System

Abstract

A system is provided.SOLUTION: A system, comprising: means including an artificial intelligence model for automatically generating a learning plan; means for receiving a request or feedback from a caregiver; means for automatically generating a report to the caregiver; and means for providing a platform for the caregiver to communicate with a nursery.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] Lack of communication between daycare centers and parents is an issue in after-school care settings. Parents want to know about their children's progress in learning and activities, and often have specific requests, but daycare centers are so busy that it is difficult to respond to individual requests. Furthermore, creating learning and activity plans and preparing reports for parents is a burden for instructors, and a way to resolve these issues is needed. [Means for solving the problem]

[0005] In order to solve the above problems, the present invention provides the following means.

[0006] We propose a system that includes a means including an artificial intelligence model for automatically generating learning plans, a means for receiving requests and feedback from parents, a means for automatically generating reports to parents, and a means for providing a platform for communication between daycare centers and parents. This system enables daycare centers to efficiently create learning plans and activity plans based on requests from parents and accurately report to parents based on daily activity records. In addition, parents can easily communicate their requests and feedback through the dedicated platform, facilitating smooth communication between daycare centers and parents.

[0007] An "artificial intelligence model" is a machine learning algorithm designed to perform a specific task automatically.

[0008] "Means for receiving requests and feedback from parents" refers to an interface that allows parents to input requests and feedback into the system and a function to store them in a database.

[0009] The "means for automatically generating reports to parents" is a system component that has the function of automatically creating reports to parents based on daily learning and activity records.

[0010] "Means for providing a platform for communication between daycare centers and parents" refers to a function that provides web services and applications that enable daycare centers and parents to communicate with each other.

[0011] "Database" refers to an information management system that stores and manages parental input data and children's daily activity records. [Brief explanation of the drawings]

[0012] [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

[0013] 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.

[0014] First, the terms used in the following description will be explained.

[0015] 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).

[0016] 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.

[0017] 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.

[0018] 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.

[0019] 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."

[0020] [First embodiment]

[0021] FIG. 1 shows an example of the configuration of a data processing system 10 according to the first embodiment.

[0022] 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.

[0023] 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).

[0024] 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.

[0025] 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.

[0026] 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.

[0027] 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.

[0028] FIG. 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0029] 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.

[0030] 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.

[0031] 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.

[0032] 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."

[0033] This invention relates to a system that facilitates communication between after-school daycare centers and parents, creates learning plans and activity plans according to parents' requests, and automatically generates reports based on daily activity records. This system provides a platform where daycare centers and parents can exchange information with each other.

[0034] Program processing

[0035] Platform-wide overview

[0036] The server creates a platform accessible to after-school care centers and parents. This platform includes a user database, a learning plan creation module, an activity plan creation module, an automatic report generation module, and a communication module. This allows parents to easily input requests and feedback, and allows daycare centers to efficiently manage children's learning and activities.

[0037] Collecting user requests

[0038] The server receives the request data entered by the parents and stores it in a database. Parents then use their devices to enter their child's individual information and requests into the platform, allowing the daycare center to respond quickly to the parents' needs.

[0039] Specific examples

[0040] Parents can input requests such as "I want to improve my child's English grades" or "I'm aiming to improve my child's athletic ability" into the device.

[0041] The server receives these requests and stores a list of requests for each parent in a database.

[0042] Automatic generation of learning and activity plans

[0043] The server uses an AI module to create learning and activity plans based on the parents' requests and the child's current status data. The server retrieves the parents' requests and the child's current status data from the database and passes it to the AI ​​module, which analyzes it and generates the optimal plan.

[0044] Specific examples

[0045] The server passes the AI ​​module the request to "improve English grades" and the child's current English grades as input data.

[0046] The AI ​​creates a plan that includes, for example, special English lessons three times a week and the use of a learning app, and returns it to the server.

[0047] The server stores this plan in a database and notifies both parents and school counselors.

[0048] Collecting daily activity records

[0049] The server collects the daily learning and activity progress records entered by the school counselor and stores them in a database. The school counselor uses a terminal to enter the children's daily activities into the platform.

[0050] Specific examples

[0051] The after-school care instructor types into the terminal, "Today we had a special English lesson, and the children actively participated."

[0052] The server receives this and stores each child's activity record in a database.

[0053] Automatic report generation

[0054] The server uses the AI ​​module to automatically generate reports for parents based on daily activity records and parental requests. The server retrieves the necessary information from the database and passes it to the AI ​​module to request the creation of a report.

[0055] Specific examples

[0056] The server retrieves information from the database, such as the progress of special English instruction and changes in the child's grades, and passes it on to the AI ​​module.

[0057] Based on this, the AI ​​generates a report such as, "Results of this week's special English instruction: Grades improved by 3 points. Next time, we will focus on strengthening listening skills."

[0058] The server sends the report to the parent's device, where the parent can view it.

[0059] Processing Feedback

[0060] The server collects feedback from parents and stores new requests and improvements in a database.

[0061] Specific examples

[0062] Parents use the devices to enter feedback into the platform, such as "I would like you to improve not only your child's English listening skills but also their speaking skills."

[0063] The server receives this, stores it in a database, and triggers an update of the learning plan if necessary.

[0064] This will facilitate smooth communication between daycare centers and parents, improving the quality of childcare.

[0065] The processing flow will be explained below.

[0066] Step 1:

[0067] The server initializes the user database and registers the after-school care center's user information. Parents create their own accounts and log in to the platform using their devices.

[0068] Step 2:

[0069] Parents use the device to input their child's individual information (such as name, grade, and interests) and requests for learning and activities (such as subjects and activities they want to strengthen, special support, etc.) The server receives this input data and stores it in a database.

[0070] Step 3:

[0071] The server retrieves data on parents' requests and children's current status from the database and sends it to the AI ​​module, which then analyzes the data and generates optimal learning and activity plans.

[0072] Step 4:

[0073] The server stores the learning and activity plans generated by the AI ​​module in a database and notifies the parents and after-school counselors of the results on the platform, who can then view the plans on their own devices.

[0074] Step 5:

[0075] The after-school instructors use the terminals to input the children's learning status and daily activities. For example, they can enter information such as, "Today we had a special English lesson, and the children actively participated." The server stores these activity records in a database.

[0076] Step 6:

[0077] The server sends data to the AI ​​module based on daily activity records and parental requests, and the AI ​​module analyzes the data and automatically generates a report for parents.

[0078] Step 7:

[0079] The server stores the generated report for parents in a database and sends it to the parents' terminal, where the parents can check the report and enter feedback on the content.

[0080] Step 8:

[0081] The server receives the feedback entered by the parent on the device and stores it in a database. The server then checks the feedback and, if necessary, requests the AI ​​module to revise the learning plan or activity plan.

[0082] This will enable smooth communication between the nursery school and parents, and enable efficient support for children's growth and learning.

[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] There is a need for a system that can facilitate smooth communication between after-school care centers and parents, efficiently create learning and activity plans according to parents' requests, and automatically generate appropriate reports based on daily activity records.Currently, communication between daycare centers and parents is complicated and requires manual data management, so automating this process to improve efficiency is a challenge.

[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 collecting and storing parental request data in a database, means for automatically generating optimal learning plans and activity plans using a generative AI model based on the parental request data and the child's current status data, means for collecting and storing daily activity records entered by after-school care instructors in a database, means for automatically generating reports using a generative AI model based on the daily activity records and parental requests, means for sending reports to parents, and means for providing a platform for communication between the daycare center and parents. This makes it possible to respond quickly to parental requests and efficiently provide appropriate learning plans and activity plans. Furthermore, automatic generation of reports based on activity records reduces the burden on daycare centers and parents.

[0088] "Parental request data" refers to data that parents enter and send to the after-school care center specific requests regarding their child's learning and activities.

[0089] The "database" is a storage device that manages and stores all data used by the system, such as parental requests, children's current status, and daily activity records.

[0090] A "generative AI model" is an artificial intelligence algorithm that analyzes data on parents' requests and children's current situation, and automatically generates optimal learning plans, activity plans, and reports.

[0091] A "learning plan" is a plan that includes specific schedules and content for achieving a child's learning goals.

[0092] An "action plan" is a plan that includes specific activities and events to support a child's growth and development.

[0093] "Daily activity records" are data that are entered and recorded daily by after-school care instructors on the progress of children's learning and activities.

[0094] The "report" is a document that summarizes the results and status of children's learning and activities, created by a generative AI model based on daily activity records and parental requests.

[0095] The "platform" is the foundation of the entire system, providing an interface for daycare centers and parents to exchange information with each other and use the system's various functions.

[0096] A "server" is a computer device that serves as the core of the entire system and stores, processes, and communicates data.

[0097] "Terminal" means a computer device or mobile device used by parents and school counselors to access the system and input or receive data.

[0098] This invention relates to a system that facilitates communication between after-school care centers and parents, efficiently creates learning plans and activity plans according to parents' requests, and automatically generates reports based on daily activity records. This system provides a platform where daycare centers and parents can exchange information with each other.

[0099] The system operates as follows.

[0100] Hardware and software configuration

[0101] 1. Server

[0102] A server is a computer device that is the core of the entire system and stores, processes, and communicates data.

[0103] 2. Terminal

[0104] Terminals are computer devices or mobile devices that allow parents and school teachers to access the system and input or receive data.

[0105] 3. Database

[0106] The database is a storage device for managing and storing data such as parents' requests, children's current status, and daily activity records.

[0107] 4. Generative AI Models

[0108] The generative AI model is an artificial intelligence algorithm that analyzes data on parents' requests and children's current situation, and automatically generates optimal learning plans, activity plans, and reports.

[0109] Specific operation of the system

[0110] 1. Gathering parents' requests

[0111] User (parent): Uses a device to access the platform's request entry screen. For example, enter a request such as "I want to improve my child's English grades."

[0112] Device: Sends the request data entered by the parent to the server.

[0113] Server: Stores the received data in the user database.

[0114] 2. Automatic generation of learning and activity plans

[0115] Server: Retrieves parental request data and child current status data from the database.

[0116] Server: Passes the acquired data to the generative AI model.

[0117] Generative AI model: Analyzes parental requests and student data to generate optimal learning and activity plans.

[0118] Server: Saves the generated plan in a database and notifies parents and school counselors.

[0119] 3. Collecting daily activity records

[0120] User (school teacher): Uses a device to enter daily activity records into the platform. For example, they might enter, "Today we had a special English lesson, and the children actively participated."

[0121] Device: Sends activity record data to the server.

[0122] Server: Stores the received data in the activity record database.

[0123] 4. Automatic report generation

[0124] Server: Retrieves daily activity records and parental request data from the database.

[0125] Server: Passes the acquired data to the generative AI model and requests it to create a report.

[0126] Generative AI model: Generates a report based on input data. For example, it generates a report such as, "Results of this week's English special instruction: Grades improved by 3 points. Next time, listening skills will be improved."

[0127] Server: Sends the generated report to the parent's device.

[0128] 5. Processing Feedback

[0129] User (parent): Uses a device to input feedback into the platform. For example, they might say, "I would like you to improve not only your English listening skills but also your speaking skills."

[0130] Terminal: Sends feedback data.

[0131] Server: Stores the received feedback in a database and updates the learning plan as needed.

[0132] Prompt Sentence Examples

[0133] 1. "Based on the parents' requests, please create the best study plan to improve your child's English grades."

[0134] 2. "Please prepare a weekly activity report based on your daily activity records."

[0135] This is expected to facilitate smoother communication between daycare centers and parents, improving the quality of childcare.

[0136] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0137] Step 1: Enter and submit your request data

[0138] User (Parent): The parent uses a device to access the platform's request entry screen, where they enter their request, such as "I want to improve my child's English grades."

[0139] Input: Request data entered by the parent.

[0140] Device: Sends the request data entered by the parent to the server.

[0141] Output: The requested data sent to the server.

[0142] Specific operation: The request is entered into the device's user interface, the input is converted into JSON format, and an HTTP request is sent to the server.

[0143] Step 2: Save your request data

[0144] Server: The server stores the received request data in a database.

[0145] Input: Request data received from the terminal.

[0146] Data processing / data calculation: Analyze the requested data, associate it with the parent ID, and insert it into the database.

[0147] Output: Request data stored in a database.

[0148] What it does: The server parses the data it receives, associates it based on the parent's ID, runs SQL queries on it, and stores it in a database.

[0149] Step 3: Request to create a lesson plan / activity plan

[0150] Server: The server retrieves the parents' request data and the child's current status data from the database and passes them to the generative AI model.

[0151] Input: Request data and current status data stored in the database.

[0152] Data processing / data calculation: The desired data and current data are formatted in JSON format and passed to the generative AI model.

[0153] Output: The data passed to the generative AI model.

[0154] What happens: The server executes SQL queries to get the required data and generates HTTP requests to the AI ​​model.

[0155] Step 4: Create a lesson plan and activity plan

[0156] Generative AI model: A generative AI model generates lesson plans and activity plans based on input data.

[0157] Input: Request data and current status data passed from the server.

[0158] Data Computing: Perform natural language processing and analysis of data to generate optimal plans.

[0159] Output: lesson plan and activity plan.

[0160] Specific actions: The AI ​​model uses the given data to generate a specific plan, such as "special English instruction three times a week."

[0161] Step 5: Notify and save your plan

[0162] Server: The server saves the generated plan in a database and notifies parents and school counselors.

[0163] Input: The plan output from the generative AI model.

[0164] Data processing / data calculation: Inserting plan data into the database and sending notifications to parents and instructors.

[0165] Output: Plans stored in the database and notifications sent.

[0166] What happens: The server saves the new plan to the database and sends emails and push notifications.

[0167] Step 6: Enter your daily activity log

[0168] User (school teacher): Uses a device to enter daily activity records into the platform. For example, they might enter, "Today we had a special English lesson, and the children actively participated."

[0169] Input: Activity record data entered by after-school care instructors.

[0170] Device: Sends activity record data to the server.

[0171] Output: Activity data sent to the server.

[0172] Specific operation: The instructor fills in the data using the input form, and the terminal converts it into JSON format and sends it to the server.

[0173] Step 7: Save your activity log

[0174] Server: The server stores the received daily activity records in a database.

[0175] Input: Activity data received from the terminal.

[0176] Data processing / data calculation: Analyze activity record data and store it in association with the child's ID.

[0177] Output: Activity log data stored in a database.

[0178] What it does: The server parses the data it receives, associates it based on the child's ID, executes an SQL query, and saves it to a database.

[0179] Step 8: Request automatic generation of report

[0180] Server: The server retrieves daily activity records and parental request data from the database, passes them to the generative AI model, and requests it to create a report.

[0181] Input: Activity record data and request data stored in the database.

[0182] Data processing / data calculation: Activity records and request data are formatted into JSON format and passed to the generative AI model.

[0183] Output: The data passed to the generative AI model.

[0184] What happens: The server executes SQL queries to get the required data and generates HTTP requests to the AI ​​model.

[0185] Step 9: Generate reports

[0186] Generative AI model: A generative AI model generates a report based on input data.

[0187] Input: Activity log data and request data passed from the server.

[0188] Data Computing: Perform natural language processing and analysis of data to generate appropriate reports.

[0189] Output: Report data.

[0190] Specific operation: Based on the given data, the AI ​​model generates a report such as, "Results of this week's special English instruction: Grades improved by 3 points. Next time, listening skills will be improved."

[0191] Step 10: Submit your report

[0192] Server: The server sends the generated report to the parent's device.

[0193] Input: Report output from a generative AI model.

[0194] Data processing / data calculation: Convert report data into PDF format and send it.

[0195] Output: Report sent to parents.

[0196] Specific operation: The server formats the generated report and sends it to the parent's device via email or push notification.

[0197] Step 11: Enter and submit your feedback

[0198] User (Parent): Parents use their devices to input feedback into the platform, for example, "I would like you to improve not only my child's English listening skills but also their speaking skills."

[0199] Input: Feedback data entered by the parent.

[0200] Device: Sends feedback data to the server.

[0201] Output: Feedback data sent to the server.

[0202] Specific operation: Parents enter data using an input form, and the device converts it into JSON format and sends it to the server.

[0203] Step 12: Storing and processing feedback

[0204] Server: The server stores the received feedback in a database and updates the learning plan as needed.

[0205] Input: Feedback data received from the device.

[0206] Data processing / data calculation: Analyze the feedback data, associate it with the parent ID, insert it into the database, and ask the generative AI model to regenerate the plan again.

[0207] Output: Feedback data stored in a database and an updated learning plan.

[0208] Specific operation: The server analyzes the feedback data, stores it in a database, and asks the AI ​​model to regenerate a plan based on the new data.

[0209] (Application example 1)

[0210] 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."

[0211] In traditional brick-and-mortar stores, there are problems such as a lack of communication between members (or guardians) and store staff (e.g., educators and instructors), difficulty in responding to individual member needs, and the tedious manual management of daily activity records. These problems also lead to delays in creating reports and reflecting feedback, which can lead to issues such as lower member (or guardian) satisfaction.

[0212] 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.

[0213] In this invention, the server includes a means for including an artificial intelligence model for automatically generating learning plans, a means for receiving requests and feedback from parents, a means for automatically generating reports to parents, a means for store staff to store daily activity records in a database, and a means for providing a platform for communication between parents and store staff. This enables prompt response to requests from parents and members, automatic generation of individual learning and activity plans, and efficient management of daily activity records. In addition, the automatic generation of reports and immediate reflection of feedback are expected to improve satisfaction among parents and members.

[0214] The "artificial intelligence model for automatically generating learning plans" is an artificial intelligence program that analyzes the requests and progress data of members and parents, and automatically generates optimal learning or activity plans.

[0215] The "means for receiving requests and feedback from parents" refers to a user interface and communication means for parents to input requests and feedback and send them to the server.

[0216] The "means for automatically generating reports to parents" is a system that uses an AI module to automatically generate reports on a regular basis based on daily activity records and requested data, and notifies parents.

[0217] The "means for store staff to store daily activity records in a database" refers to a user interface and communication means that allows store staff to input details of members' daily activities and store that data in a database.

[0218] "Means for providing a platform for parents and store staff to communicate" refers to an online platform that allows parents and store staff to exchange information in both directions.

[0219] The "database for storing data entered by guardians and members" refers to a database system for safely storing and managing requests, feedback, and other individual information entered by guardians and members.

[0220] "Means for storing members' daily activity records in a database" refers to a system for storing detailed records of members' learning and activities in a digital format in a database.

[0221] This invention relates to a system that automatically generates customized learning plans and activity plans based on the requests of members and guardians at a physical store, collects and manages daily activity records based on the plans, and reports the records to guardians. An embodiment of this system is described in detail below.

[0222] System configuration

[0223] The system of the present invention includes the following major components:

[0224] 1. Server

[0225] Artificial intelligence model that automatically generates learning plans: An artificial intelligence program that analyzes the requests and progress data of members and parents to generate the optimal plan. This model uses a generative AI model.

[0226] Database: A database system such as Amazon RDS to store parent and member input data, activity records, requests, and feedback.

[0227] Communication module: A communication interface for exchanging information with parents and staff at physical stores. Data can be exchanged via the Internet.

[0228] 2. User Device

[0229] Parental interface: A smartphone app developed with React Native to provide a screen for entering requests, viewing progress reports, and providing feedback.

[0230] Staff interface: A smartphone app developed with React Native to provide an activity record entry screen and plan confirmation screen.

[0231] Program processing overview

[0232] 1. Request collection function

[0233] The server receives requests entered by parents through a smartphone app and stores them in a database. This function uses a user interface developed with React Native and a backend built with Node.js.

[0234] Example prompt sentence:

[0235] I want to improve my English grades. My current grades are below average.

[0236] 2. Automatic learning and activity plan generation function

[0237] The server collects parent requests and member progress data from the database, passes them to the GPT-4 API, and generates an optimal plan, which is then saved in the database and notified to parents and staff.

[0238] Example prompt sentence:

[0239] Parents' request: To improve English grades. Current grades are below average.

[0240] Current data: Study time is 3 hours per week and attend English classes twice a week.

[0241] 3. Activity record collection function

[0242] The server receives daily activity records entered by store staff through a smartphone app and stores the data in a database. This function also uses a user interface developed with React Native and a backend built with Node.js.

[0243] Example prompt sentence:

[0244] Today we had a special English lesson, and the children participated enthusiastically.

[0245] 4. Automatic report generation function

[0246] The server collects activity records and request data from the database, passes them to the GPT-4 API, and generates a report, which is stored in the database and notified to the parents.

[0247] Example prompt sentence:

[0248] This week's English tutoring results: My grade went up by 3 points. Next time I'll focus on strengthening my listening skills.

[0249] 5. Feedback processing function

[0250] The server receives feedback entered by parents through a smartphone app, stores it in a database, and triggers plan updates as needed.

[0251] Example prompt sentence:

[0252] I would like you to improve not only your English listening skills but also your speaking skills.

[0253] Example

[0254] The system of the present invention enables physical facilities such as fitness clubs and cram schools to efficiently plan and manage activity records based on the individual needs of their members. Cram schools can deepen relationships of trust with parents by monitoring members' learning progress and providing regular reports to parents. Fitness clubs can also improve member satisfaction by providing individual training plans and automatically generating progress reports.

[0255] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0256] Step 1:

[0257] The user inputs their request using a smartphone app. This input data is free-form text data such as "I want to improve my English grades" or "I'm aiming to improve my athletic ability." The app sends this data to the server. Input data: Parent's request (text format). Output data: Request data sent to the server.

[0258] Step 2:

[0259] The server receives the request data sent by the user (guardian) from the smartphone app and stores the data in a database. Input data: Request data. Output data: Request data stored in the database.

[0260] Step 3:

[0261] The server retrieves the member's current status data and the guardian's request data from the database, integrates these data, and passes them to the artificial intelligence model (GPT-4 API). Input data: Request data, current status data. Output data: Plan generation request.

[0262] Step 4:

[0263] The server's AI model analyzes the input data and generates optimal learning and activity plans based on the parents' requests. The generated plans are returned to the server and stored in a database. Input data: Request data, current situation data. Output data: Generated plans.

[0264] Step 5:

[0265] The server notifies parents and store staff of the generated plan using a notification message containing the plan details. Input data: Generated plan. Output data: Notification message sent to parents and staff.

[0266] Step 6:

[0267] Users (store staff) use a smartphone app to enter their daily activity records and send the data to the server. Input data: Activity records (text format). Output data: Activity records sent to the server.

[0268] Step 7:

[0269] The server receives the activity record data sent by the user (staff member) and stores the data in the database. Input data: Activity record. Output data: Activity record stored in the database.

[0270] Step 8:

[0271] The server retrieves activity records and parental request data from the database and passes this data to an artificial intelligence model (GPT-4 API) to generate a report. Input data: Activity records, request data. Output data: Report generation request.

[0272] Step 9:

[0273] The server's AI model analyzes this data and automatically generates a report for parents. The generated report is returned to the server and stored in a database. Input data: Activity records, request data. Output data: Generated report.

[0274] Step 10:

[0275] The server notifies the parent of the generated report using a notification message containing the report. Input data: The generated report. Output data: The notification message sent to the parent.

[0276] Step 11:

[0277] The user (parent) uses a smartphone app to input feedback and sends the data to the server. Input data: Feedback (text format). Output data: Feedback data sent to the server.

[0278] Step 12:

[0279] The server receives feedback data sent by users (parents), stores the data in a database, and triggers plan updates as needed. Input data: Feedback data. Output data: Feedback data stored in the database, plan update requests.

[0280] 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.

[0281] This invention relates to a system that facilitates communication between after-school daycare centers and parents, creates learning plans and activity plans according to parents' requests, and automatically generates reports based on daily activity records. Furthermore, by combining this invention with an emotion engine that recognizes the user's emotions, it becomes possible to respond in a way that takes into consideration the emotions of parents and children.

[0282] Program processing

[0283] Platform-wide overview

[0284] The server creates a platform accessible to after-school care centers and parents. This platform includes a user database, a learning plan creation module, an activity plan creation module, an automatic report generation module, an emotion engine, and a communication module. This allows parents to easily input requests and feedback, and allows daycare centers to efficiently manage children's learning and activities.

[0285] Collecting user requests

[0286] The server receives the request data entered by the parent and stores it in a database. It then analyzes the parent's emotional data through an emotion engine and stores it together with the request data. Parents use their devices to enter their child's individual information and requests into the platform. The emotion engine analyzes emotions from the wording and tone of the input.

[0287] Specific examples

[0288] The parent types into the device, "I want to improve my child's English grades, and I'm feeling a bit anxious."

[0289] The server receives these requests, extracts the emotional data of "anxiety" using an emotion engine, and saves it in a request list for each parent.

[0290] Automatic generation of learning and activity plans

[0291] The server uses an AI module to create learning and activity plans based on the parents' requests, the child's current status data, and emotional data. By referring to the emotional data, it is possible to generate plans that take the child's needs into consideration.

[0292] Specific examples

[0293] The server passes the AI ​​module the request to "improve English grades," the emotional data of "anxiety," and the child's current English grades as input data.

[0294] For example, the AI ​​takes into account the child's impatience and creates a plan that includes special English lessons three times a week at a comfortable pace and the use of a learning app, and returns the plan to the server.

[0295] The server stores this plan in a database and notifies both parents and school counselors.

[0296] Collecting daily activity records

[0297] The teacher uses a terminal to input information about the child's learning status and daily activities, which is then stored in a database on the server. The emotion engine analyzes the child's emotions from the input text and stores this data in the database.

[0298] Specific examples

[0299] The after-school teacher types into the terminal, "Today we had a special English lesson, and the children participated actively." The emotion engine extracts the positive emotion of "active."

[0300] The server receives this and stores each child's activity record and emotional data in a database.

[0301] Automatic report generation

[0302] The server uses an AI module to automatically generate reports for parents based on daily activity records, as well as parents' requests and emotional data.

[0303] Specific examples

[0304] The server extracts information from the database about the progress of special English instruction, changes in the child's grades, and emotional data such as "positive," and passes this data to the AI ​​module.

[0305] Based on this, the AI ​​generates a report that reads, "Results of this week's special English instruction: Grades improved by 3 points. Next time, we will strengthen listening skills. The child is actively participating, so let's keep it up."

[0306] The server sends the report to the parent's device, where the parent can view it.

[0307] Processing Feedback

[0308] The server collects feedback from parents, analyzes the emotional data through an emotion engine, and stores it in a database along with any new requests or improvements.

[0309] Specific examples

[0310] Parents can input feedback into the device, such as, "I want my child to improve not only their English listening skills but also their speaking skills. I'm a little worried." The emotion engine then extracts the emotion of "anxiety."

[0311] The server receives this, stores it in a database, and triggers an update of the learning plan if necessary.

[0312] This will facilitate smooth mutual communication between the nursery school and parents, and make it possible to efficiently support children's growth and learning while also taking into consideration their emotions.

[0313] The processing flow will be explained below.

[0314] Step 1:

[0315] The server initializes the user database and registers the after-school care center's user information. Parents create their own accounts and log in to the platform using their devices.

[0316] Step 2:

[0317] Parents use the device to input their child's individual information (such as name, grade, and interests) and requests for learning and activities (such as subjects and activities to reinforce, special support, etc.). The emotion engine analyzes emotions from the input text and generates emotion data. The server receives this input data and emotion data and stores it in a database.

[0318] Step 3:

[0319] The server retrieves data on parents' requests, children's current status, and emotional data from the database and sends them to the AI ​​module, which analyzes the data and generates optimal learning and activity plans.

[0320] Step 4:

[0321] The server stores the learning and activity plans generated by the AI ​​module in a database and notifies the parents and after-school counselors of the results on the platform, who can then view the plans on their own devices.

[0322] Step 5:

[0323] After-school counselors use the terminals to input information about the children's learning status and daily activities. The emotion engine analyzes the children's emotions from the input text, and the server stores this information in a database.

[0324] Specific examples

[0325] An after-school teacher inputs, "Today we had a special English lesson, and the children participated actively." The emotion engine extracts the positive emotion of "active."

[0326] The server receives this and stores each child's activity record and emotional data in a database.

[0327] Step 6:

[0328] The server sends data to the AI ​​module based on daily activity records and parents' requests and emotional data to automatically generate reports. The AI ​​module analyzes the data and automatically generates reports for parents.

[0329] Specific examples

[0330] The server retrieves information from the database, such as progress in special English instruction, changes in the child's grades, and emotional data, and passes it on to the AI ​​module.

[0331] Based on this, the AI ​​generates a report that reads, "Results of this week's special English instruction: Grades improved by 3 points. Next time, we will strengthen listening skills. The child is actively participating, so let's keep it up."

[0332] The server sends the report to the parent's device, where the parent can view it.

[0333] Step 7:

[0334] Parents can use a device to check the report and enter their feedback on the content, and the emotion engine analyzes the emotional data from the feedback.

[0335] Specific examples

[0336] A parent enters feedback such as, "I want my child to improve not only his English listening skills but also his speaking skills. I'm a little worried." The emotion engine analyzes this and extracts the emotion data of "anxiety."

[0337] The server receives this and stores it in a database.

[0338] Step 8:

[0339] The server analyzes the feedback and emotional data from parents, and if necessary, requests the AI ​​module to update the learning plan and activity plan. The server then notifies the after-school instructor of the updated plan.

[0340] Specific examples

[0341] Based on the feedback, the server inputs into the AI ​​module the new request to "improve speaking skills" and the parent's emotional data of "anxiety."

[0342] The AI ​​module creates a new lesson plan and returns it to the server, which stores it in a database and notifies the after-school teacher.

[0343] As described above, the present invention facilitates mutual communication between daycare centers and parents, and can efficiently support children's growth and learning while taking into consideration their emotions.

[0344] Example 2

[0345] 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."

[0346] In modern society, with the increase in dual-income households, there is a need for smoother communication between parents and after-school daycare centers. Conventional systems make it difficult for parents to create learning and activity plans that meet their needs, and are therefore unable to adequately support children's development. Furthermore, there is a lack of consideration for the feelings of parents and children, making it difficult to provide an optimal learning environment.

[0347] 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.

[0348] In this invention, the server includes means including an artificial intelligence model for automatically generating learning plans and activity plans, means for receiving requests and feedback from parents, means including an emotion engine for analyzing input data and emotion data from parents, means for automatically generating reports to parents, and means for providing a platform for communication between the childcare facility and parents. This enables the creation of learning plans and activity plans based on parents' requests and appropriate responses that take into consideration the emotions of parents and children.

[0349] An "artificial intelligence model for automatically generating learning plans and activity plans" is a system that includes an algorithm for automatically creating optimal learning plans and activity plans based on the requests of parents and current data on the child.

[0350] "Means for receiving requests and feedback from parents" refers to a function that includes an interface for receiving and processing requests and feedback that parents input into the system.

[0351] The "emotion engine that analyzes parental input data and emotional data" is an analysis engine that analyzes emotions from data entered by parents and utilizes that information within the system.

[0352] "Means for automatically generating reports for parents" is a function that automatically creates reports for parents based on the child's daily activity records and progress data for the learning plan.

[0353] The "platform for communication between childcare facilities and parents" is a system that provides an online platform for childcare facilities and parents to communicate efficiently.

[0354] The "database" is a storage device that centrally stores and manages various information used by the system, such as parental input data, emotional data, and records of children's daily activities.

[0355] This invention is a system that facilitates communication between after-school care facilities and parents, creates learning plans and activity plans according to parents' requests, and automatically generates reports based on daily activity records. Furthermore, by combining it with an emotion engine that recognizes the user's emotions, it is possible to respond in a way that takes into consideration the emotions of parents and children.

[0356] Building a platform

[0357] The server creates a platform accessible to after-school care facilities and parents. This platform includes a user database, a learning plan creation module, an activity plan creation module, an automatic report generation module, an emotion engine, and a communication module. This allows parents to easily input requests and feedback, and enables after-school care facilities to efficiently manage children's learning and activities.

[0358] Collecting user requests

[0359] Users (guardians) can log in to the platform using their devices and enter their child's individual information and requests. The server receives the request data entered by the guardian and stores it in the user database. It also analyzes the guardian's emotional data through an emotion engine and stores it together with the request data.

[0360] Examples:

[0361] Parents can input requests such as, "I want to improve my child's English grades, and I'm feeling a bit anxious." The server receives these requests, extracts the emotion data for "anxiety" using an emotion engine, and saves it in a request list for each parent.

[0362] Automatic generation of learning and activity plans

[0363] The server uses an AI module to create learning and activity plans based on the parents' requests, the child's current status data, and emotional data. By referring to the emotional data, it is possible to generate plans that take the child's needs into consideration.

[0364] Examples:

[0365] The server passes the AI ​​module the request to "improve English grades," emotional data on "impatience," and the child's current English grades as input data. The AI ​​takes "impatience" into account, for example, to create a plan that incorporates special English lessons three times a week at a comfortable pace and the use of a learning app, and returns it to the server. The server stores this plan in a database and notifies both the parents and the after-school care instructor.

[0366] Collecting daily activity records

[0367] After-school instructors use terminals to input the children's daily learning status and activities. The server receives the input data and stores it in an activity record database. The emotion engine analyzes the children's emotions from the input text and stores this data in the database as well.

[0368] Examples:

[0369] The after-school teacher types into the terminal, "Today we had a special English lesson, and the children participated actively." The emotion engine extracts the positive emotion of "active." The server receives this and stores each child's activity record and emotional data in a database.

[0370] Automatic report generation

[0371] The server uses an AI module to automatically generate reports for parents based on daily activity records, as well as parents' requests and emotional data.

[0372] Examples:

[0373] The server extracts data from the database, including the progress of the English special instruction, changes in the child's grades, and emotional data such as "positive," and passes it to the AI ​​module. Based on this, the AI ​​generates a report that reads, "Results of this week's English special instruction: Grades improved by 3 points. Next time, we will strengthen listening skills. The child is actively participating, so let's keep it up." The server then sends the report to the parent's device, where the parent can review it.

[0374] Processing Feedback

[0375] The user (parent) enters feedback based on the report. The server receives the entered feedback and analyzes it through the emotion engine. The server extracts the emotion data of "anxiety" and stores it in the database along with the feedback. The server triggers an update of the learning plan as needed.

[0376] Examples:

[0377] The parent inputs feedback on the device, such as "I want my child to improve not only his English listening skills but also his speaking skills. I'm a little anxious." The server receives this, and the emotion engine extracts the emotion of "anxiety." The server saves this data and requests the AI ​​module to regenerate the plan again.

[0378] This will facilitate smooth mutual communication between childcare facilities and parents, making it possible to efficiently support children's growth and learning while also taking into consideration their emotions.

[0379] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0380] Step 1:

[0381] Users (parents) log in to the platform using their devices and input their child's individual information and requests. The input data may include requests such as "I want to improve my English grades, and I'm feeling a bit anxious." This input data is sent to the server.

[0382] Step 2:

[0383] The server receives the request data entered by the guardian and stores it in the user database. It then sends the guardian's input data to the emotion engine for emotion analysis. The emotion engine extracts the emotion data of "impatience" from the input sentence and stores it together with the request data. The input is the guardian's request data, and the output is the request data and emotion data.

[0384] Step 3:

[0385] The server calls the AI ​​module and provides the request data, the child's current situation data, and emotional data as input data. Specifically, the server passes the request, "I want to improve my English grades," the emotional data, "impatience," and the child's current English grades to the AI ​​module. The AI ​​module generates optimal learning plans and activity plans based on this data. The inputs are the request data, emotional data, and current situation data, and the output is the generated learning plan.

[0386] Step 4:

[0387] The server stores the generated plan in a database and notifies both parents and teachers. The notification includes details of the new lesson and activity plan. The input is the generated lesson plan and the output is a notification message.

[0388] Step 5:

[0389] The after-school care instructor uses a terminal to input the children's daily learning status and activities. For example, they might input, "Today we had a special English lesson, and the children actively participated." This data is sent to the server. The input is a record of daily activities, and the output is activity data that is saved on the server.

[0390] Step 6:

[0391] The server stores the received activity data in an activity record database. It then uses an emotion engine to analyze the child's emotions from the input text, extracts positive emotion data such as "active," and stores this in the database. The input is activity data, and the output is an activity record and emotion data.

[0392] Step 7:

[0393] The server uses an AI module to automatically generate a report for parents based on daily activity records and parental requests and emotional data. Specifically, the server generates a report such as, "Results of this week's special English instruction: Grades improved by 3 points. Next time, listening skills will be strengthened. The child is actively participating, so let's keep it up." The input is the activity record, request data, and emotional data, and the output is an automatically generated report.

[0394] Step 8:

[0395] The server sends the generated report to the parent's terminal, where the parent checks it. The input is the automatically generated report, and the output is the report displayed on the parent's terminal.

[0396] Step 9:

[0397] The user (parent) inputs feedback based on the report. For example, they might say, "I want you to improve not only your English listening skills but also your speaking skills. I'm a little worried." This feedback is sent to the server.

[0398] Step 10:

[0399] The server receives the input feedback and analyzes it through the emotion engine. The emotion engine extracts the emotion data of "anxiety" and stores it together with the feedback in a database. The input is the feedback data, and the output is the feedback and emotion data.

[0400] Step 11:

[0401] The server triggers an update of the learning plan as needed. Based on newly added feedback, the server requests the AI ​​module to regenerate the plan again. This allows the child's learning plan to adapt to their latest needs and emotions. The input is feedback and emotional data, and the output is an updated learning plan.

[0402] (Application example 2)

[0403] 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."

[0404] Conventional communication between after-school daycare centers and parents has been difficult to accurately reflect parents' requests and feedback, requiring a great deal of effort and time to create learning and activity plans and to report daily activity records. Furthermore, communication is often insufficient due to a lack of consideration for the feelings of parents and children. Furthermore, the lack of a system that provides learning support functions using virtual reality makes it difficult to effectively stimulate children's motivation to learn. The purpose of this invention is to solve these problems and realize smooth communication between parents and daycare centers and effective learning support.

[0405] 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.

[0406] In this invention, the server includes means for including an artificial intelligence model for automatically generating a learning plan, means for receiving requests and feedback from parents, means for automatically generating reports to parents, means for providing a platform for communication between the daycare center and parents, means for providing learning support functions using virtual reality, means for analyzing and saving the emotions of parents and children using an emotion analysis engine, and means for recording children's learning plans and daily activities via a head-mounted display. This facilitates smooth two-way communication between parents and the after-school daycare center, enabling the provision of accurate learning plans based on parents' requests and children's learning status, responses that take emotions into consideration, and automatic generation of reports. Furthermore, learning using virtual reality can effectively stimulate children's motivation to learn and provide comprehensive learning support.

[0407] The "artificial intelligence model for automatically generating learning plans" is an artificial intelligence technology that automatically creates optimal learning plans based on requests from parents and data on the child's learning situation.

[0408] "Means for receiving requests and feedback from parents" refers to an interface and system that allows parents to input requests and feedback regarding learning plans and their children's learning progress.

[0409] The "means for automatically generating reports for parents" is a system that automatically creates reports for parents based on the child's learning situation and activity records.

[0410] "Means for providing a platform for communication between childcare facilities and parents" refers to an online platform that allows childcare facility staff and parents to communicate with each other and share information.

[0411] "Means for providing learning support functions using virtual reality" refers to a system that utilizes virtual reality technology to provide educational content to children and assist them in their learning.

[0412] The "means for analyzing and storing the emotions of guardians and children using an emotion analysis engine" is a system that uses natural language processing technology to extract emotions from input data of guardians and children and stores them in a database.

[0413] "Means for recording a child's study plan and daily activities through a head-mounted display" refers to an interface and system for recording a child's study plan and daily activities using a head-mounted display.

[0414] The present invention is a learning support system that uses virtual reality technology to facilitate mutual communication between after-school daycare centers and parents, automatically generates learning plans and activity plans according to parents' requests, and automatically generates reports based on daily activity records. Furthermore, it is possible to respond in a way that takes into account the emotions of parents and children using an emotion analysis engine. This invention is implemented as follows.

[0415] First, the server contains an artificial intelligence model (e.g., TensorFlow, PyTorch) for automatically generating a learning plan. Parents use a smartphone or tablet to input their requests and feedback when setting up a learning plan. The server then analyzes the input text data using a sentiment analysis engine (e.g., Google Cloud Natural Language API) and also collects and stores the parents' emotional data.

[0416] Next, the server uses an AI module to automatically generate learning and activity plans based on requests from parents and data on the child's current situation and emotions. This allows it to propose the optimal plan based on the parent's requests and the child's learning situation. It also provides educational content to children using virtual reality technology (e.g., Oculus Rift, HTC Vive). This VR content can increase children's motivation to learn.

[0417] The after-school counselor inputs the children's daily learning plans and activities through a head-mounted display, and the server stores this data in a database. An emotion analysis engine is used to analyze the children's emotions from the input text, and this emotion data is also stored in the database. For example, if an after-school counselor inputs the content, "Today we had a special English lesson, and the children participated enthusiastically," the emotion analysis engine will extract the positive emotion of "enthusiastically."

[0418] The server uses an AI module to automatically generate reports for parents based on daily activity records and emotional data. For example, based on the progress of special English instruction, the child's improvement in grades, and emotional data such as "positive," the AI ​​could generate a report such as, "Results of this week's special English instruction: Grades improved by 3 points. Next time, listening skills will be strengthened. The child is actively participating, so let's keep it up," and the server would send the report to the parent's device.

[0419] After checking the report, parents can submit further requests or feedback. For example, if parents submit feedback such as, "I would like you to improve not only my child's English listening skills but also their speaking skills. I'm a little worried," the emotion analysis engine will extract the emotion of "anxiety," which the server will receive and store in the database, and update the learning plan as necessary.

[0420] Prompt Sentence Examples

[0421] "Please generate a study plan. The request is 'I want to improve my English grades,' the emotion is 'I'm anxious,' and the current grade is '70 points.'"

[0422] In this way, the learning support system using virtual reality of the present invention can facilitate smooth communication between parents and after-school daycare centers and effectively increase children's motivation to learn.

[0423] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0424] Step 1:

[0425] Gathering parents' requests

[0426] 1.1 Parents enter their requests for learning plans and activities using their smartphones or tablets.

[0427] 1.2 The parent's input data is sent to the server.

[0428] 1.3 The server uses an emotion analysis engine (e.g., Google Cloud Natural Language API) to analyze emotions from the text data and extract emotional data such as "anxiety" or "impatience."

[0429] Input: Text data entered by the parent

[0430] Output: Parental request data and analyzed emotion data

[0431] Step 2:

[0432] Storage of request data

[0433] 2.1 The server stores the request data and emotion data received from the parents in a database.

[0434] Input: Parental request data and emotion data

[0435] Output: Data stored in the database

[0436] Step 3:

[0437] Auto-generated learning plan

[0438] 3.1 The server inputs the saved parental request data, emotional data, and child learning status data into a generative AI model (e.g., TensorFlow, PyTorch).

[0439] 3.2 The generative AI model creates an optimal learning plan based on this data.

[0440] Input: Parental request data, emotional data, student learning status data

[0441] Output: Auto-generated learning plan

[0442] Step 4:

[0443] Plan Notification

[0444] 4.1 The server notifies the parents and after-school counselors of the generated learning plan.

[0445] 4.2 Parents and school counselors will review the notified learning plan.

[0446] Input: Auto-generated study plan

[0447] Output: Notification to parents and school counselors

[0448] Step 5:

[0449] Daily activity record

[0450] 5.1 After-school teachers will use head-mounted displays or devices to record the children's daily learning activities.

[0451] 5.2 The recorded data is sent to a server and an emotion analysis engine is used to analyze the child's emotions.

[0452] 5.3 The analyzed emotion data is stored in a database along with daily activity records.

[0453] Input: Learning activity data entered by the after-school instructor

[0454] Output: Activity records and emotion data stored in a database

[0455] Step 6:

[0456] Automatic report generation

[0457] 6.1 The server automatically generates reports for parents based on daily activity records and emotional data using a generative AI model.

[0458] Input: Daily activity records, emotional data

[0459] Output: Auto-generated parental report

[0460] Step 7:

[0461] Report notification

[0462] 7.1 The server sends the generated report to the parent's device.

[0463] 7.2 Parents will review the report and provide feedback as necessary.

[0464] Input: Auto-generated parent report

[0465] Output: Report notification to parent's device

[0466] Step 8:

[0467] Processing Feedback

[0468] 8.1 The feedback entered by the parent is sent to the server, and the emotion data is analyzed using the emotion analysis engine.

[0469] 8.2 The analyzed feedback data and emotion data are stored in a database. New learning plans are generated as needed.

[0470] Input: Parent feedback data

[0471] Output: Feedback data and emotion data stored in a database, a new learning plan

[0472] In this way, this system supports effective communication between parents and after-school daycare centers, and makes it possible to comprehensively manage and support children's learning.

[0473] 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.

[0474] 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.

[0475] 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.

[0476] [Second embodiment]

[0477] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0478] 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.

[0479] 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).

[0480] 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.

[0481] 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.

[0482] 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).

[0483] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0484] 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.

[0485] 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.

[0486] 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.

[0487] 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.

[0488] 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."

[0489] This invention relates to a system that facilitates communication between after-school daycare centers and parents, creates learning plans and activity plans according to parents' requests, and automatically generates reports based on daily activity records. This system provides a platform where daycare centers and parents can exchange information with each other.

[0490] Program processing

[0491] Platform-wide overview

[0492] The server creates a platform accessible to after-school care centers and parents. This platform includes a user database, a learning plan creation module, an activity plan creation module, an automatic report generation module, and a communication module. This allows parents to easily input requests and feedback, and allows daycare centers to efficiently manage children's learning and activities.

[0493] Collecting user requests

[0494] The server receives the request data entered by the parents and stores it in a database. Parents then use their devices to enter their child's individual information and requests into the platform, allowing the daycare center to respond quickly to the parents' needs.

[0495] Specific examples

[0496] Parents can input requests such as "I want to improve my child's English grades" or "I'm aiming to improve my child's athletic ability" into the device.

[0497] The server receives these requests and stores a list of requests for each parent in a database.

[0498] Automatic generation of learning and activity plans

[0499] The server uses an AI module to create learning and activity plans based on the parents' requests and the child's current status data. The server retrieves the parents' requests and the child's current status data from the database and passes it to the AI ​​module, which analyzes it and generates the optimal plan.

[0500] Specific examples

[0501] The server passes the AI ​​module the request to "improve English grades" and the child's current English grades as input data.

[0502] The AI ​​creates a plan that includes, for example, special English lessons three times a week and the use of a learning app, and returns it to the server.

[0503] The server stores this plan in a database and notifies both parents and school counselors.

[0504] Collecting daily activity records

[0505] The server collects the daily learning and activity progress records entered by the school counselor and stores them in a database. The school counselor uses a terminal to enter the children's daily activities into the platform.

[0506] Specific examples

[0507] The after-school care instructor types into the terminal, "Today we had a special English lesson, and the children actively participated."

[0508] The server receives this and stores each child's activity record in a database.

[0509] Automatic report generation

[0510] The server uses the AI ​​module to automatically generate reports for parents based on daily activity records and parental requests. The server retrieves the necessary information from the database and passes it to the AI ​​module to request the creation of a report.

[0511] Specific examples

[0512] The server retrieves information from the database, such as the progress of special English instruction and changes in the child's grades, and passes it on to the AI ​​module.

[0513] Based on this, the AI ​​generates a report such as, "Results of this week's special English instruction: Grades improved by 3 points. Next time, we will focus on strengthening listening skills."

[0514] The server sends the report to the parent's device, where the parent can view it.

[0515] Processing Feedback

[0516] The server collects feedback from parents and stores new requests and improvements in a database.

[0517] Specific examples

[0518] Parents use the devices to enter feedback into the platform, such as "I would like you to improve not only your child's English listening skills but also their speaking skills."

[0519] The server receives this, stores it in a database, and triggers an update of the learning plan if necessary.

[0520] This will facilitate smooth communication between daycare centers and parents, improving the quality of childcare.

[0521] The processing flow will be explained below.

[0522] Step 1:

[0523] The server initializes the user database and registers the after-school care center's user information. Parents create their own accounts and log in to the platform using their devices.

[0524] Step 2:

[0525] Parents use the device to input their child's individual information (such as name, grade, and interests) and requests for learning and activities (such as subjects and activities they want to strengthen, special support, etc.) The server receives this input data and stores it in a database.

[0526] Step 3:

[0527] The server retrieves data on parents' requests and children's current status from the database and sends it to the AI ​​module, which then analyzes the data and generates optimal learning and activity plans.

[0528] Step 4:

[0529] The server stores the learning and activity plans generated by the AI ​​module in a database and notifies the parents and after-school counselors of the results on the platform, who can then view the plans on their own devices.

[0530] Step 5:

[0531] The after-school instructors use the terminals to input the children's learning status and daily activities. For example, they can enter information such as, "Today we had a special English lesson, and the children actively participated." The server stores these activity records in a database.

[0532] Step 6:

[0533] The server sends data to the AI ​​module based on daily activity records and parental requests, and the AI ​​module analyzes the data and automatically generates a report for parents.

[0534] Step 7:

[0535] The server stores the generated report for parents in a database and sends it to the parents' terminal, where the parents can check the report and enter feedback on the content.

[0536] Step 8:

[0537] The server receives the feedback entered by the parent on the device and stores it in a database. The server then checks the feedback and, if necessary, requests the AI ​​module to revise the learning plan or activity plan.

[0538] This will enable smooth communication between the nursery school and parents, and enable efficient support for children's growth and learning.

[0539] Example 1

[0540] 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."

[0541] There is a need for a system that can facilitate smooth communication between after-school care centers and parents, efficiently create learning and activity plans according to parents' requests, and automatically generate appropriate reports based on daily activity records.Currently, communication between daycare centers and parents is complicated and requires manual data management, so automating this process to improve efficiency is a challenge.

[0542] 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.

[0543] In this invention, the server includes means for collecting and storing parental request data in a database, means for automatically generating optimal learning plans and activity plans using a generative AI model based on the parental request data and the child's current status data, means for collecting and storing daily activity records entered by after-school care instructors in a database, means for automatically generating reports using a generative AI model based on the daily activity records and parental requests, means for sending reports to parents, and means for providing a platform for communication between the daycare center and parents. This makes it possible to respond quickly to parental requests and efficiently provide appropriate learning plans and activity plans. Furthermore, automatic generation of reports based on activity records reduces the burden on daycare centers and parents.

[0544] "Parental request data" refers to data that parents enter and send to the after-school care center specific requests regarding their child's learning and activities.

[0545] The "database" is a storage device that manages and stores all data used by the system, such as parental requests, children's current status, and daily activity records.

[0546] A "generative AI model" is an artificial intelligence algorithm that analyzes data on parents' requests and children's current situation, and automatically generates optimal learning plans, activity plans, and reports.

[0547] A "learning plan" is a plan that includes specific schedules and content for achieving a child's learning goals.

[0548] An "action plan" is a plan that includes specific activities and events to support a child's growth and development.

[0549] "Daily activity records" are data that are entered and recorded daily by after-school care instructors on the progress of children's learning and activities.

[0550] The "report" is a document that summarizes the results and status of children's learning and activities, created by a generative AI model based on daily activity records and parental requests.

[0551] The "platform" is the foundation of the entire system, providing an interface for daycare centers and parents to exchange information with each other and use the system's various functions.

[0552] A "server" is a computer device that serves as the core of the entire system and stores, processes, and communicates data.

[0553] "Terminal" means a computer device or mobile device used by parents and school counselors to access the system and input or receive data.

[0554] This invention relates to a system that facilitates communication between after-school care centers and parents, efficiently creates learning plans and activity plans according to parents' requests, and automatically generates reports based on daily activity records. This system provides a platform where daycare centers and parents can exchange information with each other.

[0555] The system operates as follows.

[0556] Hardware and software configuration

[0557] 1. Server

[0558] A server is a computer device that is the core of the entire system and stores, processes, and communicates data.

[0559] 2. Terminal

[0560] Terminals are computer devices or mobile devices that allow parents and school teachers to access the system and input or receive data.

[0561] 3. Database

[0562] The database is a storage device for managing and storing data such as parents' requests, children's current status, and daily activity records.

[0563] 4. Generative AI Models

[0564] The generative AI model is an artificial intelligence algorithm that analyzes data on parents' requests and children's current situation, and automatically generates optimal learning plans, activity plans, and reports.

[0565] Specific operation of the system

[0566] 1. Gathering parents' requests

[0567] User (parent): Uses a device to access the platform's request entry screen. For example, enter a request such as "I want to improve my child's English grades."

[0568] Device: Sends the request data entered by the parent to the server.

[0569] Server: Stores the received data in the user database.

[0570] 2. Automatic generation of learning and activity plans

[0571] Server: Retrieves parental request data and child current status data from the database.

[0572] Server: Passes the acquired data to the generative AI model.

[0573] Generative AI model: Analyzes parental requests and student data to generate optimal learning and activity plans.

[0574] Server: Saves the generated plan in a database and notifies parents and school counselors.

[0575] 3. Collecting daily activity records

[0576] User (school teacher): Uses a device to enter daily activity records into the platform. For example, they might enter, "Today we had a special English lesson, and the children actively participated."

[0577] Device: Sends activity record data to the server.

[0578] Server: Stores the received data in the activity record database.

[0579] 4. Automatic report generation

[0580] Server: Retrieves daily activity records and parental request data from the database.

[0581] Server: Passes the acquired data to the generative AI model and requests it to create a report.

[0582] Generative AI model: Generates a report based on input data. For example, it generates a report such as, "Results of this week's English special instruction: Grades improved by 3 points. Next time, listening skills will be improved."

[0583] Server: Sends the generated report to the parent's device.

[0584] 5. Processing Feedback

[0585] User (parent): Uses a device to input feedback into the platform. For example, they might say, "I would like you to improve not only your English listening skills but also your speaking skills."

[0586] Terminal: Sends feedback data.

[0587] Server: Stores the received feedback in a database and updates the learning plan as needed.

[0588] Prompt Sentence Examples

[0589] 1. "Based on the parents' requests, please create the best study plan to improve your child's English grades."

[0590] 2. "Please prepare a weekly activity report based on your daily activity records."

[0591] This is expected to facilitate smoother communication between daycare centers and parents, improving the quality of childcare.

[0592] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0593] Step 1: Enter and submit your request data

[0594] User (Parent): The parent uses a device to access the platform's request entry screen, where they enter their request, such as "I want to improve my child's English grades."

[0595] Input: Request data entered by the parent.

[0596] Device: Sends the request data entered by the parent to the server.

[0597] Output: The requested data sent to the server.

[0598] Specific operation: The request is entered into the device's user interface, the input is converted into JSON format, and an HTTP request is sent to the server.

[0599] Step 2: Save your request data

[0600] Server: The server stores the received request data in a database.

[0601] Input: Request data received from the terminal.

[0602] Data processing / data calculation: Analyze the requested data, associate it with the parent ID, and insert it into the database.

[0603] Output: Request data stored in a database.

[0604] What it does: The server parses the data it receives, associates it based on the parent's ID, runs SQL queries on it, and stores it in a database.

[0605] Step 3: Request to create a lesson plan / activity plan

[0606] Server: The server retrieves the parents' request data and the child's current status data from the database and passes them to the generative AI model.

[0607] Input: Request data and current status data stored in the database.

[0608] Data processing / data calculation: The desired data and current data are formatted in JSON format and passed to the generative AI model.

[0609] Output: The data passed to the generative AI model.

[0610] What happens: The server executes SQL queries to get the required data and generates HTTP requests to the AI ​​model.

[0611] Step 4: Create a lesson plan and activity plan

[0612] Generative AI model: A generative AI model generates lesson plans and activity plans based on input data.

[0613] Input: Request data and current status data passed from the server.

[0614] Data Computing: Perform natural language processing and analysis of data to generate optimal plans.

[0615] Output: lesson plan and activity plan.

[0616] Specific actions: The AI ​​model uses the given data to generate a specific plan, such as "special English instruction three times a week."

[0617] Step 5: Notify and save your plan

[0618] Server: The server saves the generated plan in a database and notifies parents and school counselors.

[0619] Input: The plan output from the generative AI model.

[0620] Data processing / data calculation: Inserting plan data into the database and sending notifications to parents and instructors.

[0621] Output: Plans stored in the database and notifications sent.

[0622] What happens: The server saves the new plan to the database and sends emails and push notifications.

[0623] Step 6: Enter your daily activity log

[0624] User (school teacher): Uses a device to enter daily activity records into the platform. For example, they might enter, "Today we had a special English lesson, and the children actively participated."

[0625] Input: Activity record data entered by after-school care instructors.

[0626] Device: Sends activity record data to the server.

[0627] Output: Activity data sent to the server.

[0628] Specific operation: The instructor fills in the data using the input form, and the terminal converts it into JSON format and sends it to the server.

[0629] Step 7: Save your activity log

[0630] Server: The server stores the received daily activity records in a database.

[0631] Input: Activity data received from the terminal.

[0632] Data processing / data calculation: Analyze activity record data and store it in association with the child's ID.

[0633] Output: Activity log data stored in a database.

[0634] What it does: The server parses the data it receives, associates it based on the child's ID, executes an SQL query, and saves it to a database.

[0635] Step 8: Request automatic generation of report

[0636] Server: The server retrieves daily activity records and parental request data from the database, passes them to the generative AI model, and requests it to create a report.

[0637] Input: Activity record data and request data stored in the database.

[0638] Data processing / data calculation: Activity records and request data are formatted into JSON format and passed to the generative AI model.

[0639] Output: The data passed to the generative AI model.

[0640] What happens: The server executes SQL queries to get the required data and generates HTTP requests to the AI ​​model.

[0641] Step 9: Generate reports

[0642] Generative AI model: A generative AI model generates a report based on input data.

[0643] Input: Activity log data and request data passed from the server.

[0644] Data Computing: Perform natural language processing and analysis of data to generate appropriate reports.

[0645] Output: Report data.

[0646] Specific operation: Based on the given data, the AI ​​model generates a report such as, "Results of this week's special English instruction: Grades improved by 3 points. Next time, listening skills will be improved."

[0647] Step 10: Submit your report

[0648] Server: The server sends the generated report to the parent's device.

[0649] Input: Report output from a generative AI model.

[0650] Data processing / data calculation: Convert report data into PDF format and send it.

[0651] Output: Report sent to parents.

[0652] Specific operation: The server formats the generated report and sends it to the parent's device via email or push notification.

[0653] Step 11: Enter and submit your feedback

[0654] User (Parent): Parents use their devices to input feedback into the platform, for example, "I would like you to improve not only my child's English listening skills but also their speaking skills."

[0655] Input: Feedback data entered by the parent.

[0656] Device: Sends feedback data to the server.

[0657] Output: Feedback data sent to the server.

[0658] Specific operation: Parents enter data using an input form, and the device converts it into JSON format and sends it to the server.

[0659] Step 12: Storing and processing feedback

[0660] Server: The server stores the received feedback in a database and updates the learning plan as needed.

[0661] Input: Feedback data received from the device.

[0662] Data processing / data calculation: Analyze the feedback data, associate it with the parent ID, insert it into the database, and ask the generative AI model to regenerate the plan again.

[0663] Output: Feedback data stored in a database and an updated learning plan.

[0664] Specific operation: The server analyzes the feedback data, stores it in a database, and asks the AI ​​model to regenerate a plan based on the new data.

[0665] (Application example 1)

[0666] 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."

[0667] In traditional brick-and-mortar stores, there are problems such as a lack of communication between members (or guardians) and store staff (e.g., educators and instructors), difficulty in responding to individual member needs, and the tedious manual management of daily activity records. These problems also lead to delays in creating reports and reflecting feedback, which can lead to issues such as lower member (or guardian) satisfaction.

[0668] 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.

[0669] In this invention, the server includes a means for including an artificial intelligence model for automatically generating learning plans, a means for receiving requests and feedback from parents, a means for automatically generating reports to parents, a means for store staff to store daily activity records in a database, and a means for providing a platform for communication between parents and store staff. This enables prompt response to requests from parents and members, automatic generation of individual learning and activity plans, and efficient management of daily activity records. In addition, the automatic generation of reports and immediate reflection of feedback are expected to improve satisfaction among parents and members.

[0670] The "artificial intelligence model for automatically generating learning plans" is an artificial intelligence program that analyzes the requests and progress data of members and parents, and automatically generates optimal learning or activity plans.

[0671] The "means for receiving requests and feedback from parents" refers to a user interface and communication means for parents to input requests and feedback and send them to the server.

[0672] The "means for automatically generating reports to parents" is a system that uses an AI module to automatically generate reports on a regular basis based on daily activity records and requested data, and notifies parents.

[0673] The "means for store staff to store daily activity records in a database" refers to a user interface and communication means that allows store staff to input details of members' daily activities and store that data in a database.

[0674] "Means for providing a platform for parents and store staff to communicate" refers to an online platform that allows parents and store staff to exchange information in both directions.

[0675] The "database for storing data entered by guardians and members" refers to a database system for safely storing and managing requests, feedback, and other individual information entered by guardians and members.

[0676] "Means for storing members' daily activity records in a database" refers to a system for storing detailed records of members' learning and activities in a digital format in a database.

[0677] This invention relates to a system that automatically generates customized learning plans and activity plans based on the requests of members and guardians at a physical store, collects and manages daily activity records based on the plans, and reports the records to guardians. An embodiment of this system is described in detail below.

[0678] System configuration

[0679] The system of the present invention includes the following major components:

[0680] 1. Server

[0681] Artificial intelligence model that automatically generates learning plans: An artificial intelligence program that analyzes the requests and progress data of members and parents to generate the optimal plan. This model uses a generative AI model.

[0682] Database: A database system such as Amazon RDS to store parent and member input data, activity records, requests, and feedback.

[0683] Communication module: A communication interface for exchanging information with parents and staff at physical stores. Data can be exchanged via the Internet.

[0684] 2. User Device

[0685] Parental interface: A smartphone app developed with React Native to provide a screen for entering requests, viewing progress reports, and providing feedback.

[0686] Staff interface: A smartphone app developed with React Native to provide an activity record entry screen and plan confirmation screen.

[0687] Program processing overview

[0688] 1. Request collection function

[0689] The server receives requests entered by parents through a smartphone app and stores them in a database. This function uses a user interface developed with React Native and a backend built with Node.js.

[0690] Example prompt sentence:

[0691] I want to improve my English grades. My current grades are below average.

[0692] 2. Automatic learning and activity plan generation function

[0693] The server collects parent requests and member progress data from the database, passes them to the GPT-4 API, and generates an optimal plan, which is then saved in the database and notified to parents and staff.

[0694] Example prompt sentence:

[0695] Parents' request: To improve English grades. Current grades are below average.

[0696] Current data: Study time is 3 hours per week and attend English classes twice a week.

[0697] 3. Activity record collection function

[0698] The server receives daily activity records entered by store staff through a smartphone app and stores the data in a database. This function also uses a user interface developed with React Native and a backend built with Node.js.

[0699] Example prompt sentence:

[0700] Today we had a special English lesson, and the children participated enthusiastically.

[0701] 4. Automatic report generation function

[0702] The server collects activity records and request data from the database, passes them to the GPT-4 API, and generates a report, which is stored in the database and notified to the parents.

[0703] Example prompt sentence:

[0704] This week's English tutoring results: My grade went up by 3 points. Next time I'll focus on strengthening my listening skills.

[0705] 5. Feedback processing function

[0706] The server receives feedback entered by parents through a smartphone app, stores it in a database, and triggers plan updates as needed.

[0707] Example prompt sentence:

[0708] I would like you to improve not only your English listening skills but also your speaking skills.

[0709] Example

[0710] The system of the present invention enables physical facilities such as fitness clubs and cram schools to efficiently plan and manage activity records based on the individual needs of their members. Cram schools can deepen relationships of trust with parents by monitoring members' learning progress and providing regular reports to parents. Fitness clubs can also improve member satisfaction by providing individual training plans and automatically generating progress reports.

[0711] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0712] Step 1:

[0713] The user inputs their request using a smartphone app. This input data is free-form text data such as "I want to improve my English grades" or "I'm aiming to improve my athletic ability." The app sends this data to the server. Input data: Parent's request (text format). Output data: Request data sent to the server.

[0714] Step 2:

[0715] The server receives the request data sent by the user (guardian) from the smartphone app and stores the data in a database. Input data: Request data. Output data: Request data stored in the database.

[0716] Step 3:

[0717] The server retrieves the member's current status data and the guardian's request data from the database, integrates these data, and passes them to the artificial intelligence model (GPT-4 API). Input data: Request data, current status data. Output data: Plan generation request.

[0718] Step 4:

[0719] The server's AI model analyzes the input data and generates optimal learning and activity plans based on the parents' requests. The generated plans are returned to the server and stored in a database. Input data: Request data, current situation data. Output data: Generated plans.

[0720] Step 5:

[0721] The server notifies parents and store staff of the generated plan using a notification message containing the plan details. Input data: Generated plan. Output data: Notification message sent to parents and staff.

[0722] Step 6:

[0723] Users (store staff) use a smartphone app to enter their daily activity records and send the data to the server. Input data: Activity records (text format). Output data: Activity records sent to the server.

[0724] Step 7:

[0725] The server receives the activity record data sent by the user (staff member) and stores the data in the database. Input data: Activity record. Output data: Activity record stored in the database.

[0726] Step 8:

[0727] The server retrieves activity records and parental request data from the database and passes this data to an artificial intelligence model (GPT-4 API) to generate a report. Input data: Activity records, request data. Output data: Report generation request.

[0728] Step 9:

[0729] The server's AI model analyzes this data and automatically generates a report for parents. The generated report is returned to the server and stored in a database. Input data: Activity records, request data. Output data: Generated report.

[0730] Step 10:

[0731] The server notifies the parent of the generated report using a notification message containing the report. Input data: The generated report. Output data: The notification message sent to the parent.

[0732] Step 11:

[0733] The user (parent) uses a smartphone app to input feedback and sends the data to the server. Input data: Feedback (text format). Output data: Feedback data sent to the server.

[0734] Step 12:

[0735] The server receives feedback data sent by users (parents), stores the data in a database, and triggers plan updates as needed. Input data: Feedback data. Output data: Feedback data stored in the database, plan update requests.

[0736] 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.

[0737] This invention relates to a system that facilitates communication between after-school daycare centers and parents, creates learning plans and activity plans according to parents' requests, and automatically generates reports based on daily activity records. Furthermore, by combining this invention with an emotion engine that recognizes the user's emotions, it becomes possible to respond in a way that takes into consideration the emotions of parents and children.

[0738] Program processing

[0739] Platform-wide overview

[0740] The server creates a platform accessible to after-school care centers and parents. This platform includes a user database, a learning plan creation module, an activity plan creation module, an automatic report generation module, an emotion engine, and a communication module. This allows parents to easily input requests and feedback, and allows daycare centers to efficiently manage children's learning and activities.

[0741] Collecting user requests

[0742] The server receives the request data entered by the parent and stores it in a database. It then analyzes the parent's emotional data through an emotion engine and stores it together with the request data. Parents use their devices to enter their child's individual information and requests into the platform. The emotion engine analyzes emotions from the wording and tone of the input.

[0743] Specific examples

[0744] The parent types into the device, "I want to improve my child's English grades, and I'm feeling a bit anxious."

[0745] The server receives these requests, extracts the emotional data of "anxiety" using an emotion engine, and saves it in a request list for each parent.

[0746] Automatic generation of learning and activity plans

[0747] The server uses an AI module to create learning and activity plans based on the parents' requests, the child's current status data, and emotional data. By referring to the emotional data, it is possible to generate plans that take the child's needs into consideration.

[0748] Specific examples

[0749] The server passes the AI ​​module the request to "improve English grades," the emotional data of "anxiety," and the child's current English grades as input data.

[0750] For example, the AI ​​takes into account the child's impatience and creates a plan that includes special English lessons three times a week at a comfortable pace and the use of a learning app, and returns the plan to the server.

[0751] The server stores this plan in a database and notifies both parents and school counselors.

[0752] Collecting daily activity records

[0753] The teacher uses a terminal to input information about the child's learning status and daily activities, which is then stored in a database on the server. The emotion engine analyzes the child's emotions from the input text and stores this data in the database.

[0754] Specific examples

[0755] The after-school teacher types into the terminal, "Today we had a special English lesson, and the children participated actively." The emotion engine extracts the positive emotion of "active."

[0756] The server receives this and stores each child's activity record and emotional data in a database.

[0757] Automatic report generation

[0758] The server uses an AI module to automatically generate reports for parents based on daily activity records, as well as parents' requests and emotional data.

[0759] Specific examples

[0760] The server extracts information from the database about the progress of special English instruction, changes in the child's grades, and emotional data such as "positive," and passes this data to the AI ​​module.

[0761] Based on this, the AI ​​generates a report that reads, "Results of this week's special English instruction: Grades improved by 3 points. Next time, we will strengthen listening skills. The child is actively participating, so let's keep it up."

[0762] The server sends the report to the parent's device, where the parent can view it.

[0763] Processing Feedback

[0764] The server collects feedback from parents, analyzes the emotional data through an emotion engine, and stores it in a database along with any new requests or improvements.

[0765] Specific examples

[0766] Parents can input feedback into the device, such as, "I want my child to improve not only their English listening skills but also their speaking skills. I'm a little worried." The emotion engine then extracts the emotion of "anxiety."

[0767] The server receives this, stores it in a database, and triggers an update of the learning plan if necessary.

[0768] This will facilitate smooth mutual communication between the nursery school and parents, and make it possible to efficiently support children's growth and learning while also taking into consideration their emotions.

[0769] The processing flow will be explained below.

[0770] Step 1:

[0771] The server initializes the user database and registers the after-school care center's user information. Parents create their own accounts and log in to the platform using their devices.

[0772] Step 2:

[0773] Parents use the device to input their child's individual information (such as name, grade, and interests) and requests for learning and activities (such as subjects and activities to reinforce, special support, etc.). The emotion engine analyzes emotions from the input text and generates emotion data. The server receives this input data and emotion data and stores it in a database.

[0774] Step 3:

[0775] The server retrieves data on parents' requests, children's current status, and emotional data from the database and sends them to the AI ​​module, which analyzes the data and generates optimal learning and activity plans.

[0776] Step 4:

[0777] The server stores the learning and activity plans generated by the AI ​​module in a database and notifies the parents and after-school counselors of the results on the platform, who can then view the plans on their own devices.

[0778] Step 5:

[0779] After-school counselors use the terminals to input information about the children's learning status and daily activities. The emotion engine analyzes the children's emotions from the input text, and the server stores this information in a database.

[0780] Specific examples

[0781] An after-school teacher inputs, "Today we had a special English lesson, and the children participated actively." The emotion engine extracts the positive emotion of "active."

[0782] The server receives this and stores each child's activity record and emotional data in a database.

[0783] Step 6:

[0784] The server sends data to the AI ​​module based on daily activity records and parents' requests and emotional data to automatically generate reports. The AI ​​module analyzes the data and automatically generates reports for parents.

[0785] Specific examples

[0786] The server retrieves information from the database, such as progress in special English instruction, changes in the child's grades, and emotional data, and passes it on to the AI ​​module.

[0787] Based on this, the AI ​​generates a report that reads, "Results of this week's special English instruction: Grades improved by 3 points. Next time, we will strengthen listening skills. The child is actively participating, so let's keep it up."

[0788] The server sends the report to the parent's device, where the parent can view it.

[0789] Step 7:

[0790] Parents can use a device to check the report and enter their feedback on the content, and the emotion engine analyzes the emotional data from the feedback.

[0791] Specific examples

[0792] A parent enters feedback such as, "I want my child to improve not only his English listening skills but also his speaking skills. I'm a little worried." The emotion engine analyzes this and extracts the emotion data of "anxiety."

[0793] The server receives this and stores it in a database.

[0794] Step 8:

[0795] The server analyzes the feedback and emotional data from parents, and if necessary, requests the AI ​​module to update the learning plan and activity plan. The server then notifies the after-school instructor of the updated plan.

[0796] Specific examples

[0797] Based on the feedback, the server inputs into the AI ​​module the new request to "improve speaking skills" and the parent's emotional data of "anxiety."

[0798] The AI ​​module creates a new lesson plan and returns it to the server, which stores it in a database and notifies the after-school teacher.

[0799] As described above, the present invention facilitates mutual communication between daycare centers and parents, and can efficiently support children's growth and learning while taking into consideration their emotions.

[0800] Example 2

[0801] 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."

[0802] In modern society, with the increase in dual-income households, there is a need for smoother communication between parents and after-school daycare centers. Conventional systems make it difficult for parents to create learning and activity plans that meet their needs, and are therefore unable to adequately support children's development. Furthermore, there is a lack of consideration for the feelings of parents and children, making it difficult to provide an optimal learning environment.

[0803] 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.

[0804] In this invention, the server includes means including an artificial intelligence model for automatically generating learning plans and activity plans, means for receiving requests and feedback from parents, means including an emotion engine for analyzing input data and emotion data from parents, means for automatically generating reports to parents, and means for providing a platform for communication between the childcare facility and parents. This enables the creation of learning plans and activity plans based on parents' requests and appropriate responses that take into consideration the emotions of parents and children.

[0805] An "artificial intelligence model for automatically generating learning plans and activity plans" is a system that includes an algorithm for automatically creating optimal learning plans and activity plans based on the requests of parents and current data on the child.

[0806] "Means for receiving requests and feedback from parents" refers to a function that includes an interface for receiving and processing requests and feedback that parents input into the system.

[0807] The "emotion engine that analyzes parental input data and emotional data" is an analysis engine that analyzes emotions from data entered by parents and utilizes that information within the system.

[0808] "Means for automatically generating reports for parents" is a function that automatically creates reports for parents based on the child's daily activity records and progress data for the learning plan.

[0809] The "platform for communication between childcare facilities and parents" is a system that provides an online platform for childcare facilities and parents to communicate efficiently.

[0810] The "database" is a storage device that centrally stores and manages various information used by the system, such as parental input data, emotional data, and records of children's daily activities.

[0811] This invention is a system that facilitates communication between after-school care facilities and parents, creates learning plans and activity plans according to parents' requests, and automatically generates reports based on daily activity records. Furthermore, by combining it with an emotion engine that recognizes the user's emotions, it is possible to respond in a way that takes into consideration the emotions of parents and children.

[0812] Building a platform

[0813] The server creates a platform accessible to after-school care facilities and parents. This platform includes a user database, a learning plan creation module, an activity plan creation module, an automatic report generation module, an emotion engine, and a communication module. This allows parents to easily input requests and feedback, and enables after-school care facilities to efficiently manage children's learning and activities.

[0814] Collecting user requests

[0815] Users (guardians) can log in to the platform using their devices and enter their child's individual information and requests. The server receives the request data entered by the guardian and stores it in the user database. It also analyzes the guardian's emotional data through an emotion engine and stores it together with the request data.

[0816] Examples:

[0817] Parents can input requests such as, "I want to improve my child's English grades, and I'm feeling a bit anxious." The server receives these requests, extracts the emotion data for "anxiety" using an emotion engine, and saves it in a request list for each parent.

[0818] Automatic generation of learning and activity plans

[0819] The server uses an AI module to create learning and activity plans based on the parents' requests, the child's current status data, and emotional data. By referring to the emotional data, it is possible to generate plans that take the child's needs into consideration.

[0820] Examples:

[0821] The server passes the AI ​​module the request to "improve English grades," emotional data on "impatience," and the child's current English grades as input data. The AI ​​takes "impatience" into account, for example, to create a plan that incorporates special English lessons three times a week at a comfortable pace and the use of a learning app, and returns it to the server. The server stores this plan in a database and notifies both the parents and the after-school care instructor.

[0822] Collecting daily activity records

[0823] After-school instructors use terminals to input the children's daily learning status and activities. The server receives the input data and stores it in an activity record database. The emotion engine analyzes the children's emotions from the input text and stores this data in the database as well.

[0824] Examples:

[0825] The after-school teacher types into the terminal, "Today we had a special English lesson, and the children participated actively." The emotion engine extracts the positive emotion of "active." The server receives this and stores each child's activity record and emotional data in a database.

[0826] Automatic report generation

[0827] The server uses an AI module to automatically generate reports for parents based on daily activity records, as well as parents' requests and emotional data.

[0828] Examples:

[0829] The server extracts data from the database, including the progress of the English special instruction, changes in the child's grades, and emotional data such as "positive," and passes it to the AI ​​module. Based on this, the AI ​​generates a report that reads, "Results of this week's English special instruction: Grades improved by 3 points. Next time, we will strengthen listening skills. The child is actively participating, so let's keep it up." The server then sends the report to the parent's device, where the parent can review it.

[0830] Processing Feedback

[0831] The user (parent) enters feedback based on the report. The server receives the entered feedback and analyzes it through the emotion engine. The server extracts the emotion data of "anxiety" and stores it in the database along with the feedback. The server triggers an update of the learning plan as needed.

[0832] Examples:

[0833] The parent inputs feedback on the device, such as "I want my child to improve not only his English listening skills but also his speaking skills. I'm a little anxious." The server receives this, and the emotion engine extracts the emotion of "anxiety." The server saves this data and requests the AI ​​module to regenerate the plan again.

[0834] This will facilitate smooth mutual communication between childcare facilities and parents, making it possible to efficiently support children's growth and learning while also taking into consideration their emotions.

[0835] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0836] Step 1:

[0837] Users (parents) log in to the platform using their devices and input their child's individual information and requests. The input data may include requests such as "I want to improve my English grades, and I'm feeling a bit anxious." This input data is sent to the server.

[0838] Step 2:

[0839] The server receives the request data entered by the guardian and stores it in the user database. It then sends the guardian's input data to the emotion engine for emotion analysis. The emotion engine extracts the emotion data of "impatience" from the input sentence and stores it together with the request data. The input is the guardian's request data, and the output is the request data and emotion data.

[0840] Step 3:

[0841] The server calls the AI ​​module and provides the request data, the child's current situation data, and emotional data as input data. Specifically, the server passes the request, "I want to improve my English grades," the emotional data, "impatience," and the child's current English grades to the AI ​​module. The AI ​​module generates optimal learning plans and activity plans based on this data. The inputs are the request data, emotional data, and current situation data, and the output is the generated learning plan.

[0842] Step 4:

[0843] The server stores the generated plan in a database and notifies both parents and teachers. The notification includes details of the new lesson and activity plan. The input is the generated lesson plan and the output is a notification message.

[0844] Step 5:

[0845] The after-school care instructor uses a terminal to input the children's daily learning status and activities. For example, they might input, "Today we had a special English lesson, and the children actively participated." This data is sent to the server. The input is a record of daily activities, and the output is activity data that is saved on the server.

[0846] Step 6:

[0847] The server stores the received activity data in an activity record database. It then uses an emotion engine to analyze the child's emotions from the input text, extracts positive emotion data such as "active," and stores this in the database. The input is activity data, and the output is an activity record and emotion data.

[0848] Step 7:

[0849] The server uses an AI module to automatically generate a report for parents based on daily activity records and parental requests and emotional data. Specifically, the server generates a report such as, "Results of this week's special English instruction: Grades improved by 3 points. Next time, listening skills will be strengthened. The child is actively participating, so let's keep it up." The input is the activity record, request data, and emotional data, and the output is an automatically generated report.

[0850] Step 8:

[0851] The server sends the generated report to the parent's terminal, where the parent checks it. The input is the automatically generated report, and the output is the report displayed on the parent's terminal.

[0852] Step 9:

[0853] The user (parent) inputs feedback based on the report. For example, they might say, "I want you to improve not only your English listening skills but also your speaking skills. I'm a little worried." This feedback is sent to the server.

[0854] Step 10:

[0855] The server receives the input feedback and analyzes it through the emotion engine. The emotion engine extracts the emotion data of "anxiety" and stores it together with the feedback in a database. The input is the feedback data, and the output is the feedback and emotion data.

[0856] Step 11:

[0857] The server triggers an update of the learning plan as needed. Based on newly added feedback, the server requests the AI ​​module to regenerate the plan again. This allows the child's learning plan to adapt to their latest needs and emotions. The input is feedback and emotional data, and the output is an updated learning plan.

[0858] (Application example 2)

[0859] 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."

[0860] Conventional communication between after-school daycare centers and parents has been difficult to accurately reflect parents' requests and feedback, requiring a great deal of effort and time to create learning and activity plans and to report daily activity records. Furthermore, communication is often insufficient due to a lack of consideration for the feelings of parents and children. Furthermore, the lack of a system that provides learning support functions using virtual reality makes it difficult to effectively stimulate children's motivation to learn. The purpose of this invention is to solve these problems and realize smooth communication between parents and daycare centers and effective learning support.

[0861] 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.

[0862] In this invention, the server includes means for including an artificial intelligence model for automatically generating a learning plan, means for receiving requests and feedback from parents, means for automatically generating reports to parents, means for providing a platform for communication between the daycare center and parents, means for providing learning support functions using virtual reality, means for analyzing and saving the emotions of parents and children using an emotion analysis engine, and means for recording children's learning plans and daily activities via a head-mounted display. This facilitates smooth two-way communication between parents and the after-school daycare center, enabling the provision of accurate learning plans based on parents' requests and children's learning status, responses that take emotions into consideration, and automatic generation of reports. Furthermore, learning using virtual reality can effectively stimulate children's motivation to learn and provide comprehensive learning support.

[0863] The "artificial intelligence model for automatically generating learning plans" is an artificial intelligence technology that automatically creates optimal learning plans based on requests from parents and data on the child's learning situation.

[0864] "Means for receiving requests and feedback from parents" refers to an interface and system that allows parents to input requests and feedback regarding learning plans and their children's learning progress.

[0865] The "means for automatically generating reports for parents" is a system that automatically creates reports for parents based on the child's learning situation and activity records.

[0866] "Means for providing a platform for communication between childcare facilities and parents" refers to an online platform that allows childcare facility staff and parents to communicate with each other and share information.

[0867] "Means for providing learning support functions using virtual reality" refers to a system that utilizes virtual reality technology to provide educational content to children and assist them in their learning.

[0868] The "means for analyzing and storing the emotions of guardians and children using an emotion analysis engine" is a system that uses natural language processing technology to extract emotions from input data of guardians and children and stores them in a database.

[0869] "Means for recording a child's study plan and daily activities through a head-mounted display" refers to an interface and system for recording a child's study plan and daily activities using a head-mounted display.

[0870] The present invention is a learning support system that uses virtual reality technology to facilitate mutual communication between after-school daycare centers and parents, automatically generates learning plans and activity plans according to parents' requests, and automatically generates reports based on daily activity records. Furthermore, it is possible to respond in a way that takes into account the emotions of parents and children using an emotion analysis engine. This invention is implemented as follows.

[0871] First, the server contains an artificial intelligence model (e.g., TensorFlow, PyTorch) for automatically generating a learning plan. Parents use a smartphone or tablet to input their requests and feedback when setting up a learning plan. The server then analyzes the input text data using a sentiment analysis engine (e.g., Google Cloud Natural Language API) and also collects and stores the parents' emotional data.

[0872] Next, the server uses an AI module to automatically generate learning and activity plans based on requests from parents and data on the child's current situation and emotions. This allows it to propose the optimal plan based on the parent's requests and the child's learning situation. It also provides educational content to children using virtual reality technology (e.g., Oculus Rift, HTC Vive). This VR content can increase children's motivation to learn.

[0873] The after-school counselor inputs the children's daily learning plans and activities through a head-mounted display, and the server stores this data in a database. An emotion analysis engine is used to analyze the children's emotions from the input text, and this emotion data is also stored in the database. For example, if an after-school counselor inputs the content, "Today we had a special English lesson, and the children participated enthusiastically," the emotion analysis engine will extract the positive emotion of "enthusiastically."

[0874] The server uses an AI module to automatically generate reports for parents based on daily activity records and emotional data. For example, based on the progress of special English instruction, the child's improvement in grades, and emotional data such as "positive," the AI ​​could generate a report such as, "Results of this week's special English instruction: Grades improved by 3 points. Next time, listening skills will be strengthened. The child is actively participating, so let's keep it up," and the server would send the report to the parent's device.

[0875] After checking the report, parents can submit further requests or feedback. For example, if parents submit feedback such as, "I would like you to improve not only my child's English listening skills but also their speaking skills. I'm a little worried," the emotion analysis engine will extract the emotion of "anxiety," which the server will receive and store in the database, and update the learning plan as necessary.

[0876] Prompt Sentence Examples

[0877] "Please generate a study plan. The request is 'I want to improve my English grades,' the emotion is 'I'm anxious,' and the current grade is '70 points.'"

[0878] In this way, the learning support system using virtual reality of the present invention can facilitate smooth communication between parents and after-school daycare centers and effectively increase children's motivation to learn.

[0879] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0880] Step 1:

[0881] Gathering parents' requests

[0882] 1.1 Parents enter their requests for learning plans and activities using their smartphones or tablets.

[0883] 1.2 The parent's input data is sent to the server.

[0884] 1.3 The server uses an emotion analysis engine (e.g., Google Cloud Natural Language API) to analyze emotions from the text data and extract emotional data such as "anxiety" or "impatience."

[0885] Input: Text data entered by the parent

[0886] Output: Parental request data and analyzed emotion data

[0887] Step 2:

[0888] Storage of request data

[0889] 2.1 The server stores the request data and emotion data received from the parents in a database.

[0890] Input: Parental request data and emotion data

[0891] Output: Data stored in the database

[0892] Step 3:

[0893] Auto-generated learning plan

[0894] 3.1 The server inputs the saved parental request data, emotional data, and child learning status data into a generative AI model (e.g., TensorFlow, PyTorch).

[0895] 3.2 The generative AI model creates an optimal learning plan based on this data.

[0896] Input: Parental request data, emotional data, student learning status data

[0897] Output: Auto-generated learning plan

[0898] Step 4:

[0899] Plan Notification

[0900] 4.1 The server notifies the parents and after-school counselors of the generated learning plan.

[0901] 4.2 Parents and school counselors will review the notified learning plan.

[0902] Input: Auto-generated study plan

[0903] Output: Notification to parents and school counselors

[0904] Step 5:

[0905] Daily activity record

[0906] 5.1 After-school teachers will use head-mounted displays or devices to record the children's daily learning activities.

[0907] 5.2 The recorded data is sent to a server and an emotion analysis engine is used to analyze the child's emotions.

[0908] 5.3 The analyzed emotion data is stored in a database along with daily activity records.

[0909] Input: Learning activity data entered by the after-school instructor

[0910] Output: Activity records and emotion data stored in a database

[0911] Step 6:

[0912] Automatic report generation

[0913] 6.1 The server automatically generates reports for parents based on daily activity records and emotional data using a generative AI model.

[0914] Input: Daily activity records, emotional data

[0915] Output: Auto-generated parental report

[0916] Step 7:

[0917] Report notification

[0918] 7.1 The server sends the generated report to the parent's device.

[0919] 7.2 Parents will review the report and provide feedback as necessary.

[0920] Input: Auto-generated parent report

[0921] Output: Report notification to parent's device

[0922] Step 8:

[0923] Processing Feedback

[0924] 8.1 The feedback entered by the parent is sent to the server, and the emotion data is analyzed using the emotion analysis engine.

[0925] 8.2 The analyzed feedback data and emotion data are stored in a database. New learning plans are generated as needed.

[0926] Input: Parent feedback data

[0927] Output: Feedback data and emotion data stored in a database, a new learning plan

[0928] In this way, this system supports effective communication between parents and after-school daycare centers, and makes it possible to comprehensively manage and support children's learning.

[0929] 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.

[0930] 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.

[0931] 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.

[0932] [Third embodiment]

[0933] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0934] 5, the data processing system 310 includes the data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0935] 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).

[0936] 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.

[0937] 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.

[0938] 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).

[0939] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0940] 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.

[0941] 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.

[0942] 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.

[0943] 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.

[0944] 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."

[0945] This invention relates to a system that facilitates communication between after-school daycare centers and parents, creates learning plans and activity plans according to parents' requests, and automatically generates reports based on daily activity records. This system provides a platform where daycare centers and parents can exchange information with each other.

[0946] Program processing

[0947] Platform-wide overview

[0948] The server creates a platform accessible to after-school care centers and parents. This platform includes a user database, a learning plan creation module, an activity plan creation module, an automatic report generation module, and a communication module. This allows parents to easily input requests and feedback, and allows daycare centers to efficiently manage children's learning and activities.

[0949] Collecting user requests

[0950] The server receives the request data entered by the parents and stores it in a database. Parents then use their devices to enter their child's individual information and requests into the platform, allowing the daycare center to respond quickly to the parents' needs.

[0951] Specific examples

[0952] Parents can input requests such as "I want to improve my child's English grades" or "I'm aiming to improve my child's athletic ability" into the device.

[0953] The server receives these requests and stores a list of requests for each parent in a database.

[0954] Automatic generation of learning and activity plans

[0955] The server uses an AI module to create learning and activity plans based on the parents' requests and the child's current status data. The server retrieves the parents' requests and the child's current status data from the database and passes it to the AI ​​module, which analyzes it and generates the optimal plan.

[0956] Specific examples

[0957] The server passes the AI ​​module the request to "improve English grades" and the child's current English grades as input data.

[0958] The AI ​​creates a plan that includes, for example, special English lessons three times a week and the use of a learning app, and returns it to the server.

[0959] The server stores this plan in a database and notifies both parents and school counselors.

[0960] Collecting daily activity records

[0961] The server collects the daily learning and activity progress records entered by the school counselor and stores them in a database. The school counselor uses a terminal to enter the children's daily activities into the platform.

[0962] Specific examples

[0963] The after-school care instructor types into the terminal, "Today we had a special English lesson, and the children actively participated."

[0964] The server receives this and stores each child's activity record in a database.

[0965] Automatic report generation

[0966] The server uses the AI ​​module to automatically generate reports for parents based on daily activity records and parental requests. The server retrieves the necessary information from the database and passes it to the AI ​​module to request the creation of a report.

[0967] Specific examples

[0968] The server retrieves information from the database, such as the progress of special English instruction and changes in the child's grades, and passes it on to the AI ​​module.

[0969] Based on this, the AI ​​generates a report such as, "Results of this week's special English instruction: Grades improved by 3 points. Next time, we will focus on strengthening listening skills."

[0970] The server sends the report to the parent's device, where the parent can view it.

[0971] Processing Feedback

[0972] The server collects feedback from parents and stores new requests and improvements in a database.

[0973] Specific examples

[0974] Parents use the devices to enter feedback into the platform, such as "I would like you to improve not only your child's English listening skills but also their speaking skills."

[0975] The server receives this, stores it in a database, and triggers an update of the learning plan if necessary.

[0976] This will facilitate smooth communication between daycare centers and parents, improving the quality of childcare.

[0977] The processing flow will be explained below.

[0978] Step 1:

[0979] The server initializes the user database and registers the after-school care center's user information. Parents create their own accounts and log in to the platform using their devices.

[0980] Step 2:

[0981] Parents use the device to input their child's individual information (such as name, grade, and interests) and requests for learning and activities (such as subjects and activities they want to strengthen, special support, etc.) The server receives this input data and stores it in a database.

[0982] Step 3:

[0983] The server retrieves data on parents' requests and children's current status from the database and sends it to the AI ​​module, which then analyzes the data and generates optimal learning and activity plans.

[0984] Step 4:

[0985] The server stores the learning and activity plans generated by the AI ​​module in a database and notifies the parents and after-school counselors of the results on the platform, who can then view the plans on their own devices.

[0986] Step 5:

[0987] The after-school instructors use the terminals to input the children's learning status and daily activities. For example, they can enter information such as, "Today we had a special English lesson, and the children actively participated." The server stores these activity records in a database.

[0988] Step 6:

[0989] The server sends data to the AI ​​module based on daily activity records and parental requests, and the AI ​​module analyzes the data and automatically generates a report for parents.

[0990] Step 7:

[0991] The server stores the generated report for parents in a database and sends it to the parents' terminal, where the parents can check the report and enter feedback on the content.

[0992] Step 8:

[0993] The server receives the feedback entered by the parent on the device and stores it in a database. The server then checks the feedback and, if necessary, requests the AI ​​module to revise the learning plan or activity plan.

[0994] This will enable smooth communication between the nursery school and parents, and enable efficient support for children's growth and learning.

[0995] Example 1

[0996] 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."

[0997] There is a need for a system that can facilitate smooth communication between after-school care centers and parents, efficiently create learning and activity plans according to parents' requests, and automatically generate appropriate reports based on daily activity records.Currently, communication between daycare centers and parents is complicated and requires manual data management, so automating this process to improve efficiency is a challenge.

[0998] 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.

[0999] In this invention, the server includes means for collecting and storing parental request data in a database, means for automatically generating optimal learning plans and activity plans using a generative AI model based on the parental request data and the child's current status data, means for collecting and storing daily activity records entered by after-school care instructors in a database, means for automatically generating reports using a generative AI model based on the daily activity records and parental requests, means for sending reports to parents, and means for providing a platform for communication between the daycare center and parents. This makes it possible to respond quickly to parental requests and efficiently provide appropriate learning plans and activity plans. Furthermore, automatic generation of reports based on activity records reduces the burden on daycare centers and parents.

[1000] "Parental request data" refers to data that parents enter and send to the after-school care center specific requests regarding their child's learning and activities.

[1001] The "database" is a storage device that manages and stores all data used by the system, such as parental requests, children's current status, and daily activity records.

[1002] A "generative AI model" is an artificial intelligence algorithm that analyzes data on parents' requests and children's current situation, and automatically generates optimal learning plans, activity plans, and reports.

[1003] A "learning plan" is a plan that includes specific schedules and content for achieving a child's learning goals.

[1004] An "action plan" is a plan that includes specific activities and events to support a child's growth and development.

[1005] "Daily activity records" are data that are entered and recorded daily by after-school care instructors on the progress of children's learning and activities.

[1006] The "report" is a document that summarizes the results and status of children's learning and activities, created by a generative AI model based on daily activity records and parental requests.

[1007] The "platform" is the foundation of the entire system, providing an interface for daycare centers and parents to exchange information with each other and use the system's various functions.

[1008] A "server" is a computer device that serves as the core of the entire system and stores, processes, and communicates data.

[1009] "Terminal" means a computer device or mobile device used by parents and school counselors to access the system and input or receive data.

[1010] This invention relates to a system that facilitates communication between after-school care centers and parents, efficiently creates learning plans and activity plans according to parents' requests, and automatically generates reports based on daily activity records. This system provides a platform where daycare centers and parents can exchange information with each other.

[1011] The system operates as follows.

[1012] Hardware and software configuration

[1013] 1. Server

[1014] A server is a computer device that is the core of the entire system and stores, processes, and communicates data.

[1015] 2. Terminal

[1016] Terminals are computer devices or mobile devices that allow parents and school teachers to access the system and input or receive data.

[1017] 3. Database

[1018] The database is a storage device for managing and storing data such as parents' requests, children's current status, and daily activity records.

[1019] 4. Generative AI Models

[1020] The generative AI model is an artificial intelligence algorithm that analyzes data on parents' requests and children's current situation, and automatically generates optimal learning plans, activity plans, and reports.

[1021] Specific operation of the system

[1022] 1. Gathering parents' requests

[1023] User (parent): Uses a device to access the platform's request entry screen. For example, enter a request such as "I want to improve my child's English grades."

[1024] Device: Sends the request data entered by the parent to the server.

[1025] Server: Stores the received data in the user database.

[1026] 2. Automatic generation of learning and activity plans

[1027] Server: Retrieves parental request data and child current status data from the database.

[1028] Server: Passes the acquired data to the generative AI model.

[1029] Generative AI model: Analyzes parental requests and student data to generate optimal learning and activity plans.

[1030] Server: Saves the generated plan in a database and notifies parents and school counselors.

[1031] 3. Collecting daily activity records

[1032] User (school teacher): Uses a device to enter daily activity records into the platform. For example, they might enter, "Today we had a special English lesson, and the children actively participated."

[1033] Device: Sends activity record data to the server.

[1034] Server: Stores the received data in the activity record database.

[1035] 4. Automatic report generation

[1036] Server: Retrieves daily activity records and parental request data from the database.

[1037] Server: Passes the acquired data to the generative AI model and requests it to create a report.

[1038] Generative AI model: Generates a report based on input data. For example, it generates a report such as, "Results of this week's English special instruction: Grades improved by 3 points. Next time, listening skills will be improved."

[1039] Server: Sends the generated report to the parent's device.

[1040] 5. Processing Feedback

[1041] User (parent): Uses a device to input feedback into the platform. For example, they might say, "I would like you to improve not only your English listening skills but also your speaking skills."

[1042] Terminal: Sends feedback data.

[1043] Server: Stores the received feedback in a database and updates the learning plan as needed.

[1044] Prompt Sentence Examples

[1045] 1. "Based on the parents' requests, please create the best study plan to improve your child's English grades."

[1046] 2. "Please prepare a weekly activity report based on your daily activity records."

[1047] This is expected to facilitate smoother communication between daycare centers and parents, improving the quality of childcare.

[1048] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1049] Step 1: Enter and submit your request data

[1050] User (Parent): The parent uses a device to access the platform's request entry screen, where they enter their request, such as "I want to improve my child's English grades."

[1051] Input: Request data entered by the parent.

[1052] Device: Sends the request data entered by the parent to the server.

[1053] Output: The requested data sent to the server.

[1054] Specific operation: The request is entered into the device's user interface, the input is converted into JSON format, and an HTTP request is sent to the server.

[1055] Step 2: Save your request data

[1056] Server: The server stores the received request data in a database.

[1057] Input: Request data received from the terminal.

[1058] Data processing / data calculation: Analyze the requested data, associate it with the parent ID, and insert it into the database.

[1059] Output: Request data stored in a database.

[1060] What it does: The server parses the data it receives, associates it based on the parent's ID, runs SQL queries on it, and stores it in a database.

[1061] Step 3: Request to create a lesson plan / activity plan

[1062] Server: The server retrieves the parents' request data and the child's current status data from the database and passes them to the generative AI model.

[1063] Input: Request data and current status data stored in the database.

[1064] Data processing / data calculation: The desired data and current data are formatted in JSON format and passed to the generative AI model.

[1065] Output: The data passed to the generative AI model.

[1066] What happens: The server executes SQL queries to get the required data and generates HTTP requests to the AI ​​model.

[1067] Step 4: Create a lesson plan and activity plan

[1068] Generative AI model: A generative AI model generates lesson plans and activity plans based on input data.

[1069] Input: Request data and current status data passed from the server.

[1070] Data Computing: Perform natural language processing and analysis of data to generate optimal plans.

[1071] Output: lesson plan and activity plan.

[1072] Specific actions: The AI ​​model uses the given data to generate a specific plan, such as "special English instruction three times a week."

[1073] Step 5: Notify and save your plan

[1074] Server: The server saves the generated plan in a database and notifies parents and school counselors.

[1075] Input: The plan output from the generative AI model.

[1076] Data processing / data calculation: Inserting plan data into the database and sending notifications to parents and instructors.

[1077] Output: Plans stored in the database and notifications sent.

[1078] What happens: The server saves the new plan to the database and sends emails and push notifications.

[1079] Step 6: Enter your daily activity log

[1080] User (school teacher): Uses a device to enter daily activity records into the platform. For example, they might enter, "Today we had a special English lesson, and the children actively participated."

[1081] Input: Activity record data entered by after-school care instructors.

[1082] Device: Sends activity record data to the server.

[1083] Output: Activity data sent to the server.

[1084] Specific operation: The instructor fills in the data using the input form, and the terminal converts it into JSON format and sends it to the server.

[1085] Step 7: Save your activity log

[1086] Server: The server stores the received daily activity records in a database.

[1087] Input: Activity data received from the terminal.

[1088] Data processing / data calculation: Analyze activity record data and store it in association with the child's ID.

[1089] Output: Activity log data stored in a database.

[1090] What it does: The server parses the data it receives, associates it based on the child's ID, executes an SQL query, and saves it to a database.

[1091] Step 8: Request automatic generation of report

[1092] Server: The server retrieves daily activity records and parental request data from the database, passes them to the generative AI model, and requests it to create a report.

[1093] Input: Activity record data and request data stored in the database.

[1094] Data processing / data calculation: Activity records and request data are formatted into JSON format and passed to the generative AI model.

[1095] Output: The data passed to the generative AI model.

[1096] What happens: The server executes SQL queries to get the required data and generates HTTP requests to the AI ​​model.

[1097] Step 9: Generate reports

[1098] Generative AI model: A generative AI model generates a report based on input data.

[1099] Input: Activity log data and request data passed from the server.

[1100] Data Computing: Perform natural language processing and analysis of data to generate appropriate reports.

[1101] Output: Report data.

[1102] Specific operation: Based on the given data, the AI ​​model generates a report such as, "Results of this week's special English instruction: Grades improved by 3 points. Next time, listening skills will be improved."

[1103] Step 10: Submit your report

[1104] Server: The server sends the generated report to the parent's device.

[1105] Input: Report output from a generative AI model.

[1106] Data processing / data calculation: Convert report data into PDF format and send it.

[1107] Output: Report sent to parents.

[1108] Specific operation: The server formats the generated report and sends it to the parent's device via email or push notification.

[1109] Step 11: Enter and submit your feedback

[1110] User (Parent): Parents use their devices to input feedback into the platform, for example, "I would like you to improve not only my child's English listening skills but also their speaking skills."

[1111] Input: Feedback data entered by the parent.

[1112] Device: Sends feedback data to the server.

[1113] Output: Feedback data sent to the server.

[1114] Specific operation: Parents enter data using an input form, and the device converts it into JSON format and sends it to the server.

[1115] Step 12: Storing and processing feedback

[1116] Server: The server stores the received feedback in a database and updates the learning plan as needed.

[1117] Input: Feedback data received from the device.

[1118] Data processing / data calculation: Analyze the feedback data, associate it with the parent ID, insert it into the database, and ask the generative AI model to regenerate the plan again.

[1119] Output: Feedback data stored in a database and an updated learning plan.

[1120] Specific operation: The server analyzes the feedback data, stores it in a database, and asks the AI ​​model to regenerate a plan based on the new data.

[1121] (Application example 1)

[1122] 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."

[1123] In traditional brick-and-mortar stores, there are problems such as a lack of communication between members (or guardians) and store staff (e.g., educators and instructors), difficulty in responding to individual member needs, and the tedious manual management of daily activity records. These problems also lead to delays in creating reports and reflecting feedback, which can lead to issues such as lower member (or guardian) satisfaction.

[1124] 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.

[1125] In this invention, the server includes a means for including an artificial intelligence model for automatically generating learning plans, a means for receiving requests and feedback from parents, a means for automatically generating reports to parents, a means for store staff to store daily activity records in a database, and a means for providing a platform for communication between parents and store staff. This enables prompt response to requests from parents and members, automatic generation of individual learning and activity plans, and efficient management of daily activity records. In addition, the automatic generation of reports and immediate reflection of feedback are expected to improve satisfaction among parents and members.

[1126] The "artificial intelligence model for automatically generating learning plans" is an artificial intelligence program that analyzes the requests and progress data of members and parents, and automatically generates optimal learning or activity plans.

[1127] The "means for receiving requests and feedback from parents" refers to a user interface and communication means for parents to input requests and feedback and send them to the server.

[1128] The "means for automatically generating reports to parents" is a system that uses an AI module to automatically generate reports on a regular basis based on daily activity records and requested data, and notifies parents.

[1129] The "means for store staff to store daily activity records in a database" refers to a user interface and communication means that allows store staff to input details of members' daily activities and store that data in a database.

[1130] "Means for providing a platform for parents and store staff to communicate" refers to an online platform that allows parents and store staff to exchange information in both directions.

[1131] The "database for storing data entered by guardians and members" refers to a database system for safely storing and managing requests, feedback, and other individual information entered by guardians and members.

[1132] "Means for storing members' daily activity records in a database" refers to a system for storing detailed records of members' learning and activities in a digital format in a database.

[1133] This invention relates to a system that automatically generates customized learning plans and activity plans based on the requests of members and guardians at a physical store, collects and manages daily activity records based on the plans, and reports the records to guardians. An embodiment of this system is described in detail below.

[1134] System configuration

[1135] The system of the present invention includes the following major components:

[1136] 1. Server

[1137] Artificial intelligence model that automatically generates learning plans: An artificial intelligence program that analyzes the requests and progress data of members and parents to generate the optimal plan. This model uses a generative AI model.

[1138] Database: A database system such as Amazon RDS to store parent and member input data, activity records, requests, and feedback.

[1139] Communication module: A communication interface for exchanging information with parents and staff at physical stores. Data can be exchanged via the Internet.

[1140] 2. User Device

[1141] Parental interface: A smartphone app developed with React Native to provide a screen for entering requests, viewing progress reports, and providing feedback.

[1142] Staff interface: A smartphone app developed with React Native to provide an activity record entry screen and plan confirmation screen.

[1143] Program processing overview

[1144] 1. Request collection function

[1145] The server receives requests entered by parents through a smartphone app and stores them in a database. This function uses a user interface developed with React Native and a backend built with Node.js.

[1146] Example prompt sentence:

[1147] I want to improve my English grades. My current grades are below average.

[1148] 2. Automatic learning and activity plan generation function

[1149] The server collects parent requests and member progress data from the database, passes them to the GPT-4 API, and generates an optimal plan, which is then saved in the database and notified to parents and staff.

[1150] Example prompt sentence:

[1151] Parents' request: To improve English grades. Current grades are below average.

[1152] Current data: Study time is 3 hours per week and attend English classes twice a week.

[1153] 3. Activity record collection function

[1154] The server receives daily activity records entered by store staff through a smartphone app and stores the data in a database. This function also uses a user interface developed with React Native and a backend built with Node.js.

[1155] Example prompt sentence:

[1156] Today we had a special English lesson, and the children participated enthusiastically.

[1157] 4. Automatic report generation function

[1158] The server collects activity records and request data from the database, passes them to the GPT-4 API, and generates a report, which is stored in the database and notified to the parents.

[1159] Example prompt sentence:

[1160] This week's English tutoring results: My grade went up by 3 points. Next time I'll focus on strengthening my listening skills.

[1161] 5. Feedback processing function

[1162] The server receives feedback entered by parents through a smartphone app, stores it in a database, and triggers plan updates as needed.

[1163] Example prompt sentence:

[1164] I would like you to improve not only your English listening skills but also your speaking skills.

[1165] Example

[1166] The system of the present invention enables physical facilities such as fitness clubs and cram schools to efficiently plan and manage activity records based on the individual needs of their members. Cram schools can deepen relationships of trust with parents by monitoring members' learning progress and providing regular reports to parents. Fitness clubs can also improve member satisfaction by providing individual training plans and automatically generating progress reports.

[1167] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1168] Step 1:

[1169] The user inputs their request using a smartphone app. This input data is free-form text data such as "I want to improve my English grades" or "I'm aiming to improve my athletic ability." The app sends this data to the server. Input data: Parent's request (text format). Output data: Request data sent to the server.

[1170] Step 2:

[1171] The server receives the request data sent by the user (guardian) from the smartphone app and stores the data in a database. Input data: Request data. Output data: Request data stored in the database.

[1172] Step 3:

[1173] The server retrieves the member's current status data and the guardian's request data from the database, integrates these data, and passes them to the artificial intelligence model (GPT-4 API). Input data: Request data, current status data. Output data: Plan generation request.

[1174] Step 4:

[1175] The server's AI model analyzes the input data and generates optimal learning and activity plans based on the parents' requests. The generated plans are returned to the server and stored in a database. Input data: Request data, current situation data. Output data: Generated plans.

[1176] Step 5:

[1177] The server notifies parents and store staff of the generated plan using a notification message containing the plan details. Input data: Generated plan. Output data: Notification message sent to parents and staff.

[1178] Step 6:

[1179] Users (store staff) use a smartphone app to enter their daily activity records and send the data to the server. Input data: Activity records (text format). Output data: Activity records sent to the server.

[1180] Step 7:

[1181] The server receives the activity record data sent by the user (staff member) and stores the data in the database. Input data: Activity record. Output data: Activity record stored in the database.

[1182] Step 8:

[1183] The server retrieves activity records and parental request data from the database and passes this data to an artificial intelligence model (GPT-4 API) to generate a report. Input data: Activity records, request data. Output data: Report generation request.

[1184] Step 9:

[1185] The server's AI model analyzes this data and automatically generates a report for parents. The generated report is returned to the server and stored in a database. Input data: Activity records, request data. Output data: Generated report.

[1186] Step 10:

[1187] The server notifies the parent of the generated report using a notification message containing the report. Input data: The generated report. Output data: The notification message sent to the parent.

[1188] Step 11:

[1189] The user (parent) uses a smartphone app to input feedback and sends the data to the server. Input data: Feedback (text format). Output data: Feedback data sent to the server.

[1190] Step 12:

[1191] The server receives feedback data sent by users (parents), stores the data in a database, and triggers plan updates as needed. Input data: Feedback data. Output data: Feedback data stored in the database, plan update requests.

[1192] 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.

[1193] This invention relates to a system that facilitates communication between after-school daycare centers and parents, creates learning plans and activity plans according to parents' requests, and automatically generates reports based on daily activity records. Furthermore, by combining this invention with an emotion engine that recognizes the user's emotions, it becomes possible to respond in a way that takes into consideration the emotions of parents and children.

[1194] Program processing

[1195] Platform-wide overview

[1196] The server creates a platform accessible to after-school care centers and parents. This platform includes a user database, a learning plan creation module, an activity plan creation module, an automatic report generation module, an emotion engine, and a communication module. This allows parents to easily input requests and feedback, and allows daycare centers to efficiently manage children's learning and activities.

[1197] Collecting user requests

[1198] The server receives the request data entered by the parent and stores it in a database. It then analyzes the parent's emotional data through an emotion engine and stores it together with the request data. Parents use their devices to enter their child's individual information and requests into the platform. The emotion engine analyzes emotions from the wording and tone of the input.

[1199] Specific examples

[1200] The parent types into the device, "I want to improve my child's English grades, and I'm feeling a bit anxious."

[1201] The server receives these requests, extracts the emotional data of "anxiety" using an emotion engine, and saves it in a request list for each parent.

[1202] Automatic generation of learning and activity plans

[1203] The server uses an AI module to create learning and activity plans based on the parents' requests, the child's current status data, and emotional data. By referring to the emotional data, it is possible to generate plans that take the child's needs into consideration.

[1204] Specific examples

[1205] The server passes the AI ​​module the request to "improve English grades," the emotional data of "anxiety," and the child's current English grades as input data.

[1206] For example, the AI ​​takes into account the child's impatience and creates a plan that includes special English lessons three times a week at a comfortable pace and the use of a learning app, and returns the plan to the server.

[1207] The server stores this plan in a database and notifies both parents and school counselors.

[1208] Collecting daily activity records

[1209] The teacher uses a terminal to input information about the child's learning status and daily activities, which is then stored in a database on the server. The emotion engine analyzes the child's emotions from the input text and stores this data in the database.

[1210] Specific examples

[1211] The after-school teacher types into the terminal, "Today we had a special English lesson, and the children participated actively." The emotion engine extracts the positive emotion of "active."

[1212] The server receives this and stores each child's activity record and emotional data in a database.

[1213] Automatic report generation

[1214] The server uses an AI module to automatically generate reports for parents based on daily activity records, as well as parents' requests and emotional data.

[1215] Specific examples

[1216] The server extracts information from the database about the progress of special English instruction, changes in the child's grades, and emotional data such as "positive," and passes this data to the AI ​​module.

[1217] Based on this, the AI ​​generates a report that reads, "Results of this week's special English instruction: Grades improved by 3 points. Next time, we will strengthen listening skills. The child is actively participating, so let's keep it up."

[1218] The server sends the report to the parent's device, where the parent can view it.

[1219] Processing Feedback

[1220] The server collects feedback from parents, analyzes the emotional data through an emotion engine, and stores it in a database along with any new requests or improvements.

[1221] Specific examples

[1222] Parents can input feedback into the device, such as, "I want my child to improve not only their English listening skills but also their speaking skills. I'm a little worried." The emotion engine then extracts the emotion of "anxiety."

[1223] The server receives this, stores it in a database, and triggers an update of the learning plan if necessary.

[1224] This will facilitate smooth mutual communication between the nursery school and parents, and make it possible to efficiently support children's growth and learning while also taking into consideration their emotions.

[1225] The processing flow will be explained below.

[1226] Step 1:

[1227] The server initializes the user database and registers the after-school care center's user information. Parents create their own accounts and log in to the platform using their devices.

[1228] Step 2:

[1229] Parents use the device to input their child's individual information (such as name, grade, and interests) and requests for learning and activities (such as subjects and activities to reinforce, special support, etc.). The emotion engine analyzes emotions from the input text and generates emotion data. The server receives this input data and emotion data and stores it in a database.

[1230] Step 3:

[1231] The server retrieves data on parents' requests, children's current status, and emotional data from the database and sends them to the AI ​​module, which analyzes the data and generates optimal learning and activity plans.

[1232] Step 4:

[1233] The server stores the learning and activity plans generated by the AI ​​module in a database and notifies the parents and after-school counselors of the results on the platform, who can then view the plans on their own devices.

[1234] Step 5:

[1235] After-school counselors use the terminals to input information about the children's learning status and daily activities. The emotion engine analyzes the children's emotions from the input text, and the server stores this information in a database.

[1236] Specific examples

[1237] An after-school teacher inputs, "Today we had a special English lesson, and the children participated actively." The emotion engine extracts the positive emotion of "active."

[1238] The server receives this and stores each child's activity record and emotional data in a database.

[1239] Step 6:

[1240] The server sends data to the AI ​​module based on daily activity records and parents' requests and emotional data to automatically generate reports. The AI ​​module analyzes the data and automatically generates reports for parents.

[1241] Specific examples

[1242] The server retrieves information from the database, such as progress in special English instruction, changes in the child's grades, and emotional data, and passes it on to the AI ​​module.

[1243] Based on this, the AI ​​generates a report that reads, "Results of this week's special English instruction: Grades improved by 3 points. Next time, we will strengthen listening skills. The child is actively participating, so let's keep it up."

[1244] The server sends the report to the parent's device, where the parent can view it.

[1245] Step 7:

[1246] Parents can use a device to check the report and enter their feedback on the content, and the emotion engine analyzes the emotional data from the feedback.

[1247] Specific examples

[1248] A parent enters feedback such as, "I want my child to improve not only his English listening skills but also his speaking skills. I'm a little worried." The emotion engine analyzes this and extracts the emotion data of "anxiety."

[1249] The server receives this and stores it in a database.

[1250] Step 8:

[1251] The server analyzes the feedback and emotional data from parents, and if necessary, requests the AI ​​module to update the learning plan and activity plan. The server then notifies the after-school instructor of the updated plan.

[1252] Specific examples

[1253] Based on the feedback, the server inputs into the AI ​​module the new request to "improve speaking skills" and the parent's emotional data of "anxiety."

[1254] The AI ​​module creates a new lesson plan and returns it to the server, which stores it in a database and notifies the after-school teacher.

[1255] As described above, the present invention facilitates mutual communication between daycare centers and parents, and can efficiently support children's growth and learning while taking into consideration their emotions.

[1256] Example 2

[1257] 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."

[1258] In modern society, with the increase in dual-income households, there is a need for smoother communication between parents and after-school daycare centers. Conventional systems make it difficult for parents to create learning and activity plans that meet their needs, and are therefore unable to adequately support children's development. Furthermore, there is a lack of consideration for the feelings of parents and children, making it difficult to provide an optimal learning environment.

[1259] 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.

[1260] In this invention, the server includes means including an artificial intelligence model for automatically generating learning plans and activity plans, means for receiving requests and feedback from parents, means including an emotion engine for analyzing input data and emotion data from parents, means for automatically generating reports to parents, and means for providing a platform for communication between the childcare facility and parents. This enables the creation of learning plans and activity plans based on parents' requests and appropriate responses that take into consideration the emotions of parents and children.

[1261] An "artificial intelligence model for automatically generating learning plans and activity plans" is a system that includes an algorithm for automatically creating optimal learning plans and activity plans based on the requests of parents and current data on the child.

[1262] "Means for receiving requests and feedback from parents" refers to a function that includes an interface for receiving and processing requests and feedback that parents input into the system.

[1263] The "emotion engine that analyzes parental input data and emotional data" is an analysis engine that analyzes emotions from data entered by parents and utilizes that information within the system.

[1264] "Means for automatically generating reports for parents" is a function that automatically creates reports for parents based on the child's daily activity records and progress data for the learning plan.

[1265] The "platform for communication between childcare facilities and parents" is a system that provides an online platform for childcare facilities and parents to communicate efficiently.

[1266] The "database" is a storage device that centrally stores and manages various information used by the system, such as parental input data, emotional data, and records of children's daily activities.

[1267] This invention is a system that facilitates communication between after-school care facilities and parents, creates learning plans and activity plans according to parents' requests, and automatically generates reports based on daily activity records. Furthermore, by combining it with an emotion engine that recognizes the user's emotions, it is possible to respond in a way that takes into consideration the emotions of parents and children.

[1268] Building a platform

[1269] The server creates a platform accessible to after-school care facilities and parents. This platform includes a user database, a learning plan creation module, an activity plan creation module, an automatic report generation module, an emotion engine, and a communication module. This allows parents to easily input requests and feedback, and enables after-school care facilities to efficiently manage children's learning and activities.

[1270] Collecting user requests

[1271] Users (guardians) can log in to the platform using their devices and enter their child's individual information and requests. The server receives the request data entered by the guardian and stores it in the user database. It also analyzes the guardian's emotional data through an emotion engine and stores it together with the request data.

[1272] Examples:

[1273] Parents can input requests such as, "I want to improve my child's English grades, and I'm feeling a bit anxious." The server receives these requests, extracts the emotion data for "anxiety" using an emotion engine, and saves it in a request list for each parent.

[1274] Automatic generation of learning and activity plans

[1275] The server uses an AI module to create learning and activity plans based on the parents' requests, the child's current status data, and emotional data. By referring to the emotional data, it is possible to generate plans that take the child's needs into consideration.

[1276] Examples:

[1277] The server passes the AI ​​module the request to "improve English grades," emotional data on "impatience," and the child's current English grades as input data. The AI ​​takes "impatience" into account, for example, to create a plan that incorporates special English lessons three times a week at a comfortable pace and the use of a learning app, and returns it to the server. The server stores this plan in a database and notifies both the parents and the after-school care instructor.

[1278] Collecting daily activity records

[1279] After-school instructors use terminals to input the children's daily learning status and activities. The server receives the input data and stores it in an activity record database. The emotion engine analyzes the children's emotions from the input text and stores this data in the database as well.

[1280] Examples:

[1281] The after-school teacher types into the terminal, "Today we had a special English lesson, and the children participated actively." The emotion engine extracts the positive emotion of "active." The server receives this and stores each child's activity record and emotional data in a database.

[1282] Automatic report generation

[1283] The server uses an AI module to automatically generate reports for parents based on daily activity records, as well as parents' requests and emotional data.

[1284] Examples:

[1285] The server extracts data from the database, including the progress of the English special instruction, changes in the child's grades, and emotional data such as "positive," and passes it to the AI ​​module. Based on this, the AI ​​generates a report that reads, "Results of this week's English special instruction: Grades improved by 3 points. Next time, we will strengthen listening skills. The child is actively participating, so let's keep it up." The server then sends the report to the parent's device, where the parent can review it.

[1286] Processing Feedback

[1287] The user (parent) enters feedback based on the report. The server receives the entered feedback and analyzes it through the emotion engine. The server extracts the emotion data of "anxiety" and stores it in the database along with the feedback. The server triggers an update of the learning plan as needed.

[1288] Examples:

[1289] The parent inputs feedback on the device, such as "I want my child to improve not only his English listening skills but also his speaking skills. I'm a little anxious." The server receives this, and the emotion engine extracts the emotion of "anxiety." The server saves this data and requests the AI ​​module to regenerate the plan again.

[1290] This will facilitate smooth mutual communication between childcare facilities and parents, making it possible to efficiently support children's growth and learning while also taking into consideration their emotions.

[1291] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1292] Step 1:

[1293] Users (parents) log in to the platform using their devices and input their child's individual information and requests. The input data may include requests such as "I want to improve my English grades, and I'm feeling a bit anxious." This input data is sent to the server.

[1294] Step 2:

[1295] The server receives the request data entered by the guardian and stores it in the user database. It then sends the guardian's input data to the emotion engine for emotion analysis. The emotion engine extracts the emotion data of "impatience" from the input sentence and stores it together with the request data. The input is the guardian's request data, and the output is the request data and emotion data.

[1296] Step 3:

[1297] The server calls the AI ​​module and provides the request data, the child's current situation data, and emotional data as input data. Specifically, the server passes the request, "I want to improve my English grades," the emotional data, "impatience," and the child's current English grades to the AI ​​module. The AI ​​module generates optimal learning plans and activity plans based on this data. The inputs are the request data, emotional data, and current situation data, and the output is the generated learning plan.

[1298] Step 4:

[1299] The server stores the generated plan in a database and notifies both parents and teachers. The notification includes details of the new lesson and activity plan. The input is the generated lesson plan and the output is a notification message.

[1300] Step 5:

[1301] The after-school care instructor uses a terminal to input the children's daily learning status and activities. For example, they might input, "Today we had a special English lesson, and the children actively participated." This data is sent to the server. The input is a record of daily activities, and the output is activity data that is saved on the server.

[1302] Step 6:

[1303] The server stores the received activity data in an activity record database. It then uses an emotion engine to analyze the child's emotions from the input text, extracts positive emotion data such as "active," and stores this in the database. The input is activity data, and the output is an activity record and emotion data.

[1304] Step 7:

[1305] The server uses an AI module to automatically generate a report for parents based on daily activity records and parental requests and emotional data. Specifically, the server generates a report such as, "Results of this week's special English instruction: Grades improved by 3 points. Next time, listening skills will be strengthened. The child is actively participating, so let's keep it up." The input is the activity record, request data, and emotional data, and the output is an automatically generated report.

[1306] Step 8:

[1307] The server sends the generated report to the parent's terminal, where the parent checks it. The input is the automatically generated report, and the output is the report displayed on the parent's terminal.

[1308] Step 9:

[1309] The user (parent) inputs feedback based on the report. For example, they might say, "I want you to improve not only your English listening skills but also your speaking skills. I'm a little worried." This feedback is sent to the server.

[1310] Step 10:

[1311] The server receives the input feedback and analyzes it through the emotion engine. The emotion engine extracts the emotion data of "anxiety" and stores it together with the feedback in a database. The input is the feedback data, and the output is the feedback and emotion data.

[1312] Step 11:

[1313] The server triggers an update of the learning plan as needed. Based on newly added feedback, the server requests the AI ​​module to regenerate the plan again. This allows the child's learning plan to adapt to their latest needs and emotions. The input is feedback and emotional data, and the output is an updated learning plan.

[1314] (Application example 2)

[1315] 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."

[1316] Conventional communication between after-school daycare centers and parents has been difficult to accurately reflect parents' requests and feedback, requiring a great deal of effort and time to create learning and activity plans and to report daily activity records. Furthermore, communication is often insufficient due to a lack of consideration for the feelings of parents and children. Furthermore, the lack of a system that provides learning support functions using virtual reality makes it difficult to effectively stimulate children's motivation to learn. The purpose of this invention is to solve these problems and realize smooth communication between parents and daycare centers and effective learning support.

[1317] 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.

[1318] In this invention, the server includes means for including an artificial intelligence model for automatically generating a learning plan, means for receiving requests and feedback from parents, means for automatically generating reports to parents, means for providing a platform for communication between the daycare center and parents, means for providing learning support functions using virtual reality, means for analyzing and saving the emotions of parents and children using an emotion analysis engine, and means for recording children's learning plans and daily activities via a head-mounted display. This facilitates smooth two-way communication between parents and the after-school daycare center, enabling the provision of accurate learning plans based on parents' requests and children's learning status, responses that take emotions into consideration, and automatic generation of reports. Furthermore, learning using virtual reality can effectively stimulate children's motivation to learn and provide comprehensive learning support.

[1319] The "artificial intelligence model for automatically generating learning plans" is an artificial intelligence technology that automatically creates optimal learning plans based on requests from parents and data on the child's learning situation.

[1320] "Means for receiving requests and feedback from parents" refers to an interface and system that allows parents to input requests and feedback regarding learning plans and their children's learning progress.

[1321] The "means for automatically generating reports for parents" is a system that automatically creates reports for parents based on the child's learning situation and activity records.

[1322] "Means for providing a platform for communication between childcare facilities and parents" refers to an online platform that allows childcare facility staff and parents to communicate with each other and share information.

[1323] "Means for providing learning support functions using virtual reality" refers to a system that utilizes virtual reality technology to provide educational content to children and assist them in their learning.

[1324] The "means for analyzing and storing the emotions of guardians and children using an emotion analysis engine" is a system that uses natural language processing technology to extract emotions from input data of guardians and children and stores them in a database.

[1325] "Means for recording a child's study plan and daily activities through a head-mounted display" refers to an interface and system for recording a child's study plan and daily activities using a head-mounted display.

[1326] The present invention is a learning support system that uses virtual reality technology to facilitate mutual communication between after-school daycare centers and parents, automatically generates learning plans and activity plans according to parents' requests, and automatically generates reports based on daily activity records. Furthermore, it is possible to respond in a way that takes into account the emotions of parents and children using an emotion analysis engine. This invention is implemented as follows.

[1327] First, the server contains an artificial intelligence model (e.g., TensorFlow, PyTorch) for automatically generating a learning plan. Parents use a smartphone or tablet to input their requests and feedback when setting up a learning plan. The server then analyzes the input text data using a sentiment analysis engine (e.g., Google Cloud Natural Language API) and also collects and stores the parents' emotional data.

[1328] Next, the server uses an AI module to automatically generate learning and activity plans based on requests from parents and data on the child's current situation and emotions. This allows it to propose the optimal plan based on the parent's requests and the child's learning situation. It also provides educational content to children using virtual reality technology (e.g., Oculus Rift, HTC Vive). This VR content can increase children's motivation to learn.

[1329] The after-school counselor inputs the children's daily learning plans and activities through a head-mounted display, and the server stores this data in a database. An emotion analysis engine is used to analyze the children's emotions from the input text, and this emotion data is also stored in the database. For example, if an after-school counselor inputs the content, "Today we had a special English lesson, and the children participated enthusiastically," the emotion analysis engine will extract the positive emotion of "enthusiastically."

[1330] The server uses an AI module to automatically generate reports for parents based on daily activity records and emotional data. For example, based on the progress of special English instruction, the child's improvement in grades, and emotional data such as "positive," the AI ​​could generate a report such as, "Results of this week's special English instruction: Grades improved by 3 points. Next time, listening skills will be strengthened. The child is actively participating, so let's keep it up," and the server would send the report to the parent's device.

[1331] After checking the report, parents can submit further requests or feedback. For example, if parents submit feedback such as, "I would like you to improve not only my child's English listening skills but also their speaking skills. I'm a little worried," the emotion analysis engine will extract the emotion of "anxiety," which the server will receive and store in the database, and update the learning plan as necessary.

[1332] Prompt Sentence Examples

[1333] "Please generate a study plan. The request is 'I want to improve my English grades,' the emotion is 'I'm anxious,' and the current grade is '70 points.'"

[1334] In this way, the learning support system using virtual reality of the present invention can facilitate smooth communication between parents and after-school daycare centers and effectively increase children's motivation to learn.

[1335] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1336] Step 1:

[1337] Gathering parents' requests

[1338] 1.1 Parents enter their requests for learning plans and activities using their smartphones or tablets.

[1339] 1.2 The parent's input data is sent to the server.

[1340] 1.3 The server uses an emotion analysis engine (e.g., Google Cloud Natural Language API) to analyze emotions from the text data and extract emotional data such as "anxiety" or "impatience."

[1341] Input: Text data entered by the parent

[1342] Output: Parental request data and analyzed emotion data

[1343] Step 2:

[1344] Storage of request data

[1345] 2.1 The server stores the request data and emotion data received from the parents in a database.

[1346] Input: Parental request data and emotion data

[1347] Output: Data stored in the database

[1348] Step 3:

[1349] Auto-generated learning plan

[1350] 3.1 The server inputs the saved parental request data, emotional data, and child learning status data into a generative AI model (e.g., TensorFlow, PyTorch).

[1351] 3.2 The generative AI model creates an optimal learning plan based on this data.

[1352] Input: Parental request data, emotional data, student learning status data

[1353] Output: Auto-generated learning plan

[1354] Step 4:

[1355] Plan Notification

[1356] 4.1 The server notifies the parents and after-school counselors of the generated learning plan.

[1357] 4.2 Parents and school counselors will review the notified learning plan.

[1358] Input: Auto-generated study plan

[1359] Output: Notification to parents and school counselors

[1360] Step 5:

[1361] Daily activity record

[1362] 5.1 After-school teachers will use head-mounted displays or devices to record the children's daily learning activities.

[1363] 5.2 The recorded data is sent to a server and an emotion analysis engine is used to analyze the child's emotions.

[1364] 5.3 The analyzed emotion data is stored in a database along with daily activity records.

[1365] Input: Learning activity data entered by the after-school instructor

[1366] Output: Activity records and emotion data stored in a database

[1367] Step 6:

[1368] Automatic report generation

[1369] 6.1 The server automatically generates reports for parents based on daily activity records and emotional data using a generative AI model.

[1370] Input: Daily activity records, emotional data

[1371] Output: Auto-generated parental report

[1372] Step 7:

[1373] Report notification

[1374] 7.1 The server sends the generated report to the parent's device.

[1375] 7.2 Parents will review the report and provide feedback as necessary.

[1376] Input: Auto-generated parent report

[1377] Output: Report notification to parent's device

[1378] Step 8:

[1379] Processing Feedback

[1380] 8.1 The feedback entered by the parent is sent to the server, and the emotion data is analyzed using the emotion analysis engine.

[1381] 8.2 The analyzed feedback data and emotion data are stored in a database. New learning plans are generated as needed.

[1382] Input: Parent feedback data

[1383] Output: Feedback data and emotion data stored in a database, a new learning plan

[1384] In this way, this system supports effective communication between parents and after-school daycare centers, and makes it possible to comprehensively manage and support children's learning.

[1385] 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.

[1386] 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.

[1387] 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.

[1388] [Fourth embodiment]

[1389] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[1390] 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.

[1391] 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).

[1392] 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.

[1393] 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.

[1394] 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).

[1395] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[1396] 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.

[1397] 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.

[1398] 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.

[1399] 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.

[1400] 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.

[1401] 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."

[1402] This invention relates to a system that facilitates communication between after-school daycare centers and parents, creates learning plans and activity plans according to parents' requests, and automatically generates reports based on daily activity records. This system provides a platform where daycare centers and parents can exchange information with each other.

[1403] Program processing

[1404] Platform-wide overview

[1405] The server creates a platform accessible to after-school care centers and parents. This platform includes a user database, a learning plan creation module, an activity plan creation module, an automatic report generation module, and a communication module. This allows parents to easily input requests and feedback, and allows daycare centers to efficiently manage children's learning and activities.

[1406] Collecting user requests

[1407] The server receives the request data entered by the parents and stores it in a database. Parents then use their devices to enter their child's individual information and requests into the platform, allowing the daycare center to respond quickly to the parents' needs.

[1408] Specific examples

[1409] Parents can input requests such as "I want to improve my child's English grades" or "I'm aiming to improve my child's athletic ability" into the device.

[1410] The server receives these requests and stores a list of requests for each parent in a database.

[1411] Automatic generation of learning and activity plans

[1412] The server uses an AI module to create learning and activity plans based on the parents' requests and the child's current status data. The server retrieves the parents' requests and the child's current status data from the database and passes it to the AI ​​module, which analyzes it and generates the optimal plan.

[1413] Specific examples

[1414] The server passes the AI ​​module the request to "improve English grades" and the child's current English grades as input data.

[1415] The AI ​​creates a plan that includes, for example, special English lessons three times a week and the use of a learning app, and returns it to the server.

[1416] The server stores this plan in a database and notifies both parents and school counselors.

[1417] Collecting daily activity records

[1418] The server collects the daily learning and activity progress records entered by the school counselor and stores them in a database. The school counselor uses a terminal to enter the children's daily activities into the platform.

[1419] Specific examples

[1420] The after-school care instructor types into the terminal, "Today we had a special English lesson, and the children actively participated."

[1421] The server receives this and stores each child's activity record in a database.

[1422] Automatic report generation

[1423] The server uses the AI ​​module to automatically generate reports for parents based on daily activity records and parental requests. The server retrieves the necessary information from the database and passes it to the AI ​​module to request the creation of a report.

[1424] Specific examples

[1425] The server retrieves information from the database, such as the progress of special English instruction and changes in the child's grades, and passes it on to the AI ​​module.

[1426] Based on this, the AI ​​generates a report such as, "Results of this week's special English instruction: Grades improved by 3 points. Next time, we will focus on strengthening listening skills."

[1427] The server sends the report to the parent's device, where the parent can view it.

[1428] Processing Feedback

[1429] The server collects feedback from parents and stores new requests and improvements in a database.

[1430] Specific examples

[1431] Parents use the devices to enter feedback into the platform, such as "I would like you to improve not only your child's English listening skills but also their speaking skills."

[1432] The server receives this, stores it in a database, and triggers an update of the learning plan if necessary.

[1433] This will facilitate smooth communication between daycare centers and parents, improving the quality of childcare.

[1434] The processing flow will be explained below.

[1435] Step 1:

[1436] The server initializes the user database and registers the after-school care center's user information. Parents create their own accounts and log in to the platform using their devices.

[1437] Step 2:

[1438] Parents use the device to input their child's individual information (such as name, grade, and interests) and requests for learning and activities (such as subjects and activities they want to strengthen, special support, etc.) The server receives this input data and stores it in a database.

[1439] Step 3:

[1440] The server retrieves data on parents' requests and children's current status from the database and sends it to the AI ​​module, which then analyzes the data and generates optimal learning and activity plans.

[1441] Step 4:

[1442] The server stores the learning and activity plans generated by the AI ​​module in a database and notifies the parents and after-school counselors of the results on the platform, who can then view the plans on their own devices.

[1443] Step 5:

[1444] The after-school instructors use the terminals to input the children's learning status and daily activities. For example, they can enter information such as, "Today we had a special English lesson, and the children actively participated." The server stores these activity records in a database.

[1445] Step 6:

[1446] The server sends data to the AI ​​module based on daily activity records and parental requests, and the AI ​​module analyzes the data and automatically generates a report for parents.

[1447] Step 7:

[1448] The server stores the generated report for parents in a database and sends it to the parents' terminal, where the parents can check the report and enter feedback on the content.

[1449] Step 8:

[1450] The server receives the feedback entered by the parent on the device and stores it in a database. The server then checks the feedback and, if necessary, requests the AI ​​module to revise the learning plan or activity plan.

[1451] This will enable smooth communication between the nursery school and parents, and enable efficient support for children's growth and learning.

[1452] Example 1

[1453] 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."

[1454] There is a need for a system that can facilitate smooth communication between after-school care centers and parents, efficiently create learning and activity plans according to parents' requests, and automatically generate appropriate reports based on daily activity records.Currently, communication between daycare centers and parents is complicated and requires manual data management, so automating this process to improve efficiency is a challenge.

[1455] 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.

[1456] In this invention, the server includes means for collecting and storing parental request data in a database, means for automatically generating optimal learning plans and activity plans using a generative AI model based on the parental request data and the child's current status data, means for collecting and storing daily activity records entered by after-school care instructors in a database, means for automatically generating reports using a generative AI model based on the daily activity records and parental requests, means for sending reports to parents, and means for providing a platform for communication between the daycare center and parents. This makes it possible to respond quickly to parental requests and efficiently provide appropriate learning plans and activity plans. Furthermore, automatic generation of reports based on activity records reduces the burden on daycare centers and parents.

[1457] "Parental request data" refers to data that parents enter and send to the after-school care center specific requests regarding their child's learning and activities.

[1458] The "database" is a storage device that manages and stores all data used by the system, such as parental requests, children's current status, and daily activity records.

[1459] A "generative AI model" is an artificial intelligence algorithm that analyzes data on parents' requests and children's current situation, and automatically generates optimal learning plans, activity plans, and reports.

[1460] A "learning plan" is a plan that includes specific schedules and content for achieving a child's learning goals.

[1461] An "action plan" is a plan that includes specific activities and events to support a child's growth and development.

[1462] "Daily activity records" are data that are entered and recorded daily by after-school care instructors on the progress of children's learning and activities.

[1463] The "report" is a document that summarizes the results and status of children's learning and activities, created by a generative AI model based on daily activity records and parental requests.

[1464] The "platform" is the foundation of the entire system, providing an interface for daycare centers and parents to exchange information with each other and use the system's various functions.

[1465] A "server" is a computer device that serves as the core of the entire system and stores, processes, and communicates data.

[1466] "Terminal" means a computer device or mobile device used by parents and school counselors to access the system and input or receive data.

[1467] This invention relates to a system that facilitates communication between after-school care centers and parents, efficiently creates learning plans and activity plans according to parents' requests, and automatically generates reports based on daily activity records. This system provides a platform where daycare centers and parents can exchange information with each other.

[1468] The system operates as follows.

[1469] Hardware and software configuration

[1470] 1. Server

[1471] A server is a computer device that is the core of the entire system and stores, processes, and communicates data.

[1472] 2. Terminal

[1473] Terminals are computer devices or mobile devices that allow parents and school teachers to access the system and input or receive data.

[1474] 3. Database

[1475] The database is a storage device for managing and storing data such as parents' requests, children's current status, and daily activity records.

[1476] 4. Generative AI Models

[1477] The generative AI model is an artificial intelligence algorithm that analyzes data on parents' requests and children's current situation, and automatically generates optimal learning plans, activity plans, and reports.

[1478] Specific operation of the system

[1479] 1. Gathering parents' requests

[1480] User (parent): Uses a device to access the platform's request entry screen. For example, enter a request such as "I want to improve my child's English grades."

[1481] Device: Sends the request data entered by the parent to the server.

[1482] Server: Stores the received data in the user database.

[1483] 2. Automatic generation of learning and activity plans

[1484] Server: Retrieves parental request data and child current status data from the database.

[1485] Server: Passes the acquired data to the generative AI model.

[1486] Generative AI model: Analyzes parental requests and student data to generate optimal learning and activity plans.

[1487] Server: Saves the generated plan in a database and notifies parents and school counselors.

[1488] 3. Collecting daily activity records

[1489] User (school teacher): Uses a device to enter daily activity records into the platform. For example, they might enter, "Today we had a special English lesson, and the children actively participated."

[1490] Device: Sends activity record data to the server.

[1491] Server: Stores the received data in the activity record database.

[1492] 4. Automatic report generation

[1493] Server: Retrieves daily activity records and parental request data from the database.

[1494] Server: Passes the acquired data to the generative AI model and requests it to create a report.

[1495] Generative AI model: Generates a report based on input data. For example, it generates a report such as, "Results of this week's English special instruction: Grades improved by 3 points. Next time, listening skills will be improved."

[1496] Server: Sends the generated report to the parent's device.

[1497] 5. Processing Feedback

[1498] User (parent): Uses a device to input feedback into the platform. For example, they might say, "I would like you to improve not only your English listening skills but also your speaking skills."

[1499] Terminal: Sends feedback data.

[1500] Server: Stores the received feedback in a database and updates the learning plan as needed.

[1501] Prompt Sentence Examples

[1502] 1. "Based on the parents' requests, please create the best study plan to improve your child's English grades."

[1503] 2. "Please prepare a weekly activity report based on your daily activity records."

[1504] This is expected to facilitate smoother communication between daycare centers and parents, improving the quality of childcare.

[1505] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1506] Step 1: Enter and submit your request data

[1507] User (Parent): The parent uses a device to access the platform's request entry screen, where they enter their request, such as "I want to improve my child's English grades."

[1508] Input: Request data entered by the parent.

[1509] Device: Sends the request data entered by the parent to the server.

[1510] Output: The requested data sent to the server.

[1511] Specific operation: The request is entered into the device's user interface, the input is converted into JSON format, and an HTTP request is sent to the server.

[1512] Step 2: Save your request data

[1513] Server: The server stores the received request data in a database.

[1514] Input: Request data received from the terminal.

[1515] Data processing / data calculation: Analyze the requested data, associate it with the parent ID, and insert it into the database.

[1516] Output: Request data stored in a database.

[1517] What it does: The server parses the data it receives, associates it based on the parent's ID, runs SQL queries on it, and stores it in a database.

[1518] Step 3: Request to create a lesson plan / activity plan

[1519] Server: The server retrieves the parents' request data and the child's current status data from the database and passes them to the generative AI model.

[1520] Input: Request data and current status data stored in the database.

[1521] Data processing / data calculation: The desired data and current data are formatted in JSON format and passed to the generative AI model.

[1522] Output: The data passed to the generative AI model.

[1523] What happens: The server executes SQL queries to get the required data and generates HTTP requests to the AI ​​model.

[1524] Step 4: Create a lesson plan and activity plan

[1525] Generative AI model: A generative AI model generates lesson plans and activity plans based on input data.

[1526] Input: Request data and current status data passed from the server.

[1527] Data Computing: Perform natural language processing and analysis of data to generate optimal plans.

[1528] Output: lesson plan and activity plan.

[1529] Specific actions: The AI ​​model uses the given data to generate a specific plan, such as "special English instruction three times a week."

[1530] Step 5: Notify and save your plan

[1531] Server: The server saves the generated plan in a database and notifies parents and school counselors.

[1532] Input: The plan output from the generative AI model.

[1533] Data processing / data calculation: Inserting plan data into the database and sending notifications to parents and instructors.

[1534] Output: Plans stored in the database and notifications sent.

[1535] What happens: The server saves the new plan to the database and sends emails and push notifications.

[1536] Step 6: Enter your daily activity log

[1537] User (school teacher): Uses a device to enter daily activity records into the platform. For example, they might enter, "Today we had a special English lesson, and the children actively participated."

[1538] Input: Activity record data entered by after-school care instructors.

[1539] Device: Sends activity record data to the server.

[1540] Output: Activity data sent to the server.

[1541] Specific operation: The instructor fills in the data using the input form, and the terminal converts it into JSON format and sends it to the server.

[1542] Step 7: Save your activity log

[1543] Server: The server stores the received daily activity records in a database.

[1544] Input: Activity data received from the terminal.

[1545] Data processing / data calculation: Analyze activity record data and store it in association with the child's ID.

[1546] Output: Activity log data stored in a database.

[1547] What it does: The server parses the data it receives, associates it based on the child's ID, executes an SQL query, and saves it to a database.

[1548] Step 8: Request automatic generation of report

[1549] Server: The server retrieves daily activity records and parental request data from the database, passes them to the generative AI model, and requests it to create a report.

[1550] Input: Activity record data and request data stored in the database.

[1551] Data processing / data calculation: Activity records and request data are formatted into JSON format and passed to the generative AI model.

[1552] Output: The data passed to the generative AI model.

[1553] What happens: The server executes SQL queries to get the required data and generates HTTP requests to the AI ​​model.

[1554] Step 9: Generate reports

[1555] Generative AI model: A generative AI model generates a report based on input data.

[1556] Input: Activity log data and request data passed from the server.

[1557] Data Computing: Perform natural language processing and analysis of data to generate appropriate reports.

[1558] Output: Report data.

[1559] Specific operation: Based on the given data, the AI ​​model generates a report such as, "Results of this week's special English instruction: Grades improved by 3 points. Next time, listening skills will be improved."

[1560] Step 10: Submit your report

[1561] Server: The server sends the generated report to the parent's device.

[1562] Input: Report output from a generative AI model.

[1563] Data processing / data calculation: Convert report data into PDF format and send it.

[1564] Output: Report sent to parents.

[1565] Specific operation: The server formats the generated report and sends it to the parent's device via email or push notification.

[1566] Step 11: Enter and submit your feedback

[1567] User (Parent): Parents use their devices to input feedback into the platform, for example, "I would like you to improve not only my child's English listening skills but also their speaking skills."

[1568] Input: Feedback data entered by the parent.

[1569] Device: Sends feedback data to the server.

[1570] Output: Feedback data sent to the server.

[1571] Specific operation: Parents enter data using an input form, and the device converts it into JSON format and sends it to the server.

[1572] Step 12: Storing and processing feedback

[1573] Server: The server stores the received feedback in a database and updates the learning plan as needed.

[1574] Input: Feedback data received from the device.

[1575] Data processing / data calculation: Analyze the feedback data, associate it with the parent ID, insert it into the database, and ask the generative AI model to regenerate the plan again.

[1576] Output: Feedback data stored in a database and an updated learning plan.

[1577] Specific operation: The server analyzes the feedback data, stores it in a database, and asks the AI ​​model to regenerate a plan based on the new data.

[1578] (Application example 1)

[1579] 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."

[1580] In traditional brick-and-mortar stores, there are problems such as a lack of communication between members (or guardians) and store staff (e.g., educators and instructors), difficulty in responding to individual member needs, and the tedious manual management of daily activity records. These problems also lead to delays in creating reports and reflecting feedback, which can lead to issues such as lower member (or guardian) satisfaction.

[1581] 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.

[1582] In this invention, the server includes a means for including an artificial intelligence model for automatically generating learning plans, a means for receiving requests and feedback from parents, a means for automatically generating reports to parents, a means for store staff to store daily activity records in a database, and a means for providing a platform for communication between parents and store staff. This enables prompt response to requests from parents and members, automatic generation of individual learning and activity plans, and efficient management of daily activity records. In addition, the automatic generation of reports and immediate reflection of feedback are expected to improve satisfaction among parents and members.

[1583] The "artificial intelligence model for automatically generating learning plans" is an artificial intelligence program that analyzes the requests and progress data of members and parents, and automatically generates optimal learning or activity plans.

[1584] The "means for receiving requests and feedback from parents" refers to a user interface and communication means for parents to input requests and feedback and send them to the server.

[1585] The "means for automatically generating reports to parents" is a system that uses an AI module to automatically generate reports on a regular basis based on daily activity records and requested data, and notifies parents.

[1586] The "means for store staff to store daily activity records in a database" refers to a user interface and communication means that allows store staff to input details of members' daily activities and store that data in a database.

[1587] "Means for providing a platform for parents and store staff to communicate" refers to an online platform that allows parents and store staff to exchange information in both directions.

[1588] The "database for storing data entered by guardians and members" refers to a database system for safely storing and managing requests, feedback, and other individual information entered by guardians and members.

[1589] "Means for storing members' daily activity records in a database" refers to a system for storing detailed records of members' learning and activities in a digital format in a database.

[1590] This invention relates to a system that automatically generates customized learning plans and activity plans based on the requests of members and guardians at a physical store, collects and manages daily activity records based on the plans, and reports the records to guardians. An embodiment of this system is described in detail below.

[1591] System configuration

[1592] The system of the present invention includes the following major components:

[1593] 1. Server

[1594] Artificial intelligence model that automatically generates learning plans: An artificial intelligence program that analyzes the requests and progress data of members and parents to generate the optimal plan. This model uses a generative AI model.

[1595] Database: A database system such as Amazon RDS to store parent and member input data, activity records, requests, and feedback.

[1596] Communication module: A communication interface for exchanging information with parents and staff at physical stores. Data can be exchanged via the Internet.

[1597] 2. User Device

[1598] Parental interface: A smartphone app developed with React Native to provide a screen for entering requests, viewing progress reports, and providing feedback.

[1599] Staff interface: A smartphone app developed with React Native to provide an activity record entry screen and plan confirmation screen.

[1600] Program processing overview

[1601] 1. Request collection function

[1602] The server receives requests entered by parents through a smartphone app and stores them in a database. This function uses a user interface developed with React Native and a backend built with Node.js.

[1603] Example prompt sentence:

[1604] I want to improve my English grades. My current grades are below average.

[1605] 2. Automatic learning and activity plan generation function

[1606] The server collects parent requests and member progress data from the database, passes them to the GPT-4 API, and generates an optimal plan, which is then saved in the database and notified to parents and staff.

[1607] Example prompt sentence:

[1608] Parents' request: To improve English grades. Current grades are below average.

[1609] Current data: Study time is 3 hours per week and attend English classes twice a week.

[1610] 3. Activity record collection function

[1611] The server receives daily activity records entered by store staff through a smartphone app and stores the data in a database. This function also uses a user interface developed with React Native and a backend built with Node.js.

[1612] Example prompt sentence:

[1613] Today we had a special English lesson, and the children participated enthusiastically.

[1614] 4. Automatic report generation function

[1615] The server collects activity records and request data from the database, passes them to the GPT-4 API, and generates a report, which is stored in the database and notified to the parents.

[1616] Example prompt sentence:

[1617] This week's English tutoring results: My grade went up by 3 points. Next time I'll focus on strengthening my listening skills.

[1618] 5. Feedback processing function

[1619] The server receives feedback entered by parents through a smartphone app, stores it in a database, and triggers plan updates as needed.

[1620] Example prompt sentence:

[1621] I would like you to improve not only your English listening skills but also your speaking skills.

[1622] Example

[1623] The system of the present invention enables physical facilities such as fitness clubs and cram schools to efficiently plan and manage activity records based on the individual needs of their members. Cram schools can deepen relationships of trust with parents by monitoring members' learning progress and providing regular reports to parents. Fitness clubs can also improve member satisfaction by providing individual training plans and automatically generating progress reports.

[1624] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1625] Step 1:

[1626] The user inputs their request using a smartphone app. This input data is free-form text data such as "I want to improve my English grades" or "I'm aiming to improve my athletic ability." The app sends this data to the server. Input data: Parent's request (text format). Output data: Request data sent to the server.

[1627] Step 2:

[1628] The server receives the request data sent by the user (guardian) from the smartphone app and stores the data in a database. Input data: Request data. Output data: Request data stored in the database.

[1629] Step 3:

[1630] The server retrieves the member's current status data and the guardian's request data from the database, integrates these data, and passes them to the artificial intelligence model (GPT-4 API). Input data: Request data, current status data. Output data: Plan generation request.

[1631] Step 4:

[1632] The server's AI model analyzes the input data and generates optimal learning and activity plans based on the parents' requests. The generated plans are returned to the server and stored in a database. Input data: Request data, current situation data. Output data: Generated plans.

[1633] Step 5:

[1634] The server notifies parents and store staff of the generated plan using a notification message containing the plan details. Input data: Generated plan. Output data: Notification message sent to parents and staff.

[1635] Step 6:

[1636] Users (store staff) use a smartphone app to enter their daily activity records and send the data to the server. Input data: Activity records (text format). Output data: Activity records sent to the server.

[1637] Step 7:

[1638] The server receives the activity record data sent by the user (staff member) and stores the data in the database. Input data: Activity record. Output data: Activity record stored in the database.

[1639] Step 8:

[1640] The server retrieves activity records and parental request data from the database and passes this data to an artificial intelligence model (GPT-4 API) to generate a report. Input data: Activity records, request data. Output data: Report generation request.

[1641] Step 9:

[1642] The server's AI model analyzes this data and automatically generates a report for parents. The generated report is returned to the server and stored in a database. Input data: Activity records, request data. Output data: Generated report.

[1643] Step 10:

[1644] The server notifies the parent of the generated report using a notification message containing the report. Input data: The generated report. Output data: The notification message sent to the parent.

[1645] Step 11:

[1646] The user (parent) uses a smartphone app to input feedback and sends the data to the server. Input data: Feedback (text format). Output data: Feedback data sent to the server.

[1647] Step 12:

[1648] The server receives feedback data sent by users (parents), stores the data in a database, and triggers plan updates as needed. Input data: Feedback data. Output data: Feedback data stored in the database, plan update requests.

[1649] 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.

[1650] This invention relates to a system that facilitates communication between after-school daycare centers and parents, creates learning plans and activity plans according to parents' requests, and automatically generates reports based on daily activity records. Furthermore, by combining this invention with an emotion engine that recognizes the user's emotions, it becomes possible to respond in a way that takes into consideration the emotions of parents and children.

[1651] Program processing

[1652] Platform-wide overview

[1653] The server creates a platform accessible to after-school care centers and parents. This platform includes a user database, a learning plan creation module, an activity plan creation module, an automatic report generation module, an emotion engine, and a communication module. This allows parents to easily input requests and feedback, and allows daycare centers to efficiently manage children's learning and activities.

[1654] Collecting user requests

[1655] The server receives the request data entered by the parent and stores it in a database. It then analyzes the parent's emotional data through an emotion engine and stores it together with the request data. Parents use their devices to enter their child's individual information and requests into the platform. The emotion engine analyzes emotions from the wording and tone of the input.

[1656] Specific examples

[1657] The parent types into the device, "I want to improve my child's English grades, and I'm feeling a bit anxious."

[1658] The server receives these requests, extracts the emotional data of "anxiety" using an emotion engine, and saves it in a request list for each parent.

[1659] Automatic generation of learning and activity plans

[1660] The server uses an AI module to create learning and activity plans based on the parents' requests, the child's current status data, and emotional data. By referring to the emotional data, it is possible to generate plans that take the child's needs into consideration.

[1661] Specific examples

[1662] The server passes the AI ​​module the request to "improve English grades," the emotional data of "anxiety," and the child's current English grades as input data.

[1663] For example, the AI ​​takes into account the child's impatience and creates a plan that includes special English lessons three times a week at a comfortable pace and the use of a learning app, and returns the plan to the server.

[1664] The server stores this plan in a database and notifies both parents and school counselors.

[1665] Collecting daily activity records

[1666] The teacher uses a terminal to input information about the child's learning status and daily activities, which is then stored in a database on the server. The emotion engine analyzes the child's emotions from the input text and stores this data in the database.

[1667] Specific examples

[1668] The after-school teacher types into the terminal, "Today we had a special English lesson, and the children participated actively." The emotion engine extracts the positive emotion of "active."

[1669] The server receives this and stores each child's activity record and emotional data in a database.

[1670] Automatic report generation

[1671] The server uses an AI module to automatically generate reports for parents based on daily activity records, as well as parents' requests and emotional data.

[1672] Specific examples

[1673] The server extracts information from the database about the progress of special English instruction, changes in the child's grades, and emotional data such as "positive," and passes this data to the AI ​​module.

[1674] Based on this, the AI ​​generates a report that reads, "Results of this week's special English instruction: Grades improved by 3 points. Next time, we will strengthen listening skills. The child is actively participating, so let's keep it up."

[1675] The server sends the report to the parent's device, where the parent can view it.

[1676] Processing Feedback

[1677] The server collects feedback from parents, analyzes the emotional data through an emotion engine, and stores it in a database along with any new requests or improvements.

[1678] Specific examples

[1679] Parents can input feedback into the device, such as, "I want my child to improve not only their English listening skills but also their speaking skills. I'm a little worried." The emotion engine then extracts the emotion of "anxiety."

[1680] The server receives this, stores it in a database, and triggers an update of the learning plan if necessary.

[1681] This will facilitate smooth mutual communication between the nursery school and parents, and make it possible to efficiently support children's growth and learning while also taking into consideration their emotions.

[1682] The processing flow will be explained below.

[1683] Step 1:

[1684] The server initializes the user database and registers the after-school care center's user information. Parents create their own accounts and log in to the platform using their devices.

[1685] Step 2:

[1686] Parents use the device to input their child's individual information (such as name, grade, and interests) and requests for learning and activities (such as subjects and activities to reinforce, special support, etc.). The emotion engine analyzes emotions from the input text and generates emotion data. The server receives this input data and emotion data and stores it in a database.

[1687] Step 3:

[1688] The server retrieves data on parents' requests, children's current status, and emotional data from the database and sends them to the AI ​​module, which analyzes the data and generates optimal learning and activity plans.

[1689] Step 4:

[1690] The server stores the learning and activity plans generated by the AI ​​module in a database and notifies the parents and after-school counselors of the results on the platform, who can then view the plans on their own devices.

[1691] Step 5:

[1692] After-school counselors use the terminals to input information about the children's learning status and daily activities. The emotion engine analyzes the children's emotions from the input text, and the server stores this information in a database.

[1693] Specific examples

[1694] An after-school teacher inputs, "Today we had a special English lesson, and the children participated actively." The emotion engine extracts the positive emotion of "active."

[1695] The server receives this and stores each child's activity record and emotional data in a database.

[1696] Step 6:

[1697] The server sends data to the AI ​​module based on daily activity records and parents' requests and emotional data to automatically generate reports. The AI ​​module analyzes the data and automatically generates reports for parents.

[1698] Specific examples

[1699] The server retrieves information from the database, such as progress in special English instruction, changes in the child's grades, and emotional data, and passes it on to the AI ​​module.

[1700] Based on this, the AI ​​generates a report that reads, "Results of this week's special English instruction: Grades improved by 3 points. Next time, we will strengthen listening skills. The child is actively participating, so let's keep it up."

[1701] The server sends the report to the parent's device, where the parent can view it.

[1702] Step 7:

[1703] Parents can use a device to check the report and enter their feedback on the content, and the emotion engine analyzes the emotional data from the feedback.

[1704] Specific examples

[1705] A parent enters feedback such as, "I want my child to improve not only his English listening skills but also his speaking skills. I'm a little worried." The emotion engine analyzes this and extracts the emotion data of "anxiety."

[1706] The server receives this and stores it in a database.

[1707] Step 8:

[1708] The server analyzes the feedback and emotional data from parents, and if necessary, requests the AI ​​module to update the learning plan and activity plan. The server then notifies the after-school instructor of the updated plan.

[1709] Specific examples

[1710] Based on the feedback, the server inputs into the AI ​​module the new request to "improve speaking skills" and the parent's emotional data of "anxiety."

[1711] The AI ​​module creates a new lesson plan and returns it to the server, which stores it in a database and notifies the after-school teacher.

[1712] As described above, the present invention facilitates mutual communication between daycare centers and parents, and can efficiently support children's growth and learning while taking into consideration their emotions.

[1713] Example 2

[1714] 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."

[1715] In modern society, with the increase in dual-income households, there is a need for smoother communication between parents and after-school daycare centers. Conventional systems make it difficult for parents to create learning and activity plans that meet their needs, and are therefore unable to adequately support children's development. Furthermore, there is a lack of consideration for the feelings of parents and children, making it difficult to provide an optimal learning environment.

[1716] 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.

[1717] In this invention, the server includes means including an artificial intelligence model for automatically generating learning plans and activity plans, means for receiving requests and feedback from parents, means including an emotion engine for analyzing input data and emotion data from parents, means for automatically generating reports to parents, and means for providing a platform for communication between the childcare facility and parents. This enables the creation of learning plans and activity plans based on parents' requests and appropriate responses that take into consideration the emotions of parents and children.

[1718] An "artificial intelligence model for automatically generating learning plans and activity plans" is a system that includes an algorithm for automatically creating optimal learning plans and activity plans based on the requests of parents and current data on the child.

[1719] "Means for receiving requests and feedback from parents" refers to a function that includes an interface for receiving and processing requests and feedback that parents input into the system.

[1720] The "emotion engine that analyzes parental input data and emotional data" is an analysis engine that analyzes emotions from data entered by parents and utilizes that information within the system.

[1721] "Means for automatically generating reports for parents" is a function that automatically creates reports for parents based on the child's daily activity records and progress data for the learning plan.

[1722] The "platform for communication between childcare facilities and parents" is a system that provides an online platform for childcare facilities and parents to communicate efficiently.

[1723] The "database" is a storage device that centrally stores and manages various information used by the system, such as parental input data, emotional data, and records of children's daily activities.

[1724] This invention is a system that facilitates communication between after-school care facilities and parents, creates learning plans and activity plans according to parents' requests, and automatically generates reports based on daily activity records. Furthermore, by combining it with an emotion engine that recognizes the user's emotions, it is possible to respond in a way that takes into consideration the emotions of parents and children.

[1725] Building a platform

[1726] The server creates a platform accessible to after-school care facilities and parents. This platform includes a user database, a learning plan creation module, an activity plan creation module, an automatic report generation module, an emotion engine, and a communication module. This allows parents to easily input requests and feedback, and enables after-school care facilities to efficiently manage children's learning and activities.

[1727] Collecting user requests

[1728] Users (guardians) can log in to the platform using their devices and enter their child's individual information and requests. The server receives the request data entered by the guardian and stores it in the user database. It also analyzes the guardian's emotional data through an emotion engine and stores it together with the request data.

[1729] Examples:

[1730] Parents can input requests such as, "I want to improve my child's English grades, and I'm feeling a bit anxious." The server receives these requests, extracts the emotion data for "anxiety" using an emotion engine, and saves it in a request list for each parent.

[1731] Automatic generation of learning and activity plans

[1732] The server uses an AI module to create learning and activity plans based on the parents' requests, the child's current status data, and emotional data. By referring to the emotional data, it is possible to generate plans that take the child's needs into consideration.

[1733] Examples:

[1734] The server passes the AI ​​module the request to "improve English grades," emotional data on "impatience," and the child's current English grades as input data. The AI ​​takes "impatience" into account, for example, to create a plan that incorporates special English lessons three times a week at a comfortable pace and the use of a learning app, and returns it to the server. The server stores this plan in a database and notifies both the parents and the after-school care instructor.

[1735] Collecting daily activity records

[1736] After-school instructors use terminals to input the children's daily learning status and activities. The server receives the input data and stores it in an activity record database. The emotion engine analyzes the children's emotions from the input text and stores this data in the database as well.

[1737] Examples:

[1738] The after-school teacher types into the terminal, "Today we had a special English lesson, and the children participated actively." The emotion engine extracts the positive emotion of "active." The server receives this and stores each child's activity record and emotional data in a database.

[1739] Automatic report generation

[1740] The server uses an AI module to automatically generate reports for parents based on daily activity records, as well as parents' requests and emotional data.

[1741] Examples:

[1742] The server extracts data from the database, including the progress of the English special instruction, changes in the child's grades, and emotional data such as "positive," and passes it to the AI ​​module. Based on this, the AI ​​generates a report that reads, "Results of this week's English special instruction: Grades improved by 3 points. Next time, we will strengthen listening skills. The child is actively participating, so let's keep it up." The server then sends the report to the parent's device, where the parent can review it.

[1743] Processing Feedback

[1744] The user (parent) enters feedback based on the report. The server receives the entered feedback and analyzes it through the emotion engine. The server extracts the emotion data of "anxiety" and stores it in the database along with the feedback. The server triggers an update of the learning plan as needed.

[1745] Examples:

[1746] The parent inputs feedback on the device, such as "I want my child to improve not only his English listening skills but also his speaking skills. I'm a little anxious." The server receives this, and the emotion engine extracts the emotion of "anxiety." The server saves this data and requests the AI ​​module to regenerate the plan again.

[1747] This will facilitate smooth mutual communication between childcare facilities and parents, making it possible to efficiently support children's growth and learning while also taking into consideration their emotions.

[1748] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1749] Step 1:

[1750] Users (parents) log in to the platform using their devices and input their child's individual information and requests. The input data may include requests such as "I want to improve my English grades, and I'm feeling a bit anxious." This input data is sent to the server.

[1751] Step 2:

[1752] The server receives the request data entered by the guardian and stores it in the user database. It then sends the guardian's input data to the emotion engine for emotion analysis. The emotion engine extracts the emotion data of "impatience" from the input sentence and stores it together with the request data. The input is the guardian's request data, and the output is the request data and emotion data.

[1753] Step 3:

[1754] The server calls the AI ​​module and provides the request data, the child's current situation data, and emotional data as input data. Specifically, the server passes the request, "I want to improve my English grades," the emotional data, "impatience," and the child's current English grades to the AI ​​module. The AI ​​module generates optimal learning plans and activity plans based on this data. The inputs are the request data, emotional data, and current situation data, and the output is the generated learning plan.

[1755] Step 4:

[1756] The server stores the generated plan in a database and notifies both parents and teachers. The notification includes details of the new lesson and activity plan. The input is the generated lesson plan and the output is a notification message.

[1757] Step 5:

[1758] The after-school care instructor uses a terminal to input the children's daily learning status and activities. For example, they might input, "Today we had a special English lesson, and the children actively participated." This data is sent to the server. The input is a record of daily activities, and the output is activity data that is saved on the server.

[1759] Step 6:

[1760] The server stores the received activity data in an activity record database. It then uses an emotion engine to analyze the child's emotions from the input text, extracts positive emotion data such as "active," and stores this in the database. The input is activity data, and the output is an activity record and emotion data.

[1761] Step 7:

[1762] The server uses an AI module to automatically generate a report for parents based on daily activity records and parental requests and emotional data. Specifically, the server generates a report such as, "Results of this week's special English instruction: Grades improved by 3 points. Next time, listening skills will be strengthened. The child is actively participating, so let's keep it up." The input is the activity record, request data, and emotional data, and the output is an automatically generated report.

[1763] Step 8:

[1764] The server sends the generated report to the parent's terminal, where the parent checks it. The input is the automatically generated report, and the output is the report displayed on the parent's terminal.

[1765] Step 9:

[1766] The user (parent) inputs feedback based on the report. For example, they might say, "I want you to improve not only your English listening skills but also your speaking skills. I'm a little worried." This feedback is sent to the server.

[1767] Step 10:

[1768] The server receives the input feedback and analyzes it through the emotion engine. The emotion engine extracts the emotion data of "anxiety" and stores it together with the feedback in a database. The input is the feedback data, and the output is the feedback and emotion data.

[1769] Step 11:

[1770] The server triggers an update of the learning plan as needed. Based on newly added feedback, the server requests the AI ​​module to regenerate the plan again. This allows the child's learning plan to adapt to their latest needs and emotions. The input is feedback and emotional data, and the output is an updated learning plan.

[1771] (Application example 2)

[1772] 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."

[1773] Conventional communication between after-school daycare centers and parents has been difficult to accurately reflect parents' requests and feedback, requiring a great deal of effort and time to create learning and activity plans and to report daily activity records. Furthermore, communication is often insufficient due to a lack of consideration for the feelings of parents and children. Furthermore, the lack of a system that provides learning support functions using virtual reality makes it difficult to effectively stimulate children's motivation to learn. The purpose of this invention is to solve these problems and realize smooth communication between parents and daycare centers and effective learning support.

[1774] 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.

[1775] In this invention, the server includes means for including an artificial intelligence model for automatically generating a learning plan, means for receiving requests and feedback from parents, means for automatically generating reports to parents, means for providing a platform for communication between the daycare center and parents, means for providing learning support functions using virtual reality, means for analyzing and saving the emotions of parents and children using an emotion analysis engine, and means for recording children's learning plans and daily activities via a head-mounted display. This facilitates smooth two-way communication between parents and the after-school daycare center, enabling the provision of accurate learning plans based on parents' requests and children's learning status, responses that take emotions into consideration, and automatic generation of reports. Furthermore, learning using virtual reality can effectively stimulate children's motivation to learn and provide comprehensive learning support.

[1776] The "artificial intelligence model for automatically generating learning plans" is an artificial intelligence technology that automatically creates optimal learning plans based on requests from parents and data on the child's learning situation.

[1777] "Means for receiving requests and feedback from parents" refers to an interface and system that allows parents to input requests and feedback regarding learning plans and their children's learning progress.

[1778] The "means for automatically generating reports for parents" is a system that automatically creates reports for parents based on the child's learning situation and activity records.

[1779] "Means for providing a platform for communication between childcare facilities and parents" refers to an online platform that allows childcare facility staff and parents to communicate with each other and share information.

[1780] "Means for providing learning support functions using virtual reality" refers to a system that utilizes virtual reality technology to provide educational content to children and assist them in their learning.

[1781] The "means for analyzing and storing the emotions of guardians and children using an emotion analysis engine" is a system that uses natural language processing technology to extract emotions from input data of guardians and children and stores them in a database.

[1782] "Means for recording a child's study plan and daily activities through a head-mounted display" refers to an interface and system for recording a child's study plan and daily activities using a head-mounted display.

[1783] The present invention is a learning support system that uses virtual reality technology to facilitate mutual communication between after-school daycare centers and parents, automatically generates learning plans and activity plans according to parents' requests, and automatically generates reports based on daily activity records. Furthermore, it is possible to respond in a way that takes into account the emotions of parents and children using an emotion analysis engine. This invention is implemented as follows.

[1784] First, the server contains an artificial intelligence model (e.g., TensorFlow, PyTorch) for automatically generating a learning plan. Parents use a smartphone or tablet to input their requests and feedback when setting up a learning plan. The server then analyzes the input text data using a sentiment analysis engine (e.g., Google Cloud Natural Language API) and also collects and stores the parents' emotional data.

[1785] Next, the server uses an AI module to automatically generate learning and activity plans based on requests from parents and data on the child's current situation and emotions. This allows it to propose the optimal plan based on the parent's requests and the child's learning situation. It also provides educational content to children using virtual reality technology (e.g., Oculus Rift, HTC Vive). This VR content can increase children's motivation to learn.

[1786] The after-school counselor inputs the children's daily learning plans and activities through a head-mounted display, and the server stores this data in a database. An emotion analysis engine is used to analyze the children's emotions from the input text, and this emotion data is also stored in the database. For example, if an after-school counselor inputs the content, "Today we had a special English lesson, and the children participated enthusiastically," the emotion analysis engine will extract the positive emotion of "enthusiastically."

[1787] The server uses an AI module to automatically generate reports for parents based on daily activity records and emotional data. For example, based on the progress of special English instruction, the child's improvement in grades, and emotional data such as "positive," the AI ​​could generate a report such as, "Results of this week's special English instruction: Grades improved by 3 points. Next time, listening skills will be strengthened. The child is actively participating, so let's keep it up," and the server would send the report to the parent's device.

[1788] After checking the report, parents can submit further requests or feedback. For example, if parents submit feedback such as, "I would like you to improve not only my child's English listening skills but also their speaking skills. I'm a little worried," the emotion analysis engine will extract the emotion of "anxiety," which the server will receive and store in the database, and update the learning plan as necessary.

[1789] Prompt Sentence Examples

[1790] "Please generate a study plan. The request is 'I want to improve my English grades,' the emotion is 'I'm anxious,' and the current grade is '70 points.'"

[1791] In this way, the learning support system using virtual reality of the present invention can facilitate smooth communication between parents and after-school daycare centers and effectively increase children's motivation to learn.

[1792] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1793] Step 1:

[1794] Gathering parents' requests

[1795] 1.1 Parents enter their requests for learning plans and activities using their smartphones or tablets.

[1796] 1.2 The parent's input data is sent to the server.

[1797] 1.3 The server uses an emotion analysis engine (e.g., Google Cloud Natural Language API) to analyze emotions from the text data and extract emotional data such as "anxiety" or "impatience."

[1798] Input: Text data entered by the parent

[1799] Output: Parental request data and analyzed emotion data

[1800] Step 2:

[1801] Storage of request data

[1802] 2.1 The server stores the request data and emotion data received from the parents in a database.

[1803] Input: Parental request data and emotion data

[1804] Output: Data stored in the database

[1805] Step 3:

[1806] Auto-generated learning plan

[1807] 3.1 The server inputs the saved parental request data, emotional data, and child learning status data into a generative AI model (e.g., TensorFlow, PyTorch).

[1808] 3.2 The generative AI model creates an optimal learning plan based on this data.

[1809] Input: Parental request data, emotional data, student learning status data

[1810] Output: Auto-generated learning plan

[1811] Step 4:

[1812] Plan Notification

[1813] 4.1 The server notifies the parents and after-school counselors of the generated learning plan.

[1814] 4.2 Parents and school counselors will review the notified learning plan.

[1815] Input: Auto-generated study plan

[1816] Output: Notification to parents and school counselors

[1817] Step 5:

[1818] Daily activity record

[1819] 5.1 After-school teachers will use head-mounted displays or devices to record the children's daily learning activities.

[1820] 5.2 The recorded data is sent to a server and an emotion analysis engine is used to analyze the child's emotions.

[1821] 5.3 The analyzed emotion data is stored in a database along with daily activity records.

[1822] Input: Learning activity data entered by the after-school instructor

[1823] Output: Activity records and emotion data stored in a database

[1824] Step 6:

[1825] Automatic report generation

[1826] 6.1 The server automatically generates reports for parents based on daily activity records and emotional data using a generative AI model.

[1827] Input: Daily activity records, emotional data

[1828] Output: Auto-generated parental report

[1829] Step 7:

[1830] Report notification

[1831] 7.1 The server sends the generated report to the parent's device.

[1832] 7.2 Parents will review the report and provide feedback as necessary.

[1833] Input: Auto-generated parent report

[1834] Output: Report notification to parent's device

[1835] Step 8:

[1836] Processing Feedback

[1837] 8.1 The feedback entered by the parent is sent to the server, and the emotion data is analyzed using the emotion analysis engine.

[1838] 8.2 The analyzed feedback data and emotion data are stored in a database. New learning plans are generated as needed.

[1839] Input: Parent feedback data

[1840] Output: Feedback data and emotion data stored in a database, a new learning plan

[1841] In this way, this system supports effective communication between parents and after-school daycare centers, and makes it possible to comprehensively manage and support children's learning.

[1842] 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.

[1843] 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.

[1844] 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.

[1845] 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.

[1846] 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.

[1847] 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.

[1848] 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).

[1849] 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.

[1850] 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."

[1851] 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.

[1852] 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).

[1853] 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.

[1854] 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.

[1855] 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.

[1856] 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.

[1857] 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.

[1858] 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.

[1859] 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.

[1860] Furthermore, the hardware structure of these various processors can be, more specifically, an electric circuit that combines circuit eleme...

Claims

1. means including an artificial intelligence model for automatically generating a lesson plan; A means of receiving requests and feedback from parents; a means for automatically generating reports to parents; A means to provide a platform for communication between childcare centres and parents; A system including:

2. 10. The system of claim 1, further comprising a database for storing parental input data.

3. 2. The system of claim 1, further comprising means for storing a record of the child's daily activities in a database.

Images