system

Abstract

We provide the system. [Solution] A means for analyzing input data received from a terminal, A means for generating a series of steps for problem solving based on analyzed data, A means for transmitting information including the generated steps to a terminal, A system that includes this.

Description

Technical Field

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

Background Art

[0002] Patent Document 1 discloses a persona chatbot control method performed by at least one processor, the method 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] With the recent progress of generative AI technology, there are concerns that the younger generation may become overly dependent on this technology. Specifically, it is regarded as a problem that the thinking ability cannot be developed by obtaining a direct answer in learning. Instead of prohibiting it, it is required to promote appropriate use and provide an effective learning support environment.

Means for Solving the Problems

[0005] This invention provides a system that generates and presents a series of steps for problem-solving, rather than generating a direct answer to a problem entered by the user on a terminal. Data entered from the terminal is analyzed on a server, and the intent of the problem is understood using natural language processing technology. Then, based on the analysis results, the system presents a process for solving the problem and provides feedback and hints for the user's answer. This allows the user to learn problem-solving pathways and cultivate their own problem-solving skills.

[0006] A "terminal" is an electronic device used by users to input and display information.

[0007] "Input data" refers to information that a user provides to the system via their device.

[0008] "Analysis" is the process of understanding the content of input data and clarifying its intent and purpose.

[0009] "Problem-solving steps" refer to the multiple steps and ways of thinking that users need to take to solve a problem.

[0010] "Generating steps" means creating specific procedures for solving a problem.

[0011] "Natural language processing technology" is a technology that enables computers to understand and manipulate human language.

[0012] "Feedback" refers to notifying users of their actions and answers, including evaluations and suggestions for improvement.

[0013] A "hint" is something that provides additional information or clues to facilitate problem-solving. [Brief explanation of the drawing]

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

Mode for Carrying Out the Invention

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

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

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

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

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

[0020] In the following embodiments, the labeled communication I / F (Interface) is an interface including a communication processor and an antenna, etc. The communication I / F controls communication between multiple computers. Examples of communication standards applied to the communication I / F include wireless communication standards including 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), or Bluetooth (registered trademark), and the like.

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

[0022] [First Embodiment]

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

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

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

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

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

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

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

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

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

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

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

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

[0035] This system is designed as a learning support tool to enhance users' problem-solving abilities. Specifically, it has the function of allowing users to input educational problems from their devices, whereupon the server analyzes the problem and provides the necessary steps for solving it, rather than providing a direct answer.

[0036] After receiving input data from the terminal, the server analyzes the data using natural language processing techniques. The purpose of the analysis is to understand the intent of the problem and identify which category it belongs to. Through this analysis, the server generates information useful for problem solving and determines the ideas and methods to present to the user.

[0037] Next, the server generates step-by-step solution steps based on the information it has gathered, showing the user how they should think. Furthermore, it provides feedback and suggestions for improvement on the answers and processes the user has attempted, and gives additional hints as needed.

[0038] As a concrete example, if a user wants to learn how to solve quadratic equations, they would input the problem "Solve x^2 - 5x + 6 = 0" into their terminal. The server would analyze this input and determine that factorization is the appropriate solution. The server would then present the user with the steps of factorization, showing the solution such as "This equation can be decomposed into (x - 2)(x - 3) = 0". Furthermore, if the user tries a different solution, the server would provide feedback to help broaden their thinking.

[0039] This system allows users to understand problems step by step, providing an environment that fosters critical thinking and problem-solving skills.

[0040] The following describes the processing flow.

[0041] Step 1:

[0042] The terminal receives user input. The user enters the problem they want to solve in text format and prepares to send it.

[0043] Step 2:

[0044] The terminal sends user input data to the server. This data is transmitted to the server via the network and prepared for analysis.

[0045] Step 3:

[0046] The server receives data sent from the terminal. The received data is decoded and preprocessed for analysis.

[0047] Step 4:

[0048] The server uses natural language processing techniques to analyze user input. This involves text tokenization and syntactic analysis to identify the intent of the question.

[0049] Step 5:

[0050] The server identifies the category of the problem based on the analysis results. This information is then used to determine the appropriate solution and approach.

[0051] Step 6:

[0052] The server generates steps for resolving the problem. This involves constructing a step-by-step procedure for how to solve the problem.

[0053] Step 7:

[0054] The server prepares feedback and hints along with the generated steps. The feedback includes an evaluation of the user's attempts and indicates areas for improvement.

[0055] Step 8:

[0056] The server sends the generated solution steps and feedback to the terminal. This allows the user to gain insights into resolving the problem.

[0057] Step 9:

[0058] The terminal displays information received from the server to the user. The user then uses this information to solve problems themselves.

[0059] (Example 1)

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

[0061] Traditional learning support systems often provide direct answers to problems users face, making it difficult for them to develop the ability to understand the problem-solving process. Furthermore, they lack mechanisms to provide appropriate feedback and guidance for users' trial and error, potentially preventing some users from achieving their learning objectives. Therefore, there is a need for a system that supports users in understanding problems step-by-step and improving their critical thinking skills.

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

[0063] In this invention, the server includes means for analyzing input information received from a terminal, means for generating a step-by-step solution procedure for problem solving based on the analyzed information, means for transmitting information indicating a thinking method along with the generated procedure to the terminal, and means for providing feedback and additional guidance to user input. This enables the user to improve their thinking skills through the problem-solving process.

[0064] A "terminal" is a device used by a user to input and transmit information, and includes devices such as computers and smartphones.

[0065] "Received input information" refers to data related to educational issues or desired solutions that users have sent via their devices.

[0066] "Means of analysis" refers to a method of analyzing received input information using natural language processing technology to identify its intent and category.

[0067] "Means for generating a step-by-step solution procedure for problem solving" refers to a method of constructing the steps necessary to solve a problem as a series of processes, based on analyzed information.

[0068] "Information demonstrating thinking methods" refers to information that presents users with ways of thinking and approaches to problem-solving.

[0069] "Means of providing feedback and additional guidance" refers to methods of providing useful opinions and further hints regarding the results of a user's work on a task.

[0070] A "server" refers to a computer system that analyzes user input, generates solutions, and transmits them to the terminal.

[0071] This invention is a system that provides step-by-step learning support to enhance users' problem-solving abilities. The system works by having the user input educational problems from a terminal, which the server then analyzes and presents step-by-step solutions.

[0072] A terminal is a device such as a PC or smartphone that receives user input using a web browser or dedicated application. Through this process, the terminal sends the entered data to the server.

[0073] The server applies natural language processing techniques to analyze the received data. Specifically, the server uses generative AI models, such as BERT or GPT, to analyze the input information, understand its content, and identify the problem category. In this process, machine learning libraries such as TENSORFLOW® and PyTorch are used.

[0074] The server generates a step-by-step problem-solving procedure based on the analyzed information. This procedure is presented to the user along with information indicating the approach and mindset towards the problem.

[0075] As a concrete example, a user may want to learn how to solve a quadratic equation. The user enters the prompt "Solve x^2 - 5x + 6 = 0" into the terminal and sends it. The server analyzes this input and determines that factorization is the appropriate solution method. The server then generates specific solution steps, such as "This equation can be decomposed into (x - 2)(x - 3) = 0," and presents them to the user. Furthermore, even if the user tries a different solution method, the server provides feedback to support the user's thinking process.

[0076] This system allows users to improve their thinking skills and problem-solving abilities through a step-by-step problem-solving process.

[0077] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0078] Step 1:

[0079] The user inputs the problem using a device. Specifically, the user uses a web browser or a dedicated application on a PC or smartphone to input the problem in the format of "Solve x^2 - 5x + 6 = 0". This input is collected by the device and prepared to be sent to the server.

[0080] Step 2:

[0081] The terminal sends the entered data to the server. The terminal uses an internet connection to transfer the user's problem data to the server. In this case, the data format is text and arrives at the server as a prompt message.

[0082] Step 3:

[0083] The server analyzes the input data it receives. The server applies a generative AI model to the received data and uses natural language processing techniques to recognize that the input is a mathematical problem. The analysis determines that the data relates to a quadratic equation. Based on this, the server identifies an appropriate solution pattern for that category.

[0084] Step 4:

[0085] The server generates a step-by-step procedure for solving the problem. Based on the analysis results, the server designs the steps to solve the problem. Specifically, it selects a factorization method and constructs a solution step such as "This equation can be decomposed into (x - 2)(x - 3) = 0".

[0086] Step 5:

[0087] The server sends the generated steps to the terminal. The server sends the generated solution steps to the terminal in text format, along with information illustrating the thought process. This information is displayed on the user's screen.

[0088] Step 6:

[0089] The system learns based on the information the user receives. The user understands the problem-solving process based on the solution steps displayed on their device and can also try alternative solutions. In this case, the server provides feedback and sends further helpful guidance based on the user's attempts.

[0090] (Application Example 1)

[0091] 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."

[0092] In autonomous vehicles and digital transportation devices, there is a need for means to support efficient and safe movement in real time. However, conventional systems have difficulty effectively analyzing current traffic conditions and providing drivers with the optimal means of transportation. Technologies are needed to address these challenges and improve the operational efficiency and safety of vehicles.

[0093] 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.

[0094] In this invention, the server includes means for analyzing input data received from a terminal, means for generating a series of steps for problem solving based on the analyzed data, and means for proposing the optimal solution for transportation based on real-time analysis. This makes it possible to propose efficient route selection and travel methods for the transportation problems faced by the user.

[0095] A "terminal" is a device that has an interface with the user and sends and receives data.

[0096] "Input data" refers to information provided by the user through their device.

[0097] "Analysis means" refers to methods or devices for analyzing input data and understanding its content.

[0098] A "series of steps" is a set of steps that are carried out sequentially to solve a problem.

[0099] "Real-time analysis" is an analytical process that processes current information instantly and makes the results immediately available for use.

[0100] "Means of transportation" refers to methods and techniques related to the movement of vehicles and pedestrians.

[0101] A "solution" refers to a method or technology for resolving a specific problem.

[0102] A "proposal" is an action that presents a solution or option to the user.

[0103] This invention is a real-time mobility assistance system for autonomous vehicles and transportation-related digital devices. The elements constituting the system are as follows:

[0104] The server receives traffic and location information transmitted from terminals (for example, smart displays in vehicles or the driver's smartphone). This sends the input data provided by the user through the terminal to the server, and the analysis process begins.

[0105] As an analysis method, the server uses natural language processing technologies such as Google® Cloud NLP and AWS® Comprehend to understand the input data and generate a series of steps necessary for problem solving. This series of steps includes suggesting the optimal mode of transportation. Furthermore, the server leverages hardware platforms such as NVIDIA Drive to perform real-time data processing and analysis at high speed.

[0106] The terminal receives a series of instructions sent from the server and presents suggestions to the user visually or audibly. Specifically, transportation options and efficient route guidance are displayed in real time.

[0107] For example, if a user enters a prompt such as, "Analyze the current traffic conditions and show me the optimal route to my final destination and its detailed benefits," the system will suggest optimized transportation options to meet that request. Based on this prompt, the server immediately analyzes traffic data and presents the user with the best solution.

[0108] In this way, users can reach their destinations efficiently and safely, even in complex traffic conditions. Through such concrete examples, the forms in which the invention is implemented can be understood.

[0109] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0110] Step 1:

[0111] The user uses a terminal to input prompts regarding traffic information and current conditions. The entered data is sent to the server in real time. At this stage, the entered prompts are prepared as data for processing on the server.

[0112] Step 2:

[0113] The server parses the received prompt text using natural language processing tools such as Google Cloud NLP and AWS Comprehend. This analysis allows the server to understand the intent of the prompt and identify what kind of traffic-related problem it is indicating. The input is the prompt text, and the output is structured data that identifies the problem.

[0114] Step 3:

[0115] The server utilizes the NVIDIA Drive platform to evaluate real-time traffic data. Based on the analyzed prompts, it aggregates real-time traffic information obtained from various sensors and external databases, and generates data that recommends the optimal route and driving method accordingly. The input at this stage is real-time data from traffic sensors and databases, and the output is the recommended route.

[0116] Step 4:

[0117] The server sends the generated recommended routes and driving instructions to the terminal. The terminal provides this recommended information to the user visually or audibly. The output is a visual navigation guide or audio guidance, presented in a format that allows the user to act immediately.

[0118] Step 5:

[0119] The user begins their journey based on the information presented. If necessary, they can re-enter a new prompt from their terminal, allowing the server to re-evaluate the information and correct the route quickly. The input is the re-entered prompt, and the output is the new recommended route.

[0120] 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.

[0121] This system is designed to help users improve their problem-solving abilities more effectively by leveraging generative AI technology during the learning process. In particular, it aims to provide a learning process tailored to each individual user, taking into account their emotional state.

[0122] First, the user inputs the question they want to learn into their device. This input information is sent from the device to the server. The server analyzes the received data using natural language processing technology to understand the intent of the question. During this process, an emotion engine is activated to detect emotional elements included in the user's input.

[0123] The emotion engine has the ability to recognize the user's current emotional state in real time, for example, by inferring the user's emotions from keywords in text or the tone of input. Emotional information is considered an important element in subsequent analysis and feedback. This allows the server to generate appropriate responses based on the emotional state.

[0124] When the server generates steps to solve a problem, sentiment information is applied to tailor the feedback and hints provided to the user. For example, if the sentiment engine determines that the user is confused, the server will provide more detailed explanations or additional hints.

[0125] As a concrete example, suppose a user enters the problem "Solve x^2 - 5x + 6 = 0". While the server suggests factoring the quadratic equation as a solution, the emotion engine detects that the user is feeling anxious. In this case, the server provides a slower explanation and adds more concrete examples to support the user and help them proceed with confidence.

[0126] This system allows users to not only solve problems but also enjoy a learning experience that takes their emotional state into account, enabling them to learn more deeply.

[0127] The following describes the processing flow.

[0128] Step 1:

[0129] The user enters a question into the device. The user types the content they want to learn as text and prepares to press the submit button.

[0130] Step 2:

[0131] The terminal sends user input data to the server. This data is transmitted to the server via the network and prepared for reception.

[0132] Step 3:

[0133] The server receives data sent from the terminal. The received data is temporarily stored for analysis.

[0134] Step 4:

[0135] The server uses natural language processing technology to analyze the user's input. This involves tokenizing the input text and extracting important keywords and phrases.

[0136] Step 5:

[0137] The emotion engine recognizes emotions from user input. Based on specific words and sentence structures within the input, it infers the emotions the user might be feeling (e.g., confusion, anxiety, excitement).

[0138] Step 6:

[0139] The server generates problem-solving steps based on analysis results and emotional information. Along with selecting a solution, it determines the tone and level of detail of the explanation according to the emotional state.

[0140] Step 7:

[0141] The server generates feedback and hints. It prepares feedback that takes the user's emotional state into consideration, and provides appropriate hints and additional explanations. For example, if the user is feeling anxious, it provides more explanations and examples.

[0142] Step 8:

[0143] The server sends the generated information to the terminal. It transfers the problem steps, feedback, and hints to the terminal.

[0144] Step 9:

[0145] The terminal displays information received from the server to the user. Based on the displayed information, the user can learn and solve problems.

[0146] (Example 2)

[0147] 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".

[0148] Traditional learning systems often fail to consider the user's emotional state when solving problems, leading to decreased learning efficiency and insufficient understanding. In particular, providing appropriate feedback tailored to learners experiencing feelings such as anxiety or confusion was challenging.

[0149] 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.

[0150] In this invention, the server includes means for analyzing input data received from a terminal, means for understanding the intent of a problem using natural language processing technology based on the analyzed data, and means for detecting the user's emotional state. This enables the provision of personalized feedback that takes the user's emotional state into account, improving learning efficiency and deepening the learner's understanding.

[0151] A "terminal" is an information processing device used by a user to provide input data to a system.

[0152] A "server" is a central processing unit that analyzes data received from terminals, generates information for problem solving, and sends it back to the terminals.

[0153] "Means of analysis" refer to technologies for interpreting input data and converting its content into an understandable format.

[0154] "Natural language processing technology" is a technique that enables computers to understand and analyze human language, and is used to grasp the intent behind a problem.

[0155] "Means for detecting emotional states" refers to technologies that analyze and determine a user's emotions in real time based on their input.

[0156] "A series of steps for problem-solving" refers to the specific solution procedures presented to the user based on the analysis results and detected emotional states.

[0157] "Feedback information" refers to guidelines and explanations provided to help users understand and support their learning.

[0158] This invention is a learning support system aimed at improving the user's problem-solving ability by providing feedback tailored to their emotional state. The user begins using the system by inputting the problem they wish to learn about into a terminal. The terminal functions as a communication device for sending the input text data to a server. In this process, the terminal transfers data using a standard web browser or application software. A secure communication protocol (e.g., HTTPS) is used for this data transfer.

[0159] The server is equipped with the functionality to analyze data received from the terminal. This analysis process applies natural language processing and sentiment analysis technologies. For example, the server implements Google's BERT and other commonly known natural language processing models to interpret the user's problem statement. Furthermore, a sentiment analysis engine is used to detect the user's emotional state. This engine works to infer the user's emotions from keywords and context within the text.

[0160] In terms of how this system works, if a user inputs the task "solve a quadratic equation," the server generates the steps for solving the quadratic equation (e.g., factorization). If the emotion engine detects that the user is frustrated, it will provide more detailed explanations and additional examples to support the user's learning process. An example of a prompt based on this specific example would be: "Suggest the best solution to the math problem entered by the user, and provide additional hints if the user appears confused."

[0161] This configuration allows the system to respond to each user's emotional state and aims to improve learning efficiency.

[0162] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0163] Step 1:

[0164] The user inputs the problem they want to study into the terminal. Specifically, the user directly enters the problem statement into a text box and presses the submit button. This input becomes the initial data for problem analysis within the system.

[0165] Step 2:

[0166] The terminal sends the entered text data to the server. The HTTPS protocol is used for communication to ensure data security. At this stage, the input is the user's problem statement, and the output is the transmission of text data to the server.

[0167] Step 3:

[0168] The server analyzes the received text data using natural language processing techniques. Generative AI models are utilized to extract keywords and perform contextual analysis to understand the intent of the problem. The input is the received text data, and the output is the analyzed structural information of the problem.

[0169] Step 4:

[0170] Following the analysis, the server activates the emotion engine to detect the user's emotional state from the input text. Specifically, it uses an emotion analysis algorithm to analyze the tone of words and phrases in the text. The input is the user's text data, and the output is the detected emotional state of the user.

[0171] Step 5:

[0172] The server generates problem-solving steps and creates emotionally responsive feedback based on the analysis results and detected emotional states. Specifically, if the server determines the user is confused, it generates a guide including detailed explanations and additional examples. The inputs are the analysis results and emotional states, while the output is the problem-solving steps and feedback information provided to the user.

[0173] Step 6:

[0174] The terminal displays feedback and troubleshooting steps received from the server to the user. The information presented can be in text, diagrams, or interactive formats to support the user's learning process. Input is feedback information from the server, and output is the display on the user's screen.

[0175] (Application Example 2)

[0176] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as a "server" and the smart device 14 as a "terminal".

[0177] Conventional learning support systems have the challenge of not being able to adequately consider the user's emotional state, making it difficult to provide appropriate feedback. Furthermore, it is necessary to provide a dynamic learning experience that responds to the individual emotional changes of each user, but this is not fully achieved with current technology.

[0178] 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.

[0179] In this invention, the server includes means for analyzing input data received from a terminal, means for generating a series of steps for problem solving based on the analyzed data, and means for detecting the user's emotional state and adjusting feedback based on that information. This makes it possible to provide detailed feedback that responds to the user's emotions and realize an effective learning experience that takes emotional state into consideration.

[0180] A "terminal" is an electronic device used by users to input and receive information.

[0181] "Input data" refers to information transmitted by the user via their device.

[0182] "Means of analysis" refers to the technology used to understand received input data and grasp its intent.

[0183] "A series of steps for problem solving" refers to the specific steps and methods that the server generates to solve the problem presented by the user.

[0184] "Emotional state" refers to the user's emotions and psychological state, and is usually inferred using natural language processing techniques.

[0185] "Feedback" refers to response information, such as explanations and hints, provided to the user.

[0186] "Means of adjustment" refers to technologies that modify the content and format of feedback based on the user's emotional state.

[0187] A "system" is a collection of multiple elements that combine to achieve a specific function.

[0188] This invention relates to a learning support system that takes into account the user's emotional state. First, the server analyzes the input data received from the terminal. Natural language processing technology is used for this analysis to understand the content of the user's input. Based on the analysis results, the server generates a series of steps for problem solving.

[0189] The server also utilizes sentiment analysis technology to detect the user's emotional state in real time. This technology, for example, uses the Google Cloud Natural Language API. Information about the user's emotional state is a crucial element in tailoring the feedback. Specifically, if the user is feeling anxious, the server adjusts the content of the feedback, such as providing clearer explanations or additional examples.

[0190] In terms of hardware, smartphones and smart glasses are used as user input and output interfaces. On the software side, libraries and APIs for natural language processing and sentiment analysis (such as NLTK and Google Cloud Natural Language API) are used.

[0191] For example, if a user enters a request into their device saying, "I need help understanding the basics of mathematics," the server analyzes the user's intent. If the sentiment analysis determines that the user is feeling anxious, the server can generate feedback that clearly explains the basic concepts.

[0192] The generative AI model is used with prompts like the following:

[0193] "Please explain the important concepts of basic mathematics in detail for beginners."

[0194] "Please add learning tips to help reduce anxiety."

[0195] This system makes it possible to provide emotional and learning support tailored to each individual user, resulting in a more effective learning experience.

[0196] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0197] Step 1:

[0198] The user inputs the questions they want to learn using their device. The input data is sent to the server in text format. The received data is raw text information and has not yet been parsed.

[0199] Step 2:

[0200] The server analyzes the received input data using natural language processing (NLTK) techniques. Here, NLTK is used to process the data, understanding its structure and intent. As a result of the analysis, information and keywords necessary for problem-solving are extracted and passed on to the next step as structured data.

[0201] Step 3:

[0202] Based on the analysis results, the server generates a series of steps for problem solving. Using the "Generating AI Model, Prompt Sentence" in the AI ​​model, it performs calculations to create appropriate solution steps based on the received intent. This generates the problem-solving steps, which are then stored on the server as output of the solution steps.

[0203] Step 4:

[0204] The server uses the Google Cloud Natural Language API to estimate the user's emotional state. It extracts emotional information from the parsed text and performs data calculations to determine the user's emotional state. The result is output as an emotional state such as "anxiety" or "anxiety."

[0205] Step 5:

[0206] The server takes the user's emotional state into consideration and adjusts the feedback accordingly. If the emotional state is "anxiety," it will add detailed explanations and supplementary examples to the problem-solving steps. The adjusted feedback information is then formatted in a way that is easy for the user to understand.

[0207] Step 6:

[0208] The adjusted feedback and instructions are sent to the device. The user can receive feedback via the device, including how to solve the problem and detailed explanations. Based on the feedback received, the user can proceed with their learning with confidence.

[0209] 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.

[0210] 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 those described above. 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 shown 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.

[0211] 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.

[0212] [Second Embodiment]

[0213] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0214] 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.

[0215] 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).

[0216] 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.

[0217] 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.

[0218] 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).

[0219] 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.

[0220] 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.

[0221] 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.

[0222] 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.

[0223] 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.

[0224] 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".

[0225] This system is designed as a learning support tool to enhance users' problem-solving abilities. Specifically, it has the function of allowing users to input educational problems from their devices, whereupon the server analyzes the problem and provides the necessary steps for solving it, rather than providing a direct answer.

[0226] After receiving input data from the terminal, the server analyzes the data using natural language processing techniques. The purpose of the analysis is to understand the intent of the problem and identify which category it belongs to. Through this analysis, the server generates information useful for problem solving and determines the ideas and methods to present to the user.

[0227] Next, the server generates step-by-step solution steps based on the information it has gathered, showing the user how they should think. Furthermore, it provides feedback and suggestions for improvement on the answers and processes the user has attempted, and gives additional hints as needed.

[0228] As a concrete example, if a user wants to learn how to solve quadratic equations, they would input the problem "Solve x^2 - 5x + 6 = 0" into their terminal. The server would analyze this input and determine that factorization is the appropriate solution. The server would then present the user with the steps of factorization, showing the solution such as "This equation can be decomposed into (x - 2)(x - 3) = 0". Furthermore, if the user tries a different solution, the server would provide feedback to help broaden their thinking.

[0229] This system allows users to understand problems step by step, providing an environment that fosters critical thinking and problem-solving skills.

[0230] The following describes the processing flow.

[0231] Step 1:

[0232] The terminal receives user input. The user enters the problem they want to solve in text format and prepares to send it.

[0233] Step 2:

[0234] The terminal sends user input data to the server. This data is transmitted to the server via the network and prepared for analysis.

[0235] Step 3:

[0236] The server receives data sent from the terminal. The received data is decoded and preprocessed for analysis.

[0237] Step 4:

[0238] The server uses natural language processing techniques to analyze user input. This involves text tokenization and syntactic analysis to identify the intent of the question.

[0239] Step 5:

[0240] The server identifies the category of the problem based on the analysis results. This information is then used to determine the appropriate solution and approach.

[0241] Step 6:

[0242] The server generates steps for resolving the problem. This involves constructing a step-by-step procedure for how to solve the problem.

[0243] Step 7:

[0244] The server prepares feedback and hints along with the generated steps. The feedback includes an evaluation of the user's attempts and indicates areas for improvement.

[0245] Step 8:

[0246] The server sends the generated solution steps and feedback to the terminal. This allows the user to gain insights into resolving the problem.

[0247] Step 9:

[0248] The terminal displays information received from the server to the user. The user then uses this information to solve problems themselves.

[0249] (Example 1)

[0250] 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."

[0251] Traditional learning support systems often provide direct answers to problems users face, making it difficult for them to develop the ability to understand the problem-solving process. Furthermore, they lack mechanisms to provide appropriate feedback and guidance for users' trial and error, potentially preventing some users from achieving their learning objectives. Therefore, there is a need for a system that supports users in understanding problems step-by-step and improving their critical thinking skills.

[0252] 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.

[0253] In this invention, the server includes means for analyzing input information received from a terminal, means for generating a step-by-step solution procedure for problem solving based on the analyzed information, means for transmitting information indicating a thinking method along with the generated procedure to the terminal, and means for providing feedback and additional guidance to user input. This enables the user to improve their thinking skills through the problem-solving process.

[0254] A "terminal" is a device used by a user to input and transmit information, and includes devices such as computers and smartphones.

[0255] "Received input information" refers to data related to educational issues or desired solutions that users have sent via their devices.

[0256] "Means of analysis" refers to a method of analyzing received input information using natural language processing technology to identify its intent and category.

[0257] "Means for generating a step-by-step solution procedure for problem solving" refers to a method of constructing the steps necessary to solve a problem as a series of processes, based on analyzed information.

[0258] "Information demonstrating thinking methods" refers to information that presents users with ways of thinking and approaches to problem-solving.

[0259] "Means of providing feedback and additional guidance" refers to methods of providing useful opinions and further hints regarding the results of a user's work on a task.

[0260] A "server" refers to a computer system that analyzes user input, generates solutions, and transmits them to the terminal.

[0261] This invention is a system that provides step-by-step learning support to enhance users' problem-solving abilities. The system works by having the user input educational problems from a terminal, which the server then analyzes and presents step-by-step solutions.

[0262] A terminal is a device such as a PC or smartphone that receives user input using a web browser or dedicated application. Through this process, the terminal sends the entered data to the server.

[0263] The server applies natural language processing techniques to analyze the received data. Specifically, the server uses generative AI models, such as BERT or GPT, to analyze the input information, understand its content, and identify the problem category. In this process, machine learning libraries such as TensorFlow and PyTorch are used.

[0264] The server generates a step-by-step problem-solving procedure based on the analyzed information. This procedure is presented to the user along with information indicating the approach and mindset towards the problem.

[0265] As a concrete example, a user may want to learn how to solve a quadratic equation. The user enters the prompt "Solve x^2 - 5x + 6 = 0" into the terminal and sends it. The server analyzes this input and determines that factorization is the appropriate solution method. The server then generates specific solution steps, such as "This equation can be decomposed into (x - 2)(x - 3) = 0," and presents them to the user. Furthermore, even if the user tries a different solution method, the server provides feedback to support the user's thinking process.

[0266] This system allows users to improve their thinking skills and problem-solving abilities through a step-by-step problem-solving process.

[0267] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0268] Step 1:

[0269] The user inputs the problem using a device. Specifically, the user uses a web browser or a dedicated application on a PC or smartphone to input the problem in the format of "Solve x^2 - 5x + 6 = 0". This input is collected by the device and prepared to be sent to the server.

[0270] Step 2:

[0271] The terminal sends the entered data to the server. The terminal uses an internet connection to transfer the user's problem data to the server. In this case, the data format is text and arrives at the server as a prompt message.

[0272] Step 3:

[0273] The server analyzes the input data it receives. The server applies a generative AI model to the received data and uses natural language processing techniques to recognize that the input is a mathematical problem. The analysis determines that the data relates to a quadratic equation. Based on this, the server identifies an appropriate solution pattern for that category.

[0274] Step 4:

[0275] The server generates a step-by-step procedure for solving the problem. Based on the analysis results, the server designs the steps to solve the problem. Specifically, it selects a factorization method and constructs a solution step such as "This equation can be decomposed into (x - 2)(x - 3) = 0".

[0276] Step 5:

[0277] The server sends the generated steps to the terminal. The server sends the generated solution steps to the terminal in text format, along with information illustrating the thought process. This information is displayed on the user's screen.

[0278] Step 6:

[0279] The system learns based on the information the user receives. The user understands the problem-solving process based on the solution steps displayed on their device and can also try alternative solutions. In this case, the server provides feedback and sends further helpful guidance based on the user's attempts.

[0280] (Application Example 1)

[0281] 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."

[0282] In autonomous vehicles and digital devices related to transportation, there is a need for means to support efficient and safe movement in real time. However, in conventional systems, it is difficult to effectively analyze the current traffic situation and provide the driver with the optimal means of movement. To address such issues, technologies for improving the operating efficiency and safety of vehicles are necessary.

[0283] The specific processing by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following respective means.

[0284] In this invention, the server includes means for analyzing input data received from a terminal, means for generating a series of procedures for problem solving based on the analyzed data, and means for proposing an optimal solution means for transportation means based on real-time analysis. Thereby, it becomes possible to propose an efficient route selection and a means of movement for the traffic problems faced by the user.

[0285] A "terminal" is a device that has an interface with the user and transmits and receives data.

[0286] "Input data" is information provided from the user through the terminal.

[0287] "Analysis means" is a method or device for analyzing input data and understanding its content.

[0288] A "series of procedures" is a set of steps implemented step by step for problem solving.

[0289] "Real-time analysis" is a process of analyzing current information immediately and making the results immediately available for use.

[0290] "Transportation means" is a method or way related to the movement of vehicles and pedestrians.

[0291] "Solution means" is a method or technology for solving a specific problem.

[0292] A "proposal" is an action that presents a solution or option to the user.

[0293] This invention is a real-time mobility assistance system for autonomous vehicles and transportation-related digital devices. The elements constituting the system are as follows:

[0294] The server receives traffic and location information transmitted from terminals (for example, smart displays in vehicles or the driver's smartphone). This sends the input data provided by the user through the terminal to the server, and the analysis process begins.

[0295] As an analysis method, the server uses natural language processing technologies such as Google Cloud NLP and AWS Comprehend to understand the input data and generate a series of steps necessary for problem solving. This series of steps includes suggesting the optimal mode of transportation. Furthermore, the server leverages hardware platforms such as NVIDIA Drive to perform real-time data processing and analysis at high speed.

[0296] The terminal receives a series of instructions sent from the server and presents suggestions to the user visually or audibly. Specifically, transportation options and efficient route guidance are displayed in real time.

[0297] For example, if a user enters a prompt such as, "Analyze the current traffic conditions and show me the optimal route to my final destination and its detailed benefits," the system will suggest optimized transportation options to meet that request. Based on this prompt, the server immediately analyzes traffic data and presents the user with the best solution.

[0298] In this way, users can reach their destinations efficiently and safely, even in complex traffic conditions. Through such concrete examples, the forms in which the invention is implemented can be understood.

[0299] The flow of the specific process in Application Example 1 will be described using FIG. 12.

[0300] Step 1:

[0301] The user uses the terminal to input prompt sentences regarding traffic information and the current situation. The input data is transmitted to the server in real time. At this stage, the input prompt is prepared as data for processing in the server.

[0302] Step 2:

[0303] The server analyzes the received prompt sentence using natural language processing tools such as Google Cloud NLP or AWS Comprehend. Through this analysis, the server understands the intention of the prompt and identifies what traffic situation-related problem is indicated. The input is the prompt sentence, and structured data identifying the problem is generated as the output.

[0304] Step 3:

[0305] The server utilizes the NVIDIA Drive platform to evaluate real-time traffic data. Based on the analyzed prompt, it aggregates real-time traffic information obtained from various sensors and external databases and generates data recommending the optimal route and driving method accordingly. The input at this stage is the real-time data from traffic sensors and databases, and the output is the recommended route plan.

[0306] Step 4:

[0307] The server transmits the generated recommended route and driving method to the terminal. The terminal provides these recommended information to the user visually or audibly. The output is a visual navigation guide or voice guidance, presented in a format that allows the user to act immediately.

[0308] Step 5:

[0309] The user begins their journey based on the information presented. If necessary, they can re-enter a new prompt from their terminal, allowing the server to re-evaluate the information and correct the route quickly. The input is the re-entered prompt, and the output is the new recommended route.

[0310] 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.

[0311] This system is designed to help users improve their problem-solving abilities more effectively by leveraging generative AI technology during the learning process. In particular, it aims to provide a learning process tailored to each individual user, taking into account their emotional state.

[0312] First, the user inputs the question they want to learn into their device. This input information is sent from the device to the server. The server analyzes the received data using natural language processing technology to understand the intent of the question. During this process, an emotion engine is activated to detect emotional elements included in the user's input.

[0313] The emotion engine has the ability to recognize the user's current emotional state in real time, for example, by inferring the user's emotions from keywords in text or the tone of input. Emotional information is considered an important element in subsequent analysis and feedback. This allows the server to generate appropriate responses based on the emotional state.

[0314] When the server generates steps to solve a problem, sentiment information is applied to tailor the feedback and hints provided to the user. For example, if the sentiment engine determines that the user is confused, the server will provide more detailed explanations or additional hints.

[0315] As a concrete example, suppose a user enters the problem "Solve x^2 - 5x + 6 = 0". While the server suggests factoring the quadratic equation as a solution, the emotion engine detects that the user is feeling anxious. In this case, the server provides a slower explanation and adds more concrete examples to support the user and help them proceed with confidence.

[0316] This system allows users to not only solve problems but also enjoy a learning experience that takes their emotional state into account, enabling them to learn more deeply.

[0317] The following describes the processing flow.

[0318] Step 1:

[0319] The user enters a question into the device. The user types the content they want to learn as text and prepares to press the submit button.

[0320] Step 2:

[0321] The terminal sends user input data to the server. This data is transmitted to the server via the network and prepared for reception.

[0322] Step 3:

[0323] The server receives data sent from the terminal. The received data is temporarily stored for analysis.

[0324] Step 4:

[0325] The server uses natural language processing technology to analyze the user's input. This involves tokenizing the input text and extracting important keywords and phrases.

[0326] Step 5:

[0327] The emotion engine recognizes emotions from user input. Based on specific words and sentence structures within the input, it infers the emotions the user might be feeling (e.g., confusion, anxiety, excitement).

[0328] Step 6:

[0329] The server generates problem-solving steps based on analysis results and emotional information. Along with selecting a solution, it determines the tone and level of detail of the explanation according to the emotional state.

[0330] Step 7:

[0331] The server generates feedback and hints. It prepares feedback that takes the user's emotional state into consideration, and provides appropriate hints and additional explanations. For example, if the user is feeling anxious, it provides more explanations and examples.

[0332] Step 8:

[0333] The server sends the generated information to the terminal. It transfers the problem steps, feedback, and hints to the terminal.

[0334] Step 9:

[0335] The terminal displays information received from the server to the user. Based on the displayed information, the user can learn and solve problems.

[0336] (Example 2)

[0337] 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".

[0338] Traditional learning systems often fail to consider the user's emotional state when solving problems, leading to decreased learning efficiency and insufficient understanding. In particular, providing appropriate feedback tailored to learners experiencing feelings such as anxiety or confusion was challenging.

[0339] 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.

[0340] In this invention, the server includes means for analyzing input data received from a terminal, means for understanding the intent of a problem using natural language processing technology based on the analyzed data, and means for detecting the user's emotional state. This enables the provision of personalized feedback that takes the user's emotional state into account, improving learning efficiency and deepening the learner's understanding.

[0341] A "terminal" is an information processing device used by a user to provide input data to a system.

[0342] A "server" is a central processing unit that analyzes data received from terminals, generates information for problem solving, and sends it back to the terminals.

[0343] "Means of analysis" refer to technologies for interpreting input data and converting its content into an understandable format.

[0344] "Natural language processing technology" is a technique that enables computers to understand and analyze human language, and is used to grasp the intent behind a problem.

[0345] "Means for detecting emotional states" refers to technologies that analyze and determine a user's emotions in real time based on their input.

[0346] "A series of steps for problem-solving" refers to the specific solution procedures presented to the user based on the analysis results and detected emotional states.

[0347] "Feedback information" refers to guidelines and explanations provided to help users understand and support their learning.

[0348] This invention is a learning support system aimed at improving the user's problem-solving ability by providing feedback tailored to their emotional state. The user begins using the system by inputting the problem they wish to learn about into a terminal. The terminal functions as a communication device for sending the input text data to a server. In this process, the terminal transfers data using a standard web browser or application software. A secure communication protocol (e.g., HTTPS) is used for this data transfer.

[0349] The server is equipped with the functionality to analyze data received from the terminal. This analysis process applies natural language processing and sentiment analysis technologies. For example, the server implements Google's BERT and other commonly known natural language processing models to interpret the user's problem statement. Furthermore, a sentiment analysis engine is used to detect the user's emotional state. This engine works to infer the user's emotions from keywords and context within the text.

[0350] In terms of how this system works, if a user inputs the task "solve a quadratic equation," the server generates the steps for solving the quadratic equation (e.g., factorization). If the emotion engine detects that the user is frustrated, it will provide more detailed explanations and additional examples to support the user's learning process. An example of a prompt based on this specific example would be: "Suggest the best solution to the math problem entered by the user, and provide additional hints if the user appears confused."

[0351] This configuration allows the system to respond to each user's emotional state and aims to improve learning efficiency.

[0352] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0353] Step 1:

[0354] The user inputs the problem they want to study into the terminal. Specifically, the user directly enters the problem statement into a text box and presses the submit button. This input becomes the initial data for problem analysis within the system.

[0355] Step 2:

[0356] The terminal sends the entered text data to the server. The HTTPS protocol is used for communication to ensure data security. At this stage, the input is the user's problem statement, and the output is the transmission of text data to the server.

[0357] Step 3:

[0358] The server analyzes the received text data using natural language processing techniques. Generative AI models are utilized to extract keywords and perform contextual analysis to understand the intent of the problem. The input is the received text data, and the output is the analyzed structural information of the problem.

[0359] Step 4:

[0360] Following the analysis, the server activates the emotion engine to detect the user's emotional state from the input text. Specifically, it uses an emotion analysis algorithm to analyze the tone of words and phrases in the text. The input is the user's text data, and the output is the detected emotional state of the user.

[0361] Step 5:

[0362] The server generates problem-solving steps and creates emotionally responsive feedback based on the analysis results and detected emotional states. Specifically, if the server determines the user is confused, it generates a guide including detailed explanations and additional examples. The inputs are the analysis results and emotional states, while the output is the problem-solving steps and feedback information provided to the user.

[0363] Step 6:

[0364] The terminal displays feedback and troubleshooting steps received from the server to the user. The information presented can be in text, diagrams, or interactive formats to support the user's learning process. Input is feedback information from the server, and output is the display on the user's screen.

[0365] (Application Example 2)

[0366] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as the "server," and the smart glasses 214 will be referred to as the "terminal."

[0367] Conventional learning support systems have the challenge of not being able to adequately consider the user's emotional state, making it difficult to provide appropriate feedback. Furthermore, it is necessary to provide a dynamic learning experience that responds to the individual emotional changes of each user, but this is not fully achieved with current technology.

[0368] 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.

[0369] In this invention, the server includes means for analyzing input data received from a terminal, means for generating a series of steps for problem solving based on the analyzed data, and means for detecting the user's emotional state and adjusting feedback based on that information. This makes it possible to provide detailed feedback that responds to the user's emotions and realize an effective learning experience that takes emotional state into consideration.

[0370] A "terminal" is an electronic device used by users to input and receive information.

[0371] "Input data" refers to information transmitted by the user via their device.

[0372] "Means of analysis" refers to the technology used to understand received input data and grasp its intent.

[0373] "A series of steps for problem solving" refers to the specific steps and methods that the server generates to solve the problem presented by the user.

[0374] "Emotional state" refers to the user's emotions and psychological state, and is usually inferred using natural language processing techniques.

[0375] "Feedback" refers to response information, such as explanations and hints, provided to the user.

[0376] "Means of adjustment" refers to technologies that modify the content and format of feedback based on the user's emotional state.

[0377] A "system" is a collection of multiple elements that combine to achieve a specific function.

[0378] This invention relates to a learning support system that takes into account the user's emotional state. First, the server analyzes the input data received from the terminal. Natural language processing technology is used for this analysis to understand the content of the user's input. Based on the analysis results, the server generates a series of steps for problem solving.

[0379] The server also utilizes sentiment analysis technology to detect the user's emotional state in real time. This technology, for example, uses the Google Cloud Natural Language API. Information about the user's emotional state is a crucial element in tailoring the feedback. Specifically, if the user is feeling anxious, the server adjusts the content of the feedback, such as providing clearer explanations or additional examples.

[0380] In terms of hardware, smartphones and smart glasses are used as user input and output interfaces. On the software side, libraries and APIs for natural language processing and sentiment analysis (such as NLTK and Google Cloud Natural Language API) are used.

[0381] For example, if a user enters a request into their device saying, "I need help understanding the basics of mathematics," the server analyzes the user's intent. If the sentiment analysis determines that the user is feeling anxious, the server can generate feedback that clearly explains the basic concepts.

[0382] The generative AI model is used with prompts like the following:

[0383] "Please explain the important concepts of basic mathematics in detail for beginners."

[0384] "Please add learning tips to help reduce anxiety."

[0385] This system makes it possible to provide emotional and learning support tailored to each individual user, resulting in a more effective learning experience.

[0386] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0387] Step 1:

[0388] The user inputs the questions they want to learn using their device. The input data is sent to the server in text format. The received data is raw text information and has not yet been parsed.

[0389] Step 2:

[0390] The server analyzes the received input data using natural language processing (NLTK) techniques. Here, NLTK is used to process the data, understanding its structure and intent. As a result of the analysis, information and keywords necessary for problem-solving are extracted and passed on to the next step as structured data.

[0391] Step 3:

[0392] Based on the analysis results, the server generates a series of steps for problem solving. Using the "Generating AI Model, Prompt Sentence" in the AI ​​model, it performs calculations to create appropriate solution steps based on the received intent. This generates the problem-solving steps, which are then stored on the server as output of the solution steps.

[0393] Step 4:

[0394] The server uses the Google Cloud Natural Language API to estimate the user's emotional state. It extracts emotional information from the parsed text and performs data calculations to determine the user's emotional state. The result is output as an emotional state such as "anxiety" or "anxiety."

[0395] Step 5:

[0396] The server takes the user's emotional state into consideration and adjusts the feedback accordingly. If the emotional state is "anxiety," it will add detailed explanations and supplementary examples to the problem-solving steps. The adjusted feedback information is then formatted in a way that is easy for the user to understand.

[0397] Step 6:

[0398] The adjusted feedback and instructions are sent to the device. The user can receive feedback via the device, including how to solve the problem and detailed explanations. Based on the feedback received, the user can proceed with their learning with confidence.

[0399] 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.

[0400] 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 those described above. 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 shown 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.

[0401] 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.

[0402] [Third Embodiment]

[0403] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0404] 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.

[0405] 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).

[0406] 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.

[0407] 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.

[0408] 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).

[0409] 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.

[0410] 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.

[0411] 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.

[0412] 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.

[0413] 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.

[0414] 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".

[0415] This system is designed as a learning support tool to enhance users' problem-solving abilities. Specifically, it has the function of allowing users to input educational problems from their devices, whereupon the server analyzes the problem and provides the necessary steps for solving it, rather than providing a direct answer.

[0416] After receiving input data from the terminal, the server analyzes the data using natural language processing techniques. The purpose of the analysis is to understand the intent of the problem and identify which category it belongs to. Through this analysis, the server generates information useful for problem solving and determines the ideas and methods to present to the user.

[0417] Next, the server generates step-by-step solution steps based on the information it has gathered, showing the user how they should think. Furthermore, it provides feedback and suggestions for improvement on the answers and processes the user has attempted, and gives additional hints as needed.

[0418] As a concrete example, if a user wants to learn how to solve quadratic equations, they would input the problem "Solve x^2 - 5x + 6 = 0" into their terminal. The server would analyze this input and determine that factorization is the appropriate solution. The server would then present the user with the steps of factorization, showing the solution such as "This equation can be decomposed into (x - 2)(x - 3) = 0". Furthermore, if the user tries a different solution, the server would provide feedback to help broaden their thinking.

[0419] This system allows users to understand problems step by step, providing an environment that fosters critical thinking and problem-solving skills.

[0420] The following describes the processing flow.

[0421] Step 1:

[0422] The terminal receives user input. The user enters the problem they want to solve in text format and prepares to send it.

[0423] Step 2:

[0424] The terminal sends user input data to the server. This data is transmitted to the server via the network and prepared for analysis.

[0425] Step 3:

[0426] The server receives data sent from the terminal. The received data is decoded and preprocessed for analysis.

[0427] Step 4:

[0428] The server uses natural language processing techniques to analyze user input. This involves text tokenization and syntactic analysis to identify the intent of the question.

[0429] Step 5:

[0430] The server identifies the category of the problem based on the analysis results. This information is then used to determine the appropriate solution and approach.

[0431] Step 6:

[0432] The server generates steps for resolving the problem. This involves constructing a step-by-step procedure for how to solve the problem.

[0433] Step 7:

[0434] The server prepares feedback and hints along with the generated steps. The feedback includes an evaluation of the user's attempts and indicates areas for improvement.

[0435] Step 8:

[0436] The server sends the generated solution steps and feedback to the terminal. This allows the user to gain insights into resolving the problem.

[0437] Step 9:

[0438] The terminal displays information received from the server to the user. The user then uses this information to solve problems themselves.

[0439] (Example 1)

[0440] 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."

[0441] Traditional learning support systems often provide direct answers to problems users face, making it difficult for them to develop the ability to understand the problem-solving process. Furthermore, they lack mechanisms to provide appropriate feedback and guidance for users' trial and error, potentially preventing some users from achieving their learning objectives. Therefore, there is a need for a system that supports users in understanding problems step-by-step and improving their critical thinking skills.

[0442] 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.

[0443] In this invention, the server includes means for analyzing input information received from a terminal, means for generating a step-by-step solution procedure for problem solving based on the analyzed information, means for transmitting information indicating a thinking method along with the generated procedure to the terminal, and means for providing feedback and additional guidance to user input. This enables the user to improve their thinking skills through the problem-solving process.

[0444] A "terminal" is a device used by a user to input and transmit information, and includes devices such as computers and smartphones.

[0445] "Received input information" refers to data related to educational issues or desired solutions that users have sent via their devices.

[0446] "Means of analysis" refers to a method of analyzing received input information using natural language processing technology to identify its intent and category.

[0447] "Means for generating a step-by-step solution procedure for problem solving" refers to a method of constructing the steps necessary to solve a problem as a series of processes, based on analyzed information.

[0448] "Information demonstrating thinking methods" refers to information that presents users with ways of thinking and approaches to problem-solving.

[0449] "Means of providing feedback and additional guidance" refers to methods of providing useful opinions and further hints regarding the results of a user's work on a task.

[0450] A "server" refers to a computer system that analyzes user input, generates solutions, and transmits them to the terminal.

[0451] This invention is a system that provides step-by-step learning support to enhance users' problem-solving abilities. The system works by having the user input educational problems from a terminal, which the server then analyzes and presents step-by-step solutions.

[0452] A terminal is a device such as a PC or smartphone that receives user input using a web browser or dedicated application. Through this process, the terminal sends the entered data to the server.

[0453] The server applies natural language processing techniques to analyze the received data. Specifically, the server uses generative AI models, such as BERT or GPT, to analyze the input information, understand its content, and identify the problem category. In this process, machine learning libraries such as TensorFlow and PyTorch are used.

[0454] The server generates a step-by-step problem-solving procedure based on the analyzed information. This procedure is presented to the user along with information indicating the approach and mindset towards the problem.

[0455] As a concrete example, a user may want to learn how to solve a quadratic equation. The user enters the prompt "Solve x^2 - 5x + 6 = 0" into the terminal and sends it. The server analyzes this input and determines that factorization is the appropriate solution method. The server then generates specific solution steps, such as "This equation can be decomposed into (x - 2)(x - 3) = 0," and presents them to the user. Furthermore, even if the user tries a different solution method, the server provides feedback to support the user's thinking process.

[0456] This system allows users to improve their thinking skills and problem-solving abilities through a step-by-step problem-solving process.

[0457] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0458] Step 1:

[0459] The user inputs the problem using a device. Specifically, the user uses a web browser or a dedicated application on a PC or smartphone to input the problem in the format of "Solve x^2 - 5x + 6 = 0". This input is collected by the device and prepared to be sent to the server.

[0460] Step 2:

[0461] The terminal sends the entered data to the server. The terminal uses an internet connection to transfer the user's problem data to the server. In this case, the data format is text and arrives at the server as a prompt message.

[0462] Step 3:

[0463] The server analyzes the input data it receives. The server applies a generative AI model to the received data and uses natural language processing techniques to recognize that the input is a mathematical problem. The analysis determines that the data relates to a quadratic equation. Based on this, the server identifies an appropriate solution pattern for that category.

[0464] Step 4:

[0465] The server generates a step-by-step procedure for solving the problem. Based on the analysis results, the server designs the steps to solve the problem. Specifically, it selects a factorization method and constructs a solution step such as "This equation can be decomposed into (x - 2)(x - 3) = 0".

[0466] Step 5:

[0467] The server sends the generated steps to the terminal. The server sends the generated solution steps to the terminal in text format, along with information illustrating the thought process. This information is displayed on the user's screen.

[0468] Step 6:

[0469] The system learns based on the information the user receives. The user understands the problem-solving process based on the solution steps displayed on their device and can also try alternative solutions. In this case, the server provides feedback and sends further helpful guidance based on the user's attempts.

[0470] (Application Example 1)

[0471] 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."

[0472] In autonomous vehicles and digital transportation devices, there is a need for means to support efficient and safe movement in real time. However, conventional systems have difficulty effectively analyzing current traffic conditions and providing drivers with the optimal means of transportation. Technologies are needed to address these challenges and improve the operational efficiency and safety of vehicles.

[0473] 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.

[0474] In this invention, the server includes means for analyzing input data received from a terminal, means for generating a series of steps for problem solving based on the analyzed data, and means for proposing the optimal solution for transportation based on real-time analysis. This makes it possible to propose efficient route selection and travel methods for the transportation problems faced by the user.

[0475] A "terminal" is a device that has an interface with the user and sends and receives data.

[0476] "Input data" refers to information provided by the user through their device.

[0477] "Analysis means" refers to methods or devices for analyzing input data and understanding its content.

[0478] A "series of steps" is a set of steps that are carried out sequentially to solve a problem.

[0479] "Real-time analysis" is an analytical process that processes current information instantly and makes the results immediately available for use.

[0480] "Means of transportation" refers to methods and techniques related to the movement of vehicles and pedestrians.

[0481] A "solution" refers to a method or technology for resolving a specific problem.

[0482] A "proposal" is an action that presents a solution or option to the user.

[0483] This invention is a real-time mobility assistance system for autonomous vehicles and transportation-related digital devices. The elements constituting the system are as follows:

[0484] The server receives traffic and location information transmitted from terminals (for example, smart displays in vehicles or the driver's smartphone). This sends the input data provided by the user through the terminal to the server, and the analysis process begins.

[0485] As an analysis method, the server uses natural language processing technologies such as Google Cloud NLP and AWS Comprehend to understand the input data and generate a series of steps necessary for problem solving. This series of steps includes suggesting the optimal mode of transportation. Furthermore, the server leverages hardware platforms such as NVIDIA Drive to perform real-time data processing and analysis at high speed.

[0486] The terminal receives a series of instructions sent from the server and presents suggestions to the user visually or audibly. Specifically, transportation options and efficient route guidance are displayed in real time.

[0487] For example, if a user enters a prompt such as, "Analyze the current traffic conditions and show me the optimal route to my final destination and its detailed benefits," the system will suggest optimized transportation options to meet that request. Based on this prompt, the server immediately analyzes traffic data and presents the user with the best solution.

[0488] In this way, users can reach their destinations efficiently and safely, even in complex traffic conditions. Through such concrete examples, the forms in which the invention is implemented can be understood.

[0489] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0490] Step 1:

[0491] The user uses a terminal to input prompts regarding traffic information and current conditions. The entered data is sent to the server in real time. At this stage, the entered prompts are prepared as data for processing on the server.

[0492] Step 2:

[0493] The server parses the received prompt text using natural language processing tools such as Google Cloud NLP and AWS Comprehend. This analysis allows the server to understand the intent of the prompt and identify what kind of traffic-related problem it is indicating. The input is the prompt text, and the output is structured data that identifies the problem.

[0494] Step 3:

[0495] The server utilizes the NVIDIA Drive platform to evaluate real-time traffic data. Based on the analyzed prompts, it aggregates real-time traffic information obtained from various sensors and external databases, and generates data that recommends the optimal route and driving method accordingly. The input at this stage is real-time data from traffic sensors and databases, and the output is the recommended route.

[0496] Step 4:

[0497] The server sends the generated recommended routes and driving instructions to the terminal. The terminal provides this recommended information to the user visually or audibly. The output is a visual navigation guide or audio guidance, presented in a format that allows the user to act immediately.

[0498] Step 5:

[0499] The user begins their journey based on the information presented. If necessary, they can re-enter a new prompt from their terminal, allowing the server to re-evaluate the information and correct the route quickly. The input is the re-entered prompt, and the output is the new recommended route.

[0500] 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.

[0501] This system is designed to help users improve their problem-solving abilities more effectively by leveraging generative AI technology during the learning process. In particular, it aims to provide a learning process tailored to each individual user, taking into account their emotional state.

[0502] First, the user inputs the question they want to learn into their device. This input information is sent from the device to the server. The server analyzes the received data using natural language processing technology to understand the intent of the question. During this process, an emotion engine is activated to detect emotional elements included in the user's input.

[0503] The emotion engine has the ability to recognize the user's current emotional state in real time, for example, by inferring the user's emotions from keywords in text or the tone of input. Emotional information is considered an important element in subsequent analysis and feedback. This allows the server to generate appropriate responses based on the emotional state.

[0504] When the server generates steps to solve a problem, sentiment information is applied to tailor the feedback and hints provided to the user. For example, if the sentiment engine determines that the user is confused, the server will provide more detailed explanations or additional hints.

[0505] As a concrete example, suppose a user enters the problem "Solve x^2 - 5x + 6 = 0". While the server suggests factoring the quadratic equation as a solution, the emotion engine detects that the user is feeling anxious. In this case, the server provides a slower explanation and adds more concrete examples to support the user and help them proceed with confidence.

[0506] This system allows users to not only solve problems but also enjoy a learning experience that takes their emotional state into account, enabling them to learn more deeply.

[0507] The following describes the processing flow.

[0508] Step 1:

[0509] The user enters a question into the device. The user types the content they want to learn as text and prepares to press the submit button.

[0510] Step 2:

[0511] The terminal sends user input data to the server. This data is transmitted to the server via the network and prepared for reception.

[0512] Step 3:

[0513] The server receives data sent from the terminal. The received data is temporarily stored for analysis.

[0514] Step 4:

[0515] The server uses natural language processing technology to analyze the user's input. This involves tokenizing the input text and extracting important keywords and phrases.

[0516] Step 5:

[0517] The emotion engine recognizes emotions from user input. Based on specific words and sentence structures within the input, it infers the emotions the user might be feeling (e.g., confusion, anxiety, excitement).

[0518] Step 6:

[0519] The server generates problem-solving steps based on analysis results and emotional information. Along with selecting a solution, it determines the tone and level of detail of the explanation according to the emotional state.

[0520] Step 7:

[0521] The server generates feedback and hints. It prepares feedback that takes the user's emotional state into consideration, and provides appropriate hints and additional explanations. For example, if the user is feeling anxious, it provides more explanations and examples.

[0522] Step 8:

[0523] The server sends the generated information to the terminal. It transfers the problem steps, feedback, and hints to the terminal.

[0524] Step 9:

[0525] The terminal displays information received from the server to the user. Based on the displayed information, the user can learn and solve problems.

[0526] (Example 2)

[0527] 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."

[0528] Traditional learning systems often fail to consider the user's emotional state when solving problems, leading to decreased learning efficiency and insufficient understanding. In particular, providing appropriate feedback tailored to learners experiencing feelings such as anxiety or confusion was challenging.

[0529] 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.

[0530] In this invention, the server includes means for analyzing input data received from a terminal, means for understanding the intent of a problem using natural language processing technology based on the analyzed data, and means for detecting the user's emotional state. This enables the provision of personalized feedback that takes the user's emotional state into account, improving learning efficiency and deepening the learner's understanding.

[0531] A "terminal" is an information processing device used by a user to provide input data to a system.

[0532] A "server" is a central processing unit that analyzes data received from terminals, generates information for problem solving, and sends it back to the terminals.

[0533] "Means of analysis" refer to technologies for interpreting input data and converting its content into an understandable format.

[0534] "Natural language processing technology" is a technique that enables computers to understand and analyze human language, and is used to grasp the intent behind a problem.

[0535] "Means for detecting emotional states" refers to technologies that analyze and determine a user's emotions in real time based on their input.

[0536] "A series of steps for problem-solving" refers to the specific solution procedures presented to the user based on the analysis results and detected emotional states.

[0537] "Feedback information" refers to guidelines and explanations provided to help users understand and support their learning.

[0538] This invention is a learning support system aimed at improving the user's problem-solving ability by providing feedback tailored to their emotional state. The user begins using the system by inputting the problem they wish to learn about into a terminal. The terminal functions as a communication device for sending the input text data to a server. In this process, the terminal transfers data using a standard web browser or application software. A secure communication protocol (e.g., HTTPS) is used for this data transfer.

[0539] The server is equipped with the functionality to analyze data received from the terminal. This analysis process applies natural language processing and sentiment analysis technologies. For example, the server implements Google's BERT and other commonly known natural language processing models to interpret the user's problem statement. Furthermore, a sentiment analysis engine is used to detect the user's emotional state. This engine works to infer the user's emotions from keywords and context within the text.

[0540] In terms of how this system works, if a user inputs the task "solve a quadratic equation," the server generates the steps for solving the quadratic equation (e.g., factorization). If the emotion engine detects that the user is frustrated, it will provide more detailed explanations and additional examples to support the user's learning process. An example of a prompt based on this specific example would be: "Suggest the best solution to the math problem entered by the user, and provide additional hints if the user appears confused."

[0541] This configuration allows the system to respond to each user's emotional state and aims to improve learning efficiency.

[0542] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0543] Step 1:

[0544] The user inputs the problem they want to study into the terminal. Specifically, the user directly enters the problem statement into a text box and presses the submit button. This input becomes the initial data for problem analysis within the system.

[0545] Step 2:

[0546] The terminal sends the entered text data to the server. The HTTPS protocol is used for communication to ensure data security. At this stage, the input is the user's problem statement, and the output is the transmission of text data to the server.

[0547] Step 3:

[0548] The server analyzes the received text data using natural language processing techniques. Generative AI models are utilized to extract keywords and perform contextual analysis to understand the intent of the problem. The input is the received text data, and the output is the analyzed structural information of the problem.

[0549] Step 4:

[0550] Following the analysis, the server activates the emotion engine to detect the user's emotional state from the input text. Specifically, it uses an emotion analysis algorithm to analyze the tone of words and phrases in the text. The input is the user's text data, and the output is the detected emotional state of the user.

[0551] Step 5:

[0552] The server generates problem-solving steps and creates emotionally responsive feedback based on the analysis results and detected emotional states. Specifically, if the server determines the user is confused, it generates a guide including detailed explanations and additional examples. The inputs are the analysis results and emotional states, while the output is the problem-solving steps and feedback information provided to the user.

[0553] Step 6:

[0554] The terminal displays feedback and troubleshooting steps received from the server to the user. The information presented can be in text, diagrams, or interactive formats to support the user's learning process. Input is feedback information from the server, and output is the display on the user's screen.

[0555] (Application Example 2)

[0556] 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."

[0557] Conventional learning support systems have the challenge of not being able to adequately consider the user's emotional state, making it difficult to provide appropriate feedback. Furthermore, it is necessary to provide a dynamic learning experience that responds to the individual emotional changes of each user, but this is not fully achieved with current technology.

[0558] 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.

[0559] In this invention, the server includes means for analyzing input data received from a terminal, means for generating a series of steps for problem solving based on the analyzed data, and means for detecting the user's emotional state and adjusting feedback based on that information. This makes it possible to provide detailed feedback that responds to the user's emotions and realize an effective learning experience that takes emotional state into consideration.

[0560] A "terminal" is an electronic device used by users to input and receive information.

[0561] "Input data" refers to information transmitted by the user via their device.

[0562] "Means of analysis" refers to the technology used to understand received input data and grasp its intent.

[0563] "A series of steps for problem solving" refers to the specific steps and methods that the server generates to solve the problem presented by the user.

[0564] "Emotional state" refers to the user's emotions and psychological state, and is usually inferred using natural language processing techniques.

[0565] "Feedback" refers to response information, such as explanations and hints, provided to the user.

[0566] "Means of adjustment" refers to technologies that modify the content and format of feedback based on the user's emotional state.

[0567] A "system" is a collection of multiple elements that combine to achieve a specific function.

[0568] This invention relates to a learning support system that takes into account the user's emotional state. First, the server analyzes the input data received from the terminal. Natural language processing technology is used for this analysis to understand the content of the user's input. Based on the analysis results, the server generates a series of steps for problem solving.

[0569] The server also utilizes sentiment analysis technology to detect the user's emotional state in real time. This technology, for example, uses the Google Cloud Natural Language API. Information about the user's emotional state is a crucial element in tailoring the feedback. Specifically, if the user is feeling anxious, the server adjusts the content of the feedback, such as providing clearer explanations or additional examples.

[0570] In terms of hardware, smartphones and smart glasses are used as user input and output interfaces. On the software side, libraries and APIs for natural language processing and sentiment analysis (such as NLTK and Google Cloud Natural Language API) are used.

[0571] For example, if a user enters a request into their device saying, "I need help understanding the basics of mathematics," the server analyzes the user's intent. If the sentiment analysis determines that the user is feeling anxious, the server can generate feedback that clearly explains the basic concepts.

[0572] The generative AI model is used with prompts like the following:

[0573] "Please explain the important concepts of basic mathematics in detail for beginners."

[0574] "Please add learning tips to help reduce anxiety."

[0575] This system makes it possible to provide emotional and learning support tailored to each individual user, resulting in a more effective learning experience.

[0576] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0577] Step 1:

[0578] The user inputs the questions they want to learn using their device. The input data is sent to the server in text format. The received data is raw text information and has not yet been parsed.

[0579] Step 2:

[0580] The server analyzes the received input data using natural language processing (NLTK) techniques. Here, NLTK is used to process the data, understanding its structure and intent. As a result of the analysis, information and keywords necessary for problem-solving are extracted and passed on to the next step as structured data.

[0581] Step 3:

[0582] Based on the analysis results, the server generates a series of steps for problem solving. Using the "Generating AI Model, Prompt Sentence" in the AI ​​model, it performs calculations to create appropriate solution steps based on the received intent. This generates the problem-solving steps, which are then stored on the server as output of the solution steps.

[0583] Step 4:

[0584] The server uses the Google Cloud Natural Language API to estimate the user's emotional state. It extracts emotional information from the parsed text and performs data calculations to determine the user's emotional state. The result is output as an emotional state such as "anxiety" or "anxiety."

[0585] Step 5:

[0586] The server takes the user's emotional state into consideration and adjusts the feedback accordingly. If the emotional state is "anxiety," it will add detailed explanations and supplementary examples to the problem-solving steps. The adjusted feedback information is then formatted in a way that is easy for the user to understand.

[0587] Step 6:

[0588] The adjusted feedback and instructions are sent to the device. The user can receive feedback via the device, including how to solve the problem and detailed explanations. Based on the feedback received, the user can proceed with their learning with confidence.

[0589] 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.

[0590] 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 those described above. 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 shown 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.

[0591] 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.

[0592] [Fourth Embodiment]

[0593] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0594] 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.

[0595] 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).

[0596] 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.

[0597] 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.

[0598] 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).

[0599] 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.

[0600] 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.

[0601] 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.

[0602] 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.

[0603] 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.

[0604] 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.

[0605] 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".

[0606] This system is designed as a learning support tool to enhance users' problem-solving abilities. Specifically, it has the function of allowing users to input educational problems from their devices, whereupon the server analyzes the problem and provides the necessary steps for solving it, rather than providing a direct answer.

[0607] After receiving input data from the terminal, the server analyzes the data using natural language processing techniques. The purpose of the analysis is to understand the intent of the problem and identify which category it belongs to. Through this analysis, the server generates information useful for problem solving and determines the ideas and methods to present to the user.

[0608] Next, the server generates step-by-step solution steps based on the information it has gathered, showing the user how they should think. Furthermore, it provides feedback and suggestions for improvement on the answers and processes the user has attempted, and gives additional hints as needed.

[0609] As a concrete example, if a user wants to learn how to solve quadratic equations, they would input the problem "Solve x^2 - 5x + 6 = 0" into their terminal. The server would analyze this input and determine that factorization is the appropriate solution. The server would then present the user with the steps of factorization, showing the solution such as "This equation can be decomposed into (x - 2)(x - 3) = 0". Furthermore, if the user tries a different solution, the server would provide feedback to help broaden their thinking.

[0610] This system allows users to understand problems step by step, providing an environment that fosters critical thinking and problem-solving skills.

[0611] The following describes the processing flow.

[0612] Step 1:

[0613] The terminal receives user input. The user enters the problem they want to solve in text format and prepares to send it.

[0614] Step 2:

[0615] The terminal sends user input data to the server. This data is transmitted to the server via the network and prepared for analysis.

[0616] Step 3:

[0617] The server receives data sent from the terminal. The received data is decoded and preprocessed for analysis.

[0618] Step 4:

[0619] The server uses natural language processing techniques to analyze user input. This involves text tokenization and syntactic analysis to identify the intent of the question.

[0620] Step 5:

[0621] The server identifies the category of the problem based on the analysis results. This information is then used to determine the appropriate solution and approach.

[0622] Step 6:

[0623] The server generates steps for resolving the problem. This involves constructing a step-by-step procedure for how to solve the problem.

[0624] Step 7:

[0625] The server prepares feedback and hints along with the generated steps. The feedback includes an evaluation of the user's attempts and indicates areas for improvement.

[0626] Step 8:

[0627] The server sends the generated solution steps and feedback to the terminal. This allows the user to gain insights into resolving the problem.

[0628] Step 9:

[0629] The terminal displays information received from the server to the user. The user then uses this information to solve problems themselves.

[0630] (Example 1)

[0631] 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".

[0632] Traditional learning support systems often provide direct answers to problems users face, making it difficult for them to develop the ability to understand the problem-solving process. Furthermore, they lack mechanisms to provide appropriate feedback and guidance for users' trial and error, potentially preventing some users from achieving their learning objectives. Therefore, there is a need for a system that supports users in understanding problems step-by-step and improving their critical thinking skills.

[0633] 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.

[0634] In this invention, the server includes means for analyzing input information received from a terminal, means for generating a step-by-step solution procedure for problem solving based on the analyzed information, means for transmitting information indicating a thinking method along with the generated procedure to the terminal, and means for providing feedback and additional guidance to user input. This enables the user to improve their thinking skills through the problem-solving process.

[0635] A "terminal" is a device used by a user to input and transmit information, and includes devices such as computers and smartphones.

[0636] "Received input information" refers to data related to educational issues or desired solutions that users have sent via their devices.

[0637] "Means of analysis" refers to a method of analyzing received input information using natural language processing technology to identify its intent and category.

[0638] "Means for generating a step-by-step solution procedure for problem solving" refers to a method of constructing the steps necessary to solve a problem as a series of processes, based on analyzed information.

[0639] "Information demonstrating thinking methods" refers to information that presents users with ways of thinking and approaches to problem-solving.

[0640] "Means of providing feedback and additional guidance" refers to methods of providing useful opinions and further hints regarding the results of a user's work on a task.

[0641] A "server" refers to a computer system that analyzes user input, generates solutions, and transmits them to the terminal.

[0642] This invention is a system that provides step-by-step learning support to enhance users' problem-solving abilities. The system works by having the user input educational problems from a terminal, which the server then analyzes and presents step-by-step solutions.

[0643] A terminal is a device such as a PC or smartphone that receives user input using a web browser or dedicated application. Through this process, the terminal sends the entered data to the server.

[0644] The server applies natural language processing techniques to analyze the received data. Specifically, the server uses generative AI models, such as BERT or GPT, to analyze the input information, understand its content, and identify the problem category. In this process, machine learning libraries such as TensorFlow and PyTorch are used.

[0645] The server generates a step-by-step problem-solving procedure based on the analyzed information. This procedure is presented to the user along with information indicating the approach and mindset towards the problem.

[0646] As a concrete example, a user may want to learn how to solve a quadratic equation. The user enters the prompt "Solve x^2 - 5x + 6 = 0" into the terminal and sends it. The server analyzes this input and determines that factorization is the appropriate solution method. The server then generates specific solution steps, such as "This equation can be decomposed into (x - 2)(x - 3) = 0," and presents them to the user. Furthermore, even if the user tries a different solution method, the server provides feedback to support the user's thinking process.

[0647] This system allows users to improve their thinking skills and problem-solving abilities through a step-by-step problem-solving process.

[0648] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0649] Step 1:

[0650] The user inputs the problem using a device. Specifically, the user uses a web browser or a dedicated application on a PC or smartphone to input the problem in the format of "Solve x^2 - 5x + 6 = 0". This input is collected by the device and prepared to be sent to the server.

[0651] Step 2:

[0652] The terminal sends the entered data to the server. The terminal uses an internet connection to transfer the user's problem data to the server. In this case, the data format is text and arrives at the server as a prompt message.

[0653] Step 3:

[0654] The server analyzes the input data it receives. The server applies a generative AI model to the received data and uses natural language processing techniques to recognize that the input is a mathematical problem. The analysis determines that the data relates to a quadratic equation. Based on this, the server identifies an appropriate solution pattern for that category.

[0655] Step 4:

[0656] The server generates a step-by-step procedure for solving the problem. Based on the analysis results, the server designs the steps to solve the problem. Specifically, it selects a factorization method and constructs a solution step such as "This equation can be decomposed into (x - 2)(x - 3) = 0".

[0657] Step 5:

[0658] The server sends the generated steps to the terminal. The server sends the generated solution steps to the terminal in text format, along with information illustrating the thought process. This information is displayed on the user's screen.

[0659] Step 6:

[0660] The system learns based on the information the user receives. The user understands the problem-solving process based on the solution steps displayed on their device and can also try alternative solutions. In this case, the server provides feedback and sends further helpful guidance based on the user's attempts.

[0661] (Application Example 1)

[0662] 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".

[0663] In autonomous vehicles and digital transportation devices, there is a need for means to support efficient and safe movement in real time. However, conventional systems have difficulty effectively analyzing current traffic conditions and providing drivers with the optimal means of transportation. Technologies are needed to address these challenges and improve the operational efficiency and safety of vehicles.

[0664] 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.

[0665] In this invention, the server includes means for analyzing input data received from a terminal, means for generating a series of steps for problem solving based on the analyzed data, and means for proposing the optimal solution for transportation based on real-time analysis. This makes it possible to propose efficient route selection and travel methods for the transportation problems faced by the user.

[0666] A "terminal" is a device that has an interface with the user and sends and receives data.

[0667] "Input data" refers to information provided by the user through their device.

[0668] "Analysis means" refers to methods or devices for analyzing input data and understanding its content.

[0669] A "series of steps" is a set of steps that are carried out sequentially to solve a problem.

[0670] "Real-time analysis" is an analytical process that processes current information instantly and makes the results immediately available for use.

[0671] "Means of transportation" refers to methods and techniques related to the movement of vehicles and pedestrians.

[0672] A "solution" refers to a method or technology for resolving a specific problem.

[0673] A "proposal" is an action that presents a solution or option to the user.

[0674] This invention is a real-time mobility assistance system for autonomous vehicles and transportation-related digital devices. The elements constituting the system are as follows:

[0675] The server receives traffic and location information transmitted from terminals (for example, smart displays in vehicles or the driver's smartphone). This sends the input data provided by the user through the terminal to the server, and the analysis process begins.

[0676] As an analysis method, the server uses natural language processing technologies such as Google Cloud NLP and AWS Comprehend to understand the input data and generate a series of steps necessary for problem solving. This series of steps includes suggesting the optimal mode of transportation. Furthermore, the server leverages hardware platforms such as NVIDIA Drive to perform real-time data processing and analysis at high speed.

[0677] The terminal receives a series of instructions sent from the server and presents suggestions to the user visually or audibly. Specifically, transportation options and efficient route guidance are displayed in real time.

[0678] For example, if a user enters a prompt such as, "Analyze the current traffic conditions and show me the optimal route to my final destination and its detailed benefits," the system will suggest optimized transportation options to meet that request. Based on this prompt, the server immediately analyzes traffic data and presents the user with the best solution.

[0679] In this way, users can reach their destinations efficiently and safely, even in complex traffic conditions. Through such concrete examples, the forms in which the invention is implemented can be understood.

[0680] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0681] Step 1:

[0682] The user uses a terminal to input prompts regarding traffic information and current conditions. The entered data is sent to the server in real time. At this stage, the entered prompts are prepared as data for processing on the server.

[0683] Step 2:

[0684] The server parses the received prompt text using natural language processing tools such as Google Cloud NLP and AWS Comprehend. This analysis allows the server to understand the intent of the prompt and identify what kind of traffic-related problem it is indicating. The input is the prompt text, and the output is structured data that identifies the problem.

[0685] Step 3:

[0686] The server utilizes the NVIDIA Drive platform to evaluate real-time traffic data. Based on the analyzed prompts, it aggregates real-time traffic information obtained from various sensors and external databases, and generates data that recommends the optimal route and driving method accordingly. The input at this stage is real-time data from traffic sensors and databases, and the output is the recommended route.

[0687] Step 4:

[0688] The server sends the generated recommended routes and driving instructions to the terminal. The terminal provides this recommended information to the user visually or audibly. The output is a visual navigation guide or audio guidance, presented in a format that allows the user to act immediately.

[0689] Step 5:

[0690] The user begins their journey based on the information presented. If necessary, they can re-enter a new prompt from their terminal, allowing the server to re-evaluate the information and correct the route quickly. The input is the re-entered prompt, and the output is the new recommended route.

[0691] 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.

[0692] This system is designed to help users improve their problem-solving abilities more effectively by leveraging generative AI technology during the learning process. In particular, it aims to provide a learning process tailored to each individual user, taking into account their emotional state.

[0693] First, the user inputs the question they want to learn into their device. This input information is sent from the device to the server. The server analyzes the received data using natural language processing technology to understand the intent of the question. During this process, an emotion engine is activated to detect emotional elements included in the user's input.

[0694] The emotion engine has the ability to recognize the user's current emotional state in real time, for example, by inferring the user's emotions from keywords in text or the tone of input. Emotional information is considered an important element in subsequent analysis and feedback. This allows the server to generate appropriate responses based on the emotional state.

[0695] When the server generates steps to solve a problem, sentiment information is applied to tailor the feedback and hints provided to the user. For example, if the sentiment engine determines that the user is confused, the server will provide more detailed explanations or additional hints.

[0696] As a concrete example, suppose a user enters the problem "Solve x^2 - 5x + 6 = 0". While the server suggests factoring the quadratic equation as a solution, the emotion engine detects that the user is feeling anxious. In this case, the server provides a slower explanation and adds more concrete examples to support the user and help them proceed with confidence.

[0697] This system allows users to not only solve problems but also enjoy a learning experience that takes their emotional state into account, enabling them to learn more deeply.

[0698] The following describes the processing flow.

[0699] Step 1:

[0700] The user enters a question into the device. The user types the content they want to learn as text and prepares to press the submit button.

[0701] Step 2:

[0702] The terminal sends user input data to the server. This data is transmitted to the server via the network and prepared for reception.

[0703] Step 3:

[0704] The server receives data sent from the terminal. The received data is temporarily stored for analysis.

[0705] Step 4:

[0706] The server uses natural language processing technology to analyze the user's input. This involves tokenizing the input text and extracting important keywords and phrases.

[0707] Step 5:

[0708] The emotion engine recognizes emotions from user input. Based on specific words and sentence structures within the input, it infers the emotions the user might be feeling (e.g., confusion, anxiety, excitement).

[0709] Step 6:

[0710] The server generates problem-solving steps based on analysis results and emotional information. Along with selecting a solution, it determines the tone and level of detail of the explanation according to the emotional state.

[0711] Step 7:

[0712] The server generates feedback and hints. It prepares feedback that takes the user's emotional state into consideration, and provides appropriate hints and additional explanations. For example, if the user is feeling anxious, it provides more explanations and examples.

[0713] Step 8:

[0714] The server sends the generated information to the terminal. It transfers the problem steps, feedback, and hints to the terminal.

[0715] Step 9:

[0716] The terminal displays information received from the server to the user. Based on the displayed information, the user can learn and solve problems.

[0717] (Example 2)

[0718] 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".

[0719] Traditional learning systems often fail to consider the user's emotional state when solving problems, leading to decreased learning efficiency and insufficient understanding. In particular, providing appropriate feedback tailored to learners experiencing feelings such as anxiety or confusion was challenging.

[0720] 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.

[0721] In this invention, the server includes means for analyzing input data received from a terminal, means for understanding the intent of a problem using natural language processing technology based on the analyzed data, and means for detecting the user's emotional state. This enables the provision of personalized feedback that takes the user's emotional state into account, improving learning efficiency and deepening the learner's understanding.

[0722] A "terminal" is an information processing device used by a user to provide input data to a system.

[0723] A "server" is a central processing unit that analyzes data received from terminals, generates information for problem solving, and sends it back to the terminals.

[0724] "Means of analysis" refer to technologies for interpreting input data and converting its content into an understandable format.

[0725] "Natural language processing technology" is a technique that enables computers to understand and analyze human language, and is used to grasp the intent behind a problem.

[0726] "Means for detecting emotional states" refers to technologies that analyze and determine a user's emotions in real time based on their input.

[0727] "A series of steps for problem-solving" refers to the specific solution procedures presented to the user based on the analysis results and detected emotional states.

[0728] "Feedback information" refers to guidelines and explanations provided to help users understand and support their learning.

[0729] This invention is a learning support system aimed at improving the user's problem-solving ability by providing feedback tailored to their emotional state. The user begins using the system by inputting the problem they wish to learn about into a terminal. The terminal functions as a communication device for sending the input text data to a server. In this process, the terminal transfers data using a standard web browser or application software. A secure communication protocol (e.g., HTTPS) is used for this data transfer.

[0730] The server is equipped with the functionality to analyze data received from the terminal. This analysis process applies natural language processing and sentiment analysis technologies. For example, the server implements Google's BERT and other commonly known natural language processing models to interpret the user's problem statement. Furthermore, a sentiment analysis engine is used to detect the user's emotional state. This engine works to infer the user's emotions from keywords and context within the text.

[0731] In terms of how this system works, if a user inputs the task "solve a quadratic equation," the server generates the steps for solving the quadratic equation (e.g., factorization). If the emotion engine detects that the user is frustrated, it will provide more detailed explanations and additional examples to support the user's learning process. An example of a prompt based on this specific example would be: "Suggest the best solution to the math problem entered by the user, and provide additional hints if the user appears confused."

[0732] This configuration allows the system to respond to each user's emotional state and aims to improve learning efficiency.

[0733] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0734] Step 1:

[0735] The user inputs the problem they want to study into the terminal. Specifically, the user directly enters the problem statement into a text box and presses the submit button. This input becomes the initial data for problem analysis within the system.

[0736] Step 2:

[0737] The terminal sends the entered text data to the server. The HTTPS protocol is used for communication to ensure data security. At this stage, the input is the user's problem statement, and the output is the transmission of text data to the server.

[0738] Step 3:

[0739] The server analyzes the received text data using natural language processing techniques. Generative AI models are utilized to extract keywords and perform contextual analysis to understand the intent of the problem. The input is the received text data, and the output is the analyzed structural information of the problem.

[0740] Step 4:

[0741] Following the analysis, the server activates the emotion engine to detect the user's emotional state from the input text. Specifically, it uses an emotion analysis algorithm to analyze the tone of words and phrases in the text. The input is the user's text data, and the output is the detected emotional state of the user.

[0742] Step 5:

[0743] The server generates problem-solving steps and creates emotionally responsive feedback based on the analysis results and detected emotional states. Specifically, if the server determines the user is confused, it generates a guide including detailed explanations and additional examples. The inputs are the analysis results and emotional states, while the output is the problem-solving steps and feedback information provided to the user.

[0744] Step 6:

[0745] The terminal displays feedback and troubleshooting steps received from the server to the user. The information presented can be in text, diagrams, or interactive formats to support the user's learning process. Input is feedback information from the server, and output is the display on the user's screen.

[0746] (Application Example 2)

[0747] 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".

[0748] Conventional learning support systems have the challenge of not being able to adequately consider the user's emotional state, making it difficult to provide appropriate feedback. Furthermore, it is necessary to provide a dynamic learning experience that responds to the individual emotional changes of each user, but this is not fully achieved with current technology.

[0749] 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.

[0750] In this invention, the server includes means for analyzing input data received from a terminal, means for generating a series of steps for problem solving based on the analyzed data, and means for detecting the user's emotional state and adjusting feedback based on that information. This makes it possible to provide detailed feedback that responds to the user's emotions and realize an effective learning experience that takes emotional state into consideration.

[0751] A "terminal" is an electronic device used by users to input and receive information.

[0752] "Input data" refers to information transmitted by the user via their device.

[0753] "Means of analysis" refers to the technology used to understand received input data and grasp its intent.

[0754] "A series of steps for problem solving" refers to the specific steps and methods that the server generates to solve the problem presented by the user.

[0755] "Emotional state" refers to the user's emotions and psychological state, and is usually inferred using natural language processing techniques.

[0756] "Feedback" refers to response information, such as explanations and hints, provided to the user.

[0757] "Means of adjustment" refers to technologies that modify the content and format of feedback based on the user's emotional state.

[0758] A "system" is a collection of multiple elements that combine to achieve a specific function.

[0759] This invention relates to a learning support system that takes into account the user's emotional state. First, the server analyzes the input data received from the terminal. Natural language processing technology is used for this analysis to understand the content of the user's input. Based on the analysis results, the server generates a series of steps for problem solving.

[0760] The server also utilizes sentiment analysis technology to detect the user's emotional state in real time. This technology, for example, uses the Google Cloud Natural Language API. Information about the user's emotional state is a crucial element in tailoring the feedback. Specifically, if the user is feeling anxious, the server adjusts the content of the feedback, such as providing clearer explanations or additional examples.

[0761] In terms of hardware, smartphones and smart glasses are used as user input and output interfaces. On the software side, libraries and APIs for natural language processing and sentiment analysis (such as NLTK and Google Cloud Natural Language API) are used.

[0762] For example, if a user enters a request into their device saying, "I need help understanding the basics of mathematics," the server analyzes the user's intent. If the sentiment analysis determines that the user is feeling anxious, the server can generate feedback that clearly explains the basic concepts.

[0763] The generative AI model is used with prompts like the following:

[0764] "Please explain the important concepts of basic mathematics in detail for beginners."

[0765] "Please add learning tips to help reduce anxiety."

[0766] This system makes it possible to provide emotional and learning support tailored to each individual user, resulting in a more effective learning experience.

[0767] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0768] Step 1:

[0769] The user inputs the questions they want to learn using their device. The input data is sent to the server in text format. The received data is raw text information and has not yet been parsed.

[0770] Step 2:

[0771] The server analyzes the received input data using natural language processing (NLTK) techniques. Here, NLTK is used to process the data, understanding its structure and intent. As a result of the analysis, information and keywords necessary for problem-solving are extracted and passed on to the next step as structured data.

[0772] Step 3:

[0773] Based on the analysis results, the server generates a series of steps for problem solving. Using the "Generating AI Model, Prompt Sentence" in the AI ​​model, it performs calculations to create appropriate solution steps based on the received intent. This generates the problem-solving steps, which are then stored on the server as output of the solution steps.

[0774] Step 4:

[0775] The server uses the Google Cloud Natural Language API to estimate the user's emotional state. It extracts emotional information from the parsed text and performs data calculations to determine the user's emotional state. The result is output as an emotional state such as "anxiety" or "anxiety."

[0776] Step 5:

[0777] The server takes the user's emotional state into consideration and adjusts the feedback accordingly. If the emotional state is "anxiety," it will add detailed explanations and supplementary examples to the problem-solving steps. The adjusted feedback information is then formatted in a way that is easy for the user to understand.

[0778] Step 6:

[0779] The adjusted feedback and instructions are sent to the device. The user can receive feedback via the device, including how to solve the problem and detailed explanations. Based on the feedback received, the user can proceed with their learning with confidence.

[0780] 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.

[0781] 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 those described above. 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 shown 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.

[0782] 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.

[0783] 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.

[0784] 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.

[0785] 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.

[0786] 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.

[0787] 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.

[0788] 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."

[0789] 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.

[0790] 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.

[0791] 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.

[0792] 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.

[0793] 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.

[0794] 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.

[0795] 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.

[0796] 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.

[0797] 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.

[0798] 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.

[0799] 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.

[0800] 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.

[0801] The following is further disclosed regarding the embodiments described above.

[0802] (Claim 1)

[0803] A means for analyzing input data received from a terminal,

[0804] A means for generating a series of steps for problem solving based on analyzed data,

[0805] A means for transmitting information including the generated steps to a terminal,

[0806] A system that includes this.

[0807] (Claim 2)

[0808] The system according to claim 1, which analyzes user input using natural language processing technology.

[0809] (Claim 3)

[0810] The system according to claim 1, which generates learning support feedback and hints based on analyzed data.

[0811] "Example 1"

[0812] (Claim 1)

[0813] A means for analyzing input information received from a terminal,

[0814] A means for generating step-by-step problem-solving procedures based on analyzed information,

[0815] A means of transmitting information indicating the thought process along with the generated procedure to the terminal,

[0816] A means of providing feedback and additional guidance in response to user input,

[0817] A system that includes this.

[0818] (Claim 2)

[0819] The system according to claim 1, which analyzes user input using natural language processing technology and identifies the category of the input.

[0820] (Claim 3)

[0821] The system according to claim 1, which generates learning support feedback and additional guidance based on analyzed information.

[0822] "Application Example 1"

[0823] (Claim 1)

[0824] A means for analyzing input data received from a terminal,

[0825] A means for generating a series of steps for problem solving based on analyzed data,

[0826] A means of proposing the optimal solution for transportation based on real-time analysis,

[0827] A means of transmitting the generated information to the terminal,

[0828] A system that includes this.

[0829] (Claim 2)

[0830] The system according to claim 1, which analyzes user input using natural language processing technology to analyze the current movement status.

[0831] (Claim 3)

[0832] The system according to claim 1, which, based on analyzed data, presents the optimal means of transportation and generates feedback and options that contribute to improving efficiency.

[0833] "Example 2 of combining an emotion engine"

[0834] (Claim 1)

[0835] A means for analyzing input data received from a terminal,

[0836] A means of understanding the intent of a problem using natural language processing technology based on the analyzed data,

[0837] A means of detecting the user's emotional state,

[0838] A means for generating a series of steps for problem solving, taking into account the detected emotional state,

[0839] Means for transmitting generated steps and adjusted feedback information to the terminal,

[0840] A system that includes this.

[0841] (Claim 2)

[0842] The system according to claim 1, comprising emotion analysis means for analyzing user input and detecting emotional state in real time.

[0843] (Claim 3)

[0844] The system according to claim 1, which generates learning support feedback and hints based on analyzed data and detected emotional states, and adjusts them according to the emotional state of each individual user.

[0845] "Application example 2 when combining with an emotional engine"

[0846] (Claim 1)

[0847] A means for analyzing input data received from a terminal,

[0848] A means for generating a series of steps for problem solving based on analyzed data,

[0849] A means of detecting the user's emotional state and adjusting feedback based on that information,

[0850] A means for transmitting information including the generated procedure to a terminal,

[0851] A system that includes this.

[0852] (Claim 2)

[0853] The system according to claim 1, which analyzes user input using natural language processing technology.

[0854] (Claim 3)

[0855] The system according to claim 1, which analyzes the user's emotional state and adjusts learning support feedback and presentations based on that analysis. [Explanation of Symbols]

[0856] 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 analyzing input data received from a terminal, A means for generating a series of steps for problem solving based on analyzed data, A means for transmitting information including the generated steps to a terminal, A system that includes this.

2. The system according to claim 1, which analyzes user input using natural language processing technology.

3. The system according to claim 1, which generates learning support feedback and hints based on analyzed data.

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