System

Abstract

A system is provided.SOLUTION: A system comprising: means for receiving a question input by a student through a terminal; means for transmitting the received question to an artificial intelligence chatbot to generate an answer; means for transmitting the generated answer to a terminal of the student; and means for storing the student's question and the generated answer in a database.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] In traditional classes, it is difficult to respond quickly to students' questions, especially in large classes. It is difficult for teachers to answer all questions immediately. Furthermore, because teachers are unable to provide detailed answers to individual students' questions in a timely manner, students' motivation to learn can be diminished. Furthermore, questions that have already been answered are not recorded, leading to the recurrence of similar questions. [Means for solving the problem]

[0005] To solve the above problems, the present invention provides the following means: a system including means for receiving questions entered by students via their terminals, means for sending the received questions to an AI chatbot to generate answers, means for sending the generated answers to the students' terminals, and means for storing the students' questions and the generated answers in a database. The system also includes means for the AI ​​chatbot to memorize past questions and answers and quickly provide answers to similar questions, and means for a teacher to monitor students' questions and the AI ​​chatbot's answers in real time and provide supplementary information as needed. This allows for quick and accurate responses to students' questions, reducing the teacher's burden and improving learning effectiveness.

[0006] A "terminal" is an electronic device used by a user, and is a device for inputting and displaying information.

[0007] "Questions" are inquiries that students have while studying or that they seek knowledge about.

[0008] "Means for receiving" refers to a function or device for receiving data transmitted from a terminal.

[0009] An "artificial intelligence chatbot" is software that uses natural language processing technology to respond appropriately to user questions.

[0010] "Transmitting means" refers to a function or device for transferring received data to another system or device.

[0011] An "answer" is a response or explanation given to a question.

[0012] A "database" is a system that stores information systematically and allows it to be searched and referenced when needed.

[0013] "Memorizing" refers to the act of storing information so that it can be accessed later.

[0014] A "similar question" is an inquiry that has similar content or intent to a question that has been asked in the past.

[0015] "Monitoring means" refers to functions and devices that allow teachers to check the operation of the system and the information generated in real time.

[0016] "Supplemental information" is any further explanation or detail provided in addition to the basic answer.

[0017] A "system" is a set of devices and software that combines these means and functions to operate as a unified whole. [Brief explanation of the drawings]

[0018] [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 illustrating 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

[0019] 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.

[0020] First, the terms used in the following description will be explained.

[0021] 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).

[0022] 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.

[0023] 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.

[0024] 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.

[0025] 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."

[0026] [First embodiment]

[0027] FIG. 1 shows an example of the configuration of a data processing system 10 according to the first embodiment.

[0028] 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.

[0029] 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).

[0030] 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.

[0031] 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.

[0032] 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.

[0033] 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.

[0034] FIG. 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0035] 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.

[0036] 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.

[0037] 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.

[0038] 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."

[0039] MODE FOR CARRYING OUT THE INVENTION

[0040] This invention is a system that includes an AI chatbot to quickly and accurately respond to questions students have during class, reducing the burden on teachers and improving students' learning outcomes. The system mainly includes the following components: a terminal, a server, an AI chatbot, and a database.

[0041] Server Operation

[0042] The server receives question data sent from the device, forwards it to the AI ​​chatbot, generates answers, and sends the answers back to the device, recording all interactions in a database.

[0043] 1. User Authentication

[0044] The server receives the authentication information sent from the terminal and performs the authentication process.

[0045] For example, when a student logs in, the server checks the provided user ID and password against information in a database.

[0046] 2. Receiving questions

[0047] The server analyzes the question data from the terminal and temporarily stores the analysis results.

[0048] For example, if the question is "Please give me a specific example of a neutralization reaction between an acid and a base in a chemistry experiment," this is recorded as text data.

[0049] 3. Transferring questions to an AI chatbot

[0050] The server then organizes the received questions into an appropriate format for sending to the AI ​​chatbot.

[0051] Create an API request and submit your question to the AI ​​chatbot system.

[0052] 4. Receive answers from AI chatbots

[0053] The server receives the response from the AI ​​chatbot and prepares it to be sent to the device.

[0054] For example, format the answer "A specific example of a neutralization reaction is the reaction between hydrochloric acid (HCl) and sodium hydroxide (NaOH)."

[0055] 5. Sending the answer to the device

[0056] The server sends the generated answer to the user's terminal and simultaneously stores it in a database.

[0057] Device behavior

[0058] Terminals are the tools through which users (students and teachers) interface with the system. They are the primary devices through which students enter questions and receive answers.

[0059] 1. Enter your question

[0060] The user (student) enters a question in the text field and presses the submit button. The device collects this input and sends it to the server.

[0061] 2. Sending the question to the server

[0062] The terminal converts the entered question into a specific format (e.g., JSON) and sends it to the server via an HTTPS request.

[0063] 3. Receiving a response

[0064] The terminal receives the response sent from the server, analyzes it, and displays it on the screen.

[0065] User (Teacher) Role

[0066] Teachers can review the answers generated by the AI ​​chatbot and provide additional explanations or corrections as needed, thereby improving the accuracy of the answers given to students.

[0067] 1. Check and complete answers

[0068] Teachers can monitor the answers generated by the AI ​​chatbot in real time and provide additional information to fill in any gaps.

[0069] Specific examples

[0070] For example, suppose a student types the following question: "Tell me about the neutralization reaction of an acid and a base."

[0071] 1. Terminal

[0072] Students type their question into the text field and press the submit button.

[0073] 2. Server

[0074] The server receives the question and prepares it for forwarding to the AI ​​chatbot.

[0075] Send the question, "Question: Please tell me about the neutralization reaction of an acid and a base" to the AI ​​chatbot.

[0076] 3. AI Chatbots

[0077] The AI ​​chatbot analyzes the question and generates an appropriate answer:

[0078] "An example of a neutralization reaction is the reaction between hydrochloric acid (HCl) and sodium hydroxide (NaOH). The equation is HCl + NaOH → NaCl + H2O."

[0079] 4. Server

[0080] The generated response is received and sent to the terminal.

[0081] 5. Terminal

[0082] The terminal receives the response and displays it on the screen.

[0083] 6. Teacher

[0084] The teacher reviews the student's question and the AI ​​chatbot's response, providing additional explanations if necessary.

[0085] The system of the present invention allows students' questions to be answered quickly and accurately, reducing the burden on teachers and improving educational effectiveness.

[0086] The processing flow will be explained below.

[0087] Step 1:

[0088] The user (student) opens the chat interface on their device, enters a question in the text field, and clicks the "Send" button. For example, they might type, "Please tell me about the neutralization reaction of an acid and a base."

[0089] Step 2:

[0090] The terminal retrieves the entered question. The retrieved question is packaged in JSON format. The generated JSON data looks like this: {"question": "Please tell me about the neutralization reaction of an acid and a base", "userId": "student123"}

[0091] Step 3:

[0092] The device sends the packaged data to the server via an HTTPS request, using a secure communication channel.

[0093] Step 4:

[0094] The server receives the HTTPS request. It parses the received data using a JSON parser and extracts the question and user ID. For example, it extracts question and userId.

[0095] Step 5:

[0096] The server temporarily stores the retrieved question and the user ID. It then formats this data into request data to be sent to the AI ​​chatbot's API. The formatted API request is as follows: {"prompt": "Please tell me about the neutralization reaction of an acid and a base"}

[0097] Step 6:

[0098] The server sends the formatted request to the AI ​​chatbot, which sends an API request and waits for a response.

[0099] Step 7:

[0100] The AI ​​chatbot analyzes the question received from the server and uses natural language processing technology to generate the best answer to the question. The generated answer is: "A specific example of a neutralization reaction is the reaction between hydrochloric acid (HCl) and sodium hydroxide (NaOH). The reaction equation is HCl + NaOH → NaCl + H2O."

[0101] Step 8:

[0102] The AI ​​chatbot sends the generated answer to the server. The server receives this data and parses it again using a JSON parser. The received data is as follows: {"response": "A specific example of a neutralization reaction is the reaction between hydrochloric acid (HCl) and sodium hydroxide (NaOH). The reaction equation is HCl + NaOH → NaCl + H2O."}

[0103] Step 9:

[0104] The server generates an HTTP response to send the analyzed answer back to the user's (student's) device. The generated response is as follows: {"response": "A specific example of a neutralization reaction is the reaction between hydrochloric acid (HCl) and sodium hydroxide (NaOH). The reaction equation is HCl + NaOH → NaCl + H2O."}

[0105] Step 10:

[0106] The server sends the generated HTTP response to the user's device, and simultaneously stores this query and response in a database.

[0107] Step 11:

[0108] The device receives the HTTP response sent from the server, analyzes the received data with a JSON parser, and extracts the answer.

[0109] Step 12:

[0110] The device displays the analyzed answers on the chat interface so that the user (student) can check them. The student can read the answers displayed on the screen.

[0111] Step 13:

[0112] The user (teacher) monitors students' questions and the AI ​​chatbot's answers in real time during the lesson, and inputs supplementary information as needed to provide students with further explanations.

[0113] In this way, the system can respond quickly and accurately to questions from students, reducing the burden on teachers.

[0114] Example 1

[0115] 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."

[0116] In today's educational environment, it is difficult for teachers to respond quickly and accurately to all students' questions. In particular, when a large number of questions are received at once, the teacher's workload becomes excessive, leading to delayed responses and the provision of inaccurate answers. Furthermore, if students' questions cannot be resolved immediately, learning outcomes may decline. Furthermore, systems that do not effectively utilize past questions and answers often result in the same questions being asked repeatedly, reducing overall work efficiency. Furthermore, if teachers do not have a way to monitor the answers provided by generative AI models and provide supplementary information as needed, they lack confidence in the accuracy of the model's answers.

[0117] 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.

[0118] In this invention, the server includes: means for receiving questions entered by students through their terminals; means for transmitting the received questions to an AI model to generate answers; means for transmitting the generated answers to the students' terminals; means for storing the students' questions and the generated answers in data storage; means for receiving user authentication information and performing authentication; means for analyzing and temporarily storing the received questions; means for converting the received questions into a specific format and transmitting them to the AI ​​model; and means for formatting the answers received from the AI ​​model and transmitting them to the terminals. This allows for prompt and accurate responses to questions raised by students during class, reducing the burden on teachers and improving student learning outcomes. Furthermore, by utilizing past questions and answers, it is possible to efficiently respond to similar questions that repeatedly arise. Furthermore, teachers can monitor the answers provided by the generative AI model in real time and provide supplementary information as needed.

[0119] A "server" is a computer system that receives data sent from a terminal, processes it, and then sends the corresponding information to the terminal.

[0120] "Terminal" refers to a device that allows students and teachers to access and operate the system through an interface. Specifically, it includes a PC, tablet, or smartphone.

[0121] An "artificial intelligence model" is an algorithm or software that has the ability to understand natural language and generate answers to questions like a human. Specifically, this includes generative AI models and chatbots.

[0122] "Data storage" refers to a storage device or system for long-term storage of question and answer records and other necessary data. Specifically, this includes database systems.

[0123] "User authentication information" refers to information used to verify the authenticity of a user's access to a system, including, for example, a user ID and password.

[0124] "Receiving means" refers to a mechanism or process for acquiring data from the outside and processing it within the system.

[0125] "Analysis means" refers to a method or function for analyzing received data and extracting meaningful information.

[0126] "Temporary storage" is a memory or storage process used to hold data for a short period of time.

[0127] A "format conversion means" is a technique or method for converting data into a specific format so that it can be used by other systems or processes.

[0128] "Formatting" is the process of converting the generated data into a format that is easy to view for presentation to the user.

[0129] "Received questions" are doubts or inquiries that users (students) input via their terminals and that are received by the system.

[0130] A "generated answer" is a response that an artificial intelligence model generates based on a received question.

[0131] "Means for performing authentication" refers to the methods and techniques used to verify a user's authentication information and ensure that the user has legitimate access rights.

[0132] "Student" means a person who belongs to an educational institution and engages in learning activities.

[0133] "Teacher" means a person who teaches academic or technical subjects to students in an educational institution.

[0134] MODE FOR CARRYING OUT THE INVENTION

[0135] This invention is a system for quickly and accurately responding to questions students have during class. The purpose of this system is to reduce the burden on teachers and improve students' learning effectiveness. Specifically, the system consists of components such as terminals, servers, artificial intelligence models, and data storage.

[0136] Server Operation

[0137] The server receives question data from the device, transfers it to the AI ​​model to generate answers, and sends them to the device. It also records all interactions in data storage.

[0138] 1. User Authentication

[0139] The server receives the authentication information sent from the terminal and authenticates the user by checking it against a MySQL (registered trademark) database. Node.js is responsible for server-side processing.

[0140] 2. Receiving Questions

[0141] The server analyzes the question data sent from the terminal and temporarily stores it. This analysis process is performed using Python.

[0142] 3. Transferring questions to an AI chatbot

[0143] The server converts the received questions into a specific format and sends them to the artificial intelligence model, where a REST API is used to send the questions to the generative AI model (e.g., GPT-3 (registered trademark)).

[0144] 4. Receive answers from AI chatbots

[0145] The server receives the generated answers from the AI ​​chatbot and formats them using Flask (a Python microframework).

[0146] 5. Sending the answer to the device

[0147] The server sends the generated answer to the device and stores it in data storage (e.g., a MySQL database), using Socket.io for real-time communication.

[0148] Device behavior

[0149] The terminals provide the interface through which students and teachers interact with the system.

[0150] 1. Enter your question

[0151] The user (student) enters a question into the text field on the terminal and presses the send button. The terminal collects this data and sends it to the server.

[0152] 2. Sending the question to the server

[0153] The terminal converts the question data into JSON format and sends it to the server via an HTTPS request, using Axios (a JavaScript (registered trademark) library).

[0154] 3. Receiving and Displaying Responses

[0155] The device receives the response sent from the server and displays it on the screen. React.js is used for the front end.

[0156] The role of teachers

[0157] Teachers monitor the answers generated by the AI ​​chatbot in real time and provide supplementary information as needed.

[0158] 1. Check and complete answers

[0159] Teachers can view students' questions and the AI ​​chatbot's responses in real time through the system, and can enter supplemental information using Django (a Python web framework).

[0160] Specific examples

[0161] Here's a specific example: If a student types the question "Tell me about the neutralization reaction of an acid and a base":

[0162] 1. Terminal

[0163] The student types "Tell me about the neutralization reaction of an acid and a base" in the text field and presses the submit button.

[0164] 2. Server

[0165] The server receives the question, parses it, converts it into a specific format, and sends it to the generative AI model. For example, the prompt might look like this:

[0166] "Please tell me about the neutralization reaction of an acid and a base."

[0167] 3. AI Chatbots

[0168] The generative AI model receives the prompt, analyzes it, and generates an answer:

[0169] "An example of a neutralization reaction is the reaction between hydrochloric acid (HCl) and sodium hydroxide (NaOH). The equation is HCl + NaOH → NaCl + H2O."

[0170] 4. Server

[0171] The server receives this response, sends it to the terminal, and stores it in data storage.

[0172] 5. Terminal

[0173] The terminal displays the received response on the screen.

[0174] 6. Teacher

[0175] Teachers use the system to review answers and enter additional information if further clarification is needed.

[0176] In this way, the present invention can quickly and accurately respond to questions that arise during class, reducing the burden on teachers and improving students' learning effectiveness.

[0177] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0178] System processing flow

[0179] Step 1: User authentication

[0180] Specific operation: The user (student or teacher) enters the user ID and password into the terminal and presses the login button.

[0181] Input: User ID, Password

[0182] Processing: The device converts this information into JSON format and sends it to the server via an HTTPS request. The server receives the request and checks the data storage to verify the user information.

[0183] Output: Authentication token (if authentication is successful), Authentication failure message (if authentication is unsuccessful)

[0184] Technologies used: Node.js (server side), MySQL (database)

[0185] Step 2: Enter your question

[0186] Specific operation: When a user (student) has a question during class, they enter the question in the text field on their device and press the send button.

[0187] Input: Question

[0188] Processing: The terminal collects this input as text data.

[0189] Output: Text data (stored on the device)

[0190] Technologies used: HTML / CSS / JavaScript (front end)

[0191] Step 3: Sending the question to the server

[0192] Specific operation: The terminal converts the entered question into JSON format and sends it to the server via an HTTPS request.

[0193] Input: Text data (question content)

[0194] Processing: The terminal converts the text data into JSON format and sends it to the server.

[0195] Output: Question data in JSON format

[0196] Technology used: Axios (HTTP client)

[0197] Step 4: Receiving questions

[0198] Specific operation: The server receives the request, analyzes the question data, and temporarily stores it.

[0199] Input: Question data in JSON format

[0200] Processing: The server parses the received JSON data and temporarily saves it as text data.

[0201] Output: Parsed text data

[0202] Technology used: Python (data analysis)

[0203] Step 5: Transfer the question to an AI chatbot

[0204] Specific operation: The server organizes the received question into a specific format and sends it to the AI ​​chatbot as a REST API request.

[0205] Input: Parsed text data

[0206] Processing: The server converts the text data into prompt sentences and sends them to a generative AI model (such as GPT-3) using a REST API.

[0207] Output: prompt statement

[0208] Technology used: REST API

[0209] Step 6: Receive a response from the AI ​​chatbot

[0210] Specific operation: The server receives the answer from the AI ​​chatbot and formats it.

[0211] Input: prompt, AI chatbot response

[0212] Processing: The server parses the received response and formats it appropriately.

[0213] Output: Formatted response data

[0214] Technology used: Flask (a Python microframework)

[0215] Step 7: Sending the response to the device

[0216] Specific operation: The server sends the formatted response to the terminal and simultaneously stores it in data storage.

[0217] Input: Formatted response data

[0218] Processing: The server sends the answer to the terminal as an HTTPS response and stores it in the database.

[0219] Output: HTTPS response, saved to database

[0220] Technologies used: MySQL (database), Socket.io (real-time communication)

[0221] Step 8: Receive and view responses

[0222] Specific operation: The terminal receives the response from the server and displays it to the user.

[0223] Input: HTTPS response (answer data)

[0224] Processing: The device analyzes the received response data and displays it on the UI.

[0225] Output: On-screen display

[0226] Technology used: React.js (front end)

[0227] Step 9: Teacher review and completion

[0228] Specific operation: The teacher uses the device to monitor students' questions and the AI ​​chatbot's answers in real time, and enters supplementary information as needed.

[0229] Input: Student question, AI chatbot answer

[0230] Processing: The teacher reviews the answers and enters additional information if necessary.

[0231] Output:Add additional information

[0232] Technology used: Django (Python web framework)

[0233] The above is the specific processing flow of the program of the system based on the present invention, which enables the system to quickly and accurately answer students' questions.

[0234] (Application example 1)

[0235] 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."

[0236] In traditional education systems and brick-and-mortar stores, teachers and staff have to respond to numerous questions from students and customers in real time, placing a heavy burden on them. It's also difficult to provide quick and accurate answers to a large number of questions. At the same time, students and customers lack the means to quickly obtain detailed information about specific products or learning content. By solving these issues, it's necessary to reduce the burden on teachers and staff and improve student and customer satisfaction.

[0237] 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.

[0238] In this invention, the server includes a means for receiving questions entered by students through their terminals, a means for sending the received questions to an AI chatbot to generate answers, and a means for sending the generated answers to the students' terminals, thereby enabling immediate answers to questions about products in physical stores and learning content.

[0239] A "terminal" is a device through which a user inputs questions and receives answers.

[0240] An "artificial intelligence chatbot" is an artificial intelligence system that analyzes received questions and generates appropriate answers.

[0241] The "server" is a system that receives question data sent from a terminal, transfers it to an AI chatbot, and sends the generated answer to the terminal.

[0242] "Database" means a storage device for recording and managing student questions and generated answers.

[0243] "Brick and mortar store" refers to a retail store or educational facility located in a specific location that customers and students can physically visit.

[0244] Here, the embodiment for carrying out the present invention will be specifically described.

[0245] Overview of system configuration and operation

[0246] This system mainly consists of the following elements: terminals, servers, AI chatbots, and databases. These elements work together to provide quick and accurate answers to questions from students and customers. The detailed operation of each element is explained below.

[0247] Device behavior

[0248] The terminal is the main device where users (students or customers) enter questions and receive answers. Specifically, it works as follows:

[0249] 1. Question input: The user enters a question into the text field on the device and presses the send button. The device collects this input and sends it to the server.

[0250] 2. Sending the question to the server: The device converts the entered question into a specific format (e.g., JSON) and sends it to the server via an HTTPS request.

[0251] 3. Receiving the answer: The answer sent from the server is received and displayed on the screen.

[0252] Server Operation

[0253] The server receives question data sent from the device, forwards it to the AI ​​chatbot, generates answers, and sends the answers back to the device, recording all interactions in a database.

[0254] 1. User authentication: The server receives the authentication information sent from the terminal and performs the authentication process. For example, when a student logs in, the server checks the provided user ID and password against the information in the database.

[0255] 2. Question reception: The server analyzes the question data from the device and temporarily saves the analysis results. The question content is recorded as text data.

[0256] 3. Forwarding the question to the AI ​​chatbot: The server organizes the received question into a suitable format for sending to the AI ​​chatbot, creates an API request, and sends the question to the AI ​​chatbot's system.

[0257] 4. Receiving the answer from the AI ​​chatbot: The server receives the answer from the AI ​​chatbot and prepares to send it to the terminal.

[0258] 5. Sending the answer to the terminal: The generated answer is sent to the terminal and simultaneously saved in the database.

[0259] Artificial Intelligence Chatbot in Action

[0260] The AI ​​chatbot analyzes questions sent from the server and generates appropriate answers. It uses a generative AI model to understand the question and return relevant information as an answer.

[0261] Database Features

[0262] The database will record all student questions and the answers generated by the AI ​​chatbot, and will also manage user authentication information.

[0263] Hardware and software used

[0264] 1. Smartphone: The device where the user enters their question.

[0265] 2. Server: Receives question data, forwards it to the AI ​​chatbot, and generates and sends answers.

[0266] 3. Artificial Intelligence Chatbot: An artificial intelligence system that analyzes questions and generates answers.

[0267] 4. Database: Records and manages questions and answers.

[0268] 5. Libraries used: Software for making HTTP requests, such as the requests library.

[0269] Specific examples

[0270] For example, the following question and answer exchange takes place:

[0271] question

[0272] "Are there any recommended reference books related to the content of this textbook?"

[0273] answer

[0274] "One recommended reference book is 'Learn Chemistry from the Basics'. This will help you learn more deeply."

[0275] Example prompts for generative AI models

[0276] Q: Are there any recommended reference books related to the content of this textbook? A:

[0277] This allows for immediate answers to users' questions, reducing the burden on teachers and store staff and improving customer and student satisfaction.

[0278] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0279] Step 1:

[0280] The user enters a question into the text field on the device and presses the send button. The entered question is collected as text data.

[0281] Input: Question text from user

[0282] Data processing: collecting question text

[0283] Output: Collected question text

[0284] Step 2:

[0285] The terminal converts the collected question text into a specific format (e.g., JSON) and sends it to the server via an HTTPS request.

[0286] Input: Collected question text

[0287] Data processing: Convert question text into JSON format

[0288] Output: Question data in JSON format

[0289] Step 3:

[0290] The server receives the JSON-formatted question data sent from the device and first performs user authentication by checking the provided user ID and password against the information in the database.

[0291] Input: JSON format question data, user ID, password

[0292] Data processing: User ID and password verification

[0293] Output: User authentication success or failure status

[0294] Step 4:

[0295] If the authentication is successful, the server analyzes the question data and temporarily stores the analysis results.

[0296] Input: JSON format question data that was successfully authenticated

[0297] Data processing: Analysis of question text

[0298] Output: Analysis results

[0299] Step 5:

[0300] Based on the analysis results, the server organizes the question into a suitable format for forwarding to the AI ​​chatbot, and creates an API request to send the question to the AI ​​chatbot's system.

[0301] Input: Analysis results

[0302] Data processing: Creating API requests and forwarding questions

[0303] Output: Send request to AI chatbot

[0304] Step 6:

[0305] The artificial intelligence chatbot analyzes the questions it receives and generates appropriate answers using generative AI models.

[0306] Input: Question data sent through the API

[0307] Data processing: Question analysis and answer generation using generative AI models

[0308] Output: The generated answer

[0309] Step 7:

[0310] The server receives the answers generated by the AI ​​chatbot and prepares them to be sent to the device.

[0311] Input: Generated Answer

[0312] Data processing: Formatting of response data

[0313] Output: Formatted response data

[0314] Step 8:

[0315] The server sends the generated answers to the user's device and also stores all interactions in a database.

[0316] Input: Formatted response data

[0317] Data processing: Sending and saving response data

[0318] Output: Display of answers on the user's terminal and recording in the database

[0319] Step 9:

[0320] The terminal receives the response data sent from the server and displays it on the screen for the user.

[0321] Input: Response data sent from the server

[0322] Data processing: Analysis and display of response data

[0323] Output: Answer displayed on the screen

[0324] This series of processing steps allows for immediate answers to users' questions, reducing the burden on teachers and store staff and improving customer and student satisfaction.

[0325] 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.

[0326] MODE FOR CARRYING OUT THE INVENTION

[0327] This invention is a system that includes an AI chatbot to quickly and accurately respond to questions students have during class, reducing the burden on teachers and improving students' learning outcomes. Furthermore, by combining this system with an emotion engine that recognizes students' emotions, it provides more human-like responses and realizes more effective learning support.

[0328] Server Operation

[0329] The server receives question data sent from students' devices, analyzes emotions through an emotion engine, and forwards the data to an AI chatbot to generate answers. It also sends the generated answers to students' devices and records all interactions in a database.

[0330] 1. User Authentication

[0331] The server receives the authentication information sent from the terminal and performs the authentication process.

[0332] For example, when a student logs in, the server checks the provided user ID and password against information in a database.

[0333] 2. Receiving questions

[0334] The server analyzes the question data from the terminal and temporarily stores the analysis results.

[0335] For example, if the question is "Please give me a specific example of a neutralization reaction between an acid and a base in a chemistry experiment," this is recorded as text data.

[0336] 3. Question analysis using emotion engine

[0337] The server sends the received question data to the emotion recognition engine for emotion analysis.

[0338] The emotion engine analyzes the student's emotions (happiness, confusion, anger, etc.) from the question and returns the results to the server.

[0339] 4. Transferring questions to an AI chatbot

[0340] The server organizes the received questions and sentiment analysis results into an appropriate format for sending to the AI ​​chatbot.

[0341] Create an API request and submit your question to the AI ​​chatbot system.

[0342] 5. Receive answers from AI chatbots

[0343] The server receives the response from the AI ​​chatbot and prepares it to be sent to the device.

[0344] For example, format the answer "A specific example of a neutralization reaction is the reaction between hydrochloric acid (HCl) and sodium hydroxide (NaOH)."

[0345] 6. Sending the answer to the device

[0346] The server sends the generated answer to the user's terminal and simultaneously stores it in a database.

[0347] Device behavior

[0348] The terminal is a tool that users (students and teachers) use to interface with the system. It is the main device for students to enter questions and receive answers. Furthermore, it can adjust the tone of the answers based on the analysis results of the emotion engine.

[0349] 1. Enter your question

[0350] The user (student) enters a question in the text field and presses the submit button. The device collects this input and sends it to the server.

[0351] 2. Sending the question to the server

[0352] The terminal converts the entered question into a specific format (e.g., JSON) and sends it to the server via an HTTPS request.

[0353] 3. Receiving a response

[0354] The device receives the response sent from the server, analyzes it, and displays it on the screen. The response can be displayed in a tone that corresponds to the emotion.

[0355] User (Teacher) Role

[0356] Teachers can review the answers generated by the AI ​​chatbot and the results of the sentiment analysis, and provide additional explanations or corrections as needed, thereby improving the accuracy and appropriateness of the answers given to students.

[0357] 1. Check and complete answers

[0358] Teachers can monitor the answers generated by the AI ​​chatbot and the analysis results of the emotion engine in real time and input additional information to fill in any gaps.

[0359] Specific examples

[0360] For example, suppose a student types the following question: "Tell me about the neutralization reaction of an acid and a base."

[0361] 1. Terminal

[0362] Students type their question into the text field and press the submit button.

[0363] 2. Server

[0364] The server receives the question and transfers it to the emotion engine for emotion analysis. For example, it receives the emotion "confused" as the analysis result.

[0365] 3. AI Chatbots

[0366] Receive questions along with sentiment analysis results and generate appropriate answers:

[0367] "An example of a neutralization reaction is the reaction between hydrochloric acid (HCl) and sodium hydroxide (NaOH). The equation is HCl + NaOH → NaCl + H2O."

[0368] 4. Adjust the tone of your response

[0369] The tone of the generated response is adjusted based on the results of sentiment analysis, for example, a polite and gentle response to a confused student.

[0370] 5. Server

[0371] The generated response is received and sent to the terminal.

[0372] 6. Terminal

[0373] The terminal receives the response and displays it on the screen.

[0374] 7. Teacher

[0375] Teachers can review students' questions, the AI ​​chatbot's answers, and sentiment analysis results, and enter additional explanations if necessary.

[0376] The system of the present invention not only answers students' questions quickly and accurately, but also provides appropriate responses that reflect the students' emotions, thereby reducing the burden on teachers and further improving the effectiveness of education.

[0377] The processing flow will be explained below.

[0378] Step 1:

[0379] The user (student) opens the chat interface on their device, enters a question in the text field, and clicks the "Send" button. For example, they might type, "Please tell me about the neutralization reaction of an acid and a base."

[0380] Step 2:

[0381] The terminal retrieves the entered question. The retrieved question is packaged in JSON format. The generated JSON data looks like this: {"question": "Please tell me about the neutralization reaction of an acid and a base", "userId": "student123"}

[0382] Step 3:

[0383] The device sends the packaged data to the server via an HTTPS request, sending the data using secure communication.

[0384] Step 4:

[0385] The server receives the HTTPS request. It parses the received data using a JSON parser and extracts the question and user ID. For example, it extracts the question and user ID.

[0386] Step 5:

[0387] The server temporarily stores the retrieved question and user ID. It also sends the question data to the emotion engine for analysis. For example, send data to the emotion engine: {"text": "Please tell me about the neutralization reaction of acid and base"}

[0388] Step 6:

[0389] The emotion engine analyzes the data and identifies student emotions from questions, for example recognizing the emotion "confused."

[0390] Step 7:

[0391] The emotion engine sends the emotion analysis results back to the server. The server receives this and converts the question data and emotion analysis results into request data to be sent to the AI ​​chatbot. For example, {"prompt": "Please tell me about the neutralization reaction of acid and base", "emotion": "confused"}.

[0392] Step 8:

[0393] The server sends the formatted request to the AI ​​chatbot, which sends an API request and waits for a response.

[0394] Step 9:

[0395] The AI ​​chatbot generates an appropriate answer based on the question received from the server and the results of sentiment analysis. For example, it might create an answer like, "A specific example of a neutralization reaction is the reaction between hydrochloric acid (HCl) and sodium hydroxide (NaOH). The reaction equation is HCl + NaOH → NaCl + H2O."

[0396] Step 10:

[0397] The AI ​​chatbot generates an answer and sends it to the server, for example, {"response": "A specific example of a neutralization reaction is the reaction between hydrochloric acid (HCl) and sodium hydroxide (NaOH). The reaction equation is HCl + NaOH → NaCl + H2O.", "emotion": "confused"}.

[0398] Step 11:

[0399] The server receives the AI ​​chatbot's responses and the results of the sentiment analysis, and then formats the response data. Based on the sentiment analysis results, the server adjusts the tone of the response. For example, it responds to a confused student in a polite and gentle tone.

[0400] Step 12:

[0401] The server sends the formatted response data to the user's device, and simultaneously stores this exchange of queries and responses in a database.

[0402] Step 13:

[0403] The device receives the HTTP response sent from the server, analyzes the received data with a JSON parser, and extracts the answer.

[0404] Step 14:

[0405] The device displays the analyzed answers on the chat interface so that the user (student) can check them. The student can read the answers displayed on the screen.

[0406] Step 15:

[0407] The user (teacher) monitors students' questions, the AI ​​chatbot's responses, and the sentiment analysis results in real time during the lesson, and inputs supplementary information as needed to provide students with further explanations.

[0408] In this way, the system can respond quickly and accurately to questions from students, and its emotion engine can provide appropriate responses that take students' emotions into consideration. This can reduce the burden on teachers and improve learning effectiveness.

[0409] Example 2

[0410] 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."

[0411] Educational institutions are required to respond quickly and appropriately to questions that students have during class or self-study. However, conventional systems place a heavy burden on teachers, and responses to students' questions can be delayed or insufficient. Furthermore, responses that do not take into account students' feelings can lead to a loss of motivation to learn or misunderstandings. The present invention aims to solve these problems and improve students' learning outcomes.

[0412] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[0413] In this invention, the server includes means for receiving questions entered by students through their terminals, means for transmitting the received questions to an emotion analysis engine to analyze emotions, means for transmitting the questions including the emotion analysis results to an AI chatbot to generate answers, means for adjusting the tone of the generated answers based on the emotion analysis results, means for transmitting the tone-adjusted answers to the students' terminals, and means for storing the students' questions and the generated answers in a database. This makes it possible to respond quickly and appropriately to students' questions and provide appropriate responses according to the students' emotions.

[0414] "Student" means a learner enrolled in an educational institution.

[0415] "Device" means a computer or mobile device used by a student or educator.

[0416] A "question" is an inquiry that a student types into a terminal to seek clarification.

[0417] An "emotion analysis engine" is software or hardware that analyzes student emotions contained in questions.

[0418] An "artificial intelligence chatbot" is an artificial intelligence system that automatically generates answers to questions it receives.

[0419] "Tone adjustment" refers to the process of appropriately changing the wording and tone of a response based on the results of sentiment analysis.

[0420] A "server" is a central computer that handles data processing and communications for the entire system.

[0421] "Database" refers to a storage device or system for storing student questions and generated answers.

[0422] "Educator" means a person whose job is to provide education to students.

[0423] "Supplemental information" refers to additional information provided by educators to enhance students' understanding.

[0424] This invention is a system for quickly and accurately responding to questions that students have during class or self-study. The system is mainly composed of a server, terminals, a sentiment analysis engine, an AI chatbot, and a database.

[0425] A terminal is a computer or mobile device used by a student or teacher to enter questions and display answers. Students submit their questions by typing them into a text field on the terminal and pressing a submit button. The terminal also converts the questions into a format (e.g., JSON) and sends them to the server via an HTTPS request.

[0426] The server receives the question data sent from the device and temporarily stores it. The received question is sent to an emotion analysis engine, where emotion analysis is performed. This emotion analysis engine analyzes the student's emotion contained in the question (e.g., confusion, anger, joy, etc.) and returns the results to the server.

[0427] The server organizes the analyzed emotions and questions into an appropriate format and creates an API request to send to the AI ​​chatbot. The AI ​​chatbot generates an answer to the received question. It remembers past questions and answers and can quickly provide answers to similar questions. For example, in response to the question, "Please tell me about the neutralization reaction of an acid and a base," it generates the answer, "A specific example of a neutralization reaction is the reaction between hydrochloric acid (HCl) and sodium hydroxide (NaOH). The equation is HCl + NaOH → NaCl + H2O."

[0428] The server then receives the generated answer and adjusts the tone of the answer based on the emotion analysis results. For example, if the emotion analyzed is "confused," the answer will be adjusted to a more polite and gentle tone. The adjusted tone is then sent to the student's device and displayed on the screen.

[0429] Educators can monitor students' questions and the AI ​​chatbot's responses in real time through the system and provide supplementary information as needed. If an educator feels that additional explanation is needed, they can enter supplementary information at any time.

[0430] Examples of prompt statements

[0431] "Please tell me about the neutralization reaction of an acid and a base."

[0432] "Please give a brief history of ancient Rome."

[0433] "Tell me how to train a dog"

[0434] This allows for quick and appropriate responses to students' questions, and provides appropriate responses based on students' emotions, improving educational effectiveness. Furthermore, by reducing the burden on educators, it becomes possible to provide high-quality education to more students.

[0435] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0436] Step 1:

[0437] User Authentication

[0438] The server receives the user ID and password sent from the terminal.

[0439] Checks the received credentials against existing data in the database.

[0440] If authentication is successful, a session ID is issued to the student and a receipt is returned to the terminal.

[0441] Input: User ID, Password

[0442] Output: Session ID (if successful)

[0443] Step 2:

[0444] Enter a question

[0445] The terminal accepts questions that students enter into a text field.

[0446] For example, a student types, "Tell me about the neutralization reaction of an acid and a base."

[0447] Input: Question text entered by the student

[0448] Output: Question text (as is)

[0449] Step 3:

[0450] Submit a question

[0451] The terminal converts the entered question into JSON format and sends it to the server via an HTTPS request.

[0452] The transmitted data includes the question content and session ID.

[0453] Input: Question text, Session ID

[0454] Output: Question data in JSON format

[0455] Step 4:

[0456] Receiving questions

[0457] The server receives the question data sent from the terminal and temporarily stores it.

[0458] Input: JSON data sent from the terminal

[0459] Output: Temporarily saved question data

[0460] Step 5:

[0461] Emotion analysis

[0462] The server sends the received question data to the sentiment analysis engine.

[0463] The emotion analysis engine analyzes the student's emotion contained in the question and returns the result to the server.

[0464] For example, the emotion of "confusion" is analyzed.

[0465] Input: Question data

[0466] Output: Emotion analysis result (e.g. confused)

[0467] Step 6:

[0468] Transfer to AI chatbot

[0469] The server sends the question data including the emotion analysis results to the artificial intelligence chatbot.

[0470] Create an API request and organize the question and sentiment analysis results.

[0471] Input: Sentiment analysis results, question data

[0472] Output: API request

[0473] Step 7:

[0474] Receive answers from AI chatbots

[0475] The server receives the response from the artificial intelligence chatbot.

[0476] For example, the answer generated is, "A specific example of a neutralization reaction is the reaction between hydrochloric acid (HCl) and sodium hydroxide (NaOH). The reaction equation is HCl + NaOH → NaCl + H2O."

[0477] Input: API request

[0478] Output: Generated answer text

[0479] Step 8:

[0480] Adjusting the tone of your response

[0481] The server adjusts the tone of the response based on the sentiment analysis results.

[0482] For example, respond to the emotion of "confusion" with a more polite and gentle tone.

[0483] Input: Generated answer text, sentiment analysis results

[0484] Output: Tone-adjusted answer text

[0485] Step 9:

[0486] Sending answers to the device

[0487] The server sends the tone-adjusted response to the student's terminal.

[0488] At the same time, all interactions are stored in a database.

[0489] Input: Tone-adjusted answer text

[0490] Output: Submitted answers, saved data

[0491] Step 10:

[0492] Show Answers

[0493] The terminal receives the response sent from the server and displays it on the screen.

[0494] For example, it will say, "A specific example of a neutralization reaction is the reaction between hydrochloric acid (HCl) and sodium hydroxide (NaOH). The equation is HCl + NaOH → NaCl + H2O."

[0495] Input: Answer text

[0496] Output: On-screen display

[0497] Step 11:

[0498] Supplementary explanation input (teacher's role)

[0499] Teachers monitor students' questions and the AI ​​chatbot's answers in real time.

[0500] If necessary, supplementary information is input from the terminal.

[0501] Input: Teacher supplementary information

[0502] Output: Complemented answer

[0503] (Application example 2)

[0504] 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."

[0505] In traditional educational systems, responding to students' questions during class can be a significant burden on teachers, making it difficult to provide individual support. Furthermore, when students don't understand something or are confused, it's difficult to respond appropriately while taking their feelings into consideration. This can lead to a decline in students' learning effectiveness. Furthermore, in today's educational environment, where prompt and accurate responses are required, there is a lack of appropriate systems.

[0506] The identification process by the identification processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes means for receiving questions entered by students through their terminals, means for transmitting the received questions to an emotion recognition engine for emotion analysis, means for transmitting the analysis results to an AI chatbot to generate answers, means for transmitting the generated answers to the students' terminals and adjusting the tone of the answers based on the emotion analysis results, and means for storing the students' questions and the generated answers in a database. This makes it possible to not only quickly and accurately answer students' questions but also to provide appropriate responses based on the students' emotions. It also reduces the burden on teachers while further improving educational effectiveness.

[0507] "Student" means a person who uses the education system to learn.

[0508] A "terminal" is a device through which a user can input questions and receive answers. Examples include smartphones, tablets, and personal computers.

[0509] A "question" is information that a student inputs via their device. This includes questions about the learning content or requests for clarification.

[0510] The "emotion recognition engine" is a system that analyzes the emotions expressed in students' questions. For example, it has the ability to determine emotions such as joy, confusion, and anger.

[0511] "Sentiment analysis" is the process of analyzing the emotions contained in students' questions. This is done through an emotion recognition engine.

[0512] An "artificial intelligence chatbot" is a program that automatically generates answers to input questions.

[0513] "Answer" refers to the information generated by the AI ​​chatbot in response to a student's question.

[0514] "Tone" refers to the expression style of the generated answer. It is adjusted to a polite or gentle tone based on the results of sentiment analysis.

[0515] "Database" refers to an information store that stores student questions and generated answers.

[0516] "Teachers" are those who are administrators of the education system and whose role is to provide supplementary information when necessary.

[0517] System Configuration

[0518] This invention is a system that includes an AI chatbot to quickly and accurately respond to questions students have during class. The system is primarily composed of student devices, a server, and a teacher interface. It also has advanced functionality that combines an emotion recognition engine with an AI chatbot.

[0519] Hardware and software used

[0520] Hardware: Smartphone, tablet or personal computer (student device), server

[0521] Software: Python, HTTP request library (requests), AI chatbot API, emotion recognition engine API, database

[0522] Details of data processing and data calculation

[0523] 1. User authentication:

[0524] The server receives the user ID and password sent from the student's device and performs the authentication process, during which the device sends the authentication information to the server using an HTTPS request.

[0525] 2. Question reception and sentiment analysis:

[0526] When a student inputs a question through the device, the device sends the question to the server. The server receives the question and sends it to the emotion recognition engine for emotion analysis. The emotion recognition engine then analyzes the student's emotion (e.g., confusion, joy, anger, etc.) from the question and returns the result to the server.

[0527] 3. Answer generation by AI chatbots:

[0528] The server sends the received question and the results of the sentiment analysis to an AI chatbot, which uses that information to generate an appropriate answer.

[0529] 4. Tone and send your response:

[0530] The server adjusts the tone of the response received from the AI ​​chatbot based on the results of emotion analysis. For example, it selects a polite and gentle response for a confused student. The adjusted response is then sent to the device.

[0531] 5. Data storage:

[0532] The server stores the student's question and the generated answer in a database, allowing for quick responses to similar questions in the future.

[0533] Specific examples of operation procedures

[0534] Example prompt sentence:

[0535] User ID: user123

[0536] Password: password456

[0537] Question: Can you give me a concrete example of an acid-base neutralization reaction in a chemistry experiment?

[0538] For example, if a student enters a question using the prompt sentence described above, the server performs authentication and receives the question. If the server then determines using an emotion recognition engine that the student is confused, it sends that information to an AI chatbot, which generates an answer as a concrete example of a neutralization reaction: "There is a reaction between hydrochloric acid (HCl) and sodium hydroxide (NaOH). The equation is HCl + NaOH → NaCl + H2O." This answer is adjusted to a gentle tone to match the student's confused emotion and is displayed on the student's device. This series of interactions is also stored in a database.

[0539] As a result, this invention not only answers students' questions quickly and accurately, but also provides appropriate responses according to their emotions, thereby reducing the burden on teachers and improving students' learning effectiveness.

[0540] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0541] Step 1:

[0542] The server receives the user ID and password sent from the user's (student's) device. Based on the received authentication information, it compares it with the information in the database and executes the authentication process. If authentication is successful, it generates an authentication token and returns it to the device. The input is the user ID and password, and the output is the authentication token.

[0543] Step 2:

[0544] The terminal obtains the question entered by the student from the text field and sends it to the server along with the authentication token. The input is the student's question text and the authentication token, and the output is an HTTP request to the server.

[0545] Step 3:

[0546] The server receives questions sent from the device. The questions are temporarily saved and sent to the emotion recognition engine for emotion analysis. The emotion recognition engine analyzes the student's emotion from the question content and returns the results to the server. The input is the student's question text, and the output is the emotion analysis result.

[0547] Step 4:

[0548] The server sends the sentiment analysis results and question text to an AI chatbot, which then generates an appropriate answer based on that information. The input is the question text and sentiment analysis results, and the output is the generated answer.

[0549] Step 5:

[0550] The server receives the generated response and adjusts the tone of the response based on the sentiment analysis results. For example, it adjusts the tone of the response to a more gentle one for a confused student. The input is the generated response and the sentiment analysis results, and the output is the tone-adjusted response.

[0551] Step 6:

[0552] The server sends the tone-adjusted answer to the terminal and stores the student's question and the generated answer in a database. The input is the tone-adjusted answer, and the output is sending it to the terminal and storing it in the database.

[0553] Step 7:

[0554] The terminal receives the tone-adjusted response sent from the server and displays it on the screen. The input is the tone-adjusted response, and the output is the screen display.

[0555] 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.

[0556] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search engine).<URL: https: / / openai.com / blog / chatgpt> ), Gemini (registered trademark) (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0557] 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.

[0558] [Second embodiment]

[0559] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0560] 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.

[0561] 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).

[0562] 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.

[0563] 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.

[0564] 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).

[0565] 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.

[0566] 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.

[0567] 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.

[0568] 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.

[0569] 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.

[0570] 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."

[0571] MODE FOR CARRYING OUT THE INVENTION

[0572] This invention is a system that includes an AI chatbot to quickly and accurately respond to questions students have during class, reducing the burden on teachers and improving students' learning outcomes. The system mainly includes the following components: a terminal, a server, an AI chatbot, and a database.

[0573] Server Operation

[0574] The server receives question data sent from the device, forwards it to the AI ​​chatbot, generates answers, and sends the answers back to the device, recording all interactions in a database.

[0575] 1. User Authentication

[0576] The server receives the authentication information sent from the terminal and performs the authentication process.

[0577] For example, when a student logs in, the server checks the provided user ID and password against information in a database.

[0578] 2. Receiving questions

[0579] The server analyzes the question data from the terminal and temporarily stores the analysis results.

[0580] For example, if the question is "Please give me a specific example of a neutralization reaction between an acid and a base in a chemistry experiment," this is recorded as text data.

[0581] 3. Transferring questions to an AI chatbot

[0582] The server then organizes the received questions into an appropriate format for sending to the AI ​​chatbot.

[0583] Create an API request and submit your question to the AI ​​chatbot system.

[0584] 4. Receive answers from AI chatbots

[0585] The server receives the response from the AI ​​chatbot and prepares it to be sent to the device.

[0586] For example, format the answer "A specific example of a neutralization reaction is the reaction between hydrochloric acid (HCl) and sodium hydroxide (NaOH)."

[0587] 5. Sending the answer to the device

[0588] The server sends the generated answer to the user's terminal and simultaneously stores it in a database.

[0589] Device behavior

[0590] Terminals are the tools through which users (students and teachers) interface with the system. They are the primary devices through which students enter questions and receive answers.

[0591] 1. Enter your question

[0592] The user (student) enters a question in the text field and presses the submit button. The device collects this input and sends it to the server.

[0593] 2. Sending the question to the server

[0594] The terminal converts the entered question into a specific format (e.g., JSON) and sends it to the server via an HTTPS request.

[0595] 3. Receiving a response

[0596] The terminal receives the response sent from the server, analyzes it, and displays it on the screen.

[0597] User (Teacher) Role

[0598] Teachers can review the answers generated by the AI ​​chatbot and provide additional explanations or corrections as needed, thereby improving the accuracy of the answers given to students.

[0599] 1. Check and complete answers

[0600] Teachers can monitor the answers generated by the AI ​​chatbot in real time and provide additional information to fill in any gaps.

[0601] Specific examples

[0602] For example, suppose a student types the following question: "Tell me about the neutralization reaction of an acid and a base."

[0603] 1. Terminal

[0604] Students type their question into the text field and press the submit button.

[0605] 2. Server

[0606] The server receives the question and prepares it for forwarding to the AI ​​chatbot.

[0607] Send the question, "Question: Please tell me about the neutralization reaction of an acid and a base" to the AI ​​chatbot.

[0608] 3. AI Chatbots

[0609] The AI ​​chatbot analyzes the question and generates an appropriate answer:

[0610] "An example of a neutralization reaction is the reaction between hydrochloric acid (HCl) and sodium hydroxide (NaOH). The equation is HCl + NaOH → NaCl + H2O."

[0611] 4. Server

[0612] The generated response is received and sent to the terminal.

[0613] 5. Terminal

[0614] The terminal receives the response and displays it on the screen.

[0615] 6. Teacher

[0616] The teacher reviews the student's question and the AI ​​chatbot's response, providing additional explanations if necessary.

[0617] The system of the present invention allows students' questions to be answered quickly and accurately, reducing the burden on teachers and improving educational effectiveness.

[0618] The processing flow will be explained below.

[0619] Step 1:

[0620] The user (student) opens the chat interface on their device, enters a question in the text field, and clicks the "Send" button. For example, they might type, "Please tell me about the neutralization reaction of an acid and a base."

[0621] Step 2:

[0622] The terminal retrieves the entered question. The retrieved question is packaged in JSON format. The generated JSON data looks like this: {"question": "Please tell me about the neutralization reaction of an acid and a base", "userId": "student123"}

[0623] Step 3:

[0624] The device sends the packaged data to the server via an HTTPS request, using a secure communication channel.

[0625] Step 4:

[0626] The server receives the HTTPS request. It parses the received data using a JSON parser and extracts the question and user ID. For example, it extracts question and userId.

[0627] Step 5:

[0628] The server temporarily stores the retrieved question and the user ID. It then formats this data into request data to be sent to the AI ​​chatbot's API. The formatted API request is as follows: {"prompt": "Please tell me about the neutralization reaction of an acid and a base"}

[0629] Step 6:

[0630] The server sends the formatted request to the AI ​​chatbot, which sends an API request and waits for a response.

[0631] Step 7:

[0632] The AI ​​chatbot analyzes the question received from the server and uses natural language processing technology to generate the best answer to the question. The generated answer is: "A specific example of a neutralization reaction is the reaction between hydrochloric acid (HCl) and sodium hydroxide (NaOH). The reaction equation is HCl + NaOH → NaCl + H2O."

[0633] Step 8:

[0634] The AI ​​chatbot sends the generated answer to the server. The server receives this data and parses it again using a JSON parser. The received data is as follows: {"response": "A specific example of a neutralization reaction is the reaction between hydrochloric acid (HCl) and sodium hydroxide (NaOH). The reaction equation is HCl + NaOH → NaCl + H2O."}

[0635] Step 9:

[0636] The server generates an HTTP response to send the analyzed answer back to the user's (student's) device. The generated response is as follows: {"response": "A specific example of a neutralization reaction is the reaction between hydrochloric acid (HCl) and sodium hydroxide (NaOH). The reaction equation is HCl + NaOH → NaCl + H2O."}

[0637] Step 10:

[0638] The server sends the generated HTTP response to the user's device, and simultaneously stores this query and response in a database.

[0639] Step 11:

[0640] The device receives the HTTP response sent from the server, analyzes the received data with a JSON parser, and extracts the answer.

[0641] Step 12:

[0642] The device displays the analyzed answers on the chat interface so that the user (student) can check them. The student can read the answers displayed on the screen.

[0643] Step 13:

[0644] The user (teacher) monitors students' questions and the AI ​​chatbot's answers in real time during the lesson, and inputs supplementary information as needed to provide students with further explanations.

[0645] In this way, the system can respond quickly and accurately to questions from students, reducing the burden on teachers.

[0646] Example 1

[0647] 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."

[0648] In today's educational environment, it is difficult for teachers to respond quickly and accurately to all students' questions. In particular, when a large number of questions are received at once, the teacher's workload becomes excessive, leading to delayed responses and the provision of inaccurate answers. Furthermore, if students' questions cannot be resolved immediately, learning outcomes may decline. Furthermore, systems that do not effectively utilize past questions and answers often result in the same questions being asked repeatedly, reducing overall work efficiency. Furthermore, if teachers do not have a way to monitor the answers provided by generative AI models and provide supplementary information as needed, they lack confidence in the accuracy of the model's answers.

[0649] 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.

[0650] In this invention, the server includes: means for receiving questions entered by students through their terminals; means for transmitting the received questions to an AI model to generate answers; means for transmitting the generated answers to the students' terminals; means for storing the students' questions and the generated answers in data storage; means for receiving user authentication information and performing authentication; means for analyzing and temporarily storing the received questions; means for converting the received questions into a specific format and transmitting them to the AI ​​model; and means for formatting the answers received from the AI ​​model and transmitting them to the terminals. This allows for prompt and accurate responses to questions raised by students during class, reducing the burden on teachers and improving student learning outcomes. Furthermore, by utilizing past questions and answers, it is possible to efficiently respond to similar questions that repeatedly arise. Furthermore, teachers can monitor the answers provided by the generative AI model in real time and provide supplementary information as needed.

[0651] A "server" is a computer system that receives data sent from a terminal, processes it, and then sends the corresponding information to the terminal.

[0652] "Terminal" refers to a device that allows students and teachers to access and operate the system through an interface. Specifically, it includes a PC, tablet, or smartphone.

[0653] An "artificial intelligence model" is an algorithm or software that has the ability to understand natural language and generate answers to questions like a human. Specifically, this includes generative AI models and chatbots.

[0654] "Data storage" refers to a storage device or system for long-term storage of question and answer records and other necessary data. Specifically, this includes database systems.

[0655] "User authentication information" refers to information used to verify the authenticity of a user's access to a system, including, for example, a user ID and password.

[0656] "Receiving means" refers to a mechanism or process for acquiring data from the outside and processing it within the system.

[0657] "Analysis means" refers to a method or function for analyzing received data and extracting meaningful information.

[0658] "Temporary storage" is a memory or storage process used to hold data for a short period of time.

[0659] A "format conversion means" is a technique or method for converting data into a specific format so that it can be used by other systems or processes.

[0660] "Formatting" is the process of converting the generated data into a format that is easy to view for presentation to the user.

[0661] "Received questions" are doubts or inquiries that users (students) input via their terminals and that are received by the system.

[0662] A "generated answer" is a response that an artificial intelligence model generates based on a received question.

[0663] "Means for performing authentication" refers to the methods and techniques used to verify a user's authentication information and ensure that the user has legitimate access rights.

[0664] "Student" means a person who belongs to an educational institution and engages in learning activities.

[0665] "Teacher" means a person who teaches academic or technical subjects to students in an educational institution.

[0666] MODE FOR CARRYING OUT THE INVENTION

[0667] This invention is a system for quickly and accurately responding to questions students have during class. The purpose of this system is to reduce the burden on teachers and improve students' learning effectiveness. Specifically, the system consists of components such as terminals, servers, artificial intelligence models, and data storage.

[0668] Server Operation

[0669] The server receives question data from the device, transfers it to the AI ​​model to generate answers, and sends them to the device. It also records all interactions in data storage.

[0670] 1. User Authentication

[0671] The server receives the authentication information sent from the terminal and authenticates the user by checking it against the MySQL database. Node.js is responsible for server-side processing.

[0672] 2. Receiving Questions

[0673] The server analyzes the question data sent from the terminal and temporarily stores it. This analysis process is performed using Python.

[0674] 3. Transferring questions to an AI chatbot

[0675] The server converts the received questions into a specific format and sends them to the artificial intelligence model, where a REST API is used to send the questions to the generative AI model (e.g., GPT-3).

[0676] 4. Receive answers from AI chatbots

[0677] The server receives the generated answers from the AI ​​chatbot and formats them using Flask (a Python microframework).

[0678] 5. Sending the answer to the device

[0679] The server sends the generated answer to the device and stores it in data storage (e.g., a MySQL database), using Socket.io for real-time communication.

[0680] Device behavior

[0681] The terminals provide the interface through which students and teachers interact with the system.

[0682] 1. Enter your question

[0683] The user (student) enters a question into the text field on the terminal and presses the send button. The terminal collects this data and sends it to the server.

[0684] 2. Sending the question to the server

[0685] The terminal converts the question data into JSON format and sends it to the server via an HTTPS request. Here, Axios (a JavaScript library) is used.

[0686] 3. Receiving and Displaying Responses

[0687] The device receives the response sent from the server and displays it on the screen. React.js is used for the front end.

[0688] The role of teachers

[0689] Teachers monitor the answers generated by the AI ​​chatbot in real time and provide supplementary information as needed.

[0690] 1. Check and complete answers

[0691] Teachers can view students' questions and the AI ​​chatbot's responses in real time through the system, and can enter supplemental information using Django (a Python web framework).

[0692] Specific examples

[0693] Here's a specific example: If a student types the question "Tell me about the neutralization reaction of an acid and a base":

[0694] 1. Terminal

[0695] The student types "Tell me about the neutralization reaction of an acid and a base" in the text field and presses the submit button.

[0696] 2. Server

[0697] The server receives the question, parses it, converts it into a specific format, and sends it to the generative AI model. For example, the prompt might look like this:

[0698] "Please tell me about the neutralization reaction of an acid and a base."

[0699] 3. AI Chatbots

[0700] The generative AI model receives the prompt, analyzes it, and generates an answer:

[0701] "An example of a neutralization reaction is the reaction between hydrochloric acid (HCl) and sodium hydroxide (NaOH). The equation is HCl + NaOH → NaCl + H2O."

[0702] 4. Server

[0703] The server receives this response, sends it to the terminal, and stores it in data storage.

[0704] 5. Terminal

[0705] The terminal displays the received response on the screen.

[0706] 6. Teacher

[0707] Teachers use the system to review answers and enter additional information if further clarification is needed.

[0708] In this way, the present invention can quickly and accurately respond to questions that arise during class, reducing the burden on teachers and improving students' learning effectiveness.

[0709] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0710] System processing flow

[0711] Step 1: User authentication

[0712] Specific operation: The user (student or teacher) enters the user ID and password into the terminal and presses the login button.

[0713] Input: User ID, Password

[0714] Processing: The device converts this information into JSON format and sends it to the server via an HTTPS request. The server receives the request and checks the data storage to verify the user information.

[0715] Output: Authentication token (if authentication is successful), Authentication failure message (if authentication is unsuccessful)

[0716] Technologies used: Node.js (server side), MySQL (database)

[0717] Step 2: Enter your question

[0718] Specific operation: When a user (student) has a question during class, they enter the question in the text field on their device and press the send button.

[0719] Input: Question

[0720] Processing: The terminal collects this input as text data.

[0721] Output: Text data (stored on the device)

[0722] Technologies used: HTML / CSS / JavaScript (front end)

[0723] Step 3: Sending the question to the server

[0724] Specific operation: The terminal converts the entered question into JSON format and sends it to the server via an HTTPS request.

[0725] Input: Text data (question content)

[0726] Processing: The terminal converts the text data into JSON format and sends it to the server.

[0727] Output: Question data in JSON format

[0728] Technology used: Axios (HTTP client)

[0729] Step 4: Receiving questions

[0730] Specific operation: The server receives the request, analyzes the question data, and temporarily stores it.

[0731] Input: Question data in JSON format

[0732] Processing: The server parses the received JSON data and temporarily saves it as text data.

[0733] Output: Parsed text data

[0734] Technology used: Python (data analysis)

[0735] Step 5: Transfer the question to an AI chatbot

[0736] Specific operation: The server organizes the received question into a specific format and sends it to the AI ​​chatbot as a REST API request.

[0737] Input: Parsed text data

[0738] Processing: The server converts the text data into prompt sentences and sends them to a generative AI model (such as GPT-3) using a REST API.

[0739] Output: prompt statement

[0740] Technology used: REST API

[0741] Step 6: Receive a response from the AI ​​chatbot

[0742] Specific operation: The server receives the answer from the AI ​​chatbot and formats it.

[0743] Input: prompt, AI chatbot response

[0744] Processing: The server parses the received response and formats it appropriately.

[0745] Output: Formatted response data

[0746] Technology used: Flask (a Python microframework)

[0747] Step 7: Sending the response to the device

[0748] Specific operation: The server sends the formatted response to the terminal and simultaneously stores it in data storage.

[0749] Input: Formatted response data

[0750] Processing: The server sends the answer to the terminal as an HTTPS response and stores it in the database.

[0751] Output: HTTPS response, saved to database

[0752] Technologies used: MySQL (database), Socket.io (real-time communication)

[0753] Step 8: Receive and view responses

[0754] Specific operation: The terminal receives the response from the server and displays it to the user.

[0755] Input: HTTPS response (answer data)

[0756] Processing: The device analyzes the received response data and displays it on the UI.

[0757] Output: On-screen display

[0758] Technology used: React.js (front end)

[0759] Step 9: Teacher review and completion

[0760] Specific operation: The teacher uses the device to monitor students' questions and the AI ​​chatbot's answers in real time, and enters supplementary information as needed.

[0761] Input: Student question, AI chatbot answer

[0762] Processing: The teacher reviews the answers and enters additional information if necessary.

[0763] Output:Add additional information

[0764] Technology used: Django (Python web framework)

[0765] The above is the specific processing flow of the program of the system based on the present invention, which enables the system to quickly and accurately answer students' questions.

[0766] (Application example 1)

[0767] 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."

[0768] In traditional education systems and brick-and-mortar stores, teachers and staff have to respond to numerous questions from students and customers in real time, placing a heavy burden on them. It's also difficult to provide quick and accurate answers to a large number of questions. At the same time, students and customers lack the means to quickly obtain detailed information about specific products or learning content. By solving these issues, it's necessary to reduce the burden on teachers and staff and improve student and customer satisfaction.

[0769] 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.

[0770] In this invention, the server includes a means for receiving questions entered by students through their terminals, a means for sending the received questions to an AI chatbot to generate answers, and a means for sending the generated answers to the students' terminals, thereby enabling immediate answers to questions about products in physical stores and learning content.

[0771] A "terminal" is a device through which a user inputs questions and receives answers.

[0772] An "artificial intelligence chatbot" is an artificial intelligence system that analyzes received questions and generates appropriate answers.

[0773] The "server" is a system that receives question data sent from a terminal, transfers it to an AI chatbot, and sends the generated answer to the terminal.

[0774] "Database" means a storage device for recording and managing student questions and generated answers.

[0775] "Brick and mortar store" refers to a retail store or educational facility located in a specific location that customers and students can physically visit.

[0776] Here, the embodiment for carrying out the present invention will be specifically described.

[0777] Overview of system configuration and operation

[0778] This system mainly consists of the following elements: terminals, servers, AI chatbots, and databases. These elements work together to provide quick and accurate answers to questions from students and customers. The detailed operation of each element is explained below.

[0779] Device behavior

[0780] The terminal is the main device where users (students or customers) enter questions and receive answers. Specifically, it works as follows:

[0781] 1. Question input: The user enters a question into the text field on the device and presses the send button. The device collects this input and sends it to the server.

[0782] 2. Sending the question to the server: The device converts the entered question into a specific format (e.g., JSON) and sends it to the server via an HTTPS request.

[0783] 3. Receiving the answer: The answer sent from the server is received and displayed on the screen.

[0784] Server Operation

[0785] The server receives question data sent from the device, forwards it to the AI ​​chatbot, generates answers, and sends the answers back to the device, recording all interactions in a database.

[0786] 1. User authentication: The server receives the authentication information sent from the terminal and performs the authentication process. For example, when a student logs in, the server checks the provided user ID and password against the information in the database.

[0787] 2. Question reception: The server analyzes the question data from the device and temporarily saves the analysis results. The question content is recorded as text data.

[0788] 3. Forwarding the question to the AI ​​chatbot: The server organizes the received question into a suitable format for sending to the AI ​​chatbot, creates an API request, and sends the question to the AI ​​chatbot's system.

[0789] 4. Receiving the answer from the AI ​​chatbot: The server receives the answer from the AI ​​chatbot and prepares to send it to the terminal.

[0790] 5. Sending the answer to the terminal: The generated answer is sent to the terminal and simultaneously saved in the database.

[0791] Artificial Intelligence Chatbot in Action

[0792] The AI ​​chatbot analyzes questions sent from the server and generates appropriate answers. It uses a generative AI model to understand the question and return relevant information as an answer.

[0793] Database Features

[0794] The database will record all student questions and the answers generated by the AI ​​chatbot, and will also manage user authentication information.

[0795] Hardware and software used

[0796] 1. Smartphone: The device where the user enters their question.

[0797] 2. Server: Receives question data, forwards it to the AI ​​chatbot, and generates and sends answers.

[0798] 3. Artificial Intelligence Chatbot: An artificial intelligence system that analyzes questions and generates answers.

[0799] 4. Database: Records and manages questions and answers.

[0800] 5. Libraries used: Software for making HTTP requests, such as the requests library.

[0801] Specific examples

[0802] For example, the following question and answer exchange takes place:

[0803] question

[0804] "Are there any recommended reference books related to the content of this textbook?"

[0805] answer

[0806] "One recommended reference book is 'Learn Chemistry from the Basics'. This will help you learn more deeply."

[0807] Example prompts for generative AI models

[0808] Q: Are there any recommended reference books related to the content of this textbook? A:

[0809] This allows for immediate answers to users' questions, reducing the burden on teachers and store staff and improving customer and student satisfaction.

[0810] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0811] Step 1:

[0812] The user enters a question into the text field on the device and presses the send button. The entered question is collected as text data.

[0813] Input: Question text from user

[0814] Data processing: collecting question text

[0815] Output: Collected question text

[0816] Step 2:

[0817] The terminal converts the collected question text into a specific format (e.g., JSON) and sends it to the server via an HTTPS request.

[0818] Input: Collected question text

[0819] Data processing: Convert question text into JSON format

[0820] Output: Question data in JSON format

[0821] Step 3:

[0822] The server receives the JSON-formatted question data sent from the device and first performs user authentication by checking the provided user ID and password against the information in the database.

[0823] Input: JSON format question data, user ID, password

[0824] Data processing: User ID and password verification

[0825] Output: User authentication success or failure status

[0826] Step 4:

[0827] If the authentication is successful, the server analyzes the question data and temporarily stores the analysis results.

[0828] Input: JSON format question data that was successfully authenticated

[0829] Data processing: Analysis of question text

[0830] Output: Analysis results

[0831] Step 5:

[0832] Based on the analysis results, the server organizes the question into a suitable format for forwarding to the AI ​​chatbot, and creates an API request to send the question to the AI ​​chatbot's system.

[0833] Input: Analysis results

[0834] Data processing: Creating API requests and forwarding questions

[0835] Output: Send request to AI chatbot

[0836] Step 6:

[0837] The artificial intelligence chatbot analyzes the questions it receives and generates appropriate answers using generative AI models.

[0838] Input: Question data sent through the API

[0839] Data processing: Question analysis and answer generation using generative AI models

[0840] Output: The generated answer

[0841] Step 7:

[0842] The server receives the answers generated by the AI ​​chatbot and prepares them to be sent to the device.

[0843] Input: Generated Answer

[0844] Data processing: Formatting of response data

[0845] Output: Formatted response data

[0846] Step 8:

[0847] The server sends the generated answers to the user's device and also stores all interactions in a database.

[0848] Input: Formatted response data

[0849] Data processing: Sending and saving response data

[0850] Output: Display of answers on the user's terminal and recording in the database

[0851] Step 9:

[0852] The terminal receives the response data sent from the server and displays it on the screen for the user.

[0853] Input: Response data sent from the server

[0854] Data processing: Analysis and display of response data

[0855] Output: Answer displayed on the screen

[0856] This series of processing steps allows for immediate answers to users' questions, reducing the burden on teachers and store staff and improving customer and student satisfaction.

[0857] 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.

[0858] MODE FOR CARRYING OUT THE INVENTION

[0859] This invention is a system that includes an AI chatbot to quickly and accurately respond to questions students have during class, reducing the burden on teachers and improving students' learning outcomes. Furthermore, by combining this system with an emotion engine that recognizes students' emotions, it provides more human-like responses and realizes more effective learning support.

[0860] Server Operation

[0861] The server receives question data sent from students' devices, analyzes emotions through an emotion engine, and forwards the data to an AI chatbot to generate answers. It also sends the generated answers to students' devices and records all interactions in a database.

[0862] 1. User Authentication

[0863] The server receives the authentication information sent from the terminal and performs the authentication process.

[0864] For example, when a student logs in, the server checks the provided user ID and password against information in a database.

[0865] 2. Receiving questions

[0866] The server analyzes the question data from the terminal and temporarily stores the analysis results.

[0867] For example, if the question is "Please give me a specific example of a neutralization reaction between an acid and a base in a chemistry experiment," this is recorded as text data.

[0868] 3. Question analysis using emotion engine

[0869] The server sends the received question data to the emotion recognition engine for emotion analysis.

[0870] The emotion engine analyzes the student's emotions (happiness, confusion, anger, etc.) from the question and returns the results to the server.

[0871] 4. Transferring questions to an AI chatbot

[0872] The server organizes the received questions and sentiment analysis results into an appropriate format for sending to the AI ​​chatbot.

[0873] Create an API request and submit your question to the AI ​​chatbot system.

[0874] 5. Receive answers from AI chatbots

[0875] The server receives the response from the AI ​​chatbot and prepares it to be sent to the device.

[0876] For example, format the answer "A specific example of a neutralization reaction is the reaction between hydrochloric acid (HCl) and sodium hydroxide (NaOH)."

[0877] 6. Sending the answer to the device

[0878] The server sends the generated answer to the user's terminal and simultaneously stores it in a database.

[0879] Device behavior

[0880] The terminal is a tool that users (students and teachers) use to interface with the system. It is the main device for students to enter questions and receive answers. Furthermore, it can adjust the tone of the answers based on the analysis results of the emotion engine.

[0881] 1. Enter your question

[0882] The user (student) enters a question in the text field and presses the submit button. The device collects this input and sends it to the server.

[0883] 2. Sending the question to the server

[0884] The terminal converts the entered question into a specific format (e.g., JSON) and sends it to the server via an HTTPS request.

[0885] 3. Receiving a response

[0886] The device receives the response sent from the server, analyzes it, and displays it on the screen. The response can be displayed in a tone that corresponds to the emotion.

[0887] User (Teacher) Role

[0888] Teachers can review the answers generated by the AI ​​chatbot and the results of the sentiment analysis, and provide additional explanations or corrections as needed, thereby improving the accuracy and appropriateness of the answers given to students.

[0889] 1. Check and complete answers

[0890] Teachers can monitor the answers generated by the AI ​​chatbot and the analysis results of the emotion engine in real time and input additional information to fill in any gaps.

[0891] Specific examples

[0892] For example, suppose a student types the following question: "Tell me about the neutralization reaction of an acid and a base."

[0893] 1. Terminal

[0894] Students type their question into the text field and press the submit button.

[0895] 2. Server

[0896] The server receives the question and transfers it to the emotion engine for emotion analysis. For example, it receives the emotion "confused" as the analysis result.

[0897] 3. AI Chatbots

[0898] Receive questions along with sentiment analysis results and generate appropriate answers:

[0899] "An example of a neutralization reaction is the reaction between hydrochloric acid (HCl) and sodium hydroxide (NaOH). The equation is HCl + NaOH → NaCl + H2O."

[0900] 4. Adjust the tone of your response

[0901] The tone of the generated response is adjusted based on the results of sentiment analysis, for example, a polite and gentle response to a confused student.

[0902] 5. Server

[0903] The generated response is received and sent to the terminal.

[0904] 6. Terminal

[0905] The terminal receives the response and displays it on the screen.

[0906] 7. Teacher

[0907] Teachers can review students' questions, the AI ​​chatbot's answers, and sentiment analysis results, and enter additional explanations if necessary.

[0908] The system of the present invention not only answers students' questions quickly and accurately, but also provides appropriate responses that reflect the students' emotions, thereby reducing the burden on teachers and further improving the effectiveness of education.

[0909] The processing flow will be explained below.

[0910] Step 1:

[0911] The user (student) opens the chat interface on their device, enters a question in the text field, and clicks the "Send" button. For example, they might type, "Please tell me about the neutralization reaction of an acid and a base."

[0912] Step 2:

[0913] The terminal retrieves the entered question. The retrieved question is packaged in JSON format. The generated JSON data looks like this: {"question": "Please tell me about the neutralization reaction of an acid and a base", "userId": "student123"}

[0914] Step 3:

[0915] The device sends the packaged data to the server via an HTTPS request, sending the data using secure communication.

[0916] Step 4:

[0917] The server receives the HTTPS request. It parses the received data using a JSON parser and extracts the question and user ID. For example, it extracts the question and user ID.

[0918] Step 5:

[0919] The server temporarily stores the retrieved question and user ID. It also sends the question data to the emotion engine for analysis. For example, send data to the emotion engine: {"text": "Please tell me about the neutralization reaction of acid and base"}

[0920] Step 6:

[0921] The emotion engine analyzes the data and identifies student emotions from questions, for example recognizing the emotion "confused."

[0922] Step 7:

[0923] The emotion engine sends the emotion analysis results back to the server. The server receives this and converts the question data and emotion analysis results into request data to be sent to the AI ​​chatbot. For example, {"prompt": "Please tell me about the neutralization reaction of acid and base", "emotion": "confused"}.

[0924] Step 8:

[0925] The server sends the formatted request to the AI ​​chatbot, which sends an API request and waits for a response.

[0926] Step 9:

[0927] The AI ​​chatbot generates an appropriate answer based on the question received from the server and the results of sentiment analysis. For example, it might create an answer like, "A specific example of a neutralization reaction is the reaction between hydrochloric acid (HCl) and sodium hydroxide (NaOH). The reaction equation is HCl + NaOH → NaCl + H2O."

[0928] Step 10:

[0929] The AI ​​chatbot generates an answer and sends it to the server, for example, {"response": "A specific example of a neutralization reaction is the reaction between hydrochloric acid (HCl) and sodium hydroxide (NaOH). The reaction equation is HCl + NaOH → NaCl + H2O.", "emotion": "confused"}.

[0930] Step 11:

[0931] The server receives the AI ​​chatbot's responses and the results of the sentiment analysis, and then formats the response data. Based on the sentiment analysis results, the server adjusts the tone of the response. For example, it responds to a confused student in a polite and gentle tone.

[0932] Step 12:

[0933] The server sends the formatted response data to the user's device, and simultaneously stores this exchange of queries and responses in a database.

[0934] Step 13:

[0935] The device receives the HTTP response sent from the server, analyzes the received data with a JSON parser, and extracts the answer.

[0936] Step 14:

[0937] The device displays the analyzed answers on the chat interface so that the user (student) can check them. The student can read the answers displayed on the screen.

[0938] Step 15:

[0939] The user (teacher) monitors students' questions, the AI ​​chatbot's responses, and the sentiment analysis results in real time during the lesson, and inputs supplementary information as needed to provide students with further explanations.

[0940] In this way, the system can respond quickly and accurately to questions from students, and its emotion engine can provide appropriate responses that take students' emotions into consideration. This can reduce the burden on teachers and improve learning effectiveness.

[0941] Example 2

[0942] 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."

[0943] Educational institutions are required to respond quickly and appropriately to questions that students have during class or self-study. However, conventional systems place a heavy burden on teachers, and responses to students' questions can be delayed or insufficient. Furthermore, responses that do not take into account students' feelings can lead to a loss of motivation to learn or misunderstandings. The present invention aims to solve these problems and improve students' learning outcomes.

[0944] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[0945] In this invention, the server includes means for receiving questions entered by students through their terminals, means for transmitting the received questions to an emotion analysis engine to analyze emotions, means for transmitting the questions including the emotion analysis results to an AI chatbot to generate answers, means for adjusting the tone of the generated answers based on the emotion analysis results, means for transmitting the tone-adjusted answers to the students' terminals, and means for storing the students' questions and the generated answers in a database. This makes it possible to respond quickly and appropriately to students' questions and provide appropriate responses according to the students' emotions.

[0946] "Student" means a learner enrolled in an educational institution.

[0947] "Device" means a computer or mobile device used by a student or educator.

[0948] A "question" is an inquiry that a student types into a terminal to seek clarification.

[0949] An "emotion analysis engine" is software or hardware that analyzes student emotions contained in questions.

[0950] An "artificial intelligence chatbot" is an artificial intelligence system that automatically generates answers to questions it receives.

[0951] "Tone adjustment" refers to the process of appropriately changing the wording and tone of a response based on the results of sentiment analysis.

[0952] A "server" is a central computer that handles data processing and communications for the entire system.

[0953] "Database" refers to a storage device or system for storing student questions and generated answers.

[0954] "Educator" means a person whose job is to provide education to students.

[0955] "Supplemental information" refers to additional information provided by educators to enhance students' understanding.

[0956] This invention is a system for quickly and accurately responding to questions that students have during class or self-study. The system is mainly composed of a server, terminals, a sentiment analysis engine, an AI chatbot, and a database.

[0957] A terminal is a computer or mobile device used by a student or teacher to enter questions and display answers. Students submit their questions by typing them into a text field on the terminal and pressing a submit button. The terminal also converts the questions into a format (e.g., JSON) and sends them to the server via an HTTPS request.

[0958] The server receives the question data sent from the device and temporarily stores it. The received question is sent to an emotion analysis engine, where emotion analysis is performed. This emotion analysis engine analyzes the student's emotion contained in the question (e.g., confusion, anger, joy, etc.) and returns the results to the server.

[0959] The server organizes the analyzed emotions and questions into an appropriate format and creates an API request to send to the AI ​​chatbot. The AI ​​chatbot generates an answer to the received question. It remembers past questions and answers and can quickly provide answers to similar questions. For example, in response to the question, "Please tell me about the neutralization reaction of an acid and a base," it generates the answer, "A specific example of a neutralization reaction is the reaction between hydrochloric acid (HCl) and sodium hydroxide (NaOH). The equation is HCl + NaOH → NaCl + H2O."

[0960] The server then receives the generated answer and adjusts the tone of the answer based on the emotion analysis results. For example, if the emotion analyzed is "confused," the answer will be adjusted to a more polite and gentle tone. The adjusted tone is then sent to the student's device and displayed on the screen.

[0961] Educators can monitor students' questions and the AI ​​chatbot's responses in real time through the system and provide supplementary information as needed. If an educator feels that additional explanation is needed, they can enter supplementary information at any time.

[0962] Examples of prompt statements

[0963] "Please tell me about the neutralization reaction of an acid and a base."

[0964] "Please give a brief history of ancient Rome."

[0965] "Tell me how to train a dog"

[0966] This allows for quick and appropriate responses to students' questions, and provides appropriate responses based on students' emotions, improving educational effectiveness. Furthermore, by reducing the burden on educators, it becomes possible to provide high-quality education to more students.

[0967] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0968] Step 1:

[0969] User Authentication

[0970] The server receives the user ID and password sent from the terminal.

[0971] Checks the received credentials against existing data in the database.

[0972] If authentication is successful, a session ID is issued to the student and a receipt is returned to the terminal.

[0973] Input: User ID, Password

[0974] Output: Session ID (if successful)

[0975] Step 2:

[0976] Enter a question

[0977] The terminal accepts questions that students enter into a text field.

[0978] For example, a student types, "Tell me about the neutralization reaction of an acid and a base."

[0979] Input: Question text entered by the student

[0980] Output: Question text (as is)

[0981] Step 3:

[0982] Submit a question

[0983] The terminal converts the entered question into JSON format and sends it to the server via an HTTPS request.

[0984] The transmitted data includes the question content and session ID.

[0985] Input: Question text, Session ID

[0986] Output: Question data in JSON format

[0987] Step 4:

[0988] Receiving questions

[0989] The server receives the question data sent from the terminal and temporarily stores it.

[0990] Input: JSON data sent from the terminal

[0991] Output: Temporarily saved question data

[0992] Step 5:

[0993] Emotion analysis

[0994] The server sends the received question data to the sentiment analysis engine.

[0995] The emotion analysis engine analyzes the student's emotion contained in the question and returns the result to the server.

[0996] For example, the emotion of "confusion" is analyzed.

[0997] Input: Question data

[0998] Output: Emotion analysis result (e.g. confused)

[0999] Step 6:

[1000] Transfer to AI chatbot

[1001] The server sends the question data including the emotion analysis results to the artificial intelligence chatbot.

[1002] Create an API request and organize the question and sentiment analysis results.

[1003] Input: Sentiment analysis results, question data

[1004] Output: API request

[1005] Step 7:

[1006] Receive answers from AI chatbots

[1007] The server receives the response from the artificial intelligence chatbot.

[1008] For example, the answer generated is, "A specific example of a neutralization reaction is the reaction between hydrochloric acid (HCl) and sodium hydroxide (NaOH). The reaction equation is HCl + NaOH → NaCl + H2O."

[1009] Input: API request

[1010] Output: Generated answer text

[1011] Step 8:

[1012] Adjusting the tone of your response

[1013] The server adjusts the tone of the response based on the sentiment analysis results.

[1014] For example, respond to the emotion of "confusion" with a more polite and gentle tone.

[1015] Input: Generated answer text, sentiment analysis results

[1016] Output: Tone-adjusted answer text

[1017] Step 9:

[1018] Sending answers to the device

[1019] The server sends the tone-adjusted response to the student's terminal.

[1020] At the same time, all interactions are stored in a database.

[1021] Input: Tone-adjusted answer text

[1022] Output: Submitted answers, saved data

[1023] Step 10:

[1024] Show Answers

[1025] The terminal receives the response sent from the server and displays it on the screen.

[1026] For example, it will say, "A specific example of a neutralization reaction is the reaction between hydrochloric acid (HCl) and sodium hydroxide (NaOH). The equation is HCl + NaOH → NaCl + H2O."

[1027] Input: Answer text

[1028] Output: On-screen display

[1029] Step 11:

[1030] Supplementary explanation input (teacher's role)

[1031] Teachers monitor students' questions and the AI ​​chatbot's answers in real time.

[1032] If necessary, supplementary information is input from the terminal.

[1033] Input: Teacher supplementary information

[1034] Output: Complemented answer

[1035] (Application example 2)

[1036] 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."

[1037] In traditional educational systems, responding to students' questions during class can be a significant burden on teachers, making it difficult to provide individual support. Furthermore, when students don't understand something or are confused, it's difficult to respond appropriately while taking their feelings into consideration. This can lead to a decline in students' learning effectiveness. Furthermore, in today's educational environment, where prompt and accurate responses are required, there is a lack of appropriate systems.

[1038] The identification process by the identification processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes means for receiving questions entered by students through their terminals, means for transmitting the received questions to an emotion recognition engine for emotion analysis, means for transmitting the analysis results to an AI chatbot to generate answers, means for transmitting the generated answers to the students' terminals and adjusting the tone of the answers based on the emotion analysis results, and means for storing the students' questions and the generated answers in a database. This makes it possible to not only quickly and accurately answer students' questions but also to provide appropriate responses based on the students' emotions. It also reduces the burden on teachers while further improving educational effectiveness.

[1039] "Student" means a person who uses the education system to learn.

[1040] A "terminal" is a device through which a user can input questions and receive answers. Examples include smartphones, tablets, and personal computers.

[1041] A "question" is information that a student inputs via their device. This includes questions about the learning content or requests for clarification.

[1042] The "emotion recognition engine" is a system that analyzes the emotions expressed in students' questions. For example, it has the ability to determine emotions such as joy, confusion, and anger.

[1043] "Sentiment analysis" is the process of analyzing the emotions contained in students' questions. This is done through an emotion recognition engine.

[1044] An "artificial intelligence chatbot" is a program that automatically generates answers to input questions.

[1045] "Answer" refers to the information generated by the AI ​​chatbot in response to a student's question.

[1046] "Tone" refers to the expression style of the generated answer. It is adjusted to a polite or gentle tone based on the results of sentiment analysis.

[1047] "Database" refers to an information store that stores student questions and generated answers.

[1048] "Teachers" are those who are administrators of the education system and whose role is to provide supplementary information when necessary.

[1049] System Configuration

[1050] This invention is a system that includes an AI chatbot to quickly and accurately respond to questions students have during class. The system is primarily composed of student devices, a server, and a teacher interface. It also has advanced functionality that combines an emotion recognition engine with an AI chatbot.

[1051] Hardware and software used

[1052] Hardware: Smartphone, tablet or personal computer (student device), server

[1053] Software: Python, HTTP request library (requests), AI chatbot API, emotion recognition engine API, database

[1054] Details of data processing and data calculation

[1055] 1. User authentication:

[1056] The server receives the user ID and password sent from the student's device and performs the authentication process, during which the device sends the authentication information to the server using an HTTPS request.

[1057] 2. Question reception and sentiment analysis:

[1058] When a student inputs a question through the device, the device sends the question to the server. The server receives the question and sends it to the emotion recognition engine for emotion analysis. The emotion recognition engine then analyzes the student's emotion (e.g., confusion, joy, anger, etc.) from the question and returns the result to the server.

[1059] 3. Answer generation by AI chatbots:

[1060] The server sends the received question and the results of the sentiment analysis to an AI chatbot, which uses that information to generate an appropriate answer.

[1061] 4. Tone and send your response:

[1062] The server adjusts the tone of the response received from the AI ​​chatbot based on the results of emotion analysis. For example, it selects a polite and gentle response for a confused student. The adjusted response is then sent to the device.

[1063] 5. Data storage:

[1064] The server stores the student's question and the generated answer in a database, allowing for quick responses to similar questions in the future.

[1065] Specific examples of operation procedures

[1066] Example prompt sentence:

[1067] User ID: user123

[1068] Password: password456

[1069] Question: Can you give me a concrete example of an acid-base neutralization reaction in a chemistry experiment?

[1070] For example, if a student enters a question using the prompt sentence described above, the server performs authentication and receives the question. If the server then determines using an emotion recognition engine that the student is confused, it sends that information to an AI chatbot, which generates an answer as a concrete example of a neutralization reaction: "There is a reaction between hydrochloric acid (HCl) and sodium hydroxide (NaOH). The equation is HCl + NaOH → NaCl + H2O." This answer is adjusted to a gentle tone to match the student's confused emotion and is displayed on the student's device. This series of interactions is also stored in a database.

[1071] As a result, this invention not only answers students' questions quickly and accurately, but also provides appropriate responses according to their emotions, thereby reducing the burden on teachers and improving students' learning effectiveness.

[1072] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1073] Step 1:

[1074] The server receives the user ID and password sent from the user's (student's) device. Based on the received authentication information, it compares it with the information in the database and executes the authentication process. If authentication is successful, it generates an authentication token and returns it to the device. The input is the user ID and password, and the output is the authentication token.

[1075] Step 2:

[1076] The terminal obtains the question entered by the student from the text field and sends it to the server along with the authentication token. The input is the student's question text and the authentication token, and the output is an HTTP request to the server.

[1077] Step 3:

[1078] The server receives questions sent from the device. The questions are temporarily saved and sent to the emotion recognition engine for emotion analysis. The emotion recognition engine analyzes the student's emotion from the question content and returns the results to the server. The input is the student's question text, and the output is the emotion analysis result.

[1079] Step 4:

[1080] The server sends the sentiment analysis results and question text to an AI chatbot, which then generates an appropriate answer based on that information. The input is the question text and sentiment analysis results, and the output is the generated answer.

[1081] Step 5:

[1082] The server receives the generated response and adjusts the tone of the response based on the sentiment analysis results. For example, it adjusts the tone of the response to a more gentle one for a confused student. The input is the generated response and the sentiment analysis results, and the output is the tone-adjusted response.

[1083] Step 6:

[1084] The server sends the tone-adjusted answer to the terminal and stores the student's question and the generated answer in a database. The input is the tone-adjusted answer, and the output is sending it to the terminal and storing it in the database.

[1085] Step 7:

[1086] The terminal receives the tone-adjusted response sent from the server and displays it on the screen. The input is the tone-adjusted response, and the output is the screen display.

[1087] 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.

[1088] 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.

[1089] 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.

[1090] [Third embodiment]

[1091] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[1092] 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.

[1093] 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).

[1094] 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.

[1095] 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.

[1096] 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).

[1097] 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.

[1098] 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.

[1099] 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.

[1100] 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.

[1101] 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.

[1102] 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."

[1103] MODE FOR CARRYING OUT THE INVENTION

[1104] This invention is a system that includes an AI chatbot to quickly and accurately respond to questions students have during class, reducing the burden on teachers and improving students' learning outcomes. The system mainly includes the following components: a terminal, a server, an AI chatbot, and a database.

[1105] Server Operation

[1106] The server receives question data sent from the device, forwards it to the AI ​​chatbot, generates answers, and sends the answers back to the device, recording all interactions in a database.

[1107] 1. User Authentication

[1108] The server receives the authentication information sent from the terminal and performs the authentication process.

[1109] For example, when a student logs in, the server checks the provided user ID and password against information in a database.

[1110] 2. Receiving questions

[1111] The server analyzes the question data from the terminal and temporarily stores the analysis results.

[1112] For example, if the question is "Please give me a specific example of a neutralization reaction between an acid and a base in a chemistry experiment," this is recorded as text data.

[1113] 3. Transferring questions to an AI chatbot

[1114] The server then organizes the received questions into an appropriate format for sending to the AI ​​chatbot.

[1115] Create an API request and submit your question to the AI ​​chatbot system.

[1116] 4. Receive answers from AI chatbots

[1117] The server receives the response from the AI ​​chatbot and prepares it to be sent to the device.

[1118] For example, format the answer "A specific example of a neutralization reaction is the reaction between hydrochloric acid (HCl) and sodium hydroxide (NaOH)."

[1119] 5. Sending the answer to the device

[1120] The server sends the generated answer to the user's terminal and simultaneously stores it in a database.

[1121] Device behavior

[1122] Terminals are the tools through which users (students and teachers) interface with the system. They are the primary devices through which students enter questions and receive answers.

[1123] 1. Enter your question

[1124] The user (student) enters a question in the text field and presses the submit button. The device collects this input and sends it to the server.

[1125] 2. Sending the question to the server

[1126] The terminal converts the entered question into a specific format (e.g., JSON) and sends it to the server via an HTTPS request.

[1127] 3. Receiving a response

[1128] The terminal receives the response sent from the server, analyzes it, and displays it on the screen.

[1129] User (Teacher) Role

[1130] Teachers can review the answers generated by the AI ​​chatbot and provide additional explanations or corrections as needed, thereby improving the accuracy of the answers given to students.

[1131] 1. Check and complete answers

[1132] Teachers can monitor the answers generated by the AI ​​chatbot in real time and provide additional information to fill in any gaps.

[1133] Specific examples

[1134] For example, suppose a student types the following question: "Tell me about the neutralization reaction of an acid and a base."

[1135] 1. Terminal

[1136] Students type their question into the text field and press the submit button.

[1137] 2. Server

[1138] The server receives the question and prepares it for forwarding to the AI ​​chatbot.

[1139] Send the question, "Question: Please tell me about the neutralization reaction of an acid and a base" to the AI ​​chatbot.

[1140] 3. AI Chatbots

[1141] The AI ​​chatbot analyzes the question and generates an appropriate answer:

[1142] "An example of a neutralization reaction is the reaction between hydrochloric acid (HCl) and sodium hydroxide (NaOH). The equation is HCl + NaOH → NaCl + H2O."

[1143] 4. Server

[1144] The generated response is received and sent to the terminal.

[1145] 5. Terminal

[1146] The terminal receives the response and displays it on the screen.

[1147] 6. Teacher

[1148] The teacher reviews the student's question and the AI ​​chatbot's response, providing additional explanations if necessary.

[1149] The system of the present invention allows students' questions to be answered quickly and accurately, reducing the burden on teachers and improving educational effectiveness.

[1150] The processing flow will be explained below.

[1151] Step 1:

[1152] The user (student) opens the chat interface on their device, enters a question in the text field, and clicks the "Send" button. For example, they might type, "Please tell me about the neutralization reaction of an acid and a base."

[1153] Step 2:

[1154] The terminal retrieves the entered question. The retrieved question is packaged in JSON format. The generated JSON data looks like this: {"question": "Please tell me about the neutralization reaction of an acid and a base", "userId": "student123"}

[1155] Step 3:

[1156] The device sends the packaged data to the server via an HTTPS request, using a secure communication channel.

[1157] Step 4:

[1158] The server receives the HTTPS request. It parses the received data using a JSON parser and extracts the question and user ID. For example, it extracts question and userId.

[1159] Step 5:

[1160] The server temporarily stores the retrieved question and the user ID. It then formats this data into request data to be sent to the AI ​​chatbot's API. The formatted API request is as follows: {"prompt": "Please tell me about the neutralization reaction of an acid and a base"}

[1161] Step 6:

[1162] The server sends the formatted request to the AI ​​chatbot, which sends an API request and waits for a response.

[1163] Step 7:

[1164] The AI ​​chatbot analyzes the question received from the server and uses natural language processing technology to generate the best answer to the question. The generated answer is: "A specific example of a neutralization reaction is the reaction between hydrochloric acid (HCl) and sodium hydroxide (NaOH). The reaction equation is HCl + NaOH → NaCl + H2O."

[1165] Step 8:

[1166] The AI ​​chatbot sends the generated answer to the server. The server receives this data and parses it again using a JSON parser. The received data is as follows: {"response": "A specific example of a neutralization reaction is the reaction between hydrochloric acid (HCl) and sodium hydroxide (NaOH). The reaction equation is HCl + NaOH → NaCl + H2O."}

[1167] Step 9:

[1168] The server generates an HTTP response to send the analyzed answer back to the user's (student's) device. The generated response is as follows: {"response": "A specific example of a neutralization reaction is the reaction between hydrochloric acid (HCl) and sodium hydroxide (NaOH). The reaction equation is HCl + NaOH → NaCl + H2O."}

[1169] Step 10:

[1170] The server sends the generated HTTP response to the user's device, and simultaneously stores this query and response in a database.

[1171] Step 11:

[1172] The device receives the HTTP response sent from the server, analyzes the received data with a JSON parser, and extracts the answer.

[1173] Step 12:

[1174] The device displays the analyzed answers on the chat interface so that the user (student) can check them. The student can read the answers displayed on the screen.

[1175] Step 13:

[1176] The user (teacher) monitors students' questions and the AI ​​chatbot's answers in real time during the lesson, and inputs supplementary information as needed to provide students with further explanations.

[1177] In this way, the system can respond quickly and accurately to questions from students, reducing the burden on teachers.

[1178] Example 1

[1179] 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."

[1180] In today's educational environment, it is difficult for teachers to respond quickly and accurately to all students' questions. In particular, when a large number of questions are received at once, the teacher's workload becomes excessive, leading to delayed responses and the provision of inaccurate answers. Furthermore, if students' questions cannot be resolved immediately, learning outcomes may decline. Furthermore, systems that do not effectively utilize past questions and answers often result in the same questions being asked repeatedly, reducing overall work efficiency. Furthermore, if teachers do not have a way to monitor the answers provided by generative AI models and provide supplementary information as needed, they lack confidence in the accuracy of the model's answers.

[1181] 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.

[1182] In this invention, the server includes: means for receiving questions entered by students through their terminals; means for transmitting the received questions to an AI model to generate answers; means for transmitting the generated answers to the students' terminals; means for storing the students' questions and the generated answers in data storage; means for receiving user authentication information and performing authentication; means for analyzing and temporarily storing the received questions; means for converting the received questions into a specific format and transmitting them to the AI ​​model; and means for formatting the answers received from the AI ​​model and transmitting them to the terminals. This allows for prompt and accurate responses to questions raised by students during class, reducing the burden on teachers and improving student learning outcomes. Furthermore, by utilizing past questions and answers, it is possible to efficiently respond to similar questions that repeatedly arise. Furthermore, teachers can monitor the answers provided by the generative AI model in real time and provide supplementary information as needed.

[1183] A "server" is a computer system that receives data sent from a terminal, processes it, and then sends the corresponding information to the terminal.

[1184] "Terminal" refers to a device that allows students and teachers to access and operate the system through an interface. Specifically, it includes a PC, tablet, or smartphone.

[1185] An "artificial intelligence model" is an algorithm or software that has the ability to understand natural language and generate answers to questions like a human. Specifically, this includes generative AI models and chatbots.

[1186] "Data storage" refers to a storage device or system for long-term storage of question and answer records and other necessary data. Specifically, this includes database systems.

[1187] "User authentication information" refers to information used to verify the authenticity of a user's access to a system, including, for example, a user ID and password.

[1188] "Receiving means" refers to a mechanism or process for acquiring data from the outside and processing it within the system.

[1189] "Analysis means" refers to a method or function for analyzing received data and extracting meaningful information.

[1190] "Temporary storage" is a memory or storage process used to hold data for a short period of time.

[1191] A "format conversion means" is a technique or method for converting data into a specific format so that it can be used by other systems or processes.

[1192] "Formatting" is the process of converting the generated data into a format that is easy to view for presentation to the user.

[1193] "Received questions" are doubts or inquiries that users (students) input via their terminals and that are received by the system.

[1194] A "generated answer" is a response that an artificial intelligence model generates based on a received question.

[1195] "Means for performing authentication" refers to the methods and techniques used to verify a user's authentication information and ensure that the user has legitimate access rights.

[1196] "Student" means a person who belongs to an educational institution and engages in learning activities.

[1197] "Teacher" means a person who teaches academic or technical subjects to students in an educational institution.

[1198] MODE FOR CARRYING OUT THE INVENTION

[1199] This invention is a system for quickly and accurately responding to questions students have during class. The purpose of this system is to reduce the burden on teachers and improve students' learning effectiveness. Specifically, the system consists of components such as terminals, servers, artificial intelligence models, and data storage.

[1200] Server Operation

[1201] The server receives question data from the device, transfers it to the AI ​​model to generate answers, and sends them to the device. It also records all interactions in data storage.

[1202] 1. User Authentication

[1203] The server receives the authentication information sent from the terminal and authenticates the user by checking it against the MySQL database. Node.js is responsible for server-side processing.

[1204] 2. Receiving Questions

[1205] The server analyzes the question data sent from the terminal and temporarily stores it. This analysis process is performed using Python.

[1206] 3. Transferring questions to an AI chatbot

[1207] The server converts the received questions into a specific format and sends them to the artificial intelligence model, where a REST API is used to send the questions to the generative AI model (e.g., GPT-3).

[1208] 4. Receive answers from AI chatbots

[1209] The server receives the generated answers from the AI ​​chatbot and formats them using Flask (a Python microframework).

[1210] 5. Sending the answer to the device

[1211] The server sends the generated answer to the device and stores it in data storage (e.g., a MySQL database), using Socket.io for real-time communication.

[1212] Device behavior

[1213] The terminals provide the interface through which students and teachers interact with the system.

[1214] 1. Enter your question

[1215] The user (student) enters a question into the text field on the terminal and presses the send button. The terminal collects this data and sends it to the server.

[1216] 2. Sending the question to the server

[1217] The terminal converts the question data into JSON format and sends it to the server via an HTTPS request. Here, Axios (a JavaScript library) is used.

[1218] 3. Receiving and Displaying Responses

[1219] The device receives the response sent from the server and displays it on the screen. React.js is used for the front end.

[1220] The role of teachers

[1221] Teachers monitor the answers generated by the AI ​​chatbot in real time and provide supplementary information as needed.

[1222] 1. Check and complete answers

[1223] Teachers can view students' questions and the AI ​​chatbot's responses in real time through the system, and can enter supplemental information using Django (a Python web framework).

[1224] Specific examples

[1225] Here's a specific example: If a student types the question "Tell me about the neutralization reaction of an acid and a base":

[1226] 1. Terminal

[1227] The student types "Tell me about the neutralization reaction of an acid and a base" in the text field and presses the submit button.

[1228] 2. Server

[1229] The server receives the question, parses it, converts it into a specific format, and sends it to the generative AI model. For example, the prompt might look like this:

[1230] "Please tell me about the neutralization reaction of an acid and a base."

[1231] 3. AI Chatbots

[1232] The generative AI model receives the prompt, analyzes it, and generates an answer:

[1233] "An example of a neutralization reaction is the reaction between hydrochloric acid (HCl) and sodium hydroxide (NaOH). The equation is HCl + NaOH → NaCl + H2O."

[1234] 4. Server

[1235] The server receives this response, sends it to the terminal, and stores it in data storage.

[1236] 5. Terminal

[1237] The terminal displays the received response on the screen.

[1238] 6. Teacher

[1239] Teachers use the system to review answers and enter additional information if further clarification is needed.

[1240] In this way, the present invention can quickly and accurately respond to questions that arise during class, reducing the burden on teachers and improving students' learning effectiveness.

[1241] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1242] System processing flow

[1243] Step 1: User authentication

[1244] Specific operation: The user (student or teacher) enters the user ID and password into the terminal and presses the login button.

[1245] Input: User ID, Password

[1246] Processing: The device converts this information into JSON format and sends it to the server via an HTTPS request. The server receives the request and checks the data storage to verify the user information.

[1247] Output: Authentication token (if authentication is successful), Authentication failure message (if authentication is unsuccessful)

[1248] Technologies used: Node.js (server side), MySQL (database)

[1249] Step 2: Enter your question

[1250] Specific operation: When a user (student) has a question during class, they enter the question in the text field on their device and press the send button.

[1251] Input: Question

[1252] Processing: The terminal collects this input as text data.

[1253] Output: Text data (stored on the device)

[1254] Technologies used: HTML / CSS / JavaScript (front end)

[1255] Step 3: Sending the question to the server

[1256] Specific operation: The terminal converts the entered question into JSON format and sends it to the server via an HTTPS request.

[1257] Input: Text data (question content)

[1258] Processing: The terminal converts the text data into JSON format and sends it to the server.

[1259] Output: Question data in JSON format

[1260] Technology used: Axios (HTTP client)

[1261] Step 4: Receiving questions

[1262] Specific operation: The server receives the request, analyzes the question data, and temporarily stores it.

[1263] Input: Question data in JSON format

[1264] Processing: The server parses the received JSON data and temporarily saves it as text data.

[1265] Output: Parsed text data

[1266] Technology used: Python (data analysis)

[1267] Step 5: Transfer the question to an AI chatbot

[1268] Specific operation: The server organizes the received question into a specific format and sends it to the AI ​​chatbot as a REST API request.

[1269] Input: Parsed text data

[1270] Processing: The server converts the text data into prompt sentences and sends them to a generative AI model (such as GPT-3) using a REST API.

[1271] Output: prompt statement

[1272] Technology used: REST API

[1273] Step 6: Receive a response from the AI ​​chatbot

[1274] Specific operation: The server receives the answer from the AI ​​chatbot and formats it.

[1275] Input: prompt, AI chatbot response

[1276] Processing: The server parses the received response and formats it appropriately.

[1277] Output: Formatted response data

[1278] Technology used: Flask (a Python microframework)

[1279] Step 7: Sending the response to the device

[1280] Specific operation: The server sends the formatted response to the terminal and simultaneously stores it in data storage.

[1281] Input: Formatted response data

[1282] Processing: The server sends the answer to the terminal as an HTTPS response and stores it in the database.

[1283] Output: HTTPS response, saved to database

[1284] Technologies used: MySQL (database), Socket.io (real-time communication)

[1285] Step 8: Receive and view responses

[1286] Specific operation: The terminal receives the response from the server and displays it to the user.

[1287] Input: HTTPS response (answer data)

[1288] Processing: The device analyzes the received response data and displays it on the UI.

[1289] Output: On-screen display

[1290] Technology used: React.js (front end)

[1291] Step 9: Teacher review and completion

[1292] Specific operation: The teacher uses the device to monitor students' questions and the AI ​​chatbot's answers in real time, and enters supplementary information as needed.

[1293] Input: Student question, AI chatbot answer

[1294] Processing: The teacher reviews the answers and enters additional information if necessary.

[1295] Output:Add additional information

[1296] Technology used: Django (Python web framework)

[1297] The above is the specific processing flow of the program of the system based on the present invention, which enables the system to quickly and accurately answer students' questions.

[1298] (Application example 1)

[1299] 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."

[1300] In traditional education systems and brick-and-mortar stores, teachers and staff have to respond to numerous questions from students and customers in real time, placing a heavy burden on them. It's also difficult to provide quick and accurate answers to a large number of questions. At the same time, students and customers lack the means to quickly obtain detailed information about specific products or learning content. By solving these issues, it's necessary to reduce the burden on teachers and staff and improve student and customer satisfaction.

[1301] 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.

[1302] In this invention, the server includes a means for receiving questions entered by students through their terminals, a means for sending the received questions to an AI chatbot to generate answers, and a means for sending the generated answers to the students' terminals, thereby enabling immediate answers to questions about products in physical stores and learning content.

[1303] A "terminal" is a device through which a user inputs questions and receives answers.

[1304] An "artificial intelligence chatbot" is an artificial intelligence system that analyzes received questions and generates appropriate answers.

[1305] The "server" is a system that receives question data sent from a terminal, transfers it to an AI chatbot, and sends the generated answer to the terminal.

[1306] "Database" means a storage device for recording and managing student questions and generated answers.

[1307] "Brick and mortar store" refers to a retail store or educational facility located in a specific location that customers and students can physically visit.

[1308] Here, the embodiment for carrying out the present invention will be specifically described.

[1309] Overview of system configuration and operation

[1310] This system mainly consists of the following elements: terminals, servers, AI chatbots, and databases. These elements work together to provide quick and accurate answers to questions from students and customers. The detailed operation of each element is explained below.

[1311] Device behavior

[1312] The terminal is the main device where users (students or customers) enter questions and receive answers. Specifically, it works as follows:

[1313] 1. Question input: The user enters a question into the text field on the device and presses the send button. The device collects this input and sends it to the server.

[1314] 2. Sending the question to the server: The device converts the entered question into a specific format (e.g., JSON) and sends it to the server via an HTTPS request.

[1315] 3. Receiving the answer: The answer sent from the server is received and displayed on the screen.

[1316] Server Operation

[1317] The server receives question data sent from the device, forwards it to the AI ​​chatbot, generates answers, and sends the answers back to the device, recording all interactions in a database.

[1318] 1. User authentication: The server receives the authentication information sent from the terminal and performs the authentication process. For example, when a student logs in, the server checks the provided user ID and password against the information in the database.

[1319] 2. Question reception: The server analyzes the question data from the device and temporarily saves the analysis results. The question content is recorded as text data.

[1320] 3. Forwarding the question to the AI ​​chatbot: The server organizes the received question into a suitable format for sending to the AI ​​chatbot, creates an API request, and sends the question to the AI ​​chatbot's system.

[1321] 4. Receiving the answer from the AI ​​chatbot: The server receives the answer from the AI ​​chatbot and prepares to send it to the terminal.

[1322] 5. Sending the answer to the terminal: The generated answer is sent to the terminal and simultaneously saved in the database.

[1323] Artificial Intelligence Chatbot in Action

[1324] The AI ​​chatbot analyzes questions sent from the server and generates appropriate answers. It uses a generative AI model to understand the question and return relevant information as an answer.

[1325] Database Features

[1326] The database will record all student questions and the answers generated by the AI ​​chatbot, and will also manage user authentication information.

[1327] Hardware and software used

[1328] 1. Smartphone: The device where the user enters their question.

[1329] 2. Server: Receives question data, forwards it to the AI ​​chatbot, and generates and sends answers.

[1330] 3. Artificial Intelligence Chatbot: An artificial intelligence system that analyzes questions and generates answers.

[1331] 4. Database: Records and manages questions and answers.

[1332] 5. Libraries used: Software for making HTTP requests, such as the requests library.

[1333] Specific examples

[1334] For example, the following question and answer exchange takes place:

[1335] question

[1336] "Are there any recommended reference books related to the content of this textbook?"

[1337] answer

[1338] "One recommended reference book is 'Learn Chemistry from the Basics'. This will help you learn more deeply."

[1339] Example prompts for generative AI models

[1340] Q: Are there any recommended reference books related to the content of this textbook? A:

[1341] This allows for immediate answers to users' questions, reducing the burden on teachers and store staff and improving customer and student satisfaction.

[1342] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1343] Step 1:

[1344] The user enters a question into the text field on the device and presses the send button. The entered question is collected as text data.

[1345] Input: Question text from user

[1346] Data processing: collecting question text

[1347] Output: Collected question text

[1348] Step 2:

[1349] The terminal converts the collected question text into a specific format (e.g., JSON) and sends it to the server via an HTTPS request.

[1350] Input: Collected question text

[1351] Data processing: Convert question text into JSON format

[1352] Output: Question data in JSON format

[1353] Step 3:

[1354] The server receives the JSON-formatted question data sent from the device and first performs user authentication by checking the provided user ID and password against the information in the database.

[1355] Input: JSON format question data, user ID, password

[1356] Data processing: User ID and password verification

[1357] Output: User authentication success or failure status

[1358] Step 4:

[1359] If the authentication is successful, the server analyzes the question data and temporarily stores the analysis results.

[1360] Input: JSON format question data that was successfully authenticated

[1361] Data processing: Analysis of question text

[1362] Output: Analysis results

[1363] Step 5:

[1364] Based on the analysis results, the server organizes the question into a suitable format for forwarding to the AI ​​chatbot, and creates an API request to send the question to the AI ​​chatbot's system.

[1365] Input: Analysis results

[1366] Data processing: Creating API requests and forwarding questions

[1367] Output: Send request to AI chatbot

[1368] Step 6:

[1369] The artificial intelligence chatbot analyzes the questions it receives and generates appropriate answers using generative AI models.

[1370] Input: Question data sent through the API

[1371] Data processing: Question analysis and answer generation using generative AI models

[1372] Output: The generated answer

[1373] Step 7:

[1374] The server receives the answers generated by the AI ​​chatbot and prepares them to be sent to the device.

[1375] Input: Generated Answer

[1376] Data processing: Formatting of response data

[1377] Output: Formatted response data

[1378] Step 8:

[1379] The server sends the generated answers to the user's device and also stores all interactions in a database.

[1380] Input: Formatted response data

[1381] Data processing: Sending and saving response data

[1382] Output: Display of answers on the user's terminal and recording in the database

[1383] Step 9:

[1384] The terminal receives the response data sent from the server and displays it on the screen for the user.

[1385] Input: Response data sent from the server

[1386] Data processing: Analysis and display of response data

[1387] Output: Answer displayed on the screen

[1388] This series of processing steps allows for immediate answers to users' questions, reducing the burden on teachers and store staff and improving customer and student satisfaction.

[1389] 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.

[1390] MODE FOR CARRYING OUT THE INVENTION

[1391] This invention is a system that includes an AI chatbot to quickly and accurately respond to questions students have during class, reducing the burden on teachers and improving students' learning outcomes. Furthermore, by combining this system with an emotion engine that recognizes students' emotions, it provides more human-like responses and realizes more effective learning support.

[1392] Server Operation

[1393] The server receives question data sent from students' devices, analyzes emotions through an emotion engine, and forwards the data to an AI chatbot to generate answers. It also sends the generated answers to students' devices and records all interactions in a database.

[1394] 1. User Authentication

[1395] The server receives the authentication information sent from the terminal and performs the authentication process.

[1396] For example, when a student logs in, the server checks the provided user ID and password against information in a database.

[1397] 2. Receiving questions

[1398] The server analyzes the question data from the terminal and temporarily stores the analysis results.

[1399] For example, if the question is "Please give me a specific example of a neutralization reaction between an acid and a base in a chemistry experiment," this is recorded as text data.

[1400] 3. Question analysis using emotion engine

[1401] The server sends the received question data to the emotion recognition engine for emotion analysis.

[1402] The emotion engine analyzes the student's emotions (happiness, confusion, anger, etc.) from the question and returns the results to the server.

[1403] 4. Transferring questions to an AI chatbot

[1404] The server organizes the received questions and sentiment analysis results into an appropriate format for sending to the AI ​​chatbot.

[1405] Create an API request and submit your question to the AI ​​chatbot system.

[1406] 5. Receive answers from AI chatbots

[1407] The server receives the response from the AI ​​chatbot and prepares it to be sent to the device.

[1408] For example, format the answer "A specific example of a neutralization reaction is the reaction between hydrochloric acid (HCl) and sodium hydroxide (NaOH)."

[1409] 6. Sending the answer to the device

[1410] The server sends the generated answer to the user's terminal and simultaneously stores it in a database.

[1411] Device behavior

[1412] The terminal is a tool that users (students and teachers) use to interface with the system. It is the main device for students to enter questions and receive answers. Furthermore, it can adjust the tone of the answers based on the analysis results of the emotion engine.

[1413] 1. Enter your question

[1414] The user (student) enters a question in the text field and presses the submit button. The device collects this input and sends it to the server.

[1415] 2. Sending the question to the server

[1416] The terminal converts the entered question into a specific format (e.g., JSON) and sends it to the server via an HTTPS request.

[1417] 3. Receiving a response

[1418] The device receives the response sent from the server, analyzes it, and displays it on the screen. The response can be displayed in a tone that corresponds to the emotion.

[1419] User (Teacher) Role

[1420] Teachers can review the answers generated by the AI ​​chatbot and the results of the sentiment analysis, and provide additional explanations or corrections as needed, thereby improving the accuracy and appropriateness of the answers given to students.

[1421] 1. Check and complete answers

[1422] Teachers can monitor the answers generated by the AI ​​chatbot and the analysis results of the emotion engine in real time and input additional information to fill in any gaps.

[1423] Specific examples

[1424] For example, suppose a student types the following question: "Tell me about the neutralization reaction of an acid and a base."

[1425] 1. Terminal

[1426] Students type their question into the text field and press the submit button.

[1427] 2. Server

[1428] The server receives the question and transfers it to the emotion engine for emotion analysis. For example, it receives the emotion "confused" as the analysis result.

[1429] 3. AI Chatbots

[1430] Receive questions along with sentiment analysis results and generate appropriate answers:

[1431] "An example of a neutralization reaction is the reaction between hydrochloric acid (HCl) and sodium hydroxide (NaOH). The equation is HCl + NaOH → NaCl + H2O."

[1432] 4. Adjust the tone of your response

[1433] The tone of the generated response is adjusted based on the results of sentiment analysis, for example, a polite and gentle response to a confused student.

[1434] 5. Server

[1435] The generated response is received and sent to the terminal.

[1436] 6. Terminal

[1437] The terminal receives the response and displays it on the screen.

[1438] 7. Teacher

[1439] Teachers can review students' questions, the AI ​​chatbot's answers, and sentiment analysis results, and enter additional explanations if necessary.

[1440] The system of the present invention not only answers students' questions quickly and accurately, but also provides appropriate responses that reflect the students' emotions, thereby reducing the burden on teachers and further improving the effectiveness of education.

[1441] The processing flow will be explained below.

[1442] Step 1:

[1443] The user (student) opens the chat interface on their device, enters a question in the text field, and clicks the "Send" button. For example, they might type, "Please tell me about the neutralization reaction of an acid and a base."

[1444] Step 2:

[1445] The terminal retrieves the entered question. The retrieved question is packaged in JSON format. The generated JSON data looks like this: {"question": "Please tell me about the neutralization reaction of an acid and a base", "userId": "student123"}

[1446] Step 3:

[1447] The device sends the packaged data to the server via an HTTPS request, sending the data using secure communication.

[1448] Step 4:

[1449] The server receives the HTTPS request. It parses the received data using a JSON parser and extracts the question and user ID. For example, it extracts the question and user ID.

[1450] Step 5:

[1451] The server temporarily stores the retrieved question and user ID. It also sends the question data to the emotion engine for analysis. For example, send data to the emotion engine: {"text": "Please tell me about the neutralization reaction of acid and base"}

[1452] Step 6:

[1453] The emotion engine analyzes the data and identifies student emotions from questions, for example recognizing the emotion "confused."

[1454] Step 7:

[1455] The emotion engine sends the emotion analysis results back to the server. The server receives this and converts the question data and emotion analysis results into request data to be sent to the AI ​​chatbot. For example, {"prompt": "Please tell me about the neutralization reaction of acid and base", "emotion": "confused"}.

[1456] Step 8:

[1457] The server sends the formatted request to the AI ​​chatbot, which sends an API request and waits for a response.

[1458] Step 9:

[1459] The AI ​​chatbot generates an appropriate answer based on the question received from the server and the results of sentiment analysis. For example, it might create an answer like, "A specific example of a neutralization reaction is the reaction between hydrochloric acid (HCl) and sodium hydroxide (NaOH). The reaction equation is HCl + NaOH → NaCl + H2O."

[1460] Step 10:

[1461] The AI ​​chatbot generates an answer and sends it to the server, for example, {"response": "A specific example of a neutralization reaction is the reaction between hydrochloric acid (HCl) and sodium hydroxide (NaOH). The reaction equation is HCl + NaOH → NaCl + H2O.", "emotion": "confused"}.

[1462] Step 11:

[1463] The server receives the AI ​​chatbot's responses and the results of the sentiment analysis, and then formats the response data. Based on the sentiment analysis results, the server adjusts the tone of the response. For example, it responds to a confused student in a polite and gentle tone.

[1464] Step 12:

[1465] The server sends the formatted response data to the user's device, and simultaneously stores this exchange of queries and responses in a database.

[1466] Step 13:

[1467] The device receives the HTTP response sent from the server, analyzes the received data with a JSON parser, and extracts the answer.

[1468] Step 14:

[1469] The device displays the analyzed answers on the chat interface so that the user (student) can check them. The student can read the answers displayed on the screen.

[1470] Step 15:

[1471] The user (teacher) monitors students' questions, the AI ​​chatbot's responses, and the sentiment analysis results in real time during the lesson, and inputs supplementary information as needed to provide students with further explanations.

[1472] In this way, the system can respond quickly and accurately to questions from students, and its emotion engine can provide appropriate responses that take students' emotions into consideration. This can reduce the burden on teachers and improve learning effectiveness.

[1473] Example 2

[1474] 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."

[1475] Educational institutions are required to respond quickly and appropriately to questions that students have during class or self-study. However, conventional systems place a heavy burden on teachers, and responses to students' questions can be delayed or insufficient. Furthermore, responses that do not take into account students' feelings can lead to a loss of motivation to learn or misunderstandings. The present invention aims to solve these problems and improve students' learning outcomes.

[1476] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[1477] In this invention, the server includes means for receiving questions entered by students through their terminals, means for transmitting the received questions to an emotion analysis engine to analyze emotions, means for transmitting the questions including the emotion analysis results to an AI chatbot to generate answers, means for adjusting the tone of the generated answers based on the emotion analysis results, means for transmitting the tone-adjusted answers to the students' terminals, and means for storing the students' questions and the generated answers in a database. This makes it possible to respond quickly and appropriately to students' questions and provide appropriate responses according to the students' emotions.

[1478] "Student" means a learner enrolled in an educational institution.

[1479] "Device" means a computer or mobile device used by a student or educator.

[1480] A "question" is an inquiry that a student types into a terminal to seek clarification.

[1481] An "emotion analysis engine" is software or hardware that analyzes student emotions contained in questions.

[1482] An "artificial intelligence chatbot" is an artificial intelligence system that automatically generates answers to questions it receives.

[1483] "Tone adjustment" refers to the process of appropriately changing the wording and tone of a response based on the results of sentiment analysis.

[1484] A "server" is a central computer that handles data processing and communications for the entire system.

[1485] "Database" refers to a storage device or system for storing student questions and generated answers.

[1486] "Educator" means a person whose job is to provide education to students.

[1487] "Supplemental information" refers to additional information provided by educators to enhance students' understanding.

[1488] This invention is a system for quickly and accurately responding to questions that students have during class or self-study. The system is mainly composed of a server, terminals, a sentiment analysis engine, an AI chatbot, and a database.

[1489] A terminal is a computer or mobile device used by a student or teacher to enter questions and display answers. Students submit their questions by typing them into a text field on the terminal and pressing a submit button. The terminal also converts the questions into a format (e.g., JSON) and sends them to the server via an HTTPS request.

[1490] The server receives the question data sent from the device and temporarily stores it. The received question is sent to an emotion analysis engine, where emotion analysis is performed. This emotion analysis engine analyzes the student's emotion contained in the question (e.g., confusion, anger, joy, etc.) and returns the results to the server.

[1491] The server organizes the analyzed emotions and questions into an appropriate format and creates an API request to send to the AI ​​chatbot. The AI ​​chatbot generates an answer to the received question. It remembers past questions and answers and can quickly provide answers to similar questions. For example, in response to the question, "Please tell me about the neutralization reaction of an acid and a base," it generates the answer, "A specific example of a neutralization reaction is the reaction between hydrochloric acid (HCl) and sodium hydroxide (NaOH). The equation is HCl + NaOH → NaCl + H2O."

[1492] The server then receives the generated answer and adjusts the tone of the answer based on the emotion analysis results. For example, if the emotion analyzed is "confused," the answer will be adjusted to a more polite and gentle tone. The adjusted tone is then sent to the student's device and displayed on the screen.

[1493] Educators can monitor students' questions and the AI ​​chatbot's responses in real time through the system and provide supplementary information as needed. If an educator feels that additional explanation is needed, they can enter supplementary information at any time.

[1494] Examples of prompt statements

[1495] "Please tell me about the neutralization reaction of an acid and a base."

[1496] "Please give a brief history of ancient Rome."

[1497] "Tell me how to train a dog"

[1498] This allows for quick and appropriate responses to students' questions, and provides appropriate responses based on students' emotions, improving educational effectiveness. Furthermore, by reducing the burden on educators, it becomes possible to provide high-quality education to more students.

[1499] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1500] Step 1:

[1501] User Authentication

[1502] The server receives the user ID and password sent from the terminal.

[1503] Checks the received credentials against existing data in the database.

[1504] If authentication is successful, a session ID is issued to the student and a receipt is returned to the terminal.

[1505] Input: User ID, Password

[1506] Output: Session ID (if successful)

[1507] Step 2:

[1508] Enter a question

[1509] The terminal accepts questions that students enter into a text field.

[1510] For example, a student types, "Tell me about the neutralization reaction of an acid and a base."

[1511] Input: Question text entered by the student

[1512] Output: Question text (as is)

[1513] Step 3:

[1514] Submit a question

[1515] The terminal converts the entered question into JSON format and sends it to the server via an HTTPS request.

[1516] The transmitted data includes the question content and session ID.

[1517] Input: Question text, Session ID

[1518] Output: Question data in JSON format

[1519] Step 4:

[1520] Receiving questions

[1521] The server receives the question data sent from the terminal and temporarily stores it.

[1522] Input: JSON data sent from the terminal

[1523] Output: Temporarily saved question data

[1524] Step 5:

[1525] Emotion analysis

[1526] The server sends the received question data to the sentiment analysis engine.

[1527] The emotion analysis engine analyzes the student's emotion contained in the question and returns the result to the server.

[1528] For example, the emotion of "confusion" is analyzed.

[1529] Input: Question data

[1530] Output: Emotion analysis result (e.g. confused)

[1531] Step 6:

[1532] Transfer to AI chatbot

[1533] The server sends the question data including the emotion analysis results to the artificial intelligence chatbot.

[1534] Create an API request and organize the question and sentiment analysis results.

[1535] Input: Sentiment analysis results, question data

[1536] Output: API request

[1537] Step 7:

[1538] Receive answers from AI chatbots

[1539] The server receives the response from the artificial intelligence chatbot.

[1540] For example, the answer generated is, "A specific example of a neutralization reaction is the reaction between hydrochloric acid (HCl) and sodium hydroxide (NaOH). The reaction equation is HCl + NaOH → NaCl + H2O."

[1541] Input: API request

[1542] Output: Generated answer text

[1543] Step 8:

[1544] Adjusting the tone of your response

[1545] The server adjusts the tone of the response based on the sentiment analysis results.

[1546] For example, respond to the emotion of "confusion" with a more polite and gentle tone.

[1547] Input: Generated answer text, sentiment analysis results

[1548] Output: Tone-adjusted answer text

[1549] Step 9:

[1550] Sending answers to the device

[1551] The server sends the tone-adjusted response to the student's terminal.

[1552] At the same time, all interactions are stored in a database.

[1553] Input: Tone-adjusted answer text

[1554] Output: Submitted answers, saved data

[1555] Step 10:

[1556] Show Answers

[1557] The terminal receives the response sent from the server and displays it on the screen.

[1558] For example, it will say, "A specific example of a neutralization reaction is the reaction between hydrochloric acid (HCl) and sodium hydroxide (NaOH). The equation is HCl + NaOH → NaCl + H2O."

[1559] Input: Answer text

[1560] Output: On-screen display

[1561] Step 11:

[1562] Supplementary explanation input (teacher's role)

[1563] Teachers monitor students' questions and the AI ​​chatbot's answers in real time.

[1564] If necessary, supplementary information is input from the terminal.

[1565] Input: Teacher supplementary information

[1566] Output: Complemented answer

[1567] (Application example 2)

[1568] 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."

[1569] In traditional educational systems, responding to students' questions during class can be a significant burden on teachers, making it difficult to provide individual support. Furthermore, when students don't understand something or are confused, it's difficult to respond appropriately while taking their feelings into consideration. This can lead to a decline in students' learning effectiveness. Furthermore, in today's educational environment, where prompt and accurate responses are required, there is a lack of appropriate systems.

[1570] The identification process by the identification processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes means for receiving questions entered by students through their terminals, means for transmitting the received questions to an emotion recognition engine for emotion analysis, means for transmitting the analysis results to an AI chatbot to generate answers, means for transmitting the generated answers to the students' terminals and adjusting the tone of the answers based on the emotion analysis results, and means for storing the students' questions and the generated answers in a database. This makes it possible to not only quickly and accurately answer students' questions but also to provide appropriate responses based on the students' emotions. It also reduces the burden on teachers while further improving educational effectiveness.

[1571] "Student" means a person who uses the education system to learn.

[1572] A "terminal" is a device through which a user can input questions and receive answers. Examples include smartphones, tablets, and personal computers.

[1573] A "question" is information that a student inputs via their device. This includes questions about the learning content or requests for clarification.

[1574] The "emotion recognition engine" is a system that analyzes the emotions expressed in students' questions. For example, it has the ability to determine emotions such as joy, confusion, and anger.

[1575] "Sentiment analysis" is the process of analyzing the emotions contained in students' questions. This is done through an emotion recognition engine.

[1576] An "artificial intelligence chatbot" is a program that automatically generates answers to input questions.

[1577] "Answer" refers to the information generated by the AI ​​chatbot in response to a student's question.

[1578] "Tone" refers to the expression style of the generated answer. It is adjusted to a polite or gentle tone based on the results of sentiment analysis.

[1579] "Database" refers to an information store that stores student questions and generated answers.

[1580] "Teachers" are those who are administrators of the education system and whose role is to provide supplementary information when necessary.

[1581] System Configuration

[1582] This invention is a system that includes an AI chatbot to quickly and accurately respond to questions students have during class. The system is primarily composed of student devices, a server, and a teacher interface. It also has advanced functionality that combines an emotion recognition engine with an AI chatbot.

[1583] Hardware and software used

[1584] Hardware: Smartphone, tablet or personal computer (student device), server

[1585] Software: Python, HTTP request library (requests), AI chatbot API, emotion recognition engine API, database

[1586] Details of data processing and data calculation

[1587] 1. User authentication:

[1588] The server receives the user ID and password sent from the student's device and performs the authentication process, during which the device sends the authentication information to the server using an HTTPS request.

[1589] 2. Question reception and sentiment analysis:

[1590] When a student inputs a question through the device, the device sends the question to the server. The server receives the question and sends it to the emotion recognition engine for emotion analysis. The emotion recognition engine then analyzes the student's emotion (e.g., confusion, joy, anger, etc.) from the question and returns the result to the server.

[1591] 3. Answer generation by AI chatbots:

[1592] The server sends the received question and the results of the sentiment analysis to an AI chatbot, which uses that information to generate an appropriate answer.

[1593] 4. Tone and send your response:

[1594] The server adjusts the tone of the response received from the AI ​​chatbot based on the results of emotion analysis. For example, it selects a polite and gentle response for a confused student. The adjusted response is then sent to the device.

[1595] 5. Data storage:

[1596] The server stores the student's question and the generated answer in a database, allowing for quick responses to similar questions in the future.

[1597] Specific examples of operation procedures

[1598] Example prompt sentence:

[1599] User ID: user123

[1600] Password: password456

[1601] Question: Can you give me a concrete example of an acid-base neutralization reaction in a chemistry experiment?

[1602] For example, if a student enters a question using the prompt sentence described above, the server performs authentication and receives the question. If the server then determines using an emotion recognition engine that the student is confused, it sends that information to an AI chatbot, which generates an answer as a concrete example of a neutralization reaction: "There is a reaction between hydrochloric acid (HCl) and sodium hydroxide (NaOH). The equation is HCl + NaOH → NaCl + H2O." This answer is adjusted to a gentle tone to match the student's confused emotion and is displayed on the student's device. This series of interactions is also stored in a database.

[1603] As a result, this invention not only answers students' questions quickly and accurately, but also provides appropriate responses according to their emotions, thereby reducing the burden on teachers and improving students' learning effectiveness.

[1604] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1605] Step 1:

[1606] The server receives the user ID and password sent from the user's (student's) device. Based on the received authentication information, it compares it with the information in the database and executes the authentication process. If authentication is successful, it generates an authentication token and returns it to the device. The input is the user ID and password, and the output is the authentication token.

[1607] Step 2:

[1608] The terminal obtains the question entered by the student from the text field and sends it to the server along with the authentication token. The input is the student's question text and the authentication token, and the output is an HTTP request to the server.

[1609] Step 3:

[1610] The server receives questions sent from the device. The questions are temporarily saved and sent to the emotion recognition engine for emotion analysis. The emotion recognition engine analyzes the student's emotion from the question content and returns the results to the server. The input is the student's question text, and the output is the emotion analysis result.

[1611] Step 4:

[1612] The server sends the sentiment analysis results and question text to an AI chatbot, which then generates an appropriate answer based on that information. The input is the question text and sentiment analysis results, and the output is the generated answer.

[1613] Step 5:

[1614] The server receives the generated response and adjusts the tone of the response based on the sentiment analysis results. For example, it adjusts the tone of the response to a more gentle one for a confused student. The input is the generated response and the sentiment analysis results, and the output is the tone-adjusted response.

[1615] Step 6:

[1616] The server sends the tone-adjusted answer to the terminal and stores the student's question and the generated answer in a database. The input is the tone-adjusted answer, and the output is sending it to the terminal and storing it in the database.

[1617] Step 7:

[1618] The terminal receives the tone-adjusted response sent from the server and displays it on the screen. The input is the tone-adjusted response, and the output is the screen display.

[1619] 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.

[1620] 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.

[1621] 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.

[1622] [Fourth embodiment]

[1623] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[1624] 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.

[1625] 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).

[1626] 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.

[1627] 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.

[1628] 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).

[1629] 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.

[1630] 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.

[1631] 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.

[1632] 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.

[1633] 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.

[1634] 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.

[1635] 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."

[1636] MODE FOR CARRYING OUT THE INVENTION

[1637] This invention is a system that includes an AI chatbot to quickly and accurately respond to questions students have during class, reducing the burden on teachers and improving students' learning outcomes. The system mainly includes the following components: a terminal, a server, an AI chatbot, and a database.

[1638] Server Operation

[1639] The server receives question data sent from the device, forwards it to the AI ​​chatbot, generates answers, and sends the answers back to the device, recording all interactions in a database.

[1640] 1. User Authentication

[1641] The server receives the authentication information sent from the terminal and performs the authentication process.

[1642] For example, when a student logs in, the server checks the provided user ID and password against information in a database.

[1643] 2. Receiving questions

[1644] The server analyzes the question data from the terminal and temporarily stores the analysis results.

[1645] For example, if the question is "Please give me a specific example of a neutralization reaction between an acid and a base in a chemistry experiment," this is recorded as text data.

[1646] 3. Transferring questions to an AI chatbot

[1647] The server then organizes the received questions into an appropriate format for sending to the AI ​​chatbot.

[1648] Create an API request and submit your question to the AI ​​chatbot system.

[1649] 4. Receive answers from AI chatbots

[1650] The server receives the response from the AI ​​chatbot and prepares it to be sent to the device.

[1651] For example, format the answer "A specific example of a neutralization reaction is the reaction between hydrochloric acid (HCl) and sodium hydroxide (NaOH)."

[1652] 5. Sending the answer to the device

[1653] The server sends the generated answer to the user's terminal and simultaneously stores it in a database.

[1654] Device behavior

[1655] Terminals are the tools through which users (students and teachers) interface with the system. They are the primary devices through which students enter questions and receive answers.

[1656] 1. Enter your question

[1657] The user (student) enters a question in the text field and presses the submit button. The device collects this input and sends it to the server.

[1658] 2. Sending the question to the server

[1659] The terminal converts the entered question into a specific format (e.g., JSON) and sends it to the server via an HTTPS request.

[1660] 3. Receiving a response

[1661] The terminal receives the response sent from the server, analyzes it, and displays it on the screen.

[1662] User (Teacher) Role

[1663] Teachers can review the answers generated by the AI ​​chatbot and provide additional explanations or corrections as needed, thereby improving the accuracy of the answers given to students.

[1664] 1. Check and complete answers

[1665] Teachers can monitor the answers generated by the AI ​​chatbot in real time and provide additional information to fill in any gaps.

[1666] Specific examples

[1667] For example, suppose a student types the following question: "Tell me about the neutralization reaction of an acid and a base."

[1668] 1. Terminal

[1669] Students type their question into the text field and press the submit button.

[1670] 2. Server

[1671] The server receives the question and prepares it for forwarding to the AI ​​chatbot.

[1672] Send the question, "Question: Please tell me about the neutralization reaction of an acid and a base" to the AI ​​chatbot.

[1673] 3. AI Chatbots

[1674] The AI ​​chatbot analyzes the question and generates an appropriate answer:

[1675] "An example of a neutralization reaction is the reaction between hydrochloric acid (HCl) and sodium hydroxide (NaOH). The equation is HCl + NaOH → NaCl + H2O."

[1676] 4. Server

[1677] The generated response is received and sent to the terminal.

[1678] 5. Terminal

[1679] The terminal receives the response and displays it on the screen.

[1680] 6. Teacher

[1681] The teacher reviews the student's question and the AI ​​chatbot's response, providing additional explanations if necessary.

[1682] The system of the present invention allows students' questions to be answered quickly and accurately, reducing the burden on teachers and improving educational effectiveness.

[1683] The processing flow will be explained below.

[1684] Step 1:

[1685] The user (student) opens the chat interface on their device, enters a question in the text field, and clicks the "Send" button. For example, they might type, "Please tell me about the neutralization reaction of an acid and a base."

[1686] Step 2:

[1687] The terminal retrieves the entered question. The retrieved question is packaged in JSON format. The generated JSON data looks like this: {"question": "Please tell me about the neutralization reaction of an acid and a base", "userId": "student123"}

[1688] Step 3:

[1689] The device sends the packaged data to the server via an HTTPS request, using a secure communication channel.

[1690] Step 4:

[1691] The server receives the HTTPS request. It parses the received data using a JSON parser and extracts the question and user ID. For example, it extracts question and userId.

[1692] Step 5:

[1693] The server temporarily stores the retrieved question and the user ID. It then formats this data into request data to be sent to the AI ​​chatbot's API. The formatted API request is as follows: {"prompt": "Please tell me about the neutralization reaction of an acid and a base"}

[1694] Step 6:

[1695] The server sends the formatted request to the AI ​​chatbot, which sends an API request and waits for a response.

[1696] Step 7:

[1697] The AI ​​chatbot analyzes the question received from the server and uses natural language processing technology to generate the best answer to the question. The generated answer is: "A specific example of a neutralization reaction is the reaction between hydrochloric acid (HCl) and sodium hydroxide (NaOH). The reaction equation is HCl + NaOH → NaCl + H2O."

[1698] Step 8:

[1699] The AI ​​chatbot sends the generated answer to the server. The server receives this data and parses it again using a JSON parser. The received data is as follows: {"response": "A specific example of a neutralization reaction is the reaction between hydrochloric acid (HCl) and sodium hydroxide (NaOH). The reaction equation is HCl + NaOH → NaCl + H2O."}

[1700] Step 9:

[1701] The server generates an HTTP response to send the analyzed answer back to the user's (student's) device. The generated response is as follows: {"response": "A specific example of a neutralization reaction is the reaction between hydrochloric acid (HCl) and sodium hydroxide (NaOH). The reaction equation is HCl + NaOH → NaCl + H2O."}

[1702] Step 10:

[1703] The server sends the generated HTTP response to the user's device, and simultaneously stores this query and response in a database.

[1704] Step 11:

[1705] The device receives the HTTP response sent from the server, analyzes the received data with a JSON parser, and extracts the answer.

[1706] Step 12:

[1707] The device displays the analyzed answers on the chat interface so that the user (student) can check them. The student can read the answers displayed on the screen.

[1708] Step 13:

[1709] The user (teacher) monitors students' questions and the AI ​​chatbot's answers in real time during the lesson, and inputs supplementary information as needed to provide students with further explanations.

[1710] In this way, the system can respond quickly and accurately to questions from students, reducing the burden on teachers.

[1711] Example 1

[1712] 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."

[1713] In today's educational environment, it is difficult for teachers to respond quickly and accurately to all students' questions. In particular, when a large number of questions are received at once, the teacher's workload becomes excessive, leading to delayed responses and the provision of inaccurate answers. Furthermore, if students' questions cannot be resolved immediately, learning outcomes may decline. Furthermore, systems that do not effectively utilize past questions and answers often result in the same questions being asked repeatedly, reducing overall work efficiency. Furthermore, if teachers do not have a way to monitor the answers provided by generative AI models and provide supplementary information as needed, they lack confidence in the accuracy of the model's answers.

[1714] 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.

[1715] In this invention, the server includes: means for receiving questions entered by students through their terminals; means for transmitting the received questions to an AI model to generate answers; means for transmitting the generated answers to the students' terminals; means for storing the students' questions and the generated answers in data storage; means for receiving user authentication information and performing authentication; means for analyzing and temporarily storing the received questions; means for converting the received questions into a specific format and transmitting them to the AI ​​model; and means for formatting the answers received from the AI ​​model and transmitting them to the terminals. This allows for prompt and accurate responses to questions raised by students during class, reducing the burden on teachers and improving student learning outcomes. Furthermore, by utilizing past questions and answers, it is possible to efficiently respond to similar questions that repeatedly arise. Furthermore, teachers can monitor the answers provided by the generative AI model in real time and provide supplementary information as needed.

[1716] A "server" is a computer system that receives data sent from a terminal, processes it, and then sends the corresponding information to the terminal.

[1717] "Terminal" refers to a device that allows students and teachers to access and operate the system through an interface. Specifically, it includes a PC, tablet, or smartphone.

[1718] An "artificial intelligence model" is an algorithm or software that has the ability to understand natural language and generate answers to questions like a human. Specifically, this includes generative AI models and chatbots.

[1719] "Data storage" refers to a storage device or system for long-term storage of question and answer records and other necessary data. Specifically, this includes database systems.

[1720] "User authentication information" refers to information used to verify the authenticity of a user's access to a system, including, for example, a user ID and password.

[1721] "Receiving means" refers to a mechanism or process for acquiring data from the outside and processing it within the system.

[1722] "Analysis means" refers to a method or function for analyzing received data and extracting meaningful information.

[1723] "Temporary storage" is a memory or storage process used to hold data for a short period of time.

[1724] A "format conversion means" is a technique or method for converting data into a specific format so that it can be used by other systems or processes.

[1725] "Formatting" is the process of converting the generated data into a format that is easy to view for presentation to the user.

[1726] "Received questions" are doubts or inquiries that users (students) input via their terminals and that are received by the system.

[1727] A "generated answer" is a response that an artificial intelligence model generates based on a received question.

[1728] "Means for performing authentication" refers to the methods and techniques used to verify a user's authentication information and ensure that the user has legitimate access rights.

[1729] "Student" means a person who belongs to an educational institution and engages in learning activities.

[1730] "Teacher" means a person who teaches academic or technical subjects to students in an educational institution.

[1731] MODE FOR CARRYING OUT THE INVENTION

[1732] This invention is a system for quickly and accurately responding to questions students have during class. The purpose of this system is to reduce the burden on teachers and improve students' learning effectiveness. Specifically, the system consists of components such as terminals, servers, artificial intelligence models, and data storage.

[1733] Server Operation

[1734] The server receives question data from the device, transfers it to the AI ​​model to generate answers, and sends them to the device. It also records all interactions in data storage.

[1735] 1. User Authentication

[1736] The server receives the authentication information sent from the terminal and authenticates the user by checking it against the MySQL database. Node.js is responsible for server-side processing.

[1737] 2. Receiving Questions

[1738] The server analyzes the question data sent from the terminal and temporarily stores it. This analysis process is performed using Python.

[1739] 3. Transferring questions to an AI chatbot

[1740] The server converts the received questions into a specific format and sends them to the artificial intelligence model, where a REST API is used to send the questions to the generative AI model (e.g., GPT-3).

[1741] 4. Receive answers from AI chatbots

[1742] The server receives the generated answers from the AI ​​chatbot and formats them using Flask (a Python microframework).

[1743] 5. Sending the answer to the device

[1744] The server sends the generated answer to the device and stores it in data storage (e.g., a MySQL database), using Socket.io for real-time communication.

[1745] Device behavior

[1746] The terminals provide the interface through which students and teachers interact with the system.

[1747] 1. Enter your question

[1748] The user (student) enters a question into the text field on the terminal and presses the send button. The terminal collects this data and sends it to the server.

[1749] 2. Sending the question to the server

[1750] The terminal converts the question data into JSON format and sends it to the server via an HTTPS request. Here, Axios (a JavaScript library) is used.

[1751] 3. Receiving and Displaying Responses

[1752] The device receives the response sent from the server and displays it on the screen. React.js is used for the front end.

[1753] The role of teachers

[1754] Teachers monitor the answers generated by the AI ​​chatbot in real time and provide supplementary information as needed.

[1755] 1. Check and complete answers

[1756] Teachers can view students' questions and the AI ​​chatbot's responses in real time through the system, and can enter supplemental information using Django (a Python web framework).

[1757] Specific examples

[1758] Here's a specific example: If a student types the question "Tell me about the neutralization reaction of an acid and a base":

[1759] 1. Terminal

[1760] The student types "Tell me about the neutralization reaction of an acid and a base" in the text field and presses the submit button.

[1761] 2. Server

[1762] The server receives the question, parses it, converts it into a specific format, and sends it to the generative AI model. For example, the prompt might look like this:

[1763] "Please tell me about the neutralization reaction of an acid and a base."

[1764] 3. AI Chatbots

[1765] The generative AI model receives the prompt, analyzes it, and generates an answer:

[1766] "An example of a neutralization reaction is the reaction between hydrochloric acid (HCl) and sodium hydroxide (NaOH). The equation is HCl + NaOH → NaCl + H2O."

[1767] 4. Server

[1768] The server receives this response, sends it to the terminal, and stores it in data storage.

[1769] 5. Terminal

[1770] The terminal displays the received response on the screen.

[1771] 6. Teacher

[1772] Teachers use the system to review answers and enter additional information if further clarification is needed.

[1773] In this way, the present invention can quickly and accurately respond to questions that arise during class, reducing the burden on teachers and improving students' learning effectiveness.

[1774] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1775] System processing flow

[1776] Step 1: User authentication

[1777] Specific operation: The user (student or teacher) enters the user ID and password into the terminal and presses the login button.

[1778] Input: User ID, Password

[1779] Processing: The device converts this information into JSON format and sends it to the server via an HTTPS request. The server receives the request and checks the data storage to verify the user information.

[1780] Output: Authentication token (if authentication is successful), Authentication failure message (if authentication is unsuccessful)

[1781] Technologies used: Node.js (server side), MySQL (database)

[1782] Step 2: Enter your question

[1783] Specific operation: When a user (student) has a question during class, they enter the question in the text field on their device and press the send button.

[1784] Input: Question

[1785] Processing: The terminal collects this input as text data.

[1786] Output: Text data (stored on the device)

[1787] Technologies used: HTML / CSS / JavaScript (front end)

[1788] Step 3: Sending the question to the server

[1789] Specific operation: The terminal converts the entered question into JSON format and sends it to the server via an HTTPS request.

[1790] Input: Text data (question content)

[1791] Processing: The terminal converts the text data into JSON format and sends it to the server.

[1792] Output: Question data in JSON format

[1793] Technology used: Axios (HTTP client)

[1794] Step 4: Receiving questions

[1795] Specific operation: The server receives the request, analyzes the question data, and temporarily stores it.

[1796] Input: Question data in JSON format

[1797] Processing: The server parses the received JSON data and temporarily saves it as text data.

[1798] Output: Parsed text data

[1799] Technology used: Python (data analysis)

[1800] Step 5: Transfer the question to an AI chatbot

[1801] Specific operation: The server organizes the received question into a specific format and sends it to the AI ​​chatbot as a REST API request.

[1802] Input: Parsed text data

[1803] Processing: The server converts the text data into prompt sentences and sends them to a generative AI model (such as GPT-3) using a REST API.

[1804] Output: prompt statement

[1805] Technology used: REST API

[1806] Step 6: Receive a response from the AI ​​chatbot

[1807] Specific operation: The server receives the answer from the AI ​​chatbot and formats it.

[1808] Input: prompt, AI chatbot response

[1809] Processing: The server parses the received response and formats it appropriately.

[1810] Output: Formatted response data

[1811] Technology used: Flask (a Python microframework)

[1812] Step 7: Sending the response to the device

[1813] Specific operation: The server sends the formatted response to the terminal and simultaneously stores it in data storage.

[1814] Input: Formatted response data

[1815] Processing: The server sends the answer to the terminal as an HTTPS response and stores it in the database.

[1816] Output: HTTPS response, saved to database

[1817] Technologies used: MySQL (database), Socket.io (real-time communication)

[1818] Step 8: Receive and view responses

[1819] Specific operation: The terminal receives the response from the server and displays it to the user.

[1820] Input: HTTPS response (answer data)

[1821] Processing: The device analyzes the received response data and displays it on the UI.

[1822] Output: On-screen display

[1823] Technology used: React.js (front end)

[1824] Step 9: Teacher review and completion

[1825] Specific operation: The teacher uses the device to monitor students' questions and the AI ​​chatbot's answers in real time, and enters supplementary information as needed.

[1826] Input: Student question, AI chatbot answer

[1827] Processing: The teacher reviews the answers and enters additional information if necessary.

[1828] Output:Add additional information

[1829] Technology used: Django (Python web framework)

[1830] The above is the specific processing flow of the program of the system based on the present invention, which enables the system to quickly and accurately answer students' questions.

[1831] (Application example 1)

[1832] 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."

[1833] In traditional education systems and brick-and-mortar stores, teachers and staff have to respond to numerous questions from students and customers in real time, placing a heavy burden on them. It's also difficult to provide quick and accurate answers to a large number of questions. At the same time, students and customers lack the means to quickly obtain detailed information about specific products or learning content. By solving these issues, it's necessary to reduce the burden on teachers and staff and improve student and customer satisfaction.

[1834] 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.

[1835] In this invention, the server includes a means for receiving questions entered by students through their terminals, a means for sending the received questions to an AI chatbot to generate answers, and a means for sending the generated answers to the students' terminals, thereby enabling immediate answers to questions about products in physical stores and learning content.

[1836] A "terminal" is a device through which a user inputs questions and receives answers.

[1837] An "artificial intelligence chatbot" is an artificial intelligence system that analyzes received questions and generates appropriate answers.

[1838] The "server" is a system that receives question data sent from a terminal, transfers it to an AI chatbot, and sends the generated answer to the terminal.

[1839] "Database" means a storage device for recording and managing student questions and generated answers.

[1840] "Brick and mortar store" refers to a retail store or educational facility located in a specific location that customers and students can physically visit.

[1841] Here, the embodiment for carrying out the present invention will be specifically described.

[1842] Overview of system configuration and operation

[1843] This system mainly consists of the following elements: terminals, servers, AI chatbots, and databases. These elements work together to provide quick and accurate answers to questions from students and customers. The detailed operation of each element is explained below.

[1844] Device behavior

[1845] The terminal is the main device where users (students or customers) enter questions and receive answers. Specifically, it works as follows:

[1846] 1. Question input: The user enters a question into the text field on the device and presses the send button. The device collects this input and sends it to the server.

[1847] 2. Sending the question to the server: The device converts the entered question into a specific format (e.g., JSON) and sends it to the server via an HTTPS request.

[1848] 3. Receiving the answer: The answer sent from the server is received and displayed on the screen.

[1849] Server Operation

[1850] The server receives question data sent from the device, forwards it to the AI ​​chatbot, generates answers, and sends the answers back to the device, recording all interactions in a database.

[1851] 1. User authentication: The server receives the authentication information sent from the terminal and performs the authentication process. For example, when a student logs in, the server checks the provided user ID and password against the information in the database.

[1852] 2. Question reception: The server analyzes the question data from the device and temporarily saves the analysis results. The question content is recorded as text data.

[1853] 3. Forwarding the question to the AI ​​chatbot: The server organizes the received question into a suitable format for sending to the AI ​​chatbot, creates an API request, and sends the question to the AI ​​chatbot's system.

[1854] 4. Receiving the answer from the AI ​​chatbot: The server receives the answer from the AI ​​chatbot and prepares to send it to the terminal.

[1855] 5. Sending the answer to the terminal: The generated answer is sent to the terminal and simultaneously saved in the database.

[1856] Artificial Intelligence Chatbot in Action

[1857] The AI ​​chatbot analyzes questions sent from the server and generates appropriate answers. It uses a generative AI model to understand the question and return relevant information as an answer.

[1858] Database Features

[1859] The database will record all student questions and the answers generated by the AI ​​chatbot, and will also manage user authentication information.

[1860] Hardware and software used

[1861] 1. Smartphone: The device where the user enters their question.

[1862] 2. Server: Receives question data, forwards it to the AI ​​chatbot, and generates and sends answers.

[1863] 3. Artificial Intelligence Chatbot: An artificial intelligence system that analyzes questions and generates answers.

[1864] 4. Database: Records and manages questions and answers.

[1865] 5. Libraries used: Software for making HTTP requests, such as the requests library.

[1866] Specific examples

[1867] For example, the following question and answer exchange takes place:

[1868] question

[1869] "Are there any recommended reference books related to the content of this textbook?"

[1870] answer

[1871] "One recommended reference book is 'Learn Chemistry from the Basics'. This will help you learn more deeply."

[1872] Example prompts for generative AI models

[1873] Q: Are there any recommended reference books related to the content of this textbook? A:

[1874] This allows for immediate answers to users' questions, reducing the burden on teachers and store staff and improving customer and student satisfaction.

[1875] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1876] Step 1:

[1877] The user enters a question into the text field on the device and presses the send button. The entered question is collected as text data.

[1878] Input: Question text from user

[1879] Data processing: collecting question text

[1880] Output: Collected question text

[1881] Step 2:

[1882] The terminal converts the collected question text into a specific format (e.g., JSON) and sends it to the server via an HTTPS request.

[1883] Input: Collected question text

[1884] Data processing: Convert question text into JSON format

[1885] Output: Question data in JSON format

[1886] Step 3:

[1887] The server receives the JSON-formatted question data sent from the device and first performs user authentication by checking the provided user ID and password against the information in the database.

[1888] Input: JSON format question data, user ID, password

[1889] Data processing: User ID and password verification

[1890] Output: User authentication success or failure status

[1891] Step 4:

[1892] If the authentication is successful, the server analyzes the question data and temporarily stores the analysis results.

[1893] Input: JSON format question data that was successfully authenticated

[1894] Data processing: Analysis of question text

[1895] Output: Analysis results

[1896] Step 5:

[1897] Based on the analysis results, the server organizes the question into a suitable format for forwarding to the AI ​​chatbot, and creates an API request to send the question to the AI ​​chatbot's system.

[1898] Input: Analysis results

[1899] Data processing: Creating API requests and forwarding questions

[1900] Output: Send request to AI chatbot

[1901] Step 6:

[1902] The artificial intelligence chatbot analyzes the questions it receives and generates appropriate answers using generative AI models.

[1903] Input: Question data sent through the API

[1904] Data processing: Question analysis and answer generation using generative AI models

[1905] Output: The generated answer

[1906] Step 7:

[1907] The server receives the answers generated by the AI ​​chatbot and prepares them to be sent to the device.

[1908] Input: Generated Answer

[1909] Data processing: Formatting of response data

[1910] Output: Formatted response data

[1911] Step 8:

[1912] The server sends the generated answers to the user's device and also stores all interactions in a database.

[1913] Input: Formatted response data

[1914] Data processing: Sending and saving response data

[1915] Output: Display of answers on the user's terminal and recording in the database

[1916] Step 9:

[1917] The terminal receives the response data sent from the server and displays it on the screen for the user.

[1918] Input: Response data sent from the server

[1919] Data processing: Analysis and display of response data

[1920] Output: Answer displayed on the screen

[1921] This series of processing steps allows for immediate answers to users' questions, reducing the burden on teachers and store staff and improving customer and student satisfaction.

[1922] 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.

[1923] MODE FOR CARRYING OUT THE INVENTION

[1924] This invention is a system that includes an AI chatbot to quickly and accurately respond to questions students have during class, reducing the burden on teachers and improving students' learning outcomes. Furthermore, by combining this system with an emotion engine that recognizes students' emotions, it provides more human-like responses and realizes more effective learning support.

[1925] Server Operation

[1926] The server receives question data sent from students' devices, analyzes emotions through an emotion engine, and forwards the data to an AI chatbot to generate answers. It also sends the generated answers to students' devices and records all interactions in a database.

[1927] 1. User Authentication

[1928] The server receives the authentication information sent from the terminal and performs the authentication process.

[1929] For example, when a student logs in, the server checks the provided user ID and password against information in a database.

[1930] 2. Receiving questions

[1931] The server analyzes the question data from the terminal and temporarily stores the analysis results.

[1932] For example, if the question is "Please give me a specific example of a neutralization reaction between an acid and a base in a chemistry experiment," this is recorded as text data.

[1933] 3. Question analysis using emotion engine

[1934] The server sends the received question data to the emotion recognition engine for emotion analysis.

[1935] The emotion engine analyzes the student's emotions (happiness, confusion, anger, etc.) from the question and returns the results to the server.

[1936] 4. Transferring questions to an AI chatbot

[1937] The server organizes the received questions and sentiment analysis results into an appropriate format for sending to the AI ​​chatbot.

[1938] Create an API request and submit your question to the AI ​​chatbot system.

[1939] 5. Receive answers from AI chatbots

[1940] The server receives the response from the AI ​​chatbot and prepares it to be sent to the device.

[1941] For example, format the answer "A specific example of a neutralization reaction is the reaction between hydrochloric acid (HCl) and sodium hydroxide (NaOH)."

[1942] 6. Sending the answer to the device

[1943] The server sends the generated answer to the user's terminal and simultaneously stores it in a database.

[1944] Device behavior

[1945] The terminal is a tool that users (students and teachers) use to interface with the system. It is the main device for students to enter questions and receive answers. Furthermore, it can adjust the tone of the answers based on the analysis results of the emotion engine.

[1946] 1. Enter your question

[1947] The user (student) enters a question in the text field and presses the submit button. The device collects this input and sends it to the server.

[1948] 2. Sending the question to the server

[1949] The terminal converts the entered question into a specific format (e.g., JSON) and sends it to the server via an HTTPS request.

[1950] 3. Receiving a response

[1951] The device receives the response sent from the server, analyzes it, and displays it on the screen. The response can be displayed in a tone that corresponds to the emotion.

[1952] User (Teacher) Role

[1953] Teachers can review the answers generated by the AI ​​chatbot and the results of the sentiment analysis, and provide additional explanations or corrections as needed, thereby improving the accuracy and appropriateness of the answers given to students.

[1954] 1. Check and complete answers

[1955] Teachers can monitor the answers generated by the AI ​​chatbot and the analysis results of the emotion engine in real time and input additional information to fill in any gaps.

[1956] Specific examples

[1957] For example, suppose a student types the following question: "Tell me about the neutralization reaction of an acid and a base."

[1958] 1. Terminal

[1959] Students type their question into the text field and press the submit button.

[1960] 2. Server

[1961] The server receives the question and transfers it to the emotion engine for emotion analysis. For example, it receives the emotion "confused" as the analysis result.

[1962] 3. AI Chatbots

[1963] Receive questions along with sentiment analysis results and generate appropriate answers:

[1964] "An example of a neutralization reaction is the reaction between hydrochloric acid (HCl) and sodium hydroxide (NaOH). The equation is HCl + NaOH → NaCl + H2O."

[1965] 4. Adjust the tone of your response

[1966] The tone of the generated response is adjusted based on the results of sentiment analysis, for example, a polite and gentle response to a confused student.

[1967] 5. Server

[1968] The generated response is received and sent to the terminal.

[1969] 6. Terminal

[1970] The terminal receives the response and displays it on the screen.

[1971] 7. Teacher

[1972] Teachers can review students' questions, the AI ​​chatbot's answers, and sentiment analysis results, and enter additional explanations if necessary.

[1973] The system of the present invention not only answers students' questions quickly and accurately, but also provides appropriate responses that reflect the students' emotions, thereby reducing the burden on teachers and further improving the effectiveness of education.

[1974] The processing flow will be explained below.

[1975] Step 1:

[1976] The user (student) opens the chat interface on their device, enters a question in the text field, and clicks the "Send" button. For example, they might type, "Please tell me about the neutralization reaction of an acid and a base."

[1977] Step 2:

[1978] The terminal retrieves the entered question. The retrieved question is packaged in JSON format. The generated JSON data looks like this: {"question": "Please tell me about the neutralization reaction of an acid and a base", "userId": "student123"}

[1979] Step 3:

[1980] The device sends the packaged data to the server via an HTTPS request, sending the data using secure communication.

[1981] Step 4:

[1982] The server receives the HTTPS request. It parses the received data using a JSON parser and extracts the question and user ID. For example, it extracts the question and user ID.

[1983] Step 5:

[1984] The server temporarily stores the retrieved question and user ID. It also sends the question data to the emotion engine for analysis. For example, send data to the emotion engine: {"text": "Please tell me about the neutralization reaction of acid and base"}

[1985] Step 6:

[1986] The emotion engine analyzes the data and identifies student emotions from questions, for example recognizing the emotion "confused."

[1987] Step 7:

[1988] The emotion engine sends the emotion analysis results back to the server. The server receives this and converts the question data and emotion analysis results into request data to be sent to the AI ​​chatbot. For example, {"prompt": "Please tell me about the neutralization reaction of acid and base", "emotion": "confused"}.

[1989] Step 8:

[1990] The server sends the formatted request to the AI ​​chatbot, which sends an API request and waits for a response.

[1991] Step 9:

[1992] The AI ​​chatbot generates an appropriate answer based on the question received from the server and the results of sentiment analysis. For example, it might create an answer like, "A specific example of a neutralization reaction is the reaction between hydrochloric acid (HCl) and sodium hydroxide (NaOH). The reaction equation is HCl + NaOH → NaCl + H2O."

[1993] Step 10:

[1994] The AI ​​chatbot generates an answer and sends it to the server, for example, {"response": "A specific example of a neutralization reaction is the reaction between hydrochloric acid (HCl) and sodium hydroxide (NaOH). The reaction equation is HCl + NaOH → NaCl + H2O.", "emotion": "confused"}.

[1995] Step 11:

[1996] The server receives the AI ​​chatbot's responses and the results of the sentiment analysis, and then formats the response data. Based on the sentiment analysis results, the server adjusts the tone of the response. For example, it responds to a confused student in a polite and gentle tone.

[1997] Step 12:

[1998] The server sends the formatted response data to the user's device, and simultaneously stores this exchange of queries and responses in a database.

[1999] Step 13:

[2000] The device receives the HTTP response sent from the server, analyzes the received data with a JSON parser, and extracts the answer.

[2001] Step 14:

[2002] The device displays the analyzed answers on the chat interface so that the user (student) can check them. The student can read the answers displayed on the screen.

[2003] Step 15:

[2004] The user (teacher) monitors students' questions, the AI ​​chatbot's responses, and the sentiment analysis results in real time during the lesson, and inputs supplementary information as needed to provide students with further explanations.

[2005] In this way, the system can respond quickly and accurately to questions from students, and its emotion engine can provide appropriate responses that take students' emotions into consideration. This can reduce the burden on teachers and improve learning effectiveness.

[2006] Example 2

[2007] 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."

[2008] Educational institutions are required to respond quickly and appropriately to questions that students have during class or self-study. However, conventional systems place a heavy burden on teachers, and responses to students' questions can be delayed or insufficient. Furthermore, responses that do not take into account students' feelings can lead to a loss of motivation to learn or misunderstandings. The present invention aims to solve these problems and improve students' learning outcomes.

[2009] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[2010] In this invention, the server includes means for receiving questions entered by students through their terminals, means for transmitting the received questions to an emotion analysis engine to analyze emotions, means for transmitting the questions including the emotion analysis results to an AI chatbot to generate answers, means for adjusting the tone of the generated answers based on the emotion analysis results, means for transmitting the tone-adjusted answers to the students' terminals, and means for storing the students' questions and the generated answers in a database. This makes it possible to respond quickly and appropriately to students' questions and provide appropriate responses according to the students' emotions.

[2011] "Student" means a learner enrolled in an educational institution.

[2012] "Device" means a computer or mobile device used by a student or educator.

[2013] A "question" is an inquiry that a student types into a terminal to seek clarification.

[2014] An "emotion analysis engine" is software or hardware that analyzes student emotions contained in questions.

[2015] An "artificial intelligence chatbot" is an artificial intelligence system that automatically generates answers to questions it receives.

[2016] "Tone adjustment" refers to the process of appropriately changing the wording and tone of a response based on the results of sentiment analysis.

[2017] A "server" is a central computer that handles data processing and communications for the entire system.

[2018] "Database" refers to a storage device or system for storing student questions and generated answers.

[2019] "Educator" means a person whose job is to provide education to students.

[2020] "Supplemental information" refers to additional information provided by educators to enhance students' understanding.

[2021] This invention is a system for quickly and accurately responding to questions that students have during class or self-study. The system is mainly composed of a server, terminals, a sentiment analysis engine, an AI chatbot, and a database.

[2022] A terminal is a computer or mobile device used by a student or teacher to enter questions and display answers. Students submit their questions by typing them into a text field on the terminal and pressing a submit button. The terminal also converts the questions into a format (e.g., JSON) and sends them to the server via an HTTPS request.

[2023] The server receives the question data sent from the device and temporarily stores it. The received question is sent to an emotion analysis engine, where emotion analysis is performed. This emotion analysis engine analyzes the student's emotion contained in the question (e.g., confusion, anger, joy, etc.) and returns the results to the server.

[2024] The server organizes the analyzed emotions and questions into an appropriate format and creates an API request to send to the AI ​​chatbot. The AI ​​chatbot generates an answer to the received question. It remembers past questions and answers and can quickly provide answers to similar questions. For example, in response to the question, "Please tell me about the neutralization reaction of an acid and a base," it generates the answer, "A specific example of a neutralization reaction is the reaction between hydrochloric acid (HCl) and sodium hydroxide (NaOH). The equation is HCl + NaOH → NaCl + H2O."

[2025] The server then receives the generated answer and adjusts the tone of the answer based on the emotion analysis results. For example, if the emotion analyzed is "confused," the answer will be adjusted to a more polite and gentle tone. The adjusted tone is then sent to the student's device and displayed on the screen.

[2026] Educators can monitor students' questions and the AI ​​chatbot's responses in real time through the system and provide supplementary information as needed. If an educator feels that additional explanation is needed, they can enter supplementary information at any time.

[2027] Examples of prompt statements

[2028] "Please tell me about the neutralization reaction of an acid and a base."

[2029] "Please give a brief history of ancient Rome."

[2030] "Tell me how to train a dog"

[2031] This allows for quick and appropriate responses to students' questions, and provides appropriate responses based on students' emotions, improving educational effectiveness. Furthermore, by reducing the burden on educators, it becomes possible to provide high-quality education to more students.

[2032] The flow of the identification process in the second embodiment will be described with reference to FIG.

[2033] Step 1:

[2034] User Authentication

[2035] The server receives the user ID and password sent from the terminal.

[2036] Checks the received credentials against existing data in the database.

[2037] If authentication is successful, a session ID is issued to the student and a receipt is returned to the terminal.

[2038] Input: User ID, Password

[2039] Output: Session ID (if successful)

[2040] Step 2:

[2041] Enter a question

[2042] The terminal accepts questions that students enter into a text field.

[2043] For example, a student types, "Tell me about the neutralization reaction of an acid and a base."

[2044] Input: Question text entered by the student

[2045] Output: Question text (as is)

[2046] Step 3:

[2047] Submit a question

[2048] The terminal converts the entered question into JSON format and sends it to the server via an HTTPS request.

[2049] The transmitted data includes the question content and session ID.

[2050] Input: Question text, Session ID

[2051] Output: Question data in JSON format

[2052] Step 4:

[2053] Receiving questions

[2054] The server receives the question data sent from the terminal and temporarily stores it.

[2055] Input: JSON data sent from the terminal

[2056] Output: Temporarily saved question data

[2057] Step 5:

[2058] Emotion analysis

[2059] The server sends the received question data to the sentiment analysis engine.

[2060] The emotion analysis engine analyzes the student's emotion contained in the question and returns the result to the server.

[2061] For example, the emotion of "confusion" is analyzed.

[2062] Input: Question data

[2063] Output: Emotion analysis result (e.g. confused)

[2064] Step 6:

[2065] Transfer to AI chatbot

[2066] The server sends the question data including the emotion analysis results to the artificial intelligence chatbot.

[2067] Create an API request and organize the question and sentiment analysis results.

[2068] Input: Sentiment analysis results, question data

[2069] Output: API request

[2070] Step 7:

[2071] Receive answers from AI chatbots

[2072] The server receives the response from the artificial intelligence chatbot.

[2073] For example, the answer generated is, "A specific example of a neutralization reaction is the reaction between hydrochloric acid (HCl) and sodium hydroxide (NaOH). The reaction equation is HCl + NaOH → NaCl + H2O."

[2074] Input: API request

[2075] Output: Generated answer text

[2076] Step 8:

[2077] Adjusting the tone of your response

[2078] The server adjusts the tone of the response based on the sentiment analysis results.

[2079] For example, respond to the emotion of "confusion" with a more polite and gentle tone.

[2080] Input: Generated answer text, sentiment analysis results

[2081] Output: Tone-adjusted answer text

[2082] Step 9:

[2083] Sending answers to the device

[2084] The server sends the tone-adjusted response to the student's terminal.

[2085] At the same time, all interactions are stored in a database.

[2086] Input: Tone-adjusted answer text

[2087] Output: Submitted answers, saved data

[2088] Step 10:

[2089] Show Answers

[2090] The terminal receives the response sent from the server and displays it on the screen.

[2091] For example, it will say, "A specific example of a neutralization reaction is the reaction between hydrochloric acid (HCl) and sodium hydroxide (NaOH). The equation is HCl + NaOH → NaCl + H2O."

[2092] Input: Answer text

[2093] Output: On-screen display

[2094] Step 11:

[2095] Supplementary explanation input (teacher's role)

[2096] Teachers monitor students' questions and the AI ​​chatbot's answers in real time.

[2097] If necessary, supplementary information is input from the terminal.

[2098] Input: Teacher supplementary information

[2099] Output: Complemented answer

[2100] (Application example 2)

[2101] 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."

[2102] In traditional educational systems, responding to students' questions during class can be a significant burden on teachers, making it difficult to provide individual support. Furthermore, when students don't understand something or are confused, it's difficult to respond appropriately while taking their feelings into consideration. This can lead to a decline in students' learning effectiveness. Furthermore, in today's educational environment, where prompt and accurate responses are required, there is a lack of appropriate systems.

[2103] The identification process by the identification processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes means for receiving questions entered by students through their terminals, means for transmitting the received questions to an emotion recognition engine for emotion analysis, means for transmitting the analysis results to an AI chatbot to generate answers, means for transmitting the generated answers to the students' terminals and adjusting the tone of the answers based on the emotion analysis results, and means for storing the students' questions and the generated answers in a database. This makes it possible to not only quickly and accurately answer students' questions but also to provide appropriate responses based on the students' emotions. It also reduces the burden on teachers while further improving educational effectiveness.

[2104] "Student" means a person who uses the education system to learn.

[2105] A "terminal" is a device through which a user can input questions and receive answers. Examples include smartphones, tablets, and personal computers.

[2106] A "question" is information that a student inputs via their device. This includes questions about the learning content or requests for clarification.

[2107] The "emotion recognition engine" is a system that analyzes the emotions expressed in students' questions. For example, it has the ability to determine emotions such as joy, confusion, and anger.

[2108] "Sentiment analysis" is the process of analyzing the emotions contained in students' questions. This is done through an emotion recognition engine.

[2109] An "artificial intelligence chatbot" is a program that automatically generates answers to input questions.

[2110] "Answer" refers to the information generated by the AI ​​chatbot in response to a student's question.

[2111] "Tone" refers to the expression style of the generated answer. It is adjusted to a polite or gentle tone based on the results of sentiment analysis.

[2112] "Database" refers to an information store that stores student questions and generated answers.

[2113] "Teachers" are those who are administrators of the education system and whose role is to provide supplementary information when necessary.

[2114] System Configuration

[2115] This invention is a system that includes an AI chatbot to quickly and accurately respond to questions students have during class. The system is primarily composed of student devices, a server, and a teacher interface. It also has advanced functionality that combines an emotion recognition engine with an AI chatbot.

[2116] Hardware and software used

[2117] Hardware: Smartphone, tablet or personal computer (student device), server

[2118] Software: Python, HTTP request library (requests), AI chatbot API, emotion recognition engine API, database

[2119] Details of data processing and data calculation

[2120] 1. User authentication:

[2121] The server receives the user ID and password sent from the student's device and performs the authentication process, during which the device sends the authentication information to the server using an HTTPS request.

[2122] 2. Question reception and sentiment analysis:

[2123] When a student inputs a question through the device, the device sends the question to the server. The server receives the question and sends it to the emotion recognition engine for emotion analysis. The emotion recognition engine then analyzes the student's emotion (e.g., confusion, joy, anger, etc.) from the question and returns the result to the server.

[2124] 3. Answer generation by AI chatbots:

[2125] The server sends the received question and the results of the sentiment analysis to an AI chatbot, which uses that information to generate an appropriate answer.

[2126] 4. Tone and send your response:

[2127] The server adjusts the tone of the response received from the AI ​​chatbot based on the results of emotion analysis. For example, it selects a polite and gentle response for a confused student. The adjusted response is then sent to the device.

[2128] 5. Data storage:

[2129] The server stores the student's question and the generated answer in a database, allowing for quick responses to similar questions in the future.

[2130] Specific examples of operation procedures

[2131] Example prompt sentence:

[2132] User ID: user123

[2133] Password: password456

[2134] Question: Can you give me a concrete example of an acid-base neutralization reaction in a chemistry experiment?

[2135] For example, if a student enters a question using the prompt sentence described above, the server performs authentication and receives the question. If the server then determines using an emotion recognition engine that the student is confused, it sends that information to an AI chatbot, which generates an answer as a concrete example of a neutralization reaction: "There is a reaction between hydrochloric acid (HCl) and sodium hydroxide (NaOH). The equation is HCl + NaOH → NaCl + H2O." This answer is adjusted to a gentle tone to match the student's confused emotion and is displayed on the student's device. This series of interactions is also stored in a database.

[2136] As a result, this invention not only answers students' questions quickly and accurately, but also provides appropriate responses according to their emotions, thereby reducing the burden on teachers and improving students' learning effectiveness.

[2137] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[2138] Step 1:

[2139] The server receives the user ID and password sent from the user's (student's) device. Based on the received authentication information, it compares it with the information in the database and executes the authentication process. If authentication is successful, it generates an authentication token and returns it to the device. The input is the user ID and password, and the output is the authentication token.

[2140] Step 2:

[2141] The terminal obtains the question entered by the student from the text field and sends it to the server along with the authentication token. The input is the student's question text and the authentication token, and the output is an HTTP request to the server.

[2142] Step 3:

[2143] The server receives questions sent from the device. The questions are temporarily saved and sent to the emotion recognition engine for emotion analysis. The emotion recognition engine analyzes the student's emotion from the question content and returns the results to the server. The input is the student's question text, and the output is the emotion analysis result.

[2144] Step 4:

[2145] The server sends the sentiment analysis results and question text to an AI chatbot, which then generates an appropriate answer based on that information. The input is the question text and sentiment analysis results, and the output is the generated answer.

[2146] Step 5:

[2147] The server receives the generated response and adjusts the tone of the response based on the sentiment analysis results. For example, it adjusts the tone of the response to a more gentle one for a confused student. The input is the generated response and the sentiment analysis results, and the output is the tone-adjusted response.

[2148] Step 6:

[2149] The server sends the tone-adjusted answer to the terminal and stores the student's question and the generated answer in a database. The input is the tone-adjusted answer, and the output is sending it to the terminal and storing it in the database.

[2150] Step 7:

[2151] The terminal receives the tone-adjusted response sent from the server and displays it on the screen. The input is the tone-adjusted response, and the output is the screen display.

[2152] 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.

[2153] 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.

[2154] 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.

[2155] 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.

[2156] 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.

[2157] 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.

[2158] 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).

[2159] 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.

[2160] 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."

[2161] 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.

[2162] 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).

[2163] 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.

[2164] 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.

[2165] 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.

[2166] 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.

[2167] 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.

[2168] 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.

[2169] 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.

[2170] 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.

[2171] 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.

[2172] 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.

[2173] The following is further disclosed regarding the above embodiment.

[2174] (Claim 1)

[2175] a means for receiving questions entered by students via the terminals;

[2176] a means for sending the received questions to an artificial intelligence chatbot to generate answers;

[2177] a means for transmitting the generated answers to the student's device;

[2178] a means for storing student questions and generated answers in a database;

[2179] A system including:

[2180] (Claim 2)

[2181] 10. The system of claim 1, further comprising means for the artificial intelligence chatbot to remember past questions and answers and provide quick answers to similar questions.

[2182] (Claim 3)

[2183] 10. The system of claim 1, further comprising means for a teacher to monitor student questions and the artificial intelligence chatbot's answers in real time and provide supplemental information as needed.

[2184] "Example 1"

[2185] (Claim 1)

[2186] a means for receiving questions entered by students via the terminals;

[2187] means for transmitting the received question to an artificial intelligence model to generate an answer;

[2188] a means for transmitting the generated answers to the student's device;

[2189] a means for storing student questions and generated answers in data storage;

[2190] means for receiving user authentication information and performing authentication;

[2191] A means for analyzing and temporarily storing received questions;

[2192] a means for converting the received question into a specific format and sending it to an artificial intelligence model;

[2193] means for formatting and transmitting the answer received from the artificial intelligence model to the terminal;

[2194] A system including:

[2195] (Claim 2)

[2196] 10. The system of claim 1, further comprising means for the artificial intelligence model to remember past questions and answers and provide quick answers to similar questions.

[2197] (Claim 3)

[2198] 10. The system of claim 1, further comprising means for the teacher to monitor the student's questions and the artificial intelligence model's answers in real time and provide supplemental information as needed.

[2199] "Application Example 1"

[2200] (Claim 1)

[2201] a means for receiving questions entered by students via the terminals;

[2202] a means for sending the received questions to an artificial intelligence chatbot to generate answers;

[2203] a means for transmitting the generated answers to the student's device;

[2204] a means for storing student questions and generated answers in a database;

[2205] A means to provide immediate answers to questions about products and learning content in physical stores,

[2206] A system including:

[2207] (Claim 2)

[2208] 10. The system of claim 1, further comprising means for the artificial intelligence chatbot to remember past questions and answers and provide quick answers to similar questions.

[2209] (Claim 3)

[2210] 10. The system of claim 1, further comprising means for a teacher or store staff member to monitor student or customer questions and the artificial intelligence chatbot's responses in real time and provide supplemental information as needed.

[2211] "Example 2: Combining Emotion Engines"

[2212] (Claim 1)

[2213] a means for receiving questions entered by students via the terminals;

[2214] means for transmitting the received questions to a sentiment analysis engine for sentiment analysis;

[2215] a means for sending the question including the sentiment analysis result to an artificial intelligence chatbot to generate an answer;

[2216] means for adjusting the tone of the generated response based on the sentiment analysis results;

[2217] a means for transmitting the tone-adjusted responses to the student's device;

[2218] a means for storing student questions and generated answers in a database;

[2219] A system including:

[2220] (Claim 2)

[2221] 10. The system of claim 1, further comprising means for the artificial intelligence chatbot to remember past questions and answers and provide quick answers to similar questions.

[2222] (Claim 3)

[2223] 10. The system of claim 1, further comprising means for an educator to monitor student questions and the artificial intelligence chatbot's responses in real time and provide supplemental information as needed.

[2224] "Application example 2 when combining emotion engines"

[2225] (Claim 1)

[2226] a means for receiving questions entered by students via the terminals;

[2227] means for transmitting the received question to an emotion recognition engine for emotion analysis;

[2228] a means for transmitting the analysis results to an artificial intelligence chatbot to generate a response;

[2229] a means for transmitting the generated answer to a student's device and adjusting the tone of the answer based on the result of the sentiment analysis;

[2230] a means for storing student questions and generated answers in a database;

[2231] A system including:

[2232] (Claim 2)

[2233] 10. The system of claim 1, further comprising means for the artificial intelligence chatbot to remember past questions and answers and provide quick answers to similar questions.

[2234] (Claim 3)

[2235] 10. The system of claim 1, further comprising means for a teacher to monitor student questions and the artificial intelligence chatbot's answers in real time and provide supplemental information as needed. [Explanation of symbols]

[2236] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot< / url:> < / url:> < / url:> < / url:>

Claims

1. a means for receiving questions entered by students via the terminals; a means for sending the received questions to an artificial intelligence chatbot to generate answers; a means for transmitting the generated answers to the student's device; a means for storing student questions and generated answers in a database; A system including:

2. The system of claim 1 , further comprising means for the artificial intelligence chatbot to remember past questions and answers and provide quick answers to similar questions.

3. 10. The system of claim 1, further comprising means for the teacher to monitor the students' questions and the artificial intelligence chatbot's answers in real time and provide supplemental information as needed.

Images