System

Abstract

A system is provided.SOLUTION: A system for analyzing a speech of a non-school child and verbalizing his / her feelings and thoughts, comprising: means for inputting a speech of a child; means for transmitting the input speech to a server; means for receiving the speech and storing the speech in a database by the server; means for analyzing the speech and extracting feelings on the server; means for generating an appropriate linguistic expression based on the extracted feelings; and means for transmitting the generated linguistic expression to a terminal.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] Elementary and junior high school students who refuse to go to school have difficulty expressing their feelings and thoughts in words, which makes it difficult for parents and educators to provide appropriate support. The purpose of this invention is to accurately verbalize the feelings and thoughts of school-refusing children and provide them in a form that is easy for parents and educators to understand, thereby quickly and appropriately resolving the problems that school-refusing children have. [Means for solving the problem]

[0005] The present invention is a system for analyzing the utterances of children who are not attending school and verbalizing their emotions and thoughts. It includes a means for inputting the children's utterances, a means for transmitting the input utterances to a server, a means for the server to receive the utterances and store them in a database, a means for analyzing the utterances on the server and extracting emotions, a means for generating appropriate linguistic expressions based on the extracted emotions, and a means for transmitting the generated linguistic expressions to a terminal. The system also includes a means for displaying the generated linguistic expressions to parents and educators, and a means for continuously collecting and analyzing new utterances from the children, thereby providing continuous support. This allows for an accurate understanding of the emotions of children who are not attending school and enables prompt and appropriate responses.

[0006] "School-refusing children" refers to elementary and junior high school students who do not attend school and instead live at home or elsewhere.

[0007] "Utterance" refers to what a child expresses in words or writing.

[0008] "Server" refers to a computer system that has the function of receiving, storing, analyzing user input data, and returning the results.

[0009] "Database" refers to a data storage device that systematically stores information received by the server and makes it available for searching and retrieval as needed.

[0010] "Means for extracting emotions" refers to the function of analyzing and identifying a child's emotions from input statements.

[0011] "Means for generating linguistic expressions" refers to the function of expressing children's thoughts and needs in a semantically clear way based on the extracted emotions.

[0012] A "terminal" refers to a device that a user operates to communicate with a server, such as a smartphone, tablet, or personal computer.

[0013] The "means for displaying" refers to a function for visually presenting the generated analysis results to the user.

[0014] "Means for continuous collection" refers to the function of receiving new statements from children at any time, continuously collecting them as data, and analyzing them. [Brief explanation of the drawings]

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

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

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

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

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

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

[0021] In the following embodiments, a communication I / F (Interface) with a symbol is an interface including a communication processor, an antenna, etc. The communication I / F controls communication between multiple computers. Examples of communication standards applied to the communication I / F include wireless communication standards including 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), Bluetooth (registered trademark), etc.

[0022] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B." In other words, "A and / or B" means that it may be only A, only B, or a combination of A and B. Furthermore, in this specification, the same concept as "A and / or B" is also applied when three or more things are expressed connected by "and / or."

[0023] [First embodiment]

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

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

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

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

[0028] The reception device 38 includes a touch panel 38A, a microphone 38B, and the like, and receives user input. The touch panel 38A detects contact with an indicator (for example, a pen or a finger) to receive user input by the touch of the indicator. The microphone 38B detects the user's voice to receive user input by voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the data indicating the user input.

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

[0030] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 control the exchange of various information between the processor 46 and the processor 28 via the network 54.

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

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

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

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

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

[0036] DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS The embodiments for carrying out the present invention will be described in detail below.

[0037] System Overview

[0038] The system of the present invention analyzes the statements of children who are not attending school and verbalizes their feelings and thoughts. This system is broadly composed of three elements: a terminal, a server, and a user.

[0039] Terminal

[0040] The terminal is a device operated by the user, specifically a child who is not attending school. The terminal provides an interface for the child to input comments and interact with the AI ​​in a chat format. The input from the terminal is sent to a server and analyzed.

[0041] server

[0042] The server receives the child's comments sent from the device and stores them in a database. The server then analyzes the comments using a generative AI model to extract emotions. Based on the extracted emotions, the server also generates linguistic expressions that are easy for parents and educators to understand.

[0043] User

[0044] Users are primarily children who are not attending school, their parents, and educators. Children use their devices to input their comments, and parents and educators can view the analysis results provided by the server to understand the child's condition and take appropriate action.

[0045] Program processing

[0046] The program works as follows:

[0047] 1. User Input

[0048] Children can type and send messages such as "I don't want to go to school" through their devices.

[0049] 2. Data Transmission

[0050] The device sends the user's message to the server, which encodes it and sends it to the server in an HTTP request.

[0051] 3. Data Receipt and Storage

[0052] The server receives the message and stores it in a database, at which point the message is ready for analysis.

[0053] 4. Sentiment analysis

[0054] The generative AI model on the server analyzes the received messages and identifies the child's emotions, extracting emotions such as "anxiety" and "stress."

[0055] 5. Verbalization

[0056] Based on the results of the sentiment analysis, the server converts the child's feelings and thoughts into appropriate language, for example, "This child seems to be feeling a lot of stress about school."

[0057] 6. Send results

[0058] The server then sends the generated linguistic expressions back to the device, where parents and educators can view the results.

[0059] Specific examples

[0060] For example, if Mr. A doesn't like going to school, he types "I don't want to go to school" into his device. This message is sent from the device to the server, which receives it and analyzes it. As a result of the analysis, it is determined that Mr. A is feeling "anxiety" or "stress." The server generates a statement such as "Mr. A seems to be feeling a lot of stress about school" and sends this information back to the device. Parents can then view this result and provide appropriate support for Mr. A.

[0061] As described above, the system of the present invention aims to quickly resolve the problem of school refusal by accurately understanding the emotions of children who are refusing to go to school and enabling parents and educators to respond quickly and appropriately.

[0062] The processing flow will be explained below.

[0063] Step 1:

[0064] The user inputs a message through the terminal. For example, a child may input a message such as "I don't want to go to school" and press the send button.

[0065] Step 2:

[0066] The terminal sends the message entered by the user to the server, which encodes the message and sends it to the server using an HTTP request.

[0067] Step 3:

[0068] The server receives the message sent from the terminal and stores it in the database.

[0069] Step 4:

[0070] The generative AI model on the server analyzes the stored messages, using natural language processing technology to extract the child's feelings and thoughts from the content of the comments.

[0071] Step 5:

[0072] Based on the analysis results, the server expresses the child's feelings in concrete terms, for example, "This child seems to be feeling a lot of stress about school."

[0073] Step 6:

[0074] The server sends the generated language expression to the terminal, and returns the result to the terminal as an HTTP response.

[0075] Step 7:

[0076] The device displays the analysis results received from the server on the screen, providing a visual user interface to make it easier for parents and educators to view the results.

[0077] Step 8:

[0078] The user types in an additional comment, for example, a child types "I'm scared of my teacher," and sends a new message.

[0079] Step 9:

[0080] The device again sends a new message to the server, which receives the new message and stores it in its database.

[0081] Step 10:

[0082] The server then re-analyzes the new message and uses it as training data for the AI ​​model, which improves its accuracy.

[0083] Step 11:

[0084] The server sends the reanalysis results to the device, which then displays them, helping parents and educators obtain new information and take appropriate action.

[0085] By following these steps and continually repeating the cycle of analyzing, verbalizing, and providing feedback based on what is said, you can accurately understand children's emotions and provide support.

[0086] Example 1

[0087] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0088] There is a need to properly understand the feelings and thoughts of children who refuse to go to school and to provide an environment in which parents and educators can respond quickly and appropriately. However, existing technologies lack effective means to accurately analyze children's comments and extract and verbalize their emotions. This has made it difficult to obtain accurate information about children's internal states.

[0089] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[0090] In this invention, the server includes a means for an information processing device to receive utterances and store them in a data storage device, a means for analyzing the utterances using a generative AI model on the information processing device and extracting emotions, and a means for generating appropriate linguistic expressions based on prompt sentences using the generative AI model. This makes it possible to accurately analyze the emotions and thoughts of children who are not attending school and provide them in an easy-to-understand format to parents and educators.

[0091] "School-refusing children" refer to school-age children who find it difficult or impossible to attend school.

[0092] "Statements" refer to text messages or voice messages that children enter through their devices.

[0093] "Means of input" refers to a terminal or device with an interface that allows children to input text or voice.

[0094] "Means for sending" refers to the communication technology used to send the inputted remarks to the server via data communication.

[0095] "Information processing device" refers to a computer or server that has the functionality to receive, store, and analyze data.

[0096] "Data storage device" refers to a storage medium or database for storing received data.

[0097] A "generative AI model" refers to a software model that analyzes input data through natural language processing and extracts emotions and meanings.

[0098] "Prompt sentence" refers to an instruction sentence used as input to an AI model.

[0099] "Means of analysis" refers to the process of identifying emotions based on received utterances using a generative AI model.

[0100] "Means for extracting emotions" refers to the technique for extracting emotions that become clear as a result of the analysis.

[0101] "Means for generating linguistic expressions" refers to the process of converting extracted emotions into sentences in an understandable form.

[0102] "Terminal" refers to a device used by a child to receive messages from the server.

[0103] This invention relates to a system that analyzes the comments of children who are not attending school and verbalizes their feelings and thoughts. The purpose of this system is to analyze the comments entered by the children and provide the results in an appropriate format to parents and educators.

[0104] System configuration

[0105] The system of the present invention mainly consists of three elements: a terminal, a server, and a user.

[0106] Terminal

[0107] The terminal is a device operated by the user, a child who is not attending school. The terminal provides an interface for the child to input comments and interact with the AI ​​in a chat format. Input from the terminal is encoded and sent to the server using an HTTP POST request. In this system, PCs, tablets, smartphones, etc. are used as terminals.

[0108] server

[0109] The server receives the child's comments sent from the device and stores them in a database. The server then analyzes the received comments using a generative AI model to identify the emotion. This generative AI model uses OpenAI's GPT-3 or ChatGPT, for example. A prompt sentence is used to analyze the emotion, and the emotion is extracted in the following format:

[0110] Example prompt sentence:

[0111] “What is the emotion conveyed in the following sentence?

[0112] Sentence: I don't want to go to school

[0113] Emotions:”

[0114] The server generates appropriate language expressions based on the extracted emotions. These expressions are then converted into a form that is easy for parents and educators to understand. For example, the output might be something like, "This child is feeling very stressed about school."

[0115] User

[0116] Users are children who are not attending school, their parents, and educators. Children use their devices to input comments, and parents and educators can view the analysis results provided by the server to understand the child's condition and take appropriate action.

[0117] Specific examples

[0118] For example, suppose that a child named A types "I don't want to go to school" into their device. This message is sent from the device to a server, which receives it and stores it in a database. A generative AI model on the server analyzes this message and determines that A is feeling "anxiety" or "stress." Based on the analysis results, the server generates a statement such as "A seems to be feeling very stressed about school," and notifies parents and educators. This allows parents and educators to understand the child's internal state and provide appropriate support.

[0119] The system of the present invention can accurately grasp the feelings and thoughts of children who are not attending school, and provides an environment in which parents and educators can respond quickly and appropriately. As a result, it becomes possible to quickly resolve the problem of school refusal.

[0120] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0121] Step 1:

[0122] The user uses the terminal to input a statement such as "I don't want to go to school." By entering text in the input field and pressing the send button, the input of the statement is completed.

[0123] Input: A text message entered by the user.

[0124] Output: The encoded message is prepared.

[0125] Step 2:

[0126] The terminal encodes the message entered by the user and sends it to the server using an HTTP POST request, which then packages the encoded message in JSON format and sends it.

[0127] Input: The encoded text message.

[0128] Output: The HTTP POST request sent to the server.

[0129] Step 3:

[0130] The server receives messages sent from the terminal and stores them in a database. NoSQL databases (e.g., MongoDB) are often used as the database. The server records the received messages in the database.

[0131] Input: The text message included in the HTTP POST request received by the server.

[0132] Output: The message stored in the database.

[0133] Step 4:

[0134] A generative AI model on the server retrieves and analyzes messages stored in a database. A prompt sentence is used to identify the emotion of the statement, for example, the following prompt sentence: "What emotion is conveyed in the following sentence? Sentence: I don't want to go to school Emotion:." The emotion is extracted as a result of the analysis.

[0135] Input: A text message stored in the database.

[0136] Output: Emotions extracted by analysis (e.g., anxiety, stress).

[0137] Step 5:

[0138] The server uses a generative AI model to generate appropriate language expressions based on the extracted emotions. For example, it might generate a language expression such as, "Mr. A seems to be feeling very stressed about school." A text containing the emotion and its rationale is then generated.

[0139] Input: The extracted sentiment as a result of analysis.

[0140] Output: The generated linguistic expression.

[0141] Step 6:

[0142] The server sends the generated language representation to the device, which encodes it using an HTTP POST request and sends it back to the device, where it receives the information and makes it available for viewing by the user.

[0143] Input: The generated linguistic expression.

[0144] Output: The language expression sent to the terminal is displayed to the user.

[0145] (Application example 1)

[0146] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0147] The challenge is to accurately understand the emotions and psychological state of children who are not attending school, so that parents and educators can respond appropriately. There is also a need to track emotional changes in real time and quickly notify the results, thereby speeding up support for children.

[0148] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[0149] In this invention, the server includes a means for notifying relevant parties of the emotion analysis results in real time, a means for tracking changes in emotions and displaying the trends, and a means for analyzing the child's emotions using a generative AI model. This allows the child's emotions to be analyzed in real time and the relevant parties to be promptly notified of the changes, enabling appropriate support.

[0150] "School-refusing children" refer to children who, for some reason, do not go to school and spend their time at home or elsewhere.

[0151] "Means for inputting speech" refers to devices or software that provide an interface for children to input their thoughts and feelings in text form.

[0152] A "server" refers to a computer system that processes and stores data on a network.

[0153] A "database" refers to a system for efficiently storing, retrieving, and managing data.

[0154] "Means for extracting emotions" refers to algorithms or programs that use generative AI models to identify emotions from input utterances.

[0155] "Means for generating linguistic expressions" refers to a program that generates text based on the extracted emotions in a format that is easy for parents and educators to understand.

[0156] "Devices" refer to digital devices such as computers and smartphones used by children, parents, and educators.

[0157] "Means of real-time notification" refers to a push notification or message sending system that instantly communicates the results of sentiment analysis to relevant parties.

[0158] "Means for tracking changes in emotions" refers to algorithms or programs that continuously analyze children's speech and monitor and visualize emotional fluctuations.

[0159] A "generative AI model" refers to a machine learning model that uses natural language processing to analyze text data and extract emotions.

[0160] A "prompt sentence" refers to the text data input into a generative AI model, and is the source sentence that the model uses for analysis.

[0161] The system for implementing this invention is designed to analyze the comments of children who are not attending school and verbalize their feelings and thoughts. The system is broadly composed of a terminal that inputs the children's comments, a server that receives and analyzes the comments, and a means for notifying parents and educators of the results of the analysis.

[0162] Terminal

[0163] A terminal is a device used by a child (user). Specifically, it can be a smartphone, tablet, or personal computer. A terminal provides the following interfaces:

[0164] A chat-style input interface using text boxes

[0165] A button to enter and send a message

[0166] A display area for parents and educators to view the analysis results

[0167] Comments entered from the terminal are sent to the server via an HTTP request.

[0168] server

[0169] The server stores the utterances received from the device in a database and analyzes them using a generative AI model. The specific software used is as follows:

[0170] Flask (web server framework)

[0171] Hugging Face generation AI model "bert-base-uncased-emotion"

[0172] Database management systems such as PostgreSQL and MySQL

[0173] The server process is as follows:

[0174] 1. Receive comments and store them in a database.

[0175] 2. Extract the sentiment of the utterance using a generative AI model.

[0176] 3. Generate appropriate language expressions based on emotions.

[0177] 4. The generated language expression is sent back to the terminal.

[0178] The server notifies relevant parties of the results of the emotion analysis in real time, and also has the ability to track changes in emotions and display trends. For example, when B types, "I don't want to see my friends today," the server analyzes this statement as "anxiety" and generates the result, "The child is feeling anxious."

[0179] Examples of specific examples and prompts

[0180] For example, if B types, "I don't want to see my friends today," this message is sent from the device to the server. The server analyzes this statement and extracts the emotion "anxiety." The server then generates the linguistic expression, "The child is feeling the emotion of 'anxiety,'" and sends it back to the device.

[0181] Examples of prompts are:

[0182] "I don't want to see my friends today."

[0183] This will enable parents and educators to quickly understand a child's condition and take appropriate action.

[0184] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0185] Step 1:

[0186] The user (child) uses the terminal to input comments.

[0187] Input: Child's statement (e.g., "I don't want to see my friends today.")

[0188] Output: Data sent from the device to the server in speech data format

[0189] How it works: Children type their message into the chat box on their smartphone or tablet and press the "Send" button.

[0190] Step 2:

[0191] The device sends the message to the server as an HTTP request.

[0192] Input: Speech data entered into the terminal

[0193] Output: The encoded speech data sent to the server

[0194] Operation: Sends speech data as an HTTP POST request to a specific URL on the server.

[0195] Step 3:

[0196] The server stores the received comments in a database.

[0197] Input: Speech data sent to the server

[0198] Output: A database record containing the comment

[0199] How it works: The server receives an HTTP request and inserts the comment data into a database using an SQL query.

[0200] Step 4:

[0201] The server analyzes the comments and extracts the emotions.

[0202] Input: Speech data read from the database

[0203] Output: Extracted emotion data (e.g., "anxiety")

[0204] How it works: A generative AI model (e.g., "bert-base-uncased-emotion") is used to analyze speech using natural language processing to obtain emotion labels.

[0205] Step 5:

[0206] The server generates appropriate language expressions based on the extracted emotions.

[0207] Input: Extracted emotion data

[0208] Output: Generated linguistic expression (e.g., "The child is experiencing the emotion 'anxiety'.")

[0209] Operation: Based on the extracted emotions, a program is run that uses templates to generate appropriate linguistic expressions.

[0210] Step 6:

[0211] The server transmits the generated linguistic expression to the terminal.

[0212] Input: Generated linguistic expression

[0213] Output: Language expression data sent to the device

[0214] Operation: The generated language expression is sent to the terminal as an HTTP response.

[0215] Step 7:

[0216] The device displays the received language expressions to parents and educators.

[0217] Input: Linguistic expression data received from the server

[0218] Output: Language expression displayed on the terminal screen

[0219] What happens: The terminal application receives the HTTP response and displays it in the user interface.

[0220] Step 8:

[0221] The server tracks changes in the sentiment of comments and displays the trends.

[0222] Input: Multiple utterances and their emotional data accumulated in chronological order

[0223] Output: Graphs and lists showing trends in sentiment changes

[0224] Operation: Analyzes past statements and emotional data, runs a program that visualizes trends in emotional changes, and displays the results in the user interface.

[0225] This will enable us to understand the emotions of children who are not attending school in real time and respond quickly.

[0226] Furthermore, an emotion engine that estimates the user's emotion may be combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0227] DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS The embodiments for carrying out the present invention will be described in detail below.

[0228] System Overview

[0229] The system of the present invention analyzes the speech of children who are not attending school and verbalizes their feelings and thoughts. Furthermore, by combining it with an emotion engine that recognizes the user's emotions, the accuracy of the analysis can be further improved. This system is broadly composed of four elements: a terminal, a server, an emotion engine, and a user.

[0230] Terminal

[0231] The terminal is a device operated by the user, specifically a child who is not attending school. The terminal provides an interface for the child to input comments and interact with the AI ​​in a chat format. The input from the terminal is sent to a server for analysis. The terminal also has a built-in emotion engine that analyzes the user's emotions in real time and sends them to the server.

[0232] server

[0233] The server receives the children's comments sent from the device and stores them in a database. The server then analyzes the comments using a generative AI model and emotion engine to extract emotions. Based on the extracted emotions, the server also generates linguistic expressions in a form that is easy for parents and educators to understand.

[0234] Emotion Engine

[0235] The emotion engine has the ability to analyze emotions in real time from user input and dialogue. This data is immediately sent to the server, improving the analytical accuracy of the generative AI model. For example, by simultaneously analyzing a child's input and their emotional state at the time of the comment, more precise emotion analysis becomes possible.

[0236] User

[0237] Users are primarily children who are not attending school, their parents, and educators. Children use their devices to input their comments, and parents and educators can view the analysis results provided by the server to understand the child's condition and take appropriate action.

[0238] Program processing

[0239] The program works as follows:

[0240] 1. User Input

[0241] Children can type and send messages such as "I don't want to go to school" through their devices.

[0242] 2. Data Transmission

[0243] The device sends the user's message to the server, which encodes it and sends it to the server using an HTTP request.

[0244] 3. Real-time analysis using an emotion engine

[0245] The emotion engine analyzes the child's emotional state at the time of input in real time and sends the results to the server.

[0246] 4. Data Receipt and Storage

[0247] The server receives the message sent from the device and the emotion data from the emotion engine, and stores them in a database. At this stage, the message and emotion data are ready for analysis.

[0248] 5. Combined analysis of emotions and statements

[0249] The generative AI model and emotion engine on the server analyze the stored messages and emotion data, using natural language processing and emotion recognition technologies to extract the content of the comments and the emotional state.

[0250] 6. Verbalization

[0251] Based on the results of the emotion analysis, the server expresses the child's feelings and thoughts in concrete terms, for example, "This child seems to be feeling a lot of stress about school."

[0252] 7. Send results

[0253] The server sends the generated language expression to the terminal, and returns the result to the terminal as an HTTP response.

[0254] 8. Results display

[0255] The device displays the analysis results received from the server on the screen, providing a visual user interface to make it easier for parents and educators to view the results.

[0256] Specific examples

[0257] For example, if Mr. A doesn't want to go to school, he types "I don't want to go to school" into his device. When this message is sent from the device to the server, the emotion engine simultaneously analyzes Mr. A's emotions and generates emotional data such as "anxiety" and "fear." The server receives this data, and the generative AI model performs a combined analysis of the statement "I don't want to go to school" and the emotional data "anxiety" and "fear." As a result of the analysis, it is determined that Mr. A is feeling "anxiety" and "stress." The server generates a statement such as "Mr. A seems to be feeling very stressed about school" and sends this information back to the device. Parents can then view this result and provide appropriate support for Mr. A.

[0258] As described above, the system of the present invention aims to resolve the problem of school refusal at an early stage by more accurately grasping the emotions of children who are refusing to go to school and enabling parents and educators to respond quickly and appropriately.

[0259] The processing flow will be explained below.

[0260] Step 1:

[0261] The user inputs a message through the terminal. For example, a child may input a message such as "I don't want to go to school" and press the send button.

[0262] Step 2:

[0263] The emotion engine analyzes the child's emotional state in real time as the user speaks, using facial recognition, voice analysis, and contextual analysis to classify emotions.

[0264] Step 3:

[0265] The device sends the message entered by the user and the emotional data analyzed by the emotion engine to the server, where the message and emotional data are encoded and sent to the server via an HTTP request.

[0266] Step 4:

[0267] The server receives the message and emotion data sent from the device, and then stores the message and emotion data in a database.

[0268] Step 5:

[0269] The generative AI model on the server performs a combined analysis of the stored messages and emotional data, using natural language processing and emotion recognition technologies to extract the content of the speech and its corresponding emotional state.

[0270] Step 6:

[0271] Based on the analysis results, the server expresses the child's feelings in concrete terms, for example, "This child seems to be feeling a lot of stress about school."

[0272] Step 7:

[0273] The server sends the generated language expression to the terminal, and returns the result to the terminal as an HTTP response.

[0274] Step 8:

[0275] The device displays the analysis results received from the server on the screen, providing a visual user interface to make it easier for parents and educators to view the results.

[0276] Step 9:

[0277] The user types in an additional comment, for example, a child types "I'm scared of my teacher," and sends a new message.

[0278] Step 10:

[0279] The device sends the new message and the new emotion data analyzed by the emotion engine back to the server, which receives the new data and stores it in a database.

[0280] Step 11:

[0281] The server re-analyzes the new message and emotion data and uses it as training data for the AI ​​model and emotion engine, improving the accuracy of the analysis.

[0282] Step 12:

[0283] The server sends the reanalysis results to the device, which then displays them, helping parents and educators obtain new information and take appropriate action.

[0284] This process involves a continuous cycle of emotion analysis, verbalization, and feedback based on what is said, resulting in a system that can accurately grasp children's emotions and provide appropriate support.

[0285] Example 2

[0286] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0287] Properly understanding the words and thoughts of school-refusing children and providing appropriate support based on that understanding is an important issue in the educational field. However, children may have difficulty verbalizing their own feelings and thoughts, or their feelings may be difficult to understand, making it difficult for parents and educators to respond appropriately. The present invention aims to solve these issues by providing a system that accurately understands the feelings and thoughts of school-refusing children and enables prompt and appropriate responses based on that understanding.

[0288] The identification process by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means. In this invention, the server includes means for inputting children's comments, means for transmitting the input comments to the server, means equipped with an emotion engine that analyzes emotions in real time, means for the server to receive the comments and emotion data and store them in a database, means for analyzing the comments and emotion data on the server and extracting emotions, means for generating appropriate linguistic expressions based on the extracted emotions, and means for transmitting the generated linguistic expressions to a terminal. This makes it possible to analyze the emotions and thoughts of children who are not attending school in real time and provide appropriate support based on the analysis.

[0289] "Futoko children" refer to children who do not attend school regularly and spend their time at home or elsewhere.

[0290] "Comments" refer to messages and comments that children enter through their devices.

[0291] A "server" refers to a computer system that receives speech and emotional data, stores it in a database, and analyzes it.

[0292] "Terminal" refers to an input / output device that children can operate directly to input comments.

[0293] An "emotion engine" refers to software that has the ability to analyze emotions in real time from user input and dialogue.

[0294] "Database" refers to an information management system for storing speech data and emotional data.

[0295] A "generative AI model" refers to artificial intelligence technology that analyzes the statements and emotional data of children who are not attending school and generates appropriate language expressions.

[0296] "Linguistic expressions" refer to sentences generated by the generative AI model to explain children's emotions and thoughts.

[0297] An "HTTP request" refers to a type of communication protocol for sending data from a terminal to a server.

[0298] "Analysis" refers to the process of extracting and understanding emotions and thoughts based on received statements and emotional data.

[0299] "Guardians" refers to the parents or supervisors of children who are not attending school.

[0300] "Educational personnel" refers to professionals such as teachers and counselors who provide guidance and support to children in educational settings.

[0301] This invention is a system that analyzes the speech of children who are not attending school and verbalizes their feelings and thoughts. This system is composed of multiple elements, such as a terminal, a server, an emotion engine, and a user. Each element is described in detail below.

[0302] Terminal

[0303] The terminal is a device operated by the user, specifically a child who is not attending school. The terminal provides an interface for the child to input comments and interact with the AI ​​in a chat format. For example, a tablet or smartphone can be used as the terminal. Input from the terminal is sent to a server for analysis. The terminal also has a built-in emotion engine that analyzes the user's emotions in real time and sends them to the server.

[0304] server

[0305] The server receives the child's utterances sent from the device and stores them in a database. The server then analyzes the utterances using a generative AI model and emotion engine to extract emotions. For example, the server can use cloud services such as Amazon Web Services (AWS) and Microsoft Azure. Based on the extracted emotions, the server also generates linguistic expressions that are easy for parents and educators to understand.

[0306] Emotion Engine

[0307] The emotion engine has the ability to analyze emotions in real time from user input and dialogue. For example, the emotion engine uses Google Cloud's Natural Language API or IBM Watson's emotion recognition function. This data is immediately sent to the server, improving the analytical accuracy of the generative AI model. For example, by simultaneously analyzing the words entered by a child and their emotional state at the time of the words, more precise emotion analysis becomes possible.

[0308] User

[0309] Users are primarily children who are not attending school, their parents, and educators. Children use their devices to input their comments, and parents and educators can view the analysis results provided by the server to understand the child's condition and take appropriate action.

[0310] Specific examples

[0311] For example, if Mr. A doesn't want to go to school, he types "I don't want to go to school" into his device. When this message is sent from the device to the server, the emotion engine simultaneously analyzes Mr. A's emotions and generates emotional data such as "anxiety" and "fear." The server receives this data, and the generative AI model performs a combined analysis of the statement "I don't want to go to school" and the emotional data "anxiety" and "fear." As a result of the analysis, it is determined that Mr. A is feeling "anxiety" and "stress." The server generates a statement such as "Mr. A seems to be feeling very stressed about school" and sends this information back to the device. Parents can then view this result and provide appropriate support for Mr. A.

[0312] Prompt Sentence Examples

[0313] For example, the prompt to input to the generative AI model would be:

[0314] "When a child types 'I don't want to go to school,' the emotion engine analyzes it as 'anxiety' and 'fear.' Please provide a detailed analysis based on this child's emotions and statements."

[0315] This system allows for a more accurate understanding of the emotions of the users, i.e., children who are not attending school, and enables parents and educators to take appropriate action quickly through the devices and servers.

[0316] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0317] Step 1: User Input

[0318] The user (child) inputs a message such as "I don't want to go to school" through the terminal and presses the send button. The input is in text format and is prepared for transmission to the server through the terminal interface. The input at this stage is the user's spoken text.

[0319] Step 2: Send data

[0320] The device encodes the user's input message into JSON format and sends it to the server using an HTTP request. This request also includes the user's session information. Specifically, the device encodes the message and sends a POST request to the " / api / message" endpoint. The output is the encoded message data sent to the server.

[0321] Step 3: Real-time analysis by emotion engine

[0322] The device's emotion engine analyzes facial and voice data captured during user input and determines the user's emotional state in real time. The input is the captured facial and voice data, which is analyzed to generate emotion data such as "anxiety" or "fear." The generated emotion data is also sent to the server using an HTTP request. The output is the analyzed emotion data.

[0323] Step 4: Receiving and storing data

[0324] The server receives messages and emotion data sent from the device and stores them in a database. Specifically, messages are stored in the "messages" table, and emotion data is stored in the "emotions" table. The input is the encoded data received from the device, and the output is the database in its saved state.

[0325] Step 5: Combined analysis of emotions and statements

[0326] The generative AI model and emotion engine running on the server perform a comprehensive analysis of the messages and emotion data stored in the database. Specifically, the generative AI model analyzes the message content using natural language processing technology, and the emotion engine analyzes the emotion data. The input is the stored message and emotion data, and the output is the analysis results.

[0327] Step 6: Verbalize

[0328] The server converts the child's feelings and thoughts into concrete words based on the analysis results obtained from the generative AI model and emotion engine. For example, it verbalizes the child's feelings and thoughts in the form of, "This child seems to be feeling a lot of stress about school." The input is the analysis result data, and the output is a linguistic expression that humans can understand.

[0329] Step 7: Send results

[0330] The server sends the language expression generated as the analysis result to the terminal. The result is returned in JSON format as an HTTP response. Specifically, the result is sent as a POST request to the " / api / result" endpoint. The input is the generated language expression, and the output is the analysis result sent to the terminal.

[0331] Step 8: View the results

[0332] The analysis results received by the device from the server are displayed on the user interface. To make the results easy for parents and educators to understand, not only text messages but also visual graphs and icons are displayed. The input is the analysis results received from the server, and the output is the results displayed on the device's user interface.

[0333] As a result, the comments of the user, a child who is not attending school, are analyzed in real time, and their feelings and thoughts are expressed in concrete words, enabling parents and educators to respond quickly and appropriately.

[0334] (Application example 2)

[0335] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0336] Children who refuse to go to school often find it difficult to express their feelings and thoughts appropriately. As a result, parents and educators often have difficulty accurately understanding the child's condition and are unable to respond appropriately. Real-time emotion analysis and rapid response are also required, but current systems do not easily achieve this.

[0337] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 2 is realized by the following means.

[0338] In this invention, the server includes means for inputting comments from children, means for transmitting the input comments to the server, means for the server to receive the comments and store them in a database, means for analyzing the comments on the server and extracting emotions, means for generating appropriate linguistic expressions based on the extracted emotions, means for transmitting the generated linguistic expressions to the terminal, means for real-time analysis using an emotion engine, and means for transmitting the results as alerts to the terminals of parents and educators. This makes it possible to analyze children's emotions in real time and quickly notify parents and educators of the analysis results.

[0339] "School-refusing children" refer to children who have difficulty attending school.

[0340] "Statements" refers to text messages or voice messages typed by the child.

[0341] "Terminal" refers to the device used by a child to input comments.

[0342] "Server" is a computer system that receives, stores, and analyzes data sent from a terminal.

[0343] The "database" is a data storage system that is stored on a server and that stores children's comments and analysis results.

[0344] An "emotion engine" is a software engine that analyzes user emotions from utterances and other input data.

[0345] "Real-time analysis means" refers to a means for immediately analyzing a message after it is sent.

[0346] A "generative AI model" is an artificial intelligence that generates appropriate language expressions based on user statements and emotional data.

[0347] An "alert" is a warning message sent to parents and educators based on the analysis results.

[0348] "Guardian" refers to an adult who supports a child in their daily life, including the child's parents.

[0349] "Educational professionals" refers to professionals involved in children's education, such as teachers and counselors.

[0350] DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS The embodiments for carrying out the present invention will be described in detail below.

[0351] Overall system configuration

[0352] The system of the present invention analyzes the statements of children who are not attending school and verbalizes their feelings and thoughts. This system consists of a terminal that inputs the children's statements, a server, an emotion engine, and devices such as smartphones of parents and educators.

[0353] Terminal

[0354] The terminal is a device operated by the child. The child uses the terminal to input text messages and voice comments. The terminal is provided with an input interface through which the child inputs their comments. The comments are not stored on the terminal but are immediately sent to the server.

[0355] server

[0356] The server receives the utterances sent from the device and stores them in a database. The server then uses a generative AI model and emotion engine to analyze the utterances and extract emotions. The extracted emotion data and utterance data are then analyzed in a composite manner to generate an appropriate linguistic expression. The generated linguistic expression is then sent back to the device.

[0357] Emotion Engine

[0358] The emotion engine has the ability to analyze emotions from user comments in real time. The emotion engine analyzes the content of comments and the emotional state at the time of commenting, and generates emotion data for the comment. The generated emotion data is immediately sent to the server and used for analysis.

[0359] Devices for parents and educators

[0360] The analysis results sent from the server are displayed in real time on the devices of parents and educators. The analysis results are displayed in a visually easy-to-understand format and are sent to parents and educators as push notifications, for example, allowing for a prompt response.

[0361] Hardware and software used

[0362] Devices: Smartphones, tablets, smart glasses, etc.

[0363] Server: A cloud-based server, such as AWS (Amazon Web Services) or GCP (Google Cloud Platform).

[0364] Emotion Engine: Emotion Engine API.

[0365] Generative AI models: Natural language processing models such as GPT-4.

[0366] Database: A relational database such as MySQL or PostgreSQL.

[0367] Specific examples

[0368] For example, a child might type "I don't want to go to school" into their device. This message is immediately sent from the device to the server. At the same time, the emotion engine generates emotion data such as "anxiety" or "fear." The server receives this data, and the generative AI model performs a comprehensive analysis. As a result of the analysis, a linguistic expression is generated: "This child is feeling very stressed about school." This information is sent as an alert to the parent's smartphone, allowing them to respond quickly.

[0369] Examples of prompts:

[0370] Use a sentiment analysis engine to analyze the sentiment of the following message: "I don't want to go to school."

[0371] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0372] Step 1:

[0373] The user uses the device to input a message. For example, a child might type "I don't want to go to school" into their smartphone. The input text message is temporarily stored in the device's memory. The input at this stage is text data before it is sent from the device to the server.

[0374] Step 2:

[0375] The device sends the input message to the server using an HTTP request. At this time, the text data is encoded and sent securely. The input is a text message, and the output is sent to the server in the form of an HTTP request.

[0376] Step 3:

[0377] The server receives the messages sent from the device, decodes the received data, and stores it as a text message in a database. This database also stores the child's past messages and emotional data. The input is the text data received via the HTTP request, and the output is the stored database record.

[0378] Step 4:

[0379] The server sends the message data to the emotion engine, which analyzes the emotion in real time. The emotion engine analyzes the message text, generates emotion data such as "anxiety" or "fear," and sends it back to the server. Here, emotion recognition is performed based on the input message text, and the emotion data is output.

[0380] Step 5:

[0381] The server uses a generative AI model to perform a comprehensive analysis of the received emotion data and utterance data. The generative AI model analyzes the emotion data and utterance text obtained from the emotion engine, and generates a specific linguistic expression such as "This child is feeling very stressed about school." The input is emotion data and utterance text, and the output is a linguistic expression resulting from the analysis.

[0382] Step 6:

[0383] The server sends the generated linguistic expression to the device of the parent or guardian or educator as an HTTP response. The sent data is immediately displayed on the smartphone or other device, and the parent or guardian or educator is notified as an alert. The input is the generated linguistic expression, and the output is a notification message.

[0384] Step 7:

[0385] Parents and educators can check the analysis results displayed on the device and take prompt action depending on the child's situation. The input is the notification message sent from the server, and the output is the specific response action taken by the parent or educator.

[0386] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0387] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0388] In the above embodiment, an example in which the specific process is performed by the data processing device 12 has been given, but the technology of the present disclosure is not limited to this, and the specific process may be performed by the smart device 14.

[0389] [Second embodiment]

[0390] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0391] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0392] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0393] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, and the camera 42 are also connected to the bus 52.

[0394] The microphone 238 receives instructions and the like from the user 20 by receiving voice uttered by the user 20. The microphone 238 captures the voice uttered by the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio in accordance with instructions from the processor 46.

[0395] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the surroundings of user 20 (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0396] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0397] Fig. 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Fig. 4, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0398] The specific processing program 56 is an example of a "program" according to the technology of the present disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0399] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0400] In the smart glasses 214, the reception output process is performed by the processor 46. A reception output program 60 is stored in the storage 50. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[0401] Next, a description will be given of the identification process performed by the identification processing unit 290 of the data processing device 12. In the following description, the data processing device 12 will be referred to as the "server" and the smart glasses 214 will be referred to as the "terminal."

[0402] DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS The embodiments for carrying out the present invention will be described in detail below.

[0403] System Overview

[0404] The system of the present invention analyzes the statements of children who are not attending school and verbalizes their feelings and thoughts. This system is broadly composed of three elements: a terminal, a server, and a user.

[0405] Terminal

[0406] The terminal is a device operated by the user, specifically a child who is not attending school. The terminal provides an interface for the child to input comments and interact with the AI ​​in a chat format. The input from the terminal is sent to a server and analyzed.

[0407] server

[0408] The server receives the child's comments sent from the device and stores them in a database. The server then analyzes the comments using a generative AI model to extract emotions. Based on the extracted emotions, the server also generates linguistic expressions that are easy for parents and educators to understand.

[0409] User

[0410] Users are primarily children who are not attending school, their parents, and educators. Children use their devices to input their comments, and parents and educators can view the analysis results provided by the server to understand the child's condition and take appropriate action.

[0411] Program processing

[0412] The program works as follows:

[0413] 1. User Input

[0414] Children can type and send messages such as "I don't want to go to school" through their devices.

[0415] 2. Data Transmission

[0416] The device sends the user's message to the server, which encodes it and sends it to the server in an HTTP request.

[0417] 3. Data Receipt and Storage

[0418] The server receives the message and stores it in a database, at which point the message is ready for analysis.

[0419] 4. Sentiment analysis

[0420] The generative AI model on the server analyzes the received messages and identifies the child's emotions, extracting emotions such as "anxiety" and "stress."

[0421] 5. Verbalization

[0422] Based on the results of the sentiment analysis, the server converts the child's feelings and thoughts into appropriate language, for example, "This child seems to be feeling a lot of stress about school."

[0423] 6. Send results

[0424] The server then sends the generated linguistic expressions back to the device, where parents and educators can view the results.

[0425] Specific examples

[0426] For example, if Mr. A doesn't like going to school, he types "I don't want to go to school" into his device. This message is sent from the device to the server, which receives it and analyzes it. As a result of the analysis, it is determined that Mr. A is feeling "anxiety" or "stress." The server generates a statement such as "Mr. A seems to be feeling a lot of stress about school" and sends this information back to the device. Parents can then view this result and provide appropriate support for Mr. A.

[0427] As described above, the system of the present invention aims to quickly resolve the problem of school refusal by accurately understanding the emotions of children who are refusing to go to school and enabling parents and educators to respond quickly and appropriately.

[0428] The processing flow will be explained below.

[0429] Step 1:

[0430] The user inputs a message through the terminal. For example, a child may input a message such as "I don't want to go to school" and press the send button.

[0431] Step 2:

[0432] The terminal sends the message entered by the user to the server, which encodes the message and sends it to the server using an HTTP request.

[0433] Step 3:

[0434] The server receives the message sent from the terminal and stores it in the database.

[0435] Step 4:

[0436] The generative AI model on the server analyzes the stored messages, using natural language processing technology to extract the child's feelings and thoughts from the content of the comments.

[0437] Step 5:

[0438] Based on the analysis results, the server expresses the child's feelings in concrete terms, for example, "This child seems to be feeling a lot of stress about school."

[0439] Step 6:

[0440] The server sends the generated language expression to the terminal, and returns the result to the terminal as an HTTP response.

[0441] Step 7:

[0442] The device displays the analysis results received from the server on the screen, providing a visual user interface to make it easier for parents and educators to view the results.

[0443] Step 8:

[0444] The user types in an additional comment, for example, a child types "I'm scared of my teacher," and sends a new message.

[0445] Step 9:

[0446] The device again sends a new message to the server, which receives the new message and stores it in its database.

[0447] Step 10:

[0448] The server then re-analyzes the new message and uses it as training data for the AI ​​model, which improves its accuracy.

[0449] Step 11:

[0450] The server sends the reanalysis results to the device, which then displays them, helping parents and educators obtain new information and take appropriate action.

[0451] By following these steps and continually repeating the cycle of analyzing, verbalizing, and providing feedback based on what is said, you can accurately understand children's emotions and provide support.

[0452] Example 1

[0453] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0454] There is a need to properly understand the feelings and thoughts of children who refuse to go to school and to provide an environment in which parents and educators can respond quickly and appropriately. However, existing technologies lack effective means to accurately analyze children's comments and extract and verbalize their emotions. This has made it difficult to obtain accurate information about children's internal states.

[0455] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[0456] In this invention, the server includes a means for an information processing device to receive utterances and store them in a data storage device, a means for analyzing the utterances using a generative AI model on the information processing device and extracting emotions, and a means for generating appropriate linguistic expressions based on prompt sentences using the generative AI model. This makes it possible to accurately analyze the emotions and thoughts of children who are not attending school and provide them in an easy-to-understand format to parents and educators.

[0457] "School-refusing children" refer to school-age children who find it difficult or impossible to attend school.

[0458] "Statements" refer to text messages or voice messages that children enter through their devices.

[0459] "Means of input" refers to a terminal or device with an interface that allows children to input text or voice.

[0460] "Means for sending" refers to the communication technology used to send the inputted remarks to the server via data communication.

[0461] "Information processing device" refers to a computer or server that has the functionality to receive, store, and analyze data.

[0462] "Data storage device" refers to a storage medium or database for storing received data.

[0463] A "generative AI model" refers to a software model that analyzes input data through natural language processing and extracts emotions and meanings.

[0464] "Prompt sentence" refers to an instruction sentence used as input to an AI model.

[0465] "Means of analysis" refers to the process of identifying emotions based on received utterances using a generative AI model.

[0466] "Means for extracting emotions" refers to the technique for extracting emotions that become clear as a result of the analysis.

[0467] "Means for generating linguistic expressions" refers to the process of converting extracted emotions into sentences in an understandable form.

[0468] "Terminal" refers to a device used by a child to receive messages from the server.

[0469] This invention relates to a system that analyzes the comments of children who are not attending school and verbalizes their feelings and thoughts. The purpose of this system is to analyze the comments entered by the children and provide the results in an appropriate format to parents and educators.

[0470] System configuration

[0471] The system of the present invention mainly consists of three elements: a terminal, a server, and a user.

[0472] Terminal

[0473] The terminal is a device operated by the user, a child who is not attending school. The terminal provides an interface for the child to input comments and interact with the AI ​​in a chat format. Input from the terminal is encoded and sent to the server using an HTTP POST request. In this system, PCs, tablets, smartphones, etc. are used as terminals.

[0474] server

[0475] The server receives the child's comments sent from the device and stores them in a database. The server then analyzes the received comments using a generative AI model to identify the emotion. This generative AI model uses OpenAI's GPT-3 or ChatGPT, for example. A prompt sentence is used to analyze the emotion, and the emotion is extracted in the following format:

[0476] Example prompt sentence:

[0477] “What is the emotion conveyed in the following sentence?

[0478] Sentence: I don't want to go to school

[0479] Emotions:”

[0480] The server generates appropriate language expressions based on the extracted emotions. These expressions are then converted into a form that is easy for parents and educators to understand. For example, the output might be something like, "This child is feeling very stressed about school."

[0481] User

[0482] Users are children who are not attending school, their parents, and educators. Children use their devices to input comments, and parents and educators can view the analysis results provided by the server to understand the child's condition and take appropriate action.

[0483] Specific examples

[0484] For example, suppose that a child named A types "I don't want to go to school" into their device. This message is sent from the device to a server, which receives it and stores it in a database. A generative AI model on the server analyzes this message and determines that A is feeling "anxiety" or "stress." Based on the analysis results, the server generates a statement such as "A seems to be feeling very stressed about school," and notifies parents and educators. This allows parents and educators to understand the child's internal state and provide appropriate support.

[0485] The system of the present invention can accurately grasp the feelings and thoughts of children who are not attending school, and provides an environment in which parents and educators can respond quickly and appropriately. As a result, it becomes possible to quickly resolve the problem of school refusal.

[0486] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0487] Step 1:

[0488] The user uses the terminal to input a statement such as "I don't want to go to school." By entering text in the input field and pressing the send button, the input of the statement is completed.

[0489] Input: A text message entered by the user.

[0490] Output: The encoded message is prepared.

[0491] Step 2:

[0492] The terminal encodes the message entered by the user and sends it to the server using an HTTP POST request, which then packages the encoded message in JSON format and sends it.

[0493] Input: The encoded text message.

[0494] Output: The HTTP POST request sent to the server.

[0495] Step 3:

[0496] The server receives messages sent from the terminal and stores them in a database. NoSQL databases (e.g., MongoDB) are often used as the database. The server records the received messages in the database.

[0497] Input: The text message included in the HTTP POST request received by the server.

[0498] Output: The message stored in the database.

[0499] Step 4:

[0500] A generative AI model on the server retrieves and analyzes messages stored in a database. A prompt sentence is used to identify the emotion of the statement, for example, the following prompt sentence: "What emotion is conveyed in the following sentence? Sentence: I don't want to go to school Emotion:." The emotion is extracted as a result of the analysis.

[0501] Input: A text message stored in the database.

[0502] Output: Emotions extracted by analysis (e.g., anxiety, stress).

[0503] Step 5:

[0504] The server uses a generative AI model to generate appropriate language expressions based on the extracted emotions. For example, it might generate a language expression such as, "Mr. A seems to be feeling very stressed about school." A text containing the emotion and its rationale is then generated.

[0505] Input: The extracted sentiment as a result of analysis.

[0506] Output: The generated linguistic expression.

[0507] Step 6:

[0508] The server sends the generated language representation to the device, which encodes it using an HTTP POST request and sends it back to the device, where it receives the information and makes it available for viewing by the user.

[0509] Input: The generated linguistic expression.

[0510] Output: The language expression sent to the terminal is displayed to the user.

[0511] (Application example 1)

[0512] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0513] The challenge is to accurately understand the emotions and psychological state of children who are not attending school, so that parents and educators can respond appropriately. There is also a need to track emotional changes in real time and quickly notify the results, thereby speeding up support for children.

[0514] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[0515] In this invention, the server includes a means for notifying relevant parties of the emotion analysis results in real time, a means for tracking changes in emotions and displaying the trends, and a means for analyzing the child's emotions using a generative AI model. This allows the child's emotions to be analyzed in real time and the relevant parties to be promptly notified of the changes, enabling appropriate support.

[0516] "School-refusing children" refer to children who, for some reason, do not go to school and spend their time at home or elsewhere.

[0517] "Means for inputting speech" refers to devices or software that provide an interface for children to input their thoughts and feelings in text form.

[0518] A "server" refers to a computer system that processes and stores data on a network.

[0519] A "database" refers to a system for efficiently storing, retrieving, and managing data.

[0520] "Means for extracting emotions" refers to algorithms or programs that use generative AI models to identify emotions from input utterances.

[0521] "Means for generating linguistic expressions" refers to a program that generates text based on the extracted emotions in a format that is easy for parents and educators to understand.

[0522] "Devices" refer to digital devices such as computers and smartphones used by children, parents, and educators.

[0523] "Means of real-time notification" refers to a push notification or message sending system that instantly communicates the results of sentiment analysis to relevant parties.

[0524] "Means for tracking emotional changes" refers to algorithms or programs that continuously analyze children's speech and monitor and visualize emotional fluctuations.

[0525] A "generative AI model" refers to a machine learning model that uses natural language processing to analyze text data and extract emotions.

[0526] A "prompt sentence" refers to the text data input into a generative AI model, and is the source sentence that the model uses for analysis.

[0527] The system for implementing this invention is designed to analyze the comments of children who are not attending school and verbalize their feelings and thoughts. The system is broadly composed of a terminal that inputs the children's comments, a server that receives and analyzes the comments, and a means for notifying parents and educators of the results of the analysis.

[0528] Terminal

[0529] A terminal is a device used by a child (user). Specifically, it can be a smartphone, tablet, or personal computer. A terminal provides the following interfaces:

[0530] A chat-style input interface using text boxes

[0531] A button to enter and send a message

[0532] A display area for parents and educators to view the analysis results

[0533] Comments entered from the terminal are sent to the server via an HTTP request.

[0534] server

[0535] The server stores the utterances received from the device in a database and analyzes them using a generative AI model. The specific software used includes:

[0536] Flask (web server framework)

[0537] Hugging Face generation AI model "bert-base-uncased-emotion"

[0538] Database management systems such as PostgreSQL and MySQL

[0539] The server process is as follows:

[0540] 1. Receive comments and store them in a database.

[0541] 2. Extract the sentiment of the utterance using a generative AI model.

[0542] 3. Generate appropriate language expressions based on emotions.

[0543] 4. The generated language expression is sent back to the terminal.

[0544] The server notifies relevant parties of the results of the emotion analysis in real time, and also has the ability to track changes in emotions and display trends. For example, when B types, "I don't want to see my friends today," the server analyzes this statement as "anxiety" and generates the result, "The child is feeling anxious."

[0545] Examples of specific examples and prompts

[0546] For example, if B types, "I don't want to see my friends today," this message is sent from the device to the server. The server analyzes this statement and extracts the emotion "anxiety." The server then generates the linguistic expression, "The child is feeling the emotion of 'anxiety,'" and sends it back to the device.

[0547] Examples of prompts are:

[0548] "I don't want to see my friends today."

[0549] This will enable parents and educators to quickly understand a child's condition and take appropriate action.

[0550] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0551] Step 1:

[0552] The user (child) uses the terminal to input comments.

[0553] Input: Child's statement (e.g., "I don't want to see my friends today.")

[0554] Output: Data sent from the device to the server in speech data format

[0555] How it works: Children type their message into the chat box on their smartphone or tablet and press the "Send" button.

[0556] Step 2:

[0557] The device sends the message to the server as an HTTP request.

[0558] Input: Speech data entered into the terminal

[0559] Output: The encoded speech data sent to the server

[0560] Operation: Sends speech data as an HTTP POST request to a specific URL on the server.

[0561] Step 3:

[0562] The server stores the received comments in a database.

[0563] Input: Speech data sent to the server

[0564] Output: A database record containing the comment

[0565] How it works: The server receives an HTTP request and inserts the comment data into a database using an SQL query.

[0566] Step 4:

[0567] The server analyzes the comments and extracts the emotions.

[0568] Input: Speech data read from the database

[0569] Output: Extracted emotion data (e.g., "anxiety")

[0570] How it works: A generative AI model (e.g., "bert-base-uncased-emotion") is used to analyze speech using natural language processing to obtain emotion labels.

[0571] Step 5:

[0572] The server generates appropriate language expressions based on the extracted emotions.

[0573] Input: Extracted emotion data

[0574] Output: Generated linguistic expression (e.g., "The child is experiencing the emotion 'anxiety'.")

[0575] Operation: Based on the extracted emotions, a program is run that uses templates to generate appropriate linguistic expressions.

[0576] Step 6:

[0577] The server transmits the generated linguistic expression to the terminal.

[0578] Input: Generated linguistic expression

[0579] Output: Language expression data sent to the device

[0580] Operation: The generated language expression is sent to the terminal as an HTTP response.

[0581] Step 7:

[0582] The device displays the received language expressions to parents and educators.

[0583] Input: Linguistic expression data received from the server

[0584] Output: Language expression displayed on the terminal screen

[0585] What happens: The terminal application receives the HTTP response and displays it in the user interface.

[0586] Step 8:

[0587] The server tracks changes in the sentiment of comments and displays the trends.

[0588] Input: Multiple utterances and their emotional data accumulated in chronological order

[0589] Output: Graphs and lists showing trends in sentiment changes

[0590] Operation: Analyzes past statements and emotional data, runs a program that visualizes trends in emotional changes, and displays the results in the user interface.

[0591] This will enable us to understand the emotions of children who are not attending school in real time and respond quickly.

[0592] Furthermore, an emotion engine that estimates the user's emotion may be further combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59, and perform identification processing using the user's emotion.

[0593] DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS The embodiments for carrying out the present invention will be described in detail below.

[0594] System Overview

[0595] The system of the present invention analyzes the speech of children who are not attending school and verbalizes their feelings and thoughts. Furthermore, by combining it with an emotion engine that recognizes the user's emotions, the accuracy of the analysis can be further improved. This system is broadly composed of four elements: a terminal, a server, an emotion engine, and a user.

[0596] Terminal

[0597] The terminal is a device operated by the user, specifically a child who is not attending school. The terminal provides an interface for the child to input comments and interact with the AI ​​in a chat format. The input from the terminal is sent to a server for analysis. The terminal also has a built-in emotion engine that analyzes the user's emotions in real time and sends them to the server.

[0598] server

[0599] The server receives the children's comments sent from the device and stores them in a database. The server then analyzes the comments using a generative AI model and emotion engine to extract emotions. Based on the extracted emotions, the server also generates linguistic expressions in a form that is easy for parents and educators to understand.

[0600] Emotion Engine

[0601] The emotion engine has the ability to analyze emotions in real time from user input and dialogue. This data is immediately sent to the server, improving the analytical accuracy of the generative AI model. For example, by simultaneously analyzing a child's input and their emotional state at the time of the comment, more precise emotion analysis becomes possible.

[0602] User

[0603] Users are primarily children who are not attending school, their parents, and educators. Children use their devices to input their comments, and parents and educators can view the analysis results provided by the server to understand the child's condition and take appropriate action.

[0604] Program processing

[0605] The program works as follows:

[0606] 1. User Input

[0607] Children can type and send messages such as "I don't want to go to school" through their devices.

[0608] 2. Data Transmission

[0609] The device sends the user's message to the server, which encodes it and sends it to the server using an HTTP request.

[0610] 3. Real-time analysis using an emotion engine

[0611] The emotion engine analyzes the child's emotional state at the time of input in real time and sends the results to the server.

[0612] 4. Data Receipt and Storage

[0613] The server receives the message sent from the device and the emotion data from the emotion engine, and stores them in a database. At this stage, the message and emotion data are ready for analysis.

[0614] 5. Combined analysis of emotions and statements

[0615] The generative AI model and emotion engine on the server analyze the stored messages and emotion data, using natural language processing and emotion recognition technologies to extract the content of the comments and the emotional state.

[0616] 6. Verbalization

[0617] Based on the results of the emotion analysis, the server expresses the child's feelings and thoughts in concrete terms, for example, "This child seems to be feeling a lot of stress about school."

[0618] 7. Send results

[0619] The server sends the generated language expression to the terminal, and returns the result to the terminal as an HTTP response.

[0620] 8. Results display

[0621] The device displays the analysis results received from the server on the screen, providing a visual user interface to make it easier for parents and educators to view the results.

[0622] Specific examples

[0623] For example, if Mr. A doesn't want to go to school, he types "I don't want to go to school" into his device. When this message is sent from the device to the server, the emotion engine simultaneously analyzes Mr. A's emotions and generates emotional data such as "anxiety" and "fear." The server receives this data, and the generative AI model performs a combined analysis of the statement "I don't want to go to school" and the emotional data "anxiety" and "fear." As a result of the analysis, it is determined that Mr. A is feeling "anxiety" and "stress." The server generates a statement such as "Mr. A seems to be feeling very stressed about school" and sends this information back to the device. Parents can then view this result and provide appropriate support for Mr. A.

[0624] As described above, the system of the present invention aims to resolve the problem of school refusal at an early stage by more accurately grasping the emotions of children who are refusing to go to school and enabling parents and educators to respond quickly and appropriately.

[0625] The processing flow will be explained below.

[0626] Step 1:

[0627] The user inputs a message through the terminal. For example, a child may input a message such as "I don't want to go to school" and press the send button.

[0628] Step 2:

[0629] The emotion engine analyzes the child's emotional state in real time as the user speaks, using facial recognition, voice analysis, and contextual analysis to classify emotions.

[0630] Step 3:

[0631] The device sends the message entered by the user and the emotional data analyzed by the emotion engine to the server, where the message and emotional data are encoded and sent to the server via an HTTP request.

[0632] Step 4:

[0633] The server receives the message and emotion data sent from the device, and then stores the message and emotion data in a database.

[0634] Step 5:

[0635] The generative AI model on the server performs a combined analysis of the stored messages and emotional data, using natural language processing and emotion recognition technologies to extract the content of the speech and its corresponding emotional state.

[0636] Step 6:

[0637] Based on the analysis results, the server expresses the child's feelings in concrete terms, for example, "This child seems to be feeling a lot of stress about school."

[0638] Step 7:

[0639] The server sends the generated language expression to the terminal, and returns the result to the terminal as an HTTP response.

[0640] Step 8:

[0641] The device displays the analysis results received from the server on the screen, providing a visual user interface to make it easier for parents and educators to view the results.

[0642] Step 9:

[0643] The user types in an additional comment, for example, a child types "I'm scared of my teacher," and sends a new message.

[0644] Step 10:

[0645] The device sends the new message and the new emotion data analyzed by the emotion engine back to the server, which receives the new data and stores it in a database.

[0646] Step 11:

[0647] The server re-analyzes the new message and emotion data and uses it as training data for the AI ​​model and emotion engine, improving the accuracy of the analysis.

[0648] Step 12:

[0649] The server sends the reanalysis results to the device, which then displays them, helping parents and educators obtain new information and take appropriate action.

[0650] This process involves a continuous cycle of emotion analysis, verbalization, and feedback based on what is said, resulting in a system that can accurately grasp children's emotions and provide appropriate support.

[0651] Example 2

[0652] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0653] Properly understanding the words and thoughts of school-refusing children and providing appropriate support based on that understanding is an important issue in the educational field. However, children may have difficulty verbalizing their own feelings and thoughts, or their feelings may be difficult to understand, making it difficult for parents and educators to respond appropriately. The present invention aims to solve these issues by providing a system that accurately understands the feelings and thoughts of school-refusing children and enables prompt and appropriate responses based on that understanding.

[0654] The identification process by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means. In this invention, the server includes means for inputting children's comments, means for transmitting the input comments to the server, means equipped with an emotion engine that analyzes emotions in real time, means for the server to receive the comments and emotion data and store them in a database, means for analyzing the comments and emotion data on the server and extracting emotions, means for generating appropriate linguistic expressions based on the extracted emotions, and means for transmitting the generated linguistic expressions to a terminal. This makes it possible to analyze the emotions and thoughts of children who are not attending school in real time and provide appropriate support based on the analysis.

[0655] "Futoko children" refer to children who do not attend school regularly and spend their time at home or elsewhere.

[0656] "Comments" refer to messages and comments that children enter through their devices.

[0657] A "server" refers to a computer system that receives speech and emotional data, stores it in a database, and analyzes it.

[0658] "Terminal" refers to an input / output device that children can directly operate and use to input comments.

[0659] An "emotion engine" refers to software that has the ability to analyze emotions in real time from user input and dialogue.

[0660] "Database" refers to an information management system for storing speech data and emotional data.

[0661] A "generative AI model" refers to artificial intelligence technology that analyzes the statements and emotional data of children who are not attending school and generates appropriate language expressions.

[0662] "Linguistic expressions" refer to sentences generated by the generative AI model to explain children's emotions and thoughts.

[0663] An "HTTP request" refers to a type of communication protocol for sending data from a terminal to a server.

[0664] "Analysis" refers to the process of extracting and understanding emotions and thoughts based on received statements and emotional data.

[0665] "Guardians" refers to the parents or supervisors of children who are not attending school.

[0666] "Educational personnel" refers to professionals such as teachers and counselors who provide guidance and support to children in educational settings.

[0667] This invention is a system that analyzes the speech of children who are not attending school and verbalizes their feelings and thoughts. This system is composed of multiple elements, including a terminal, a server, an emotion engine, and a user. Each element is described in detail below.

[0668] Terminal

[0669] The terminal is a device operated by the user, specifically a child who is not attending school. The terminal provides an interface for the child to input comments and interact with the AI ​​in a chat format. For example, a tablet or smartphone can be used as the terminal. Input from the terminal is sent to a server for analysis. The terminal also has a built-in emotion engine that analyzes the user's emotions in real time and sends them to the server.

[0670] server

[0671] The server receives the child's utterances sent from the device and stores them in a database. The server then analyzes the utterances using a generative AI model and emotion engine to extract emotions. For example, the server can use cloud services such as Amazon Web Services (AWS) and Microsoft Azure. Based on the extracted emotions, the server also generates linguistic expressions that are easy for parents and educators to understand.

[0672] Emotion Engine

[0673] The emotion engine has the ability to analyze emotions in real time from user input and dialogue. For example, the emotion engine uses Google Cloud's Natural Language API or IBM Watson's emotion recognition function. This data is immediately sent to the server, improving the analytical accuracy of the generative AI model. For example, by simultaneously analyzing the words entered by a child and their emotional state at the time of the words, more precise emotion analysis becomes possible.

[0674] User

[0675] Users are primarily children who are not attending school, their parents, and educators. Children use their devices to input their comments, and parents and educators can view the analysis results provided by the server to understand the child's condition and take appropriate action.

[0676] Specific examples

[0677] For example, if Mr. A doesn't want to go to school, he types "I don't want to go to school" into his device. When this message is sent from the device to the server, the emotion engine simultaneously analyzes Mr. A's emotions and generates emotional data such as "anxiety" and "fear." The server receives this data, and the generative AI model performs a combined analysis of the statement "I don't want to go to school" and the emotional data "anxiety" and "fear." As a result of the analysis, it is determined that Mr. A is feeling "anxiety" and "stress." The server generates a statement such as "Mr. A seems to be feeling very stressed about school" and sends this information back to the device. Parents can then view this result and provide appropriate support for Mr. A.

[0678] Prompt Sentence Examples

[0679] For example, the prompt to input to the generative AI model would be:

[0680] "When a child types 'I don't want to go to school,' the emotion engine analyzes it as 'anxiety' and 'fear.' Please provide a detailed analysis based on this child's emotions and statements."

[0681] This system allows for a more accurate understanding of the emotions of the users, i.e., children who are not attending school, and enables parents and educators to take appropriate action quickly through the devices and servers.

[0682] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0683] Step 1: User Input

[0684] The user (child) inputs a message such as "I don't want to go to school" through the terminal and presses the send button. The input is in text format and is prepared for transmission to the server through the terminal interface. The input at this stage is the user's spoken text.

[0685] Step 2: Send data

[0686] The device encodes the user's input message into JSON format and sends it to the server using an HTTP request. This request also includes the user's session information. Specifically, the device encodes the message and sends a POST request to the " / api / message" endpoint. The output is the encoded message data sent to the server.

[0687] Step 3: Real-time analysis by emotion engine

[0688] The device's emotion engine analyzes facial and voice data captured during user input and determines the user's emotional state in real time. The input is the captured facial and voice data, which is analyzed to generate emotion data such as "anxiety" or "fear." The generated emotion data is also sent to the server using an HTTP request. The output is the analyzed emotion data.

[0689] Step 4: Receiving and storing data

[0690] The server receives messages and emotion data sent from the device and stores them in a database. Specifically, messages are stored in the "messages" table, and emotion data is stored in the "emotions" table. The input is the encoded data received from the device, and the output is the database in its saved state.

[0691] Step 5: Combined analysis of emotions and statements

[0692] The generative AI model and emotion engine running on the server perform a comprehensive analysis of the messages and emotion data stored in the database. Specifically, the generative AI model analyzes the message content using natural language processing technology, and the emotion engine analyzes the emotion data. The input is the stored message and emotion data, and the output is the analysis results.

[0693] Step 6: Verbalize

[0694] The server converts the child's feelings and thoughts into concrete words based on the analysis results obtained from the generative AI model and emotion engine. For example, it verbalizes the child's feelings and thoughts in the form of, "This child seems to be feeling a lot of stress about school." The input is the analysis result data, and the output is a linguistic expression that humans can understand.

[0695] Step 7: Send results

[0696] The server sends the language expression generated as the analysis result to the terminal. The result is returned in JSON format as an HTTP response. Specifically, the result is sent as a POST request to the " / api / result" endpoint. The input is the generated language expression, and the output is the analysis result sent to the terminal.

[0697] Step 8: View the results

[0698] The analysis results received by the device from the server are displayed on the user interface. To make the results easy for parents and educators to understand, not only text messages but also visual graphs and icons are displayed. The input is the analysis results received from the server, and the output is the results displayed on the device's user interface.

[0699] As a result, the comments of the user, a child who is not attending school, are analyzed in real time, and their feelings and thoughts are expressed in concrete words, enabling parents and educators to respond quickly and appropriately.

[0700] (Application example 2)

[0701] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0702] Children who refuse to go to school often find it difficult to express their feelings and thoughts appropriately. As a result, parents and educators often have difficulty accurately understanding the child's condition and are unable to respond appropriately. Real-time emotion analysis and rapid response are also required, but current systems do not easily achieve this.

[0703] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 2 is realized by the following means.

[0704] In this invention, the server includes means for inputting comments from children, means for transmitting the input comments to the server, means for the server to receive the comments and store them in a database, means for analyzing the comments on the server and extracting emotions, means for generating appropriate linguistic expressions based on the extracted emotions, means for transmitting the generated linguistic expressions to the terminal, means for real-time analysis using an emotion engine, and means for transmitting the results as alerts to the terminals of parents and educators. This makes it possible to analyze children's emotions in real time and quickly notify parents and educators of the analysis results.

[0705] "School-refusing children" refer to children who have difficulty attending school.

[0706] "Statements" refers to text messages or voice messages typed by the child.

[0707] "Terminal" refers to the device used by a child to input comments.

[0708] "Server" is a computer system that receives, stores, and analyzes data sent from a terminal.

[0709] The "database" is a data storage system that is stored on a server and that stores children's comments and analysis results.

[0710] An "emotion engine" is a software engine that analyzes user emotions from utterances and other input data.

[0711] "Real-time analysis means" refers to a means for immediately analyzing a message after it is sent.

[0712] A "generative AI model" is an artificial intelligence that generates appropriate language expressions based on user statements and emotional data.

[0713] An "alert" is a warning message sent to parents and educators based on the analysis results.

[0714] "Guardian" refers to an adult who supports a child in their daily life, including the child's parents.

[0715] "Educational professionals" refers to professionals involved in children's education, such as teachers and counselors.

[0716] DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS The embodiments for carrying out the present invention will be described in detail below.

[0717] Overall system configuration

[0718] The system of the present invention analyzes the statements of children who are not attending school and verbalizes their feelings and thoughts. This system consists of a terminal that inputs the children's statements, a server, an emotion engine, and devices such as smartphones of parents and educators.

[0719] Terminal

[0720] The terminal is a device operated by the child. The child uses the terminal to input text messages and voice comments. The terminal is provided with an input interface through which the child inputs their comments. The comments are not stored on the terminal but are immediately sent to the server.

[0721] server

[0722] The server receives the utterances sent from the device and stores them in a database. The server then uses a generative AI model and emotion engine to analyze the utterances and extract emotions. The extracted emotion data and utterance data are then analyzed in a composite manner to generate an appropriate linguistic expression. The generated linguistic expression is then sent back to the device.

[0723] Emotion Engine

[0724] The emotion engine has the ability to analyze emotions from user comments in real time. The emotion engine analyzes the content of comments and the emotional state at the time of commenting, and generates emotion data for the comment. The generated emotion data is immediately sent to the server and used for analysis.

[0725] Devices for parents and educators

[0726] The analysis results sent from the server are displayed in real time on the devices of parents and educators. The analysis results are displayed in a visually easy-to-understand format and are sent to parents and educators as push notifications, for example, allowing for quick response.

[0727] Hardware and software used

[0728] Devices: Smartphones, tablets, smart glasses, etc.

[0729] Server: A cloud-based server, such as AWS (Amazon Web Services) or GCP (Google Cloud Platform).

[0730] Emotion Engine: Emotion Engine API.

[0731] Generative AI models: Natural language processing models such as GPT-4.

[0732] Database: A relational database such as MySQL or PostgreSQL.

[0733] Specific examples

[0734] For example, a child might type "I don't want to go to school" into their device. This message is immediately sent from the device to the server. At the same time, the emotion engine generates emotion data such as "anxiety" or "fear." The server receives this data, and the generative AI model performs a comprehensive analysis. As a result of the analysis, a linguistic expression is generated: "This child is feeling very stressed about school." This information is sent as an alert to the parent's smartphone, allowing them to respond quickly.

[0735] Examples of prompts:

[0736] Use a sentiment analysis engine to analyze the sentiment of the following message: "I don't want to go to school."

[0737] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0738] Step 1:

[0739] The user uses the device to input a message. For example, a child might type "I don't want to go to school" into their smartphone. The input text message is temporarily stored in the device's memory. The input at this stage is text data before it is sent from the device to the server.

[0740] Step 2:

[0741] The device sends the input message to the server using an HTTP request. At this time, the text data is encoded and sent securely. The input is a text message, and the output is sent to the server in the form of an HTTP request.

[0742] Step 3:

[0743] The server receives the messages sent from the device, decodes the received data, and stores it as a text message in a database. This database also stores the child's past messages and emotional data. The input is the text data received via the HTTP request, and the output is the stored database record.

[0744] Step 4:

[0745] The server sends the message data to the emotion engine, which analyzes the emotion in real time. The emotion engine analyzes the message text, generates emotion data such as "anxiety" or "fear," and sends it back to the server. Here, emotion recognition is performed based on the input message text, and the emotion data is output.

[0746] Step 5:

[0747] The server uses a generative AI model to perform a comprehensive analysis of the received emotion data and utterance data. The generative AI model analyzes the emotion data and utterance text obtained from the emotion engine, and generates a specific linguistic expression such as "This child is feeling very stressed about school." The input is emotion data and utterance text, and the output is a linguistic expression resulting from the analysis.

[0748] Step 6:

[0749] The server sends the generated linguistic expression to the device of the parent or educator as an HTTP response. The sent data is immediately displayed on the smartphone or other device, and the parent or educator is notified as an alert. The input is the generated linguistic expression, and the output is a notification message.

[0750] Step 7:

[0751] Parents and educators can check the analysis results displayed on the device and take prompt action depending on the child's situation. The input is the notification message sent from the server, and the output is the specific response action taken by the parent or educator.

[0752] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0753] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0754] In the above embodiment, an example in which the specific processing is performed by the data processing device 12 has been given, but the technology of the present disclosure is not limited to this, and the specific processing may be performed by the smart glasses 214.

[0755] [Third embodiment]

[0756] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0757] 5, the data processing system 310 includes the data processing device 12 and a headset type terminal 314. An example of the data processing device 12 is a server.

[0758] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0759] The headset type terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a display 343. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the display 343 are also connected to the bus 52.

[0760] The microphone 238 receives instructions and the like from the user 20 by receiving voice uttered by the user 20. The microphone 238 captures the voice uttered by the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio in accordance with instructions from the processor 46.

[0761] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the surroundings of user 20 (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0762] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0763] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset type terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0764] The specific processing program 56 is an example of a "program" according to the technology of the present disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0765] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0766] In the headset type terminal 314, a reception output process is performed by the processor 46. A reception output program 60 is stored in the storage 50. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[0767] Next, a description will be given of the identification process performed by the identification processing unit 290 of the data processing device 12. In the following description, the data processing device 12 will be referred to as the "server" and the headset type terminal 314 will be referred to as the "terminal."

[0768] DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS The embodiments for carrying out the present invention will be described in detail below.

[0769] System Overview

[0770] The system of the present invention analyzes the statements of children who are not attending school and verbalizes their feelings and thoughts. This system is broadly composed of three elements: a terminal, a server, and a user.

[0771] Terminal

[0772] The terminal is a device operated by the user, specifically a child who is not attending school. The terminal provides an interface for the child to input comments and interact with the AI ​​in a chat format. The input from the terminal is sent to a server and analyzed.

[0773] server

[0774] The server receives the child's comments sent from the device and stores them in a database. The server then analyzes the comments using a generative AI model to extract emotions. Based on the extracted emotions, the server also generates linguistic expressions that are easy for parents and educators to understand.

[0775] User

[0776] Users are primarily children who are not attending school, their parents, and educators. Children use their devices to input their comments, and parents and educators can view the analysis results provided by the server to understand the child's condition and take appropriate action.

[0777] Program processing

[0778] The program works as follows:

[0779] 1. User Input

[0780] Children can type and send messages such as "I don't want to go to school" through their devices.

[0781] 2. Data Transmission

[0782] The device sends the user's message to the server, which encodes it and sends it to the server in an HTTP request.

[0783] 3. Data Receipt and Storage

[0784] The server receives the message and stores it in a database, at which point the message is ready for analysis.

[0785] 4. Sentiment analysis

[0786] The generative AI model on the server analyzes the received messages and identifies the child's emotions, extracting emotions such as "anxiety" and "stress."

[0787] 5. Verbalization

[0788] Based on the results of the sentiment analysis, the server converts the child's feelings and thoughts into appropriate language, for example, "This child seems to be feeling a lot of stress about school."

[0789] 6. Send results

[0790] The server then sends the generated linguistic expressions back to the device, where parents and educators can view the results.

[0791] Specific examples

[0792] For example, if Mr. A doesn't like going to school, he types "I don't want to go to school" into his device. This message is sent from the device to the server, which receives it and analyzes it. As a result of the analysis, it is determined that Mr. A is feeling "anxiety" or "stress." The server generates a statement such as "Mr. A seems to be feeling a lot of stress about school" and sends this information back to the device. Parents can then view this result and provide appropriate support for Mr. A.

[0793] As described above, the system of the present invention aims to quickly resolve the problem of school refusal by accurately understanding the emotions of children who are refusing to go to school and enabling parents and educators to respond quickly and appropriately.

[0794] The processing flow will be explained below.

[0795] Step 1:

[0796] The user inputs a message through the terminal. For example, a child may input a message such as "I don't want to go to school" and press the send button.

[0797] Step 2:

[0798] The terminal sends the message entered by the user to the server, which encodes the message and sends it to the server using an HTTP request.

[0799] Step 3:

[0800] The server receives the message sent from the terminal and stores it in the database.

[0801] Step 4:

[0802] The generative AI model on the server analyzes the stored messages, using natural language processing technology to extract the child's feelings and thoughts from the content of the comments.

[0803] Step 5:

[0804] Based on the analysis results, the server expresses the child's feelings in concrete terms, for example, "This child seems to be feeling a lot of stress about school."

[0805] Step 6:

[0806] The server sends the generated language expression to the terminal, and returns the result to the terminal as an HTTP response.

[0807] Step 7:

[0808] The device displays the analysis results received from the server on the screen, providing a visual user interface to make it easier for parents and educators to view the results.

[0809] Step 8:

[0810] The user types in an additional comment, for example, a child types "I'm scared of my teacher," and sends a new message.

[0811] Step 9:

[0812] The device again sends a new message to the server, which receives the new message and stores it in its database.

[0813] Step 10:

[0814] The server then re-analyzes the new message and uses it as training data for the AI ​​model, which improves its accuracy.

[0815] Step 11:

[0816] The server sends the reanalysis results to the device, which then displays them, helping parents and educators obtain new information and take appropriate action.

[0817] By following these steps and continually repeating the cycle of analyzing, verbalizing, and providing feedback based on what is said, you can accurately understand children's emotions and provide support.

[0818] Example 1

[0819] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[0820] There is a need to properly understand the feelings and thoughts of children who refuse to go to school and to provide an environment in which parents and educators can respond quickly and appropriately. However, existing technologies lack effective means to accurately analyze children's comments and extract and verbalize their emotions. This has made it difficult to obtain accurate information about children's internal states.

[0821] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[0822] In this invention, the server includes a means for an information processing device to receive utterances and store them in a data storage device, a means for analyzing the utterances using a generative AI model on the information processing device and extracting emotions, and a means for generating appropriate linguistic expressions based on prompt sentences using the generative AI model. This makes it possible to accurately analyze the emotions and thoughts of children who are not attending school and provide them in an easy-to-understand format to parents and educators.

[0823] "School-refusing children" refer to school-age children who find it difficult or impossible to attend school.

[0824] "Statements" refer to text messages or voice messages that children enter through their devices.

[0825] "Means of input" refers to a terminal or device with an interface that allows children to input text or voice.

[0826] "Means for sending" refers to the communication technology used to send the inputted remarks to the server via data communication.

[0827] "Information processing device" refers to a computer or server that has the functionality to receive, store, and analyze data.

[0828] "Data storage device" refers to a storage medium or database for storing received data.

[0829] A "generative AI model" refers to a software model that analyzes input data through natural language processing and extracts emotions and meanings.

[0830] "Prompt sentence" refers to an instruction sentence used as input to an AI model.

[0831] "Means of analysis" refers to the process of identifying emotions based on received utterances using a generative AI model.

[0832] "Means for extracting emotions" refers to the technique for extracting emotions that become clear as a result of the analysis.

[0833] "Means for generating linguistic expressions" refers to the process of converting extracted emotions into sentences in an understandable form.

[0834] "Terminal" refers to a device used by a child to receive messages from the server.

[0835] This invention relates to a system that analyzes the comments of children who are not attending school and verbalizes their feelings and thoughts. The purpose of this system is to analyze the comments entered by the children and provide the results in an appropriate format to parents and educators.

[0836] System configuration

[0837] The system of the present invention mainly consists of three elements: a terminal, a server, and a user.

[0838] Terminal

[0839] The terminal is a device operated by the user, a child who is not attending school. The terminal provides an interface for the child to input comments and interact with the AI ​​in a chat format. Input from the terminal is encoded and sent to the server using an HTTP POST request. In this system, PCs, tablets, smartphones, etc. are used as terminals.

[0840] server

[0841] The server receives the child's comments sent from the device and stores them in a database. The server then analyzes the received comments using a generative AI model to identify the emotion. This generative AI model uses OpenAI's GPT-3 or ChatGPT, for example. A prompt sentence is used to analyze the emotion, and the emotion is extracted in the following format:

[0842] Example prompt sentence:

[0843] “What is the emotion conveyed in the following sentence?

[0844] Sentence: I don't want to go to school

[0845] Emotions:”

[0846] The server generates appropriate language expressions based on the extracted emotions. These expressions are then converted into a form that is easy for parents and educators to understand. For example, the output might be something like, "This child is feeling very stressed about school."

[0847] User

[0848] Users are children who are not attending school, their parents, and educators. Children use their devices to input comments, and parents and educators can view the analysis results provided by the server to understand the child's condition and take appropriate action.

[0849] Specific examples

[0850] For example, suppose that a child named A types "I don't want to go to school" into their device. This message is sent from the device to a server, which receives it and stores it in a database. A generative AI model on the server analyzes this message and determines that A is feeling "anxiety" or "stress." Based on the analysis results, the server generates a statement such as "A seems to be feeling very stressed about school," and notifies parents and educators. This allows parents and educators to understand the child's internal state and provide appropriate support.

[0851] The system of the present invention can accurately grasp the feelings and thoughts of children who are not attending school, and provides an environment in which parents and educators can respond quickly and appropriately. As a result, it becomes possible to quickly resolve the problem of school refusal.

[0852] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0853] Step 1:

[0854] The user uses the terminal to input a statement such as "I don't want to go to school." By entering text in the input field and pressing the send button, the input of the statement is completed.

[0855] Input: A text message entered by the user.

[0856] Output: The encoded message is prepared.

[0857] Step 2:

[0858] The terminal encodes the message entered by the user and sends it to the server using an HTTP POST request, which then packages the encoded message in JSON format and sends it.

[0859] Input: The encoded text message.

[0860] Output: The HTTP POST request sent to the server.

[0861] Step 3:

[0862] The server receives messages sent from the terminal and stores them in a database. NoSQL databases (e.g., MongoDB) are often used as the database. The server records the received messages in the database.

[0863] Input: The text message included in the HTTP POST request received by the server.

[0864] Output: The message stored in the database.

[0865] Step 4:

[0866] A generative AI model on the server retrieves and analyzes messages stored in a database. A prompt sentence is used to identify the emotion of the statement, for example, the following prompt sentence: "What emotion is conveyed in the following sentence? Sentence: I don't want to go to school Emotion:." The emotion is extracted as a result of the analysis.

[0867] Input: A text message stored in the database.

[0868] Output: Emotions extracted by analysis (e.g., anxiety, stress).

[0869] Step 5:

[0870] The server uses a generative AI model to generate appropriate language expressions based on the extracted emotions. For example, it might generate a language expression such as, "Mr. A seems to be feeling very stressed about school." A text containing the emotion and its rationale is then generated.

[0871] Input: The extracted sentiment as a result of analysis.

[0872] Output: The generated linguistic expression.

[0873] Step 6:

[0874] The server sends the generated language representation to the device, which encodes it using an HTTP POST request and sends it back to the device, where it receives the information and makes it available for viewing by the user.

[0875] Input: The generated linguistic expression.

[0876] Output: The language expression sent to the terminal is displayed to the user.

[0877] (Application example 1)

[0878] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[0879] The challenge is to accurately understand the emotions and psychological state of children who are not attending school, so that parents and educators can respond appropriately. There is also a need to track emotional changes in real time and quickly notify the results, thereby speeding up support for children.

[0880] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[0881] In this invention, the server includes a means for notifying relevant parties of the emotion analysis results in real time, a means for tracking changes in emotions and displaying the trends, and a means for analyzing the child's emotions using a generative AI model. This allows the child's emotions to be analyzed in real time and the relevant parties to be promptly notified of the changes, enabling appropriate support.

[0882] "School-refusing children" refer to children who, for some reason, do not go to school and spend their time at home or elsewhere.

[0883] "Means for inputting speech" refers to devices or software that provide an interface for children to input their thoughts and feelings in text form.

[0884] A "server" refers to a computer system that processes and stores data on a network.

[0885] A "database" refers to a system for efficiently storing, retrieving, and managing data.

[0886] "Means for extracting emotions" refers to algorithms or programs that use generative AI models to identify emotions from input utterances.

[0887] "Means for generating linguistic expressions" refers to a program that generates text based on the extracted emotions in a format that is easy for parents and educators to understand.

[0888] "Devices" refer to digital devices such as computers and smartphones used by children, parents, and educators.

[0889] "Means of real-time notification" refers to a push notification or message sending system that instantly communicates the results of sentiment analysis to relevant parties.

[0890] "Means for tracking emotional changes" refers to algorithms or programs that continuously analyze children's speech and monitor and visualize emotional fluctuations.

[0891] A "generative AI model" refers to a machine learning model that uses natural language processing to analyze text data and extract emotions.

[0892] A "prompt sentence" refers to the text data input into a generative AI model, and is the source sentence that the model uses for analysis.

[0893] The system for implementing this invention is designed to analyze the comments of children who are not attending school and verbalize their feelings and thoughts. The system is broadly composed of a terminal that inputs the children's comments, a server that receives and analyzes the comments, and a means for notifying parents and educators of the results of the analysis.

[0894] Terminal

[0895] A terminal is a device used by a child (user). Specifically, it can be a smartphone, tablet, or personal computer. A terminal provides the following interfaces:

[0896] A chat-style input interface using text boxes

[0897] A button to enter and send a message

[0898] A display area for parents and educators to view the analysis results

[0899] Comments entered from the terminal are sent to the server via an HTTP request.

[0900] server

[0901] The server stores the utterances received from the device in a database and analyzes them using a generative AI model. The specific software used includes:

[0902] Flask (web server framework)

[0903] Hugging Face generation AI model "bert-base-uncased-emotion"

[0904] Database management systems such as PostgreSQL and MySQL

[0905] The server process is as follows:

[0906] 1. Receive comments and store them in a database.

[0907] 2. Extract the sentiment of the utterance using a generative AI model.

[0908] 3. Generate appropriate language expressions based on emotions.

[0909] 4. The generated language expression is sent back to the terminal.

[0910] The server notifies relevant parties of the results of the emotion analysis in real time, and also has the ability to track changes in emotions and display trends. For example, when B types, "I don't want to see my friends today," the server analyzes this statement as "anxiety" and generates the result, "The child is feeling anxious."

[0911] Examples of specific examples and prompts

[0912] For example, if B types, "I don't want to see my friends today," this message is sent from the device to the server. The server analyzes this statement and extracts the emotion "anxiety." The server then generates the linguistic expression, "The child is feeling the emotion of 'anxiety,'" and sends it back to the device.

[0913] Examples of prompts are:

[0914] "I don't want to see my friends today."

[0915] This will enable parents and educators to quickly understand a child's condition and take appropriate action.

[0916] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0917] Step 1:

[0918] The user (child) uses the terminal to input comments.

[0919] Input: Child's statement (e.g., "I don't want to see my friends today.")

[0920] Output: Data sent from the device to the server in speech data format

[0921] How it works: Children type their message into the chat box on their smartphone or tablet and press the "Send" button.

[0922] Step 2:

[0923] The device sends the message to the server as an HTTP request.

[0924] Input: Speech data entered into the terminal

[0925] Output: The encoded speech data sent to the server

[0926] Operation: Sends speech data as an HTTP POST request to a specific URL on the server.

[0927] Step 3:

[0928] The server stores the received comments in a database.

[0929] Input: Speech data sent to the server

[0930] Output: A database record containing the comment

[0931] How it works: The server receives an HTTP request and inserts the comment data into a database using an SQL query.

[0932] Step 4:

[0933] The server analyzes the comments and extracts the emotions.

[0934] Input: Speech data read from the database

[0935] Output: Extracted emotion data (e.g., "anxiety")

[0936] How it works: A generative AI model (e.g., "bert-base-uncased-emotion") is used to analyze speech using natural language processing to obtain emotion labels.

[0937] Step 5:

[0938] The server generates appropriate language expressions based on the extracted emotions.

[0939] Input: Extracted emotion data

[0940] Output: Generated linguistic expression (e.g., "The child is experiencing the emotion 'anxiety'.")

[0941] Operation: Based on the extracted emotions, a program is run that uses templates to generate appropriate linguistic expressions.

[0942] Step 6:

[0943] The server transmits the generated linguistic expression to the terminal.

[0944] Input: Generated linguistic expression

[0945] Output: Language expression data sent to the device

[0946] Operation: The generated language expression is sent to the terminal as an HTTP response.

[0947] Step 7:

[0948] The device displays the received language expressions to parents and educators.

[0949] Input: Linguistic expression data received from the server

[0950] Output: Language expression displayed on the terminal screen

[0951] What happens: The terminal application receives the HTTP response and displays it in the user interface.

[0952] Step 8:

[0953] The server tracks changes in the sentiment of comments and displays the trends.

[0954] Input: Multiple utterances and their emotional data accumulated in chronological order

[0955] Output: Graphs and lists showing trends in sentiment changes

[0956] Operation: Analyzes past statements and emotional data, runs a program that visualizes trends in emotional changes, and displays the results in the user interface.

[0957] This will enable us to understand the emotions of children who are not attending school in real time and respond quickly.

[0958] Furthermore, an emotion engine that estimates the user's emotion may be further combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59, and perform identification processing using the user's emotion.

[0959] DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS The embodiments for carrying out the present invention will be described in detail below.

[0960] System Overview

[0961] The system of the present invention analyzes the speech of children who are not attending school and verbalizes their feelings and thoughts. Furthermore, by combining it with an emotion engine that recognizes the user's emotions, the accuracy of the analysis can be further improved. This system is broadly composed of four elements: a terminal, a server, an emotion engine, and a user.

[0962] Terminal

[0963] The terminal is a device operated by the user, specifically a child who is not attending school. The terminal provides an interface for the child to input comments and interact with the AI ​​in a chat format. The input from the terminal is sent to a server for analysis. The terminal also has a built-in emotion engine that analyzes the user's emotions in real time and sends them to the server.

[0964] server

[0965] The server receives the children's comments sent from the device and stores them in a database. The server then analyzes the comments using a generative AI model and emotion engine to extract emotions. Based on the extracted emotions, the server also generates linguistic expressions in a form that is easy for parents and educators to understand.

[0966] Emotion Engine

[0967] The emotion engine has the ability to analyze emotions in real time from user input and dialogue. This data is immediately sent to the server, improving the analytical accuracy of the generative AI model. For example, by simultaneously analyzing a child's input and their emotional state at the time of the comment, more precise emotion analysis becomes possible.

[0968] User

[0969] Users are primarily children who are not attending school, their parents, and educators. Children use their devices to input their comments, and parents and educators can view the analysis results provided by the server to understand the child's condition and take appropriate action.

[0970] Program processing

[0971] The program works as follows:

[0972] 1. User Input

[0973] Children can type and send messages such as "I don't want to go to school" through their devices.

[0974] 2. Data Transmission

[0975] The device sends the user's message to the server, which encodes it and sends it to the server using an HTTP request.

[0976] 3. Real-time analysis using an emotion engine

[0977] The emotion engine analyzes the child's emotional state at the time of input in real time and sends the results to the server.

[0978] 4. Data Receipt and Storage

[0979] The server receives the message sent from the device and the emotion data from the emotion engine, and stores them in a database. At this stage, the message and emotion data are ready for analysis.

[0980] 5. Combined analysis of emotions and statements

[0981] The generative AI model and emotion engine on the server analyze the stored messages and emotion data, using natural language processing and emotion recognition technologies to extract the content of the comments and the emotional state.

[0982] 6. Verbalization

[0983] Based on the results of the emotion analysis, the server expresses the child's feelings and thoughts in concrete terms, for example, "This child seems to be feeling a lot of stress about school."

[0984] 7. Send results

[0985] The server sends the generated language expression to the terminal, and returns the result to the terminal as an HTTP response.

[0986] 8. Results display

[0987] The device displays the analysis results received from the server on the screen, providing a visual user interface to make it easier for parents and educators to view the results.

[0988] Specific examples

[0989] For example, if Mr. A doesn't want to go to school, he types "I don't want to go to school" into his device. When this message is sent from the device to the server, the emotion engine simultaneously analyzes Mr. A's emotions and generates emotional data such as "anxiety" and "fear." The server receives this data, and the generative AI model performs a combined analysis of the statement "I don't want to go to school" and the emotional data "anxiety" and "fear." As a result of the analysis, it is determined that Mr. A is feeling "anxiety" and "stress." The server generates a statement such as "Mr. A seems to be feeling very stressed about school" and sends this information back to the device. Parents can then view this result and provide appropriate support for Mr. A.

[0990] As described above, the system of the present invention aims to resolve the problem of school refusal at an early stage by more accurately grasping the emotions of children who are refusing to go to school and enabling parents and educators to respond quickly and appropriately.

[0991] The processing flow will be explained below.

[0992] Step 1:

[0993] The user inputs a message through the terminal. For example, a child may input a message such as "I don't want to go to school" and press the send button.

[0994] Step 2:

[0995] The emotion engine analyzes the child's emotional state in real time as the user speaks, using facial recognition, voice analysis, and contextual analysis to classify emotions.

[0996] Step 3:

[0997] The device sends the message entered by the user and the emotional data analyzed by the emotion engine to the server, where the message and emotional data are encoded and sent to the server via an HTTP request.

[0998] Step 4:

[0999] The server receives the message and emotion data sent from the device, and then stores the message and emotion data in a database.

[1000] Step 5:

[1001] The generative AI model on the server performs a combined analysis of the stored messages and emotional data, using natural language processing and emotion recognition technologies to extract the content of the speech and its corresponding emotional state.

[1002] Step 6:

[1003] Based on the analysis results, the server expresses the child's feelings in concrete terms, for example, "This child seems to be feeling a lot of stress about school."

[1004] Step 7:

[1005] The server sends the generated language expression to the terminal, and returns the result to the terminal as an HTTP response.

[1006] Step 8:

[1007] The device displays the analysis results received from the server on the screen, providing a visual user interface to make it easier for parents and educators to view the results.

[1008] Step 9:

[1009] The user types in an additional comment, for example, a child types "I'm scared of my teacher," and sends a new message.

[1010] Step 10:

[1011] The device sends the new message and the new emotion data analyzed by the emotion engine back to the server, which receives the new data and stores it in a database.

[1012] Step 11:

[1013] The server re-analyzes the new message and emotion data and uses it as training data for the AI ​​model and emotion engine, improving the accuracy of the analysis.

[1014] Step 12:

[1015] The server sends the reanalysis results to the device, which then displays them, helping parents and educators obtain new information and take appropriate action.

[1016] This process involves a continuous cycle of emotion analysis, verbalization, and feedback based on what is said, resulting in a system that can accurately grasp children's emotions and provide appropriate support.

[1017] Example 2

[1018] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[1019] Properly understanding the words and thoughts of school-refusing children and providing appropriate support based on that understanding is an important issue in the educational field. However, children may have difficulty verbalizing their own feelings and thoughts, or their feelings may be difficult to understand, making it difficult for parents and educators to respond appropriately. The present invention aims to solve these issues by providing a system that accurately understands the feelings and thoughts of school-refusing children and enables prompt and appropriate responses based on that understanding.

[1020] The identification process by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means. In this invention, the server includes means for inputting children's comments, means for transmitting the input comments to the server, means equipped with an emotion engine that analyzes emotions in real time, means for the server to receive the comments and emotion data and store them in a database, means for analyzing the comments and emotion data on the server and extracting emotions, means for generating appropriate linguistic expressions based on the extracted emotions, and means for transmitting the generated linguistic expressions to a terminal. This makes it possible to analyze the emotions and thoughts of children who are not attending school in real time and provide appropriate support based on the analysis.

[1021] "Futoko children" refer to children who do not attend school regularly and spend their time at home or elsewhere.

[1022] "Comments" refer to messages and comments that children enter through their devices.

[1023] A "server" refers to a computer system that receives speech and emotional data, stores it in a database, and analyzes it.

[1024] "Terminal" refers to an input / output device that children can directly operate and use to input comments.

[1025] An "emotion engine" refers to software that has the ability to analyze emotions in real time from user input and dialogue.

[1026] "Database" refers to an information management system for storing speech data and emotional data.

[1027] A "generative AI model" refers to artificial intelligence technology that analyzes the statements and emotional data of children who are not attending school and generates appropriate language expressions.

[1028] "Linguistic expressions" refer to sentences generated by the generative AI model to explain children's emotions and thoughts.

[1029] An "HTTP request" refers to a type of communication protocol for sending data from a terminal to a server.

[1030] "Analysis" refers to the process of extracting and understanding emotions and thoughts based on received statements and emotional data.

[1031] "Guardians" refers to the parents or supervisors of children who are not attending school.

[1032] "Educational personnel" refers to professionals such as teachers and counselors who provide guidance and support to children in educational settings.

[1033] This invention is a system that analyzes the speech of children who are not attending school and verbalizes their feelings and thoughts. This system is composed of multiple elements, including a terminal, a server, an emotion engine, and a user. Each element is described in detail below.

[1034] Terminal

[1035] The terminal is a device operated by the user, specifically a child who is not attending school. The terminal provides an interface for the child to input comments and interact with the AI ​​in a chat format. For example, a tablet or smartphone can be used as the terminal. Input from the terminal is sent to a server for analysis. The terminal also has a built-in emotion engine that analyzes the user's emotions in real time and sends them to the server.

[1036] server

[1037] The server receives the child's utterances sent from the device and stores them in a database. The server then analyzes the utterances using a generative AI model and emotion engine to extract emotions. For example, the server can use cloud services such as Amazon Web Services (AWS) and Microsoft Azure. Based on the extracted emotions, the server also generates linguistic expressions that are easy for parents and educators to understand.

[1038] Emotion Engine

[1039] The emotion engine has the ability to analyze emotions in real time from user input and dialogue. For example, the emotion engine uses Google Cloud's Natural Language API or IBM Watson's emotion recognition function. This data is immediately sent to the server, improving the analytical accuracy of the generative AI model. For example, by simultaneously analyzing the words entered by a child and their emotional state at the time of the words, more precise emotion analysis becomes possible.

[1040] User

[1041] Users are primarily children who are not attending school, their parents, and educators. Children use their devices to input their comments, and parents and educators can view the analysis results provided by the server to understand the child's condition and take appropriate action.

[1042] Specific examples

[1043] For example, if Mr. A doesn't want to go to school, he types "I don't want to go to school" into his device. When this message is sent from the device to the server, the emotion engine simultaneously analyzes Mr. A's emotions and generates emotional data such as "anxiety" and "fear." The server receives this data, and the generative AI model performs a combined analysis of the statement "I don't want to go to school" and the emotional data "anxiety" and "fear." As a result of the analysis, it is determined that Mr. A is feeling "anxiety" and "stress." The server generates a statement such as "Mr. A seems to be feeling very stressed about school" and sends this information back to the device. Parents can then view this result and provide appropriate support for Mr. A.

[1044] Prompt Sentence Examples

[1045] For example, the prompt to input to the generative AI model would be:

[1046] "When a child types 'I don't want to go to school,' the emotion engine analyzes it as 'anxiety' and 'fear.' Please provide a detailed analysis based on this child's emotions and statements."

[1047] This system allows for a more accurate understanding of the emotions of the users, i.e., children who are not attending school, and enables parents and educators to take appropriate action quickly through the devices and servers.

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

[1049] Step 1: User Input

[1050] The user (child) inputs a message such as "I don't want to go to school" through the terminal and presses the send button. The input is in text format and is prepared for transmission to the server through the terminal interface. The input at this stage is the user's spoken text.

[1051] Step 2: Send data

[1052] The device encodes the user's input message into JSON format and sends it to the server using an HTTP request. This request also includes the user's session information. Specifically, the device encodes the message and sends a POST request to the " / api / message" endpoint. The output is the encoded message data sent to the server.

[1053] Step 3: Real-time analysis by emotion engine

[1054] The device's emotion engine analyzes facial and voice data captured during user input and determines the user's emotional state in real time. The input is the captured facial and voice data, which is analyzed to generate emotion data such as "anxiety" or "fear." The generated emotion data is also sent to the server using an HTTP request. The output is the analyzed emotion data.

[1055] Step 4: Receiving and storing data

[1056] The server receives messages and emotion data sent from the device and stores them in a database. Specifically, messages are stored in the "messages" table, and emotion data is stored in the "emotions" table. The input is the encoded data received from the device, and the output is the database in its saved state.

[1057] Step 5: Combined analysis of emotions and statements

[1058] The generative AI model and emotion engine running on the server perform a comprehensive analysis of the messages and emotion data stored in the database. Specifically, the generative AI model analyzes the message content using natural language processing technology, and the emotion engine analyzes the emotion data. The input is the stored message and emotion data, and the output is the analysis results.

[1059] Step 6: Verbalize

[1060] The server converts the child's feelings and thoughts into concrete words based on the analysis results obtained from the generative AI model and emotion engine. For example, it verbalizes the child's feelings and thoughts in the form of, "This child seems to be feeling a lot of stress about school." The input is the analysis result data, and the output is a linguistic expression that humans can understand.

[1061] Step 7: Send results

[1062] The server sends the language expression generated as the analysis result to the terminal. The result is returned in JSON format as an HTTP response. Specifically, the result is sent as a POST request to the " / api / result" endpoint. The input is the generated language expression, and the output is the analysis result sent to the terminal.

[1063] Step 8: View the results

[1064] The analysis results received by the device from the server are displayed on the user interface. To make the results easy for parents and educators to understand, not only text messages but also visual graphs and icons are displayed. The input is the analysis results received from the server, and the output is the results displayed on the device's user interface.

[1065] As a result, the comments of the user, a child who is not attending school, are analyzed in real time, and their feelings and thoughts are expressed in concrete words, enabling parents and educators to respond quickly and appropriately.

[1066] (Application example 2)

[1067] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[1068] Children who refuse to go to school often find it difficult to express their feelings and thoughts appropriately. As a result, parents and educators often have difficulty accurately understanding the child's condition and are unable to respond appropriately. Real-time emotion analysis and rapid response are also required, but current systems do not easily achieve this.

[1069] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 2 is realized by the following means.

[1070] In this invention, the server includes means for inputting comments from children, means for transmitting the input comments to the server, means for the server to receive the comments and store them in a database, means for analyzing the comments on the server and extracting emotions, means for generating appropriate linguistic expressions based on the extracted emotions, means for transmitting the generated linguistic expressions to the terminal, means for real-time analysis using an emotion engine, and means for transmitting the results as alerts to the terminals of parents and educators. This makes it possible to analyze children's emotions in real time and quickly notify parents and educators of the analysis results.

[1071] "School-refusing children" refer to children who have difficulty attending school.

[1072] "Statements" refers to text messages or voice messages typed by the child.

[1073] "Terminal" refers to the device used by a child to input comments.

[1074] "Server" is a computer system that receives, stores, and analyzes data sent from a terminal.

[1075] The "database" is a data storage system that is stored on a server and that stores children's comments and analysis results.

[1076] An "emotion engine" is a software engine that analyzes user emotions from utterances and other input data.

[1077] "Real-time analysis means" refers to a means for immediately analyzing a message after it is sent.

[1078] A "generative AI model" is an artificial intelligence that generates appropriate language expressions based on user statements and emotional data.

[1079] An "alert" is a warning message sent to parents and educators based on the analysis results.

[1080] "Guardian" refers to an adult who supports a child in their daily life, including the child's parents.

[1081] "Educational professionals" refers to professionals involved in children's education, such as teachers and counselors.

[1082] DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS The embodiments for carrying out the present invention will be described in detail below.

[1083] Overall system configuration

[1084] The system of the present invention analyzes the statements of children who are not attending school and verbalizes their feelings and thoughts. This system consists of a terminal that inputs the children's statements, a server, an emotion engine, and devices such as smartphones of parents and educators.

[1085] Terminal

[1086] The terminal is a device operated by the child. The child uses the terminal to input text messages and voice comments. The terminal is provided with an input interface through which the child inputs their comments. The comments are not stored on the terminal but are immediately sent to the server.

[1087] server

[1088] The server receives the utterances sent from the device and stores them in a database. The server then uses a generative AI model and emotion engine to analyze the utterances and extract emotions. The extracted emotion data and utterance data are then analyzed in a composite manner to generate an appropriate linguistic expression. The generated linguistic expression is then sent back to the device.

[1089] Emotion Engine

[1090] The emotion engine has the ability to analyze emotions from user comments in real time. The emotion engine analyzes the content of comments and the emotional state at the time of commenting, and generates emotion data for the comment. The generated emotion data is immediately sent to the server and used for analysis.

[1091] Devices for parents and educators

[1092] The analysis results sent from the server are displayed in real time on the devices of parents and educators. The analysis results are displayed in a visually easy-to-understand format and are sent to parents and educators as push notifications, for example, allowing for quick response.

[1093] Hardware and software used

[1094] Devices: Smartphones, tablets, smart glasses, etc.

[1095] Server: A cloud-based server, such as AWS (Amazon Web Services) or GCP (Google Cloud Platform).

[1096] Emotion Engine: Emotion Engine API.

[1097] Generative AI models: Natural language processing models such as GPT-4.

[1098] Database: A relational database such as MySQL or PostgreSQL.

[1099] Specific examples

[1100] For example, a child might type "I don't want to go to school" into their device. This message is immediately sent from the device to the server. At the same time, the emotion engine generates emotion data such as "anxiety" or "fear." The server receives this data, and the generative AI model performs a comprehensive analysis. As a result of the analysis, a linguistic expression is generated: "This child is feeling very stressed about school." This information is sent as an alert to the parent's smartphone, allowing them to respond quickly.

[1101] Examples of prompts:

[1102] Use a sentiment analysis engine to analyze the sentiment of the following message: "I don't want to go to school."

[1103] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1104] Step 1:

[1105] The user uses the device to input a message. For example, a child might type "I don't want to go to school" into their smartphone. The input text message is temporarily stored in the device's memory. The input at this stage is text data before it is sent from the device to the server.

[1106] Step 2:

[1107] The device sends the input message to the server using an HTTP request. At this time, the text data is encoded and sent securely. The input is a text message, and the output is sent to the server in the form of an HTTP request.

[1108] Step 3:

[1109] The server receives the messages sent from the device, decodes the received data, and stores it as a text message in a database. This database also stores the child's past messages and emotional data. The input is the text data received via the HTTP request, and the output is the stored database record.

[1110] Step 4:

[1111] The server sends the message data to the emotion engine, which analyzes the emotion in real time. The emotion engine analyzes the message text, generates emotion data such as "anxiety" or "fear," and sends it back to the server. Here, emotion recognition is performed based on the input message text, and the emotion data is output.

[1112] Step 5:

[1113] The server uses a generative AI model to perform a comprehensive analysis of the received emotion data and utterance data. The generative AI model analyzes the emotion data and utterance text obtained from the emotion engine, and generates a specific linguistic expression such as "This child is feeling very stressed about school." The input is emotion data and utterance text, and the output is a linguistic expression resulting from the analysis.

[1114] Step 6:

[1115] The server sends the generated linguistic expression to the device of the parent or educator as an HTTP response. The sent data is immediately displayed on the smartphone or other device, and the parent or educator is notified as an alert. The input is the generated linguistic expression, and the output is a notification message.

[1116] Step 7:

[1117] Parents and educators can check the analysis results displayed on the device and take prompt action depending on the child's situation. The input is the notification message sent from the server, and the output is the specific response action taken by the parent or educator.

[1118] The specific processing unit 290 transmits the result of the specific processing to the headset type terminal 314. In the headset type terminal 314, the control unit 46A causes the speaker 240 and the display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[1119] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[1120] In the above embodiment, an example was given in which the specific processing is performed by the data processing device 12, but the technology of the present disclosure is not limited to this, and the specific processing may be performed by the headset type terminal 314.

[1121] [Fourth embodiment]

[1122] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[1123] 7, a data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[1124] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[1125] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a control target 443. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the control target 443 are also connected to the bus 52.

[1126] The microphone 238 receives instructions and the like from the user 20 by receiving voice uttered by the user 20. The microphone 238 captures the voice uttered by the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio in accordance with instructions from the processor 46.

[1127] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the surroundings of user 20 (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[1128] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[1129] The control object 443 includes a display device, LEDs in the eyes, and motors for driving the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

[1130] Fig. 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Fig. 8, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[1131] The specific processing program 56 is an example of a "program" according to the technology of the present disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[1132] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[1133] In the robot 414, the processor 46 performs the reception output process. A reception output program 60 is stored in the storage 50. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[1134] Next, a description will be given of the specific processing performed by the specific processing unit 290 of the data processing device 12. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1135] DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS The embodiments for carrying out the present invention will be described in detail below.

[1136] System Overview

[1137] The system of the present invention analyzes the statements of children who are not attending school and verbalizes their feelings and thoughts. This system is broadly composed of three elements: a terminal, a server, and a user.

[1138] Terminal

[1139] The terminal is a device operated by the user, specifically a child who is not attending school. The terminal provides an interface for the child to input comments and interact with the AI ​​in a chat format. The input from the terminal is sent to a server and analyzed.

[1140] server

[1141] The server receives the child's comments sent from the device and stores them in a database. The server then analyzes the comments using a generative AI model to extract emotions. Based on the extracted emotions, the server also generates linguistic expressions that are easy for parents and educators to understand.

[1142] User

[1143] Users are primarily children who are not attending school, their parents, and educators. Children use their devices to input their comments, and parents and educators can view the analysis results provided by the server to understand the child's condition and take appropriate action.

[1144] Program processing

[1145] The program works as follows:

[1146] 1. User Input

[1147] Children can type and send messages such as "I don't want to go to school" through their devices.

[1148] 2. Data Transmission

[1149] The device sends the user's message to the server, which encodes it and sends it to the server in an HTTP request.

[1150] 3. Data Receipt and Storage

[1151] The server receives the message and stores it in a database, at which point the message is ready for analysis.

[1152] 4. Sentiment analysis

[1153] The generative AI model on the server analyzes the received messages and identifies the child's emotions, extracting emotions such as "anxiety" and "stress."

[1154] 5. Verbalization

[1155] Based on the results of the sentiment analysis, the server converts the child's feelings and thoughts into appropriate language, for example, "This child seems to be feeling a lot of stress about school."

[1156] 6. Send results

[1157] The server then sends the generated linguistic expressions back to the device, where parents and educators can view the results.

[1158] Specific examples

[1159] For example, if Mr. A doesn't like going to school, he types "I don't want to go to school" into his device. This message is sent from the device to the server, which receives it and analyzes it. As a result of the analysis, it is determined that Mr. A is feeling "anxiety" or "stress." The server generates a statement such as "Mr. A seems to be feeling a lot of stress about school" and sends this information back to the device. Parents can then view this result and provide appropriate support for Mr. A.

[1160] As described above, the system of the present invention aims to quickly resolve the problem of school refusal by accurately understanding the emotions of children who are refusing to go to school and enabling parents and educators to respond quickly and appropriately.

[1161] The processing flow will be explained below.

[1162] Step 1:

[1163] The user inputs a message through the terminal. For example, a child may input a message such as "I don't want to go to school" and press the send button.

[1164] Step 2:

[1165] The terminal sends the message entered by the user to the server, which encodes the message and sends it to the server using an HTTP request.

[1166] Step 3:

[1167] The server receives the message sent from the terminal and stores it in the database.

[1168] Step 4:

[1169] The generative AI model on the server analyzes the stored messages, using natural language processing technology to extract the child's feelings and thoughts from the content of the comments.

[1170] Step 5:

[1171] Based on the analysis results, the server expresses the child's feelings in concrete terms, for example, "This child seems to be feeling a lot of stress about school."

[1172] Step 6:

[1173] The server sends the generated language expression to the terminal, and returns the result to the terminal as an HTTP response.

[1174] Step 7:

[1175] The device displays the analysis results received from the server on the screen, providing a visual user interface to make it easier for parents and educators to view the results.

[1176] Step 8:

[1177] The user types in an additional comment, for example, a child types "I'm scared of my teacher," and sends a new message.

[1178] Step 9:

[1179] The device again sends a new message to the server, which receives the new message and stores it in its database.

[1180] Step 10:

[1181] The server then re-analyzes the new message and uses it as training data for the AI ​​model, which improves its accuracy.

[1182] Step 11:

[1183] The server sends the reanalysis results to the device, which then displays them, helping parents and educators obtain new information and take appropriate action.

[1184] By following these steps and continually repeating the cycle of analyzing, verbalizing, and providing feedback based on what is said, you can accurately understand children's emotions and provide support.

[1185] Example 1

[1186] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1187] There is a need to properly understand the feelings and thoughts of children who refuse to go to school and to provide an environment in which parents and educators can respond quickly and appropriately. However, existing technologies lack effective means to accurately analyze children's comments and extract and verbalize their emotions. This has made it difficult to obtain accurate information about children's internal states.

[1188] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[1189] In this invention, the server includes a means for an information processing device to receive utterances and store them in a data storage device, a means for analyzing the utterances using a generative AI model on the information processing device and extracting emotions, and a means for generating appropriate linguistic expressions based on prompt sentences using the generative AI model. This makes it possible to accurately analyze the emotions and thoughts of children who are not attending school and provide them in an easy-to-understand format to parents and educators.

[1190] "School-refusing children" refer to school-age children who find it difficult or impossible to attend school.

[1191] "Statements" refer to text messages or voice messages that children enter through their devices.

[1192] "Means of input" refers to a terminal or device with an interface that allows children to input text or voice.

[1193] "Means for sending" refers to the communication technology used to send the inputted remarks to the server via data communication.

[1194] "Information processing device" refers to a computer or server that has the functionality to receive, store, and analyze data.

[1195] "Data storage device" refers to a storage medium or database for storing received data.

[1196] A "generative AI model" refers to a software model that analyzes input data through natural language processing and extracts emotions and meanings.

[1197] "Prompt sentence" refers to an instruction sentence used as input to an AI model.

[1198] "Means of analysis" refers to the process of identifying emotions based on received utterances using a generative AI model.

[1199] "Means for extracting emotions" refers to the technique for extracting emotions that become clear as a result of the analysis.

[1200] "Means for generating linguistic expressions" refers to the process of converting extracted emotions into sentences in an understandable form.

[1201] "Terminal" refers to a device used by a child to receive messages from the server.

[1202] This invention relates to a system that analyzes the comments of children who are not attending school and verbalizes their feelings and thoughts. The purpose of this system is to analyze the comments entered by the children and provide the results in an appropriate format to parents and educators.

[1203] System configuration

[1204] The system of the present invention mainly consists of three elements: a terminal, a server, and a user.

[1205] Terminal

[1206] The terminal is a device operated by the user, a child who is not attending school. The terminal provides an interface for the child to input comments and interact with the AI ​​in a chat format. Input from the terminal is encoded and sent to the server using an HTTP POST request. In this system, PCs, tablets, smartphones, etc. are used as terminals.

[1207] server

[1208] The server receives the child's comments sent from the device and stores them in a database. The server then analyzes the received comments using a generative AI model to identify the emotion. This generative AI model uses OpenAI's GPT-3 or ChatGPT, for example. A prompt sentence is used to analyze the emotion, and the emotion is extracted in the following format:

[1209] Example prompt sentence:

[1210] “What is the emotion conveyed in the following sentence?

[1211] Sentence: I don't want to go to school

[1212] Emotions:”

[1213] The server generates appropriate language expressions based on the extracted emotions. These expressions are then converted into a form that is easy for parents and educators to understand. For example, the output might be something like, "This child is feeling very stressed about school."

[1214] User

[1215] Users are children who are not attending school, their parents, and educators. Children use their devices to input comments, and parents and educators can view the analysis results provided by the server to understand the child's condition and take appropriate action.

[1216] Specific examples

[1217] For example, suppose that a child named A types "I don't want to go to school" into their device. This message is sent from the device to a server, which receives it and stores it in a database. A generative AI model on the server analyzes this message and determines that A is feeling "anxiety" or "stress." Based on the analysis results, the server generates a statement such as "A seems to be feeling very stressed about school," and notifies parents and educators. This allows parents and educators to understand the child's internal state and provide appropriate support.

[1218] The system of the present invention can accurately grasp the feelings and thoughts of children who are not attending school, and provides an environment in which parents and educators can respond quickly and appropriately. As a result, it becomes possible to quickly resolve the problem of school refusal.

[1219] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1220] Step 1:

[1221] The user uses the terminal to input a statement such as "I don't want to go to school." By entering text in the input field and pressing the send button, the input of the statement is completed.

[1222] Input: A text message entered by the user.

[1223] Output: The encoded message is prepared.

[1224] Step 2:

[1225] The terminal encodes the message entered by the user and sends it to the server using an HTTP POST request, which then packages the encoded message in JSON format and sends it.

[1226] Input: The encoded text message.

[1227] Output: The HTTP POST request sent to the server.

[1228] Step 3:

[1229] The server receives messages sent from the terminal and stores them in a database. NoSQL databases (e.g., MongoDB) are often used as the database. The server records the received messages in the database.

[1230] Input: The text message included in the HTTP POST request received by the server.

[1231] Output: The message stored in the database.

[1232] Step 4:

[1233] A generative AI model on the server retrieves and analyzes messages stored in a database. A prompt sentence is used to identify the emotion of the statement, for example, the following prompt sentence: "What emotion is conveyed in the following sentence? Sentence: I don't want to go to school Emotion:." The emotion is extracted as a result of the analysis.

[1234] Input: A text message stored in the database.

[1235] Output: Emotions extracted by analysis (e.g., anxiety, stress).

[1236] Step 5:

[1237] The server uses a generative AI model to generate appropriate language expressions based on the extracted emotions. For example, it might generate a language expression such as, "Mr. A seems to be feeling very stressed about school." A text containing the emotion and its rationale is then generated.

[1238] Input: The extracted sentiment as a result of analysis.

[1239] Output: The generated linguistic expression.

[1240] Step 6:

[1241] The server sends the generated language representation to the device, which encodes it using an HTTP POST request and sends it back to the device, where it receives the information and makes it available for viewing by the user.

[1242] Input: The generated linguistic expression.

[1243] Output: The language expression sent to the terminal is displayed to the user.

[1244] (Application example 1)

[1245] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1246] The challenge is to accurately understand the emotions and psychological state of children who are not attending school, so that parents and educators can respond appropriately. There is also a need to track emotional changes in real time and quickly notify the results, thereby speeding up support for children.

[1247] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[1248] In this invention, the server includes a means for notifying relevant parties of the emotion analysis results in real time, a means for tracking changes in emotions and displaying the trends, and a means for analyzing the child's emotions using a generative AI model. This allows the child's emotions to be analyzed in real time and the relevant parties to be promptly notified of the changes, enabling appropriate support.

[1249] "School-refusing children" refer to children who, for some reason, do not go to school and spend their time at home or elsewhere.

[1250] "Means for inputting speech" refers to devices or software that provide an interface for children to input their thoughts and feelings in text form.

[1251] A "server" refers to a computer system that processes and stores data on a network.

[1252] A "database" refers to a system for efficiently storing, retrieving, and managing data.

[1253] "Means for extracting emotions" refers to algorithms or programs that use generative AI models to identify emotions from input utterances.

[1254] "Means for generating linguistic expressions" refers to a program that generates text based on the extracted emotions in a format that is easy for parents and educators to understand.

[1255] "Devices" refer to digital devices such as computers and smartphones used by children, parents, and educators.

[1256] "Means of real-time notification" refers to a push notification or message sending system that instantly communicates the results of sentiment analysis to relevant parties.

[1257] "Means for tracking emotional changes" refers to algorithms or programs that continuously analyze children's speech and monitor and visualize emotional fluctuations.

[1258] A "generative AI model" refers to a machine learning model that uses natural language processing to analyze text data and extract emotions.

[1259] A "prompt sentence" refers to the text data input into a generative AI model, and is the source sentence that the model uses for analysis.

[1260] The system for implementing this invention is designed to analyze the comments of children who are not attending school and verbalize their feelings and thoughts. The system is broadly composed of a terminal that inputs the children's comments, a server that receives and analyzes the comments, and a means for notifying parents and educators of the results of the analysis.

[1261] Terminal

[1262] A terminal is a device used by a child (user). Specifically, it can be a smartphone, tablet, or personal computer. A terminal provides the following interfaces:

[1263] A chat-style input interface using text boxes

[1264] A button to enter and send a message

[1265] A display area for parents and educators to view the analysis results

[1266] Comments entered from the terminal are sent to the server via an HTTP request.

[1267] server

[1268] The server stores the utterances received from the device in a database and analyzes them using a generative AI model. The specific software used includes:

[1269] Flask (web server framework)

[1270] Hugging Face generation AI model "bert-base-uncased-emotion"

[1271] Database management systems such as PostgreSQL and MySQL

[1272] The server process is as follows:

[1273] 1. Receive comments and store them in a database.

[1274] 2. Extract the sentiment of the utterance using a generative AI model.

[1275] 3. Generate appropriate language expressions based on emotions.

[1276] 4. The generated language expression is sent back to the terminal.

[1277] The server notifies relevant parties of the results of the emotion analysis in real time, and also has the ability to track changes in emotions and display trends. For example, when B types, "I don't want to see my friends today," the server analyzes this statement as "anxiety" and generates the result, "The child is feeling anxious."

[1278] Examples of specific examples and prompts

[1279] For example, if B types, "I don't want to see my friends today," this message is sent from the device to the server. The server analyzes this statement and extracts the emotion "anxiety." The server then generates the linguistic expression, "The child is feeling the emotion of 'anxiety,'" and sends it back to the device.

[1280] Examples of prompts are:

[1281] "I don't want to see my friends today."

[1282] This will enable parents and educators to quickly understand a child's condition and take appropriate action.

[1283] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1284] Step 1:

[1285] The user (child) uses the terminal to input comments.

[1286] Input: Child's statement (e.g., "I don't want to see my friends today.")

[1287] Output: Data sent from the device to the server in speech data format

[1288] How it works: Children type their message into the chat box on their smartphone or tablet and press the "Send" button.

[1289] Step 2:

[1290] The device sends the message to the server as an HTTP request.

[1291] Input: Speech data entered into the terminal

[1292] Output: The encoded speech data sent to the server

[1293] Operation: Sends speech data as an HTTP POST request to a specific URL on the server.

[1294] Step 3:

[1295] The server stores the received comments in a database.

[1296] Input: Speech data sent to the server

[1297] Output: A database record containing the comment

[1298] How it works: The server receives an HTTP request and inserts the comment data into a database using an SQL query.

[1299] Step 4:

[1300] The server analyzes the comments and extracts the emotions.

[1301] Input: Speech data read from the database

[1302] Output: Extracted emotion data (e.g., "anxiety")

[1303] How it works: A generative AI model (e.g., "bert-base-uncased-emotion") is used to analyze speech using natural language processing to obtain emotion labels.

[1304] Step 5:

[1305] The server generates appropriate language expressions based on the extracted emotions.

[1306] Input: Extracted emotion data

[1307] Output: Generated linguistic expression (e.g., "The child is experiencing the emotion 'anxiety'.")

[1308] Operation: Based on the extracted emotions, a program is run that uses templates to generate appropriate linguistic expressions.

[1309] Step 6:

[1310] The server transmits the generated linguistic expression to the terminal.

[1311] Input: Generated linguistic expression

[1312] Output: Language expression data sent to the device

[1313] Operation: The generated language expression is sent to the terminal as an HTTP response.

[1314] Step 7:

[1315] The device displays the received language expressions to parents and educators.

[1316] Input: Linguistic expression data received from the server

[1317] Output: Language expression displayed on the terminal screen

[1318] What happens: The terminal application receives the HTTP response and displays it in the user interface.

[1319] Step 8:

[1320] The server tracks changes in the sentiment of comments and displays the trends.

[1321] Input: Multiple utterances and their emotional data accumulated in chronological order

[1322] Output: Graphs and lists showing trends in sentiment changes

[1323] Operation: Analyzes past statements and emotional data, runs a program that visualizes trends in emotional changes, and displays the results in the user interface.

[1324] This will enable us to understand the emotions of children who are not attending school in real time and respond quickly.

[1325] Furthermore, an emotion engine that estimates the user's emotion may be further combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59, and perform identification processing using the user's emotion.

[1326] DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS The embodiments for carrying out the present invention will be described in detail below.

[1327] System Overview

[1328] The system of the present invention analyzes the speech of children who are not attending school and verbalizes their feelings and thoughts. Furthermore, by combining it with an emotion engine that recognizes the user's emotions, the accuracy of the analysis can be further improved. This system is broadly composed of four elements: a terminal, a server, an emotion engine, and a user.

[1329] Terminal

[1330] The terminal is a device operated by the user, specifically a child who is not attending school. The terminal provides an interface for the child to input comments and interact with the AI ​​in a chat format. The input from the terminal is sent to a server for analysis. The terminal also has a built-in emotion engine that analyzes the user's emotions in real time and sends them to the server.

[1331] server

[1332] The server receives the children's comments sent from the device and stores them in a database. The server then analyzes the comments using a generative AI model and emotion engine to extract emotions. Based on the extracted emotions, the server also generates linguistic expressions in a form that is easy for parents and educators to understand.

[1333] Emotion Engine

[1334] The emotion engine has the ability to analyze emotions in real time from user input and dialogue. This data is immediately sent to the server, improving the analytical accuracy of the generative AI model. For example, by simultaneously analyzing a child's input and their emotional state at the time of the comment, more precise emotion analysis becomes possible.

[1335] User

[1336] Users are primarily children who are not attending school, their parents, and educators. Children use their devices to input their comments, and parents and educators can view the analysis results provided by the server to understand the child's condition and take appropriate action.

[1337] Program processing

[1338] The program works as follows:

[1339] 1. User Input

[1340] Children can type and send messages such as "I don't want to go to school" through their devices.

[1341] 2. Data Transmission

[1342] The device sends the user's message to the server, which encodes it and sends it to the server using an HTTP request.

[1343] 3. Real-time analysis using an emotion engine

[1344] The emotion engine analyzes the child's emotional state at the time of input in real time and sends the results to the server.

[1345] 4. Data Receipt and Storage

[1346] The server receives the message sent from the device and the emotion data from the emotion engine, and stores them in a database. At this stage, the message and emotion data are ready for analysis.

[1347] 5. Combined analysis of emotions and statements

[1348] The generative AI model and emotion engine on the server analyze the stored messages and emotion data, using natural language processing and emotion recognition technologies to extract the content of the comments and the emotional state.

[1349] 6. Verbalization

[1350] Based on the results of the emotion analysis, the server expresses the child's feelings and thoughts in concrete terms, for example, "This child seems to be feeling a lot of stress about school."

[1351] 7. Send results

[1352] The server sends the generated language expression to the terminal, and returns the result to the terminal as an HTTP response.

[1353] 8. Results display

[1354] The device displays the analysis results received from the server on the screen, providing a visual user interface to make it easier for parents and educators to view the results.

[1355] Specific examples

[1356] For example, if Mr. A doesn't want to go to school, he types "I don't want to go to school" into his device. When this message is sent from the device to the server, the emotion engine simultaneously analyzes Mr. A's emotions and generates emotional data such as "anxiety" and "fear." The server receives this data, and the generative AI model performs a combined analysis of the statement "I don't want to go to school" and the emotional data "anxiety" and "fear." As a result of the analysis, it is determined that Mr. A is feeling "anxiety" and "stress." The server generates a statement such as "Mr. A seems to be feeling very stressed about school" and sends this information back to the device. Parents can then view this result and provide appropriate support for Mr. A.

[1357] As described above, the system of the present invention aims to resolve the problem of school refusal at an early stage by more accurately grasping the emotions of children who are refusing to go to school and enabling parents and educators to respond quickly and appropriately.

[1358] The processing flow will be explained below.

[1359] Step 1:

[1360] The user inputs a message through the terminal. For example, a child may input a message such as "I don't want to go to school" and press the send button.

[1361] Step 2:

[1362] The emotion engine analyzes the child's emotional state in real time as the user speaks, using facial recognition, voice analysis, and contextual analysis to classify emotions.

[1363] Step 3:

[1364] The device sends the message entered by the user and the emotional data analyzed by the emotion engine to the server, where the message and emotional data are encoded and sent to the server via an HTTP request.

[1365] Step 4:

[1366] The server receives the message and emotion data sent from the device, and then stores the message and emotion data in a database.

[1367] Step 5:

[1368] The generative AI model on the server performs a combined analysis of the stored messages and emotional data, using natural language processing and emotion recognition technologies to extract the content of the speech and its corresponding emotional state.

[1369] Step 6:

[1370] Based on the analysis results, the server expresses the child's feelings in concrete terms, for example, "This child seems to be feeling a lot of stress about school."

[1371] Step 7:

[1372] The server sends the generated language expression to the terminal, and returns the result to the terminal as an HTTP response.

[1373] Step 8:

[1374] The device displays the analysis results received from the server on the screen, providing a visual user interface to make it easier for parents and educators to view the results.

[1375] Step 9:

[1376] The user types in an additional comment, for example, a child types "I'm scared of my teacher," and sends a new message.

[1377] Step 10:

[1378] The device sends the new message and the new emotion data analyzed by the emotion engine back to the server, which receives the new data and stores it in a database.

[1379] Step 11:

[1380] The server re-analyzes the new message and emotion data and uses it as training data for the AI ​​model and emotion engine, improving the accuracy of the analysis.

[1381] Step 12:

[1382] The server sends the reanalysis results to the device, which then displays them, helping parents and educators obtain new information and take appropriate action.

[1383] This process involves a continuous cycle of emotion analysis, verbalization, and feedback based on what is said, resulting in a system that can accurately grasp children's emotions and provide appropriate support.

[1384] Example 2

[1385] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1386] Properly understanding the words and thoughts of school-refusing children and providing appropriate support based on that understanding is an important issue in the educational field. However, children may have difficulty verbalizing their own feelings and thoughts, or their feelings may be difficult to understand, making it difficult for parents and educators to respond appropriately. The present invention aims to solve these issues by providing a system that accurately understands the feelings and thoughts of school-refusing children and enables prompt and appropriate responses based on that understanding.

[1387] The identification process by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means. In this invention, the server includes means for inputting children's comments, means for transmitting the input comments to the server, means equipped with an emotion engine that analyzes emotions in real time, means for the server to receive the comments and emotion data and store them in a database, means for analyzing the comments and emotion data on the server and extracting emotions, means for generating appropriate linguistic expressions based on the extracted emotions, and means for transmitting the generated linguistic expressions to a terminal. This makes it possible to analyze the emotions and thoughts of children who are not attending school in real time and provide appropriate support based on the analysis.

[1388] "Futoko children" refer to children who do not attend school regularly and spend their time at home or elsewhere.

[1389] "Comments" refer to messages and comments that children enter through their devices.

[1390] A "server" refers to a computer system that receives speech and emotional data, stores it in a database, and analyzes it.

[1391] "Terminal" refers to an input / output device that children can directly operate and use to input comments.

[1392] An "emotion engine" refers to software that has the ability to analyze emotions in real time from user input and dialogue.

[1393] "Database" refers to an information management system for storing speech data and emotional data.

[1394] A "generative AI model" refers to artificial intelligence technology that analyzes the statements and emotional data of children who are not attending school and generates appropriate language expressions.

[1395] "Linguistic expressions" refer to sentences generated by the generative AI model to explain children's emotions and thoughts.

[1396] An "HTTP request" refers to a type of communication protocol for sending data from a terminal to a server.

[1397] "Analysis" refers to the process of extracting and understanding emotions and thoughts based on received statements and emotional data.

[1398] "Guardians" refers to the parents or supervisors of children who are not attending school.

[1399] "Educational personnel" refers to professionals such as teachers and counselors who provide guidance and support to children in educational settings.

[1400] This invention is a system that analyzes the speech of children who are not attending school and verbalizes their feelings and thoughts. This system is composed of multiple elements, including a terminal, a server, an emotion engine, and a user. Each element is described in detail below.

[1401] Terminal

[1402] The terminal is a device operated by the user, specifically a child who is not attending school. The terminal provides an interface for the child to input comments and interact with the AI ​​in a chat format. For example, a tablet or smartphone can be used as the terminal. Input from the terminal is sent to a server for analysis. The terminal also has a built-in emotion engine that analyzes the user's emotions in real time and sends them to the server.

[1403] server

[1404] The server receives the child's utterances sent from the device and stores them in a database. The server then analyzes the utterances using a generative AI model and emotion engine to extract emotions. For example, the server can use cloud services such as Amazon Web Services (AWS) and Microsoft Azure. Based on the extracted emotions, the server also generates linguistic expressions that are easy for parents and educators to understand.

[1405] Emotion Engine

[1406] The emotion engine has the ability to analyze emotions in real time from user input and dialogue. For example, the emotion engine uses Google Cloud's Natural Language API or IBM Watson's emotion recognition function. This data is immediately sent to the server, improving the analytical accuracy of the generative AI model. For example, by simultaneously analyzing the words entered by a child and their emotional state at the time of the words, more precise emotion analysis becomes possible.

[1407] User

[1408] Users are primarily children who are not attending school, their parents, and educators. Children use their devices to input their comments, and parents and educators can view the analysis results provided by the server to understand the child's condition and take appropriate action.

[1409] Specific examples

[1410] For example, if Mr. A doesn't want to go to school, he types "I don't want to go to school" into his device. When this message is sent from the device to the server, the emotion engine simultaneously analyzes Mr. A's emotions and generates emotional data such as "anxiety" and "fear." The server receives this data, and the generative AI model performs a combined analysis of the statement "I don't want to go to school" and the emotional data "anxiety" and "fear." As a result of the analysis, it is determined that Mr. A is feeling "anxiety" and "stress." The server generates a statement such as "Mr. A seems to be feeling very stressed about school" and sends this information back to the device. Parents can then view this result and provide appropriate support for Mr. A.

[1411] Prompt Sentence Examples

[1412] For example, the prompt to input to the generative AI model would be:

[1413] "When a child types 'I don't want to go to school,' the emotion engine analyzes it as 'anxiety' and 'fear.' Please provide a detailed analysis based on this child's emotions and statements."

[1414] This system allows for a more accurate understanding of the emotions of the users, i.e., children who are not attending school, and enables parents and educators to take appropriate action quickly through the devices and servers.

[1415] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1416] Step 1: User Input

[1417] The user (child) inputs a message such as "I don't want to go to school" through the terminal and presses the send button. The input is in text format and is prepared for transmission to the server through the terminal interface. The input at this stage is the user's spoken text.

[1418] Step 2: Send data

[1419] The device encodes the user's input message into JSON format and sends it to the server using an HTTP request. This request also includes the user's session information. Specifically, the device encodes the message and sends a POST request to the " / api / message" endpoint. The output is the encoded message data sent to the server.

[1420] Step 3: Real-time analysis by emotion engine

[1421] The device's emotion engine analyzes facial and voice data captured during user input and determines the user's emotional state in real time. The input is the captured facial and voice data, which is analyzed to generate emotion data such as "anxiety" or "fear." The generated emotion data is also sent to the server using an HTTP request. The output is the analyzed emotion data.

[1422] Step 4: Receiving and storing data

[1423] The server receives messages and emotion data sent from the device and stores them in a database. Specifically, messages are stored in the "messages" table, and emotion data is stored in the "emotions" table. The input is the encoded data received from the device, and the output is the database in its saved state.

[1424] Step 5: Combined analysis of emotions and statements

[1425] The generative AI model and emotion engine running on the server perform a comprehensive analysis of the messages and emotion data stored in the database. Specifically, the generative AI model analyzes the message content using natural language processing technology, and the emotion engine analyzes the emotion data. The input is the stored message and emotion data, and the output is the analysis results.

[1426] Step 6: Verbalize

[1427] The server converts the child's feelings and thoughts into concrete words based on the analysis results obtained from the generative AI model and emotion engine. For example, it verbalizes the child's feelings and thoughts in the form of, "This child seems to be feeling a lot of stress about school." The input is the analysis result data, and the output is a linguistic expression that humans can understand.

[1428] Step 7: Send results

[1429] The server sends the language expression generated as the analysis result to the terminal. The result is returned in JSON format as an HTTP response. Specifically, the result is sent as a POST request to the " / api / result" endpoint. The input is the generated language expression, and the output is the analysis result sent to the terminal.

[1430] Step 8: View the results

[1431] The analysis results received by the device from the server are displayed on the user interface. To make the results easy for parents and educators to understand, not only text messages but also visual graphs and icons are displayed. The input is the analysis results received from the server, and the output is the results displayed on the device's user interface.

[1432] As a result, the comments of the user, a child who is not attending school, are analyzed in real time, and their feelings and thoughts are expressed in concrete words, enabling parents and educators to respond quickly and appropriately.

[1433] (Application example 2)

[1434] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1435] Children who refuse to go to school often find it difficult to express their feelings and thoughts appropriately. As a result, parents and educators often have difficulty accurately understanding the child's condition and are unable to respond appropriately. Real-time emotion analysis and rapid response are also required, but current systems do not easily achieve this.

[1436] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 2 is realized by the following means.

[1437] In this invention, the server includes means for inputting comments from children, means for transmitting the input comments to the server, means for the server to receive the comments and store them in a database, means for analyzing the comments on the server and extracting emotions, means for generating appropriate linguistic expressions based on the extracted emotions, means for transmitting the generated linguistic expressions to the terminal, means for real-time analysis using an emotion engine, and means for transmitting the results as alerts to the terminals of parents and educators. This makes it possible to analyze children's emotions in real time and quickly notify parents and educators of the analysis results.

[1438] "School-refusing children" refer to children who have difficulty attending school.

[1439] "Statements" refers to text messages or voice messages typed by the child.

[1440] "Terminal" refers to the device used by a child to input comments.

[1441] "Server" is a computer system that receives, stores, and analyzes data sent from a terminal.

[1442] The "database" is a data storage system that is stored on a server and that stores children's comments and analysis results.

[1443] An "emotion engine" is a software engine that analyzes user emotions from utterances and other input data.

[1444] "Real-time analysis means" refers to a means for immediately analyzing a message after it is sent.

[1445] A "generative AI model" is an artificial intelligence that generates appropriate language expressions based on user statements and emotional data.

[1446] An "alert" is a warning message sent to parents and educators based on the analysis results.

[1447] "Guardian" refers to an adult who supports a child in their daily life, including the child's parents.

[1448] "Educational professionals" refers to professionals involved in children's education, such as teachers and counselors.

[1449] DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS The embodiments for carrying out the present invention will be described in detail below.

[1450] Overall system configuration

[1451] The system of the present invention analyzes the statements of children who are not attending school and verbalizes their feelings and thoughts. This system consists of a terminal that inputs the children's statements, a server, an emotion engine, and devices such as smartphones of parents and educators.

[1452] Terminal

[1453] The terminal is a device operated by the child. The child uses the terminal to input text messages and voice comments. The terminal is provided with an input interface through which the child inputs their comments. The comments are not stored on the terminal but are immediately sent to the server.

[1454] server

[1455] The server receives the utterances sent from the device and stores them in a database. The server then uses a generative AI model and emotion engine to analyze the utterances and extract emotions. The extracted emotion data and utterance data are then analyzed in a composite manner to generate an appropriate linguistic expression. The generated linguistic expression is then sent back to the device.

[1456] Emotion Engine

[1457] The emotion engine has the ability to analyze emotions from user comments in real time. The emotion engine analyzes the content of comments and the emotional state at the time of commenting, and generates emotion data for the comment. The generated emotion data is immediately sent to the server and used for analysis.

[1458] Devices for parents and educators

[1459] The analysis results sent from the server are displayed in real time on the devices of parents and educators. The analysis results are displayed in a visually easy-to-understand format and are sent to parents and educators as push notifications, for example, allowing for quick response.

[1460] Hardware and software used

[1461] Devices: Smartphones, tablets, smart glasses, etc.

[1462] Server: A cloud-based server, such as AWS (Amazon Web Services) or GCP (Google Cloud Platform).

[1463] Emotion Engine: Emotion Engine API.

[1464] Generative AI models: Natural language processing models such as GPT-4.

[1465] Database: A relational database such as MySQL or PostgreSQL.

[1466] Specific examples

[1467] For example, a child might type "I don't want to go to school" into their device. This message is immediately sent from the device to the server. At the same time, the emotion engine generates emotion data such as "anxiety" or "fear." The server receives this data, and the generative AI model performs a comprehensive analysis. As a result of the analysis, a linguistic expression is generated: "This child is feeling very stressed about school." This information is sent as an alert to the parent's smartphone, allowing them to respond quickly.

[1468] Examples of prompts:

[1469] Use a sentiment analysis engine to analyze the sentiment of the following message: "I don't want to go to school."

[1470] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1471] Step 1:

[1472] The user uses the device to input a message. For example, a child might type "I don't want to go to school" into their smartphone. The input text message is temporarily stored in the device's memory. The input at this stage is text data before it is sent from the device to the server.

[1473] Step 2:

[1474] The device sends the input message to the server using an HTTP request. At this time, the text data is encoded and sent securely. The input is a text message, and the output is sent to the server in the form of an HTTP request.

[1475] Step 3:

[1476] The server receives the messages sent from the device, decodes the received data, and stores it as a text message in a database. This database also stores the child's past messages and emotional data. The input is the text data received via the HTTP request, and the output is the stored database record.

[1477] Step 4:

[1478] The server sends the message data to the emotion engine, which analyzes the emotion in real time. The emotion engine analyzes the message text, generates emotion data such as "anxiety" or "fear," and sends it back to the server. Here, emotion recognition is performed based on the input message text, and the emotion data is output.

[1479] Step 5:

[1480] The server uses a generative AI model to perform a comprehensive analysis of the received emotion data and utterance data. The generative AI model analyzes the emotion data and utterance text obtained from the emotion engine, and generates a specific linguistic expression such as "This child is feeling very stressed about school." The input is emotion data and utterance text, and the output is a linguistic expression resulting from the analysis.

[1481] Step 6:

[1482] The server sends the generated linguistic expression to the device of the parent or educator as an HTTP response. The sent data is immediately displayed on the smartphone or other device, and the parent or educator is notified as an alert. The input is the generated linguistic expression, and the output is a notification message.

[1483] Step 7:

[1484] Parents and educators can check the analysis results displayed on the device and take prompt action depending on the child's situation. The input is the notification message sent from the server, and the output is the specific response action taken by the parent or educator.

[1485] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the control target 443 to output the result of the specific processing. The microphone 238 acquires voice indicating a user input regarding the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[1486] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[1487] In the above embodiment, an example was given in which the specific processing is performed by the data processing device 12, but the technology of the present disclosure is not limited to this, and the specific processing may be performed by the robot 414.

[1488] The emotion identification model 59 as an emotion engine may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[1489] FIG. 9 is a diagram illustrating an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. Emotions closer to the center of the concentric circles are more primitive. Emotions representing states and actions arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion includes both affect and mental states. Emotions generally generated from reactions occurring in the brain are arranged on the left side of the concentric circles. Emotions generally induced by situational judgment are arranged on the right side of the concentric circles. Emotions generally generated from reactions occurring in the brain and induced by situational judgment are arranged on the upper and lower sides of the concentric circles. Furthermore, the emotion of "pleasure" is arranged on the upper side of the concentric circles, and the emotion of "discomfort" is arranged on the lower side. In this way, in the emotion map 400, multiple emotions are mapped based on the structure by which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[1490] These emotions are distributed in the 3 o'clock direction on emotion map 400, and typically fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

[1491] The inside of emotion map 400 represents what is going on in the mind, and the outside of emotion map 400 represents behavior, so the further you go outside emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[1492] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is indicated, and when they approach the ideal, a state of pleasure is indicated. Emotions can also be created for robots, automobiles, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is indicated, and when they approach the ideal, a state of pleasure is indicated. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on Voice Emotion Recognition and Emotional Brain Physiological Signal Analysis Systems, Tokushima University, Doctoral Dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the "reaction" domain, where sensation is dominant. The right half of the emotion map lists emotions belonging to the "situation" domain, where situational awareness is dominant.

[1493] The emotion map defines two emotions that promote learning. One is a negative emotion on the situation side, around the middle of "repentance" or "reflection." In other words, this occurs when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is a positive emotion on the response side, around "desire." In other words, this occurs when the robot experiences positive feelings such as "I want more" or "I want to know more."

[1494] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple pieces of training data that are combinations of user input and emotion values ​​indicating each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions that are located close to each other have similar values, as in the emotion map 900 shown in FIG. 10. FIG. 10 shows an example in which multiple emotions, "relieved," "calm," and "reassuring," have similar emotion values.

[1495] The system according to the present disclosure has been described above mainly with respect to the functions of the data processing device 12, but the system according to the present disclosure is not necessarily implemented on a server. The system according to the present disclosure may be implemented as a general information processing system. The present disclosure may be implemented, for example, as a software program running on a personal computer or an application running on a smartphone, etc. The method according to the present disclosure may be provided to users in the form of SaaS (Software as a Service).

[1496] In the above embodiment, an example was given in which the specific processing is performed by one computer 22, but the technology of the present disclosure is not limited to this, and the specific processing may be distributed and performed by a plurality of computers including the computer 22. For example, the data generation model 58 may be provided in an external device of the data processing device 12, and data may be generated in the external device in accordance with input data.

[1497] In the above embodiment, an example in which the specific processing program 56 is stored in the storage 32 has been described, but the technology of the present disclosure is not limited to this. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-transitory storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-transitory storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes the specific processing in accordance with the specific processing program 56.

[1498] 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.

[1499] It is not necessary to store all of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store all of the specific processing program 56 in the storage 32; only a portion of the specific processing program 56 may be stored.

[1500] The hardware resource for executing a specific process can be any of the following processors: An example of a processor is a CPU, which is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. Another example of a processor is a dedicated electrical circuit, such as an FPGA (Field-Programmable Gate Array), a PLD (Programmable Logic Device), or an ASIC (Application Specific Integrated Circuit), which is a processor with a circuit configuration designed specifically for executing a specific process. Each processor has built-in or connected memory, and each processor uses the memory to execute the specific process.

[1501] The hardware resource that executes the specific processing may be configured with one of these various processors, or may be configured with a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Also, the hardware resource that executes the specific processing may be a single processor.

[1502] As an example of a system configured with a single processor, first, one processor is configured by combining one or more CPUs and software, and this processor functions as a hardware resource that executes a specific process. Second, there is a system that uses a processor that realizes the functions of an entire system including multiple hardware resources that execute a specific process on a single IC chip, as typified by SoC (System-on-a-chip). In this way, a specific process is realized using one or more of the above-mentioned various processors as hardware resources.

[1503] Furthermore, the hardware structure of these various processors can be, more specifically, an electric circuit that combines circuit elements such as semiconductor devices. The specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps may be deleted, new steps may be added, or the processing order may be rearranged, without departing from the spirit of the invention.

[1504] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, to avoid confusion and facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[1505] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference.

[1506] The following is further disclosed regarding the above embodiment.

[1507] (Claim 1)

[1508] It is a system that analyzes the statements of children who are not attending school and verbalizes their feelings and thoughts.

[1509] a means for inputting child utterances;

[1510] means for transmitting the inputted utterance to a server;

[1511] A means for the server to receive and store the comments in a database;

[1512] A means of analyzing comments on the server and extracting emotions,

[1513] A means for generating an appropriate linguistic expression based on the extracted emotion;

[1514] means for transmitting the generated linguistic expression to a terminal;

[1515] A system including:

[1516] (Claim 2)

[1517] 10. The system of claim 1, further comprising means for displaying the generated verbal expression to a parent or educator.

[1518] (Claim 3)

[1519] 10. The system of claim 1, further comprising means for continuously collecting and analyzing new utterances from the child.

[1520] "Example 1"

[1521] (Claim 1)

[1522] It is a system that analyzes the statements of children who are not attending school and verbalizes their feelings and thoughts.

[1523] a means for inputting child utterances;

[1524] means for transmitting the inputted utterance to the information processing device;

[1525] a means for an information processing device to receive messages and store them in a data storage device;

[1526] A means for analyzing utterances and extracting emotions using a generative AI model on an information processing device;

[1527] A means for generating an appropriate linguistic expression based on a prompt sentence using a generative AI model;

[1528] means for transmitting the generated linguistic expression to a terminal;

[1529] A system including:

[1530] (Claim 2)

[1531] 10. The system of claim 1, further comprising means for displaying the generated verbal expression to a parent or educator.

[1532] (Claim 3)

[1533] 10. The system of claim 1, further comprising means for continuously collecting and analyzing new utterances from the child.

[1534] "Application Example 1"

[1535] (Claim 1)

[1536] It is a system that analyzes the statements of children who are not attending school and verbalizes their feelings and thoughts.

[1537] a means for inputting child utterances;

[1538] means for transmitting the inputted utterance to a server;

[1539] A means for the server to receive and store the comments in a database;

[1540] A means of analyzing comments on the server and extracting emotions,

[1541] A means for generating an appropriate linguistic expression based on the extracted emotion;

[1542] means for transmitting the generated linguistic expression to a terminal;

[1543] A means of notifying relevant parties of the results of emotion analysis in real time,

[1544] a means for tracking changes in sentiment and displaying trends;

[1545] A system including:

[1546] (Claim 2)

[1547] 10. The system of claim 1, further comprising means for displaying the generated verbal expression to a parent or educator.

[1548] (Claim 3)

[1549] 10. The system of claim 1, further comprising means for continuously collecting and analyzing new utterances from the child.

[1550] "Example 2: Combining Emotion Engines"

[1551] (Claim 1)

[1552] It is a system that analyzes the statements of children who are not attending school and verbalizes their feelings and thoughts.

[1553] a means for inputting child utterances;

[1554] means for transmitting the inputted utterance to a server;

[1555] A means having an emotion engine for analyzing emotions in real time;

[1556] A means for the server to receive the utterances and emotion data and store them in a database;

[1557] A means for analyzing speech and emotion data on the server and extracting emotions;

[1558] A means for generating an appropriate linguistic expression based on the extracted emotion;

[1559] means for transmitting the generated linguistic expression to a terminal;

[1560] A system including:

[1561] (Claim 2)

[1562] 10. The system of claim 1, further comprising means for displaying the generated verbal expression to a parent or educator.

[1563] (Claim 3)

[1564] 10. The system of claim 1, further comprising means for analyzing facial and voice data captured while the child is speaking to determine an emotional state.

[1565] "Application example 2 when combining emotion engines"

[1566] (Claim 1)

[1567] It is a system that analyzes the statements of children who are not attending school and verbalizes their feelings and thoughts.

[1568] a means for inputting child utterances;

[1569] means for transmitting the inputted utterance to a server;

[1570] A means for the server to receive and store the comments in a database;

[1571] A means of analyzing comments on the server and extracting emotions,

[1572] A means for generating an appropriate linguistic expression based on the extracted emotion;

[1573] means for transmitting the generated linguistic expression to a terminal;

[1574] Real-time analysis using an emotion engine,

[1575] A means to send the results as alerts to the devices of parents and educators,

[1576] A system including:

[1577] (Claim 2)

[1578] 10. The system of claim 1, further comprising means for displaying the generated verbal expression to a parent or educator.

[1579] (Claim 3)

[1580] 10. The system of claim 1, further comprising means for continuously collecting and analyzing new utterances from the child. [Explanation of symbols]

[1581] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot< / url:> < / url:> < / url:> < / url:>

Claims

1. It is a system that analyzes the statements of children who are not attending school and verbalizes their feelings and thoughts. a means for inputting child utterances; means for transmitting the inputted utterance to a server; A means for the server to receive and store the comments in a database; A means of analyzing comments on the server and extracting emotions, A means for generating an appropriate linguistic expression based on the extracted emotion; means for transmitting the generated linguistic expression to a terminal; A system including:

2. The system of claim 1 , further comprising means for displaying the generated linguistic expression to a parent or educator.

3. The system of claim 1 further comprising means for continuously collecting and analyzing new utterances from the child.

Images