system

Abstract

We provide the system. [Solution] A means for receiving input data from a user and analyzing said input data using natural language processing technology, A means for generating an appropriate response based on the analyzed input data, A means of presenting the generated response to the user, 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, including steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to an explanation of a character of the chatbot, encoding the prompt, and inputting the encoded prompt into a language model to generate a chatbot utterance in response to the user utterance.

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In modern dual-income families, parents have limited time to adequately support their children's learning. Especially in the intensifying competition for junior high school entrance examinations, it is difficult to appropriately grasp the learning progress and weak areas of children and provide efficient support. Furthermore, for busy parents, keeping track of their children's progress and making learning plans is a significant burden. Therefore, there is a need to create an environment where children can study autonomously and effectively, and parents can provide support at appropriate times.

Means for Solving the Problems

[0005] This invention provides a system that analyzes user input data using natural language processing technology and generates and presents appropriate responses based on that analysis. This allows children to immediately ask questions about things they don't understand and deepen their comprehension. Furthermore, this invention collects the user's learning history and performance data, and automatically generates and provides personalized learning plans based on the analysis results. This enables children to learn efficiently. In addition, it provides a dashboard for parents that visualizes learning progress and points of difficulty, and includes regular reports and alert functions for parents, creating a system that allows parents to provide appropriate support.

[0006] "Input data" refers to all information that a user provides to the system, including in various formats such as text, audio, and images.

[0007] "Natural language processing technology" refers to the technology that enables computers to understand, interpret, and generate human language, and is particularly used when analyzing text and voice input from users.

[0008] "Analysis" refers to the process of examining and understanding data and information in detail for a specific purpose, and extracting meaning and intent from them.

[0009] "Response" refers to the answer or information that the system provides in response to user input, and the content generated to facilitate understanding.

[0010] "Learning history" refers to a record of the user's past learning activities, including completed assignments, content covered, and level of achievement.

[0011] "Performance data" refers to data that shows the results and achievements of a user's learning activities, and includes indicators such as comprehension level and answer speed.

[0012] A "personalized learning plan" refers to a learning activity plan optimized for a specific user, based on their current level of understanding and goals.

[0013] A "dashboard" is an interface in which a system visually presents information to users and parents, such as visualizing learning progress.

[0014] The "alert function" is a feature that notifies users or parents when certain conditions are met, with the purpose of informing them about the continuation of tasks or any issues with progress. [Brief explanation of the drawing]

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

Mode for Carrying Out the Invention

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

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

[0018] 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.

[0019] 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.

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

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

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

[0023] [First Embodiment]

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

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

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

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

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

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

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

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

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

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

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

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

[0036] This invention relates to an interactive learning support system for users, aimed at supporting the learning of children in dual-income households. This system improves the quality of children's learning by providing real-time question answering, optimization of learning plans, and visualization of learning progress. Furthermore, it enables efficient learning support within the home through timely information provision and notifications to parents.

[0037] System configuration and operation

[0038] Question answering function

[0039] The server receives question data submitted by the user, including text and voice input. The server uses natural language processing techniques to analyze the question and generate the most appropriate response. This response is sent back to the user's device and provided to the user in real time, either displayed or spoken aloud. For example, if a user asks, "What does this English word mean?", the server translates the word's meaning, provides an explanation, and returns the answer.

[0040] Optimizing your learning plan

[0041] The server collects the user's learning history and performance data, and analyzes this data using AI technology. This identifies the user's strengths and weaknesses and generates a personalized learning plan. The generated plan is sent to the user's device, and the user uses it to work on their daily studies. For example, if a user has a weak understanding of a particular subject, a plan will be provided that includes many related problems to strengthen that subject.

[0042] Visualization of learning progress and support for parents

[0043] Parents can access a dashboard through their device that visualizes their child's learning progress. This dashboard displays learning achievements, areas of difficulty, and progress status. The server generates reports regularly and sends them to parents weekly and monthly. Furthermore, if a specific learning task remains unresolved, the server sends an alert to the parent. In this way, parents can understand their child's learning situation in real time and provide the necessary support.

[0044] Specific example

[0045] Suppose a user (child) finds a math problem they can't solve and asks the system for help. The user's device sends the question to the server, which generates an answer including how to solve the problem and related explanations. The answer is notified to the user in real time, and the user deepens their understanding by reading the displayed explanation. Afterwards, the user's progress is saved in the system and used for future learning plans. Parents can also check through a dashboard whether the child has been able to solve the problem and provide additional support as needed.

[0046] This invention creates an environment where children can efficiently overcome learning challenges, and parents can provide effective support even when they are busy.

[0047] The following describes the processing flow.

[0048] Step 1:

[0049] Users enter questions into the device to solve problems they don't understand. Input methods include voice or text.

[0050] Step 2:

[0051] The terminal converts the input data into an appropriate format (text format) and sends it to the server. In the case of voice input, speech recognition technology is used to convert it to text.

[0052] Step 3:

[0053] The server analyzes the received text data using natural language processing technology to accurately understand the intent of the user's question.

[0054] Step 4:

[0055] The server references internal databases and knowledge bases to generate appropriate answers to user questions. These generated answers include relevant information and additional explanations.

[0056] Step 5:

[0057] The server sends the generated response to the user's device in text format.

[0058] Step 6:

[0059] The device presents the received response to the user. The presentation method is either screen display or audio playback.

[0060] Step 7:

[0061] The user refers to the provided answers to understand or solve the problem.

[0062] Step 8:

[0063] The device collects data on the user's learning progress and question history, and sends it to the server. This data is used to optimize future learning plans.

[0064] (Example 1)

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

[0066] In dual-income households, a challenge exists in supporting children's learning: it is difficult for parents to efficiently understand their children's learning progress and provide appropriate support. Furthermore, it is challenging to provide personalized learning plans while monitoring users' learning progress in real time. These challenges need to be addressed to improve the quality of children's learning.

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

[0068] In this invention, the server includes means for receiving input data from a user and analyzing the input data using information processing technology, a generation device for generating an appropriate response based on the analyzed input data, and an output device for presenting the generated response to the user. This makes it possible to answer user questions quickly and accurately.

[0069] "Information processing technology" refers to the technology used to analyze data obtained from users and understand its meaning and context.

[0070] A "generation device" is a device that constructs an appropriate response to present to the user based on the analyzed data.

[0071] An "output device" is a device that communicates the generated response to the user visually or audibly.

[0072] A "data storage device" is a device that saves user activity records and learning progress for use in future analysis and planning.

[0073] A "generative AI model" is an artificial intelligence technology that generates appropriate and skillful responses based on user prompts.

[0074] A "prompt statement" is a statement input to a generative AI model, containing instructions and conditions for the model to generate a response.

[0075] A "learning plan" refers to a learning schedule and content tailored to the user's learning progress and individual needs.

[0076] A "display device" is a device that provides an interface that allows users to visually check their learning progress.

[0077] A "notification function" is a feature that issues a warning to the user or parent when certain conditions are met.

[0078] This invention is a system for supporting the learning of children in dual-income households, and is realized through the interaction of a server, a terminal, and a user. First, the user inputs learning-related questions via the terminal. Questions can be entered in text or voice format. The terminal transfers the input data to the server. The server analyzes the received data using natural language processing technology. Information processing technologies such as Python's NLTK library and spaCy are used for the analysis. This allows the server to understand the user's intent and the content of the question.

[0079] The server launches a generative AI model based on the analyzed data. This model is input with specific prompt sentences. For example, if a user asks, "What does this English word mean?", the prompt sentence input to the model will be in the format of "Please explain the meaning of the English word '". The generative AI model generates a response to the user's question based on this prompt sentence.

[0080] The generated response is sent from the server to the terminal, which then provides it to the user either visually or audibly. The terminal's screen is used for display, while text-to-speech technology is used for audio output. For example, if the user asks about the meaning of "resilience," the server generates a response such as "resilience means the ability to recover from difficulties" and displays it on the terminal.

[0081] Furthermore, the server records the user's learning activities and saves their progress to a data storage device. This allows the user's individual learning history, strengths, and weaknesses to be identified and used to generate their next learning plan. In this way, users can progress in their learning based on individually tailored learning content.

[0082] Furthermore, parents can access a dashboard through their device that visualizes their child's learning progress. This dashboard displays learning achievements, areas of difficulty, and progress status, providing parents with timely and accurate information. Based on this, parents can provide appropriate learning support for their children.

[0083] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0084] Step 1:

[0085] The user enters the topic or question they want to learn about into the terminal. This input can be in text or voice format. The terminal converts the user's input into a format (e.g., JSON) for transmission as digital data to the server. This digital data becomes the input to the server.

[0086] Step 2:

[0087] The server analyzes the digital data received from the terminal. This analysis uses natural language processing techniques to extract important keywords and context from the data. This analysis identifies the intent behind the user's question. The analyzed data is then used as input for a generative AI model.

[0088] Step 3:

[0089] The server inputs a prompt sentence into the generative AI model based on the analysis results. This prompt sentence includes context and constraints related to the user's question. For example, the prompt sentence "Please explain the meaning of the English word 'resilience'" might be input into the generative AI model.

[0090] Step 4:

[0091] The generative AI model generates an appropriate response based on the input prompt sentence. In this response generation process, the AI ​​model constructs sentences using natural language generation technology. The generated response becomes the server output and is converted back into a format such as JSON.

[0092] Step 5:

[0093] The server sends the generated response to the terminal. The terminal displays the received response on the screen or outputs it as speech using text-to-speech technology to present it to the user. This allows the user to receive the actual response and deepen their learning.

[0094] Step 6:

[0095] The server stores user question-answer data in a database and records it as the user's learning progress. This record allows for the accumulation of the user's learning history and performance, which is then used to optimize future learning plans.

[0096] (Application Example 1)

[0097] 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."

[0098] In today's dual-income households, it is difficult for parents to understand their children's learning progress and provide appropriate support. Furthermore, children themselves lack the resources necessary to learn efficiently. In this context, real-time learning support and continuous monitoring of learning progress are needed. Moreover, learning support needs to be presented in a way that is engaging and interesting to children.

[0099] 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.

[0100] In this invention, the server includes means for receiving input data from a user and analyzing the input data using natural language processing technology, means for generating an appropriate response based on the analyzed input data, means for presenting the generated response to the user, means for using an artificial intelligence model for generating the answer, and means equipped with a support device that provides interaction in voice or text format. This enhances learning support for children in dual-income households, making it possible for parents and children to easily grasp the learning situation and create an environment where learning can be done efficiently.

[0101] "Input data" refers to information provided by the user, expressed in a format such as natural language.

[0102] "Natural language processing technology" is computer technology used to analyze and understand human language.

[0103] "Analysis" is the process of breaking down data and information into smaller parts to understand their meaning and structure.

[0104] An "artificial intelligence model" is a system that learns from vast amounts of data and performs judgments and reasoning like a human when performing a specific task.

[0105] A "support device" is a device that interacts with the user, provides information, and supports tasks.

[0106] "Learning history" refers to a record of the learning activities a user has undertaken in the past.

[0107] "Performance data" refers to data used to evaluate a user's learning results and progress.

[0108] A "learning plan" is a set of activities and tasks designed to help a user learn efficiently.

[0109] A "dashboard" is an interface for visually displaying and managing data.

[0110] A "report" is a document that summarizes the aggregated results and analysis of data over a specific period.

[0111] An "alert function" is a feature that notifies users or administrators when certain conditions are met.

[0112] A learning support system plays a crucial role in realizing this invention. The system interacts with the user and has multiple functions to support learning. First, the server analyzes the input data received from the user terminal using natural language processing technology.

[0113] The server uses a generative AI model based on the analyzed data to generate the optimal response. Examples of such generative AI models include OpenAI's GPT model. The generated response is sent to the user's device and presented via screen or audio.

[0114] Furthermore, the server collects the user's learning history and performance data. Based on this, it automatically generates personalized learning plans using machine learning algorithms. These learning plans are presented to the user via their device or a robot in their home to support their learning activities.

[0115] Parental support is also crucial. Users' learning progress and problems are visualized on a dashboard, allowing parents to monitor progress in real time. The server also generates weekly or monthly reports, which are automatically sent to parents via email. An alert function is also included, notifying parents if specific issues remain unresolved.

[0116] For example, when a user (a child) inputs a question about the meaning of an English word into the system, the server performs natural language processing and uses an AI model to explain the meaning of the word. Examples of prompts used to instruct the generative AI model include, "I want to know the meaning of this English word. Please search for it," and "Tell me more about fractions."

[0117] This system will enable children from dual-income families to continue their studies efficiently, and will create an environment where parents can support their children's learning with peace of mind.

[0118] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0119] Step 1:

[0120] The user enters learning questions using a device. This input data is in text or audio format, and the device sends this data to the server. The entered content becomes the basis for subsequent analysis.

[0121] Step 2:

[0122] The server receives input data sent by the user and performs text analysis using natural language processing techniques. Here, important information is extracted from the input data, and data processing is performed to understand the structure of the text.

[0123] Step 3:

[0124] Based on the analyzed input data, the server uses a generative AI model to generate an appropriate response. The analysis results are input to the model as prompts, and the AI ​​generates a human-readable response based on its learned knowledge. For example, in response to an input asking for the meaning of a word, it generates an explanation.

[0125] Step 4:

[0126] The generated response is sent back from the server to the terminal, which then presents it to the user. During presentation, the information is provided to the user in an intuitively understandable format through screen display or audio output.

[0127] Step 5:

[0128] The user's learning history and performance data are periodically sent from the device to the server. The server receives this data, performs calculations to analyze the user's strengths and weaknesses, and generates a personalized learning plan.

[0129] Step 6:

[0130] The generated learning plan is sent to the device, which then presents it to the user. The robot may also support learning activities within the home, flexibly adjusting the plan according to the user's progress.

[0131] Step 7:

[0132] Information is also provided to parents. The server visualizes the user's learning progress on a dashboard and automatically sends weekly and monthly reports to parents. If a specific issue is not resolved, an alert function is used to notify parents.

[0133] 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.

[0134] The present invention aims to enrich and enhance the user's learning experience by incorporating an emotion engine into an interactive learning support system. This system analyzes user input data in real time, combines natural language processing and emotion recognition technology to provide optimal responses, and supports the user's learning progress through personalized learning plans.

[0135] System configuration and operation

[0136] Adaptive Question Answering Function

[0137] The server receives questions from the user in voice or text format. After analyzing the intent of the question using natural language processing techniques, the sentiment engine analyzes the user's emotional state. This allows the server to generate an appropriate response that takes the user's emotions and perceptions into account. For example, if the sentiment engine determines that the user is confused, the server will provide a response that includes a more helpful and polite explanation.

[0138] Optimizing individual learning plans

[0139] The server continuously collects and analyzes the user's learning history and emotional data obtained by the emotion engine to generate a learning plan tailored to the user's needs. The generated plan is modified according to the user's emotional state and sent to the user. For example, if the user is in a stressful situation, the system will suggest a plan that includes a more relaxed approach.

[0140] Visualization of learning progress and emotions

[0141] The device displays learning progress and emotional patterns to users and parents through a visual dashboard. The server provides parents with information weekly or monthly through regularly generated reports. If certain emotional patterns may negatively impact learning, the system alerts parents to encourage early intervention. This allows parents to provide appropriate support to ensure users continue learning in a better state.

[0142] Specific example

[0143] For example, if a user shows frustration while working on a history problem, the device detects this through its emotion engine. Based on that emotional state, the server may suggest history-related videos or game-style problems that contain relaxing content. The device presents these to the user to help prevent learning from stalling. Parents can also use the dashboard to see under what circumstances their child experiences stress during learning and use that information to consider countermeasures.

[0144] This invention enables learning support that takes into account the user's emotional state, thereby providing a more adaptive and effective learning environment.

[0145] The following describes the processing flow.

[0146] Step 1:

[0147] When a user encounters a problem they don't understand during learning, they can enter their question into the device via voice or text.

[0148] Step 2:

[0149] The terminal converts the input voice data into text data using speech recognition technology and prepares to send it to the server.

[0150] Step 3:

[0151] The server receives text data and uses natural language processing techniques to analyze the intent of the question.

[0152] Step 4:

[0153] The server uses an emotion engine to analyze the user's emotional state from their input. For example, it can determine whether the user is confused or irritated based on their tone of voice and chosen words.

[0154] Step 5:

[0155] The server generates an appropriate response based on the analysis results. This includes taking the user's emotional state into consideration, providing more helpful and clearer explanations, as well as encouraging messages.

[0156] Step 6:

[0157] The server sends the generated response to the terminal in text format.

[0158] Step 7:

[0159] The device displays the received response to the user. The display is either shown as text on the screen or played as audio.

[0160] Step 8:

[0161] The user reconsiders the problem based on the information provided. If no improvement is seen, they can ask additional questions, and the system will provide further support.

[0162] Step 9:

[0163] The device collects data on the user's learning progress and emotional state and sends it to the server. This data is used to optimize future learning plans and report to parents.

[0164] Step 10:

[0165] The server updates personalized learning plans based on the collected data and generates reports and status updates for the user's parents as needed.

[0166] (Example 2)

[0167] 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".

[0168] Conventional learning support systems do not take into account the user's emotional state, making it difficult to maintain the user's interest and concentration, resulting in insufficient learning effectiveness. This invention aims to achieve more effective learning support by analyzing the user's emotional state in real time and providing responses and learning plans accordingly.

[0169] 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.

[0170] In this invention, the server includes means for receiving input information from the user and analyzing the input information using natural language processing technology, means for generating an appropriate response based on the analyzed input information and emotion data, and means for analyzing the user's emotional state and adjusting the tone of the generated response. This enables adaptive learning support that responds to the user's emotions.

[0171] "Input information" refers to audio or text data received from the user.

[0172] "Natural language processing technology" is a technology that uses computers to analyze human language and understand its intent and meaning.

[0173] "Emotional data" refers to information that indicates an emotional state, extracted from user input.

[0174] "Response" refers to a reply or instruction to the user that is generated based on the user's input information and sentiment data.

[0175] An "information display device" refers to a digital device or screen display means used by a user to receive responses from a system.

[0176] A "learning plan" is a personalized learning guideline and schedule designed based on the user's learning progress and emotional data.

[0177] The "warning function" is a feature that notifies parents if certain conditions persist.

[0178] This invention is an interactive system that supports user learning and incorporates emotion recognition capabilities. When a user provides input information in voice or text format through a terminal, the terminal sends that information to a server. The server analyzes the input information using natural language processing technology and an emotion recognition engine to understand the user's intent and extract emotion data.

[0179] The server uses a generative AI model to generate appropriate responses based on the analysis results. The generative AI model adjusts the tone and content according to the user's emotional state, forming a response optimized for the individual user. This response is sent to the terminal and presented to the user visually or audibly.

[0180] In particular, this system performs long-term analysis of the user's learning history and acquired emotional data to dynamically generate a learning plan tailored to the user. It also includes features to visualize learning progress and emotional patterns through an information display device, providing this information to both the user and their parents. This allows parents to understand their child's learning progress through regular reports and provide effective support.

[0181] For example, if a user is feeling frustrated with a historical issue, the server can suggest relaxing video content or game-like problems based on their emotional data. This allows the user to continue learning in a new way.

[0182] The following prompt can be used as an example input to the generative AI model: "Describe the steps to use the AI ​​model to propose a learning support system that responds to the user's emotional state. Include specific examples of responses based on sentiment analysis when the user asks a question."

[0183] This invention is a system that enables the optimization of learning by taking into account the user's emotional state, thereby providing a more effective learning experience.

[0184] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0185] Step 1:

[0186] The user enters questions or requests into the terminal in voice or text format. The terminal receives this as input information and sends it to the server. At this stage, it is important that the input information is correctly received by the system, and the terminal may ask for confirmation using the user interface.

[0187] Step 2:

[0188] The server passes the received input information to a natural language processing engine for analysis. Specifically, the information is tokenized, and syntactic and semantic analysis are performed. This clarifies the intent of the input information and identifies the user's request. The output obtained in this process consists of analyzed syntactic data and metadata of the user's intent.

[0189] Step 3:

[0190] The server then uses an emotion recognition engine to analyze the user's emotional state based on the data obtained in the previous step. It identifies the user's emotions by evaluating their voice tone and emotional vocabulary in text. The resulting emotional data is then used in the subsequent response generation process.

[0191] Step 4:

[0192] The server uses a generative AI model to generate appropriate responses from parsed syntactic and sentiment data. In this process, the model adjusts tone and detail according to sentiment and selects content. The generated responses are designed to be delivered to the user in the most optimal way. The output is the generated text or audio response data.

[0193] Step 5:

[0194] The server sends the generated response data to the terminal, which then presents it to the user. The user receives the response via on-screen text or audio output. At this stage, the terminal optimizes its interface to ensure the response is properly conveyed to the user.

[0195] Step 6:

[0196] The server continuously collects the user's learning history and sentiment data, and uses this information to generate a personalized learning plan. This data processing includes trend analysis of past learning history and pattern recognition of sentiment data. The generated plan is adjusted to the user's learning needs and sent to the device.

[0197] Step 7:

[0198] The device visualizes information, including learning plans, progress, and emotional patterns, and displays it to the user and their parents as a dashboard. This visualization output includes detailed reports using graphs and charts. This allows for continuous monitoring of the user's learning progress and determination of necessary support.

[0199] (Application Example 2)

[0200] 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 device 14 will be referred to as the "terminal."

[0201] In today's learning environment, there is a challenge in appropriately addressing the emotions and progress of individual learners. Traditional learning systems provide uniform materials and plans without considering learners' feelings, which can lead to decreased motivation and reduced opportunities for effective learning. Furthermore, parents have limited means of obtaining information to understand their child's emotional state and respond appropriately.

[0202] 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.

[0203] In this invention, the server includes means for receiving data from the user and analyzing the data using natural language processing technology, means for generating an appropriate response based on the analyzed data, means for presenting the generated response to the user, and means for recognizing the user's emotional state and adjusting the response based on that emotion. This enables the provision of appropriate responses and learning plans that correspond to the learner's emotions, and flexible and effective learning support that meets individual needs.

[0204] "User" refers to an individual or learner who uses the learning support system.

[0205] "Data" refers to information provided by the user, including voice, text, historical information, and emotional states.

[0206] "Natural language processing technology" is a technology that enables computers to understand, analyze, and generate responses to human language.

[0207] "Means" refers to a process, apparatus, or technical method used to perform a particular function.

[0208] "Response" refers to the reply or feedback that a system generates in response to user input.

[0209] "Emotional state" refers to the changes and tendencies in emotions that a user exhibits during the learning process.

[0210] A "learning plan" is a structured plan that represents a learning approach and materials optimized according to the individual user's needs and feelings.

[0211] An "interface" refers to the means or platform by which users and systems exchange information with each other.

[0212] A "guardian" refers to an individual who is responsible for supporting and monitoring a learner's learning progress and emotional state.

[0213] A "warning function" is a mechanism designed to cause a system to take a specific action when certain states or conditions are met.

[0214] To implement this invention, a system is required for analyzing voice or text data input by a user in real time. The system utilizes hardware for receiving data transmitted from the user, such as a terminal with a microphone. The received data is analyzed using natural language processing technology, and software that understands its intent, such as NLTK (Natural Language Toolkit) or Google's speech recognition service, is used. Based on the analyzed data, a generative AI model is used to generate a response, which is then presented to the user. Furthermore, to perform emotion recognition, a library for emotion analysis, such as EmotionDetector, is utilized to identify the user's emotional state.

[0215] The server generates a personalized learning plan based on this information, adjusts it as needed based on the user's emotional state, and then sends it to the device. This learning plan is created using a program such as LearningPlanGenerator. The device presents the learning plan and progress to the user through a visual interface, enabling the visualization of emotions. This information is provided not only to the user but also to parents, who receive it as periodic reports.

[0216] Specifically, when a user says something like "I don't understand" while learning English, the device recognizes this, and the server determines through sentiment analysis that the user is confused. In this case, the server can offer words of encouragement and suggest learning videos that are appropriate for the user.

[0217] When using the generative AI model, the following prompts should be considered:

[0218] "Please be kind and encouraging to users who are confused."

[0219] "Please suggest appropriate learning materials based on emotions."

[0220] With the above configuration, the system can provide a flexible and effective learning environment, enriching the user's learning experience.

[0221] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0222] Step 1:

[0223] The user provides input to the system via voice or text. The terminal receives the input data and converts it to text data. Here, speech recognition is used to convert the voice data to text and send it as input data. The input is raw voice data, and the output is text data.

[0224] Step 2:

[0225] The server analyzes the received text data using natural language processing techniques. Specifically, it utilizes libraries such as NLTK to construct a data structure to understand the intent of the question. This allows it to identify the user's intent and areas of interest. The input is text data, and the output is the analyzed data structure.

[0226] Step 3:

[0227] The server uses the analyzed data structure to invoke a generative AI model and generate an appropriate response. Here, a prompt is applied to the model to generate an emotionally resonant response. The input is the analyzed data structure, and the output is the generated response text.

[0228] Step 4:

[0229] The server analyzes the user's emotional state using an emotion analysis library. It utilizes EmotionDetector to identify emotions from the user's textual expressions and records that emotion data. Input is text data, and output is emotion data.

[0230] Step 5:

[0231] The server uses LearningPlanGenerator to generate a personalized learning plan based on collected sentiment data and the user's past history data. This creates a learning sequence best suited to the user's current state. The input is sentiment data and history data, and the output is the personalized learning plan.

[0232] Step 6:

[0233] The generated response text and learning plan are sent to the terminal, which then presents the information to the user through a visual interface. Here, the information is displayed on the screen, and the user can take the next action based on it. The input is the response text and learning plan, and the output is the visual presentation to the user.

[0234] Step 7:

[0235] The device visualizes the user's learning progress and emotional state, and generates and sends periodic reports to parents. The data is displayed as graphs and charts through visualization tools. Inputs are user progress data and emotional data, and outputs are reports for parents.

[0236] 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.

[0237] 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.

[0238] 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.

[0239] [Second Embodiment]

[0240] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0241] 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.

[0242] 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).

[0243] 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.

[0244] 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.

[0245] 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).

[0246] 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.

[0247] 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.

[0248] 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.

[0249] 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.

[0250] 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.

[0251] 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".

[0252] This invention relates to an interactive learning support system for users, aimed at supporting the learning of children in dual-income households. This system improves the quality of children's learning by providing real-time question answering, optimization of learning plans, and visualization of learning progress. Furthermore, it enables efficient learning support within the home through timely information provision and notifications to parents.

[0253] System configuration and operation

[0254] Question answering function

[0255] The server receives question data submitted by the user, including text and voice input. The server uses natural language processing techniques to analyze the question and generate the most appropriate response. This response is sent back to the user's device and provided to the user in real time, either displayed or spoken aloud. For example, if a user asks, "What does this English word mean?", the server translates the word's meaning, provides an explanation, and returns the answer.

[0256] Optimizing your learning plan

[0257] The server collects the user's learning history and performance data, and analyzes this data using AI technology. This identifies the user's strengths and weaknesses and generates a personalized learning plan. The generated plan is sent to the user's device, and the user uses it to work on their daily studies. For example, if a user has a weak understanding of a particular subject, a plan will be provided that includes many related problems to strengthen that subject.

[0258] Visualization of learning progress and support for parents

[0259] Parents can access a dashboard through their device that visualizes their child's learning progress. This dashboard displays learning achievements, areas of difficulty, and progress status. The server generates reports regularly and sends them to parents weekly and monthly. Furthermore, if a specific learning task remains unresolved, the server sends an alert to the parent. In this way, parents can understand their child's learning situation in real time and provide the necessary support.

[0260] Specific example

[0261] Suppose a user (child) finds a math problem they can't solve and asks the system for help. The user's device sends the question to the server, which generates an answer including how to solve the problem and related explanations. The answer is notified to the user in real time, and the user deepens their understanding by reading the displayed explanation. Afterwards, the user's progress is saved in the system and used for future learning plans. Parents can also check through a dashboard whether the child has been able to solve the problem and provide additional support as needed.

[0262] This invention creates an environment where children can efficiently overcome learning challenges, and parents can provide effective support even when they are busy.

[0263] The following describes the processing flow.

[0264] Step 1:

[0265] Users enter questions into the device to solve problems they don't understand. Input methods include voice or text.

[0266] Step 2:

[0267] The terminal converts the input data into an appropriate format (text format) and sends it to the server. In the case of voice input, speech recognition technology is used to convert it to text.

[0268] Step 3:

[0269] The server analyzes the received text data using natural language processing technology to accurately understand the intent of the user's question.

[0270] Step 4:

[0271] The server references internal databases and knowledge bases to generate appropriate answers to user questions. These generated answers include relevant information and additional explanations.

[0272] Step 5:

[0273] The server sends the generated response to the user's device in text format.

[0274] Step 6:

[0275] The device presents the received response to the user. The presentation method is either screen display or audio playback.

[0276] Step 7:

[0277] The user refers to the provided answers to understand or solve the problem.

[0278] Step 8:

[0279] The device collects data on the user's learning progress and question history, and sends it to the server. This data is used to optimize future learning plans.

[0280] (Example 1)

[0281] 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."

[0282] In dual-income households, a challenge exists in supporting children's learning: it is difficult for parents to efficiently understand their children's learning progress and provide appropriate support. Furthermore, it is challenging to provide personalized learning plans while monitoring users' learning progress in real time. These challenges need to be addressed to improve the quality of children's learning.

[0283] 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.

[0284] In this invention, the server includes means for receiving input data from a user and analyzing the input data by means of information processing technology, a generating device for generating an appropriate response based on the analyzed input data, and an output device for presenting the generated response to the user. This makes it possible to quickly and accurately answer the user's questions.

[0285] "Information processing technology" is a technology for analyzing data obtained from a user and understanding its meaning and context.

[0286] The "generating device" is a device for constructing an appropriate response to be presented to the user based on the analyzed data.

[0287] The "output device" is a device for visually or audibly transmitting the generated response to the user.

[0288] The "data storage device" is a device for storing the user's activity records and learning progress for future analysis and planning.

[0289] The "generative AI model" is an artificial intelligence technology for generating appropriate and skillful responses based on prompts from the user.

[0290] The "prompt text" is the text input to the generative AI model, which includes instructions and conditions for the model to generate a response.

[0291] The "learning plan" refers to the learning schedule and content adjusted according to the user's learning progress and individual needs.

[0292] The "display device" is a device that provides an interface through which the user can visually confirm their learning progress.

[0293] The "notification function" is a function that issues a warning to the user or their parent when a specific condition is met.

[0294] This invention is a system for supporting the learning of children in dual-income households, and is realized through the interaction of a server, a terminal, and a user. First, the user inputs learning-related questions via the terminal. Questions can be entered in text or voice format. The terminal transfers the input data to the server. The server analyzes the received data using natural language processing technology. Information processing technologies such as Python's NLTK library and spaCy are used for the analysis. This allows the server to understand the user's intent and the content of the question.

[0295] The server launches a generative AI model based on the analyzed data. This model is input with specific prompt sentences. For example, if a user asks, "What does this English word mean?", the prompt sentence input to the model will be in the format of "Please explain the meaning of the English word '". The generative AI model generates a response to the user's question based on this prompt sentence.

[0296] The generated response is sent from the server to the terminal, which then provides it to the user either visually or audibly. The terminal's screen is used for display, while text-to-speech technology is used for audio output. For example, if the user asks about the meaning of "resilience," the server generates a response such as "resilience means the ability to recover from difficulties" and displays it on the terminal.

[0297] Furthermore, the server records the user's learning activities and saves their progress to a data storage device. This allows the user's individual learning history, strengths, and weaknesses to be identified and used to generate their next learning plan. In this way, users can progress in their learning based on individually tailored learning content.

[0298] Furthermore, parents can access a dashboard through their device that visualizes their child's learning progress. This dashboard displays learning achievements, areas of difficulty, and progress status, providing parents with timely and accurate information. Based on this, parents can provide appropriate learning support for their children.

[0299] The flow of the specific process in Example 1 will be described with reference to FIG. 11.

[0300] Step 1:

[0301] The user inputs the topic or question they want to learn into the terminal. This input is made in text or voice form. The terminal converts the user's input into a format (e.g., JSON format) to transmit it as digital data to the server. This digital data serves as the input to the server.

[0302] Step 2:

[0303] The server analyzes the digital data received from the terminal. The analysis is performed using natural language processing technology to extract important keywords and context from the data. Through this analysis, the intention of the user's question is identified. The analyzed data is used as input to the generative AI model.

[0304] Step 3:

[0305] The server inputs a prompt sentence based on the analysis result into the generative AI model. This prompt sentence is a sentence that includes the context and constraints related to the user's question. For example, a prompt sentence such as "Please explain the meaning of the English word'resilience'" is input into the generative AI model.

[0306] Step 4:

[0307] The generative AI model generates an appropriate response based on the input prompt sentence. In this response generation process, the AI model constructs sentences using natural language generation technology. The generated response becomes the output of the server and is converted back into a format such as JSON.

[0308] Step 5:

[0309] The server sends the generated response to the terminal. The terminal displays the received response on the screen or outputs it as speech using text-to-speech technology to present it to the user. This allows the user to receive the actual response and deepen their learning.

[0310] Step 6:

[0311] The server stores user question-answer data in a database and records it as the user's learning progress. This record allows for the accumulation of the user's learning history and performance, which is then used to optimize future learning plans.

[0312] (Application Example 1)

[0313] 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."

[0314] In today's dual-income households, it is difficult for parents to understand their children's learning progress and provide appropriate support. Furthermore, children themselves lack the resources necessary to learn efficiently. In this context, real-time learning support and continuous monitoring of learning progress are needed. Moreover, learning support needs to be presented in a way that is engaging and interesting to children.

[0315] 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.

[0316] In this invention, the server includes means for receiving input data from a user and analyzing the input data using natural language processing technology, means for generating an appropriate response based on the analyzed input data, means for presenting the generated response to the user, means for using an artificial intelligence model for generating the answer, and means equipped with a support device that provides interaction in voice or text format. This enhances learning support for children in dual-income households, making it possible for parents and children to easily grasp the learning situation and create an environment where learning can be done efficiently.

[0317] "Input data" refers to information provided by the user, expressed in a format such as natural language.

[0318] "Natural language processing technology" is computer technology used to analyze and understand human language.

[0319] "Analysis" is the process of breaking down data and information into smaller parts to understand their meaning and structure.

[0320] An "artificial intelligence model" is a system that learns from vast amounts of data and performs judgments and reasoning like a human when performing a specific task.

[0321] A "support device" is a device that interacts with the user, provides information, and supports tasks.

[0322] "Learning history" refers to a record of the learning activities a user has undertaken in the past.

[0323] "Performance data" refers to data used to evaluate a user's learning results and progress.

[0324] A "learning plan" is a set of activities and tasks designed to help a user learn efficiently.

[0325] A "dashboard" is an interface for visually displaying and managing data.

[0326] A "report" is a document that summarizes the aggregated results and analysis of data over a specific period.

[0327] An "alert function" is a feature that notifies users or administrators when certain conditions are met.

[0328] A learning support system plays a crucial role in realizing this invention. The system interacts with the user and has multiple functions to support learning. First, the server analyzes the input data received from the user terminal using natural language processing technology.

[0329] The server uses a generative AI model based on the analyzed data to generate the optimal response. Examples of such generative AI models include OpenAI's GPT model. The generated response is sent to the user's device and presented on the screen or via audio.

[0330] Furthermore, the server collects the user's learning history and performance data. Based on this, it automatically generates personalized learning plans using machine learning algorithms. These learning plans are presented to the user via their device or a robot in their home to support their learning activities.

[0331] Parental support is also crucial. Users' learning progress and problems are visualized on a dashboard, allowing parents to monitor progress in real time. The server also generates weekly or monthly reports, which are automatically sent to parents via email. An alert function is also included, notifying parents if specific issues remain unresolved.

[0332] For example, when a user (a child) inputs a question about the meaning of an English word into the system, the server performs natural language processing and uses an AI model to explain the meaning of the word. Examples of prompts used to instruct the generative AI model include, "I want to know the meaning of this English word. Please search for it," and "Tell me more about fractions."

[0333] This system will enable children from dual-income families to continue their studies efficiently, and will create an environment where parents can support their children's learning with peace of mind.

[0334] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0335] Step 1:

[0336] The user enters learning questions using a device. This input data is in text or audio format, and the device sends this data to the server. The entered content becomes the basis for subsequent analysis.

[0337] Step 2:

[0338] The server receives input data sent by the user and performs text analysis using natural language processing techniques. Here, important information is extracted from the input data, and data processing is performed to understand the structure of the text.

[0339] Step 3:

[0340] Based on the analyzed input data, the server uses a generative AI model to generate an appropriate response. The analysis results are input to the model as prompts, and the AI ​​generates a human-readable response based on its learned knowledge. For example, in response to an input asking for the meaning of a word, it generates an explanation.

[0341] Step 4:

[0342] The generated response is sent back from the server to the terminal, which then presents it to the user. During presentation, the information is provided to the user in an intuitively understandable format through screen display or audio output.

[0343] Step 5:

[0344] The user's learning history and performance data are periodically sent from the device to the server. The server receives this data, performs calculations to analyze the user's strengths and weaknesses, and generates a personalized learning plan.

[0345] Step 6:

[0346] The generated learning plan is sent to the device, which then presents it to the user. The robot may also support learning activities within the home, flexibly adjusting the plan according to the user's progress.

[0347] Step 7:

[0348] Information is also provided to parents. The server visualizes the user's learning progress on a dashboard and automatically sends weekly and monthly reports to parents. If a specific issue is not resolved, an alert function is used to notify parents.

[0349] 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.

[0350] The present invention aims to enrich and enhance the user's learning experience by incorporating an emotion engine into an interactive learning support system. This system analyzes user input data in real time, combines natural language processing and emotion recognition technology to provide optimal responses, and supports the user's learning progress through personalized learning plans.

[0351] System configuration and operation

[0352] Adaptive Question Answering Function

[0353] The server receives questions from the user in voice or text format. After analyzing the intent of the question using natural language processing techniques, the sentiment engine analyzes the user's emotional state. This allows the server to generate an appropriate response that takes the user's emotions and perceptions into account. For example, if the sentiment engine determines that the user is confused, the server will provide a response that includes a more helpful and polite explanation.

[0354] Optimizing individual learning plans

[0355] The server continuously collects and analyzes the user's learning history and emotional data obtained by the emotion engine to generate a learning plan tailored to the user's needs. The generated plan is modified according to the user's emotional state and sent to the user. For example, if the user is in a stressful situation, the system will suggest a plan that includes a more relaxed approach.

[0356] Visualization of learning progress and emotions

[0357] The device displays learning progress and emotional patterns to users and parents through a visual dashboard. The server provides parents with information weekly or monthly through regularly generated reports. If certain emotional patterns may negatively impact learning, the system alerts parents to encourage early intervention. This allows parents to provide appropriate support to ensure users continue learning in a better state.

[0358] Specific example

[0359] For example, if a user shows frustration while working on a history problem, the device detects this through its emotion engine. Based on that emotional state, the server may suggest history-related videos or game-style problems that contain relaxing content. The device presents these to the user to help prevent learning from stalling. Parents can also use the dashboard to see under what circumstances their child experiences stress during learning and use that information to consider countermeasures.

[0360] This invention enables learning support that takes into account the user's emotional state, thereby providing a more adaptive and effective learning environment.

[0361] The following describes the processing flow.

[0362] Step 1:

[0363] When a user encounters a problem they don't understand during learning, they can enter their question into the device via voice or text.

[0364] Step 2:

[0365] The terminal converts the input voice data into text data using speech recognition technology and prepares to send it to the server.

[0366] Step 3:

[0367] The server receives text data and uses natural language processing techniques to analyze the intent of the question.

[0368] Step 4:

[0369] The server uses an emotion engine to analyze the user's emotional state from their input. For example, it can determine whether the user is confused or irritated based on their tone of voice and chosen words.

[0370] Step 5:

[0371] The server generates an appropriate response based on the analysis results. This includes taking the user's emotional state into consideration, providing more helpful and clearer explanations, as well as encouraging messages.

[0372] Step 6:

[0373] The server sends the generated response to the terminal in text format.

[0374] Step 7:

[0375] The device displays the received response to the user. The display is either shown as text on the screen or played as audio.

[0376] Step 8:

[0377] The user reconsiders the problem based on the information provided. If no improvement is seen, they can ask additional questions, and the system will provide further support.

[0378] Step 9:

[0379] The device collects data on the user's learning progress and emotional state and sends it to the server. This data is used to optimize future learning plans and report to parents.

[0380] Step 10:

[0381] The server updates personalized learning plans based on the collected data and generates reports and status updates for the user's parents as needed.

[0382] (Example 2)

[0383] 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".

[0384] Conventional learning support systems do not take into account the user's emotional state, making it difficult to maintain the user's interest and concentration, resulting in insufficient learning effectiveness. This invention aims to achieve more effective learning support by analyzing the user's emotional state in real time and providing responses and learning plans accordingly.

[0385] 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.

[0386] In this invention, the server includes means for receiving input information from the user and analyzing the input information using natural language processing technology, means for generating an appropriate response based on the analyzed input information and emotion data, and means for analyzing the user's emotional state and adjusting the tone of the generated response. This enables adaptive learning support that responds to the user's emotions.

[0387] "Input information" refers to audio or text data received from the user.

[0388] "Natural language processing technology" is a technology that uses computers to analyze human language and understand its intent and meaning.

[0389] "Emotional data" refers to information that indicates an emotional state, extracted from user input.

[0390] "Response" refers to a reply or instruction to the user that is generated based on the user's input information and sentiment data.

[0391] An "information display device" refers to a digital device or screen display means used by a user to receive responses from a system.

[0392] A "learning plan" is a personalized learning guideline and schedule designed based on the user's learning progress and emotional data.

[0393] The "warning function" is a feature that notifies parents if certain conditions persist.

[0394] This invention is an interactive system that supports user learning and incorporates emotion recognition capabilities. When a user provides input information in voice or text format through a terminal, the terminal sends that information to a server. The server analyzes the input information using natural language processing technology and an emotion recognition engine to understand the user's intent and extract emotion data.

[0395] The server uses a generative AI model to generate appropriate responses based on the analysis results. The generative AI model adjusts the tone and content according to the user's emotional state, forming a response optimized for the individual user. This response is sent to the terminal and presented to the user visually or audibly.

[0396] In particular, this system performs long-term analysis of the user's learning history and acquired emotional data to dynamically generate a learning plan tailored to the user. It also includes features to visualize learning progress and emotional patterns through an information display device, providing this information to both the user and their parents. This allows parents to understand their child's learning progress through regular reports and provide effective support.

[0397] For example, if a user is feeling frustrated with a historical issue, the server can suggest relaxing video content or game-like problems based on their emotional data. This allows the user to continue learning in a new way.

[0398] The following prompt can be used as an example input to the generative AI model: "Describe the steps to use the AI ​​model to propose a learning support system that responds to the user's emotional state. Include specific examples of responses based on sentiment analysis when the user asks a question."

[0399] This invention is a system that enables the optimization of learning by taking into account the user's emotional state, thereby providing a more effective learning experience.

[0400] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0401] Step 1:

[0402] The user enters questions or requests into the terminal in voice or text format. The terminal receives this as input information and sends it to the server. At this stage, it is important that the input information is correctly received by the system, and the terminal may ask for confirmation using the user interface.

[0403] Step 2:

[0404] The server passes the received input information to a natural language processing engine for analysis. Specifically, the information is tokenized, and syntactic and semantic analysis are performed. This clarifies the intent of the input information and identifies the user's request. The output obtained in this process consists of analyzed syntactic data and metadata of the user's intent.

[0405] Step 3:

[0406] The server then uses an emotion recognition engine to analyze the user's emotional state based on the data obtained in the previous step. It identifies the user's emotions by evaluating their voice tone and emotional vocabulary in text. The resulting emotional data is then used in the subsequent response generation process.

[0407] Step 4:

[0408] The server uses a generative AI model to generate appropriate responses from parsed syntactic and sentiment data. In this process, the model adjusts tone and detail according to sentiment and selects content. The generated responses are designed to be delivered to the user in the most optimal way. The output is the generated text or audio response data.

[0409] Step 5:

[0410] The server sends the generated response data to the terminal, which then presents it to the user. The user receives the response via on-screen text or audio output. At this stage, the terminal optimizes its interface to ensure the response is properly conveyed to the user.

[0411] Step 6:

[0412] The server continuously collects the user's learning history and sentiment data, and uses this information to generate a personalized learning plan. This data processing includes trend analysis of past learning history and pattern recognition of sentiment data. The generated plan is adjusted to the user's learning needs and sent to the device.

[0413] Step 7:

[0414] The device visualizes information, including learning plans, progress, and emotional patterns, and displays it to the user and their parents as a dashboard. This visualization output includes detailed reports using graphs and charts. This allows for continuous monitoring of the user's learning progress and determination of necessary support.

[0415] (Application Example 2)

[0416] 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 as the "terminal".

[0417] In today's learning environment, there is a challenge in appropriately addressing the emotions and progress of individual learners. Traditional learning systems provide uniform materials and plans without considering learners' feelings, which can lead to decreased motivation and reduced opportunities for effective learning. Furthermore, parents have limited means of obtaining information to understand their child's emotional state and respond appropriately.

[0418] 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.

[0419] In this invention, the server includes means for receiving data from the user and analyzing the data using natural language processing technology, means for generating an appropriate response based on the analyzed data, means for presenting the generated response to the user, and means for recognizing the user's emotional state and adjusting the response based on that emotion. This enables the provision of appropriate responses and learning plans that correspond to the learner's emotions, and flexible and effective learning support that meets individual needs.

[0420] "User" refers to an individual or learner who uses the learning support system.

[0421] "Data" refers to information provided by the user, including voice, text, historical information, and emotional states.

[0422] "Natural language processing technology" is a technology that enables computers to understand, analyze, and generate responses to human language.

[0423] "Means" refers to a process, apparatus, or technical method used to perform a particular function.

[0424] "Response" refers to the reply or feedback that a system generates in response to user input.

[0425] "Emotional state" refers to the changes and tendencies in emotions that a user exhibits during the learning process.

[0426] A "learning plan" is a structured plan that represents a learning approach and materials optimized according to the individual user's needs and feelings.

[0427] An "interface" refers to the means or platform by which users and systems exchange information with each other.

[0428] A "guardian" refers to an individual who is responsible for supporting and monitoring a learner's learning progress and emotional state.

[0429] A "warning function" is a mechanism designed to cause a system to take a specific action when certain states or conditions are met.

[0430] To implement this invention, a system is required for analyzing user-inputted voice or text data in real time. The system utilizes hardware for receiving data transmitted from the user, such as a terminal with a microphone. The received data is analyzed using natural language processing technology, and software that understands its intent, such as NLTK (Natural Language Toolkit) or Google's speech recognition service, is used. Based on the analyzed data, a generative AI model is used to generate a response, which is then presented to the user. Furthermore, to perform emotion recognition, a library for emotion analysis, such as EmotionDetector, is utilized to identify the user's emotional state.

[0431] The server generates a personalized learning plan based on this information, adjusts it as needed based on the user's emotional state, and then sends it to the device. This learning plan is created using a program such as LearningPlanGenerator. The device presents the learning plan and progress to the user through a visual interface, enabling the visualization of emotions. This information is provided not only to the user but also to parents, who receive it as periodic reports.

[0432] Specifically, when a user says something like "I don't understand" while learning English, the device recognizes this, and the server determines through sentiment analysis that the user is confused. In this case, the server can offer words of encouragement and suggest learning videos that are appropriate for the user.

[0433] When using the generative AI model, the following prompts should be considered:

[0434] "Please be kind and encouraging to users who are confused."

[0435] "Please suggest appropriate learning materials based on emotions."

[0436] With the above configuration, the system can provide a flexible and effective learning environment, enriching the user's learning experience.

[0437] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0438] Step 1:

[0439] The user provides input to the system via voice or text. The terminal receives the input data and converts it to text data. Here, speech recognition is used to convert the voice data to text and send it as input data. The input is raw voice data, and the output is text data.

[0440] Step 2:

[0441] The server analyzes the received text data using natural language processing techniques. Specifically, it utilizes libraries such as NLTK to construct a data structure to understand the intent of the question. This allows it to identify the user's intent and areas of interest. The input is text data, and the output is the analyzed data structure.

[0442] Step 3:

[0443] The server uses the analyzed data structure to invoke a generative AI model and generate an appropriate response. Here, a prompt is applied to the model to generate an emotionally resonant response. The input is the analyzed data structure, and the output is the generated response text.

[0444] Step 4:

[0445] The server analyzes the user's emotional state using an emotion analysis library. It utilizes EmotionDetector to identify emotions from the user's textual expressions and records that emotion data. Input is text data, and output is emotion data.

[0446] Step 5:

[0447] The server uses LearningPlanGenerator to generate a personalized learning plan based on collected sentiment data and the user's past history data. This creates a learning sequence best suited to the user's current state. The input is sentiment data and history data, and the output is the personalized learning plan.

[0448] Step 6:

[0449] The generated response text and learning plan are sent to the terminal, which then presents the information to the user through a visual interface. Here, the information is displayed on the screen, and the user can take the next action based on it. The input is the response text and learning plan, and the output is the visual presentation to the user.

[0450] Step 7:

[0451] The device visualizes the user's learning progress and emotional state, and generates and sends periodic reports to parents. The data is displayed as graphs and charts through visualization tools. Inputs are user progress data and emotional data, and outputs are reports for parents.

[0452] 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.

[0453] 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.

[0454] 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.

[0455] [Third Embodiment]

[0456] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0457] 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.

[0458] 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).

[0459] 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.

[0460] 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.

[0461] 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).

[0462] 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.

[0463] 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.

[0464] 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.

[0465] 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.

[0466] 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.

[0467] 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".

[0468] This invention relates to an interactive learning support system for users, aimed at supporting the learning of children in dual-income households. This system improves the quality of children's learning by providing real-time question answering, optimization of learning plans, and visualization of learning progress. Furthermore, it enables efficient learning support within the home through timely information provision and notifications to parents.

[0469] System configuration and operation

[0470] Question answering function

[0471] The server receives question data submitted by the user, including text and voice input. The server uses natural language processing techniques to analyze the question and generate the most appropriate response. This response is sent back to the user's device and provided to the user in real time, either displayed or spoken aloud. For example, if a user asks, "What does this English word mean?", the server translates the word's meaning, provides an explanation, and returns the answer.

[0472] Optimizing your learning plan

[0473] The server collects the user's learning history and performance data, and analyzes this data using AI technology. This identifies the user's strengths and weaknesses and generates a personalized learning plan. The generated plan is sent to the user's device, and the user uses it to work on their daily studies. For example, if a user has a weak understanding of a particular subject, a plan will be provided that includes many related problems to strengthen that subject.

[0474] Visualization of learning progress and support for parents

[0475] Parents can access a dashboard through their device that visualizes their child's learning progress. This dashboard displays learning achievements, areas of difficulty, and progress status. The server generates reports regularly and sends them to parents weekly and monthly. Furthermore, if a specific learning task remains unresolved, the server sends an alert to the parent. In this way, parents can understand their child's learning situation in real time and provide the necessary support.

[0476] Specific example

[0477] Suppose a user (child) finds a math problem they can't solve and asks the system for help. The user's device sends the question to the server, which generates an answer including how to solve the problem and related explanations. The answer is notified to the user in real time, and the user deepens their understanding by reading the displayed explanation. Afterwards, the user's progress is saved in the system and used for future learning plans. Parents can also check through a dashboard whether the child has been able to solve the problem and provide additional support as needed.

[0478] This invention creates an environment where children can efficiently overcome learning challenges, and parents can provide effective support even when they are busy.

[0479] The following describes the processing flow.

[0480] Step 1:

[0481] Users enter questions into the device to solve problems they don't understand. Input methods include voice or text.

[0482] Step 2:

[0483] The terminal converts the input data into an appropriate format (text format) and sends it to the server. In the case of voice input, speech recognition technology is used to convert it to text.

[0484] Step 3:

[0485] The server analyzes the received text data using natural language processing technology to accurately understand the intent of the user's question.

[0486] Step 4:

[0487] The server references internal databases and knowledge bases to generate appropriate answers to user questions. These generated answers include relevant information and additional explanations.

[0488] Step 5:

[0489] The server sends the generated response to the user's device in text format.

[0490] Step 6:

[0491] The device presents the received response to the user. The presentation method is either screen display or audio playback.

[0492] Step 7:

[0493] The user refers to the provided answers to understand or solve the problem.

[0494] Step 8:

[0495] The device collects data on the user's learning progress and question history, and sends it to the server. This data is used to optimize future learning plans.

[0496] (Example 1)

[0497] 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."

[0498] In dual-income households, a challenge exists in supporting children's learning: it is difficult for parents to efficiently understand their children's learning progress and provide appropriate support. Furthermore, it is challenging to provide personalized learning plans while monitoring users' learning progress in real time. These challenges need to be addressed to improve the quality of children's learning.

[0499] 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.

[0500] In this invention, the server includes means for receiving input data from a user and analyzing the input data using information processing technology, a generation device for generating an appropriate response based on the analyzed input data, and an output device for presenting the generated response to the user. This makes it possible to answer user questions quickly and accurately.

[0501] "Information processing technology" refers to the technology used to analyze data obtained from users and understand its meaning and context.

[0502] A "generation device" is a device that constructs an appropriate response to present to the user based on the analyzed data.

[0503] An "output device" is a device that communicates the generated response to the user visually or audibly.

[0504] A "data storage device" is a device that saves user activity records and learning progress for use in future analysis and planning.

[0505] A "generative AI model" is an artificial intelligence technology that generates appropriate and skillful responses based on user prompts.

[0506] A "prompt statement" is a statement input to a generative AI model, containing instructions and conditions for the model to generate a response.

[0507] A "learning plan" refers to a learning schedule and content tailored to the user's learning progress and individual needs.

[0508] A "display device" is a device that provides an interface that allows users to visually check their learning progress.

[0509] A "notification function" is a feature that issues a warning to the user or parent when certain conditions are met.

[0510] This invention is a system for supporting the learning of children in dual-income households, and is realized through the interaction of a server, a terminal, and a user. First, the user inputs learning-related questions via the terminal. Questions can be entered in text or voice format. The terminal transfers the input data to the server. The server analyzes the received data using natural language processing technology. Information processing technologies such as Python's NLTK library and spaCy are used for the analysis. This allows the server to understand the user's intent and the content of the question.

[0511] The server launches a generative AI model based on the analyzed data. This model is input with specific prompt sentences. For example, if a user asks, "What does this English word mean?", the prompt sentence input to the model will be in the format of "Please explain the meaning of the English word '". The generative AI model generates a response to the user's question based on this prompt sentence.

[0512] The generated response is sent from the server to the terminal, which then provides it to the user either visually or audibly. The terminal's screen is used for display, while text-to-speech technology is used for audio output. For example, if the user asks about the meaning of "resilience," the server generates a response such as "resilience means the ability to recover from difficulties" and displays it on the terminal.

[0513] Furthermore, the server records the user's learning activities and saves their progress to a data storage device. This allows the user's individual learning history, strengths, and weaknesses to be identified and used to generate their next learning plan. In this way, users can progress in their learning based on individually tailored learning content.

[0514] Furthermore, parents can access a dashboard through their device that visualizes their child's learning progress. This dashboard displays learning achievements, areas of difficulty, and progress status, providing parents with timely and accurate information. Based on this, parents can provide appropriate learning support for their children.

[0515] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0516] Step 1:

[0517] The user enters the topic or question they want to learn about into the terminal. This input can be in text or voice format. The terminal converts the user's input into a format (e.g., JSON) for transmission as digital data to the server. This digital data becomes the input to the server.

[0518] Step 2:

[0519] The server analyzes the digital data received from the terminal. This analysis uses natural language processing techniques to extract important keywords and context from the data. This analysis identifies the intent behind the user's question. The analyzed data is then used as input for a generative AI model.

[0520] Step 3:

[0521] The server inputs a prompt sentence into the generative AI model based on the analysis results. This prompt sentence includes context and constraints related to the user's question. For example, the prompt sentence "Please explain the meaning of the English word 'resilience'" might be input into the generative AI model.

[0522] Step 4:

[0523] The generative AI model generates an appropriate response based on the input prompt sentence. In this response generation process, the AI ​​model constructs sentences using natural language generation technology. The generated response becomes the server output and is converted back into a format such as JSON.

[0524] Step 5:

[0525] The server sends the generated response to the terminal. The terminal displays the received response on the screen or outputs it as speech using text-to-speech technology to present it to the user. This allows the user to receive the actual response and deepen their learning.

[0526] Step 6:

[0527] The server stores user question-answer data in a database and records it as the user's learning progress. This record allows for the accumulation of the user's learning history and performance, which is then used to optimize future learning plans.

[0528] (Application Example 1)

[0529] 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."

[0530] In today's dual-income households, it is difficult for parents to understand their children's learning progress and provide appropriate support. Furthermore, children themselves lack the resources necessary to learn efficiently. In this context, real-time learning support and continuous monitoring of learning progress are needed. Moreover, learning support needs to be presented in a way that is engaging and interesting to children.

[0531] 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.

[0532] In this invention, the server includes means for receiving input data from a user and analyzing the input data using natural language processing technology, means for generating an appropriate response based on the analyzed input data, means for presenting the generated response to the user, means for using an artificial intelligence model for generating the answer, and means equipped with a support device that provides interaction in voice or text format. This enhances learning support for children in dual-income households, making it possible for parents and children to easily grasp the learning situation and create an environment where learning can be done efficiently.

[0533] "Input data" refers to information provided by the user, expressed in a format such as natural language.

[0534] "Natural language processing technology" is computer technology used to analyze and understand human language.

[0535] "Analysis" is the process of breaking down data and information into smaller parts to understand their meaning and structure.

[0536] An "artificial intelligence model" is a system that learns from vast amounts of data and performs judgments and reasoning like a human when performing a specific task.

[0537] A "support device" is a device that interacts with the user, provides information, and supports tasks.

[0538] "Learning history" refers to a record of the learning activities a user has undertaken in the past.

[0539] "Performance data" refers to data used to evaluate a user's learning results and progress.

[0540] A "learning plan" is a set of activities and tasks designed to help a user learn efficiently.

[0541] A "dashboard" is an interface for visually displaying and managing data.

[0542] A "report" is a document that summarizes the aggregated results and analysis of data over a specific period.

[0543] An "alert function" is a feature that notifies users or administrators when certain conditions are met.

[0544] A learning support system plays a crucial role in realizing this invention. The system interacts with the user and has multiple functions to support learning. First, the server analyzes the input data received from the user terminal using natural language processing technology.

[0545] The server uses a generative AI model based on the analyzed data to generate the optimal response. Examples of such generative AI models include OpenAI's GPT model. The generated response is sent to the user's device and presented on the screen or via audio.

[0546] Furthermore, the server collects the user's learning history and performance data. Based on this, it automatically generates personalized learning plans using machine learning algorithms. These learning plans are presented to the user via their device or a robot in their home to support their learning activities.

[0547] Parental support is also crucial. Users' learning progress and problems are visualized on a dashboard, allowing parents to monitor progress in real time. The server also generates weekly or monthly reports, which are automatically sent to parents via email. An alert function is also included, notifying parents if specific issues remain unresolved.

[0548] For example, when a user (a child) inputs a question about the meaning of an English word into the system, the server performs natural language processing and uses an AI model to explain the meaning of the word. Examples of prompts used to instruct the generative AI model include, "I want to know the meaning of this English word. Please search for it," and "Tell me more about fractions."

[0549] This system will enable children from dual-income families to continue their studies efficiently, and will create an environment where parents can support their children's learning with peace of mind.

[0550] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0551] Step 1:

[0552] The user enters learning questions using a device. This input data is in text or audio format, and the device sends this data to the server. The entered content becomes the basis for subsequent analysis.

[0553] Step 2:

[0554] The server receives input data sent by the user and performs text analysis using natural language processing techniques. Here, important information is extracted from the input data, and data processing is performed to understand the structure of the text.

[0555] Step 3:

[0556] Based on the analyzed input data, the server uses a generative AI model to generate an appropriate response. The analysis results are input to the model as prompts, and the AI ​​generates a human-readable response based on its learned knowledge. For example, in response to an input asking for the meaning of a word, it generates an explanation.

[0557] Step 4:

[0558] The generated response is sent back from the server to the terminal, which then presents it to the user. During presentation, the information is provided to the user in an intuitively understandable format through screen display or audio output.

[0559] Step 5:

[0560] The user's learning history and performance data are periodically sent from the device to the server. The server receives this data, performs calculations to analyze the user's strengths and weaknesses, and generates a personalized learning plan.

[0561] Step 6:

[0562] The generated learning plan is sent to the device, which then presents it to the user. The robot may also support learning activities within the home, flexibly adjusting the plan according to the user's progress.

[0563] Step 7:

[0564] Information is also provided to parents. The server visualizes the user's learning progress on a dashboard and automatically sends weekly and monthly reports to parents. If a specific issue is not resolved, an alert function is used to notify parents.

[0565] 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.

[0566] The present invention aims to enrich and enhance the user's learning experience by incorporating an emotion engine into an interactive learning support system. This system analyzes user input data in real time, combines natural language processing and emotion recognition technology to provide optimal responses, and supports the user's learning progress through personalized learning plans.

[0567] System configuration and operation

[0568] Adaptive Question Answering Function

[0569] The server receives questions from the user in voice or text format. After analyzing the intent of the question using natural language processing techniques, the sentiment engine analyzes the user's emotional state. This allows the server to generate an appropriate response that takes the user's emotions and perceptions into account. For example, if the sentiment engine determines that the user is confused, the server will provide a response that includes a more helpful and polite explanation.

[0570] Optimizing individual learning plans

[0571] The server continuously collects and analyzes the user's learning history and emotional data obtained by the emotion engine to generate a learning plan tailored to the user's needs. The generated plan is modified according to the user's emotional state and sent to the user. For example, if the user is in a stressful situation, the system will suggest a plan that includes a more relaxed approach.

[0572] Visualization of learning progress and emotions

[0573] The device displays learning progress and emotional patterns to users and parents through a visual dashboard. The server provides parents with information weekly or monthly through regularly generated reports. If certain emotional patterns may negatively impact learning, the system alerts parents to encourage early intervention. This allows parents to provide appropriate support to ensure users continue learning in a better state.

[0574] Specific example

[0575] For example, if a user shows frustration while working on a history problem, the device detects this through its emotion engine. Based on that emotional state, the server may suggest history-related videos or game-style problems that contain relaxing content. The device presents these to the user to help prevent learning from stalling. Parents can also use the dashboard to see under what circumstances their child experiences stress during learning and use that information to consider countermeasures.

[0576] This invention enables learning support that takes into account the user's emotional state, thereby providing a more adaptive and effective learning environment.

[0577] The following describes the processing flow.

[0578] Step 1:

[0579] When a user encounters a problem they don't understand during learning, they can enter their question into the device via voice or text.

[0580] Step 2:

[0581] The terminal converts the input voice data into text data using speech recognition technology and prepares to send it to the server.

[0582] Step 3:

[0583] The server receives text data and uses natural language processing techniques to analyze the intent of the question.

[0584] Step 4:

[0585] The server uses an emotion engine to analyze the user's emotional state from their input. For example, it can determine whether the user is confused or irritated based on their tone of voice and chosen words.

[0586] Step 5:

[0587] The server generates an appropriate response based on the analysis results. This includes taking the user's emotional state into consideration, providing more helpful and clearer explanations, as well as encouraging messages.

[0588] Step 6:

[0589] The server sends the generated response to the terminal in text format.

[0590] Step 7:

[0591] The device displays the received response to the user. The display is either shown as text on the screen or played as audio.

[0592] Step 8:

[0593] The user reconsiders the problem based on the information provided. If no improvement is seen, they can ask additional questions, and the system will provide further support.

[0594] Step 9:

[0595] The device collects data on the user's learning progress and emotional state and sends it to the server. This data is used to optimize future learning plans and report to parents.

[0596] Step 10:

[0597] The server updates personalized learning plans based on the collected data and generates reports and status updates for the user's parents as needed.

[0598] (Example 2)

[0599] 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."

[0600] Conventional learning support systems do not take into account the user's emotional state, making it difficult to maintain the user's interest and concentration, resulting in insufficient learning effectiveness. This invention aims to achieve more effective learning support by analyzing the user's emotional state in real time and providing responses and learning plans accordingly.

[0601] 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.

[0602] In this invention, the server includes means for receiving input information from the user and analyzing the input information using natural language processing technology, means for generating an appropriate response based on the analyzed input information and emotion data, and means for analyzing the user's emotional state and adjusting the tone of the generated response. This enables adaptive learning support that responds to the user's emotions.

[0603] "Input information" refers to audio or text data received from the user.

[0604] "Natural language processing technology" is a technology that uses computers to analyze human language and understand its intent and meaning.

[0605] "Emotional data" refers to information that indicates an emotional state, extracted from user input.

[0606] "Response" refers to a reply or instruction to the user that is generated based on the user's input information and sentiment data.

[0607] An "information display device" refers to a digital device or screen display means used by a user to receive responses from a system.

[0608] A "learning plan" is a personalized learning guideline and schedule designed based on the user's learning progress and emotional data.

[0609] The "warning function" is a feature that notifies parents if certain conditions persist.

[0610] This invention is an interactive system that supports user learning and incorporates emotion recognition capabilities. When a user provides input information in voice or text format through a terminal, the terminal sends that information to a server. The server analyzes the input information using natural language processing technology and an emotion recognition engine to understand the user's intent and extract emotion data.

[0611] The server uses a generative AI model to generate appropriate responses based on the analysis results. The generative AI model adjusts the tone and content according to the user's emotional state, forming a response optimized for the individual user. This response is sent to the terminal and presented to the user visually or audibly.

[0612] In particular, this system performs long-term analysis of the user's learning history and acquired emotional data to dynamically generate a learning plan tailored to the user. It also includes features to visualize learning progress and emotional patterns through an information display device, providing this information to both the user and their parents. This allows parents to understand their child's learning progress through regular reports and provide effective support.

[0613] For example, if a user is feeling frustrated with a historical issue, the server can suggest relaxing video content or game-like problems based on their emotional data. This allows the user to continue learning in a new way.

[0614] The following prompt can be used as an example input to the generative AI model: "Describe the steps to use the AI ​​model to propose a learning support system that responds to the user's emotional state. Include specific examples of responses based on sentiment analysis when the user asks a question."

[0615] This invention is a system that enables the optimization of learning by taking into account the user's emotional state, thereby providing a more effective learning experience.

[0616] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0617] Step 1:

[0618] The user enters questions or requests into the terminal in voice or text format. The terminal receives this as input information and sends it to the server. At this stage, it is important that the input information is correctly received by the system, and the terminal may ask for confirmation using the user interface.

[0619] Step 2:

[0620] The server passes the received input information to a natural language processing engine for analysis. Specifically, the information is tokenized, and syntactic and semantic analysis are performed. This clarifies the intent of the input information and identifies the user's request. The output obtained in this process consists of analyzed syntactic data and metadata of the user's intent.

[0621] Step 3:

[0622] The server then uses an emotion recognition engine to analyze the user's emotional state based on the data obtained in the previous step. It identifies the user's emotions by evaluating their voice tone and emotional vocabulary in text. The resulting emotional data is then used in the subsequent response generation process.

[0623] Step 4:

[0624] The server uses a generative AI model to generate appropriate responses from parsed syntactic and sentiment data. In this process, the model adjusts tone and detail according to sentiment and selects content. The generated responses are designed to be delivered to the user in the most optimal way. The output is the generated text or audio response data.

[0625] Step 5:

[0626] The server sends the generated response data to the terminal, which then presents it to the user. The user receives the response via on-screen text or audio output. At this stage, the terminal optimizes its interface to ensure the response is properly conveyed to the user.

[0627] Step 6:

[0628] The server continuously collects the user's learning history and sentiment data, and uses this information to generate a personalized learning plan. This data processing includes trend analysis of past learning history and pattern recognition of sentiment data. The generated plan is adjusted to the user's learning needs and sent to the device.

[0629] Step 7:

[0630] The device visualizes information, including learning plans, progress, and emotional patterns, and displays it to the user and their parents as a dashboard. This visualization output includes detailed reports using graphs and charts. This allows for continuous monitoring of the user's learning progress and determination of necessary support.

[0631] (Application Example 2)

[0632] 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."

[0633] In today's learning environment, there is a challenge in appropriately addressing the emotions and progress of individual learners. Traditional learning systems provide uniform materials and plans without considering learners' feelings, which can lead to decreased motivation and reduced opportunities for effective learning. Furthermore, parents have limited means of obtaining information to understand their child's emotional state and respond appropriately.

[0634] 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.

[0635] In this invention, the server includes means for receiving data from the user and analyzing the data using natural language processing technology, means for generating an appropriate response based on the analyzed data, means for presenting the generated response to the user, and means for recognizing the user's emotional state and adjusting the response based on that emotion. This enables the provision of appropriate responses and learning plans that correspond to the learner's emotions, and flexible and effective learning support that meets individual needs.

[0636] "User" refers to an individual or learner who uses the learning support system.

[0637] "Data" refers to information provided by the user, including voice, text, historical information, and emotional states.

[0638] "Natural language processing technology" is a technology that enables computers to understand, analyze, and generate responses to human language.

[0639] "Means" refers to a process, apparatus, or technical method used to perform a particular function.

[0640] "Response" refers to the reply or feedback that a system generates in response to user input.

[0641] "Emotional state" refers to the changes and tendencies in emotions that a user exhibits during the learning process.

[0642] A "learning plan" is a structured plan that represents a learning approach and materials optimized according to the individual user's needs and feelings.

[0643] An "interface" refers to the means or platform by which users and systems exchange information with each other.

[0644] A "guardian" refers to an individual who is responsible for supporting and monitoring a learner's learning progress and emotional state.

[0645] A "warning function" is a mechanism designed to cause a system to take a specific action when certain states or conditions are met.

[0646] To implement this invention, a system is required for analyzing user-inputted voice or text data in real time. The system utilizes hardware for receiving data transmitted from the user, such as a terminal with a microphone. The received data is analyzed using natural language processing technology, and software that understands its intent, such as NLTK (Natural Language Toolkit) or Google's speech recognition service, is used. Based on the analyzed data, a generative AI model is used to generate a response, which is then presented to the user. Furthermore, to perform emotion recognition, a library for emotion analysis, such as EmotionDetector, is utilized to identify the user's emotional state.

[0647] The server generates a personalized learning plan based on this information, adjusts it as needed based on the user's emotional state, and then sends it to the device. This learning plan is created using a program such as LearningPlanGenerator. The device presents the learning plan and progress to the user through a visual interface, enabling the visualization of emotions. This information is provided not only to the user but also to parents, who receive it as periodic reports.

[0648] Specifically, when a user says something like "I don't understand" while learning English, the device recognizes this, and the server determines through sentiment analysis that the user is confused. In this case, the server can offer words of encouragement and suggest learning videos that are appropriate for the user.

[0649] When using the generative AI model, the following prompts should be considered:

[0650] "Please be kind and encouraging to users who are confused."

[0651] "Please suggest appropriate learning materials based on emotions."

[0652] With the above configuration, the system can provide a flexible and effective learning environment, enriching the user's learning experience.

[0653] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0654] Step 1:

[0655] The user provides input to the system via voice or text. The terminal receives the input data and converts it to text data. Here, speech recognition is used to convert the voice data to text and send it as input data. The input is raw voice data, and the output is text data.

[0656] Step 2:

[0657] The server analyzes the received text data using natural language processing techniques. Specifically, it utilizes libraries such as NLTK to construct a data structure to understand the intent of the question. This allows it to identify the user's intent and areas of interest. The input is text data, and the output is the analyzed data structure.

[0658] Step 3:

[0659] The server uses the analyzed data structure to invoke a generative AI model and generate an appropriate response. Here, a prompt is applied to the model to generate an emotionally resonant response. The input is the analyzed data structure, and the output is the generated response text.

[0660] Step 4:

[0661] The server analyzes the user's emotional state using an emotion analysis library. It utilizes EmotionDetector to identify emotions from the user's textual expressions and records that emotion data. Input is text data, and output is emotion data.

[0662] Step 5:

[0663] The server uses LearningPlanGenerator to generate a personalized learning plan based on collected sentiment data and the user's past history data. This creates a learning sequence best suited to the user's current state. The input is sentiment data and history data, and the output is the personalized learning plan.

[0664] Step 6:

[0665] The generated response text and learning plan are sent to the terminal, which then presents the information to the user through a visual interface. Here, the information is displayed on the screen, and the user can take the next action based on it. The input is the response text and learning plan, and the output is the visual presentation to the user.

[0666] Step 7:

[0667] The device visualizes the user's learning progress and emotional state, and generates and sends periodic reports to parents. The data is displayed as graphs and charts through visualization tools. Inputs are user progress data and emotional data, and outputs are reports for parents.

[0668] 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.

[0669] 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.

[0670] 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.

[0671] [Fourth Embodiment]

[0672] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0673] 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.

[0674] 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).

[0675] 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.

[0676] 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.

[0677] 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).

[0678] 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.

[0679] 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.

[0680] 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.

[0681] 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.

[0682] 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.

[0683] 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.

[0684] 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".

[0685] This invention relates to an interactive learning support system for users, aimed at supporting the learning of children in dual-income households. This system improves the quality of children's learning by providing real-time question answering, optimization of learning plans, and visualization of learning progress. Furthermore, it enables efficient learning support within the home through timely information provision and notifications to parents.

[0686] System configuration and operation

[0687] Question answering function

[0688] The server receives question data submitted by the user, including text and voice input. The server uses natural language processing techniques to analyze the question and generate the most appropriate response. This response is sent back to the user's device and provided to the user in real time, either displayed or spoken aloud. For example, if a user asks, "What does this English word mean?", the server translates the word's meaning, provides an explanation, and returns the answer.

[0689] Optimizing your learning plan

[0690] The server collects the user's learning history and performance data, and analyzes this data using AI technology. This identifies the user's strengths and weaknesses and generates a personalized learning plan. The generated plan is sent to the user's device, and the user uses it to work on their daily studies. For example, if a user has a weak understanding of a particular subject, a plan will be provided that includes many related problems to strengthen that subject.

[0691] Visualization of learning progress and support for parents

[0692] Parents can access a dashboard through their device that visualizes their child's learning progress. This dashboard displays learning achievements, areas of difficulty, and progress status. The server generates reports regularly and sends them to parents weekly and monthly. Furthermore, if a specific learning task remains unresolved, the server sends an alert to the parent. In this way, parents can understand their child's learning situation in real time and provide the necessary support.

[0693] Specific example

[0694] Suppose a user (child) finds a math problem they can't solve and asks the system for help. The user's device sends the question to the server, which generates an answer including how to solve the problem and related explanations. The answer is notified to the user in real time, and the user deepens their understanding by reading the displayed explanation. Afterwards, the user's progress is saved in the system and used for future learning plans. Parents can also check through a dashboard whether the child has been able to solve the problem and provide additional support as needed.

[0695] This invention creates an environment where children can efficiently overcome learning challenges, and parents can provide effective support even when they are busy.

[0696] The following describes the processing flow.

[0697] Step 1:

[0698] Users enter questions into the device to solve problems they don't understand. Input methods include voice or text.

[0699] Step 2:

[0700] The terminal converts the input data into an appropriate format (text format) and sends it to the server. In the case of voice input, speech recognition technology is used to convert it to text.

[0701] Step 3:

[0702] The server analyzes the received text data using natural language processing technology to accurately understand the intent of the user's question.

[0703] Step 4:

[0704] The server references internal databases and knowledge bases to generate appropriate answers to user questions. These generated answers include relevant information and additional explanations.

[0705] Step 5:

[0706] The server sends the generated response to the user's device in text format.

[0707] Step 6:

[0708] The device presents the received response to the user. The presentation method is either screen display or audio playback.

[0709] Step 7:

[0710] The user refers to the provided answers to understand or solve the problem.

[0711] Step 8:

[0712] The device collects data on the user's learning progress and question history, and sends it to the server. This data is used to optimize future learning plans.

[0713] (Example 1)

[0714] 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".

[0715] In dual-income households, a challenge exists in supporting children's learning: it is difficult for parents to efficiently understand their children's learning progress and provide appropriate support. Furthermore, it is challenging to provide personalized learning plans while monitoring users' learning progress in real time. These challenges need to be addressed to improve the quality of children's learning.

[0716] 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.

[0717] In this invention, the server includes means for receiving input data from a user and analyzing the input data using information processing technology, a generation device for generating an appropriate response based on the analyzed input data, and an output device for presenting the generated response to the user. This makes it possible to answer user questions quickly and accurately.

[0718] "Information processing technology" refers to the technology used to analyze data obtained from users and understand its meaning and context.

[0719] A "generation device" is a device that constructs an appropriate response to present to the user based on the analyzed data.

[0720] An "output device" is a device that communicates the generated response to the user visually or audibly.

[0721] A "data storage device" is a device that saves user activity records and learning progress for use in future analysis and planning.

[0722] A "generative AI model" is an artificial intelligence technology that generates appropriate and skillful responses based on user prompts.

[0723] A "prompt statement" is a statement input to a generative AI model, containing instructions and conditions for the model to generate a response.

[0724] A "learning plan" refers to a learning schedule and content tailored to the user's learning progress and individual needs.

[0725] A "display device" is a device that provides an interface that allows users to visually check their learning progress.

[0726] A "notification function" is a feature that issues a warning to the user or parent when certain conditions are met.

[0727] This invention is a system for supporting the learning of children in dual-income households, and is realized through the interaction of a server, a terminal, and a user. First, the user inputs learning-related questions via the terminal. Questions can be entered in text or voice format. The terminal transfers the input data to the server. The server analyzes the received data using natural language processing technology. Information processing technologies such as Python's NLTK library and spaCy are used for the analysis. This allows the server to understand the user's intent and the content of the question.

[0728] The server launches a generative AI model based on the analyzed data. This model is input with specific prompt sentences. For example, if a user asks, "What does this English word mean?", the prompt sentence input to the model will be in the format of "Please explain the meaning of the English word '". The generative AI model generates a response to the user's question based on this prompt sentence.

[0729] The generated response is sent from the server to the terminal, which then provides it to the user either visually or audibly. The terminal's screen is used for display, while text-to-speech technology is used for audio output. For example, if the user asks about the meaning of "resilience," the server generates a response such as "resilience means the ability to recover from difficulties" and displays it on the terminal.

[0730] Furthermore, the server records the user's learning activities and saves their progress to a data storage device. This allows the user's individual learning history, strengths, and weaknesses to be identified and used to generate their next learning plan. In this way, users can progress in their learning based on individually tailored learning content.

[0731] Furthermore, parents can access a dashboard through their device that visualizes their child's learning progress. This dashboard displays learning achievements, areas of difficulty, and progress status, providing parents with timely and accurate information. Based on this, parents can provide appropriate learning support for their children.

[0732] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0733] Step 1:

[0734] The user enters the topic or question they want to learn about into the terminal. This input can be in text or voice format. The terminal converts the user's input into a format (e.g., JSON) for transmission as digital data to the server. This digital data becomes the input to the server.

[0735] Step 2:

[0736] The server analyzes the digital data received from the terminal. This analysis uses natural language processing techniques to extract important keywords and context from the data. This analysis identifies the intent behind the user's question. The analyzed data is then used as input for a generative AI model.

[0737] Step 3:

[0738] The server inputs a prompt sentence into the generative AI model based on the analysis results. This prompt sentence includes context and constraints related to the user's question. For example, the prompt sentence "Please explain the meaning of the English word 'resilience'" might be input into the generative AI model.

[0739] Step 4:

[0740] The generative AI model generates an appropriate response based on the input prompt sentence. In this response generation process, the AI ​​model constructs sentences using natural language generation technology. The generated response becomes the server output and is converted back into a format such as JSON.

[0741] Step 5:

[0742] The server sends the generated response to the terminal. The terminal displays the received response on the screen or outputs it as speech using text-to-speech technology to present it to the user. This allows the user to receive the actual response and deepen their learning.

[0743] Step 6:

[0744] The server stores user question-answer data in a database and records it as the user's learning progress. This record allows for the accumulation of the user's learning history and performance, which is then used to optimize future learning plans.

[0745] (Application Example 1)

[0746] 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".

[0747] In today's dual-income households, it is difficult for parents to understand their children's learning progress and provide appropriate support. Furthermore, children themselves lack the resources necessary to learn efficiently. In this context, real-time learning support and continuous monitoring of learning progress are needed. Moreover, learning support needs to be presented in a way that is engaging and interesting to children.

[0748] 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.

[0749] In this invention, the server includes means for receiving input data from a user and analyzing the input data using natural language processing technology, means for generating an appropriate response based on the analyzed input data, means for presenting the generated response to the user, means for using an artificial intelligence model for generating the answer, and means equipped with a support device that provides interaction in voice or text format. This enhances learning support for children in dual-income households, making it possible for parents and children to easily grasp the learning situation and create an environment where learning can be done efficiently.

[0750] "Input data" refers to information provided by the user, expressed in a format such as natural language.

[0751] "Natural language processing technology" is computer technology used to analyze and understand human language.

[0752] "Analysis" is the process of breaking down data and information into smaller parts to understand their meaning and structure.

[0753] An "artificial intelligence model" is a system that learns from vast amounts of data and performs judgments and reasoning like a human when performing a specific task.

[0754] A "support device" is a device that interacts with the user, provides information, and supports tasks.

[0755] "Learning history" refers to a record of the learning activities a user has undertaken in the past.

[0756] "Performance data" refers to data used to evaluate a user's learning results and progress.

[0757] A "learning plan" is a set of activities and tasks designed to help a user learn efficiently.

[0758] A "dashboard" is an interface for visually displaying and managing data.

[0759] A "report" is a document that summarizes the aggregated results and analysis of data over a specific period.

[0760] An "alert function" is a feature that notifies users or administrators when certain conditions are met.

[0761] A learning support system plays a crucial role in realizing this invention. The system interacts with the user and has multiple functions to support learning. First, the server analyzes the input data received from the user terminal using natural language processing technology.

[0762] The server uses a generative AI model based on the analyzed data to generate the optimal response. Examples of such generative AI models include OpenAI's GPT model. The generated response is sent to the user's device and presented on the screen or via audio.

[0763] Furthermore, the server collects the user's learning history and performance data. Based on this, it automatically generates personalized learning plans using machine learning algorithms. These learning plans are presented to the user via their device or a robot in their home to support their learning activities.

[0764] Parental support is also crucial. Users' learning progress and problems are visualized on a dashboard, allowing parents to monitor progress in real time. The server also generates weekly or monthly reports, which are automatically sent to parents via email. An alert function is also included, notifying parents if specific issues remain unresolved.

[0765] For example, when a user (a child) inputs a question about the meaning of an English word into the system, the server performs natural language processing and uses an AI model to explain the meaning of the word. Examples of prompts used to instruct the generative AI model include, "I want to know the meaning of this English word. Please search for it," and "Tell me more about fractions."

[0766] This system will enable children from dual-income families to continue their studies efficiently, and will create an environment where parents can support their children's learning with peace of mind.

[0767] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0768] Step 1:

[0769] The user enters learning questions using a device. This input data is in text or audio format, and the device sends this data to the server. The entered content becomes the basis for subsequent analysis.

[0770] Step 2:

[0771] The server receives input data sent by the user and performs text analysis using natural language processing techniques. Here, important information is extracted from the input data, and data processing is performed to understand the structure of the text.

[0772] Step 3:

[0773] Based on the analyzed input data, the server uses a generative AI model to generate an appropriate response. The analysis results are input to the model as prompts, and the AI ​​generates a human-readable response based on its learned knowledge. For example, in response to an input asking for the meaning of a word, it generates an explanation.

[0774] Step 4:

[0775] The generated response is sent back from the server to the terminal, which then presents it to the user. During presentation, the information is provided to the user in an intuitively understandable format through screen display or audio output.

[0776] Step 5:

[0777] The user's learning history and performance data are periodically sent from the device to the server. The server receives this data, performs calculations to analyze the user's strengths and weaknesses, and generates a personalized learning plan.

[0778] Step 6:

[0779] The generated learning plan is sent to the device, which then presents it to the user. The robot may also support learning activities within the home, flexibly adjusting the plan according to the user's progress.

[0780] Step 7:

[0781] Information is also provided to parents. The server visualizes the user's learning progress on a dashboard and automatically sends weekly and monthly reports to parents. If a specific issue is not resolved, an alert function is used to notify parents.

[0782] 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.

[0783] The present invention aims to enrich and enhance the user's learning experience by incorporating an emotion engine into an interactive learning support system. This system analyzes user input data in real time, combines natural language processing and emotion recognition technology to provide optimal responses, and supports the user's learning progress through personalized learning plans.

[0784] System configuration and operation

[0785] Adaptive Question Answering Function

[0786] The server receives questions from the user in voice or text format. After analyzing the intent of the question using natural language processing techniques, the sentiment engine analyzes the user's emotional state. This allows the server to generate an appropriate response that takes the user's emotions and perceptions into account. For example, if the sentiment engine determines that the user is confused, the server will provide a response that includes a more helpful and polite explanation.

[0787] Optimizing individual learning plans

[0788] The server continuously collects and analyzes the user's learning history and emotional data obtained by the emotion engine to generate a learning plan tailored to the user's needs. The generated plan is modified according to the user's emotional state and sent to the user. For example, if the user is in a stressful situation, the system will suggest a plan that includes a more relaxed approach.

[0789] Visualization of learning progress and emotions

[0790] The device displays learning progress and emotional patterns to users and parents through a visual dashboard. The server provides parents with information weekly or monthly through regularly generated reports. If certain emotional patterns may negatively impact learning, the system alerts parents to encourage early intervention. This allows parents to provide appropriate support to ensure users continue learning in a better state.

[0791] Specific example

[0792] For example, if a user shows frustration while working on a history problem, the device detects this through its emotion engine. Based on that emotional state, the server may suggest history-related videos or game-style problems that contain relaxing content. The device presents these to the user to help prevent learning from stalling. Parents can also use the dashboard to see under what circumstances their child experiences stress during learning and use that information to consider countermeasures.

[0793] This invention enables learning support that takes into account the user's emotional state, thereby providing a more adaptive and effective learning environment.

[0794] The following describes the processing flow.

[0795] Step 1:

[0796] When a user encounters a problem they don't understand during learning, they can enter their question into the device via voice or text.

[0797] Step 2:

[0798] The terminal converts the input voice data into text data using speech recognition technology and prepares to send it to the server.

[0799] Step 3:

[0800] The server receives text data and uses natural language processing techniques to analyze the intent of the question.

[0801] Step 4:

[0802] The server uses an emotion engine to analyze the user's emotional state from their input. For example, it can determine whether the user is confused or irritated based on their tone of voice and chosen words.

[0803] Step 5:

[0804] The server generates an appropriate response based on the analysis results. This includes taking the user's emotional state into consideration, providing more helpful and clearer explanations, as well as encouraging messages.

[0805] Step 6:

[0806] The server sends the generated response to the terminal in text format.

[0807] Step 7:

[0808] The device displays the received response to the user. The display is either shown as text on the screen or played as audio.

[0809] Step 8:

[0810] The user reconsiders the problem based on the information provided. If no improvement is seen, they can ask additional questions, and the system will provide further support.

[0811] Step 9:

[0812] The device collects data on the user's learning progress and emotional state and sends it to the server. This data is used to optimize future learning plans and report to parents.

[0813] Step 10:

[0814] The server updates personalized learning plans based on the collected data and generates reports and status updates for the user's parents as needed.

[0815] (Example 2)

[0816] 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".

[0817] Conventional learning support systems do not take into account the user's emotional state, making it difficult to maintain the user's interest and concentration, resulting in insufficient learning effectiveness. This invention aims to achieve more effective learning support by analyzing the user's emotional state in real time and providing responses and learning plans accordingly.

[0818] 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.

[0819] In this invention, the server includes means for receiving input information from the user and analyzing the input information using natural language processing technology, means for generating an appropriate response based on the analyzed input information and emotion data, and means for analyzing the user's emotional state and adjusting the tone of the generated response. This enables adaptive learning support that responds to the user's emotions.

[0820] "Input information" refers to audio or text data received from the user.

[0821] "Natural language processing technology" is a technology that uses computers to analyze human language and understand its intent and meaning.

[0822] "Emotional data" refers to information that indicates an emotional state, extracted from user input.

[0823] "Response" refers to a reply or instruction to the user that is generated based on the user's input information and sentiment data.

[0824] An "information display device" refers to a digital device or screen display means used by a user to receive responses from a system.

[0825] A "learning plan" is a personalized learning guideline and schedule designed based on the user's learning progress and emotional data.

[0826] The "warning function" is a feature that notifies parents if certain conditions persist.

[0827] This invention is an interactive system that supports user learning and incorporates emotion recognition capabilities. When a user provides input information in voice or text format through a terminal, the terminal sends that information to a server. The server analyzes the input information using natural language processing technology and an emotion recognition engine to understand the user's intent and extract emotion data.

[0828] The server uses a generative AI model to generate appropriate responses based on the analysis results. The generative AI model adjusts the tone and content according to the user's emotional state, forming a response optimized for the individual user. This response is sent to the terminal and presented to the user visually or audibly.

[0829] In particular, this system performs long-term analysis of the user's learning history and acquired emotional data to dynamically generate a learning plan tailored to the user. It also includes features to visualize learning progress and emotional patterns through an information display device, providing this information to both the user and their parents. This allows parents to understand their child's learning progress through regular reports and provide effective support.

[0830] For example, if a user is feeling frustrated with a historical issue, the server can suggest relaxing video content or game-like problems based on their emotional data. This allows the user to continue learning in a new way.

[0831] The following prompt can be used as an example input to the generative AI model: "Describe the steps to use the AI ​​model to propose a learning support system that responds to the user's emotional state. Include specific examples of responses based on sentiment analysis when the user asks a question."

[0832] This invention is a system that enables the optimization of learning by taking into account the user's emotional state, thereby providing a more effective learning experience.

[0833] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0834] Step 1:

[0835] The user enters questions or requests into the terminal in voice or text format. The terminal receives this as input information and sends it to the server. At this stage, it is important that the input information is correctly received by the system, and the terminal may ask for confirmation using the user interface.

[0836] Step 2:

[0837] The server passes the received input information to a natural language processing engine for analysis. Specifically, the information is tokenized, and syntactic and semantic analysis are performed. This clarifies the intent of the input information and identifies the user's request. The output obtained in this process consists of analyzed syntactic data and metadata of the user's intent.

[0838] Step 3:

[0839] The server then uses an emotion recognition engine to analyze the user's emotional state based on the data obtained in the previous step. It identifies the user's emotions by evaluating their voice tone and emotional vocabulary in text. The resulting emotional data is then used in the subsequent response generation process.

[0840] Step 4:

[0841] The server uses a generative AI model to generate appropriate responses from parsed syntactic and sentiment data. In this process, the model adjusts tone and detail according to sentiment and selects content. The generated responses are designed to be delivered to the user in the most optimal way. The output is the generated text or audio response data.

[0842] Step 5:

[0843] The server sends the generated response data to the terminal, which then presents it to the user. The user receives the response via on-screen text or audio output. At this stage, the terminal optimizes its interface to ensure the response is properly conveyed to the user.

[0844] Step 6:

[0845] The server continuously collects the user's learning history and sentiment data, and uses this information to generate a personalized learning plan. This data processing includes trend analysis of past learning history and pattern recognition of sentiment data. The generated plan is adjusted to the user's learning needs and sent to the device.

[0846] Step 7:

[0847] The device visualizes information, including learning plans, progress, and emotional patterns, and displays it to the user and their parents as a dashboard. This visualization output includes detailed reports using graphs and charts. This allows for continuous monitoring of the user's learning progress and determination of necessary support.

[0848] (Application Example 2)

[0849] 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".

[0850] In today's learning environment, there is a challenge in appropriately addressing the emotions and progress of individual learners. Traditional learning systems provide uniform materials and plans without considering learners' feelings, which can lead to decreased motivation and reduced opportunities for effective learning. Furthermore, parents have limited means of obtaining information to understand their child's emotional state and respond appropriately.

[0851] 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.

[0852] In this invention, the server includes means for receiving data from the user and analyzing the data using natural language processing technology, means for generating an appropriate response based on the analyzed data, means for presenting the generated response to the user, and means for recognizing the user's emotional state and adjusting the response based on that emotion. This enables the provision of appropriate responses and learning plans that correspond to the learner's emotions, and flexible and effective learning support that meets individual needs.

[0853] "User" refers to an individual or learner who uses the learning support system.

[0854] "Data" refers to information provided by the user, including voice, text, historical information, and emotional states.

[0855] "Natural language processing technology" is a technology that enables computers to understand, analyze, and generate responses to human language.

[0856] "Means" refers to a process, apparatus, or technical method used to perform a particular function.

[0857] "Response" refers to the reply or feedback that a system generates in response to user input.

[0858] "Emotional state" refers to the changes and tendencies in emotions that a user exhibits during the learning process.

[0859] A "learning plan" is a structured plan that represents a learning approach and materials optimized according to the individual user's needs and feelings.

[0860] An "interface" refers to the means or platform by which users and systems exchange information with each other.

[0861] A "guardian" refers to an individual who is responsible for supporting and monitoring a learner's learning progress and emotional state.

[0862] A "warning function" is a mechanism designed to cause a system to take a specific action when certain states or conditions are met.

[0863] To implement this invention, a system is required for analyzing user-inputted voice or text data in real time. The system utilizes hardware for receiving data transmitted from the user, such as a terminal with a microphone. The received data is analyzed using natural language processing technology, and software that understands its intent, such as NLTK (Natural Language Toolkit) or Google's speech recognition service, is used. Based on the analyzed data, a generative AI model is used to generate a response, which is then presented to the user. Furthermore, to perform emotion recognition, a library for emotion analysis, such as EmotionDetector, is utilized to identify the user's emotional state.

[0864] The server generates a personalized learning plan based on this information, adjusts it as needed based on the user's emotional state, and then sends it to the device. This learning plan is created using a program such as LearningPlanGenerator. The device presents the learning plan and progress to the user through a visual interface, enabling the visualization of emotions. This information is provided not only to the user but also to parents, who receive it as periodic reports.

[0865] Specifically, when a user says something like "I don't understand" while learning English, the device recognizes this, and the server determines through sentiment analysis that the user is confused. In this case, the server can offer words of encouragement and suggest learning videos that are appropriate for the user.

[0866] When using the generative AI model, the following prompts should be considered:

[0867] "Please be kind and encouraging to users who are confused."

[0868] "Please suggest appropriate learning materials based on emotions."

[0869] With the above configuration, the system can provide a flexible and effective learning environment, enriching the user's learning experience.

[0870] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0871] Step 1:

[0872] The user provides input to the system via voice or text. The terminal receives the input data and converts it to text data. Here, speech recognition is used to convert the voice data to text and send it as input data. The input is raw voice data, and the output is text data.

[0873] Step 2:

[0874] The server analyzes the received text data using natural language processing techniques. Specifically, it utilizes libraries such as NLTK to construct a data structure to understand the intent of the question. This allows it to identify the user's intent and areas of interest. The input is text data, and the output is the analyzed data structure.

[0875] Step 3:

[0876] The server uses the analyzed data structure to invoke a generative AI model and generate an appropriate response. Here, a prompt is applied to the model to generate an emotionally resonant response. The input is the analyzed data structure, and the output is the generated response text.

[0877] Step 4:

[0878] The server analyzes the user's emotional state using an emotion analysis library. It utilizes EmotionDetector to identify emotions from the user's textual expressions and records that emotion data. Input is text data, and output is emotion data.

[0879] Step 5:

[0880] The server uses LearningPlanGenerator to generate a personalized learning plan based on collected sentiment data and the user's past history data. This creates a learning sequence best suited to the user's current state. The input is sentiment data and history data, and the output is the personalized learning plan.

[0881] Step 6:

[0882] The generated response text and learning plan are sent to the terminal, which then presents the information to the user through a visual interface. Here, the information is displayed on the screen, and the user can take the next action based on it. The input is the response text and learning plan, and the output is the visual presentation to the user.

[0883] Step 7:

[0884] The device visualizes the user's learning progress and emotional state, and generates and sends periodic reports to parents. The data is displayed as graphs and charts through visualization tools. Inputs are user progress data and emotional data, and outputs are reports for parents.

[0885] 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.

[0886] 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.

[0887] 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.

[0888] 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.

[0889] 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.

[0890] 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.

[0891] 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.

[0892] 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.

[0893] 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."

[0894] 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.

[0895] 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.

[0896] 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.

[0897] 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.

[0898] 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.

[0899] 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.

[0900] 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.

[0901] 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.

[0902] 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.

[0903] 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.

[0904] 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.

[0905] 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.

[0906] The following is further disclosed regarding the embodiments described above.

[0907] (Claim 1)

[0908] A means for receiving input data from a user and analyzing said input data using natural language processing technology,

[0909] A means for generating an appropriate response based on the analyzed input data,

[0910] A means of presenting the generated response to the user,

[0911] A system that includes this.

[0912] (Claim 2)

[0913] A means for collecting and analyzing user learning history and performance data,

[0914] A means for automatically generating personalized learning plans based on analysis results,

[0915] A means of sending the generated learning plan to the user's device,

[0916] The system according to claim 1, including the following:

[0917] (Claim 3)

[0918] We provide a dashboard to visualize users' learning progress and problems.

[0919] A means of generating and sending weekly or monthly reports to parents,

[0920] A means of providing an alert function that notifies parents if a specific problem persists for a certain period of time,

[0921] The system according to claim 1, including the following:

[0922] "Example 1"

[0923] (Claim 1)

[0924] A means for receiving input data from a user and analyzing said input data using information processing technology,

[0925] A generation device that generates an appropriate response based on the analyzed input data,

[0926] An output device that presents the generated response to the user,

[0927] A means for recording the user's question-answering activities and storing the learning progress in a data storage device,

[0928] A means for inputting a prompt sentence into a generative AI model and generating a response,

[0929] A system that includes this.

[0930] (Claim 2)

[0931] A means for collecting and analyzing user learning history and performance data,

[0932] A means for automatically generating an individualized learning plan based on the analysis results,

[0933] A means for sending the generated learning plan to the user's device,

[0934] The system according to claim 1, including the following:

[0935] (Claim 3)

[0936] It provides a display device to visualize the user's learning progress and challenges.

[0937] A means of generating and sending reports to parents on a regular basis,

[0938] A means of providing a notification function that warns parents if a specific issue has not been resolved,

[0939] The system according to claim 1, including the following:

[0940] "Application Example 1"

[0941] (Claim 1)

[0942] A means for receiving input data from a user and analyzing said input data using natural language processing technology,

[0943] A means for generating an appropriate response based on the analyzed input data,

[0944] A means of presenting the generated response to the user,

[0945] Means of using artificial intelligence models to generate answers,

[0946] A means equipped with an assistive device that provides interaction in voice or text format,

[0947] A system that includes this.

[0948] (Claim 2)

[0949] A means for collecting and analyzing user learning history and performance data,

[0950] A means for automatically generating personalized learning plans based on analysis results,

[0951] A means of sending the generated learning plan to the user's device,

[0952] Means for presenting and implementing these learning plans via robots,

[0953] The system according to claim 1, including the following:

[0954] (Claim 3)

[0955] We provide a dashboard to visualize users' learning progress and problems.

[0956] A means of generating and sending weekly or monthly reports to parents,

[0957] A means of providing an alert function that notifies parents if a specific problem persists for a certain period of time,

[0958] A means of continuously monitoring learning progress and providing feedback via a robot,

[0959] The system according to claim 1, including the following:

[0960] "Example 2 of combining an emotion engine"

[0961] (Claim 1)

[0962] A means for receiving input information from a user and analyzing said input information using natural language processing technology,

[0963] A means for generating an appropriate response based on analyzed input information and sentiment data,

[0964] A means of analyzing the user's emotional state and adjusting the tone of the generated response,

[0965] A means for presenting the generated response on an information display device,

[0966] A system that includes this.

[0967] (Claim 2)

[0968] A means for collecting and analyzing user learning history and sentiment data,

[0969] A means for automatically generating and adjusting individualized learning plans based on analysis results and emotional data,

[0970] Means for transmitting the generated learning plan to the user's device,

[0971] The system according to claim 1, including the following:

[0972] (Claim 3)

[0973] We provide an information display device to visualize the user's learning progress and emotional patterns.

[0974] A means of generating and sending regular reports to parents,

[0975] A means of providing a warning function that notifies parents if a specific emotional pattern or learning challenge persists for a certain period of time,

[0976] The system according to claim 1, including the following:

[0977] "Application example 2 when combining with an emotional engine"

[0978] (Claim 1)

[0979] A means for receiving data from users and analyzing said data using natural language processing technology,

[0980] A means for generating an appropriate response based on the analyzed data,

[0981] A means of presenting the generated response to the user,

[0982] A means of recognizing the user's emotional state and adjusting the response based on that emotion,

[0983] A system that includes this.

[0984] (Claim 2)

[0985] A means for collecting user history information and sentiment data, and for analyzing said data,

[0986] Based on the analysis results, a means is provided to generate an individualized learning plan and adjust it according to the emotional state,

[0987] A means for transmitting the generated learning plan to the user's device,

[0988] The system according to claim 1, including the following:

[0989] (Claim 3)

[0990] It provides an interface to visualize the user's learning progress and emotions.

[0991] A means of periodically generating and sending information to parents,

[0992] A means of providing a warning function that notifies parents when certain emotional patterns may affect learning,

[0993] The system according to claim 1, including the following: [Explanation of symbols]

[0994] 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 for receiving input data from a user and analyzing said input data using natural language processing technology, A means for generating an appropriate response based on the analyzed input data, A means of presenting the generated response to the user, A system that includes this.

2. A means for collecting and analyzing user learning history and performance data, A means for automatically generating personalized learning plans based on analysis results, A means of sending the generated learning plan to the user's device, The system according to claim 1, including the following:

3. We provide a dashboard to visualize users' learning progress and problems. A means of generating and sending weekly or monthly reports to parents, A means of providing an alert function that notifies parents if a specific problem persists for a certain period of time, The system according to claim 1, including the following:

Images