system

Abstract

We provide the system. [Solution] A device that receives and stores notifications from an information processing device, A device that automatically sends received notifications to a terminal, A device that acquires and analyzes information related to academic performance and proposes support based on the results, A communication device for providing real-time notifications of academic information, A device that automatically proposes individualized instruction plans based on learning tendencies, A system that includes this.

Description

Technical Field

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

Background Art

[0002] Patent Document 1 discloses a method for controlling a persona chatbot, which is performed by at least one processor, and includes steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to an explanation of a 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

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the current education system, the information transmission between educational institutions and parents is insufficient, and there are often delays in important notifications and grasping the academic progress of students. In addition, due to the fragmented management of academic information and the lack of cooperation between parents and educational institutions, it is difficult to implement prompt educational support for students. In such a current situation, it has become difficult to construct an optimal educational environment for the three parties of parents, educational institutions, and students.

Means for Solving the Problems

[0005] This invention provides a system that includes means for receiving notifications from an information processing device and storing them in a database. It also includes means for automatically sending the received notifications to a parent's terminal, and further includes means for acquiring student academic information, analyzing it using a specific algorithm, and proposing learning support to parents and educational institutions based on the results. This system enables immediate information sharing, identification of academic trends, and automatic generation of administrative information, thereby improving the quality of education and enabling prompt and appropriate support.

[0006] An "information processing device" is a part of a computer system that has the functions of receiving, processing, and storing digital data.

[0007] A "notification" is a message or alert containing important information sent by an educational institution to parents or other stakeholders.

[0008] A "database" is a structured collection of data designed to efficiently store, manage, and retrieve information.

[0009] A "terminal" is an electronic device used by a user to interact with an information processing device.

[0010] "Academic information" refers to data related to the progress of education, such as students' grades, attendance records, and assignment submission status.

[0011] An "algorithm" is a logical set of steps or computational processes that define how to solve a specific problem.

[0012] "Learning support" refers to additional instruction and support provided with the aim of improving students' academic performance.

[0013] An "educational institution" is an organization that provides education, such as a school or similar institution.

[0014] A "system" refers to a configuration in which multiple devices or components work together to achieve a specific function. [Brief explanation of the drawing]

[0015] [Figure 1] This is a conceptual diagram showing an example of the configuration of a data processing system according to the first embodiment. [Figure 2] This is a conceptual diagram showing an example of the essential functions of a data processing device and a smart device according to the first embodiment. [Figure 3] This is a conceptual diagram showing an example of the configuration of a data processing system according to the second embodiment. [Figure 4] This is a conceptual diagram showing an example of the main functions of a data processing device and smart glasses according to the second embodiment. [Figure 5] This is a conceptual diagram showing an example of the configuration of a data processing system according to the third embodiment. [Figure 6] This is a conceptual diagram showing an example of the main functions of a data processing device and a headset-type terminal according to the third embodiment. [Figure 7] This is a conceptual diagram showing an example of the configuration of a data processing system according to the fourth embodiment. [Figure 8] This is a conceptual diagram showing an example of the main functions of a data processing device and a robot according to the fourth embodiment. [Figure 9] This shows an emotion map where multiple emotions are mapped. [Figure 10] This shows an emotion map where multiple emotions are mapped. [Figure 11] This is a sequence diagram showing the processing flow of the data processing system in Example 1. [Figure 12] This is a sequence diagram showing the processing flow of the data processing system in Application Example 1. [Figure 13] This is a sequence diagram showing the processing flow of the data processing system in Example 2, which incorporates an emotion engine. [Figure 14]It is a sequence diagram showing the processing flow of a data processing system in Application Example 2 when a sentiment engine is combined.

Embodiments for Carrying Out the Invention

[0016] Hereinafter, an example of an embodiment of a system according to the technology of the present disclosure will be described with reference to the accompanying drawings.

[0017] First, the terms used in the following description will be explained.

[0018] In the following embodiments, a numbered processor (hereinafter simply referred to as "processor") may be a single arithmetic unit or a combination of multiple arithmetic units. Also, the processor may be a single type of arithmetic unit or a combination of multiple types of arithmetic units. Examples of arithmetic units include a CPU (Central Processing Unit), a GPU (Graphics Processing Unit), a GPGPU (General-Purpose computing on Graphics Processing Units), an APU (Accelerated Processing Unit), and the like.

[0019] In the following embodiments, a numbered RAM (Random Access Memory) is a memory in which information is temporarily stored and is used as a work memory by the processor.

[0020] In the following embodiments, a numbered storage is one or more non-volatile storage devices that store various programs and various parameters, etc. Examples of non-volatile storage devices include flash memory (SSD (Solid State Drive)), magnetic disks (e.g., hard disks), or magnetic tapes, etc.

[0021] In the following embodiments, the signed communication interface (I / F) is an interface that includes a communication processor and an antenna, etc. The communication interface manages communication between multiple computers. Examples of communication standards applicable to the communication interface include wireless communication standards such as 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), or Bluetooth (registered trademark).

[0022] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B." That is, "A and / or B" means that it may be A alone, or B alone, or a combination of A and B. Furthermore, in this specification, the same concept as "A and / or B" applies when expressing three or more things linked by "and / or."

[0023] [First Embodiment]

[0024] Figure 1 shows an example of the configuration of the data processing system 10 according to the first embodiment.

[0025] As shown in Figure 1, the data processing system 10 includes a data processing device 12 and a smart device 14. An example of the data processing device 12 is a server.

[0026] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0027] The smart device 14 comprises a computer 36, a reception device 38, an output device 40, a camera 42, and a communication interface 44. The computer 36 comprises a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The reception device 38, output device 40, and camera 42 are also connected to the bus 52.

[0028] The reception device 38 is equipped with a touch panel 38A and a microphone 38B, etc., and receives user input. The touch panel 38A receives user input by detecting contact with an object (e.g., a pen or finger). The microphone 38B receives user input by detecting the user's voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the data indicating the user input.

[0029] The output device 40 includes a display 40A and a speaker 40B, and presents data to the user 20 by outputting the data in a form perceptible to the user 20 (e.g., audio and / or text). The display 40A displays visible information such as text and images according to instructions from the processor 46. The speaker 40B outputs audio according to instructions from the processor 46. The camera 42 is a small digital camera equipped with an optical system such as a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0030] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various types of information between processor 46 and processor 28 via network 54.

[0031] Figure 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0032] As shown in Figure 2, in the data processing device 12, a specific processing is performed by the processor 28. A specific processing program 56 is stored in the storage 32. The specific processing program 56 is an example of a "program" related to the technology of this disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0033] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0034] In the smart device 14, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The reception output program 60 is used in conjunction with a specific processing program 56 by the data processing system 10. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0035] Next, the specific processing performed by the specific processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the smart device 14 as the "terminal".

[0036] The system of this invention is designed to streamline information exchange between educational institutions and parents, enabling immediate monitoring of academic progress and appropriate learning support. Its embodiments are described below.

[0037] This system consists of an information processing device with three main functions, enabling the automation of information transmission and academic support. Specifically, it includes a "parent support function," a "student affairs support function," and a "performance analysis function."

[0038] First, the server has the function of receiving important notifications from educational institutions and storing them in a database. This database maintains a history of notifications and is configured in a way that allows for easy reference of past communications. Next, the server automatically sends the received notifications to each parent's device. In this process, the device displays the notification as a pop-up message or email to ensure that no information is overlooked.

[0039] Next, the server automates administrative tasks. Specifically, it is responsible for generating necessary documents for educational institutions and distributing them to relevant parties. For example, it has a function to automatically generate invitations for parent-teacher meetings from templates and send them based on a selected recipient list.

[0040] Furthermore, the server comprehensively processes students' academic information. Specifically, it has the capability to analyze academic information, including grades, attendance records, and assignment submission history. Based on the analysis, it detects changes in trends and anomalies, identifies the need for learning support, and forms appropriate guidance and additional assignment suggestions. This information is then notified to teachers and parents (users) by the server, facilitating early intervention to improve students' academic performance.

[0041] The introduction of this system will provide academic progress information to relevant parties in real time, eliminating delays and fragmentation in information transmission. For example, if a student shows a consistent decline in recent math grades, the server will detect this and report it to parents and teachers. Furthermore, individualized learning programs and additional instruction will be suggested for that student, enabling rapid improvement in their learning.

[0042] As described above, this invention embodies a form that strengthens cooperation between educational institutions and parents and provides students with the optimal learning environment.

[0043] The following describes the processing flow.

[0044] Step 1:

[0045] The server receives important notifications from educational institutions. These notifications may include announcements about class visits or changes to event schedules. This information is immediately stored in the server's database.

[0046] Step 2:

[0047] The server analyzes received notifications and optimizes their content for parents. For example, it selects message templates based on the notification content and customizes notifications as needed.

[0048] Step 3:

[0049] The server automatically sends optimized notifications to the parent's registered email address or application, ensuring that information reaches parents quickly and reliably.

[0050] Step 4:

[0051] The device displays received notifications to the user. These notifications are received as pop-ups or emails, allowing parents to review and respond accordingly.

[0052] Step 5:

[0053] Parents, as users, take necessary actions based on the notification. For example, they might prepare for a school visit.

[0054] Step 6:

[0055] The server also automatically generates information related to administrative tasks (e.g., parent-teacher meeting announcements) and requests confirmation from the educational institution's administrator. After confirmation, it sends the necessary documents to the relevant parties.

[0056] Step 7:

[0057] The server regularly updates student grades and attendance information and reflects it in the database. This ensures that the most up-to-date academic information is always maintained.

[0058] Step 8:

[0059] The server uses specific algorithms to analyze student data. For example, it can detect patterns of declining academic performance and generate information to pinpoint the causes.

[0060] Step 9:

[0061] Based on the analysis results, the server notifies parents and educational institutions with suggestions for appropriate learning support. This allows educational institutions to provide additional instruction as needed.

[0062] Step 10:

[0063] Teachers and parents, who are users of the system, can consider and implement specific support measures for students based on the learning support information provided by the server.

[0064] (Example 1)

[0065] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server," and the smart device 14 will be referred to as the "terminal."

[0066] In recent years, there has been a growing demand for more efficient communication between educational institutions and parents. In particular, it is crucial to quickly and accurately grasp students' academic information and provide necessary learning support. However, traditional systems suffer from delays in information transmission and require significant manpower and time for analyzing academic evaluations, making efficient support difficult. Furthermore, there is a lack of mechanisms to quickly propose appropriate educational strategies to students who require individualized learning support.

[0067] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0068] In this invention, the server includes means for receiving notifications from communication units and storing them in a storage medium, means for automatically transmitting the received notifications to an associated output device, and means for acquiring and analyzing education-related information and proposing instructional support based on the results. This enables real-time information sharing between educational institutions and parents. In particular, automatic analysis within the server allows for rapid identification of trends in academic evaluation, and furthermore, the generative model can propose an optimal learning support plan. As a result, individualized learning support for students can be advanced more effectively and efficiently.

[0069] A "communication unit" refers to a device or system that can transmit or receive information.

[0070] "Notification" refers to information or messages intended to inform other devices or people of specific information or messages.

[0071] A "storage medium" refers to a physical or digital medium used to store data or information.

[0072] "Means" refer to the methods or mechanisms used to achieve a specific objective.

[0073] An "output device" is a device used to display processed information to the user.

[0074] "Education-related information" refers to data on student performance, attendance, assignment submissions, and other information related to education.

[0075] "Analysis" refers to the process of extracting useful insights from data and information.

[0076] "Instructional support" refers to assistance and advice provided to help students learn and encourage improvement.

[0077] A "generative model" is an algorithm or system that uses data to automatically generate new information, such as proposals.

[0078] One embodiment of this invention is a system designed to streamline information exchange between educational institutions and parents and to promote academic support for students. This system includes several components, as described below, and provides a seamless flow of information as a whole.

[0079] The server uses a communication interface to receive notifications from educational institutions. This interface automatically receives various notifications via an API and stores them on an internal storage medium. The stored data is indexed and managed in a database. The server accesses this data using SQL queries and uses it as needed.

[0080] The server automatically forwards received notifications to the relevant parent's device. This forwarding is done via email using the SMTP protocol or mobile push notifications using Firebase Cloud Messaging (FMC). This feature allows users to instantly view notifications.

[0081] Furthermore, the server utilizes a generative AI model to analyze education-related information stored in the database, such as student grades and attendance data. Specifically, it cleanses and formats the data using Python and Pandas, and then performs analysis using a machine learning model with Scikit-learn. As a result, it identifies academic trends and immediately generates suggestions if individualized instructional support is needed.

[0082] The proposed learning support content is then notified to teachers and parents by the server. The notification includes information derived from the generating AI model and is delivered as a prompt. Specific examples include "Please check the latest notifications from your educational institution" and "The student's math grades are declining. Additional learning support is needed."

[0083] This system facilitates rapid information sharing between educational institutions and parents, enabling appropriate academic support for students. This is expected to lead to improved academic performance.

[0084] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0085] Step 1:

[0086] The server receives notifications sent from educational institutions. The input is an electronic notification sent via an API. Upon receiving this notification, the server parses the data and stores it in an appropriate format on a storage medium. This involves using SQL queries to store the information in a database. The output is the notification data stored on the storage medium.

[0087] Step 2:

[0088] The server prepares the notification data stored on the storage medium for delivery to the parent's device. The input is the notification data stored in step 1. The server generates an email using the SMTP protocol or configures a push notification using Firebase Cloud Messaging (FMC). The output is the deliverable notification message.

[0089] Step 3:

[0090] The terminal receives notification messages sent from the server and displays them to the user as visual alerts. The input is the notification message sent from the server. The terminal presents this to the user as a pop-up message or email. The output is easily accessible notification information displayed on the user's screen.

[0091] Step 4:

[0092] The server retrieves student academic information stored on a storage medium and analyzes it using a generative AI model. The input consists of educational data such as student grades, attendance records, and assignment submission history. The server processes this data using Python and Pandas, and then analyzes it using a machine learning model with Scikit-learn. The output includes academic trends and anomaly detection results.

[0093] Step 5:

[0094] The server generates necessary instructional support using an AI model based on the results of the academic performance analysis. The input is the analysis results from step 4. Based on the analysis results, the server creates an optimized learning support plan and generates it as a notification. The output is a prompt message containing specific instructional suggestions.

[0095] Step 6:

[0096] The server notifies the user (teacher or parent) of the generated prompt text for the lesson suggestion. The input is the prompt text generated in step 5. The server delivers the suggestion via email or in-app notification. The output is the learning support information based on the prompt text received by the teacher or parent.

[0097] (Application Example 1)

[0098] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server," and the smart device 14 will be referred to as the "terminal."

[0099] There is a growing need for systems that streamline information exchange between educational institutions and parents, and allow for real-time monitoring of students' academic progress. However, current systems typically suffer from information delays and fragmentation, making it difficult to provide prompt learning support. Solving this problem and providing effective academic support is essential.

[0100] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0101] In this invention, the server includes a device that receives and stores notifications from an information processing device, a device that automatically transmits the received notifications to a terminal, a device that acquires and analyzes academic information and proposes support based on the results, a communication device for real-time notification of academic information, and a device that automatically proposes an individualized instruction plan based on learning trends. This enables efficient information transmission and real-time academic support.

[0102] An "information processing device" is a device that receives and processes data, manages it appropriately, and performs necessary functions based on that data.

[0103] A "notification" is a message sent to inform a user of specific information.

[0104] A "terminal" refers to a device used by a user to receive and manipulate information.

[0105] A "server" is a central computer system that provides data and services to multiple terminals or users.

[0106] "Academic information" refers to information related to a student's activities at an educational institution, such as their grades, attendance record, and assignment submission status.

[0107] "Analysis" refers to the process of thoroughly analyzing data and deriving useful information and trends from it.

[0108] A "communication device" refers to a combination of hardware or software for sending and receiving information, and is particularly useful for enabling real-time communication.

[0109] An "individualized instruction plan" is a customized learning support plan designed to meet the individual learning needs of each student.

[0110] In implementing this invention, the server receives notifications from educational institutions and stores them in a database. This database is configured to retain a history of past notifications and to allow for quick access as needed. The server then automatically sends the notifications to the terminals of parents and educators. The terminals are responsible for displaying the notifications as pop-up messages or emails to ensure they reach the users.

[0111] This system centrally manages and analyzes students' academic performance information on a server. The analysis uses specific algorithms to identify performance trends and generate foundational data for providing learning support. Based on the analysis results, the server notifies parents and teachers to propose necessary guidance or support plans.

[0112] For example, if a student's math grades consistently decline, the server detects this change and immediately sends a notification to the parent's device. This allows for swift action to be taken. Furthermore, an individualized tutoring plan is automatically generated and provided to both the parent and the educator.

[0113] The generative AI model can also be used to generate new learning support prompts. For example, it is possible to create a prompt like this: "Please suggest the content of the notification to parents if a particular student's grades decline."

[0114] This series of processes involving servers and terminals enables rapid and effective communication between educational institutions and parents, allowing for appropriate measures to be taken to improve students' academic performance.

[0115] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0116] Step 1:

[0117] The server receives notifications from educational institutions. The input is notification data sent from educational institutions, which the server stores in its database. Specifically, notification content (such as meeting announcements) arrives at the server and is saved in the database, making it available for later reference.

[0118] Step 2:

[0119] The server sends received notifications to the device. The input is the notification data stored in the database, and the output is the notification displayed on the device. The server automatically sends this notification as an email or pop-up to the selected device. This ensures that the user receives the latest information.

[0120] Step 3:

[0121] The server collects and analyzes students' academic information. Inputs include academic data such as grades, attendance, and assignment submission status, while output is the analysis results. Specifically, the server sorts each student's data and performs analysis to identify trends. If an anomaly is detected during this process, it is recorded.

[0122] Step 4:

[0123] The server proposes support based on the analysis results. The input is the analysis results of academic information, and the output is the proposed support program. The server utilizes a generative AI model to generate individualized tutoring plans and additional advice based on the student's needs. This series of suggestions is provided to the user as a notification.

[0124] Step 5:

[0125] The device receives and displays notifications. The input is notification data sent from the server, and the output is information displayed to the user. Specifically, the device displays notifications via pop-up messages or email to help the user take quick action based on the information.

[0126] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0127] This invention incorporates an emotion engine that recognizes user emotions into a system that improves communication between educational institutions, parents, and students, and utilizes emotional information for academic support. An embodiment of this system is described below.

[0128] This system consists of an information processing unit, an emotion engine, a database, and a communication interface. At the heart of the system is the information processing unit, which receives notifications from educational institutions, stores them in the database, analyzes the content of the notifications, and automatically sends them to the parents' devices.

[0129] Here, the emotion engine has the function of recognizing the user's emotional state in real time and appropriately adjusting the notification content using that information. Specifically, the server analyzes the parent's recent emotional state using the emotion engine and flexibly changes the notification content based on that data. For example, conventional notification messages can be improved to be expressed in a more acceptable way.

[0130] After a notification is sent, the device presents it to the parent. This notification incorporates customization based on sentiment analysis. This improves the parent's access to and understanding of the information.

[0131] Furthermore, in addition to students' academic information, the server collects emotional data obtained by the emotion engine and records it in a database. Based on this information, the server feeds the emotional data back to educational institutions and develops academic support plans that take into account the students' psychological factors. Specifically, if the emotion engine determines that a student is experiencing stress with a particular subject, the server will suggest additional support or follow-up from teachers accordingly.

[0132] Overall, this invention improves the accuracy of information transmission while simultaneously enabling more personalized responses through an understanding of emotions, thereby enhancing the students' learning experience.

[0133] The following describes the processing flow.

[0134] Step 1:

[0135] The server receives notification data from educational institutions. These notifications include information about school events and emergency contacts.

[0136] Step 2:

[0137] The server stores received notifications in a database. This maintains a history of notifications, which can then be accessed when needed.

[0138] Step 3:

[0139] The server retrieves the caregiver's recent emotional state through the emotion engine. The emotion engine analyzes emotions using past data and sensor information.

[0140] Step 4:

[0141] The server optimizes the content of notifications based on the acquired emotional information. For example, if the emotion is negative, the notification message is modified to use softer language.

[0142] Step 5:

[0143] The server sends optimized notifications to the parent's device. These notifications are sent via email or app notifications.

[0144] Step 6:

[0145] The device displays incoming notifications as pop-ups to ensure they reach the parent / guardian. This allows the parent / guardian to check the information immediately.

[0146] Step 7:

[0147] The user (parent / guardian) will check the notification and take appropriate action. For example, if there is a request to participate, they will adjust their schedule.

[0148] Step 8:

[0149] The server continuously collects students' academic and emotional data. This data is integrated into a grade database and emotional history.

[0150] Step 9:

[0151] The server analyzes the collected data and applies specific algorithms to identify academic trends and emotional patterns.

[0152] Step 10:

[0153] Based on the analysis results, the server provides individualized learning support suggestions to educational institutions. This enables teachers to provide appropriate guidance that takes into account students' academic performance and emotional state.

[0154] Step 11:

[0155] The teachers, as users of the system, will consider the suggestions and plan and implement measures to address student issues. They will also collaborate with parents as needed.

[0156] Through this process, educational institutions and parents will be able to collaborate more easily and provide more personalized support to students.

[0157] (Example 2)

[0158] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server" and the smart device 14 as the "terminal".

[0159] As the importance of improving the accuracy of information transmission and providing individualized support between educational institutions, parents, and students increases, traditional systems often transmit information in only one direction, making it difficult to provide information that takes emotional context into account. Furthermore, there is a problem that individualized support is insufficient because academic support plans based on students' emotional states have not been adequately developed.

[0160] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0161] In this invention, the server includes means for receiving notifications from information processing equipment and storing them in an information storage medium; means for automatically transmitting the received notifications to the parent's communication device; means for analyzing the parent's emotional state using an emotion analysis engine and adjusting the notification content; and means for acquiring student academic and emotional information, analyzing it as needed, and proposing learning support to the parent and educational institution based on the results. This enables the provision of information based on emotional state and the planning of individualized academic support.

[0162] An "information processing device" is an electronic device that has the capability to manage information, such as receiving, storing, transmitting, and analyzing data.

[0163] "Information storage media" refers to equipment used to store digital information, and includes databases and storage devices.

[0164] A "communication device" is a device that has means for sending and receiving information, and specifically includes mobile phones and tablets.

[0165] An "emotion analysis engine" is an algorithm or platform that analyzes text and audio to determine emotional states such as positive, negative, or neutral.

[0166] "Academic information" refers to information related to learning activities at educational institutions, such as students' grades, attendance records, and lesson content.

[0167] "Emotional information" refers to data that represents the psychological state of a specific individual, and is obtained through text analysis and behavioral analysis.

[0168] "Analysis" is the process of examining collected data in detail to identify specific patterns or trends.

[0169] The embodiments for carrying out this invention are shown below.

[0170] This system consists of an information processing unit, an emotion analysis engine, a data storage medium, and a communication interface. The main component, the information processing unit, receives notifications from educational institutions, stores them in the data storage medium, analyzes the content of those notifications, and automatically transmits them to the parents' communication devices.

[0171] The server receives notifications sent from educational institutions via an information processing device. The HTTP protocol is used, and the data is structured in JSON format. The received information is recorded on a data storage medium. A relational database management system can be used as the data storage medium. Specific software commonly used for this purpose includes MySQL®.

[0172] The server analyzes the parent's emotional state using an emotion analysis engine. This emotion analysis utilizes natural language processing techniques, such as commercial or open-source emotion analysis APIs. The analysis results indicate the parent's emotional state, and the server adjusts the notification content based on this information. For example, if the emotional state is found to be negative, a text generation AI model is used to make the notification content positive. The generation AI model can be improved by inputting prompts to produce more appropriate expressions.

[0173] The device displays customized notifications to parents. These devices are typically smartphones or tablets, and the information is presented as push notifications. These notifications contain information optimized for each parent and are presented in an easy-to-understand format.

[0174] Parents, as users, can access and understand information more effectively through the notifications they receive. For example, if a notification uses phrasing such as, "There is a special event at school. Please consider participating," it can create a more positive impression.

[0175] In addition, as a concrete example, an example of a prompt is shown. Using the prompt "Use the emotion engine to customize notifications to facilitate communication between educational institutions and parents. Suggest ways to make the information more acceptable while considering the emotional state of parents," the AI ​​model generates appropriate notification content.

[0176] Overall, this system not only ensures the accurate and effective transmission of information, but also enables sophisticated, personalized responses based on emotional information, offering a new approach to academic support.

[0177] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0178] Step 1:

[0179] The server receives notifications from educational institutions. These notifications are sent via the HTTP protocol in JSON format. The server parses this data and converts it into a valid data format. The input is notification data in JSON format, and the output is parsed structured data. The parsed data is broken down into elements such as the notification message, sender, and date and time of transmission.

[0180] Step 2:

[0181] The server stores the analyzed notification data in an information storage medium. A relational database system is used to maintain data integrity during storage. The input is structured notification data, and the output is the status of completion for storage into the database. Specifically, an INSERT operation is performed on the database.

[0182] Step 3:

[0183] The server collects past feedback and message data to obtain the emotional state of parents and inputs it into an emotion analysis engine. The input is past text data, and the output is an emotion score (positive, negative, neutral). Natural language processing tools are used for text analysis.

[0184] Step 4:

[0185] The server utilizes a generative AI model to adjust notification content based on sentiment analysis results. The input is the analyzed sentiment score and the original notification content, and the output is the adjusted notification message. Specifically, the prompt sentence is input to the generative AI model to obtain an improved expression.

[0186] Step 5:

[0187] The server sends a pre-arranged notification message to the parent's device. Here, the communication infrastructure is used to deliver the message based on the device's token ID. The inputs are the pre-arranged notification message and the device token, and the output is a delivery completion notification.

[0188] Step 6:

[0189] The device displays the received notification message on its screen. The input is the notification message sent from the server, and the output is a visual notification display to the user. Specifically, this involves displaying push notifications.

[0190] Step 7:

[0191] The user (parent / guardian) checks and understands the notifications displayed on the device. The input is the content displayed on the device, and the output is the user's understanding of the information. A concrete example is the user opening the notification to view detailed information.

[0192] Step 8:

[0193] The server generates feedback for educational institutions based on students' academic performance information and analyzed sentiment data. Inputs are academic and sentiment data, and output is academic support suggestions for educational institutions. These suggestions include additional instructional support and follow-up.

[0194] (Application Example 2)

[0195] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as a "server" and the smart device 14 as a "terminal".

[0196] In modern educational settings, information is frequently exchanged between educational institutions, parents, and students. However, conventional notifications often fail to consider the emotional state of the recipient, resulting in important information not being properly conveyed. Furthermore, the lack of individualized academic support makes it difficult to maximize students' learning experiences. This invention aims to solve these problems.

[0197] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0198] In this invention, the server includes means for receiving notifications from an information processing device and storing them in a storage device; means for automatically sending the received notifications to the parent's terminal; means for acquiring and analyzing student academic information and proposing learning support to the parent and educational institution based on the results; means for recognizing the user's emotions and adjusting the notification content based on that information; and means for aggregating emotional data and feeding it back to the educational institution to optimize the learning support plan. As a result, the transmission of information is adjusted to the emotional state of the recipient, enabling more effective learning support.

[0199] An "information processing device" is an electronic device used to receive notifications and data, store them in a storage device, and adjust the content of notifications.

[0200] A "storage device" is a computer storage medium used to record and save received notifications and data.

[0201] A "device" refers to a portable communication device used by a parent or guardian that has the function of receiving and displaying notifications.

[0202] "Academic information" refers to educational data such as the content of education and academic performance evaluations related to students.

[0203] "Analysis" is the process of evaluating collected data and extracting useful information.

[0204] An "emotion engine" is a software function that recognizes a user's emotions and suggests countermeasures based on that information.

[0205] "Adjusting notification content" means changing the content and wording of a notification message based on the recipient's emotional state.

[0206] "Feedback" is the process of providing data and insights based on results and achievements to be used for improvement.

[0207] An "academic support plan" is a curriculum and instructional plan designed to improve students' learning.

[0208] To realize this invention, the server, terminal, and user must work in coordination. The server first receives notifications from educational institutions through an information processing device and stores them in a storage device. Next, the server uses an emotion engine to analyze the emotional state of parents in real time. Based on this analysis, the server adjusts the notification content and automatically sends the notification to the terminal in a more acceptable form.

[0209] The device displays a pre-configured notification received from the server to the user. This notification incorporates customization by the emotion engine, ensuring the user can easily understand the information. Once the user reviews the information, their emotional response is analyzed again by the emotion engine, which is then used to refine future notifications.

[0210] Furthermore, the server aggregates students' academic and emotional data and provides feedback to educational institutions. This allows educational institutions to develop academic support plans tailored to each student's psychological state and academic ability.

[0211] For example, if a parent is experiencing stress at work and receives a notification stating, "There is a school event tomorrow," the server might adjust the notification to something like, "We're looking forward to tomorrow! We hope you'll join us for the school event," making the parent feel less burdened.

[0212] An example of a prompt message would be: "Please tell me how to modify notifications based on parental sentiment data. Please adjust the notification content to a stress-reducing tone."

[0213] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0214] Step 1:

[0215] The server receives notifications from educational institutions via an information processing device. The input is notification data generated by the educational institution, and the output includes storage in a storage device. This data is stored directly in the storage device, ready for use in subsequent processing.

[0216] Step 2:

[0217] The server uses an emotion engine to analyze the parent's current emotional state. The input is emotional data such as facial expressions and voice collected from the parent's device, and the output is emotional evaluation data based on the analysis results. This provides the basis for determining how to adjust notification content.

[0218] Step 3:

[0219] The server adjusts the content of the original notification data based on the sentiment evaluation data. The inputs are the notification data saved in step 1 and the sentiment evaluation data from step 2, and the output is the adjusted notification message. In this step, a generative AI model is used to optimize the wording of the notification according to the parent's emotions.

[0220] Step 4:

[0221] The adjusted notification is sent to the parent's device. The device receives this data and displays the notification to the parent (user) via the screen. The input is the adjusted notification message, and the output is the user's recognition and understanding.

[0222] Step 5:

[0223] After the user acknowledges the notification, their reaction is analyzed again by the emotion engine. The input is the user's emotional response data, and the output is emotional feedback data recording this reaction. This accumulates data that can be used to adjust future notifications.

[0224] Step 6:

[0225] The server aggregates student academic information and emotional feedback data and provides it to educational institutions. Inputs are academic and emotional data related to each student, and output is a dataset for educational institutions. This allows educational institutions to obtain the information needed to develop academic support plans that take students' psychological states into account.

[0226] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0227] Data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of data generation model 58 is ChatGPT (registered trademark) (Internet search).<URL: https: / / openai.com / blog / chatgpt> ), Gemini (registered trademark) (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0228] In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and the specific processing may also be performed by the smart device 14.

[0229] [Second Embodiment]

[0230] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0231] As shown in Figure 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0232] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0233] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication interface 44. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, and camera 42 are also connected to the bus 52.

[0234] The microphone 238 receives voice signals from the user 20 and receives instructions from the user 20. The microphone 238 captures the voice signals from the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0235] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the area around the user 20 (for example, an imaging range defined by a field of view equivalent to the width of a typical healthy person's field of vision).

[0236] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0237] Figure 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Figure 4, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0238] The specific processing program 56 is an example of a "program" relating to the technology of this disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0239] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0240] In the smart glasses 214, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0241] Next, the identification processing performed by the identification processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the smart glasses 214 will be referred to as the "terminal".

[0242] The system of this invention is designed to streamline information exchange between educational institutions and parents, enabling immediate monitoring of academic progress and appropriate learning support. Its embodiments are described below.

[0243] This system consists of an information processing device with three main functions, enabling the automation of information transmission and academic support. Specifically, it includes a "parent support function," a "student affairs support function," and a "performance analysis function."

[0244] First, the server has the function of receiving important notifications from educational institutions and storing them in a database. This database maintains a history of notifications and is configured in a way that allows for easy reference of past communications. Next, the server automatically sends the received notifications to each parent's device. In this process, the device displays the notification as a pop-up message or email to ensure that no information is overlooked.

[0245] Next, the server automates administrative tasks. Specifically, it is responsible for generating necessary documents for educational institutions and distributing them to relevant parties. For example, it has a function to automatically generate invitations for parent-teacher meetings from templates and send them based on a selected recipient list.

[0246] Furthermore, the server comprehensively processes students' academic information. Specifically, it has the capability to analyze academic information, including grades, attendance records, and assignment submission history. Based on the analysis, it detects changes in trends and anomalies, identifies the need for learning support, and forms appropriate guidance and additional assignment suggestions. This information is then notified to teachers and parents (users) by the server, facilitating early intervention to improve students' academic performance.

[0247] The introduction of this system will provide academic progress information to relevant parties in real time, eliminating delays and fragmentation in information transmission. For example, if a student shows a consistent decline in recent math grades, the server will detect this and report it to parents and teachers. Furthermore, individualized learning programs and additional instruction will be suggested for that student, enabling rapid improvement in their learning.

[0248] As described above, this invention embodies a form that strengthens cooperation between educational institutions and parents and provides students with the optimal learning environment.

[0249] The following describes the processing flow.

[0250] Step 1:

[0251] The server receives important notifications from educational institutions. These notifications may include announcements about class visits or changes to event schedules. This information is immediately stored in the server's database.

[0252] Step 2:

[0253] The server analyzes received notifications and optimizes their content for parents. For example, it selects message templates based on the notification content and customizes notifications as needed.

[0254] Step 3:

[0255] The server automatically sends optimized notifications to the parent's registered email address or application, ensuring that information reaches parents quickly and reliably.

[0256] Step 4:

[0257] The device displays received notifications to the user. These notifications are received as pop-ups or emails, allowing parents to review and respond accordingly.

[0258] Step 5:

[0259] Parents, as users, take necessary actions based on the notification. For example, they might prepare for a school visit.

[0260] Step 6:

[0261] The server also automatically generates information related to administrative tasks (e.g., parent-teacher meeting announcements) and requests confirmation from the educational institution's administrator. After confirmation, it sends the necessary documents to the relevant parties.

[0262] Step 7:

[0263] The server regularly updates student grades and attendance information and reflects it in the database. This ensures that the most up-to-date academic information is always maintained.

[0264] Step 8:

[0265] The server uses specific algorithms to analyze student data. For example, it can detect patterns of declining academic performance and generate information to pinpoint the causes.

[0266] Step 9:

[0267] Based on the analysis results, the server notifies parents and educational institutions with suggestions for appropriate learning support. This allows educational institutions to provide additional instruction as needed.

[0268] Step 10:

[0269] Teachers and parents, who are users of the system, can consider and implement specific support measures for students based on the learning support information provided by the server.

[0270] (Example 1)

[0271] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server" and the smart glasses 214 will be referred to as the "terminal".

[0272] In recent years, there has been a growing demand for more efficient communication between educational institutions and parents. In particular, it is crucial to quickly and accurately grasp students' academic information and provide necessary learning support. However, traditional systems suffer from delays in information transmission and require significant manpower and time for analyzing academic evaluations, making efficient support difficult. Furthermore, there is a lack of mechanisms to quickly propose appropriate educational strategies to students who require individualized learning support.

[0273] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0274] In this invention, the server includes means for receiving notifications from communication units and storing them in a storage medium, means for automatically transmitting the received notifications to an associated output device, and means for acquiring and analyzing education-related information and proposing instructional support based on the results. This enables real-time information sharing between educational institutions and parents. In particular, automatic analysis within the server allows for rapid identification of trends in academic evaluation, and furthermore, the generative model can propose an optimal learning support plan. As a result, individualized learning support for students can be advanced more effectively and efficiently.

[0275] A "communication unit" refers to a device or system that can transmit or receive information.

[0276] "Notification" refers to information or messages intended to inform other devices or people of specific information or messages.

[0277] A "storage medium" refers to a physical or digital medium used to store data or information.

[0278] "Means" refer to the methods or mechanisms used to achieve a specific objective.

[0279] An "output device" is a device used to display processed information to the user.

[0280] "Education-related information" refers to data on student performance, attendance, assignment submissions, and other information related to education.

[0281] "Analysis" refers to the process of extracting useful insights from data and information.

[0282] "Instructional support" refers to assistance and advice provided to help students learn and encourage improvement.

[0283] A "generative model" is an algorithm or system that uses data to automatically generate new information, such as proposals.

[0284] The embodiment for implementing this invention is a system constructed to streamline information transmission between educational institutions and guardians and to facilitate academic support for students. This system includes a plurality of components described below and provides a seamless flow of information as a whole.

[0285] The server uses a communication interface to receive notifications from educational institutions. This interface automatically receives various notifications through an API and stores them in an internal storage medium. The stored data is indexed and managed in a database. The server accesses this data using SQL queries and utilizes it as needed.

[0286] The server automatically transfers the received notifications to the terminals of relevant guardians. The transfer is carried out by email transmission using the SMTP protocol or mobile push notifications using Firebase Cloud Messaging (FMC). With this function, users can immediately check the notifications.

[0287] Furthermore, the server utilizes a generative AI model to analyze educational-related information accumulated in the database, such as students' grades and attendance information. Specifically, Python and Pandas are used to clean and format the data, and then the analysis is performed using a machine learning model with Scikit-learn. As a result, academic trends are identified, and if individual tutoring support is needed, proposals are immediately generated.

[0288] The proposed learning support content is notified to teachers and guardians again by the server. The notification includes the content obtained from the generative AI model and is delivered as a prompt text. Specific examples include "Please check the latest notifications from the educational institution." and "The student's math grade is declining. Additional learning support is needed."

[0289] This system facilitates rapid information sharing between educational institutions and parents, enabling appropriate academic support for students. This is expected to lead to improved academic performance.

[0290] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0291] Step 1:

[0292] The server receives notifications sent from educational institutions. The input is an electronic notification sent via an API. Upon receiving this notification, the server parses the data and stores it in an appropriate format on a storage medium. This involves using SQL queries to store the information in a database. The output is the notification data stored on the storage medium.

[0293] Step 2:

[0294] The server prepares the notification data stored on the storage medium for delivery to the parent's device. The input is the notification data stored in step 1. The server generates an email using the SMTP protocol or configures a push notification using Firebase Cloud Messaging (FMC). The output is the deliverable notification message.

[0295] Step 3:

[0296] The terminal receives notification messages sent from the server and displays them to the user as visual alerts. The input is the notification message sent from the server. The terminal presents this to the user as a pop-up message or email. The output is easily accessible notification information displayed on the user's screen.

[0297] Step 4:

[0298] The server retrieves student academic information stored on a storage medium and analyzes it using a generative AI model. The input consists of educational data such as student grades, attendance records, and assignment submission history. The server processes this data using Python and Pandas, and then analyzes it using a machine learning model with Scikit-learn. The output includes academic trends and anomaly detection results.

[0299] Step 5:

[0300] The server generates necessary instructional support using an AI model based on the results of the academic performance analysis. The input is the analysis results from step 4. Based on the analysis results, the server creates an optimized learning support plan and generates it as a notification. The output is a prompt message containing specific instructional suggestions.

[0301] Step 6:

[0302] The server notifies the user (teacher or parent) of the generated prompt text for the lesson suggestion. The input is the prompt text generated in step 5. The server delivers the suggestion via email or in-app notification. The output is the learning support information based on the prompt text received by the teacher or parent.

[0303] (Application Example 1)

[0304] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server," and the smart glasses 214 will be referred to as the "terminal."

[0305] There is a growing need for systems that streamline information exchange between educational institutions and parents, and allow for real-time monitoring of students' academic progress. However, current systems typically suffer from information delays and fragmentation, making it difficult to provide prompt learning support. Solving this problem and providing effective academic support is essential.

[0306] The specific processing by the specific processing unit 290 of the data processing apparatus 12 in Application Example 1 is realized by the following means.

[0307] In this invention, the server includes: a device that receives a notification from an information processing device and stores it; a device that automatically transmits the received notification to a terminal; a device that acquires information related to schoolwork, performs analysis, and proposes support based on the result; a communication device for performing real-time notification of schoolwork information; and a device that automatically proposes an individualized guidance plan based on learning tendencies. This enables the efficiency of information transmission and real-time schoolwork support.

[0308] The "information processing device" is a device that receives, processes, and appropriately manages data and executes necessary functions based on it.

[0309] A "notification" is a message sent to inform a user of specific information.

[0310] A "terminal" refers to a device for a user to receive and operate information.

[0311] A "server" is a central computer system that provides data and services to multiple terminals and users.

[0312] "Information related to schoolwork" refers to information related to activities in educational institutions such as students' grades, attendance status, and assignment submission status.

[0313] "Analysis" refers to the process of analyzing data in detail and deriving useful information and trends from it.

[0314] A "communication device" refers to a combination of hardware and software for transmitting and receiving information, especially enabling real-time communication.

[0315] An "individualized guidance plan" is a customized learning support plan designed according to the learning needs of each student.

[0316] In implementing this invention, the server receives notifications from educational institutions and stores them in a database. This database is configured to retain a history of past notifications and to allow for quick access as needed. The server then automatically sends the notifications to the terminals of parents and educators. The terminals are responsible for displaying the notifications as pop-up messages or emails to ensure they reach the users.

[0317] This system centrally manages and analyzes students' academic performance information on a server. The analysis uses specific algorithms to identify performance trends and generate foundational data for providing learning support. Based on the analysis results, the server notifies parents and teachers to propose necessary guidance or support plans.

[0318] For example, if a student's math grades consistently decline, the server detects this change and immediately sends a notification to the parent's device. This allows for swift action to be taken. Furthermore, an individualized tutoring plan is automatically generated and provided to both the parent and the educator.

[0319] The generative AI model can also be used to generate new learning support prompts. For example, it is possible to create a prompt like this: "Please suggest the content of the notification to parents if a particular student's grades decline."

[0320] This series of processes involving servers and terminals enables rapid and effective communication between educational institutions and parents, allowing for appropriate measures to be taken to improve students' academic performance.

[0321] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0322] Step 1:

[0323] The server receives notifications from educational institutions. The input is notification data sent from educational institutions, which the server stores in its database. Specifically, notification content (such as meeting announcements) arrives at the server and is saved in the database, making it available for later reference.

[0324] Step 2:

[0325] The server sends received notifications to the device. The input is the notification data stored in the database, and the output is the notification displayed on the device. The server automatically sends this notification as an email or pop-up to the selected device. This ensures that the user receives the latest information.

[0326] Step 3:

[0327] The server collects and analyzes students' academic information. Inputs include academic data such as grades, attendance, and assignment submission status, while output is the analysis results. Specifically, the server sorts each student's data and performs analysis to identify trends. If an anomaly is detected during this process, it is recorded.

[0328] Step 4:

[0329] The server proposes support based on the analysis results. The input is the analysis results of academic information, and the output is the proposed support program. The server utilizes a generative AI model to generate individualized tutoring plans and additional advice based on the student's needs. This series of suggestions is provided to the user as a notification.

[0330] Step 5:

[0331] The device receives and displays notifications. The input is notification data sent from the server, and the output is information displayed to the user. Specifically, the device displays notifications via pop-up messages or email to help the user take quick action based on the information.

[0332] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0333] This invention incorporates an emotion engine that recognizes user emotions into a system that improves communication between educational institutions, parents, and students, and utilizes emotional information for academic support. An embodiment of this system is described below.

[0334] This system consists of an information processing unit, an emotion engine, a database, and a communication interface. At the heart of the system is the information processing unit, which receives notifications from educational institutions, stores them in the database, analyzes the content of the notifications, and automatically sends them to the parents' devices.

[0335] Here, the emotion engine has the function of recognizing the user's emotional state in real time and appropriately adjusting the notification content using that information. Specifically, the server analyzes the parent's recent emotional state using the emotion engine and flexibly changes the notification content based on that data. For example, conventional notification messages can be improved to be expressed in a more acceptable way.

[0336] After a notification is sent, the device presents it to the parent. This notification incorporates customization based on sentiment analysis. This improves the parent's access to and understanding of the information.

[0337] Furthermore, in addition to students' academic information, the server collects emotional data obtained by the emotion engine and records it in a database. Based on this information, the server feeds the emotional data back to educational institutions and develops academic support plans that take into account the students' psychological factors. Specifically, if the emotion engine determines that a student is experiencing stress with a particular subject, the server will suggest additional support or follow-up from teachers accordingly.

[0338] Overall, this invention improves the accuracy of information transmission while simultaneously enabling more personalized responses through an understanding of emotions, thereby enhancing the students' learning experience.

[0339] The following describes the processing flow.

[0340] Step 1:

[0341] The server receives notification data from educational institutions. These notifications include information about school events and emergency contacts.

[0342] Step 2:

[0343] The server stores received notifications in a database. This maintains a history of notifications, which can then be accessed when needed.

[0344] Step 3:

[0345] The server retrieves the caregiver's recent emotional state through the emotion engine. The emotion engine analyzes emotions using past data and sensor information.

[0346] Step 4:

[0347] The server optimizes the content of notifications based on the acquired emotional information. For example, if the emotion is negative, the notification message is modified to use softer language.

[0348] Step 5:

[0349] The server sends optimized notifications to the parent's device. These notifications are sent via email or app notifications.

[0350] Step 6:

[0351] The device displays incoming notifications as pop-ups to ensure they reach the parent / guardian. This allows the parent / guardian to check the information immediately.

[0352] Step 7:

[0353] The user (parent / guardian) will check the notification and take appropriate action. For example, if there is a request to participate, they will adjust their schedule.

[0354] Step 8:

[0355] The server continuously collects students' academic and emotional data. This data is integrated into a grade database and emotional history.

[0356] Step 9:

[0357] The server analyzes the collected data and applies specific algorithms to identify academic trends and emotional patterns.

[0358] Step 10:

[0359] Based on the analysis results, the server provides individualized learning support suggestions to educational institutions. This enables teachers to provide appropriate guidance that takes into account students' academic performance and emotional state.

[0360] Step 11:

[0361] The teachers, as users of the system, will consider the suggestions and plan and implement measures to address student issues. They will also collaborate with parents as needed.

[0362] Through this process, educational institutions and parents will be able to collaborate more easily and provide more personalized support to students.

[0363] (Example 2)

[0364] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server" and the smart glasses 214 will be referred to as the "terminal".

[0365] As the importance of improving the accuracy of information transmission and providing individualized support between educational institutions, parents, and students increases, traditional systems often transmit information in only one direction, making it difficult to provide information that takes emotional context into account. Furthermore, there is a problem that individualized support is insufficient because academic support plans based on students' emotional states have not been adequately developed.

[0366] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0367] In this invention, the server includes means for receiving notifications from information processing equipment and storing them in an information storage medium; means for automatically transmitting the received notifications to the parent's communication device; means for analyzing the parent's emotional state using an emotion analysis engine and adjusting the notification content; and means for acquiring student academic and emotional information, analyzing it as needed, and proposing learning support to the parent and educational institution based on the results. This enables the provision of information based on emotional state and the planning of individualized academic support.

[0368] An "information processing device" is an electronic device that has the capability to manage information, such as receiving, storing, transmitting, and analyzing data.

[0369] "Information storage media" refers to equipment used to store digital information, and includes databases and storage devices.

[0370] A "communication device" is a device that has means for sending and receiving information, and specifically includes mobile phones and tablets.

[0371] An "emotion analysis engine" is an algorithm or platform that analyzes text and audio to determine emotional states such as positive, negative, or neutral.

[0372] "Academic information" refers to information related to learning activities at educational institutions, such as students' grades, attendance records, and lesson content.

[0373] "Emotional information" refers to data that represents the psychological state of a specific individual, and is obtained through text analysis and behavioral analysis.

[0374] "Analysis" is the process of examining collected data in detail to identify specific patterns or trends.

[0375] The embodiments for carrying out this invention are shown below.

[0376] This system consists of an information processing unit, an emotion analysis engine, a data storage medium, and a communication interface. The main component, the information processing unit, receives notifications from educational institutions, stores them in the data storage medium, analyzes the content of those notifications, and automatically transmits them to the parents' communication devices.

[0377] The server receives notifications sent from educational institutions via an information processing device. The HTTP protocol is used, and the data is structured in JSON format. The received information is recorded on a data storage medium. A relational database management system can be used as the data storage medium. Specifically, MySQL is commonly used software for this purpose.

[0378] The server analyzes the parent's emotional state using an emotion analysis engine. This emotion analysis utilizes natural language processing techniques, such as commercial or open-source emotion analysis APIs. The analysis results indicate the parent's emotional state, and the server adjusts the notification content based on this information. For example, if the emotional state is found to be negative, a text generation AI model is used to make the notification content positive. The generation AI model can be improved by inputting prompts to produce more appropriate expressions.

[0379] The device displays customized notifications to parents. These devices are typically smartphones or tablets, and the information is presented as push notifications. These notifications contain information optimized for each parent and are presented in an easy-to-understand format.

[0380] Parents, as users, can access and understand information more effectively through the notifications they receive. For example, if a notification uses phrasing such as, "There is a special event at school. Please consider participating," it can create a more positive impression.

[0381] In addition, as a concrete example, an example of a prompt is shown. Using the prompt "Use the emotion engine to customize notifications to facilitate communication between educational institutions and parents. Suggest ways to make the information more acceptable while considering the emotional state of parents," the AI ​​model generates appropriate notification content.

[0382] Overall, this system not only ensures the accurate and effective transmission of information, but also enables sophisticated, personalized responses based on emotional information, offering a new approach to academic support.

[0383] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0384] Step 1:

[0385] The server receives notifications from educational institutions. These notifications are sent via the HTTP protocol in JSON format. The server parses this data and converts it into a valid data format. The input is notification data in JSON format, and the output is parsed structured data. The parsed data is broken down into elements such as the notification message, sender, and date and time of transmission.

[0386] Step 2:

[0387] The server stores the analyzed notification data in an information storage medium. A relational database system is used to maintain data integrity during storage. The input is structured notification data, and the output is the status of completion for storage into the database. Specifically, an INSERT operation is performed on the database.

[0388] Step 3:

[0389] The server collects past feedback and message data to obtain the emotional state of parents and inputs it into an emotion analysis engine. The input is past text data, and the output is an emotion score (positive, negative, neutral). Natural language processing tools are used for text analysis.

[0390] Step 4:

[0391] The server utilizes a generative AI model to adjust notification content based on sentiment analysis results. The input is the analyzed sentiment score and the original notification content, and the output is the adjusted notification message. Specifically, the prompt sentence is input to the generative AI model to obtain an improved expression.

[0392] Step 5:

[0393] The server sends a pre-arranged notification message to the parent's device. Here, the communication infrastructure is used to deliver the message based on the device's token ID. The inputs are the pre-arranged notification message and the device token, and the output is a delivery completion notification.

[0394] Step 6:

[0395] The device displays the received notification message on its screen. The input is the notification message sent from the server, and the output is a visual notification display to the user. Specifically, this involves displaying push notifications.

[0396] Step 7:

[0397] The user (parent / guardian) checks and understands the notifications displayed on the device. The input is the content displayed on the device, and the output is the user's understanding of the information. A concrete example is the user opening the notification to view detailed information.

[0398] Step 8:

[0399] The server generates feedback for educational institutions based on students' academic performance information and analyzed sentiment data. Inputs are academic and sentiment data, and output is academic support suggestions for educational institutions. These suggestions include additional instructional support and follow-up.

[0400] (Application Example 2)

[0401] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as the "server," and the smart glasses 214 will be referred to as the "terminal."

[0402] In modern educational settings, information is frequently exchanged between educational institutions, parents, and students. However, conventional notifications often fail to consider the emotional state of the recipient, resulting in important information not being properly conveyed. Furthermore, the lack of individualized academic support makes it difficult to maximize students' learning experiences. This invention aims to solve these problems.

[0403] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0404] In this invention, the server includes means for receiving notifications from an information processing device and storing them in a storage device; means for automatically sending the received notifications to the parent's terminal; means for acquiring and analyzing student academic information and proposing learning support to the parent and educational institution based on the results; means for recognizing the user's emotions and adjusting the notification content based on that information; and means for aggregating emotional data and feeding it back to the educational institution to optimize the learning support plan. As a result, the transmission of information is adjusted to the emotional state of the recipient, enabling more effective learning support.

[0405] An "information processing device" is an electronic device used to receive notifications and data, store them in a storage device, and adjust the content of notifications.

[0406] A "storage device" is a computer storage medium used to record and save received notifications and data.

[0407] A "device" refers to a portable communication device used by a parent or guardian that has the function of receiving and displaying notifications.

[0408] "Academic information" refers to educational data such as the content of education and academic performance evaluations related to students.

[0409] "Analysis" is the process of evaluating collected data and extracting useful information.

[0410] An "emotion engine" is a software function that recognizes a user's emotions and suggests countermeasures based on that information.

[0411] "Adjusting notification content" means changing the content and wording of a notification message based on the recipient's emotional state.

[0412] "Feedback" is the process of providing data and insights based on results and achievements to be used for improvement.

[0413] An "academic support plan" is a curriculum and instructional plan designed to improve students' learning.

[0414] To realize this invention, the server, terminal, and user must work in coordination. The server first receives notifications from educational institutions through an information processing device and stores them in a storage device. Next, the server uses an emotion engine to analyze the emotional state of parents in real time. Based on this analysis, the server adjusts the notification content and automatically sends the notification to the terminal in a more acceptable form.

[0415] The device displays a pre-configured notification received from the server to the user. This notification incorporates customization by the emotion engine, ensuring the user can easily understand the information. Once the user reviews the information, their emotional response is analyzed again by the emotion engine, which is then used to refine future notifications.

[0416] Furthermore, the server aggregates students' academic and emotional data and provides feedback to educational institutions. This allows educational institutions to develop academic support plans tailored to each student's psychological state and academic ability.

[0417] For example, if a parent is experiencing stress at work and receives a notification stating, "There is a school event tomorrow," the server might adjust the notification to something like, "We're looking forward to tomorrow! We hope you'll join us for the school event," making the parent feel less burdened.

[0418] An example of a prompt message would be: "Please tell me how to modify notifications based on parental sentiment data. Please adjust the notification content to a stress-reducing tone."

[0419] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0420] Step 1:

[0421] The server receives notifications from educational institutions via an information processing device. The input is notification data generated by the educational institution, and the output includes storage in a storage device. This data is stored directly in the storage device, ready for use in subsequent processing.

[0422] Step 2:

[0423] The server uses an emotion engine to analyze the parent's current emotional state. The input is emotional data such as facial expressions and voice collected from the parent's device, and the output is emotional evaluation data based on the analysis results. This provides the basis for determining how to adjust notification content.

[0424] Step 3:

[0425] The server adjusts the content of the original notification data based on the sentiment evaluation data. The inputs are the notification data saved in step 1 and the sentiment evaluation data from step 2, and the output is the adjusted notification message. In this step, a generative AI model is used to optimize the wording of the notification according to the parent's emotions.

[0426] Step 4:

[0427] The adjusted notification is sent to the parent's device. The device receives this data and displays the notification to the parent (user) via the screen. The input is the adjusted notification message, and the output is the user's recognition and understanding.

[0428] Step 5:

[0429] After the user acknowledges the notification, their reaction is analyzed again by the emotion engine. The input is the user's emotional response data, and the output is emotional feedback data recording this reaction. This accumulates data that can be used to adjust future notifications.

[0430] Step 6:

[0431] The server aggregates student academic information and emotional feedback data and provides it to educational institutions. Inputs are academic and emotional data related to each student, and output is a dataset for educational institutions. This allows educational institutions to obtain the information needed to develop academic support plans that take students' psychological states into account.

[0432] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0433] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). One example of data generation model 58 is ChatGPT (Internet search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0434] In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and the specific processing may also be performed by the smart glasses 214.

[0435] [Third Embodiment]

[0436] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0437] As shown in Figure 5, the data processing system 310 includes a data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0438] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0439] The headset terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a display 343. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and display 343 are also connected to the bus 52.

[0440] The microphone 238 receives voice signals from the user 20 and receives instructions from the user 20. The microphone 238 captures the voice signals from the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0441] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the area around the user 20 (for example, an imaging range defined by a field of view equivalent to the width of a typical healthy person's field of vision).

[0442] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0443] Figure 6 shows an example of the main functions of the data processing device 12 and the headset terminal 314. As shown in Figure 6, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0444] The specific processing program 56 is an example of a "program" relating to the technology of this disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0445] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0446] In the headset terminal 314, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0447] Next, the specific processing performed by the specific processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the headset terminal 314 will be referred to as the "terminal".

[0448] The system of this invention is designed to streamline information exchange between educational institutions and parents, enabling immediate monitoring of academic progress and appropriate learning support. Its embodiments are described below.

[0449] This system consists of an information processing device with three main functions, enabling the automation of information transmission and academic support. Specifically, it includes a "parent support function," a "student affairs support function," and a "performance analysis function."

[0450] First, the server has the function of receiving important notifications from educational institutions and storing them in a database. This database maintains a history of notifications and is configured in a way that allows for easy reference of past communications. Next, the server automatically sends the received notifications to each parent's device. In this process, the device displays the notification as a pop-up message or email to ensure that no information is overlooked.

[0451] Next, the server automates administrative tasks. Specifically, it is responsible for generating necessary documents for educational institutions and distributing them to relevant parties. For example, it has a function to automatically generate invitations for parent-teacher meetings from templates and send them based on a selected recipient list.

[0452] Furthermore, the server comprehensively processes students' academic information. Specifically, it has the capability to analyze academic information, including grades, attendance records, and assignment submission history. Based on the analysis, it detects changes in trends and anomalies, identifies the need for learning support, and forms appropriate guidance and additional assignment suggestions. This information is then notified to teachers and parents (users) by the server, facilitating early intervention to improve students' academic performance.

[0453] The introduction of this system will provide academic progress information to relevant parties in real time, eliminating delays and fragmentation in information transmission. For example, if a student shows a consistent decline in recent math grades, the server will detect this and report it to parents and teachers. Furthermore, individualized learning programs and additional instruction will be suggested for that student, enabling rapid improvement in their learning.

[0454] As described above, this invention embodies a form that strengthens cooperation between educational institutions and parents and provides students with the optimal learning environment.

[0455] The following describes the processing flow.

[0456] Step 1:

[0457] The server receives important notifications from educational institutions. These notifications may include announcements about class visits or changes to event schedules. This information is immediately stored in the server's database.

[0458] Step 2:

[0459] The server analyzes received notifications and optimizes their content for parents. For example, it selects message templates based on the notification content and customizes notifications as needed.

[0460] Step 3:

[0461] The server automatically sends optimized notifications to the parent's registered email address or application, ensuring that information reaches parents quickly and reliably.

[0462] Step 4:

[0463] The device displays received notifications to the user. These notifications are received as pop-ups or emails, allowing parents to review and respond accordingly.

[0464] Step 5:

[0465] Parents, as users, take necessary actions based on the notification. For example, they might prepare for a school visit.

[0466] Step 6:

[0467] The server also automatically generates information related to administrative tasks (e.g., parent-teacher meeting announcements) and requests confirmation from the educational institution's administrator. After confirmation, it sends the necessary documents to the relevant parties.

[0468] Step 7:

[0469] The server regularly updates student grades and attendance information and reflects it in the database. This ensures that the most up-to-date academic information is always maintained.

[0470] Step 8:

[0471] The server uses specific algorithms to analyze student data. For example, it can detect patterns of declining academic performance and generate information to pinpoint the causes.

[0472] Step 9:

[0473] Based on the analysis results, the server notifies parents and educational institutions with suggestions for appropriate learning support. This allows educational institutions to provide additional instruction as needed.

[0474] Step 10:

[0475] Teachers and parents, who are users of the system, can consider and implement specific support measures for students based on the learning support information provided by the server.

[0476] (Example 1)

[0477] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server," and the headset-type terminal 314 will be referred to as the "terminal."

[0478] In recent years, there has been a growing demand for more efficient communication between educational institutions and parents. In particular, it is crucial to quickly and accurately grasp students' academic information and provide necessary learning support. However, traditional systems suffer from delays in information transmission and require significant manpower and time for analyzing academic evaluations, making efficient support difficult. Furthermore, there is a lack of mechanisms to quickly propose appropriate educational strategies to students who require individualized learning support.

[0479] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0480] In this invention, the server includes means for receiving notifications from communication units and storing them in a storage medium, means for automatically transmitting the received notifications to an associated output device, and means for acquiring and analyzing education-related information and proposing instructional support based on the results. This enables real-time information sharing between educational institutions and parents. In particular, automatic analysis within the server allows for rapid identification of trends in academic evaluation, and furthermore, the generative model can propose an optimal learning support plan. As a result, individualized learning support for students can be advanced more effectively and efficiently.

[0481] A "communication unit" refers to a device or system that can transmit or receive information.

[0482] "Notification" refers to information or messages intended to inform other devices or people of specific information or messages.

[0483] A "storage medium" refers to a physical or digital medium used to store data or information.

[0484] "Means" refer to the methods or mechanisms used to achieve a specific objective.

[0485] An "output device" is a device used to display processed information to the user.

[0486] "Education-related information" refers to data on student performance, attendance, assignment submissions, and other information related to education.

[0487] "Analysis" refers to the process of extracting useful insights from data and information.

[0488] "Instructional support" refers to assistance and advice provided to help students learn and encourage improvement.

[0489] A "generative model" is an algorithm or system that uses data to automatically generate new information, such as proposals.

[0490] One embodiment of this invention is a system designed to streamline information exchange between educational institutions and parents and to promote academic support for students. This system includes several components, as described below, and provides a seamless flow of information as a whole.

[0491] The server uses a communication interface to receive notifications from educational institutions. This interface automatically receives various notifications via an API and stores them on an internal storage medium. The stored data is indexed and managed in a database. The server accesses this data using SQL queries and uses it as needed.

[0492] The server automatically forwards received notifications to the relevant parent's device. This forwarding is done via email using the SMTP protocol or mobile push notifications using Firebase Cloud Messaging (FMC). This feature allows users to instantly view notifications.

[0493] Furthermore, the server utilizes a generative AI model to analyze education-related information stored in the database, such as student grades and attendance data. Specifically, it cleanses and formats the data using Python and Pandas, and then performs analysis using a machine learning model with Scikit-learn. As a result, it identifies academic trends and immediately generates suggestions if individualized instructional support is needed.

[0494] The proposed learning support content is then notified to teachers and parents by the server. The notification includes information derived from the generating AI model and is delivered as a prompt. Specific examples include "Please check the latest notifications from your educational institution" and "The student's math grades are declining. Additional learning support is needed."

[0495] This system facilitates rapid information sharing between educational institutions and parents, enabling appropriate academic support for students. This is expected to lead to improved academic performance.

[0496] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0497] Step 1:

[0498] The server receives notifications sent from educational institutions. The input is an electronic notification sent via an API. Upon receiving this notification, the server parses the data and stores it in an appropriate format on a storage medium. This involves using SQL queries to store the information in a database. The output is the notification data stored on the storage medium.

[0499] Step 2:

[0500] The server prepares the notification data stored on the storage medium for delivery to the parent's device. The input is the notification data stored in step 1. The server generates an email using the SMTP protocol or configures a push notification using Firebase Cloud Messaging (FMC). The output is the deliverable notification message.

[0501] Step 3:

[0502] The terminal receives notification messages sent from the server and displays them to the user as visual alerts. The input is the notification message sent from the server. The terminal presents this to the user as a pop-up message or email. The output is easily accessible notification information displayed on the user's screen.

[0503] Step 4:

[0504] The server retrieves student academic information stored on a storage medium and analyzes it using a generative AI model. The input consists of educational data such as student grades, attendance records, and assignment submission history. The server processes this data using Python and Pandas, and then analyzes it using a machine learning model with Scikit-learn. The output includes academic trends and anomaly detection results.

[0505] Step 5:

[0506] The server generates necessary instructional support using an AI model based on the results of the academic performance analysis. The input is the analysis results from step 4. Based on the analysis results, the server creates an optimized learning support plan and generates it as a notification. The output is a prompt message containing specific instructional suggestions.

[0507] Step 6:

[0508] The server notifies the user (teacher or parent) of the generated prompt text for the lesson suggestion. The input is the prompt text generated in step 5. The server delivers the suggestion via email or in-app notification. The output is the learning support information based on the prompt text received by the teacher or parent.

[0509] (Application Example 1)

[0510] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server," and the headset-type terminal 314 will be referred to as the "terminal."

[0511] There is a growing need for systems that streamline information exchange between educational institutions and parents, and allow for real-time monitoring of students' academic progress. However, current systems typically suffer from information delays and fragmentation, making it difficult to provide prompt learning support. Solving this problem and providing effective academic support is essential.

[0512] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0513] In this invention, the server includes a device that receives and stores notifications from an information processing device, a device that automatically transmits the received notifications to a terminal, a device that acquires and analyzes academic information and proposes support based on the results, a communication device for real-time notification of academic information, and a device that automatically proposes an individualized instruction plan based on learning trends. This enables efficient information transmission and real-time academic support.

[0514] An "information processing device" is a device that receives and processes data, manages it appropriately, and performs necessary functions based on that data.

[0515] A "notification" is a message sent to inform a user of specific information.

[0516] A "terminal" refers to a device used by a user to receive and manipulate information.

[0517] A "server" is a central computer system that provides data and services to multiple terminals or users.

[0518] "Academic information" refers to information related to a student's activities at an educational institution, such as their grades, attendance record, and assignment submission status.

[0519] "Analysis" refers to the process of thoroughly analyzing data and deriving useful information and trends from it.

[0520] A "communication device" refers to a combination of hardware or software for sending and receiving information, and is particularly useful for enabling real-time communication.

[0521] An "individualized instruction plan" is a customized learning support plan designed to meet the individual learning needs of each student.

[0522] In implementing this invention, the server receives notifications from educational institutions and stores them in a database. This database is configured to retain a history of past notifications and to allow for quick access as needed. The server then automatically sends the notifications to the terminals of parents and educators. The terminals are responsible for displaying the notifications as pop-up messages or emails to ensure they reach the users.

[0523] This system centrally manages and analyzes students' academic performance information on a server. The analysis uses specific algorithms to identify performance trends and generate foundational data for providing learning support. Based on the analysis results, the server notifies parents and teachers to propose necessary guidance or support plans.

[0524] For example, if a student's math grades consistently decline, the server detects this change and immediately sends a notification to the parent's device. This allows for swift action to be taken. Furthermore, an individualized tutoring plan is automatically generated and provided to both the parent and the educator.

[0525] The generative AI model can also be used to generate new learning support prompts. For example, it is possible to create a prompt like this: "Please suggest the content of the notification to parents if a particular student's grades decline."

[0526] This series of processes involving servers and terminals enables rapid and effective communication between educational institutions and parents, allowing for appropriate measures to be taken to improve students' academic performance.

[0527] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0528] Step 1:

[0529] The server receives notifications from educational institutions. The input is notification data sent from educational institutions, which the server stores in its database. Specifically, notification content (such as meeting announcements) arrives at the server and is saved in the database, making it available for later reference.

[0530] Step 2:

[0531] The server sends received notifications to the device. The input is the notification data stored in the database, and the output is the notification displayed on the device. The server automatically sends this notification as an email or pop-up to the selected device. This ensures that the user receives the latest information.

[0532] Step 3:

[0533] The server collects and analyzes students' academic information. Inputs include academic data such as grades, attendance, and assignment submission status, while output is the analysis results. Specifically, the server sorts each student's data and performs analysis to identify trends. If an anomaly is detected during this process, it is recorded.

[0534] Step 4:

[0535] The server proposes support based on the analysis results. The input is the analysis results of academic information, and the output is the proposed support program. The server utilizes a generative AI model to generate individualized tutoring plans and additional advice based on the student's needs. This series of suggestions is provided to the user as a notification.

[0536] Step 5:

[0537] The device receives and displays notifications. The input is notification data sent from the server, and the output is information displayed to the user. Specifically, the device displays notifications via pop-up messages or email to help the user take quick action based on the information.

[0538] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0539] This invention incorporates an emotion engine that recognizes user emotions into a system that improves communication between educational institutions, parents, and students, and utilizes emotional information for academic support. An embodiment of this system is described below.

[0540] This system consists of an information processing unit, an emotion engine, a database, and a communication interface. At the heart of the system is the information processing unit, which receives notifications from educational institutions, stores them in the database, analyzes the content of the notifications, and automatically sends them to the parents' devices.

[0541] Here, the emotion engine has the function of recognizing the user's emotional state in real time and appropriately adjusting the notification content using that information. Specifically, the server analyzes the parent's recent emotional state using the emotion engine and flexibly changes the notification content based on that data. For example, conventional notification messages can be improved to be expressed in a more acceptable way.

[0542] After a notification is sent, the device presents it to the parent. This notification incorporates customization based on sentiment analysis. This improves the parent's access to and understanding of the information.

[0543] Furthermore, in addition to students' academic information, the server collects emotional data obtained by the emotion engine and records it in a database. Based on this information, the server feeds the emotional data back to educational institutions and develops academic support plans that take into account the students' psychological factors. Specifically, if the emotion engine determines that a student is experiencing stress with a particular subject, the server will suggest additional support or follow-up from teachers accordingly.

[0544] Overall, this invention improves the accuracy of information transmission while simultaneously enabling more personalized responses through an understanding of emotions, thereby enhancing the students' learning experience.

[0545] The following describes the processing flow.

[0546] Step 1:

[0547] The server receives notification data from educational institutions. These notifications include information about school events and emergency contacts.

[0548] Step 2:

[0549] The server stores received notifications in a database. This maintains a history of notifications, which can then be accessed when needed.

[0550] Step 3:

[0551] The server retrieves the caregiver's recent emotional state through the emotion engine. The emotion engine analyzes emotions using past data and sensor information.

[0552] Step 4:

[0553] The server optimizes the content of notifications based on the acquired emotional information. For example, if the emotion is negative, the notification message is modified to use softer language.

[0554] Step 5:

[0555] The server sends optimized notifications to the parent's device. These notifications are sent via email or app notifications.

[0556] Step 6:

[0557] The device displays incoming notifications as pop-ups to ensure they reach the parent / guardian. This allows the parent / guardian to check the information immediately.

[0558] Step 7:

[0559] The user (parent / guardian) will check the notification and take appropriate action. For example, if there is a request to participate, they will adjust their schedule.

[0560] Step 8:

[0561] The server continuously collects students' academic and emotional data. This data is integrated into a grade database and emotional history.

[0562] Step 9:

[0563] The server analyzes the collected data and applies specific algorithms to identify academic trends and emotional patterns.

[0564] Step 10:

[0565] Based on the analysis results, the server provides individualized learning support suggestions to educational institutions. This enables teachers to provide appropriate guidance that takes into account students' academic performance and emotional state.

[0566] Step 11:

[0567] The teachers, as users of the system, will consider the suggestions and plan and implement measures to address student issues. They will also collaborate with parents as needed.

[0568] Through this process, educational institutions and parents will be able to collaborate more easily and provide more personalized support to students.

[0569] (Example 2)

[0570] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server," and the headset-type terminal 314 will be referred to as the "terminal."

[0571] As the importance of improving the accuracy of information transmission and providing individualized support between educational institutions, parents, and students increases, traditional systems often transmit information in only one direction, making it difficult to provide information that takes emotional context into account. Furthermore, there is a problem that individualized support is insufficient because academic support plans based on students' emotional states have not been adequately developed.

[0572] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0573] In this invention, the server includes means for receiving notifications from information processing equipment and storing them in an information storage medium; means for automatically transmitting the received notifications to the parent's communication device; means for analyzing the parent's emotional state using an emotion analysis engine and adjusting the notification content; and means for acquiring student academic and emotional information, analyzing it as needed, and proposing learning support to the parent and educational institution based on the results. This enables the provision of information based on emotional state and the planning of individualized academic support.

[0574] An "information processing device" is an electronic device that has the capability to manage information, such as receiving, storing, transmitting, and analyzing data.

[0575] "Information storage media" refers to equipment used to store digital information, and includes databases and storage devices.

[0576] A "communication device" is a device that has means for sending and receiving information, and specifically includes mobile phones and tablets.

[0577] An "emotion analysis engine" is an algorithm or platform that analyzes text and audio to determine emotional states such as positive, negative, or neutral.

[0578] "Academic information" refers to information related to learning activities at educational institutions, such as students' grades, attendance records, and lesson content.

[0579] "Emotional information" refers to data that represents the psychological state of a specific individual, and is obtained through text analysis and behavioral analysis.

[0580] "Analysis" is the process of examining collected data in detail to identify specific patterns or trends.

[0581] The embodiments for carrying out this invention are shown below.

[0582] This system consists of an information processing unit, an emotion analysis engine, a data storage medium, and a communication interface. The main component, the information processing unit, receives notifications from educational institutions, stores them in the data storage medium, analyzes the content of those notifications, and automatically transmits them to the parents' communication devices.

[0583] The server receives notifications sent from educational institutions via an information processing device. The HTTP protocol is used, and the data is structured in JSON format. The received information is recorded on a data storage medium. A relational database management system can be used as the data storage medium. Specifically, MySQL is commonly used software for this purpose.

[0584] The server analyzes the parent's emotional state using an emotion analysis engine. This emotion analysis utilizes natural language processing techniques, such as commercial or open-source emotion analysis APIs. The analysis results indicate the parent's emotional state, and the server adjusts the notification content based on this information. For example, if the emotional state is found to be negative, a text generation AI model is used to make the notification content positive. The generation AI model can be improved by inputting prompts to produce more appropriate expressions.

[0585] The device displays customized notifications to parents. These devices are typically smartphones or tablets, and the information is presented as push notifications. These notifications contain information optimized for each parent and are presented in an easy-to-understand format.

[0586] Parents, as users, can access and understand information more effectively through the notifications they receive. For example, if a notification uses phrasing such as, "There is a special event at school. Please consider participating," it can create a more positive impression.

[0587] In addition, as a concrete example, an example of a prompt is shown. Using the prompt "Use the emotion engine to customize notifications to facilitate communication between educational institutions and parents. Suggest ways to make the information more acceptable while considering the emotional state of parents," the AI ​​model generates appropriate notification content.

[0588] Overall, this system not only ensures the accurate and effective transmission of information, but also enables sophisticated, personalized responses based on emotional information, offering a new approach to academic support.

[0589] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0590] Step 1:

[0591] The server receives notifications from educational institutions. These notifications are sent via the HTTP protocol in JSON format. The server parses this data and converts it into a valid data format. The input is notification data in JSON format, and the output is parsed structured data. The parsed data is broken down into elements such as the notification message, sender, and date and time of transmission.

[0592] Step 2:

[0593] The server stores the analyzed notification data in an information storage medium. A relational database system is used to maintain data integrity during storage. The input is structured notification data, and the output is the status of completion for storage into the database. Specifically, an INSERT operation is performed on the database.

[0594] Step 3:

[0595] The server collects past feedback and message data to obtain the emotional state of parents and inputs it into an emotion analysis engine. The input is past text data, and the output is an emotion score (positive, negative, neutral). Natural language processing tools are used for text analysis.

[0596] Step 4:

[0597] The server utilizes a generative AI model to adjust notification content based on sentiment analysis results. The input is the analyzed sentiment score and the original notification content, and the output is the adjusted notification message. Specifically, the prompt sentence is input to the generative AI model to obtain an improved expression.

[0598] Step 5:

[0599] The server sends a pre-arranged notification message to the parent's device. Here, the communication infrastructure is used to deliver the message based on the device's token ID. The inputs are the pre-arranged notification message and the device token, and the output is a delivery completion notification.

[0600] Step 6:

[0601] The device displays the received notification message on its screen. The input is the notification message sent from the server, and the output is a visual notification display to the user. Specifically, this involves displaying push notifications.

[0602] Step 7:

[0603] The user (parent / guardian) checks and understands the notifications displayed on the device. The input is the content displayed on the device, and the output is the user's understanding of the information. A concrete example is the user opening the notification to view detailed information.

[0604] Step 8:

[0605] The server generates feedback for educational institutions based on students' academic performance information and analyzed sentiment data. Inputs are academic and sentiment data, and output is academic support suggestions for educational institutions. These suggestions include additional instructional support and follow-up.

[0606] (Application Example 2)

[0607] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as the "server," and the headset-type terminal 314 will be referred to as the "terminal."

[0608] In modern educational settings, information is frequently exchanged between educational institutions, parents, and students. However, conventional notifications often fail to consider the emotional state of the recipient, resulting in important information not being properly conveyed. Furthermore, the lack of individualized academic support makes it difficult to maximize students' learning experiences. This invention aims to solve these problems.

[0609] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0610] In this invention, the server includes means for receiving notifications from an information processing device and storing them in a storage device; means for automatically sending the received notifications to the parent's terminal; means for acquiring and analyzing student academic information and proposing learning support to the parent and educational institution based on the results; means for recognizing the user's emotions and adjusting the notification content based on that information; and means for aggregating emotional data and feeding it back to the educational institution to optimize the learning support plan. As a result, the transmission of information is adjusted to the emotional state of the recipient, enabling more effective learning support.

[0611] An "information processing device" is an electronic device used to receive notifications and data, store them in a storage device, and adjust the content of notifications.

[0612] A "storage device" is a computer storage medium used to record and save received notifications and data.

[0613] A "device" refers to a portable communication device used by a parent or guardian that has the function of receiving and displaying notifications.

[0614] "Academic information" refers to educational data such as the content of education and academic performance evaluations related to students.

[0615] "Analysis" is the process of evaluating collected data and extracting useful information.

[0616] An "emotion engine" is a software function that recognizes a user's emotions and suggests countermeasures based on that information.

[0617] "Adjusting notification content" means changing the content and wording of a notification message based on the recipient's emotional state.

[0618] "Feedback" is the process of providing data and insights based on results and achievements to be used for improvement.

[0619] An "academic support plan" is a curriculum and instructional plan designed to improve students' learning.

[0620] To realize this invention, the server, terminal, and user must work in coordination. The server first receives notifications from educational institutions through an information processing device and stores them in a storage device. Next, the server uses an emotion engine to analyze the emotional state of parents in real time. Based on this analysis, the server adjusts the notification content and automatically sends the notification to the terminal in a more acceptable form.

[0621] The device displays a pre-configured notification received from the server to the user. This notification incorporates customization by the emotion engine, ensuring the user can easily understand the information. Once the user reviews the information, their emotional response is analyzed again by the emotion engine, which is then used to refine future notifications.

[0622] Furthermore, the server aggregates students' academic and emotional data and provides feedback to educational institutions. This allows educational institutions to develop academic support plans tailored to each student's psychological state and academic ability.

[0623] For example, if a parent is experiencing stress at work and receives a notification stating, "There is a school event tomorrow," the server might adjust the notification to something like, "We're looking forward to tomorrow! We hope you'll join us for the school event," making the parent feel less burdened.

[0624] An example of a prompt message would be: "Please tell me how to modify notifications based on parental sentiment data. Please adjust the notification content to a stress-reducing tone."

[0625] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0626] Step 1:

[0627] The server receives notifications from educational institutions via an information processing device. The input is notification data generated by the educational institution, and the output includes storage in a storage device. This data is stored directly in the storage device, ready for use in subsequent processing.

[0628] Step 2:

[0629] The server uses an emotion engine to analyze the parent's current emotional state. The input is emotional data such as facial expressions and voice collected from the parent's device, and the output is emotional evaluation data based on the analysis results. This provides the basis for determining how to adjust notification content.

[0630] Step 3:

[0631] The server adjusts the content of the original notification data based on the sentiment evaluation data. The inputs are the notification data saved in step 1 and the sentiment evaluation data from step 2, and the output is the adjusted notification message. In this step, a generative AI model is used to optimize the wording of the notification according to the parent's emotions.

[0632] Step 4:

[0633] The adjusted notification is sent to the parent's device. The device receives this data and displays the notification to the parent (user) via the screen. The input is the adjusted notification message, and the output is the user's recognition and understanding.

[0634] Step 5:

[0635] After the user acknowledges the notification, their reaction is analyzed again by the emotion engine. The input is the user's emotional response data, and the output is emotional feedback data recording this reaction. This accumulates data that can be used to adjust future notifications.

[0636] Step 6:

[0637] The server aggregates student academic information and emotional feedback data and provides it to educational institutions. Inputs are academic and emotional data related to each student, and output is a dataset for educational institutions. This allows educational institutions to obtain the information needed to develop academic support plans that take students' psychological states into account.

[0638] The specific processing unit 290 transmits the result of the specific processing to the headset terminal 314. In the headset terminal 314, the control unit 46A causes the speaker 240 and display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0639] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). One example of data generation model 58 is ChatGPT (Internet search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0640] In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and specific processing may also be performed by the headset terminal 314.

[0641] [Fourth Embodiment]

[0642] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0643] As shown in Figure 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0644] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0645] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a controlled object 443. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and controlled object 443 are also connected to the bus 52.

[0646] The microphone 238 receives voice signals from the user 20 and receives instructions from the user 20. The microphone 238 captures the voice signals from the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0647] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the area around the user 20 (for example, an imaging range defined by a field of view equivalent to the width of a typical healthy person's field of vision).

[0648] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0649] The controlled object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the robot 414's emotions can be expressed by controlling these motors. Furthermore, the robot 414's facial expressions can also be expressed by controlling the illumination state of the LEDs in its eyes.

[0650] Figure 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Figure 8, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0651] The specific processing program 56 is an example of a "program" relating to the technology of this disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0652] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0653] In robot 414, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0654] Next, the specific processing performed by the specific processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0655] The system of this invention is designed to streamline information exchange between educational institutions and parents, enabling immediate monitoring of academic progress and appropriate learning support. Its embodiments are described below.

[0656] This system consists of an information processing device with three main functions, enabling the automation of information transmission and academic support. Specifically, it includes a "parent support function," a "student affairs support function," and a "performance analysis function."

[0657] First, the server has the function of receiving important notifications from educational institutions and storing them in a database. This database maintains a history of notifications and is configured in a way that allows for easy reference of past communications. Next, the server automatically sends the received notifications to each parent's device. In this process, the device displays the notification as a pop-up message or email to ensure that no information is overlooked.

[0658] Next, the server automates administrative tasks. Specifically, it is responsible for generating necessary documents for educational institutions and distributing them to relevant parties. For example, it has a function to automatically generate invitations for parent-teacher meetings from templates and send them based on a selected recipient list.

[0659] Furthermore, the server comprehensively processes students' academic information. Specifically, it has the capability to analyze academic information, including grades, attendance records, and assignment submission history. Based on the analysis, it detects changes in trends and anomalies, identifies the need for learning support, and forms appropriate guidance and additional assignment suggestions. This information is then notified to teachers and parents (users) by the server, facilitating early intervention to improve students' academic performance.

[0660] The introduction of this system will provide academic progress information to relevant parties in real time, eliminating delays and fragmentation in information transmission. For example, if a student shows a consistent decline in recent math grades, the server will detect this and report it to parents and teachers. Furthermore, individualized learning programs and additional instruction will be suggested for that student, enabling rapid improvement in their learning.

[0661] As described above, this invention embodies a form that strengthens cooperation between educational institutions and parents and provides students with the optimal learning environment.

[0662] The following describes the processing flow.

[0663] Step 1:

[0664] The server receives important notifications from educational institutions. These notifications may include announcements about class visits or changes to event schedules. This information is immediately stored in the server's database.

[0665] Step 2:

[0666] The server analyzes received notifications and optimizes their content for parents. For example, it selects message templates based on the notification content and customizes notifications as needed.

[0667] Step 3:

[0668] The server automatically sends optimized notifications to the parent's registered email address or application, ensuring that information reaches parents quickly and reliably.

[0669] Step 4:

[0670] The device displays received notifications to the user. These notifications are received as pop-ups or emails, allowing parents to review and respond accordingly.

[0671] Step 5:

[0672] Parents, as users, take necessary actions based on the notification. For example, they might prepare for a school visit.

[0673] Step 6:

[0674] The server also automatically generates information related to administrative tasks (e.g., parent-teacher meeting announcements) and requests confirmation from the educational institution's administrator. After confirmation, it sends the necessary documents to the relevant parties.

[0675] Step 7:

[0676] The server regularly updates student grades and attendance information and reflects it in the database. This ensures that the most up-to-date academic information is always maintained.

[0677] Step 8:

[0678] The server uses specific algorithms to analyze student data. For example, it can detect patterns of declining academic performance and generate information to pinpoint the causes.

[0679] Step 9:

[0680] Based on the analysis results, the server notifies parents and educational institutions with suggestions for appropriate learning support. This allows educational institutions to provide additional instruction as needed.

[0681] Step 10:

[0682] Teachers and parents, who are users of the system, can consider and implement specific support measures for students based on the learning support information provided by the server.

[0683] (Example 1)

[0684] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0685] In recent years, there has been a growing demand for more efficient communication between educational institutions and parents. In particular, it is crucial to quickly and accurately grasp students' academic information and provide necessary learning support. However, traditional systems suffer from delays in information transmission and require significant manpower and time for analyzing academic evaluations, making efficient support difficult. Furthermore, there is a lack of mechanisms to quickly propose appropriate educational strategies to students who require individualized learning support.

[0686] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0687] In this invention, the server includes means for receiving notifications from communication units and storing them in a storage medium, means for automatically transmitting the received notifications to an associated output device, and means for acquiring and analyzing education-related information and proposing instructional support based on the results. This enables real-time information sharing between educational institutions and parents. In particular, automatic analysis within the server allows for rapid identification of trends in academic evaluation, and furthermore, the generative model can propose an optimal learning support plan. As a result, individualized learning support for students can be advanced more effectively and efficiently.

[0688] A "communication unit" refers to a device or system that can transmit or receive information.

[0689] "Notification" refers to information or messages intended to inform other devices or people of specific information or messages.

[0690] A "storage medium" refers to a physical or digital medium used to store data or information.

[0691] "Means" refer to the methods or mechanisms used to achieve a specific objective.

[0692] An "output device" is a device used to display processed information to the user.

[0693] "Education-related information" refers to data on student performance, attendance, assignment submissions, and other information related to education.

[0694] "Analysis" refers to the process of extracting useful insights from data and information.

[0695] "Instructional support" refers to assistance and advice provided to help students learn and encourage improvement.

[0696] A "generative model" is an algorithm or system that uses data to automatically generate new information, such as proposals.

[0697] One embodiment of this invention is a system designed to streamline information exchange between educational institutions and parents and to promote academic support for students. This system includes several components, as described below, and provides a seamless flow of information as a whole.

[0698] The server uses a communication interface to receive notifications from educational institutions. This interface automatically receives various notifications via an API and stores them on an internal storage medium. The stored data is indexed and managed in a database. The server accesses this data using SQL queries and uses it as needed.

[0699] The server automatically forwards received notifications to the relevant parent's device. This forwarding is done via email using the SMTP protocol or mobile push notifications using Firebase Cloud Messaging (FMC). This feature allows users to instantly view notifications.

[0700] Furthermore, the server utilizes a generative AI model to analyze education-related information stored in the database, such as student grades and attendance data. Specifically, it cleanses and formats the data using Python and Pandas, and then performs analysis using a machine learning model with Scikit-learn. As a result, it identifies academic trends and immediately generates suggestions if individualized instructional support is needed.

[0701] The proposed learning support content is then notified to teachers and parents by the server. The notification includes information derived from the generating AI model and is delivered as a prompt. Specific examples include "Please check the latest notifications from your educational institution" and "The student's math grades are declining. Additional learning support is needed."

[0702] This system facilitates rapid information sharing between educational institutions and parents, enabling appropriate academic support for students. This is expected to lead to improved academic performance.

[0703] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0704] Step 1:

[0705] The server receives notifications sent from educational institutions. The input is an electronic notification sent via an API. Upon receiving this notification, the server parses the data and stores it in an appropriate format on a storage medium. This involves using SQL queries to store the information in a database. The output is the notification data stored on the storage medium.

[0706] Step 2:

[0707] The server prepares the notification data stored on the storage medium for delivery to the parent's device. The input is the notification data stored in step 1. The server generates an email using the SMTP protocol or configures a push notification using Firebase Cloud Messaging (FMC). The output is the deliverable notification message.

[0708] Step 3:

[0709] The terminal receives notification messages sent from the server and displays them to the user as visual alerts. The input is the notification message sent from the server. The terminal presents this to the user as a pop-up message or email. The output is easily accessible notification information displayed on the user's screen.

[0710] Step 4:

[0711] The server retrieves student academic information stored on a storage medium and analyzes it using a generative AI model. The input consists of educational data such as student grades, attendance records, and assignment submission history. The server processes this data using Python and Pandas, and then analyzes it using a machine learning model with Scikit-learn. The output includes academic trends and anomaly detection results.

[0712] Step 5:

[0713] The server generates necessary instructional support using an AI model based on the results of the academic performance analysis. The input is the analysis results from step 4. Based on the analysis results, the server creates an optimized learning support plan and generates it as a notification. The output is a prompt message containing specific instructional suggestions.

[0714] Step 6:

[0715] The server notifies the user (teacher or parent) of the generated prompt text for the lesson suggestion. The input is the prompt text generated in step 5. The server delivers the suggestion via email or in-app notification. The output is the learning support information based on the prompt text received by the teacher or parent.

[0716] (Application Example 1)

[0717] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0718] There is a growing need for systems that streamline information exchange between educational institutions and parents, and allow for real-time monitoring of students' academic progress. However, current systems typically suffer from information delays and fragmentation, making it difficult to provide prompt learning support. Solving this problem and providing effective academic support is essential.

[0719] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0720] In this invention, the server includes a device that receives and stores notifications from an information processing device, a device that automatically transmits the received notifications to a terminal, a device that acquires and analyzes academic information and proposes support based on the results, a communication device for real-time notification of academic information, and a device that automatically proposes an individualized instruction plan based on learning trends. This enables efficient information transmission and real-time academic support.

[0721] An "information processing device" is a device that receives and processes data, manages it appropriately, and performs necessary functions based on that data.

[0722] A "notification" is a message sent to inform a user of specific information.

[0723] A "terminal" refers to a device used by a user to receive and manipulate information.

[0724] A "server" is a central computer system that provides data and services to multiple terminals or users.

[0725] "Academic information" refers to information related to a student's activities at an educational institution, such as their grades, attendance record, and assignment submission status.

[0726] "Analysis" refers to the process of thoroughly analyzing data and deriving useful information and trends from it.

[0727] A "communication device" refers to a combination of hardware or software for sending and receiving information, and is particularly useful for enabling real-time communication.

[0728] An "individualized instruction plan" is a customized learning support plan designed to meet the individual learning needs of each student.

[0729] In implementing this invention, the server receives notifications from educational institutions and stores them in a database. This database is configured to retain a history of past notifications and to allow for quick access as needed. The server then automatically sends the notifications to the terminals of parents and educators. The terminals are responsible for displaying the notifications as pop-up messages or emails to ensure they reach the users.

[0730] This system centrally manages and analyzes students' academic performance information on a server. The analysis uses specific algorithms to identify performance trends and generate foundational data for providing learning support. Based on the analysis results, the server notifies parents and teachers to propose necessary guidance or support plans.

[0731] For example, if a student's math grades consistently decline, the server detects this change and immediately sends a notification to the parent's device. This allows for swift action to be taken. Furthermore, an individualized tutoring plan is automatically generated and provided to both the parent and the educator.

[0732] The generative AI model can also be used to generate new learning support prompts. For example, it is possible to create a prompt like this: "Please suggest the content of the notification to parents if a particular student's grades decline."

[0733] This series of processes involving servers and terminals enables rapid and effective communication between educational institutions and parents, allowing for appropriate measures to be taken to improve students' academic performance.

[0734] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0735] Step 1:

[0736] The server receives notifications from educational institutions. The input is notification data sent from educational institutions, which the server stores in its database. Specifically, notification content (such as meeting announcements) arrives at the server and is saved in the database, making it available for later reference.

[0737] Step 2:

[0738] The server sends received notifications to the device. The input is the notification data stored in the database, and the output is the notification displayed on the device. The server automatically sends this notification as an email or pop-up to the selected device. This ensures that the user receives the latest information.

[0739] Step 3:

[0740] The server collects and analyzes students' academic information. Inputs include academic data such as grades, attendance, and assignment submission status, while output is the analysis results. Specifically, the server sorts each student's data and performs analysis to identify trends. If an anomaly is detected during this process, it is recorded.

[0741] Step 4:

[0742] The server proposes support based on the analysis results. The input is the analysis results of academic information, and the output is the proposed support program. The server utilizes a generative AI model to generate individualized tutoring plans and additional advice based on the student's needs. This series of suggestions is provided to the user as a notification.

[0743] Step 5:

[0744] The device receives and displays notifications. The input is notification data sent from the server, and the output is information displayed to the user. Specifically, the device displays notifications via pop-up messages or email to help the user take quick action based on the information.

[0745] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0746] This invention incorporates an emotion engine that recognizes user emotions into a system that improves communication between educational institutions, parents, and students, and utilizes emotional information for academic support. An embodiment of this system is described below.

[0747] This system consists of an information processing unit, an emotion engine, a database, and a communication interface. At the heart of the system is the information processing unit, which receives notifications from educational institutions, stores them in the database, analyzes the content of the notifications, and automatically sends them to the parents' devices.

[0748] Here, the emotion engine has the function of recognizing the user's emotional state in real time and appropriately adjusting the notification content using that information. Specifically, the server analyzes the parent's recent emotional state using the emotion engine and flexibly changes the notification content based on that data. For example, conventional notification messages can be improved to be expressed in a more acceptable way.

[0749] After a notification is sent, the device presents it to the parent. This notification incorporates customization based on sentiment analysis. This improves the parent's access to and understanding of the information.

[0750] Furthermore, in addition to students' academic information, the server collects emotional data obtained by the emotion engine and records it in a database. Based on this information, the server feeds the emotional data back to educational institutions and develops academic support plans that take into account the students' psychological factors. Specifically, if the emotion engine determines that a student is experiencing stress with a particular subject, the server will suggest additional support or follow-up from teachers accordingly.

[0751] Overall, this invention improves the accuracy of information transmission while simultaneously enabling more personalized responses through an understanding of emotions, thereby enhancing the students' learning experience.

[0752] The following describes the processing flow.

[0753] Step 1:

[0754] The server receives notification data from educational institutions. These notifications include information about school events and emergency contacts.

[0755] Step 2:

[0756] The server stores received notifications in a database. This maintains a history of notifications, which can then be accessed when needed.

[0757] Step 3:

[0758] The server retrieves the caregiver's recent emotional state through the emotion engine. The emotion engine analyzes emotions using past data and sensor information.

[0759] Step 4:

[0760] The server optimizes the content of notifications based on the acquired emotional information. For example, if the emotion is negative, the notification message is modified to use softer language.

[0761] Step 5:

[0762] The server sends optimized notifications to the parent's device. These notifications are sent via email or app notifications.

[0763] Step 6:

[0764] The device displays incoming notifications as pop-ups to ensure they reach the parent / guardian. This allows the parent / guardian to check the information immediately.

[0765] Step 7:

[0766] The user (parent / guardian) will check the notification and take appropriate action. For example, if there is a request to participate, they will adjust their schedule.

[0767] Step 8:

[0768] The server continuously collects students' academic and emotional data. This data is integrated into a grade database and emotional history.

[0769] Step 9:

[0770] The server analyzes the collected data and applies specific algorithms to identify academic trends and emotional patterns.

[0771] Step 10:

[0772] Based on the analysis results, the server provides individualized learning support suggestions to educational institutions. This enables teachers to provide appropriate guidance that takes into account students' academic performance and emotional state.

[0773] Step 11:

[0774] The teachers, as users of the system, will consider the suggestions and plan and implement measures to address student issues. They will also collaborate with parents as needed.

[0775] Through this process, educational institutions and parents will be able to collaborate more easily and provide more personalized support to students.

[0776] (Example 2)

[0777] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0778] As the importance of improving the accuracy of information transmission and providing individualized support between educational institutions, parents, and students increases, traditional systems often transmit information in only one direction, making it difficult to provide information that takes emotional context into account. Furthermore, there is a problem that individualized support is insufficient because academic support plans based on students' emotional states have not been adequately developed.

[0779] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0780] In this invention, the server includes means for receiving notifications from information processing equipment and storing them in an information storage medium; means for automatically transmitting the received notifications to the parent's communication device; means for analyzing the parent's emotional state using an emotion analysis engine and adjusting the notification content; and means for acquiring student academic and emotional information, analyzing it as needed, and proposing learning support to the parent and educational institution based on the results. This enables the provision of information based on emotional state and the planning of individualized academic support.

[0781] An "information processing device" is an electronic device that has the capability to manage information, such as receiving, storing, transmitting, and analyzing data.

[0782] "Information storage media" refers to equipment used to store digital information, and includes databases and storage devices.

[0783] A "communication device" is a device that has means for sending and receiving information, and specifically includes mobile phones and tablets.

[0784] An "emotion analysis engine" is an algorithm or platform that analyzes text and audio to determine emotional states such as positive, negative, or neutral.

[0785] "Academic information" refers to information related to learning activities at educational institutions, such as students' grades, attendance records, and lesson content.

[0786] "Emotional information" refers to data that represents the psychological state of a specific individual, and is obtained through text analysis and behavioral analysis.

[0787] "Analysis" is the process of examining collected data in detail to identify specific patterns or trends.

[0788] The embodiments for carrying out this invention are shown below.

[0789] This system consists of an information processing unit, an emotion analysis engine, a data storage medium, and a communication interface. The main component, the information processing unit, receives notifications from educational institutions, stores them in the data storage medium, analyzes the content of those notifications, and automatically transmits them to the parents' communication devices.

[0790] The server receives notifications sent from educational institutions via an information processing device. The HTTP protocol is used, and the data is structured in JSON format. The received information is recorded on a data storage medium. A relational database management system can be used as the data storage medium. Specifically, MySQL is commonly used software for this purpose.

[0791] The server analyzes the parent's emotional state using an emotion analysis engine. This emotion analysis utilizes natural language processing techniques, such as commercial or open-source emotion analysis APIs. The analysis results indicate the parent's emotional state, and the server adjusts the notification content based on this information. For example, if the emotional state is found to be negative, a text generation AI model is used to make the notification content positive. The generation AI model can be improved by inputting prompts to produce more appropriate expressions.

[0792] The device displays customized notifications to parents. These devices are typically smartphones or tablets, and the information is presented as push notifications. These notifications contain information optimized for each parent and are presented in an easy-to-understand format.

[0793] Parents, as users, can access and understand information more effectively through the notifications they receive. For example, if a notification uses phrasing such as, "There is a special event at school. Please consider participating," it can create a more positive impression.

[0794] In addition, as a concrete example, an example of a prompt is shown. Using the prompt "Use the emotion engine to customize notifications to facilitate communication between educational institutions and parents. Suggest ways to make the information more acceptable while considering the emotional state of parents," the AI ​​model generates appropriate notification content.

[0795] Overall, this system not only ensures the accurate and effective transmission of information, but also enables sophisticated, personalized responses based on emotional information, offering a new approach to academic support.

[0796] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0797] Step 1:

[0798] The server receives notifications from educational institutions. These notifications are sent via the HTTP protocol in JSON format. The server parses this data and converts it into a valid data format. The input is notification data in JSON format, and the output is parsed structured data. The parsed data is broken down into elements such as the notification message, sender, and date and time of transmission.

[0799] Step 2:

[0800] The server stores the analyzed notification data in an information storage medium. A relational database system is used to maintain data integrity during storage. The input is structured notification data, and the output is the status of completion for storage into the database. Specifically, an INSERT operation is performed on the database.

[0801] Step 3:

[0802] The server collects past feedback and message data to obtain the emotional state of parents and inputs it into an emotion analysis engine. The input is past text data, and the output is an emotion score (positive, negative, neutral). Natural language processing tools are used for text analysis.

[0803] Step 4:

[0804] The server utilizes a generative AI model to adjust notification content based on sentiment analysis results. The input is the analyzed sentiment score and the original notification content, and the output is the adjusted notification message. Specifically, the prompt sentence is input to the generative AI model to obtain an improved expression.

[0805] Step 5:

[0806] The server sends a pre-arranged notification message to the parent's device. Here, the communication infrastructure is used to deliver the message based on the device's token ID. The inputs are the pre-arranged notification message and the device token, and the output is a delivery completion notification.

[0807] Step 6:

[0808] The device displays the received notification message on its screen. The input is the notification message sent from the server, and the output is a visual notification display to the user. Specifically, this involves displaying push notifications.

[0809] Step 7:

[0810] The user (parent / guardian) checks and understands the notifications displayed on the device. The input is the content displayed on the device, and the output is the user's understanding of the information. A concrete example is the user opening the notification to view detailed information.

[0811] Step 8:

[0812] The server generates feedback for educational institutions based on students' academic performance information and analyzed sentiment data. Inputs are academic and sentiment data, and output is academic support suggestions for educational institutions. These suggestions include additional instructional support and follow-up.

[0813] (Application Example 2)

[0814] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0815] In modern educational settings, information is frequently exchanged between educational institutions, parents, and students. However, conventional notifications often fail to consider the emotional state of the recipient, resulting in important information not being properly conveyed. Furthermore, the lack of individualized academic support makes it difficult to maximize students' learning experiences. This invention aims to solve these problems.

[0816] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0817] In this invention, the server includes means for receiving notifications from an information processing device and storing them in a storage device; means for automatically sending the received notifications to the parent's terminal; means for acquiring and analyzing student academic information and proposing learning support to the parent and educational institution based on the results; means for recognizing the user's emotions and adjusting the notification content based on that information; and means for aggregating emotional data and feeding it back to the educational institution to optimize the learning support plan. As a result, the transmission of information is adjusted to the emotional state of the recipient, enabling more effective learning support.

[0818] An "information processing device" is an electronic device used to receive notifications and data, store them in a storage device, and adjust the content of notifications.

[0819] A "storage device" is a computer storage medium used to record and save received notifications and data.

[0820] A "device" refers to a portable communication device used by a parent or guardian that has the function of receiving and displaying notifications.

[0821] "Academic information" refers to educational data such as the content of education and academic performance evaluations related to students.

[0822] "Analysis" is the process of evaluating collected data and extracting useful information.

[0823] An "emotion engine" is a software function that recognizes a user's emotions and suggests countermeasures based on that information.

[0824] "Adjusting notification content" means changing the content and wording of a notification message based on the recipient's emotional state.

[0825] "Feedback" is the process of providing data and insights based on results and achievements to be used for improvement.

[0826] An "academic support plan" is a curriculum and instructional plan designed to improve students' learning.

[0827] To realize this invention, the server, terminal, and user must work in coordination. The server first receives notifications from educational institutions through an information processing device and stores them in a storage device. Next, the server uses an emotion engine to analyze the emotional state of parents in real time. Based on this analysis, the server adjusts the notification content and automatically sends the notification to the terminal in a more acceptable form.

[0828] The device displays a pre-configured notification received from the server to the user. This notification incorporates customization by the emotion engine, ensuring the user can easily understand the information. Once the user reviews the information, their emotional response is analyzed again by the emotion engine, which is then used to refine future notifications.

[0829] Furthermore, the server aggregates students' academic and emotional data and provides feedback to educational institutions. This allows educational institutions to develop academic support plans tailored to each student's psychological state and academic ability.

[0830] For example, if a parent is experiencing stress at work and receives a notification stating, "There is a school event tomorrow," the server might adjust the notification to something like, "We're looking forward to tomorrow! We hope you'll join us for the school event," making the parent feel less burdened.

[0831] An example of a prompt message would be: "Please tell me how to modify notifications based on parental sentiment data. Please adjust the notification content to a stress-reducing tone."

[0832] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0833] Step 1:

[0834] The server receives notifications from educational institutions via an information processing device. The input is notification data generated by the educational institution, and the output includes storage in a storage device. This data is stored directly in the storage device, ready for use in subsequent processing.

[0835] Step 2:

[0836] The server uses an emotion engine to analyze the parent's current emotional state. The input is emotional data such as facial expressions and voice collected from the parent's device, and the output is emotional evaluation data based on the analysis results. This provides the basis for determining how to adjust notification content.

[0837] Step 3:

[0838] The server adjusts the content of the original notification data based on the sentiment evaluation data. The inputs are the notification data saved in step 1 and the sentiment evaluation data from step 2, and the output is the adjusted notification message. In this step, a generative AI model is used to optimize the wording of the notification according to the parent's emotions.

[0839] Step 4:

[0840] The adjusted notification is sent to the parent's device. The device receives this data and displays the notification to the parent (user) via the screen. The input is the adjusted notification message, and the output is the user's recognition and understanding.

[0841] Step 5:

[0842] After the user acknowledges the notification, their reaction is analyzed again by the emotion engine. The input is the user's emotional response data, and the output is emotional feedback data recording this reaction. This accumulates data that can be used to adjust future notifications.

[0843] Step 6:

[0844] The server aggregates student academic information and emotional feedback data and provides it to educational institutions. Inputs are academic and emotional data related to each student, and output is a dataset for educational institutions. This allows educational institutions to obtain the information needed to develop academic support plans that take students' psychological states into account.

[0845] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the controlled object 443 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0846] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). One example of data generation model 58 is ChatGPT (Internet search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0847] 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 this disclosure is not limited thereto, and the specific processing may also be performed by the robot 414.

[0848] Furthermore, the emotion identification model 59, acting as an emotion engine, may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to a specific mapping, which is an emotion map (see Figure 9). Similarly, the emotion identification model 59 may also determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0849] Figure 9 shows an emotion map 400 in which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. The closer to the center of the concentric circles, the more primitive the emotions are located. Further out of the concentric circles, emotions representing states and actions arising from mental states are located. Emotion is a concept that includes feelings and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions occurring in the brain are located. On the right side of the concentric circles, emotions that are generally induced by situational judgment are located. Above and below the concentric circles, emotions that are generally generated from reactions occurring in the brain and induced by situational judgment are located. In addition, the emotion of "pleasure" is located on the upper side of the concentric circles, and the emotion of "displeasure" is located on the lower side. Thus, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions arise, and emotions that are likely to occur simultaneously are mapped close together.

[0850] These emotions are distributed at the 3 o'clock position on the Emotion Map 400, and usually fluctuate between feelings of security and anxiety. In the right half of the Emotion Map 400, situational awareness takes precedence over internal feelings, resulting in a calm impression.

[0851] The inside of the Emotion Map 400 represents inner thoughts, while the outside represents actions. Therefore, the further you go from the outside of the Emotion Map 400, the more visible (expressed in actions) your emotions become.

[0852] Here, human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. Similarly, in robots, cars, motorcycles, etc., emotions can be created based on various balances, such as posture and battery level. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. The emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on a system for analyzing brain physiological signals of speech emotion recognition and emotion, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map contains emotions belonging to a region called "response," where sensation is dominant. The right half of the emotion map contains emotions belonging to a region called "situation," where situational awareness is dominant.

[0853] The emotion map defines two emotions that promote learning. One is the emotion around the middle of the negative "repentance" and "reflection" on the situation side. In other words, it is when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is the emotion around the positive "desire" on the reaction side. In other words, it is when the robot has positive feelings such as "I want more" or "I want to know more."

[0854] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​representing each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple training data sets, which are combinations of user input and emotion values ​​representing each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions located close together have similar values, as shown in the emotion map 900 in Figure 10. Figure 10 shows an example where multiple emotions such as "reassured," "calm," and "confident" have similar emotion values.

[0855] The above description primarily focuses on the functions of the data processing device 12 in relation to this disclosure. However, the system related to this disclosure is not necessarily implemented on a server. The system related to this disclosure may be implemented as a general information processing system. This disclosure may be implemented, for example, as a software program that runs on a personal computer or as an application that runs on a smartphone. The method related to this disclosure may be provided to users in SaaS (Software as a Service) format.

[0856] In the above embodiment, an example was given in which a specific process is performed by a single computer 22. However, the technology of this disclosure is not limited thereto, and a distributed processing of the specific process may be performed by multiple computers, including computer 22. For example, a data generation model 58 may be provided in an external device of the data processing device 12, and the external device may generate data according to the input data.

[0857] In the above embodiment, an example was given in which the specific processing program 56 is stored in the storage 32, but the technology of this disclosure is not limited thereto. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-temporary storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-temporary storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes specific processing according to the specific processing program 56.

[0858] 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.

[0859] Furthermore, it is not necessary to store the entirety of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store the entirety of the specific processing program 56 in the storage 32; it is acceptable to store only a portion of the specific processing program 56.

[0860] The following types of processors can be used as hardware resources to perform specific processing. Examples of processors include a CPU, a general-purpose processor that functions as a hardware resource to perform specific processing by executing software, i.e., a program. Other examples of processors include dedicated electrical circuits, such as FPGAs (Field-Programmable Gate Arrays), PLDs (Programmable Logic Devices), or ASICs (Application Specific Integrated Circuits), which have circuit configurations specifically designed to perform specific processing. All of these processors have built-in or connected memory, and all of them perform specific processing by using memory.

[0861] The hardware resource that performs a specific process may consist of one of these various processors, or it may consist of a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Alternatively, the hardware resource that performs a specific process may consist of a single processor.

[0862] Examples of configurations using a single processor include, firstly, a configuration in which one or more CPUs and software are combined to form a single processor, and this processor functions as a hardware resource that performs a specific process. Secondly, there is a configuration using a processor that realizes the functions of the entire system, including multiple hardware resources that perform a specific process, on a single IC chip, as exemplified by SoCs (System-on-a-chip). In this way, a specific process is realized using one or more of the above types of processors as hardware resources.

[0863] Furthermore, the hardware structure of these various processors can more specifically utilize electrical circuits that combine circuit elements such as semiconductor devices. Also, the specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps can be deleted, new steps added, or the processing order rearranged, as long as it does not deviate from the main purpose.

[0864] The descriptions and illustrations presented above are detailed explanations of the technical aspects of this disclosure and are merely examples of the technical aspects. For example, the above descriptions of the structure, function, operation, and effect are examples of the structure, function, operation, and effect of the technical aspects of this disclosure. Therefore, it goes without saying that you may delete unnecessary parts, add new elements, or replace elements in the descriptions and illustrations presented above, as long as you do not deviate from the essence of the technical aspects of this disclosure. Furthermore, in order to avoid confusion and facilitate understanding of the technical aspects of this disclosure, explanations of common technical knowledge and the like that do not require special explanation to enable the implementation of the technical aspects of this disclosure have been omitted from the descriptions and illustrations presented above.

[0865] All documents, patent applications, and technical standards described herein are incorporated by reference to the same extent as if each individual document, patent application, and technical standard were specifically and individually noted to be incorporated by reference.

[0866] The following is further disclosed regarding the embodiments described above.

[0867] (Claim 1)

[0868] A means for receiving notifications from an information processing device and storing them in a database,

[0869] A means of automatically sending received notifications to the parent's device,

[0870] A means of acquiring and analyzing students' academic information and proposing learning support to parents and educational institutions based on the results,

[0871] A system that includes this.

[0872] (Claim 2)

[0873] The system according to claim 1, which automatically generates information related to administrative processing and makes it ready for transmission to the necessary stakeholders.

[0874] (Claim 3)

[0875] The system according to claim 1, which identifies trends in grade evaluation using a specific algorithm based on the analysis of academic information.

[0876] "Example 1"

[0877] (Claim 1)

[0878] A means for receiving notifications from a communication unit and storing them in a storage medium,

[0879] A means for automatically sending received notifications to the relevant output device,

[0880] A means of acquiring and analyzing education-related information and proposing instructional support based on the results,

[0881] A means of identifying trends in academic evaluation using anomaly detection algorithms,

[0882] A means of constructing and notifying the proposed content using a generative model,

[0883] A system that includes this.

[0884] (Claim 2)

[0885] The system according to claim 1, which uses document generation technology to make information automatically available for distribution to relevant parties.

[0886] (Claim 3)

[0887] The system according to claim 1, which notifies the user of an individual learning program corresponding to the analysis results.

[0888] "Application Example 1"

[0889] (Claim 1)

[0890] A device that receives and stores notifications from an information processing device,

[0891] A device that automatically sends received notifications to a terminal,

[0892] A device that acquires and analyzes information related to academic performance and proposes support based on the results,

[0893] A communication device for providing real-time notifications of academic information,

[0894] A device that automatically proposes individualized instruction plans based on learning tendencies,

[0895] A system that includes this.

[0896] (Claim 2)

[0897] The system according to claim 1, which automatically generates information related to administrative processing and makes it available for transmission to the necessary stakeholders.

[0898] (Claim 3)

[0899] The system according to claim 1, which identifies trends in grade evaluation using a specific calculation method based on the processing of academic information.

[0900] "Example 2 of combining an emotion engine"

[0901] (Claim 1)

[0902] A means for receiving notifications from information processing equipment and storing them in an information storage medium,

[0903] A means of automatically sending received notifications to the parent's communication device,

[0904] A means of analyzing the emotional state of parents using an emotion analysis engine and adjusting the content of notifications accordingly,

[0905] A means of acquiring and analyzing students' academic and emotional information, and based on the results, proposing learning support to parents and educational institutions.

[0906] ...

[0907] A system that includes this.

[0908] (Claim 2)

[0909] The system according to claim 1, which automatically generates administrative information and makes it ready for transmission to the necessary stakeholders.

[0910] (Claim 3)

[0911] The system according to claim 1, which uses a specific algorithm to identify performance evaluations and emotional trends based on the analysis of academic and emotional information.

[0912] "Application example 2 when combining with an emotional engine"

[0913] (Claim 1)

[0914] A means for receiving notifications from an information processing device and storing them in a storage device,

[0915] A means of automatically sending received notifications to the parent's device,

[0916] A means of acquiring and analyzing students' academic information and proposing learning support to parents and educational institutions based on the results,

[0917] A means of recognizing the user's emotions and adjusting the notification content based on that information,

[0918] A means of aggregating emotional data and feeding it back to educational institutions to optimize academic support plans,

[0919] A system that includes this.

[0920] (Claim 2)

[0921] The system according to claim 1, which automatically generates information related to business operations and makes it ready for transmission to the necessary stakeholders.

[0922] (Claim 3)

[0923] The system according to claim 1, which identifies trends in academic performance evaluation using specific rules based on the analysis of academic information. [Explanation of symbols]

[0924] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots< / url:> < / url:> < / url:> < / url:>

Claims

1. A device that receives and stores notifications from an information processing device, A device that automatically sends received notifications to a terminal, A device that acquires and analyzes information related to academic performance and proposes support based on the results, A communication device for providing real-time notifications of academic information, A device that automatically proposes individualized instruction plans based on learning tendencies, A system that includes this.

2. The system according to claim 1, which automatically generates information related to administrative processing and makes it available for transmission to the necessary stakeholders.

3. The system according to claim 1, which identifies trends in grade evaluation using a specific calculation method based on the processing of academic information.

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