system

Abstract

We provide the system. [Solution] A means of using generative artificial intelligence to analyze questions from educational institutions and generate appropriate learning responses, A means for sending the generated learning response to the user's terminal, A means for displaying learning responses sent to the user's terminal, A system that includes this.

Description

Technical Field

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

Background Art

[0002] Patent Document 1 discloses a method for controlling a persona chatbot, which is performed by at least one processor, and includes steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to an explanation of the chatbot's character, encoding the prompt, and inputting the encoded prompt into a language model to generate a chatbot utterance that responds to the user utterance.

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In educational institutions, there is a need for individualized guidance that responds immediately to the questions and learning progress of each student. However, there is a problem that the human burden on teachers increases, and it is difficult to provide efficient and individualized educational support. In addition, with the increasing need for online education, there is a need for support that allows students to learn at their own pace anytime and anywhere.

Means for Solving the Problems

[0005] The present invention relates to an educational support system utilizing generative artificial intelligence, which includes means for analyzing questions input from educational facilities and generating appropriate learning answers. The generated learning answers are transmitted to a user terminal and displayed on the terminal. Furthermore, by including means for generating and providing personalized learning content, and means for managing students' learning progress and presenting learning plans based on data, the system realizes efficient and personalized educational support.

[0006] "Generative artificial intelligence" is an artificial intelligence technology that can analyze user input and generate appropriate responses in natural language.

[0007] An "educational institution" is an institution or organization whose purpose is to provide education to students, and includes schools, cram schools, and the like.

[0008] "Means for analyzing questions" refers to technologies that receive questions entered by users, analyze their content, and perform the process of understanding them.

[0009] "Means for generating learned responses" refer to processing technologies that create appropriate knowledge provision and problem-solving methods based on analyzed questions.

[0010] A "user terminal" is an electronic device used by a user to input or output information, and includes personal computers, tablets, smartphones, etc.

[0011] "Personalized learning content" refers to learning materials and problem sets that are customized to each student's individual learning needs and progress.

[0012] "Methods for managing learning progress" refer to technologies for recording and evaluating students' learning status and achievements, and using that information to determine what they should learn next.

[0013] "Methods for presenting learning plans" refer to techniques that show students specific learning procedures and goals based on managed learning progress data. [Brief explanation of the drawing]

[0014] [Figure 1] It is a conceptual diagram showing an example of the configuration of a data processing system according to the first embodiment. [Figure 2] It is a conceptual diagram showing an example of the main functions of a data processing device and a smart device according to the first embodiment. [Figure 3] It is a conceptual diagram showing an example of the configuration of a data processing system according to the second embodiment. [Figure 4] It is a conceptual diagram showing an example of the main functions of a data processing device and smart glasses according to the second embodiment. [Figure 5] It is a conceptual diagram showing an example of the configuration of a data processing system according to the third embodiment. [Figure 6] It is a conceptual diagram showing an example of the main functions of a data processing device and a headset-type terminal according to the third embodiment. [Figure 7] It is a conceptual diagram showing an example of the configuration of a data processing system according to the fourth embodiment. [Figure 8] It is a conceptual diagram showing an example of the main functions of a data processing device and a robot according to the fourth embodiment. [Figure 9] It shows an emotion map to which a plurality of emotions are mapped. [Figure 10] It shows an emotion map to which a plurality of emotions are mapped. [Figure 11] It is a sequence diagram showing the processing flow of the data processing system in Example 1. [Figure 12] It is a sequence diagram showing the processing flow of the data processing system in Application Example 1. [Figure 13] It is a sequence diagram showing the processing flow of the data processing system in Example 2 when an emotion engine is combined. [Figure 14] It is a sequence diagram showing the processing flow of the data processing system in Application Example 2 when an emotion engine is combined.

Mode for Carrying Out the Invention

[0015] Hereinafter, an example of an embodiment of a system according to the technology of the present disclosure will be described with reference to the accompanying drawings.

[0016] First, the terms used in the following description will be explained.

[0017] In the following embodiments, the numbered processor (hereinafter simply referred to as "processor") may be a single arithmetic unit or a combination of multiple arithmetic units. Also, the processor may be a single type of arithmetic unit or a combination of multiple types of arithmetic units. Examples of arithmetic units include a CPU (Central Processing Unit), a GPU (Graphics Processing Unit), a GPGPU (General-Purpose computing on Graphics Processing Units), an APU (Accelerated Processing Unit), and the like.

[0018] In the following embodiments, the numbered RAM (Random Access Memory) is a memory in which information is temporarily stored and is used as a work memory by the processor.

[0019] In the following embodiments, the numbered storage is one or more non-volatile storage devices that store various programs and various parameters, etc. Examples of non-volatile storage devices include flash memory (SSD (Solid State Drive)), magnetic disks (e.g., hard disks), or magnetic tapes, etc.

[0020] In the following embodiments, the signed communication interface (I / F) is an interface that includes a communication processor and an antenna, etc. The communication interface manages communication between multiple computers. Examples of communication standards applicable to the communication interface include wireless communication standards such as 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), or Bluetooth (registered trademark).

[0021] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B." That is, "A and / or B" means that it may be A alone, or B alone, or a combination of A and B. Furthermore, in this specification, the same concept as "A and / or B" applies when expressing three or more things linked by "and / or."

[0022] [First Embodiment]

[0023] Figure 1 shows an example of the configuration of the data processing system 10 according to the first embodiment.

[0024] As shown in Figure 1, the data processing system 10 includes a data processing device 12 and a smart device 14. An example of the data processing device 12 is a server.

[0025] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0026] The smart device 14 comprises a computer 36, a reception device 38, an output device 40, a camera 42, and a communication interface 44. The computer 36 comprises a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The reception device 38, output device 40, and camera 42 are also connected to the bus 52.

[0027] The reception device 38 is equipped with a touch panel 38A and a microphone 38B, etc., and receives user input. The touch panel 38A receives user input by detecting contact with an object (e.g., a pen or finger). The microphone 38B receives user input by detecting the user's voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the data indicating the user input.

[0028] The output device 40 includes a display 40A and a speaker 40B, and presents data to the user 20 by outputting the data in a form perceptible to the user 20 (e.g., audio and / or text). The display 40A displays visible information such as text and images according to instructions from the processor 46. The speaker 40B outputs audio according to instructions from the processor 46. The camera 42 is a small digital camera equipped with an optical system such as a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0029] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various types of information between processor 46 and processor 28 via network 54.

[0030] Figure 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0031] As shown in Figure 2, in the data processing device 12, a specific processing is performed by the processor 28. A specific processing program 56 is stored in the storage 32. The specific processing program 56 is an example of a "program" related to the technology of this disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0032] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0033] In the smart device 14, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The reception output program 60 is used in conjunction with a specific processing program 56 by the data processing system 10. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0034] Next, the specific processing performed by the specific processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the smart device 14 as the "terminal".

[0035] The system of this invention consists of a server equipped with generative artificial intelligence, a terminal used by the user, and the user themselves. The server's main role is to analyze questions from educational institutions and handle a series of processes to generate appropriate learning answers. When the user inputs a question from the terminal, that information is sent to the server.

[0036] Server operation

[0037] When the server receives a question from a terminal, it inputs it into a question analysis module. This module analyzes the question using natural language processing techniques to identify the problem to be solved. After analysis, the server's generated AI model produces an appropriate trained response based on the results. This response is then formatted and sent to the terminal.

[0038] Terminal operation

[0039] When the terminal receives a response from the server, it displays that information appropriately in the user interface. This allows the user to confirm their answers. The terminal also has the functionality to track the user's learning progress and periodically send that data to the server.

[0040] User actions

[0041] Users can input questions and topics they want to understand during their learning process via their device. Based on the answers displayed on the device from the server, they can then proceed with their learning. Furthermore, users can monitor their learning progress through their device and effectively progress according to the generated learning plan.

[0042] As a concrete example, consider a student studying math problems who types "I want to understand the basic concepts of differentiation." This question is sent from the terminal to the server, where the server's AI searches for and generates the necessary information. The answer, including the definition, usage, and examples of differentiation, is then displayed on the terminal, allowing the student to deepen their understanding on the spot.

[0043] In this way, the system of the present invention realizes efficient and individualized educational support.

[0044] The following describes the processing flow.

[0045] Step 1:

[0046] The user uses their device to input questions that arise during their learning in text format. For example, they might input, "Please explain the trigonometric formulas in detail."

[0047] Step 2:

[0048] The terminal converts the text entered by the user into the appropriate format and sends it to the server. Additional data, such as the user's login information, is also added as needed.

[0049] Step 3:

[0050] The server receives data sent from the terminal and passes it to the question analysis module. The question analysis module uses natural language processing to analyze the intent of the user's question.

[0051] Step 4:

[0052] The AI ​​model on the server generates appropriate trained answers based on data obtained from the question analysis module. These answers include information about trigonometric functions that the user asked about.

[0053] Step 5:

[0054] The server formats the generated response and sends it back to the terminal as a data packet for transmission. This formatting includes readability and highlighting of necessary information.

[0055] Step 6:

[0056] The terminal receives data from the server, analyzes its contents, and displays it on the user interface. Users can refer to the information displayed on the terminal and use it to aid their learning.

[0057] Step 7:

[0058] Users progress through the learning process based on the information provided, and continue the learning cycle by entering the next step or new questions after resolving their doubts.

[0059] (Example 1)

[0060] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server," and the smart device 14 will be referred to as the "terminal."

[0061] Traditional educational support systems have faced challenges in providing timely and appropriate learning responses tailored to individual learners' needs, thus failing to maximize learning effectiveness. Furthermore, they struggled to effectively manage learners' progress and provide appropriate feedback. This hindered the development and implementation of efficient learning plans.

[0062] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0063] In this invention, the server includes means for analyzing natural language questions from educational institutions and extracting necessary information, means for generating appropriate learning responses using a generative AI model, and means for recording the user's learning progress and transmitting that data to the server device. This enables the provision of personalized learning responses to learners in real time, making efficient and effective learning support possible.

[0064] "Generative artificial intelligence" is an artificial intelligence technology that analyzes natural language and generates appropriate answers or content in response to individual questions and requests.

[0065] An "educational institution" is a facility or organization that provides knowledge and skills to learners.

[0066] "Natural language" refers to the language that humans use on a daily basis, and is a linguistic form that is expressed through sound or writing.

[0067] A "generative AI model" is an algorithmic model that performs natural language processing and response generation by learning from large amounts of data.

[0068] A "user device" is an electronic terminal that learners can directly operate to input questions and check answers.

[0069] A "user interface" is a visual or operational framework for how a user interacts with a device or system.

[0070] "Learning progress" refers to indicators or data that show how far a learner is progressing toward a specific learning objective.

[0071] "Personalized learning content" refers to educational materials and information that are customized according to each learner's level of understanding and interests.

[0072] The system of this invention consists of a server equipped with generative artificial intelligence, a terminal used by learners, and the learners themselves. The server receives questions from educational institutions and generates appropriate learning answers based on them. Specifically, a question analysis module using natural language processing technology runs on the server and extracts important information from the input questions. Python's NLTK library and SpaCy are used for the analysis.

[0073] The server uses a generative AI model to generate trained responses based on the extracted information. This model includes a natural language generation algorithm based on a large training dataset, and for example, leverages widely used language model frameworks. The generated responses are formatted into a data format and sent to the terminal.

[0074] The device displays learning responses received from the server through a user interface. The UI uses HTML and JavaScript (registered trademark) and is designed to provide information in a visually clear and easy-to-use format. Furthermore, the device can record the user's learning progress and periodically send it to the server.

[0075] Users can use their devices to input questions or topics they want to learn about on the spot. For example, by entering a prompt such as "I want to understand the basic concepts of differentiation," the generative AI model will provide a specific explanation, supporting the learner's understanding. Users can also check their progress through their devices, enabling them to learn more efficiently. In this way, the entire system works together to provide personalized educational support for learners.

[0076] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0077] Step 1:

[0078] The user enters a question into the input field on the terminal. For example, they might enter a prompt in text format such as, "I want to understand the basic concepts of differentiation." The input data is converted to JSON format on the terminal and prepared for transmission to the server.

[0079] Step 2:

[0080] The terminal sends the user's input question to the server as an HTTP request in JSON format. The input contains the user's question, and the server receives it as output. AJAX technology is used to achieve asynchronous communication.

[0081] Step 3:

[0082] The server extracts questions from the received JSON data. Natural language processing techniques are used to analyze the input text data and identify important keywords and phrases. Python's NLTK library and SpaCy are used for this analysis. The analysis results clarify the problems that need to be solved.

[0083] Step 4:

[0084] The server inputs the analysis results into a generating AI model, which then generates appropriate trained responses. This model is trained on a large dataset and includes natural language generation algorithms. The input is the analysis results, and the output is specific and relevant responses.

[0085] Step 5:

[0086] The server formats the generated response into HTML or JSON format and sends this data to the terminal. The output data is formatted for easy display in the user interface. The formatted data is returned to the terminal as an HTTP response.

[0087] Step 6:

[0088] The terminal analyzes the response data received from the server and prepares it for display in the user interface. JavaScript is used to dynamically update the page. HTML is used to ensure a clear and user-friendly presentation. The user reviews the displayed responses and continues their learning.

[0089] Step 7:

[0090] The device records the user's learning progress and sends data to the server at regular intervals. Input includes learning history and progress data, while output is stored in a database used for adjusting the learning process. This data is later used for analysis and optimizing the learning plan.

[0091] (Application Example 1)

[0092] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server," and the smart device 14 will be referred to as the "terminal."

[0093] In modern education, providing effective learning support tailored to the individual needs of learners is crucial. However, traditional educational support systems have shortcomings in providing personalized learning experiences for individual learners and efficiently delivering information on local educational events. In addition, accurately understanding learners' progress and presenting appropriate learning plans based on that progress remains a challenge.

[0094] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0095] In this invention, the server includes means for analyzing questions from educational organizations using generative artificial intelligence and generating appropriate learning responses; means for transmitting the generated learning responses to a user device; means for displaying the learning responses transmitted to the user device; and means for acquiring and presenting location-based educational event information. This enables personalized educational experiences and efficient information delivery.

[0096] "Generative artificial intelligence" is a type of artificial intelligence that generates responses in natural language based on input data.

[0097] An "educational organization" refers to an organization or institution that provides education-related services, such as schools or online learning platforms.

[0098] "Learning response" refers to the answers and explanations provided by generative artificial intelligence in response to a learner's question.

[0099] "User device" refers to a device used by an individual user, including smartphones and tablets.

[0100] "Display means" refers to an interface for visually representing data on a user device, and includes screens and displays.

[0101] "Educational event information" refers to information about learning activities such as lectures and seminars held both within and outside the local area.

[0102] "Location-based information" refers to information related to a specific geographical area and is useful for providing data that takes the user's current location into account.

[0103] The system for realizing this invention mainly consists of a server, a user terminal, and a generative AI model. The server uses state-of-the-art natural language processing technology to receive and analyze questions from educational organizations. Based on this analysis, the generative AI model generates appropriate learned responses and sends them to the user terminal. The server uses software such as Python and HTTP request libraries to efficiently receive and send data.

[0104] User terminals include devices such as smartphones and tablets, and display received learning responses on the user interface. The terminals also allow learners to access local educational event information and retrieve educational resources in real time.

[0105] As a concrete example, suppose the server receives a prompt from a user asking, "Please tell me the date of the next online programming workshop." In response to this question, the server analyzes the situation and uses AI to generate an appropriate response. Then, it sends the response to the user's terminal and displays it along with the learning event information, making it easy for the user to prepare for participation.

[0106] This process enables the present invention to provide efficient and personalized educational support.

[0107] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0108] Step 1:

[0109] The user enters the question they want to know via the terminal. The entered question is sent to the server through the user interface on the terminal. The input is text data from the user, and the output is request information to the server.

[0110] Step 2:

[0111] The server uses natural language processing techniques to analyze questions received from terminals. Specifically, it identifies the intent of the question and extracts the necessary information. In this process, the user's question is received as input, and the analyzed problem statement is generated as output.

[0112] Step 3:

[0113] Based on the analysis results, a generative AI model on the server generates an appropriate learned response. In this process, the input is the problem statement, and the output is a natural language response to be presented to the user. The generative AI model performs data retrieval and generation to include relevant learning content and event information.

[0114] Step 4:

[0115] The generated learning response is formatted and then sent to the user's terminal. This process takes the generated response sentence as input and produces a formatted information packet as output.

[0116] Step 5:

[0117] The terminal displays received learning responses on its user interface. It also simultaneously provides information on relevant local educational events. Input is information sent from the server, and output is a visual display on the terminal.

[0118] Through this process, users will gain access to personalized learning content and real-time educational event information.

[0119] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0120] This invention relates to an educational support system that combines generative artificial intelligence and an emotion engine, and consists of a server, a terminal, and a user. The server is responsible for analyzing questions received from the user and generating appropriate learning answers. In addition, the server is equipped with an emotion engine that recognizes the emotions contained in the user's input and applies them to the generated learning answers.

[0121] Server operation

[0122] The server receives user emotion data along with the questions sent from the terminal. An emotion analysis module uses this data to identify the user's emotional state and passes the result to a generative AI model. The generative AI model generates learned responses that correspond to the emotional state. For example, if the user is feeling stressed or anxious, the system will choose more encouraging words and add additional information to make the explanation easier to understand.

[0123] Terminal operation

[0124] The device analyzes the response received from the server and displays it in an emotion-sensitive interface. For example, if the user is frustrated because they don't understand something, the device will explain the information step by step and provide step-by-step instructions as needed to alleviate their anxiety.

[0125] User actions

[0126] Users input questions that arise during their learning using their devices, and their emotions are naturally reflected in the text and audio of their input. For example, when a student working on a math problem inputs the question, "I don't understand this equation," the system takes the user's emotions into account based on that input and provides the most appropriate answer and approach.

[0127] This system not only makes learning content more personalized, but also enables support that responds immediately to the user's emotions, thereby improving learning effectiveness.

[0128] The following describes the processing flow.

[0129] Step 1:

[0130] Users input questions via text or voice through their devices. In this process, their emotions are often naturally reflected in their input. For example, they might input something like, "I keep getting this problem wrong, and I'm starting to lose confidence."

[0131] Step 2:

[0132] The terminal receives input data from the user and sends it to a text analysis module. Here, the user's input is organized as digital data, and packets are prepared to be sent to the server.

[0133] Step 3:

[0134] The server receives data sent from the terminal and passes it to the emotion analysis module. The emotion analysis module uses natural language processing technology to identify emotions from the user's input and processes that emotional information to combine it with the generating AI. For example, it recognizes a state of the user losing confidence as "anxiety."

[0135] Step 4:

[0136] The generative AI model generates optimal learned responses based on the received questions and sentiment data. If the user is feeling anxious, the AI ​​generates responses that include more detailed and step-by-step explanations, incorporating content that soothes the user's feelings.

[0137] Step 5:

[0138] The server formats the generated response and sends it back to the terminal. This formatting process includes adjusting the visual arrangement and presentation of information to make it easy for the user to see and understand.

[0139] Step 6:

[0140] The device displays received responses in the user interface, reproducing additional, emotionally sensitive visual information and interactive elements as needed. The responses are presented in a way that is easy for the user to read and use.

[0141] Step 7:

[0142] Users review the information displayed on their devices and use it to enhance their learning. Receiving emotion-based feedback along with the generated content can increase their motivation for the next learning step.

[0143] (Example 2)

[0144] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server" and the smart device 14 as the "terminal".

[0145] Existing educational support systems do not adequately consider the emotional state of individual learners, making it difficult to provide support that responds immediately to the anxieties and confusions they experience. As a result, learners' motivation decreases, and learning effectiveness is not fully realized.

[0146] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0147] In this invention, the server includes means for analyzing questions from information providers using generative artificial intelligence and generating appropriate educational responses; means for analyzing the emotional state contained in the question input and adjusting the educational response based on the emotional analysis results; and means for transmitting the generated educational response to the user interface. This makes it possible to provide personalized educational support that responds immediately to the learner's emotions.

[0148] "Generative artificial intelligence" is an artificial intelligence technology that can analyze user input data and generate new information or answers based on that data.

[0149] An "information provider" is an organization whose purpose is to provide educational information to learners. Specifically, this includes schools and cram schools.

[0150] "Educational responses" refer to answers and explanations generated in response to learners' questions and tasks, and are provided as aids to learning.

[0151] "Emotional state" refers to the user's emotional and psychological state, including emotions such as stress, anxiety, and joy.

[0152] The "emotion analysis result" is the determination of the emotional state based on the user's input data, and the response content is adjusted based on this result.

[0153] A "user interface" is a means of exchanging information between a system and a user, and includes things like screen displays and audio output.

[0154] This embodiment of the invention is a system that provides personalized educational support, consisting of a server, a terminal, and a user. The server utilizes generative artificial intelligence to analyze questions from the user and uses an emotion analysis module to determine the user's emotional state. This generates responses that are appropriate for each individual user.

[0155] First, the user uses a terminal to input a question in text or voice. The terminal sends the user's input data to the server, which also includes information about the user's emotions. The server receives this data and uses an emotion analysis module to analyze the user's emotions. For example, if a prompt such as "I'm frustrated because I don't understand this task" is entered, the emotion analysis module will identify emotional states such as "confused" or "anxious."

[0156] Subsequently, the generative AI model generates an educational response appropriate to the user's emotions based on the emotion analysis results. This response may include encouraging content for the user, such as, "Don't worry. Let's break down the problem and think about it simply first." The generated response is then sent from the server to the terminal.

[0157] The terminal displays responses received from the server in a way that is appropriate for the user. For example, if the user is anxious, it provides an interface that presents information step by step to aid understanding. This allows the user to have a personalized learning experience and improves learning efficiency.

[0158] This system aims to improve learner motivation by adjusting educational content according to the learner's emotions and providing individualized learning support. This goes beyond mere knowledge transfer, achieving comprehensive educational support that responds directly to emotions.

[0159] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0160] Step 1:

[0161] Users input questions using a device. Input is primarily in text or voice format. For example, if a user asks a question about a math problem, they might input something like, "I don't understand this equation." Because the input data naturally reflects the user's emotions, the device prepares to send it directly to the server.

[0162] Step 2:

[0163] The terminal sends user input data to the server. This data includes the question content and, to the extent possible, sentiment information. The transmitted data becomes input for processing on the server side.

[0164] Step 3:

[0165] The server analyzes the received data and first uses an emotion analysis module to identify the user's emotional state. In this process, natural language processing techniques are used to extract emotions from text and speech. For example, if the user input is "I don't understand," the server will output emotional states such as "confused" or "anxious."

[0166] Step 4:

[0167] The server's AI model generates educational responses tailored to the user's emotions based on the analysis results. The key here is that the generated responses are not merely answers, but also considerate of the user's feelings. For example, for an anxious user, the model might output a response that includes encouragement, such as, "First, try to relax and break down this problem into smaller steps."

[0168] Step 5:

[0169] The server sends the generated educational response to the terminal. This response is tailored to the user's needs because it is adjusted according to the sentiment analysis results. The output from the server is a text message that includes specific answers and emotional considerations.

[0170] Step 6:

[0171] The terminal receives responses sent from the server and displays them in a format suitable for the user. The terminal presents the responses in stages, taking into account the user's emotional state, and adds additional explanations as needed. For example, the terminal might display a message such as, "Let's focus on this part first, then move on to the next step."

[0172] This series of processes allows users to receive personalized learning support that is tailored to their emotions.

[0173] (Application Example 2)

[0174] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as a "server" and the smart device 14 as a "terminal".

[0175] There is a growing need to provide flexible and effective communication and care that responds to the emotions of users in nursing facilities and at home. Conventional systems have struggled to grasp users' emotions in real time and provide optimal support based on that understanding. This has resulted in a lack of appropriate care tailored to individual emotional states, hindering efficient support.

[0176] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0177] In this invention, the server includes means for analyzing questions from the facility and generating appropriate responses using generative artificial intelligence, means for identifying the user's emotional state based on emotion analysis technology, and means for adjusting the response and information presentation method according to the user's emotional state. This makes it possible to provide information that is appropriate to the user's emotions, thereby realizing more personalized and effective care support.

[0178] "Generative artificial intelligence" refers to an artificial intelligence system that automatically generates responses and content based on user requests.

[0179] A "facility" refers to a place where users gather to participate in specific activities or receive services, and specifically includes nursing homes, educational facilities, and so on.

[0180] A "means for analyzing questions" is a mechanism that aims to understand the user's question and derive an appropriate response.

[0181] A "means for generating responses" is a system that creates answers or instructions suitable for the user based on analyzed information.

[0182] "User device" refers to an electronic device used directly by the user as an interface, and includes smartphones, tablets, computers, and other similar devices.

[0183] "Emotion analysis technology" is a technology that identifies a user's emotional state from their facial expressions and voice data.

[0184] "Emotional state" refers to the mental or emotional state that a user experiences, such as stress or joy.

[0185] A "means of adjustment" refers to a mechanism for changing or optimizing the operation or response content according to specific purposes or conditions.

[0186] The system implementing this invention combines generative artificial intelligence and emotion analysis technology. The server uses a generative AI model to analyze questions from users of nursing homes and home care services and generate appropriate responses. User devices, including smartphones, tablets, and smart glasses, are responsible for displaying the responses received from the server.

[0187] The server uses an emotion analysis module to analyze the user's facial expressions and voice data to identify their emotional state. This analysis utilizes hardware such as cameras and microphones. The identified emotional state is input into a generative AI model, which generates a response tailored to the user's emotions. The response is adjusted according to the user's emotional state; for example, a user experiencing stress will receive a response that provides reassurance.

[0188] For example, if the emotional analysis indicates that the user is tired, the generative AI model might suggest, "How about taking a short break?" An example of this prompt might be, "The user seems a little irritated right now. Please advise on appropriate ways to talk to them or provide care in this situation." This makes it possible to provide more appropriate and flexible care to the user.

[0189] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0190] Step 1:

[0191] The user enters a question through their device. This input can be in the form of voice data or text. The system receives the input data and prepares to send it to the server.

[0192] Step 2:

[0193] The server analyzes the received question and audio data. Using an emotion analysis module, it identifies the emotional state from the input audio data or text. Specifically, it performs text analysis using natural language processing techniques and determines the emotion through speech tone analysis. This process outputs the emotional state.

[0194] Step 3:

[0195] The server operates a generative AI model based on the identified emotional state. Using prompts, it generates responses appropriate to the emotional state. The input includes data on the detected emotional state, and the AI ​​model outputs an appropriate response based on this data.

[0196] Step 4:

[0197] The server sends the generated response to the user's device. This output data includes language and suggestions that are sensitive to the user's feelings.

[0198] Step 5:

[0199] The terminal displays the received response. This includes text display as visual information and speech synthesis for audio output. Specific actions include adjusting the display format or playing voice commands.

[0200] Step 6:

[0201] The user decides on their next action based on the information received from the device. Their understanding of and reaction to this response initiates the next cycle as new input.

[0202] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0203] Data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of data generation model 58 is ChatGPT (registered trademark) (Internet search).<URL: https: / / openai.com / blog / chatgpt> ), Gemini (registered trademark) (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0204] In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and the specific processing may also be performed by the smart device 14.

[0205] [Second Embodiment]

[0206] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0207] As shown in Figure 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0208] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0209] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication interface 44. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, and camera 42 are also connected to the bus 52.

[0210] The microphone 238 receives voice signals from the user 20 and receives instructions from the user 20. The microphone 238 captures the voice signals from the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0211] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the area around the user 20 (for example, an imaging range defined by a field of view equivalent to the width of a typical healthy person's field of vision).

[0212] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0213] Figure 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Figure 4, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0214] The specific processing program 56 is an example of a "program" relating to the technology of this disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0215] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0216] In the smart glasses 214, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0217] Next, the identification processing performed by the identification processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the smart glasses 214 will be referred to as the "terminal".

[0218] The system of this invention consists of a server equipped with generative artificial intelligence, a terminal used by the user, and the user themselves. The server's main role is to analyze questions from educational institutions and handle a series of processes to generate appropriate learning answers. When the user inputs a question from the terminal, that information is sent to the server.

[0219] Server operation

[0220] When the server receives a question from a terminal, it inputs it into a question analysis module. This module analyzes the question using natural language processing techniques to identify the problem to be solved. After analysis, the server's generated AI model produces an appropriate trained response based on the results. This response is then formatted and sent to the terminal.

[0221] Terminal operation

[0222] When the terminal receives a response from the server, it displays that information appropriately in the user interface. This allows the user to confirm their answers. The terminal also has the functionality to track the user's learning progress and periodically send that data to the server.

[0223] User actions

[0224] Users can input questions and topics they want to understand during their learning process via their device. Based on the answers displayed on the device from the server, they can then proceed with their learning. Furthermore, users can monitor their learning progress through their device and effectively progress according to the generated learning plan.

[0225] As a concrete example, consider a student studying math problems who types "I want to understand the basic concepts of differentiation." This question is sent from the terminal to the server, where the server's AI searches for and generates the necessary information. The answer, including the definition, usage, and examples of differentiation, is then displayed on the terminal, allowing the student to deepen their understanding on the spot.

[0226] In this way, the system of the present invention realizes efficient and individualized educational support.

[0227] The following describes the processing flow.

[0228] Step 1:

[0229] The user uses their device to input questions that arise during their learning in text format. For example, they might input, "Please explain the trigonometric formulas in detail."

[0230] Step 2:

[0231] The terminal converts the text entered by the user into the appropriate format and sends it to the server. Additional data, such as the user's login information, is also added as needed.

[0232] Step 3:

[0233] The server receives data sent from the terminal and passes it to the question analysis module. The question analysis module uses natural language processing to analyze the intent of the user's question.

[0234] Step 4:

[0235] The AI ​​model on the server generates appropriate trained answers based on data obtained from the question analysis module. These answers include information about trigonometric functions that the user asked about.

[0236] Step 5:

[0237] The server formats the generated response and sends it back to the terminal as a data packet for transmission. This formatting includes readability and highlighting of necessary information.

[0238] Step 6:

[0239] The terminal receives data from the server, analyzes its contents, and displays it on the user interface. Users can refer to the information displayed on the terminal and use it to aid their learning.

[0240] Step 7:

[0241] Users progress through the learning process based on the information provided, and continue the learning cycle by entering the next step or new questions after resolving their doubts.

[0242] (Example 1)

[0243] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server," and the smart glasses 214 will be referred to as the "terminal."

[0244] Traditional educational support systems have faced challenges in providing timely and appropriate learning responses tailored to individual learners' needs, thus failing to maximize learning effectiveness. Furthermore, they struggled to effectively manage learners' progress and provide appropriate feedback. This hindered the development and implementation of efficient learning plans.

[0245] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0246] In this invention, the server includes means for analyzing natural language questions from educational institutions and extracting necessary information, means for generating appropriate learning responses using a generative AI model, and means for recording the user's learning progress and transmitting that data to the server device. This enables the provision of personalized learning responses to learners in real time, making efficient and effective learning support possible.

[0247] "Generative artificial intelligence" is an artificial intelligence technology that analyzes natural language and generates appropriate answers or content in response to individual questions and requests.

[0248] An "educational institution" is a facility or organization that provides knowledge and skills to learners.

[0249] "Natural language" refers to the language that humans use on a daily basis, and is a linguistic form that is expressed through sound or writing.

[0250] A "generative AI model" is an algorithmic model that performs natural language processing and response generation by learning from large amounts of data.

[0251] A "user device" is an electronic terminal that learners can directly operate to input questions and check answers.

[0252] A "user interface" is a visual or operational framework for how a user interacts with a device or system.

[0253] "Learning progress" refers to indicators or data that show how far a learner is progressing toward a specific learning objective.

[0254] "Personalized learning content" refers to educational materials and information that are customized according to each learner's level of understanding and interests.

[0255] The system of this invention consists of a server equipped with generative artificial intelligence, a terminal used by learners, and the learners themselves. The server receives questions from educational institutions and generates appropriate learning answers based on them. Specifically, a question analysis module using natural language processing technology runs on the server and extracts important information from the input questions. Python's NLTK library and SpaCy are used for the analysis.

[0256] The server uses a generative AI model to generate trained responses based on the extracted information. This model includes a natural language generation algorithm based on a large training dataset, and for example, leverages widely used language model frameworks. The generated responses are formatted into a data format and sent to the terminal.

[0257] The device displays learning responses received from the server through a user interface. The UI uses HTML and JavaScript, designed to provide information in a visually clear and easy-to-use format. Furthermore, the device can record the user's learning progress and periodically send it to the server.

[0258] Users can use their devices to input questions or topics they want to learn about on the spot. For example, by entering a prompt such as "I want to understand the basic concepts of differentiation," the generative AI model will provide a specific explanation, supporting the learner's understanding. Users can also check their progress through their devices, enabling them to learn more efficiently. In this way, the entire system works together to provide personalized educational support for learners.

[0259] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0260] Step 1:

[0261] The user enters a question into the input field on the terminal. For example, they might enter a prompt in text format such as, "I want to understand the basic concepts of differentiation." The input data is converted to JSON format on the terminal and prepared for transmission to the server.

[0262] Step 2:

[0263] The terminal sends the user's input question to the server as an HTTP request in JSON format. The input contains the user's question, and the server receives it as output. AJAX technology is used to achieve asynchronous communication.

[0264] Step 3:

[0265] The server extracts questions from the received JSON data. Natural language processing techniques are used to analyze the input text data and identify important keywords and phrases. Python's NLTK library and SpaCy are used for this analysis. The analysis results clarify the problems that need to be solved.

[0266] Step 4:

[0267] The server inputs the analysis results into a generating AI model, which then generates appropriate trained responses. This model is trained on a large dataset and includes natural language generation algorithms. The input is the analysis results, and the output is specific and relevant responses.

[0268] Step 5:

[0269] The server formats the generated response into HTML or JSON format and sends this data to the terminal. The output data is formatted for easy display in the user interface. The formatted data is returned to the terminal as an HTTP response.

[0270] Step 6:

[0271] The terminal analyzes the response data received from the server and prepares it for display in the user interface. JavaScript is used to dynamically update the page. HTML is used to ensure a clear and user-friendly presentation. The user reviews the displayed responses and continues their learning.

[0272] Step 7:

[0273] The device records the user's learning progress and sends data to the server at regular intervals. Input includes learning history and progress data, while output is stored in a database used for adjusting the learning process. This data is later used for analysis and optimizing the learning plan.

[0274] (Application Example 1)

[0275] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server," and the smart glasses 214 will be referred to as the "terminal."

[0276] In modern education, providing effective learning support tailored to the individual needs of learners is crucial. However, traditional educational support systems have shortcomings in providing personalized learning experiences for individual learners and efficiently delivering information on local educational events. In addition, accurately understanding learners' progress and presenting appropriate learning plans based on that progress remains a challenge.

[0277] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0278] In this invention, the server includes means for analyzing questions from educational organizations using generative artificial intelligence and generating appropriate learning responses; means for transmitting the generated learning responses to a user device; means for displaying the learning responses transmitted to the user device; and means for acquiring and presenting location-based educational event information. This enables personalized educational experiences and efficient information delivery.

[0279] "Generative artificial intelligence" is a type of artificial intelligence that generates responses in natural language based on input data.

[0280] An "educational organization" refers to an organization or institution that provides education-related services, such as schools or online learning platforms.

[0281] "Learning response" refers to the answers and explanations provided by generative artificial intelligence in response to a learner's question.

[0282] "User device" refers to a device used by an individual user, including smartphones and tablets.

[0283] The "display means" is an interface for visually presenting data in a user device, referring to a screen or display.

[0284] The "education event information" is information regarding learning activities such as lectures and seminars held inside and outside the region.

[0285] The "location-based information" refers to information related to a specific geographical area, which is useful for providing data considering the user's current location.

[0286] The system for realizing this invention mainly consists of a server, a user terminal, and a generative AI model. The server receives questions from educational organizations and uses the latest natural language processing technology to analyze them. Based on this analysis, the generative AI model generates an appropriate learning response and sends it to the user terminal. The server uses software such as Python and HTTP request libraries to efficiently receive and send data.

[0287] The user terminal includes devices such as smartphones and tablets, and displays the learning response received on the user interface. The terminal also has a function that enables learners to access education event information within the region and obtain educational resources in real time.

[0288] As a specific example, assume that the server receives a prompt from the user such as "Please tell me the schedule of the next online programming workshop." For this question, the server performs analysis and creates an appropriate response using generative AI. Then, it sends the answer to the user terminal and displays it together with the learning event information, enabling the user to easily prepare for participation.

[0289] Through this process, the present invention realizes efficient and individualized educational support.

[0290] The flow of the specific process in Application Example 1 will be described using FIG. 12.

[0291] Step 1:

[0292] The user enters the question they want to know via the terminal. The entered question is sent to the server through the user interface on the terminal. The input is text data from the user, and the output is request information to the server.

[0293] Step 2:

[0294] The server uses natural language processing techniques to analyze questions received from terminals. Specifically, it identifies the intent of the question and extracts the necessary information. In this process, the user's question is received as input, and the analyzed problem statement is generated as output.

[0295] Step 3:

[0296] Based on the analysis results, a generative AI model on the server generates an appropriate learned response. In this process, the input is the problem statement, and the output is a natural language response to be presented to the user. The generative AI model performs data retrieval and generation to include relevant learning content and event information.

[0297] Step 4:

[0298] The generated learning response is formatted and then sent to the user's terminal. This process takes the generated response sentence as input and produces a formatted information packet as output.

[0299] Step 5:

[0300] The terminal displays received learning responses on its user interface. It also simultaneously provides information on relevant local educational events. Input is information sent from the server, and output is a visual display on the terminal.

[0301] Through this series of processes, the user can access personalized learning content and real-time education event information.

[0302] Furthermore, an emotion engine for estimating the user's emotions may be combined. That is, the specific processing unit 290 may estimate the user's emotions using the emotion recognition model 59 and perform specific processing using the user's emotions.

[0303] The present invention is an education support system that combines a generative artificial intelligence and an emotion engine, and is composed of a server, a terminal, and a user. The server analyzes the questions received from the user and plays a role in generating appropriate learning answers. In addition, the server is equipped with an emotion engine that recognizes the emotions included in the user's input and applies them to the generated learning answers.

[0304] Server Operations

[0305] The server receives the user's emotion data together with the questions sent from the terminal. The emotion analysis module identifies the user's emotional state based on this data and passes the result to the generative AI model. The generative AI model generates a learning answer according to the emotional state. For example, when the user is feeling stress or anxiety, the system selects more encouraging words and adds additional information to make the explanation easier to understand.

[0306] Terminal Operations

[0307] The terminal analyzes the answer received from the server and displays it on an interface according to the emotion. For example, when the user is irritated because they don't understand something, the terminal explains the provided information step by step and presents a step-by-step explanation if necessary, in order to relieve the anxiety.

[0308] User Operations

[0309] Users input questions that arise during their learning using their devices, and their emotions are naturally reflected in the text and audio of their input. For example, when a student working on a math problem inputs the question, "I don't understand this equation," the system takes the user's emotions into account based on that input and provides the most appropriate answer and approach.

[0310] This system not only makes learning content more personalized, but also enables support that responds immediately to the user's emotions, thereby improving learning effectiveness.

[0311] The following describes the processing flow.

[0312] Step 1:

[0313] Users input questions via text or voice through their devices. In this process, their emotions are often naturally reflected in their input. For example, they might input something like, "I keep getting this problem wrong, and I'm starting to lose confidence."

[0314] Step 2:

[0315] The terminal receives input data from the user and sends it to a text analysis module. Here, the user's input is organized as digital data, and packets are prepared to be sent to the server.

[0316] Step 3:

[0317] The server receives data sent from the terminal and passes it to the emotion analysis module. The emotion analysis module uses natural language processing technology to identify emotions from the user's input and processes that emotional information to combine it with the generating AI. For example, it recognizes a state of the user losing confidence as "anxiety."

[0318] Step 4:

[0319] The generative AI model generates optimal learned responses based on the received questions and sentiment data. If the user is feeling anxious, the AI ​​generates responses that include more detailed and step-by-step explanations, incorporating content that soothes the user's feelings.

[0320] Step 5:

[0321] The server formats the generated response and sends it back to the terminal. This formatting process includes adjusting the visual arrangement and presentation of information to make it easy for the user to see and understand.

[0322] Step 6:

[0323] The device displays received responses in the user interface, reproducing additional, emotionally sensitive visual information and interactive elements as needed. The responses are presented in a way that is easy for the user to read and use.

[0324] Step 7:

[0325] Users review the information displayed on their devices and use it to enhance their learning. Receiving emotion-based feedback along with the generated content can increase their motivation for the next learning step.

[0326] (Example 2)

[0327] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server" and the smart glasses 214 will be referred to as the "terminal".

[0328] Existing educational support systems do not adequately consider the emotional state of individual learners, making it difficult to provide support that responds immediately to the anxieties and confusions they experience. As a result, learners' motivation decreases, and learning effectiveness is not fully realized.

[0329] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0330] In this invention, the server includes means for analyzing questions from information providers using generative artificial intelligence and generating appropriate educational responses; means for analyzing the emotional state contained in the question input and adjusting the educational response based on the emotional analysis results; and means for transmitting the generated educational response to the user interface. This makes it possible to provide personalized educational support that responds immediately to the learner's emotions.

[0331] "Generative artificial intelligence" is an artificial intelligence technology that can analyze user input data and generate new information or answers based on that data.

[0332] An "information provider" is an organization whose purpose is to provide educational information to learners. Specifically, this includes schools and cram schools.

[0333] "Educational responses" refer to answers and explanations generated in response to learners' questions and tasks, and are provided as aids to learning.

[0334] "Emotional state" refers to the user's emotional and psychological state, including emotions such as stress, anxiety, and joy.

[0335] The "emotion analysis result" is the determination of the emotional state based on the user's input data, and the response content is adjusted based on this result.

[0336] A "user interface" is a means of exchanging information between a system and a user, and includes things like screen displays and audio output.

[0337] This embodiment of the invention is a system that provides personalized educational support, consisting of a server, a terminal, and a user. The server utilizes generative artificial intelligence to analyze questions from the user and uses an emotion analysis module to determine the user's emotional state. This generates responses that are appropriate for each individual user.

[0338] First, the user uses a terminal to input a question in text or voice. The terminal sends the user's input data to the server, which also includes information about the user's emotions. The server receives this data and uses an emotion analysis module to analyze the user's emotions. For example, if a prompt such as "I'm frustrated because I don't understand this task" is entered, the emotion analysis module will identify emotional states such as "confused" or "anxious."

[0339] Subsequently, the generative AI model generates an educational response appropriate to the user's emotions based on the emotion analysis results. This response may include encouraging content for the user, such as, "Don't worry. Let's break down the problem and think about it simply first." The generated response is then sent from the server to the terminal.

[0340] The terminal displays responses received from the server in a way that is appropriate for the user. For example, if the user is anxious, it provides an interface that presents information step by step to aid understanding. This allows the user to have a personalized learning experience and improves learning efficiency.

[0341] This system aims to improve learner motivation by adjusting educational content according to the learner's emotions and providing individualized learning support. This goes beyond mere knowledge transfer, achieving comprehensive educational support that responds directly to emotions.

[0342] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0343] Step 1:

[0344] Users input questions using a device. Input is primarily in text or voice format. For example, if a user asks a question about a math problem, they might input something like, "I don't understand this equation." Because the input data naturally reflects the user's emotions, the device prepares to send it directly to the server.

[0345] Step 2:

[0346] The terminal sends user input data to the server. This data includes the question content and, to the extent possible, sentiment information. The transmitted data becomes input for processing on the server side.

[0347] Step 3:

[0348] The server analyzes the received data and first uses an emotion analysis module to identify the user's emotional state. In this process, natural language processing techniques are used to extract emotions from text and speech. For example, if the user input is "I don't understand," the server will output emotional states such as "confused" or "anxious."

[0349] Step 4:

[0350] The server's AI model generates educational responses tailored to the user's emotions based on the analysis results. The key here is that the generated responses are not merely answers, but also considerate of the user's feelings. For example, for an anxious user, the model might output a response that includes encouragement, such as, "First, try to relax and break down this problem into smaller steps."

[0351] Step 5:

[0352] The server sends the generated educational response to the terminal. This response is tailored to the user's needs because it is adjusted according to the sentiment analysis results. The output from the server is a text message that includes specific answers and emotional considerations.

[0353] Step 6:

[0354] The terminal receives responses sent from the server and displays them in a format suitable for the user. The terminal presents the responses in stages, taking into account the user's emotional state, and adds additional explanations as needed. For example, the terminal might display a message such as, "Let's focus on this part first, then move on to the next step."

[0355] This series of processes allows users to receive personalized learning support that is tailored to their emotions.

[0356] (Application Example 2)

[0357] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as the "server," and the smart glasses 214 will be referred to as the "terminal."

[0358] There is a growing need to provide flexible and effective communication and care that responds to the emotions of users in nursing facilities and at home. Conventional systems have struggled to grasp users' emotions in real time and provide optimal support based on that understanding. This has resulted in a lack of appropriate care tailored to individual emotional states, hindering efficient support.

[0359] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0360] In this invention, the server includes means for analyzing questions from the facility and generating appropriate responses using generative artificial intelligence, means for identifying the user's emotional state based on emotion analysis technology, and means for adjusting the response and information presentation method according to the user's emotional state. This makes it possible to provide information that is appropriate to the user's emotions, thereby realizing more personalized and effective care support.

[0361] "Generative artificial intelligence" refers to an artificial intelligence system that automatically generates responses and content based on user requests.

[0362] A "facility" refers to a place where users gather to participate in specific activities or receive services, and specifically includes nursing homes, educational facilities, and so on.

[0363] A "means for analyzing questions" is a mechanism that aims to understand the user's question and derive an appropriate response.

[0364] A "means for generating responses" is a system that creates answers or instructions suitable for the user based on analyzed information.

[0365] "User device" refers to an electronic device used directly by the user as an interface, and includes smartphones, tablets, computers, and other similar devices.

[0366] "Emotion analysis technology" is a technology that identifies a user's emotional state from their facial expressions and voice data.

[0367] "Emotional state" refers to the mental or emotional state that a user experiences, such as stress or joy.

[0368] A "means of adjustment" refers to a mechanism for changing or optimizing the operation or response content according to specific purposes or conditions.

[0369] The system implementing this invention combines generative artificial intelligence and emotion analysis technology. The server uses a generative AI model to analyze questions from users of nursing homes and home care services and generate appropriate responses. User devices, including smartphones, tablets, and smart glasses, are responsible for displaying the responses received from the server.

[0370] The server uses an emotion analysis module to analyze the user's facial expressions and voice data to identify their emotional state. This analysis utilizes hardware such as cameras and microphones. The identified emotional state is input into a generative AI model, which generates a response tailored to the user's emotions. The response is adjusted according to the user's emotional state; for example, a user experiencing stress will receive a response that provides reassurance.

[0371] For example, if the emotional analysis indicates that the user is tired, the generative AI model might suggest, "How about taking a short break?" An example of this prompt might be, "The user seems a little irritated right now. Please advise on appropriate ways to talk to them or provide care in this situation." This makes it possible to provide more appropriate and flexible care to the user.

[0372] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0373] Step 1:

[0374] The user enters a question through their device. This input can be in the form of voice data or text. The system receives the input data and prepares to send it to the server.

[0375] Step 2:

[0376] The server analyzes the received question and audio data. Using an emotion analysis module, it identifies the emotional state from the input audio data or text. Specifically, it performs text analysis using natural language processing techniques and determines the emotion through speech tone analysis. This process outputs the emotional state.

[0377] Step 3:

[0378] The server operates a generative AI model based on the identified emotional state. Using prompts, it generates responses appropriate to the emotional state. The input includes data on the detected emotional state, and the AI ​​model outputs an appropriate response based on this data.

[0379] Step 4:

[0380] The server sends the generated response to the user's device. This output data includes language and suggestions that are sensitive to the user's feelings.

[0381] Step 5:

[0382] The terminal displays the received response. This includes text display as visual information and speech synthesis for audio output. Specific actions include adjusting the display format or playing voice commands.

[0383] Step 6:

[0384] The user decides on their next action based on the information received from the device. Their understanding of and reaction to this response initiates the next cycle as new input.

[0385] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0386] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). One example of data generation model 58 is ChatGPT (Internet search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0387] In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and the specific processing may also be performed by the smart glasses 214.

[0388] [Third Embodiment]

[0389] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0390] As shown in Figure 5, the data processing system 310 includes a data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0391] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0392] The headset terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a display 343. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and display 343 are also connected to the bus 52.

[0393] The microphone 238 receives voice signals from the user 20 and receives instructions from the user 20. The microphone 238 captures the voice signals from the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0394] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the area around the user 20 (for example, an imaging range defined by a field of view equivalent to the width of a typical healthy person's field of vision).

[0395] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0396] Figure 6 shows an example of the main functions of the data processing device 12 and the headset terminal 314. As shown in Figure 6, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0397] The specific processing program 56 is an example of a "program" relating to the technology of this disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0398] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0399] In the headset terminal 314, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0400] Next, the specific processing performed by the specific processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the headset terminal 314 will be referred to as the "terminal".

[0401] The system of this invention consists of a server equipped with generative artificial intelligence, a terminal used by the user, and the user themselves. The server's main role is to analyze questions from educational institutions and handle a series of processes to generate appropriate learning answers. When the user inputs a question from the terminal, that information is sent to the server.

[0402] Server operation

[0403] When the server receives a question from a terminal, it inputs it into a question analysis module. This module analyzes the question using natural language processing techniques to identify the problem to be solved. After analysis, the server's generated AI model produces an appropriate trained response based on the results. This response is then formatted and sent to the terminal.

[0404] Terminal operation

[0405] When the terminal receives a response from the server, it displays that information appropriately in the user interface. This allows the user to confirm their answers. The terminal also has the functionality to track the user's learning progress and periodically send that data to the server.

[0406] User actions

[0407] Users can input questions and topics they want to understand during their learning process via their device. Based on the answers displayed on the device from the server, they can then proceed with their learning. Furthermore, users can monitor their learning progress through their device and effectively progress according to the generated learning plan.

[0408] As a concrete example, consider a student studying math problems who types "I want to understand the basic concepts of differentiation." This question is sent from the terminal to the server, where the server's AI searches for and generates the necessary information. The answer, including the definition, usage, and examples of differentiation, is then displayed on the terminal, allowing the student to deepen their understanding on the spot.

[0409] In this way, the system of the present invention realizes efficient and individualized educational support.

[0410] The following describes the processing flow.

[0411] Step 1:

[0412] The user uses their device to input questions that arise during their learning in text format. For example, they might input, "Please explain the trigonometric formulas in detail."

[0413] Step 2:

[0414] The terminal converts the text entered by the user into the appropriate format and sends it to the server. Additional data, such as the user's login information, is also added as needed.

[0415] Step 3:

[0416] The server receives data sent from the terminal and passes it to the question analysis module. The question analysis module uses natural language processing to analyze the intent of the user's question.

[0417] Step 4:

[0418] The AI ​​model on the server generates appropriate trained answers based on data obtained from the question analysis module. These answers include information about trigonometric functions that the user asked about.

[0419] Step 5:

[0420] The server formats the generated response and sends it back to the terminal as a data packet for transmission. This formatting includes readability and highlighting of necessary information.

[0421] Step 6:

[0422] The terminal receives data from the server, analyzes its contents, and displays it on the user interface. Users can refer to the information displayed on the terminal and use it to aid their learning.

[0423] Step 7:

[0424] Users progress through the learning process based on the information provided, and continue the learning cycle by entering the next step or new questions after resolving their doubts.

[0425] (Example 1)

[0426] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server," and the headset-type terminal 314 will be referred to as the "terminal."

[0427] Traditional educational support systems have faced challenges in providing timely and appropriate learning responses tailored to individual learners' needs, thus failing to maximize learning effectiveness. Furthermore, they struggled to effectively manage learners' progress and provide appropriate feedback. This hindered the development and implementation of efficient learning plans.

[0428] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0429] In this invention, the server includes means for analyzing natural language questions from educational institutions and extracting necessary information, means for generating appropriate learning responses using a generative AI model, and means for recording the user's learning progress and transmitting that data to the server device. This enables the provision of personalized learning responses to learners in real time, making efficient and effective learning support possible.

[0430] "Generative artificial intelligence" is an artificial intelligence technology that analyzes natural language and generates appropriate answers or content in response to individual questions and requests.

[0431] An "educational institution" is a facility or organization that provides knowledge and skills to learners.

[0432] "Natural language" refers to the language that humans use on a daily basis, and is a linguistic form that is expressed through sound or writing.

[0433] A "generative AI model" is an algorithmic model that performs natural language processing and response generation by learning from large amounts of data.

[0434] A "user device" is an electronic terminal that learners can directly operate to input questions and check answers.

[0435] A "user interface" is a visual or operational framework for how a user interacts with a device or system.

[0436] "Learning progress" refers to indicators or data that show how far a learner is progressing toward a specific learning objective.

[0437] "Personalized learning content" refers to educational materials and information that are customized according to each learner's level of understanding and interests.

[0438] The system of this invention consists of a server equipped with generative artificial intelligence, a terminal used by learners, and the learners themselves. The server receives questions from educational institutions and generates appropriate learning answers based on them. Specifically, a question analysis module using natural language processing technology runs on the server and extracts important information from the input questions. Python's NLTK library and SpaCy are used for the analysis.

[0439] The server uses a generative AI model to generate trained responses based on the extracted information. This model includes a natural language generation algorithm based on a large training dataset, and for example, leverages widely used language model frameworks. The generated responses are formatted into a data format and sent to the terminal.

[0440] The device displays learning responses received from the server through a user interface. The UI uses HTML and JavaScript, designed to provide information in a visually clear and easy-to-use format. Furthermore, the device can record the user's learning progress and periodically send it to the server.

[0441] Users can use their devices to input questions or topics they want to learn about on the spot. For example, by entering a prompt such as "I want to understand the basic concepts of differentiation," the generative AI model will provide a specific explanation, supporting the learner's understanding. Users can also check their progress through their devices, enabling them to learn more efficiently. In this way, the entire system works together to provide personalized educational support for learners.

[0442] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0443] Step 1:

[0444] The user enters a question into the input field on the terminal. For example, they might enter a prompt in text format such as, "I want to understand the basic concepts of differentiation." The input data is converted to JSON format on the terminal and prepared for transmission to the server.

[0445] Step 2:

[0446] The terminal sends the user's input question to the server as an HTTP request in JSON format. The input contains the user's question, and the server receives it as output. AJAX technology is used to achieve asynchronous communication.

[0447] Step 3:

[0448] The server extracts questions from the received JSON data. Natural language processing techniques are used to analyze the input text data and identify important keywords and phrases. Python's NLTK library and SpaCy are used for this analysis. The analysis results clarify the problems that need to be solved.

[0449] Step 4:

[0450] The server inputs the analysis results into a generating AI model, which then generates appropriate trained responses. This model is trained on a large dataset and includes natural language generation algorithms. The input is the analysis results, and the output is specific and relevant responses.

[0451] Step 5:

[0452] The server formats the generated response into HTML or JSON format and sends this data to the terminal. The output data is formatted for easy display in the user interface. The formatted data is returned to the terminal as an HTTP response.

[0453] Step 6:

[0454] The terminal analyzes the response data received from the server and prepares it for display in the user interface. JavaScript is used to dynamically update the page. HTML is used to ensure a clear and user-friendly presentation. The user reviews the displayed responses and continues their learning.

[0455] Step 7:

[0456] The device records the user's learning progress and sends data to the server at regular intervals. Input includes learning history and progress data, while output is stored in a database used for adjusting the learning process. This data is later used for analysis and optimizing the learning plan.

[0457] (Application Example 1)

[0458] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server," and the headset-type terminal 314 will be referred to as the "terminal."

[0459] In modern education, providing effective learning support tailored to the individual needs of learners is crucial. However, traditional educational support systems have shortcomings in providing personalized learning experiences for individual learners and efficiently delivering information on local educational events. In addition, accurately understanding learners' progress and presenting appropriate learning plans based on that progress remains a challenge.

[0460] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0461] In this invention, the server includes means for analyzing questions from educational organizations using generative artificial intelligence and generating appropriate learning responses; means for transmitting the generated learning responses to a user device; means for displaying the learning responses transmitted to the user device; and means for acquiring and presenting location-based educational event information. This enables personalized educational experiences and efficient information delivery.

[0462] "Generative artificial intelligence" is a type of artificial intelligence that generates responses in natural language based on input data.

[0463] An "educational organization" refers to an organization or institution that provides education-related services, such as schools or online learning platforms.

[0464] "Learning response" refers to the answers and explanations provided by generative artificial intelligence in response to a learner's question.

[0465] "User device" refers to a device used by an individual user, including smartphones and tablets.

[0466] "Display means" refers to an interface for visually representing data on a user device, and includes screens and displays.

[0467] "Educational event information" refers to information about learning activities such as lectures and seminars held both within and outside the local area.

[0468] "Location-based information" refers to information related to a specific geographical area and is useful for providing data that takes the user's current location into account.

[0469] The system for realizing this invention mainly consists of a server, a user terminal, and a generative AI model. The server uses state-of-the-art natural language processing technology to receive and analyze questions from educational organizations. Based on this analysis, the generative AI model generates appropriate learned responses and sends them to the user terminal. The server uses software such as Python and HTTP request libraries to efficiently receive and send data.

[0470] User terminals include devices such as smartphones and tablets, and display received learning responses on the user interface. The terminals also allow learners to access local educational event information and retrieve educational resources in real time.

[0471] As a concrete example, suppose the server receives a prompt from a user asking, "Please tell me the date of the next online programming workshop." In response to this question, the server analyzes the situation and uses AI to generate an appropriate response. Then, it sends the response to the user's terminal and displays it along with the learning event information, making it easy for the user to prepare for participation.

[0472] This process enables the present invention to provide efficient and personalized educational support.

[0473] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0474] Step 1:

[0475] The user enters the question they want to know via the terminal. The entered question is sent to the server through the user interface on the terminal. The input is text data from the user, and the output is request information to the server.

[0476] Step 2:

[0477] The server uses natural language processing techniques to analyze questions received from terminals. Specifically, it identifies the intent of the question and extracts the necessary information. In this process, the user's question is received as input, and the analyzed problem statement is generated as output.

[0478] Step 3:

[0479] Based on the analysis results, a generative AI model on the server generates an appropriate learned response. In this process, the input is the problem statement, and the output is a natural language response to be presented to the user. The generative AI model performs data retrieval and generation to include relevant learning content and event information.

[0480] Step 4:

[0481] The generated learning response is formatted and then sent to the user's terminal. This process takes the generated response sentence as input and produces a formatted information packet as output.

[0482] Step 5:

[0483] The terminal displays received learning responses on its user interface. It also simultaneously provides information on relevant local educational events. Input is information sent from the server, and output is a visual display on the terminal.

[0484] Through this process, users will gain access to personalized learning content and real-time educational event information.

[0485] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0486] This invention relates to an educational support system that combines generative artificial intelligence and an emotion engine, and consists of a server, a terminal, and a user. The server is responsible for analyzing questions received from the user and generating appropriate learning answers. In addition, the server is equipped with an emotion engine that recognizes the emotions contained in the user's input and applies them to the generated learning answers.

[0487] Server operation

[0488] The server receives user emotion data along with the questions sent from the terminal. An emotion analysis module uses this data to identify the user's emotional state and passes the result to a generative AI model. The generative AI model generates learned responses that correspond to the emotional state. For example, if the user is feeling stressed or anxious, the system will choose more encouraging words and add additional information to make the explanation easier to understand.

[0489] Terminal operation

[0490] The device analyzes the response received from the server and displays it in an emotion-sensitive interface. For example, if the user is frustrated because they don't understand something, the device will explain the information step by step and provide step-by-step instructions as needed to alleviate their anxiety.

[0491] User actions

[0492] Users input questions that arise during their learning using their devices, and their emotions are naturally reflected in the text and audio of their input. For example, when a student working on a math problem inputs the question, "I don't understand this equation," the system takes the user's emotions into account based on that input and provides the most appropriate answer and approach.

[0493] This system not only makes learning content more personalized, but also enables support that responds immediately to the user's emotions, thereby improving learning effectiveness.

[0494] The following describes the processing flow.

[0495] Step 1:

[0496] Users input questions via text or voice through their devices. In this process, their emotions are often naturally reflected in their input. For example, they might input something like, "I keep getting this problem wrong, and I'm starting to lose confidence."

[0497] Step 2:

[0498] The terminal receives input data from the user and sends it to a text analysis module. Here, the user's input is organized as digital data, and packets are prepared to be sent to the server.

[0499] Step 3:

[0500] The server receives data sent from the terminal and passes it to the emotion analysis module. The emotion analysis module uses natural language processing technology to identify emotions from the user's input and processes that emotional information to combine it with the generating AI. For example, it recognizes a state of the user losing confidence as "anxiety."

[0501] Step 4:

[0502] The generative AI model generates optimal learned responses based on the received questions and sentiment data. If the user is feeling anxious, the AI ​​generates responses that include more detailed and step-by-step explanations, incorporating content that soothes the user's feelings.

[0503] Step 5:

[0504] The server formats the generated response and sends it back to the terminal. This formatting process includes adjusting the visual arrangement and presentation of information to make it easy for the user to see and understand.

[0505] Step 6:

[0506] The device displays received responses in the user interface, reproducing additional, emotionally sensitive visual information and interactive elements as needed. The responses are presented in a way that is easy for the user to read and use.

[0507] Step 7:

[0508] Users review the information displayed on their devices and use it to enhance their learning. Receiving emotion-based feedback along with the generated content can increase their motivation for the next learning step.

[0509] (Example 2)

[0510] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server," and the headset-type terminal 314 will be referred to as the "terminal."

[0511] Existing educational support systems do not adequately consider the emotional state of individual learners, making it difficult to provide support that responds immediately to the anxieties and confusions they experience. As a result, learners' motivation decreases, and learning effectiveness is not fully realized.

[0512] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0513] In this invention, the server includes means for analyzing questions from information providers using generative artificial intelligence and generating appropriate educational responses; means for analyzing the emotional state contained in the question input and adjusting the educational response based on the emotional analysis results; and means for transmitting the generated educational response to the user interface. This makes it possible to provide personalized educational support that responds immediately to the learner's emotions.

[0514] "Generative artificial intelligence" is an artificial intelligence technology that can analyze user input data and generate new information or answers based on that data.

[0515] An "information provider" is an organization whose purpose is to provide educational information to learners. Specifically, this includes schools and cram schools.

[0516] "Educational responses" refer to answers and explanations generated in response to learners' questions and tasks, and are provided as aids to learning.

[0517] "Emotional state" refers to the user's emotional and psychological state, including emotions such as stress, anxiety, and joy.

[0518] The "emotion analysis result" is the determination of the emotional state based on the user's input data, and the response content is adjusted based on this result.

[0519] A "user interface" is a means of exchanging information between a system and a user, and includes things like screen displays and audio output.

[0520] This embodiment of the invention is a system that provides personalized educational support, consisting of a server, a terminal, and a user. The server utilizes generative artificial intelligence to analyze questions from the user and uses an emotion analysis module to determine the user's emotional state. This generates responses that are appropriate for each individual user.

[0521] First, the user uses a terminal to input a question in text or voice. The terminal sends the user's input data to the server, which also includes information about the user's emotions. The server receives this data and uses an emotion analysis module to analyze the user's emotions. For example, if a prompt such as "I'm frustrated because I don't understand this task" is entered, the emotion analysis module will identify emotional states such as "confused" or "anxious."

[0522] Subsequently, the generative AI model generates an educational response appropriate to the user's emotions based on the emotion analysis results. This response may include encouraging content for the user, such as, "Don't worry. Let's break down the problem and think about it simply first." The generated response is then sent from the server to the terminal.

[0523] The terminal displays responses received from the server in a way that is appropriate for the user. For example, if the user is anxious, it provides an interface that presents information step by step to aid understanding. This allows the user to have a personalized learning experience and improves learning efficiency.

[0524] This system aims to improve learner motivation by adjusting educational content according to the learner's emotions and providing individualized learning support. This goes beyond mere knowledge transfer, achieving comprehensive educational support that responds directly to emotions.

[0525] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0526] Step 1:

[0527] Users input questions using a device. Input is primarily in text or voice format. For example, if a user asks a question about a math problem, they might input something like, "I don't understand this equation." Because the input data naturally reflects the user's emotions, the device prepares to send it directly to the server.

[0528] Step 2:

[0529] The terminal sends user input data to the server. This data includes the question content and, to the extent possible, sentiment information. The transmitted data becomes input for processing on the server side.

[0530] Step 3:

[0531] The server analyzes the received data and first uses an emotion analysis module to identify the user's emotional state. In this process, natural language processing techniques are used to extract emotions from text and speech. For example, if the user input is "I don't understand," the server will output emotional states such as "confused" or "anxious."

[0532] Step 4:

[0533] The server's AI model generates educational responses tailored to the user's emotions based on the analysis results. The key here is that the generated responses are not merely answers, but also considerate of the user's feelings. For example, for an anxious user, the model might output a response that includes encouragement, such as, "First, try to relax and break down this problem into smaller steps."

[0534] Step 5:

[0535] The server sends the generated educational response to the terminal. This response is tailored to the user's needs because it is adjusted according to the sentiment analysis results. The output from the server is a text message that includes specific answers and emotional considerations.

[0536] Step 6:

[0537] The terminal receives responses sent from the server and displays them in a format suitable for the user. The terminal presents the responses in stages, taking into account the user's emotional state, and adds additional explanations as needed. For example, the terminal might display a message such as, "Let's focus on this part first, then move on to the next step."

[0538] This series of processes allows users to receive personalized learning support that is tailored to their emotions.

[0539] (Application Example 2)

[0540] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as the "server," and the headset-type terminal 314 will be referred to as the "terminal."

[0541] There is a growing need to provide flexible and effective communication and care that responds to the emotions of users in nursing facilities and at home. Conventional systems have struggled to grasp users' emotions in real time and provide optimal support based on that understanding. This has resulted in a lack of appropriate care tailored to individual emotional states, hindering efficient support.

[0542] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0543] In this invention, the server includes means for analyzing questions from the facility and generating appropriate responses using generative artificial intelligence, means for identifying the user's emotional state based on emotion analysis technology, and means for adjusting the response and information presentation method according to the user's emotional state. This makes it possible to provide information that is appropriate to the user's emotions, thereby realizing more personalized and effective care support.

[0544] "Generative artificial intelligence" refers to an artificial intelligence system that automatically generates responses and content based on user requests.

[0545] A "facility" refers to a place where users gather to participate in specific activities or receive services, and specifically includes nursing homes, educational facilities, and so on.

[0546] A "means for analyzing questions" is a mechanism that aims to understand the user's question and derive an appropriate response.

[0547] A "means for generating responses" is a system that creates answers or instructions suitable for the user based on analyzed information.

[0548] "User device" refers to an electronic device used directly by the user as an interface, and includes smartphones, tablets, computers, and other similar devices.

[0549] "Emotion analysis technology" is a technology that identifies a user's emotional state from their facial expressions and voice data.

[0550] "Emotional state" refers to the mental or emotional state that a user experiences, such as stress or joy.

[0551] A "means of adjustment" refers to a mechanism for changing or optimizing the operation or response content according to specific purposes or conditions.

[0552] The system implementing this invention combines generative artificial intelligence and emotion analysis technology. The server uses a generative AI model to analyze questions from users of nursing homes and home care services and generate appropriate responses. User devices, including smartphones, tablets, and smart glasses, are responsible for displaying the responses received from the server.

[0553] The server uses an emotion analysis module to analyze the user's facial expressions and voice data to identify their emotional state. This analysis utilizes hardware such as cameras and microphones. The identified emotional state is input into a generative AI model, which generates a response tailored to the user's emotions. The response is adjusted according to the user's emotional state; for example, a user experiencing stress will receive a response that provides reassurance.

[0554] For example, if the emotional analysis indicates that the user is tired, the generative AI model might suggest, "How about taking a short break?" An example of this prompt might be, "The user seems a little irritated right now. Please advise on appropriate ways to talk to them or provide care in this situation." This makes it possible to provide more appropriate and flexible care to the user.

[0555] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0556] Step 1:

[0557] The user enters a question through their device. This input can be in the form of voice data or text. The system receives the input data and prepares to send it to the server.

[0558] Step 2:

[0559] The server analyzes the received question and audio data. Using an emotion analysis module, it identifies the emotional state from the input audio data or text. Specifically, it performs text analysis using natural language processing techniques and determines the emotion through speech tone analysis. This process outputs the emotional state.

[0560] Step 3:

[0561] The server operates a generative AI model based on the identified emotional state. Using prompts, it generates responses appropriate to the emotional state. The input includes data on the detected emotional state, and the AI ​​model outputs an appropriate response based on this data.

[0562] Step 4:

[0563] The server sends the generated response to the user's device. This output data includes language and suggestions that are sensitive to the user's feelings.

[0564] Step 5:

[0565] The terminal displays the received response. This includes text display as visual information and speech synthesis for audio output. Specific actions include adjusting the display format or playing voice commands.

[0566] Step 6:

[0567] The user decides on their next action based on the information received from the device. Their understanding of and reaction to this response initiates the next cycle as new input.

[0568] The specific processing unit 290 transmits the result of the specific processing to the headset terminal 314. In the headset terminal 314, the control unit 46A causes the speaker 240 and display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0569] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). One example of data generation model 58 is ChatGPT (Internet search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0570] In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and specific processing may also be performed by the headset terminal 314.

[0571] [Fourth Embodiment]

[0572] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0573] As shown in Figure 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0574] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0575] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a controlled object 443. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and controlled object 443 are also connected to the bus 52.

[0576] The microphone 238 receives voice signals from the user 20 and receives instructions from the user 20. The microphone 238 captures the voice signals from the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0577] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the area around the user 20 (for example, an imaging range defined by a field of view equivalent to the width of a typical healthy person's field of vision).

[0578] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0579] The controlled object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the robot 414's emotions can be expressed by controlling these motors. Furthermore, the robot 414's facial expressions can also be expressed by controlling the illumination state of the LEDs in its eyes.

[0580] Figure 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Figure 8, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0581] The specific processing program 56 is an example of a "program" relating to the technology of this disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0582] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0583] In robot 414, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0584] Next, the specific processing performed by the specific processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0585] The system of this invention consists of a server equipped with generative artificial intelligence, a terminal used by the user, and the user themselves. The server's main role is to analyze questions from educational institutions and handle a series of processes to generate appropriate learning answers. When the user inputs a question from the terminal, that information is sent to the server.

[0586] Server operation

[0587] When the server receives a question from a terminal, it inputs it into a question analysis module. This module analyzes the question using natural language processing techniques to identify the problem to be solved. After analysis, the server's generated AI model produces an appropriate trained response based on the results. This response is then formatted and sent to the terminal.

[0588] Terminal operation

[0589] When the terminal receives a response from the server, it displays that information appropriately in the user interface. This allows the user to confirm their answers. The terminal also has the functionality to track the user's learning progress and periodically send that data to the server.

[0590] User actions

[0591] Users can input questions and topics they want to understand during their learning process via their device. Based on the answers displayed on the device from the server, they can then proceed with their learning. Furthermore, users can monitor their learning progress through their device and effectively progress according to the generated learning plan.

[0592] As a concrete example, consider a student studying math problems who types "I want to understand the basic concepts of differentiation." This question is sent from the terminal to the server, where the server's AI searches for and generates the necessary information. The answer, including the definition, usage, and examples of differentiation, is then displayed on the terminal, allowing the student to deepen their understanding on the spot.

[0593] In this way, the system of the present invention realizes efficient and individualized educational support.

[0594] The following describes the processing flow.

[0595] Step 1:

[0596] The user uses their device to input questions that arise during their learning in text format. For example, they might input, "Please explain the trigonometric formulas in detail."

[0597] Step 2:

[0598] The terminal converts the text entered by the user into the appropriate format and sends it to the server. Additional data, such as the user's login information, is also added as needed.

[0599] Step 3:

[0600] The server receives data sent from the terminal and passes it to the question analysis module. The question analysis module uses natural language processing to analyze the intent of the user's question.

[0601] Step 4:

[0602] The AI ​​model on the server generates appropriate trained answers based on data obtained from the question analysis module. These answers include information about trigonometric functions that the user asked about.

[0603] Step 5:

[0604] The server formats the generated response and sends it back to the terminal as a data packet for transmission. This formatting includes readability and highlighting of necessary information.

[0605] Step 6:

[0606] The terminal receives data from the server, analyzes its contents, and displays it on the user interface. Users can refer to the information displayed on the terminal and use it to aid their learning.

[0607] Step 7:

[0608] Users progress through the learning process based on the information provided, and continue the learning cycle by entering the next step or new questions after resolving their doubts.

[0609] (Example 1)

[0610] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0611] Traditional educational support systems have faced challenges in providing timely and appropriate learning responses tailored to individual learners' needs, thus failing to maximize learning effectiveness. Furthermore, they struggled to effectively manage learners' progress and provide appropriate feedback. This hindered the development and implementation of efficient learning plans.

[0612] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0613] In this invention, the server includes means for analyzing natural language questions from educational institutions and extracting necessary information, means for generating appropriate learning responses using a generative AI model, and means for recording the user's learning progress and transmitting that data to the server device. This enables the provision of personalized learning responses to learners in real time, making efficient and effective learning support possible.

[0614] "Generative artificial intelligence" is an artificial intelligence technology that analyzes natural language and generates appropriate answers or content in response to individual questions and requests.

[0615] An "educational institution" is a facility or organization that provides knowledge and skills to learners.

[0616] "Natural language" refers to the language that humans use on a daily basis, and is a linguistic form that is expressed through sound or writing.

[0617] A "generative AI model" is an algorithmic model that performs natural language processing and response generation by learning from large amounts of data.

[0618] A "user device" is an electronic terminal that learners can directly operate to input questions and check answers.

[0619] A "user interface" is a visual or operational framework for how a user interacts with a device or system.

[0620] "Learning progress" refers to indicators or data that show how far a learner is progressing toward a specific learning objective.

[0621] "Personalized learning content" refers to educational materials and information that are customized according to each learner's level of understanding and interests.

[0622] The system of this invention consists of a server equipped with generative artificial intelligence, a terminal used by learners, and the learners themselves. The server receives questions from educational institutions and generates appropriate learning answers based on them. Specifically, a question analysis module using natural language processing technology runs on the server and extracts important information from the input questions. Python's NLTK library and SpaCy are used for the analysis.

[0623] The server uses a generative AI model to generate trained responses based on the extracted information. This model includes a natural language generation algorithm based on a large training dataset, and for example, leverages widely used language model frameworks. The generated responses are formatted into a data format and sent to the terminal.

[0624] The device displays learning responses received from the server through a user interface. The UI uses HTML and JavaScript, designed to provide information in a visually clear and easy-to-use format. Furthermore, the device can record the user's learning progress and periodically send it to the server.

[0625] Users can use their devices to input questions or topics they want to learn about on the spot. For example, by entering a prompt such as "I want to understand the basic concepts of differentiation," the generative AI model will provide a specific explanation, supporting the learner's understanding. Users can also check their progress through their devices, enabling them to learn more efficiently. In this way, the entire system works together to provide personalized educational support for learners.

[0626] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0627] Step 1:

[0628] The user enters a question into the input field on the terminal. For example, they might enter a prompt in text format such as, "I want to understand the basic concepts of differentiation." The input data is converted to JSON format on the terminal and prepared for transmission to the server.

[0629] Step 2:

[0630] The terminal sends the user's input question to the server as an HTTP request in JSON format. The input contains the user's question, and the server receives it as output. AJAX technology is used to achieve asynchronous communication.

[0631] Step 3:

[0632] The server extracts questions from the received JSON data. Natural language processing techniques are used to analyze the input text data and identify important keywords and phrases. Python's NLTK library and SpaCy are used for this analysis. The analysis results clarify the problems that need to be solved.

[0633] Step 4:

[0634] The server inputs the analysis results into a generating AI model, which then generates appropriate trained responses. This model is trained on a large dataset and includes natural language generation algorithms. The input is the analysis results, and the output is specific and relevant responses.

[0635] Step 5:

[0636] The server formats the generated response into HTML or JSON format and sends this data to the terminal. The output data is formatted for easy display in the user interface. The formatted data is returned to the terminal as an HTTP response.

[0637] Step 6:

[0638] The terminal analyzes the response data received from the server and prepares it for display in the user interface. JavaScript is used to dynamically update the page. HTML is used to ensure a clear and user-friendly presentation. The user reviews the displayed responses and continues their learning.

[0639] Step 7:

[0640] The device records the user's learning progress and sends data to the server at regular intervals. Input includes learning history and progress data, while output is stored in a database used for adjusting the learning process. This data is later used for analysis and optimizing the learning plan.

[0641] (Application Example 1)

[0642] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0643] In modern education, providing effective learning support tailored to the individual needs of learners is crucial. However, traditional educational support systems have shortcomings in providing personalized learning experiences for individual learners and efficiently delivering information on local educational events. In addition, accurately understanding learners' progress and presenting appropriate learning plans based on that progress remains a challenge.

[0644] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0645] In this invention, the server includes means for analyzing questions from educational organizations using generative artificial intelligence and generating appropriate learning responses; means for transmitting the generated learning responses to a user device; means for displaying the learning responses transmitted to the user device; and means for acquiring and presenting location-based educational event information. This enables personalized educational experiences and efficient information delivery.

[0646] "Generative artificial intelligence" is a type of artificial intelligence that generates responses in natural language based on input data.

[0647] An "educational organization" refers to an organization or institution that provides education-related services, such as schools or online learning platforms.

[0648] "Learning response" refers to the answers and explanations provided by generative artificial intelligence in response to a learner's question.

[0649] "User device" refers to a device used by an individual user, including smartphones and tablets.

[0650] "Display means" refers to an interface for visually representing data on a user device, and includes screens and displays.

[0651] "Educational event information" refers to information about learning activities such as lectures and seminars held both within and outside the local area.

[0652] "Location-based information" refers to information related to a specific geographical area and is useful for providing data that takes the user's current location into account.

[0653] The system for realizing this invention mainly consists of a server, a user terminal, and a generative AI model. The server uses state-of-the-art natural language processing technology to receive and analyze questions from educational organizations. Based on this analysis, the generative AI model generates appropriate learned responses and sends them to the user terminal. The server uses software such as Python and HTTP request libraries to efficiently receive and send data.

[0654] User terminals include devices such as smartphones and tablets, and display received learning responses on the user interface. The terminals also allow learners to access local educational event information and retrieve educational resources in real time.

[0655] As a concrete example, suppose the server receives a prompt from a user asking, "Please tell me the date of the next online programming workshop." In response to this question, the server analyzes the situation and uses AI to generate an appropriate response. Then, it sends the response to the user's terminal and displays it along with the learning event information, making it easy for the user to prepare for participation.

[0656] This process enables the present invention to provide efficient and personalized educational support.

[0657] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0658] Step 1:

[0659] The user enters the question they want to know via the terminal. The entered question is sent to the server through the user interface on the terminal. The input is text data from the user, and the output is request information to the server.

[0660] Step 2:

[0661] The server uses natural language processing techniques to analyze questions received from terminals. Specifically, it identifies the intent of the question and extracts the necessary information. In this process, the user's question is received as input, and the analyzed problem statement is generated as output.

[0662] Step 3:

[0663] Based on the analysis results, a generative AI model on the server generates an appropriate learned response. In this process, the input is the problem statement, and the output is a natural language response to be presented to the user. The generative AI model performs data retrieval and generation to include relevant learning content and event information.

[0664] Step 4:

[0665] The generated learning response is formatted and then sent to the user's terminal. This process takes the generated response sentence as input and produces a formatted information packet as output.

[0666] Step 5:

[0667] The terminal displays received learning responses on its user interface. It also simultaneously provides information on relevant local educational events. Input is information sent from the server, and output is a visual display on the terminal.

[0668] Through this process, users will gain access to personalized learning content and real-time educational event information.

[0669] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0670] This invention relates to an educational support system that combines generative artificial intelligence and an emotion engine, and consists of a server, a terminal, and a user. The server is responsible for analyzing questions received from the user and generating appropriate learning answers. In addition, the server is equipped with an emotion engine that recognizes the emotions contained in the user's input and applies them to the generated learning answers.

[0671] Server operation

[0672] The server receives user emotion data along with the questions sent from the terminal. An emotion analysis module uses this data to identify the user's emotional state and passes the result to a generative AI model. The generative AI model generates learned responses that correspond to the emotional state. For example, if the user is feeling stressed or anxious, the system will choose more encouraging words and add additional information to make the explanation easier to understand.

[0673] Terminal operation

[0674] The device analyzes the response received from the server and displays it in an emotion-sensitive interface. For example, if the user is frustrated because they don't understand something, the device will explain the information step by step and provide step-by-step instructions as needed to alleviate their anxiety.

[0675] User actions

[0676] Users input questions that arise during their learning using their devices, and their emotions are naturally reflected in the text and audio of their input. For example, when a student working on a math problem inputs the question, "I don't understand this equation," the system takes the user's emotions into account based on that input and provides the most appropriate answer and approach.

[0677] This system not only makes learning content more personalized, but also enables support that responds immediately to the user's emotions, thereby improving learning effectiveness.

[0678] The following describes the processing flow.

[0679] Step 1:

[0680] Users input questions via text or voice through their devices. In this process, their emotions are often naturally reflected in their input. For example, they might input something like, "I keep getting this problem wrong, and I'm starting to lose confidence."

[0681] Step 2:

[0682] The terminal receives input data from the user and sends it to a text analysis module. Here, the user's input is organized as digital data, and packets are prepared to be sent to the server.

[0683] Step 3:

[0684] The server receives data sent from the terminal and passes it to the emotion analysis module. The emotion analysis module uses natural language processing technology to identify emotions from the user's input and processes that emotional information to combine it with the generating AI. For example, it recognizes a state of the user losing confidence as "anxiety."

[0685] Step 4:

[0686] The generative AI model generates optimal learned responses based on the received questions and sentiment data. If the user is feeling anxious, the AI ​​generates responses that include more detailed and step-by-step explanations, incorporating content that soothes the user's feelings.

[0687] Step 5:

[0688] The server formats the generated response and sends it back to the terminal. This formatting process includes adjusting the visual arrangement and presentation of information to make it easy for the user to see and understand.

[0689] Step 6:

[0690] The device displays received responses in the user interface, reproducing additional, emotionally sensitive visual information and interactive elements as needed. The responses are presented in a way that is easy for the user to read and use.

[0691] Step 7:

[0692] Users review the information displayed on their devices and use it to enhance their learning. Receiving emotion-based feedback along with the generated content can increase their motivation for the next learning step.

[0693] (Example 2)

[0694] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0695] Existing educational support systems do not adequately consider the emotional state of individual learners, making it difficult to provide support that responds immediately to the anxieties and confusions they experience. As a result, learners' motivation decreases, and learning effectiveness is not fully realized.

[0696] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0697] In this invention, the server includes means for analyzing questions from information providers using generative artificial intelligence and generating appropriate educational responses; means for analyzing the emotional state contained in the question input and adjusting the educational response based on the emotional analysis results; and means for transmitting the generated educational response to the user interface. This makes it possible to provide personalized educational support that responds immediately to the learner's emotions.

[0698] "Generative artificial intelligence" is an artificial intelligence technology that can analyze user input data and generate new information or answers based on that data.

[0699] An "information provider" is an organization whose purpose is to provide educational information to learners. Specifically, this includes schools and cram schools.

[0700] "Educational responses" refer to answers and explanations generated in response to learners' questions and tasks, and are provided as aids to learning.

[0701] "Emotional state" refers to the user's emotional and psychological state, including emotions such as stress, anxiety, and joy.

[0702] The "emotion analysis result" is the determination of the emotional state based on the user's input data, and the response content is adjusted based on this result.

[0703] A "user interface" is a means of exchanging information between a system and a user, and includes things like screen displays and audio output.

[0704] This embodiment of the invention is a system that provides personalized educational support, consisting of a server, a terminal, and a user. The server utilizes generative artificial intelligence to analyze questions from the user and uses an emotion analysis module to determine the user's emotional state. This generates responses that are appropriate for each individual user.

[0705] First, the user uses a terminal to input a question in text or voice. The terminal sends the user's input data to the server, which also includes information about the user's emotions. The server receives this data and uses an emotion analysis module to analyze the user's emotions. For example, if a prompt such as "I'm frustrated because I don't understand this task" is entered, the emotion analysis module will identify emotional states such as "confused" or "anxious."

[0706] Subsequently, the generative AI model generates an educational response appropriate to the user's emotions based on the emotion analysis results. This response may include encouraging content for the user, such as, "Don't worry. Let's break down the problem and think about it simply first." The generated response is then sent from the server to the terminal.

[0707] The terminal displays responses received from the server in a way that is appropriate for the user. For example, if the user is anxious, it provides an interface that presents information step by step to aid understanding. This allows the user to have a personalized learning experience and improves learning efficiency.

[0708] This system aims to improve learner motivation by adjusting educational content according to the learner's emotions and providing individualized learning support. This goes beyond mere knowledge transfer, achieving comprehensive educational support that responds directly to emotions.

[0709] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0710] Step 1:

[0711] Users input questions using a device. Input is primarily in text or voice format. For example, if a user asks a question about a math problem, they might input something like, "I don't understand this equation." Because the input data naturally reflects the user's emotions, the device prepares to send it directly to the server.

[0712] Step 2:

[0713] The terminal sends user input data to the server. This data includes the question content and, to the extent possible, sentiment information. The transmitted data becomes input for processing on the server side.

[0714] Step 3:

[0715] The server analyzes the received data and first uses an emotion analysis module to identify the user's emotional state. In this process, natural language processing techniques are used to extract emotions from text and speech. For example, if the user input is "I don't understand," the server will output emotional states such as "confused" or "anxious."

[0716] Step 4:

[0717] The server's AI model generates educational responses tailored to the user's emotions based on the analysis results. The key here is that the generated responses are not merely answers, but also considerate of the user's feelings. For example, for an anxious user, the model might output a response that includes encouragement, such as, "First, try to relax and break down this problem into smaller steps."

[0718] Step 5:

[0719] The server sends the generated educational response to the terminal. This response is tailored to the user's needs because it is adjusted according to the sentiment analysis results. The output from the server is a text message that includes specific answers and emotional considerations.

[0720] Step 6:

[0721] The terminal receives responses sent from the server and displays them in a format suitable for the user. The terminal presents the responses in stages, taking into account the user's emotional state, and adds additional explanations as needed. For example, the terminal might display a message such as, "Let's focus on this part first, then move on to the next step."

[0722] This series of processes allows users to receive personalized learning support that is tailored to their emotions.

[0723] (Application Example 2)

[0724] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0725] There is a growing need to provide flexible and effective communication and care that responds to the emotions of users in nursing facilities and at home. Conventional systems have struggled to grasp users' emotions in real time and provide optimal support based on that understanding. This has resulted in a lack of appropriate care tailored to individual emotional states, hindering efficient support.

[0726] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0727] In this invention, the server includes means for analyzing questions from the facility and generating appropriate responses using generative artificial intelligence, means for identifying the user's emotional state based on emotion analysis technology, and means for adjusting the response and information presentation method according to the user's emotional state. This makes it possible to provide information that is appropriate to the user's emotions, thereby realizing more personalized and effective care support.

[0728] "Generative artificial intelligence" refers to an artificial intelligence system that automatically generates responses and content based on user requests.

[0729] A "facility" refers to a place where users gather to participate in specific activities or receive services, and specifically includes nursing homes, educational facilities, and so on.

[0730] A "means for analyzing questions" is a mechanism that aims to understand the user's question and derive an appropriate response.

[0731] A "means for generating responses" is a system that creates answers or instructions suitable for the user based on analyzed information.

[0732] "User device" refers to an electronic device used directly by the user as an interface, and includes smartphones, tablets, computers, and other similar devices.

[0733] "Emotion analysis technology" is a technology that identifies a user's emotional state from their facial expressions and voice data.

[0734] "Emotional state" refers to the mental or emotional state that a user experiences, such as stress or joy.

[0735] A "means of adjustment" refers to a mechanism for changing or optimizing the operation or response content according to specific purposes or conditions.

[0736] The system implementing this invention combines generative artificial intelligence and emotion analysis technology. The server uses a generative AI model to analyze questions from users of nursing homes and home care services and generate appropriate responses. User devices, including smartphones, tablets, and smart glasses, are responsible for displaying the responses received from the server.

[0737] The server uses an emotion analysis module to analyze the user's facial expressions and voice data to identify their emotional state. This analysis utilizes hardware such as cameras and microphones. The identified emotional state is input into a generative AI model, which generates a response tailored to the user's emotions. The response is adjusted according to the user's emotional state; for example, a user experiencing stress will receive a response that provides reassurance.

[0738] For example, if the emotional analysis indicates that the user is tired, the generative AI model might suggest, "How about taking a short break?" An example of this prompt might be, "The user seems a little irritated right now. Please advise on appropriate ways to talk to them or provide care in this situation." This makes it possible to provide more appropriate and flexible care to the user.

[0739] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0740] Step 1:

[0741] The user enters a question through their device. This input can be in the form of voice data or text. The system receives the input data and prepares to send it to the server.

[0742] Step 2:

[0743] The server analyzes the received question and audio data. Using an emotion analysis module, it identifies the emotional state from the input audio data or text. Specifically, it performs text analysis using natural language processing techniques and determines the emotion through speech tone analysis. This process outputs the emotional state.

[0744] Step 3:

[0745] The server operates a generative AI model based on the identified emotional state. Using prompts, it generates responses appropriate to the emotional state. The input includes data on the detected emotional state, and the AI ​​model outputs an appropriate response based on this data.

[0746] Step 4:

[0747] The server sends the generated response to the user's device. This output data includes language and suggestions that are sensitive to the user's feelings.

[0748] Step 5:

[0749] The terminal displays the received response. This includes text display as visual information and speech synthesis for audio output. Specific actions include adjusting the display format or playing voice commands.

[0750] Step 6:

[0751] The user decides on their next action based on the information received from the device. Their understanding of and reaction to this response initiates the next cycle as new input.

[0752] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the controlled object 443 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0753] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). One example of data generation model 58 is ChatGPT (Internet search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0754] In the above embodiment, an example was given in which the specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and the specific processing may also be performed by the robot 414.

[0755] Furthermore, the emotion identification model 59, acting as an emotion engine, may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to a specific mapping, which is an emotion map (see Figure 9). Similarly, the emotion identification model 59 may also determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0756] Figure 9 shows an emotion map 400 in which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. The closer to the center of the concentric circles, the more primitive the emotions are located. Further out of the concentric circles, emotions representing states and actions arising from mental states are located. Emotion is a concept that includes feelings and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions occurring in the brain are located. On the right side of the concentric circles, emotions that are generally induced by situational judgment are located. Above and below the concentric circles, emotions that are generally generated from reactions occurring in the brain and induced by situational judgment are located. In addition, the emotion of "pleasure" is located on the upper side of the concentric circles, and the emotion of "displeasure" is located on the lower side. Thus, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions arise, and emotions that are likely to occur simultaneously are mapped close together.

[0757] These emotions are distributed at the 3 o'clock position on the Emotion Map 400, and usually fluctuate between feelings of security and anxiety. In the right half of the Emotion Map 400, situational awareness takes precedence over internal feelings, resulting in a calm impression.

[0758] The inside of the Emotion Map 400 represents inner thoughts, while the outside represents actions. Therefore, the further you go from the outside of the Emotion Map 400, the more visible (expressed in actions) your emotions become.

[0759] Here, human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. Similarly, in robots, cars, motorcycles, etc., emotions can be created based on various balances, such as posture and battery level. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. The emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on a system for analyzing brain physiological signals of speech emotion recognition and emotion, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map contains emotions belonging to a region called "response," where sensation is dominant. The right half of the emotion map contains emotions belonging to a region called "situation," where situational awareness is dominant.

[0760] The emotion map defines two emotions that promote learning. One is the emotion around the middle of the negative "repentance" and "reflection" on the situation side. In other words, it is when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is the emotion around the positive "desire" on the reaction side. In other words, it is when the robot has positive feelings such as "I want more" or "I want to know more."

[0761] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​representing each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple training data sets, which are combinations of user input and emotion values ​​representing each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions located close together have similar values, as shown in the emotion map 900 in Figure 10. Figure 10 shows an example where multiple emotions such as "reassured," "calm," and "confident" have similar emotion values.

[0762] The above description primarily focuses on the functions of the data processing device 12 in relation to this disclosure. However, the system related to this disclosure is not necessarily implemented on a server. The system related to this disclosure may be implemented as a general information processing system. This disclosure may be implemented, for example, as a software program that runs on a personal computer or as an application that runs on a smartphone. The method related to this disclosure may be provided to users in SaaS (Software as a Service) format.

[0763] In the above embodiment, an example was given in which a specific process is performed by a single computer 22. However, the technology of this disclosure is not limited thereto, and a distributed processing of the specific process may be performed by multiple computers, including computer 22. For example, a data generation model 58 may be provided in an external device of the data processing device 12, and the external device may generate data according to the input data.

[0764] In the above embodiment, an example was given in which the specific processing program 56 is stored in the storage 32, but the technology of this disclosure is not limited thereto. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-temporary storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-temporary storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes specific processing according to the specific processing program 56.

[0765] 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.

[0766] Furthermore, it is not necessary to store the entirety of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store the entirety of the specific processing program 56 in the storage 32; it is acceptable to store only a portion of the specific processing program 56.

[0767] The following types of processors can be used as hardware resources to perform specific processing. Examples of processors include a CPU, a general-purpose processor that functions as a hardware resource to perform specific processing by executing software, i.e., a program. Other examples of processors include dedicated electrical circuits, such as FPGAs (Field-Programmable Gate Arrays), PLDs (Programmable Logic Devices), or ASICs (Application Specific Integrated Circuits), which have circuit configurations specifically designed to perform specific processing. All of these processors have built-in or connected memory, and all of them perform specific processing by using memory.

[0768] The hardware resource that performs a specific process may consist of one of these various processors, or it may consist of a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Alternatively, the hardware resource that performs a specific process may consist of a single processor.

[0769] Examples of configurations using a single processor include, firstly, a configuration in which one or more CPUs and software are combined to form a single processor, and this processor functions as a hardware resource that performs a specific process. Secondly, there is a configuration using a processor that realizes the functions of the entire system, including multiple hardware resources that perform a specific process, on a single IC chip, as exemplified by SoCs (System-on-a-chip). In this way, a specific process is realized using one or more of the above types of processors as hardware resources.

[0770] Furthermore, the hardware structure of these various processors can more specifically utilize electrical circuits that combine circuit elements such as semiconductor devices. Also, the specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps can be deleted, new steps added, or the processing order rearranged, as long as it does not deviate from the main purpose.

[0771] The descriptions and illustrations presented above are detailed explanations of the technical aspects of this disclosure and are merely examples of the technical aspects. For example, the above descriptions of the structure, function, operation, and effect are examples of the structure, function, operation, and effect of the technical aspects of this disclosure. Therefore, it goes without saying that you may delete unnecessary parts, add new elements, or replace elements in the descriptions and illustrations presented above, as long as you do not deviate from the essence of the technical aspects of this disclosure. Furthermore, in order to avoid confusion and facilitate understanding of the technical aspects of this disclosure, explanations of common technical knowledge and the like that do not require special explanation to enable the implementation of the technical aspects of this disclosure have been omitted from the descriptions and illustrations presented above.

[0772] All documents, patent applications, and technical standards described herein are incorporated by reference to the same extent as if each individual document, patent application, and technical standard were specifically and individually noted to be incorporated by reference.

[0773] The following is further disclosed regarding the embodiments described above.

[0774] (Claim 1)

[0775] A means of using generative artificial intelligence to analyze questions from educational institutions and generate appropriate learning responses,

[0776] A means for sending the generated learning response to the user's terminal,

[0777] A means for displaying learning responses sent to the user's terminal,

[0778] A system that includes this.

[0779] (Claim 2)

[0780] The system according to claim 1, characterized in that it has means for generating and providing personalized learning content based on questions entered by educational institutions.

[0781] (Claim 3)

[0782] The system according to claim 1, characterized in that it has means for managing the user's learning progress and presenting a learning plan based on that data.

[0783] "Example 1"

[0784] (Claim 1)

[0785] A method for analyzing natural language questions from educational institutions using generative artificial intelligence and extracting necessary information,

[0786] A means for generating appropriate learned responses using a generative AI model based on extracted information,

[0787] A means for transmitting the generated learning response in data format to a user device,

[0788] A means for displaying the learned answers sent to the user's device on the user interface, allowing the user to confirm them,

[0789] A means for recording the user's learning progress and transmitting that data to a server device,

[0790] A system that includes this.

[0791] (Claim 2)

[0792] The system according to claim 1, characterized by generating personalized learning content and providing it to a user device.

[0793] (Claim 3)

[0794] The system according to claim 1, characterized in that it presents an adjusted learning plan based on the user's learning progress.

[0795] "Application Example 1"

[0796] (Claim 1)

[0797] A means of analyzing questions from educational organizations and generating appropriate learned responses using generative artificial intelligence,

[0798] Means for transmitting the generated learned response to the user device,

[0799] Means for displaying the learning response sent to the user device,

[0800] A means of obtaining and presenting location-based educational event information,

[0801] A system that includes this.

[0802] (Claim 2)

[0803] The system according to claim 1, characterized by having means for generating and providing personalized learning materials based on the questions entered.

[0804] (Claim 3)

[0805] The system according to claim 1, characterized by having means for supervising learners' progress and providing a learning plan based on that data.

[0806] "Example 2 of combining an emotion engine"

[0807] (Claim 1)

[0808] A means of using generative artificial intelligence to analyze questions from information providers and generate appropriate educational responses,

[0809] A means for analyzing the emotional state contained in the question input and adjusting the educational response based on the emotional analysis results,

[0810] A means for sending the generated educational response to the user interface,

[0811] A means for displaying educational responses sent to the user interface in an emotion-responsive format,

[0812] A system that includes this.

[0813] (Claim 2)

[0814] The system according to claim 1, characterized in that it has means for generating and providing personalized educational content based on input questions and taking into account the results of sentiment analysis.

[0815] (Claim 3)

[0816] The system according to claim 1, characterized by having a feedback mechanism based on the user's emotional state and means for presenting an educational plan based on that data.

[0817] "Application example 2 when combining with an emotional engine"

[0818] (Claim 1)

[0819] A means for analyzing questions from a facility and generating appropriate responses using generative artificial intelligence,

[0820] Means for transmitting the generated response to the user device,

[0821] Means for displaying responses sent to the user device,

[0822] A means of identifying a user's emotional state based on emotion analysis technology,

[0823] A means of adjusting the response and information presentation method according to the user's emotional state,

[0824] A system that includes this.

[0825] (Claim 2)

[0826] The system according to claim 1, characterized by having means for generating and providing personalized response content based on the results of identifying an emotional state.

[0827] (Claim 3)

[0828] The system according to claim 1, characterized in that it has means for managing the emotional state and learning progress of users, presenting a support plan based on that data, and providing care support. [Explanation of Symbols]

[0829] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots< / url:> < / url:> < / url:> < / url:>

Claims

1. A means of using generative artificial intelligence to analyze questions from educational institutions and generate appropriate learning responses, A means for sending the generated learning response to the user's terminal, A means for displaying learning responses sent to the user's terminal, A system that includes this.

2. The system according to claim 1, characterized in that it has means for generating and providing personalized learning content based on questions entered by educational institutions.

3. The system according to claim 1, characterized in that it has means for managing the user's learning progress and presenting a learning plan based on that data.

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