.

[1240] Step 11:

[1241] The server receives user feedback, stores it in a database, and prepares for reanalysis and model improvement based on the feedback.

[1242] Step 12:

[1243] The server analyzes the feedback and, if necessary, modifies the proposal and updates the training data for the generative AI model, thereby improving the accuracy of future proposals.

[1244] The above are the specific processing steps of this system. This series of processes allows users to easily find the best way to raise their children, thereby reducing anxiety about raising children.

[1245] Example 1

[1246] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1247] In today's world, child-raising methods are extremely diverse, making it difficult for parents to choose the most appropriate method for their children. Furthermore, collecting information on local educational institutions and child-raising methods and making recommendations tailored to individual children requires a great deal of time and effort. Therefore, there is a need for a system that can efficiently and accurately suggest individual child-raising methods.

[1248] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[1249] In this invention, the server includes: means for a user to input basic information about a child; means for the server to receive and store the input data; means for the server to cross-reference and analyze the collected external database information with the user data; means for the server to suggest appropriate educational institutions, upbringing methods, and health management methods based on the analysis results; means for the user to receive the suggestions; means for receiving and storing feedback from the user; means for the server to analyze the feedback, revise the suggestions, and improve the model; means for the server to periodically collect and analyze external databases; means for the terminal to send feedback data to the server; and means for the server to use prompt sentences to cause the generative AI model to perform analysis. This makes it possible to quickly and accurately suggest the optimal upbringing method for each individual child.

[1250] A "user" is an individual who uses the system to input basic information about their child and receive suggestions on how to raise them.

[1251] "Server" means a central processing unit that receives and stores input data, cross-references user data with collected external database information, analyzes it, and generates appropriate recommendations.

[1252] "Terminal" means a device on which a user inputs basic information and confirms and sends suggestions and feedback.

[1253] "Basic information" includes information such as the child's name, age, gender, interests, and health status.

[1254] "External database information" refers to information such as educational institution information, training methods, and health data for a specific region.

[1255] "Cross-referencing" is the process of comparing external database information with user data to find correlations.

[1256] "Analysis" is the process of evaluation and judgment to generate optimal proposals based on collected data and user data.

[1257] A "generative AI model" is an artificial intelligence that analyzes input information and suggests optimal training methods.

[1258] A "prompt statement" is an instruction statement entered into a generative AI model to cause it to perform analysis.

[1259] "Feedback" refers to information that a user inputs into the system, such as opinions or questions about the proposed content.

[1260] "Suggestions" are advice on selecting educational institutions, training methods, health management methods, etc. provided based on the analysis results.

[1261] The present invention is a system that utilizes a generative AI model to individually propose child-raising methods. This system generates and proposes optimal child-raising methods based on basic information about the child provided by the user. A specific embodiment of this system is described below.

[1262] Hardware and Software Configuration

[1263] User: Users access the system using a user device such as a smartphone or PC. The user device must be connected to the Internet, and operations are performed using a dedicated application or web browser.

[1264] Device: The device is where users enter basic information and review and enter suggestions and feedback. The device temporarily stores the information in local storage and then sends it to the server. The software used includes web technologies such as HTML, CSS, and JavaScript.

[1265] Server: The server is built using cloud services such as AWS. The server stores data received from users, collects external database information, and performs cross-referencing and analysis. Python is used for server-side programming, and MongoDB is used as the database.

[1266] Data collection and analysis

[1267] Server: The server periodically collects information from external databases, such as information on educational institutions in a specific region, training methods, and health data. This collection is performed using web scraping with Python libraries (e.g., BeautifulSoup). The collected data is stored in MongoDB.

[1268] Analysis: The server uses the collected external data and basic information entered by the user to perform analysis using a generative AI model (e.g., GPT-4). The AI ​​model operates based on the prompt text and generates appropriate suggestions.

[1269] Suggestions and Feedback

[1270] Terminal: The server generates suggestions, which are sent to the terminal and displayed to the user. The suggestions include educational institutions, training methods, and health management methods.

[1271] User: The user reviews the proposal and enters questions or feedback. The feedback is sent from the device to the server. The server analyzes the received feedback and modifies the proposal or improves the generative AI model.

[1272] Specific examples

[1273] 1. The user downloads and launches the app, entering basic information such as their child's name, age, gender, interests, and health status.

[1274] 2. The device temporarily stores the entered information in local storage and sends it to the server.

[1275] 3. The server receives the information and stores it securely in a database.

[1276] 4. The server periodically collects information on educational institutions and training methods from external databases and stores it in an analysis database.

[1277] 5. The server sends a prompt to the generative AI model and begins analysis. For example, the prompt might read, "The user's child is interested in music. Please suggest nearby music schools."

[1278] 6. The AI ​​model analyzes and generates recommendations, such as "ABC music school in X city, XYZ online piano lessons."

[1279] 7. The server sends the generated proposal to the terminal and displays it to the user.

[1280] 8. The user reviews the proposal and provides feedback, such as "the proposed school does not exist nearby."

[1281] 9. The device sends the feedback to the server, which receives it.

[1282] 10. The server analyzes the feedback and modifies the suggestions and improves the AI ​​model.

[1283] This series of operations allows the system to provide the best training method for the user's needs. The system will be tested in India, data will be collected, and the system will be rolled out to Japan and other countries.

[1284] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1285] Step 1:

[1286] User registration and data entry

[1287] User: The user downloads the application and is presented with a new registration screen when they launch it for the first time. Here, they enter their child's basic information (name, age, gender, interests, health status, etc.). The user presses the "Confirm Input" button to receive output based on the input (basic information).

[1288] Terminal: The input information is temporarily stored in local storage, converted to JSON format, and prepared for transmission. The data is saved in a local file as output based on the input (basic information).

[1289] Terminal: Once ready to send, it sends data to the server via API. The data is sent to the server as output based on the input (local data).

[1290] Step 2:

[1291] Data reception and storage

[1292] Server: Receives data sent from the terminal. Based on the input (user data), the server validates the data. If validation is successful, the data is securely stored in a database (e.g., AWS RDS). A save completion message is generated as output based on the input (sent data).

[1293] Step 3:

[1294] External Data Collection and Cross-referencing

[1295] Server: The server periodically collects information from external databases, such as information on educational institutions in a specific region, development methods, and health data, using Python libraries (e.g., BeautifulSoup). The collected data is converted into JSON format as output based on the input (web page URL).

[1296] Server: Collected data is stored in MongoDB and cross-referenced with user data. Highly relevant data is extracted as output based on the input (user data and collected data).

[1297] Step 4:

[1298] AI-based analysis and proposal generation

[1299] Server: Generates prompts for analysis and inputs them into a generative AI model (e.g., GPT-4). For example, a prompt containing the following content is used: "The user's child is interested in music. Please suggest nearby music schools." The optimal suggestion is generated as output based on the input (prompt and analysis data).

[1300] Example: If a user inputs "My child is interested in music," the AI ​​will generate suggestions for local music schools and online lessons. The output will be suggestions such as "Music School ABC in XYZ City" and "Online Piano Lessons XYZ."

[1301] Step 5:

[1302] Presentation of proposal content

[1303] Terminal: Receives the proposal content generated by the server and displays it on the user's terminal. The proposal content is displayed on the terminal screen as an output based on the input (proposal data).

[1304] User: The user reviews the proposal and considers the content and relevance of the information. As an output, the user presses the "Confirm" button.

[1305] Step 6:

[1306] Enter and submit feedback

[1307] User: Enters questions or feedback about the proposal. For example, provides feedback such as "the proposed school does not exist nearby." Based on the input (feedback), the user presses the "Submit" button.

[1308] Terminal: Converts feedback data into JSON format and sends it to the server. Data is sent to the server as output based on the input (feedback data).

[1309] Step 7:

[1310] Analyze feedback and improve

[1311] Server: Analyzes the received feedback data and, if necessary, modifies the proposal content and improves the AI ​​model. The analysis results are generated as output based on the input (feedback data), and the model is updated. The next time a proposal is made, a more accurate proposal will be made based on this.

[1312] The above steps realize a system that can quickly and accurately provide a training method that is optimal for the user's needs.

[1313] (Application example 1)

[1314] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1315] Traditional child development methods often rely on general guidelines and standard educational measures, lacking personalized suggestions tailored to each child's characteristics and interests. In particular, there are no effective systems for using regional educational institutions and development data, or handling user feedback. As a result, parents have to expend a great deal of effort selecting the appropriate educational institution and development method, making it difficult to find the optimal development environment.

[1316] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[1317] In this invention, the server includes: a means for a user to input basic information; a means for the server to receive and store the input data; a means for the server to cross-reference and analyze the collected external database information and user data; a means for the server to suggest appropriate educational institutions, development methods, and health management methods based on the analysis results; a means for the user to receive the suggestions; a means for receiving and storing feedback from the user; a means for the server to analyze the feedback and revise the suggestions and improve the model; a means for the user to provide questions or additional feedback based on the suggestions; and a means for continuously training the generative AI model using the collected feedback to improve the accuracy of the suggestions. This makes it possible to suggest the most appropriate development methods and educational institutions for each individual child, reducing the burden on parents and maximizing their children's abilities.

[1318] "Means for users to input basic information" refers to an input interface that allows users to input their child's name, age, gender, interests, health status, etc. through the application.

[1319] "Means for the server to receive and store the entered data" refers to the function by which the server receives the basic information entered by the user and stores it in a database.

[1320] "Means for the server to cross-reference and analyze collected external database information and user data" refers to the process by which the server compares and analyzes information collected from multiple external databases with data entered by the user.

[1321] "Means for the server to suggest appropriate educational institutions, upbringing methods, and health management methods based on the analysis results" refers to the function of suggesting the most suitable educational institutions, upbringing methods, and health management methods for children based on the data collected by the server and the analysis results.

[1322] "Means for users to receive suggestions" refers to a mechanism that allows users to view the suggestions sent from the server through the application.

[1323] "Means for receiving and storing feedback from users" refers to a function that allows users to send opinions and questions about the proposal content, and the server receives and stores them.

[1324] "Means for the server to analyze feedback and revise proposals and improve the model" refers to the process by which the server analyzes the feedback it receives and uses it to revise proposals and improve the AI ​​model.

[1325] "Means for users to ask questions or provide additional feedback based on the suggestions" refers to an interface that allows users to ask additional questions or provide feedback about the suggestions received from the server.

[1326] "Means of continuously training the generative AI model using collected feedback to improve the accuracy of suggestions" refers to the process of continuously training the generative AI model using collected user feedback to make the next suggestions more accurate.

[1327] This invention is a system that utilizes generative AI to individually suggest child-raising methods. Based on basic information about the child provided by the user, the system suggests educational institutions, extracurricular activities, discipline methods, health management, etc. Below, we will explain in detail how this system works.

[1328] User registration and data entry

[1329] When a user downloads a smartphone application for the first time, they are directed to a new registration screen where they enter their child's basic information (name, age, gender, interests, health status, etc.). This information is temporarily stored in the device's local storage and then sent to the server. The server stores the received data in a database and generates an individual user profile.

[1330] Data collection and cross-referencing

[1331] The server periodically collects external databases such as educational institution information, training methods, and health data, including data for specific regions (multi-regional). The server cross-references the collected external data with user data to extract and analyze similarities.

[1332] AI analysis and proposal generation

[1333] The server uses a generative AI model to analyze the collected external data and user data. The AI ​​determines the child's interests and characteristics from the information entered by the user and identifies the optimal development method based on that. For example, if a user enters that their child is interested in music, the AI ​​will suggest local music schools and online lessons. The AI ​​model used utilizes machine learning frameworks such as TensorFlow.

[1334] Presenting proposals and collecting feedback

[1335] The suggestions generated by the server are sent to the terminal and displayed to the user. The user can review the suggestions and enter questions or feedback. For example, they can enter feedback such as "the suggested school does not exist nearby," and this feedback is also sent to the server. The suggestions include educational institutions, extracurricular activities, discipline methods, health management methods, etc.

[1336] Feedback analysis and continuous improvement

[1337] The server analyzes the received feedback and, if necessary, modifies the suggestions and improves the model. The collected feedback is used as training data for the generative AI model, improving the accuracy of future suggestions. This allows the system to continuously provide the optimal upbringing method for each individual child.

[1338] Examples of prompt statements

[1339] "Generate the best musical education recommendations for your child with the EduCare app."

[1340] ---

[1341] The system consists of a backend server built using the Django framework and a smartphone application. A generative AI model using Python and TensorFlow is implemented on the server side to analyze data and provide generated suggestions. PostgreSQL is used as the database to store and manage user profiles and feedback information. Users can intuitively enter information through the interface and receive suggestions.

[1342] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1343] Step 1:

[1344] The user downloads the smartphone application and enters basic information about their child (such as name, age, gender, interests, and health status) on the new registration screen. The information entered here is temporarily saved in the device's local storage. The input data is formatted in a structured data format such as JSON, which makes it ready to be sent to the server.

[1345] Step 2:

[1346] The terminal sends the basic information entered by the user to the server. The sent data is received by the server and stored in a database. The data is stored in an SQL database (specifically, PostgreSQL), and a user profile is generated. Database operations are performed on the input data, and a query is executed to insert the data into a table.

[1347] Step 3:

[1348] The server periodically collects data from external databases (such as educational institution information, development methods, and health data). This data collection is done via APIs and web crawling. The collected data is temporarily stored on the server and later cross-referenced with user data. The data collection results are stored as structured data.

[1349] Step 4:

[1350] The server cross-references the collected external data with the user's basic information and performs analysis. This analysis utilizes a generative AI model (using TensorFlow). Specifically, the AI ​​model uses user data and external data as input to identify the optimal training method and educational institution and generate recommendations. This model is pre-trained using techniques such as supervised learning and reinforcement learning.

[1351] Step 5:

[1352] The server sends the generated suggestions to the device. The device then displays the received suggestions to the user. This display is done using the smartphone app's UI components (e.g., list view or card view) so that the user can easily check the content. The output suggestions consist of specific items, such as "nearby music schools."

[1353] Step 6:

[1354] The user checks the displayed suggestions and enters feedback if necessary. The feedback can be questions or opinions about the suggestions. The feedback entered by the user is saved on the device and later sent to the server. The feedback data is packaged in JSON format.

[1355] Step 7:

[1356] The server receives the feedback submitted by the user and stores it in a database, again using a SQL database and running queries to insert data into the feedback table.

[1357] Step 8:

[1358] The server analyzes the stored feedback and uses it as data to continuously train the generative AI model, thereby improving the accuracy of future suggestions. This process involves retraining and reevaluating the AI ​​model, and updating the suggestion algorithm.

[1359] Step 9:

[1360] Based on the updated AI model and newly collected data, the server generates recommendations for future users, and this process is repeated, continuously improving the accuracy and efficiency of the entire system.

[1361] Examples of prompt statements

[1362] "Generate the best musical education recommendations for your child with the EduCare app."

[1363] ---

[1364] The above are the specific processing steps for carrying out the present invention. We have clarified how data is input, processed, calculated, and output in each step, and shown an effective method for carrying out the present invention.

[1365] Furthermore, an emotion engine that estimates the user's emotion may be further combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59, and perform identification processing using the user's emotion.

[1366] This invention combines an emotion engine with a generative AI-based training method proposal system to achieve customization that closely reflects the user's emotions. This system operates in the following steps.

[1367] 1. User registration and data entry procedures

[1368] User: After downloading and launching the application, the user is directed to a new registration screen. The user enters basic information about the child (name, age, gender, interests, health status, etc.). There is also an emotion input field, where the user can enter their current state of mind or emotions.

[1369] 2. Procedure for emotion analysis using the emotion engine

[1370] Device: The basic information and emotion information entered by the user are temporarily stored in local storage and prepared for transmission. After final confirmation of the input, the data, including the emotion information, is sent to the server.

[1371] Server: The received data is stored in a database. The emotion engine analyzes the user's emotion data and reflects the emotional trends in the analysis results.

[1372] 3. Data collection and cross-referencing procedures

[1373] Server: Periodically collects external databases such as information on educational institutions, training methods, health data, etc. in the test region (e.g., India). The collected data is cross-referenced with user data.

[1374] Example: Comparing data from educational institutions in urban India with user profiles in Japan to extract similarities.

[1375] 4. AI-based analysis and proposal generation procedure

[1376] Server: Generative AI analyzes the collected external data and user data. The AI ​​determines the child's characteristics and interests from the user's input information and emotional data, and determines the optimal development method based on that.

[1377] Example: If a user types "my child is interested in music" and selects "anxiety" as the emotion, the AI ​​will suggest local music schools and online lessons, as well as ways to support the child in easing their anxiety.

[1378] 5. Procedure for presenting proposals and collecting emotional feedback

[1379] Device: The server generates suggestions and sends them to the device, where they are displayed to the user. These suggestions include educational institutions, extracurricular activities, discipline methods, and health management methods. A field for entering emotions about the suggestions is also displayed.

[1380] User: Checks the proposal and enters feedback. For emotional feedback, the user can select emotions such as "happiness" or "dissatisfaction" and enter specific opinions.

[1381] Device: Sends feedback and emotion data to the server.

[1382] 6. Feedback analysis and continuous improvement procedures

[1383] Server: Analyzes the received feedback and emotion data and stores it in a database. The emotion engine analyzes the emotion data of the feedback and prepares it to be reflected in the next proposal.

[1384] Example: If the AI ​​receives feedback such as "The proposed music school is good, but transportation is inconvenient" along with emotion data of "dissatisfied," it will generate new suggestions that also take transportation convenience into account.

[1385] 7. Continuous system improvement

[1386] Server: Continually trains the generative AI model based on feedback to improve the accuracy of suggestions, and updates the emotion engine to make suggestions more relevant to the user's emotions.

[1387] Through these steps, the system can propose training methods that take the user's emotions into consideration and provide the optimal training plan that meets their individual needs.The system will be tested in India and data will be collected, with plans to expand to Japan and other countries.

[1388] The above is a detailed description of the mode for carrying out the invention. The purpose of the present invention is to realize suggestions that take into consideration the user's feelings, thereby reducing anxiety about child-rearing and enabling children to reach their full potential.

[1389] The processing flow will be explained below.

[1390] Step 1:

[1391] The user downloads and launches the application. A new registration screen appears, and the user enters basic information about the child (name, age, gender, interests, and health status). An emotion input field also appears, allowing the user to select or enter their current state of mind or emotion.

[1392] Step 2:

[1393] The device temporarily stores the basic information and emotion information entered by the user in local storage. After the input contents are confirmed, the device prepares and sends the data to the server.

[1394] Step 3:

[1395] The server receives the user data and emotion data sent from the device, stores the received data in a database, and creates a user profile.

[1396] Step 4:

[1397] The server periodically collects external databases such as information on educational institutions in the test area (e.g., India), training methods, health data, etc. The collected external data is stored in the database.

[1398] Step 5:

[1399] The server cross-references the user profile with the collected external data, and AI analyzes both to find the right upbringing method and educational institution for the user's child.

[1400] Step 6:

[1401] The server's generative AI analyzes the cross-referenced data and the user's emotional data to identify the optimal upbringing method, educational institution, and health care method, taking into account the child's characteristics and interests, as well as the user's emotions.

[1402] Example: If a user types "my child is interested in science" and selects "confident" as the emotion, the AI ​​generates suggestions for local science clubs and online science courses. By taking the user's "confident" emotion into account and providing more detailed information, the AI ​​maintains a sense of security in parenting.

[1403] Step 7:

[1404] The server generates specific developmental suggestions based on the analysis results. These suggestions include educational institutions, extracurricular activities, discipline methods, and health management methods. The suggestions also reflect the emotional data selected by the user.

[1405] Step 8:

[1406] The server sends the suggestion to the user's device, and an emotional feedback field is displayed on the user interface along with the suggestion.

[1407] Step 9:

[1408] The user confirms the proposal, selects emotions such as "joy," "dissatisfaction," or "relief" as emotional feedback, and also enters specific opinions and impressions.

[1409] Example: If you are happy with the proposed science club, select "Delighted" and enter a comment such as "It fits perfectly with what I'm interested in and I'm very happy."

[1410] Step 10:

[1411] The device transmits the user's feedback and emotion data to the server, where the feedback data is ready to be analyzed.

[1412] Step 11:

[1413] The server analyzes the received feedback and emotional data. The analysis results are stored in a database, and the generative AI model is updated and improved based on the feedback analysis. This improves the accuracy of future suggestions.

[1414] Step 12:

[1415] The server uses the improved AI model to generate new suggestions as needed and sends them to the user, enabling the system to provide the optimal training method and personalized suggestions that are sensitive to the user's emotions.

[1416] The above are the specific processing steps of this system. Through this series of processes, the system aims to quickly and accurately propose child-rearing methods that take into consideration the user's feelings, reduce anxiety about child-rearing, and maximize the child's potential.

[1417] Example 2

[1418] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1419] There is a need to provide personalized training methods based on the characteristics, interests, and current emotions of the training recipient. However, conventional systems were unable to take the user's emotions into account, and their suggestions were general and insufficient for individualization. Furthermore, they lacked the functionality to analyze the quality of feedback and emotions and reflect them in future suggestions, making continuous improvement difficult.

[1420] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[1421] In this invention, the server includes means for the user to input basic information about the person being trained and their current emotional information, means for the terminal to temporarily store the input data and transmit it to the server, means for the server to store the received data and analyze the emotional information using an emotional engine, means for the server to cross-reference and analyze the collected external database information with the user data, means for the server to suggest appropriate educational institutions, training methods, and health management methods based on the analysis results, means for the user to receive the suggestions and input feedback, means for the terminal to transmit the user's feedback and emotional data to the server, and means for the server to analyze the feedback and generate next suggestions and improve the model. This enables customized training suggestions that are tailored to the emotions of each individual user.

[1422] "User" refers to an individual or organization that uses the system to input information about the person being trained and receive proposals.

[1423] "Training target" refers to the child or student who receives the proposed training method or educational plan.

[1424] The "emotion engine" is a software module that analyzes the emotional information entered by the user and determines the tendency of that emotion.

[1425] "External database information" refers to information obtained from external resources, such as data on educational institutions, data on development methods, and health data, which is collected for the system to reference.

[1426] "Cross-reference" is a process that compares user data with external database information and extracts commonalities and relationships.

[1427] "Generative AI" is artificial intelligence that generates optimal suggestions based on user input and collected data.

[1428] "Feedback" refers to the opinions and emotional information provided by a user after receiving a suggestion.

[1429] "Terminal" refers to an electronic device that a user uses to input information and communicate with a server.

[1430] A "server" is a central computer system that processes received data and generates offers.

[1431] "Suggestions" are recommendations on educational institutions, training methods, health management methods, etc. provided by the server based on the analysis results.

[1432] "Model improvement" is the process of incorporating user feedback to improve the accuracy of generative AI.

[1433] The present invention is a system that proposes an optimal training method based on the characteristics, interests, and emotions of a person to be trained. To implement this system, the following hardware and software are used.

[1434] Users download the application and enter basic information about the child they are raising, as well as their current emotional state. For example, they can enter the child's name, age, gender, interests, health condition, and current state of mind and emotions. The user's input is done on a device (electronic device such as a smartphone or tablet), which temporarily stores the input data in local storage. After confirmation, the data is sent to the server.

[1435] The server stores the received data in a database. The emotional information is analyzed using an emotional engine. For example, IBM Watson or Microsoft Azure's emotional analysis API can be used as the emotional engine. The analysis results are reflected in the database.

[1436] The server then collects education, upbringing, and health data from external databases, including government education databases and professional organization repositories. This data is then cross-referenced with the user data. For example, it collects information on educational institutions, upbringing, and health data for a specified region and extracts commonalities and associations with the user data.

[1437] A generative AI model (e.g., GPT-4) determines the optimal upbringing method based on the cross-referenced data, user input, and emotional data, and generates suggestions. These suggestions may include educational institutions, extracurricular activities, discipline methods, and health management methods. Support methods may also be included. For example, if a user inputs "my child is interested in music" and selects "anxiety" as the emotion, the generative AI may suggest local music schools and online lessons, as well as support methods to alleviate anxiety.

[1438] An example of a prompt is as follows:

[1439] "What music school would you recommend for a 3-year-old boy who is interested in music, and how can I ease his concerns?"

[1440] The suggestions are sent to the device and displayed to the user. The user checks the suggestions and enters feedback. The feedback can include specific opinions such as "transportation is inconvenient" and emotions such as "dissatisfaction." The device then sends this feedback to the server.

[1441] The server analyzes the feedback and reflects it in the next recommendation. This data is used to train the generative AI model, which continuously improves the model. This allows the system to provide customized training suggestions that are in tune with each individual user's emotions.

[1442] The system of the present invention promotes individualized education and training, reduces user anxiety, and maximizes the abilities of trainees. This system will be tested in a specific region, and data will be collected before it can be expanded to other regions.

[1443] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1444] Step 1:

[1445] The user enters basic information and emotional information about the person being trained.

[1446] Input: The user enters the name, age, gender, interests, health status, and current state and feelings of the person being fostered.

[1447] How it works: A user launches the application and enters information into each field on the registration screen, specifically using text boxes and drop-down menus.

[1448] Output: The input data is temporarily stored in the device's local storage.

[1449] Step 2:

[1450] The device temporarily stores the data and sends it to the server

[1451] Input: Basic information and emotional information entered by the user is stored in the device's local storage.

[1452] How it works: When the user clicks the "Send" button, the device converts this data into JSON format and sends it to the server.

[1453] Output: The server receives the data in JSON format.

[1454] Step 3:

[1455] The server stores the data and analyzes it with the emotion engine

[1456] Input: Basic information and emotion information received by the server from the device in JSON format.

[1457] How it works: The server stores the received data in a database and analyzes the emotional information using an emotion engine (e.g., IBM Watson or Microsoft Azure's emotion analysis API). The analysis results are added to the database.

[1458] Output: The sentiment analysis results are stored in a database.

[1459] Step 4:

[1460] Server collects external database information

[1461] Input: A trigger that requires external database information.

[1462] How it works: The server periodically uses API requests to collect information about educational institutions, development practices, and health data for a specified region, e.g., by retrieving data in JSON format from a government education database.

[1463] Output: Collected external data is stored in a database.

[1464] Step 5:

[1465] Server performs cross-referencing and analysis

[1466] Input: User data and external database information.

[1467] How it works: The server cross-references user data with information from external databases to extract commonalities and relationships. This is done using data analysis algorithms.

[1468] Output: Cross-reference results and analysis results are provided.

[1469] Step 6:

[1470] Generative AI generates suggestions

[1471] Input: User data, sentiment analysis results, cross-reference results.

[1472] How it works: Generative AI (e.g., GPT-4) uses these inputs to determine the optimal training method and generate specific suggestions. For example, if the interest is "music" and the emotion is "anxiety," the AI ​​will suggest local music schools and online lessons, along with support methods to alleviate anxiety.

[1473] Output: Customized development suggestions.

[1474] Step 7:

[1475] Users submit suggestions and feedback

[1476] Input: Development suggestions generated by generative AI.

[1477] How it works: The suggestions are displayed on the user's device. The user reviews the suggestions and enters their emotional feedback and specific opinions through a feedback form.

[1478] Output: User feedback data is sent from the device to the server.

[1479] Step 8:

[1480] The server analyzes the feedback and improves the model

[1481] Input: Feedback data submitted by the user.

[1482] How it works: The server stores the feedback in a database and analyzes the emotional information in the feedback using an emotion engine. The results of this analysis are used to train a generative AI model and are reflected in the next proposal.

[1483] Output: An improved generative AI model.

[1484] Through these specific processing steps, the system is able to continuously provide and improve customized development suggestions while being sensitive to the user's emotions.

[1485] (Application example 2)

[1486] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1487] Conventional autonomous vehicles have the problem of being unable to provide optimal routes and entertainment options that take into account the emotional state of passengers. This has resulted in cases where the riding experience is not necessarily comfortable for each individual user. Furthermore, there has been a problem with continuous improvement of the system due to insufficient collection and analysis of feedback.

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

[1489] In this invention, the server includes: a means for a user to input basic information and emotional information of a target person; a means for the server to receive and store the input data; a means for the server to analyze the received emotional data and analyze emotional trends; a means for the server to cross-reference and analyze collected external database information with the user data; a means for the server to suggest optimal routes and entertainment options for the autonomous vehicle based on the analysis results; a means for the user to receive the suggestions; a means for the server to receive and store feedback from the user; and a means for the server to analyze the feedback, revise the suggestions, and improve the model. This makes it possible to provide optimal routes and entertainment options that take into account the emotional state of passengers, thereby improving the riding experience and achieving continuous improvement of the system.

[1490] "Means for users to input basic information and emotional information of a subject" refers to an interface that allows users to input basic information about themselves or others and their current emotional state into the system.

[1491] "Means for the server to receive and store input data" refers to the function of the server receiving data input by the user via the network and storing that data in a database.

[1492] "Means for analyzing the emotional data received by the server and analyzing the emotional trends" refers to the function of the server processing the emotional data received from the user using an analysis program and identifying emotional patterns and trends.

[1493] "Means for the server to cross-reference and analyze collected external database information with user data" refers to the function of comparing and analyzing information collected from external databases with data provided by users.

[1494] "Means for the server to suggest optimal routes and entertainment options for autonomous vehicles based on the analysis results" refers to a function that suggests optimal routes for autonomous vehicles and entertainment options suitable for users based on data analyzed by the server.

[1495] The "means for the user to receive the proposal" refers to an interface that allows the user to check and receive the proposal content generated by the server.

[1496] "Means for receiving and storing feedback from users" refers to a function in which users input their thoughts and opinions about the proposal content, and the server receives and stores them in a database.

[1497] "Means for the server to analyze feedback, revise proposals, and improve the model" refers to the function by which the server analyzes the user feedback collected, revises the next proposal based on the analysis results, and further improves the model of the entire system.

[1498] The present invention provides a system for analyzing a user's emotional state in real time and suggesting optimal routes and entertainment options for an autonomous vehicle.

[1499] System configuration and hardware / software overview

[1500] The system includes user devices such as smartphones and tablets, a communication network, and a server. Its main software components include an emotion engine, a generative AI model, a database, and an external data collection module.

[1501] User terminal: Provides an interface for users to input basic and emotional information, including touchscreen and voice input capabilities.

[1502] Emotion engine: Software that analyzes emotional information entered by users in real time and detects emotional trends.

[1503] Generative AI model: An AI that generates optimal routes and entertainment options based on user and external data.

[1504] Database: A storage system for storing user data, emotion data, external data, feedback, etc.

[1505] External data collection module: A module for periodically collecting external information such as traffic conditions and weather data.

[1506] Program processing overview

[1507] 1. User registration and data entry: A user launches the application and enters basic information such as name, age, gender, destination, current emotional state, etc. This data is temporarily stored in local storage and then sent to the server.

[1508] 2. Emotion data analysis: The server uses an emotion engine to analyze the received emotion data in real time, detect emotional trends, and then save the analysis results in a database.

[1509] 3. External data collection and cross-referencing: The external data collection module is used to periodically collect external information such as traffic and weather data and cross-reference it with user data.

[1510] 4. Analysis and recommendation generation: Generative AI models analyze the cross-referenced data to generate optimal routes and entertainment options for the user.

[1511] 5. Proposal presentation and feedback collection: The proposal is sent to the user's device, where the user can review it and provide feedback. Feedback about the proposed plan is sent to the server.

[1512] 6. Feedback analysis and system improvement: The server analyzes the received feedback and stores it in a database to continuously improve the quality of the emotion engine and generative AI model.

[1513] Specific examples

[1514] For example, if a user enters the following information:

[1515] Name: Yamada Taro

[1516] Age: 35

[1517] Gender: Male

[1518] Destination: Workplace

[1519] Current emotional state: Anxiety

[1520] The emotion engine analyzes the input "anxiety" and suggests routes, music, videos, and other entertainment that will help the user relax. The generative AI model combines real-time traffic and weather data to calculate the optimal route. The user then selects and uses the suggested route and entertainment options through the app.

[1521] Prompt Sentence Examples

[1522] Name: Yamada Taro

[1523] Age: 35

[1524] Gender: Male

[1525] Destination: Workplace

[1526] Current emotional state: Anxiety

[1527] In this way, the present invention is able to take into account the user's emotional state and provide a personally optimized self-driving vehicle experience.

[1528] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1529] Step 1:

[1530] A user launches the application and enters basic information such as name, age, gender, destination, and current emotional state.

[1531] Input: User's basic information and emotional information

[1532] Output: User data saved in local storage

[1533] How it works: The user enters the required information into the input form on their device, and the input is temporarily saved in local storage. The data is then ready to be sent to the server.

[1534] Step 2:

[1535] The server receives the user's input data and stores it in a database, including the emotion data.

[1536] Input: User data stored in local storage

[1537] Output: User data and emotion data stored in a database

[1538] Operation: The server receives data from the user terminal via the network and stores it in a database.

[1539] Step 3:

[1540] The server uses an emotion engine to analyze the received emotion data and detect the trend of emotions.

[1541] Input: Emotion data stored in a database

[1542] Output: Emotional tendency data as the analysis result

[1543] How it works: The emotion engine analyzes emotion data, identifies positive, negative, or specific emotional states (e.g., anxiety, joy), and stores the results in a database.

[1544] Step 4:

[1545] The external data collection module is used to periodically collect external information such as traffic conditions and weather.

[1546] Input: Traffic and weather data from external APIs and databases

[1547] Output: External data stored on the server

[1548] How it works: The server periodically calls an external API to get the latest traffic and weather information and stores it in a database.

[1549] Step 5:

[1550] The server cross-references and analyzes the collected external data with the user data.

[1551] Input: User data in the database, sentiment data, and external data

[1552] Output: Optimal routes and entertainment options as analysis results

[1553] How it works: A generative AI model uses this data to perform analysis and generate optimal routes and entertainment options.

[1554] Step 6:

[1555] The server transmits the generated proposal to the user terminal, and the user receives it.

[1556] Input: Suggested best route and entertainment options

[1557] Output: Proposal displayed on the user's device

[1558] Operation: The server sends the generated results to the user's terminal and displays them on the interface for the user to check.

[1559] Step 7:

[1560] The user inputs feedback about the proposal and sends it to the server.

[1561] Input: User feedback (e.g., satisfaction with the proposal, specific opinions)

[1562] Output: Feedback data stored on the server

[1563] How it works: Users enter their opinions through a feedback form, and the server receives the data and stores it in a database.

[1564] Step 8:

[1565] The server analyzes the received feedback and uses it to revise the proposals and improve the system for the next time.

[1566] Input: Feedback data

[1567] Output: Revised proposal and improved system model

[1568] How it works: The server analyzes the feedback data and uses it as training data for the generative AI model and emotion engine to improve the accuracy of the next suggestion.

[1569] The above are the specific processing steps for carrying out the present invention.

[1570] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the control target 443 to output the result of the specific processing. The microphone 238 acquires voice indicating a user input regarding the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[1571] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[1572] In the above embodiment, an example was given in which the specific processing is performed by the data processing device 12, but the technology of the present disclosure is not limited to this, and the specific processing may be performed by the robot 414.

[1573] The emotion identification model 59 as an emotion engine may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[1574] FIG. 9 is a diagram illustrating an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. Emotions closer to the center of the concentric circles are more primitive. Emotions representing states and actions arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion includes both affect and mental states. Emotions generally generated from reactions occurring in the brain are arranged on the left side of the concentric circles. Emotions generally induced by situational judgment are arranged on the right side of the concentric circles. Emotions generally generated from reactions occurring in the brain and induced by situational judgment are arranged on the upper and lower sides of the concentric circles. Furthermore, the emotion of "pleasure" is arranged on the upper side of the concentric circles, and the emotion of "discomfort" is arranged on the lower side. In this way, in the emotion map 400, multiple emotions are mapped based on the structure by which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[1575] These emotions are distributed in the 3 o'clock direction on emotion map 400, and typically fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

[1576] The inside of emotion map 400 represents what is going on in the mind, and the outside of emotion map 400 represents behavior, so the further you go outside emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[1577] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is indicated, and when they approach the ideal, a state of pleasure is indicated. Emotions can also be created for robots, automobiles, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is indicated, and when they approach the ideal, a state of pleasure is indicated. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on Voice Emotion Recognition and Emotional Brain Physiological Signal Analysis Systems, Tokushima University, Doctoral Dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the "reaction" domain, where sensation is dominant. The right half of the emotion map lists emotions belonging to the "situation" domain, where situational awareness is dominant.

[1578] The emotion map defines two emotions that promote learning. One is a negative emotion on the situation side, around the middle of "repentance" or "reflection." In other words, this occurs when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is a positive emotion on the response side, around "desire." In other words, this occurs when the robot experiences positive feelings such as "I want more" or "I want to know more."

[1579] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple pieces of training data that are combinations of user input and emotion values ​​indicating each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions that are located close to each other have similar values, as in the emotion map 900 shown in FIG. 10. FIG. 10 shows an example in which multiple emotions, "relieved," "calm," and "reassuring," have similar emotion values.

[1580] The system according to the present disclosure has been described above mainly with respect to the functions of the data processing device 12, but the system according to the present disclosure is not necessarily implemented on a server. The system according to the present disclosure may be implemented as a general information processing system. The present disclosure may be implemented, for example, as a software program running on a personal computer or an application running on a smartphone, etc. The method according to the present disclosure may be provided to users in the form of SaaS (Software as a Service).

[1581] In the above embodiment, an example was given in which the specific processing is performed by one computer 22, but the technology of the present disclosure is not limited to this, and the specific processing may be distributed and performed by a plurality of computers including the computer 22. For example, the data generation model 58 may be provided in an external device of the data processing device 12, and data may be generated in the external device in accordance with input data.

[1582] In the above embodiment, an example in which the specific processing program 56 is stored in the storage 32 has been described, but the technology of the present disclosure is not limited to this. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-transitory storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-transitory storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes the specific processing in accordance with the specific processing program 56.

[1583] 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.

[1584] It is not necessary to store all of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store all of the specific processing program 56 in the storage 32; only a portion of the specific processing program 56 may be stored.

[1585] The hardware resource for executing a specific process can be any of the following processors: An example of a processor is a CPU, which is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. Another example of a processor is a dedicated electrical circuit, such as an FPGA (Field-Programmable Gate Array), a PLD (Programmable Logic Device), or an ASIC (Application Specific Integrated Circuit), which is a processor with a circuit configuration designed specifically for executing a specific process. Each processor has built-in or connected memory, and each processor uses the memory to execute the specific process.

[1586] The hardware resource that executes the specific processing may be configured with one of these various processors, or may be configured with a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Also, the hardware resource that executes the specific processing may be a single processor.

[1587] As an example of a system configured with a single processor, first, one processor is configured by combining one or more CPUs and software, and this processor functions as a hardware resource that executes a specific process. Second, there is a system that uses a processor that realizes the functions of an entire system including multiple hardware resources that execute a specific process on a single IC chip, as typified by SoC (System-on-a-chip). In this way, a specific process is realized using one or more of the above-mentioned various processors as hardware resources.

[1588] Furthermore, the hardware structure of these various processors can be, more specifically, an electric circuit that combines circuit elements such as semiconductor devices. The specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps may be deleted, new steps may be added, or the processing order may be rearranged, without departing from the spirit of the invention.

[1589] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, to avoid confusion and facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[1590] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference.

[1591] The following is further disclosed regarding the above embodiment.

[1592] (Claim 1)

[1593] a means for the user to input basic information about the child;

[1594] means for the server to receive and store the input data;

[1595] a means for the server to cross-reference and analyze the collected external database information and user data;

[1596] A means for the server to suggest appropriate educational institutions, training methods, and health management methods based on the analysis results;

[1597] a means for the user to receive suggestions;

[1598] means for receiving and storing feedback from users;

[1599] The system includes a means for the server to analyze the feedback and revise the suggestions and improve the model.

[1600] (Claim 2)

[1601] The system of claim 1, further comprising means for utilizing generative AI when suggesting educational institutions, development methods, and health management methods.

[1602] (Claim 3)

[1603] 2. The system according to claim 1, further comprising means for using development data, educational institution data, and health data from a specific region (such as India) as the external database information to be collected.

[1604] "Example 1"

[1605] (Claim 1)

[1606] a means for the user to input basic information about the child;

[1607] means for the server to receive and store the input data;

[1608] a means for the server to cross-reference and analyze the collected external database information and user data;

[1609] A means for the server to suggest appropriate educational institutions, training methods, and health management methods based on the analysis results;

[1610] a means for the user to receive suggestions;

[1611] means for receiving and storing feedback from users;

[1612] a means for the server to analyze the feedback and revise the proposals and improve the model; and

[1613] A means for the server to periodically collect and analyze external databases;

[1614] means for the terminal to transmit feedback data to a server;

[1615] A system including a means by which a server uses prompt sentences to cause a generative AI model to perform analysis.

[1616] (Claim 2)

[1617] The system of claim 1, further comprising means for utilizing generative AI when suggesting educational institutions, development methods, and health management methods.

[1618] (Claim 3)

[1619] 2. The system according to claim 1, further comprising means for using development data, educational institution data, and health data for a specific region as the external database information to be collected.

[1620] "Application Example 1"

[1621] (Claim 1)

[1622] a means for the user to input basic information about the child;

[1623] means for the server to receive and store the input data;

[1624] a means for the server to cross-reference and analyze the collected external database information and user data;

[1625] A means for the server to suggest appropriate educational institutions, training methods, and health management methods based on the analysis results;

[1626] a means for the user to receive suggestions;

[1627] means for receiving and storing feedback from users;

[1628] a means for the server to analyze the feedback and revise the proposals and improve the model; and

[1629] a means for users to ask questions or provide additional feedback based on the suggestions;

[1630] We will use the collected feedback to continuously train our generative AI model and improve the accuracy of our suggestions.

[1631] Including system.

[1632] (Claim 2)

[1633] The system of claim 1, further comprising means for utilizing generative AI to provide insights based on a user profile when suggesting educational institutions, development methods, and health management methods.

[1634] (Claim 3)

[1635] 2. The system according to claim 1, further comprising means for using multi-regional educational institution data, development data, and health data as external database information to be collected.

[1636] "Example 2: Combining Emotion Engines"

[1637] (Claim 1)

[1638] A means for a user to input basic information about the person being trained and their emotional information at that time;

[1639] A means for the terminal to temporarily store input data and transmit it to a server;

[1640] a means for storing the received data by the server and analyzing the emotion information using an emotion engine;

[1641] a means for the server to cross-reference and analyze the collected external database information and user data;

[1642] A means for the server to suggest appropriate educational institutions, training methods, and health management methods based on the analysis results;

[1643] a means for users to receive suggestions and enter feedback;

[1644] means for the terminal to transmit user feedback and emotion data to a server;

[1645] The system includes a means for the server to analyze the feedback and generate suggestions and improve the model next time.

[1646] (Claim 2)

[1647] The system of claim 1, further comprising means for using generative AI to suggest appropriate educational institutions, training methods, and health management methods.

[1648] (Claim 3)

[1649] 2. The system of claim 1, further comprising means for collecting external database information including development data, educational institution data, and health data for a specific region.

[1650] "Application example 2 when combining emotion engines"

[1651] (Claim 1)

[1652] A means for a user to input basic information and emotional information of a subject;

[1653] means for the server to receive and store the input data;

[1654] A means for analyzing the emotion data received by the server and analyzing the tendency of the emotion;

[1655] a means for the server to cross-reference and analyze the collected external database information and user data;

[1656] A means for the server to suggest optimal routes and entertainment options for the autonomous vehicle based on the analysis results; and

[1657] a means for the user to receive suggestions;

[1658] means for receiving and storing feedback from users;

[1659] The system includes a means for the server to analyze the feedback and revise the suggestions and improve the model.

[1660] (Claim 2)

[1661] The system of claim 1, further comprising means for utilizing generative AI to suggest optimal educational institutions, training methods, health management methods, autonomous vehicle routes, and entertainment options.

[1662] (Claim 3)

[1663] 10. The system of claim 1, further comprising means for using traffic data, weather data, educational institution data, and health data for a particular area as the external database information to be collected. [Explanation of symbols]

[1664] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot< / url:> < / url:> < / url:> < / url:>

Claims

1. a means for the user to input basic information about the child; means for the server to receive and store the input data; a means for the server to cross-reference and analyze the collected external database information and user data; A means for the server to suggest appropriate educational institutions, training methods, and health management methods based on the analysis results; a means for the user to receive suggestions; means for receiving and storing feedback from users; The system includes a means for the server to analyze the feedback and revise the suggestions and improve the model.

2. The system according to claim 1, further comprising means for utilizing generative AI when proposing educational institutions, training methods, and health management methods.

3. 2. The system according to claim 1, further comprising means for using development data, educational institution data, and health data of a specific region as the external database information to be collected.

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