system

Abstract

We provide the system. [Solution] Means for acquiring user ability data and goal information, A means for analyzing the user's ability data and goal information and generating an individualized learning plan, Means for presenting the learning plan on the user's display device, A means for collecting the user's learning progress and dynamically updating the learning plan, A means of aggregating learning progress and displaying it visually to administrators and users, 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, including steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to an explanation of a chatbot character, encoding the prompt, and inputting the encoded prompt into a language model to generate a chatbot utterance as a response to the user utterance.

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In many companies, group training and self-study for improving employees' capabilities are carried out, but these methods have not provided the optimal learning method for each employee. As a result, companies feel that they cannot conduct efficient training, and individual employees also have the problem that they have to learn within limited time without clearly knowing the capabilities they need.

Means for Solving the Problems

[0005] To address this challenge, the proposed system acquires user ability data and goal information, analyzes this data, and generates personalized learning plans. Furthermore, these learning plans are displayed on the user's display device, dynamically collecting the user's learning progress and updating the plan as needed. In this way, the system aims to achieve efficient and streamlined learning, meeting the needs of both employees and the company.

[0006] "User capability data" refers to information that indicates the current skill and knowledge level of each individual user.

[0007] "Goal information" refers to information about the future career and skill goals that the user wishes to achieve.

[0008] An "individualized learning plan" is a learning progress plan that is customized for a specific user based on their ability data and goal information.

[0009] A "display device" is an electronic device that allows users to visually check information such as study plans.

[0010] "Learning progress" refers to information indicating how far a user has progressed in their learning based on their learning plan.

[0011] "Dynamic updating" means modifying the learning plan in real time or on a situational basis, depending on learning progress and other relevant information.

[0012] "Visual display" means providing information in a format that can be easily understood at a glance on a display device, such as in the form of diagrams, graphs, or text. [Brief explanation of the drawing]

[0013] [Figure 1] This is a conceptual diagram showing an example of the configuration of a data processing system according to the first embodiment. [Figure 2] This is a conceptual diagram showing an example of the essential functions of a data processing device and a smart device according to the first embodiment. [Figure 3] 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.

Embodiments for Carrying Out the Invention

[0014] 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.

[0015] First, the language used in the following description will be explained.

[0016] In the following embodiments, the numbered processor (hereinafter simply referred to as "processor") may be a single arithmetic unit or a combination of multiple arithmetic units. Also, the processor may be a single type of arithmetic unit or a combination of multiple types of arithmetic units. Examples of arithmetic units include a CPU (Central Processing Unit), a GPU (Graphics Processing Unit), a GPGPU (General-Purpose computing on Graphics Processing Units), an APU (Accelerated Processing Unit), and the like.

[0017] In the following embodiments, the numbered RAM (Random Access Memory) is a memory in which information is temporarily stored and is used as a work memory by the processor.

[0018] In the following embodiments, the numbered storage is one or more non-volatile storage devices that store various programs and various parameters and the like. Examples of non-volatile storage devices include flash memory (SSD (Solid State Drive)), magnetic disks (for example, hard disks), or magnetic tapes, and the like.

[0019] In the following embodiments, the numbered communication I / F (Interface) is an interface including a communication processor and an antenna and the like. 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).

[0020] 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."

[0021] [First Embodiment]

[0022] Figure 1 shows an example of the configuration of the data processing system 10 according to the first embodiment.

[0023] 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.

[0024] 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).

[0025] 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.

[0026] 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.

[0027] 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.

[0028] 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.

[0029] Figure 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0030] 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.

[0031] 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.

[0032] 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.

[0033] 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".

[0034] Embodiments of the present invention are systems for efficiently supporting employee skill development within a company. The system functions primarily through interaction between servers, terminals, and users.

[0035] Users input their skill data and goal information on their device. This input includes their current skill level, skills they want to acquire in the future, and career goals. The device structures this data and sends it to the server.

[0036] The server analyzes the received data. This analysis utilizes natural language processing techniques and machine learning algorithms, generating a personalized learning plan for each user by comparing it with company-specific skill requirements and the latest industry information. The learning plan includes short, easy-to-learn videos, quiz-style materials, and reading materials to aid in deeper understanding.

[0037] The generated learning plan is sent from the server to the user's device and presented to the user so they can start learning at their preferred time. The device records the user's learning progress in real time and sends data such as achievement level and comprehension level back to the server.

[0038] The server analyzes the received learning progress data and dynamically updates the learning plan as needed. This ensures that each user continuously receives appropriate learning content based on the latest information. Furthermore, by comprehensively aggregating learning progress and results and providing this information visualized in a dashboard format to both the company and the user, the growth process can be clearly understood.

[0039] This system allows users to utilize their free time between tasks and learn effectively at their own pace. Furthermore, companies can centrally manage the skill development of their entire workforce, fostering competitive talent. For example, if a user wants to improve their digital marketing skills, this system provides video learning materials and related quizzes on the latest digital marketing technologies, and the learning plan is adjusted according to the user's needs.

[0040] The following describes the processing flow.

[0041] Step 1:

[0042] The user enters their skill level, career goals, areas of interest, etc., on the device. The device formats this data and saves it to a database for the next processing step.

[0043] Step 2:

[0044] The terminal sends formatted user data to the server. The server receives the data, performs an integrity check, and prepares for analysis.

[0045] Step 3:

[0046] The server begins analysis based on the received user data. Using natural language processing and machine learning algorithms, it generates a personalized learning plan that matches the user's skill requirements and the company's goals.

[0047] Step 4:

[0048] The server generates a learning plan and sends it to the user's device for review. The device displays the received learning plan in its user interface and prompts the user to begin learning.

[0049] Step 5:

[0050] The user progresses through the learning process on their device, recording their progress and results. The device continuously sends this data to the server, which then monitors their progress.

[0051] Step 6:

[0052] The server analyzes progress data sent from the terminal to assess learning achievement and make adjustments for the next step. It automatically updates the learning plan as needed, providing an optimized learning experience.

[0053] Step 7:

[0054] The server aggregates user learning progress and displays it on a dashboard in a format that is easy for both the company and the user to understand. This allows users to track their own progress, and the company to grasp overall skill improvement.

[0055] (Example 1)

[0056] 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."

[0057] To effectively support employee skill development within companies and organizations, it is necessary to develop and continuously manage training plans tailored to individual needs. However, traditional methods have made it difficult to provide training content appropriate for each user in real time, to flexibly modify plans as progress is made, and to visually grasp the results.

[0058] 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.

[0059] In this invention, the server includes means for acquiring user ability information and purpose information, means for analyzing the user ability information and purpose information and generating an individualized educational plan, and means for providing access to educational materials using an application of an external educational platform. This enables the generation of an appropriate educational plan tailored to each user's needs and flexible updating of the plan according to progress.

[0060] "User" refers to an individual or member of an organization who uses the system to improve their own capabilities.

[0061] "Capability information" refers to data about the level of technology, knowledge, and skills that a user currently possesses.

[0062] "Purpose information" refers to information about the abilities, skills, or career goals that the user wishes to achieve in the future.

[0063] An "educational plan" refers to a plan that presents individualized learning content and schedules based on analyzed user ability and goal information.

[0064] A "display device" refers to a terminal or screen that users use to visually confirm information.

[0065] "Educational progress" refers to data that shows how far users have actually progressed in their learning based on their educational plan.

[0066] An "external education platform" refers to an external digital service that provides online learning content and materials accessible to users.

[0067] "Analysis" refers to the process of analyzing data based on information entered by users to generate appropriate educational plans.

[0068] This invention is a system designed to support efficient employee skill development within a company. The system functions primarily through interaction between servers, terminals, and users.

[0069] Users input their personal skills data and goal information using a terminal. Specifically, they use a GUI-based input form to enter their current skill level, desired skills, and career goals. The terminal structures this information in JSON format and sends it to the server using a secure protocol (e.g., HTTPS).

[0070] The server analyzes the received data. This analysis utilizes natural language processing techniques (e.g., spaCy) and machine learning algorithms (e.g., TENSORFLOW® and PyTorch). Based on this analysis, the server generates personalized training plans, comparing them with company-specific skill requirements and up-to-date industry information. Furthermore, it leverages APIs from external online education platforms (e.g., Coursera and Udemy) to integrate accompanying video materials and quiz-style learning tasks into the training plans.

[0071] The device displays the generated educational plan on a dashboard, allowing users to begin learning at any time. Educational progress is recorded in real time, and data on the user's progress and understanding is sent back to the server. The server analyzes this data and dynamically updates the educational plan as needed. This ensures that users always have access to the most appropriate educational content.

[0072] For example, if a user wants to improve their digital marketing skills, the system will provide video tutorials and quizzes related to the latest digital marketing technologies. An example of a prompt that utilizes a generative AI model is, "Generate an optimal educational plan based on the user's current data science skills and goals." This prompt is designed to allow the AI ​​model to generate customized educational content tailored to the specific user's needs.

[0073] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0074] Step 1:

[0075] Users input their skill data and goal information using a terminal. Specifically, they fill in their skill level, desired skills, and career goals in an input form via a GUI. The entered information is converted into JSON format. At this stage, the input is text information, and the output is structured data.

[0076] Step 2:

[0077] The terminal sends structured JSON data to the server using a secure protocol (e.g., HTTPS). This communication is encrypted to ensure data confidentiality. The input here is data in JSON format, and the output is the completion of data transmission to the server.

[0078] Step 3:

[0079] The server analyzes the received data. Specifically, it uses natural language processing techniques (e.g., spaCy) to syntactically parse the input information and machine learning algorithms (e.g., TensorFlow) to identify skill gaps. The input consists of user ability data and goal information, and the output is the analysis results.

[0080] Step 4:

[0081] The server generates personalized learning plans based on the analysis results. These plans include learning materials collected using APIs from external online education platforms (e.g., Coursera and Udemy). The input is the analysis results, and the output is the learning plan.

[0082] Step 5:

[0083] The terminal displays the educational plan received from the server on the user's dashboard. The user uses this dashboard to review the educational plan and begin learning. The input is the educational plan, and the output is the information presented to the user.

[0084] Step 6:

[0085] The device records the user's progress as they advance through the learning process. Specifically, this includes the learning materials used, completed modules, and quiz accuracy rates. The progress data is recorded in JSON format. The input is user behavior data, and the output is real-time updated progress information.

[0086] Step 7:

[0087] The server analyzes progress data and dynamically updates the educational plan using a generated AI model as needed. If the analysis reveals delays in skill acquisition, additional materials are suggested. The input is progress data, and the output is the updated educational plan.

[0088] (Application Example 1)

[0089] 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."

[0090] For employees and individuals to efficiently improve specific skills, personalized learning plans and timely feedback are crucial. However, current systems struggle to effectively provide real-time learning support and feedback, which leads to decreased learning efficiency.

[0091] 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.

[0092] In this invention, the server includes means for acquiring user ability data and goal information, means for generating personalized learning plans, and means for providing learning support via home-use automated devices and providing instruction and feedback in real time. This enables users to effectively progress through specific learning at their own pace while staying at home.

[0093] A "user" is an individual who uses the system to input ability data and goal information and receives a learning plan.

[0094] "Competency data" refers to information about the user's current skill level and job capabilities.

[0095] "Goal information" refers to information about the skills and career goals that the user wants to achieve.

[0096] A "learning plan" is a personalized learning program generated based on the user's ability data and goal information.

[0097] A "display device" is a device used to present learning plans and progress information to users.

[0098] "Learning progress" refers to information about the progress and level of understanding that users have achieved based on their learning plan.

[0099] "Household automated devices" refer to devices installed in homes that provide learning support, such as robots and digital assistants.

[0100] "Real-time" is a concept that refers to processing or information presentation taking place in real time.

[0101] The system for implementing this invention functions primarily through interaction between a server, a home automated device, and a user. The user utilizes a home automated device installed in their home to input ability data and goal information via voice or touchscreen. This includes the user's current ability level, desired skills, and career goals. The home automated device immediately transmits the input data to the server, ensuring the data is securely structured.

[0102] The server uses Python to analyze the received data using natural language processing techniques such as Spacy and BERT. The analysis process utilizes machine learning algorithms (e.g., Scikit-learn and TensorFlow) to generate personalized learning plans optimized for each user, comparing them with company skill requirements and up-to-date industry information. These plans include videos, question-and-answer materials, and reading comprehension materials.

[0103] The generated learning plan is sent from the server to a home automated device, allowing the user to begin learning at their own pace. The home automated device records the user's learning progress in real time and provides timely feedback. The progress data is then sent back to the server, which dynamically updates the learning plan based on this data.

[0104] As a concrete example, consider a scenario where a user inputs "I want to learn digital marketing." A home automation device could play a video on "social media strategy" and present questions in a quiz format, such as "What are the elements of a successful campaign?" An example of a prompt for a generative AI model would be, "Please generate a plan for a user to learn a new cooking skill. Please include the cooking category, the technologies used (videos, quizzes), and progress tracking."

[0105] This system allows users to efficiently improve their skills at home and enhance the quality of their learning through real-time feedback.

[0106] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0107] Step 1:

[0108] Users input their ability data and goal information using home-based automated devices. This input includes their current skill level, desired skills, and career goals, via voice recognition or touchscreen. The input information is structured by the home-based automated device and then sent to a server.

[0109] Step 2:

[0110] The server analyzes the received capability data and target information using natural language processing with Spacy and BERT. First, the data is converted to text format, and then skill mapping is performed based on machine learning algorithms. This prepares the server to generate the most suitable skill plan for the user.

[0111] Step 3:

[0112] The server generates individual learning plans using Scikit-learn and TensorFlow based on the analyzed data. These learning plans include engaging videos, quiz-style learning materials, and relevant reading materials. These materials are designed to be easy to learn in a short amount of time.

[0113] Step 4:

[0114] The server sends the generated learning plan to the home automated device. The home automated device then presents the received learning plan to the user through visual and audio interfaces. The user can decide when to start learning.

[0115] Step 5:

[0116] Users engage in learning activities based on their learning plan through home-based automated devices. These devices monitor the user's learning progress in real time, continuously recording their level of understanding and progress. This data is later transmitted to a server.

[0117] Step 6:

[0118] The server analyzes the received learning progress data and dynamically updates the learning plan as needed. This ensures that users receive the most up-to-date and optimal learning content. Furthermore, the progress data is visually presented to users and administrators, allowing them to understand their specific progress.

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

[0120] This invention provides a system that supports employees within a company in engaging in individually optimized learning, and can further enhance the learning experience, particularly by incorporating an emotion engine. The system operates through interaction between the server, terminals, and users, with each component working in coordination.

[0121] Users input their skill level and goal information using a device. Based on this initial input, the device organizes the data and prepares it for transmission to the server. After transmission, the server generates a customized learning plan for each user based on the received data. The plan includes videos, question-and-answer format materials, and reading comprehension materials, and the selection of these materials takes into account not only the user's ability data but also their emotional state as recognized by the emotion engine.

[0122] The emotion engine analyzes the user's voice and facial expression data to recognize their current emotional state. This information is sent to the server, and the learning plan is modified accordingly. For example, if the emotion engine determines that the user is tired, the server can switch to shorter, easier-to-understand learning materials.

[0123] The generated learning plan is presented to the user via the device. The user proceeds with their learning based on the presented content. Learning progress and changes in the user's emotions are recorded in real time by the device and sent back to the server.

[0124] The server continuously analyzes this progress information and sentiment data, dynamically updating the learning plan to maximize learning effectiveness. It also provides enterprise administrators and users with a dashboard that visualizes progress and sentiment status, allowing for a clear understanding of individual growth and learning effectiveness.

[0125] As a concrete example, suppose a user wants to improve their presentation skills, which they find difficult. If the emotional engine detects stress while the user is learning using the materials, the system will suggest temporarily switching from impractical materials to materials on easily applicable relaxation techniques. In this way, it is possible to alleviate the user's psychological burden while effectively supporting skill improvement.

[0126] The following describes the processing flow.

[0127] Step 1:

[0128] The user uses the device to input their ability data and goal information. The device formats this data and prepares it for transmission.

[0129] Step 2:

[0130] The terminal transmits the user's ability data and goal information to the server. The server receives the data, verifies its integrity, and then stores it in an analysis database.

[0131] Step 3:

[0132] The server uses natural language processing and machine learning algorithms to analyze the user's learning needs based on the received data. Based on the analysis results, it generates a personalized learning plan.

[0133] Step 4:

[0134] The emotion engine uses the user's voice information and facial expression data to recognize their current emotional state. The device then sends this emotional data to the server.

[0135] Step 5:

[0136] The server dynamically adjusts the learning plan to reflect the user's emotional state. For example, if the user is feeling anxious, the server will change the content to prioritize easier-to-understand material.

[0137] Step 6:

[0138] The device displays the updated learning plan to the user. The user then proceeds with their studies according to the new learning materials.

[0139] Step 7:

[0140] The device records the user's learning progress and updated sentiment state in real time and periodically sends this information to the server.

[0141] Step 8:

[0142] The server analyzes the transmitted data, makes any necessary adjustments, and provides progress and sentiment data to companies and users through a dashboard that visualizes these metrics. This allows for objective verification of the learning process's effectiveness.

[0143] (Example 2)

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

[0145] Modern companies require employees to learn efficiently according to their individual skill levels and goals. However, providing an optimized training plan for each employee while maintaining a learning environment that takes their emotional state into consideration is challenging. In particular, traditional systems do not adequately address the impact of employees' emotional states on learning effectiveness. Solving this problem is essential.

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

[0147] In this invention, the server includes means for acquiring the user's personal data and goal information, means for analyzing the user's voice and facial expression data to recognize their emotional state, and means for adjusting the educational plan based on the recognized emotional state. This makes it possible for the individualized educational plan to be dynamically optimized according to the emotional state.

[0148] "Personal data" refers to information about individual users, such as their skill level and learning history.

[0149] "Goal information" refers to information that indicates the specific learning or skill goals that the user wants to achieve.

[0150] An "educational plan" is a collection of learning materials and tasks that are generated based on the individual needs and skill levels of the users.

[0151] "Emotional state" refers to the user's current emotions and psychological state, and is analyzed from voice and facial expression data.

[0152] A "display device" is an electronic device used by users to view educational plans, and includes computers, tablets, and other similar devices.

[0153] "Progress" refers to the rate at which users are progressing in their learning activities and their level of achievement.

[0154] "Visually displaying" means presenting data and information to users and administrators in a graphical manner.

[0155] "Multimedia teaching materials" refer to educational content that utilizes a variety of media formats, such as video, audio, and text.

[0156] "Interactive learning materials" are materials that allow users to directly participate in and progress through the learning process, and include quizzes and simulations.

[0157] "Emotion recognition technology" is a technology that identifies emotions by analyzing the user's voice and facial expressions.

[0158] This invention is a system for providing an individualized learning environment within a company. Users input their skill level and learning goals into a terminal. The terminal can be a computer or tablet, and this information can be entered through a dedicated application. After the user inputs the information, the terminal organizes it and prepares it for transmission to the server.

[0159] The server receives information transmitted from the terminal. Based on this information, the server uses a generative AI model to create an individualized educational plan. Specifically, a generative AI model implemented using programming languages ​​such as Python or Java® analyzes the user's personal data and proposes an educational plan that combines multimedia and interactive teaching materials.

[0160] The user provides voice and facial expression data through the device's camera and microphone. The device collects this data and analyzes the user's emotional state using emotion recognition technology. For example, OpenCV could be used for face recognition and Librosa for voice analysis.

[0161] The server has the ability to dynamically adjust the learning plan based on emotional data. If a user experiences stress during learning, the server can detect this and change the learning plan to make it more effective and efficient in a shorter amount of time. This allows users to continue learning at their own pace while reducing stress.

[0162] The device presents the user with a tailored learning plan. The user learns based on the provided materials and manages their own progress. The device records learning progress and changes in the user's emotions in real time and sends this information to the server. The server continuously analyzes this information and readjusts the learning plan to maximize learning effectiveness.

[0163] This system can integrate progress information and emotional state, providing a visually displayed dashboard for administrators and users. For example, suppose a user wants to improve their presentation skills. In this case, the system can provide learning materials incorporating relaxation techniques based on the user's emotional state, thereby reducing the learning burden and supporting effective skill improvement.

[0164] Examples of prompts for generative AI models:

[0165] "Please create an effective learning plan to improve presentation skills, especially including relaxation techniques that can help users when they feel stressed."

[0166] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0167] Step 1:

[0168] Users operate a terminal to input their skill level and learning goals. The input data includes specific goals, such as improving marketing skills or mastering specific analytical methods. The terminal structures this user data and prepares it for transmission to the server. The input skill information and goal data are organized into a dataset to identify the user's learning needs.

[0169] Step 2:

[0170] The terminal sends organized user data to the server. The server receives this data and uses a generative AI model to create an optimized educational plan for the user. Specifically, an AI algorithm written in Python evaluates and analyzes the data and selects learning materials that match the user's goals. The input is the user's personal data, and the output is a personalized educational plan that includes appropriate learning materials and programs.

[0171] Step 3:

[0172] The user provides voice and facial expressions during the learning process through the camera and microphone connected to the device. The device uses emotion recognition technology to analyze voice patterns and facial movements to determine the user's emotional state. For example, it receives data such as whether the user is laughing or frowning as input and evaluates the emotional response. The output is information about the user's current emotional state.

[0173] Step 4:

[0174] The server adjusts the learning plan based on the analyzed emotional data. For example, if the server determines that a user is experiencing stress during learning, it will switch to materials that are easier and more relaxing to learn from. This process dynamically optimizes the learning plan, using the input emotional data to maximize learning effectiveness.

[0175] Step 5:

[0176] The device presents the user with an optimized learning plan. Specifically, it displays the learning materials to be used via pop-up notifications and a dashboard. The user continues learning using these materials and acquires the necessary knowledge. During this process, responses to the learning materials are recorded again as input data on the device and sent to the server.

[0177] Step 6:

[0178] The server continuously analyzes collected learning and sentiment data and visualizes progress information in a dashboard format. Administrators and users can use this visualized dashboard to evaluate learning effectiveness and determine the next course of action. Inputs are the user's progress and sentiment state, while outputs include performance summaries and improvement suggestions.

[0179] (Application Example 2)

[0180] 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".

[0181] There is a lack of systems that consider the impact of emotional changes and stress on learning efficiency when employees engage in learning tailored to their individual abilities and goals. Therefore, recognizing employees' emotional states in real time and adjusting learning plans accordingly is essential for improving the learning experience.

[0182] 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.

[0183] In this invention, the server includes means for acquiring user ability data and goal information, means for generating an individualized learning plan, and means for analyzing the user's emotional state using an emotion engine and adjusting the learning plan. This makes it possible to provide learning optimized for each user and to flexibly change the learning plan based on the emotional state.

[0184] "Users" refer to employees or individuals who utilize the learning system and are entities that aim to improve their own abilities by receiving the system's services.

[0185] "Ability data" refers to information that indicates a user's current skill level and level of knowledge, and is an element that is used as a foundation for learning.

[0186] "Target information" refers to information that shows the specific learning objectives and outcomes that users aim to achieve.

[0187] A "learning plan" is a customized plan based on the user's ability data and goal information, designed to guide them through their learning content and progress.

[0188] An "emotion engine" is a technology that infers a user's emotional state by analyzing their voice and facial expression data.

[0189] A "presentation device" is a device or apparatus that allows users to visually confirm generated learning plans and learning materials.

[0190] "Emotional state" refers to the psychological condition and emotions of a user, and is a factor that affects the efficiency of learning.

[0191] "Relaxation techniques" are methods and techniques used to reduce stress in users and create an environment where they can concentrate on learning.

[0192] A "manager" is a person or role responsible for overseeing the operation of the entire learning system and understanding the learning progress and emotional state of users.

[0193] To implement this invention, it is necessary to build a system that effectively coordinates between the server, terminal, and user. The server acquires the user's ability data and goal information and generates an individualized learning plan. In generating the learning plan, natural language processing and machine learning are utilized to select learning materials appropriate to the user's current skill level.

[0194] The emotion engine analyzes the user's voice and facial expression data acquired through the device to infer the user's emotional state. This makes it possible to adjust the learning content in a timely manner according to the user's emotional changes. Specifically, if high stress is detected as a factor that reduces learning efficiency, the server will instruct the system to switch to learning materials that include relaxation techniques.

[0195] Users review the learning plan generated through the display device and proceed with their learning. Data on learning progress and emotional state is transmitted to the server in real time for continuous analysis. Based on this data, the server dynamically updates the learning plan and continues to provide the most suitable learning materials.

[0196] A concrete example is a personal assistant robot supporting English language learning. In this case, the robot would engage in everyday conversation with the user, and if it senses that the user is feeling stressed, it would instruct the user to switch to learning materials in a game format that allow for more relaxed learning. An example of a prompt when using a generative AI model would be, "Based on the user's current state, please suggest learning materials that will help them relax."

[0197] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0198] Step 1:

[0199] The terminal receives user ability data and goal information as input. The user enters their skill level and learning goals into the terminal. This data is then ready to be sent to the server.

[0200] Step 2:

[0201] The server uses natural language processing and machine learning to analyze the received ability data and goal information. This analysis generates a personalized learning plan for each user, which includes learning materials such as videos and practice problems.

[0202] Step 3:

[0203] The device collects the user's voice and facial expression data as input to the emotion engine. As the user progresses through the learning process, voice and facial expressions are automatically recorded, and the data is ready to be sent to the server.

[0204] Step 4:

[0205] The server uses an emotion engine to analyze the received audio and facial expression data and recognizes the user's emotional state as output. This analysis determines whether the user is experiencing stress, and the learning plan is adjusted accordingly if necessary.

[0206] Step 5:

[0207] The server sends the adjusted learning plan to the terminal and presents it to the user through a display device. The user can then continue learning based on the newly suggested learning materials.

[0208] Step 6:

[0209] The device records learning progress data and refreshed sentiment data in real time and periodically sends them to the server. This allows for centralized management of the user's progress.

[0210] Step 7:

[0211] The server analyzes the collected progress information and sentiment data to dynamically update the next learning plan. This continuous updating maximizes the learning effect.

[0212] 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.

[0213] Data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of data generation model 58 is ChatGPT (registered trademark) (Internet search).<URL: https: / / openai.com / blog / chatgpt> ), Gemini (registered trademark) (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0214] 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.

[0215] [Second Embodiment]

[0216] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0217] 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.

[0218] 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).

[0219] 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.

[0220] 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.

[0221] 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).

[0222] 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.

[0223] 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.

[0224] 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.

[0225] 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.

[0226] 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.

[0227] 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".

[0228] Embodiments of the present invention are systems for efficiently supporting employee skill development within a company. The system functions primarily through interaction between servers, terminals, and users.

[0229] Users input their skill data and goal information on their device. This input includes their current skill level, skills they want to acquire in the future, and career goals. The device structures this data and sends it to the server.

[0230] The server analyzes the received data. This analysis utilizes natural language processing techniques and machine learning algorithms, generating a personalized learning plan for each user by comparing it with company-specific skill requirements and the latest industry information. The learning plan includes short, easy-to-learn videos, quiz-style materials, and reading materials to aid in deeper understanding.

[0231] The generated learning plan is sent from the server to the user's device and presented to the user so they can start learning at their preferred time. The device records the user's learning progress in real time and sends data such as achievement level and comprehension level back to the server.

[0232] The server analyzes the received learning progress data and dynamically updates the learning plan as needed. This ensures that each user continuously receives appropriate learning content based on the latest information. Furthermore, by comprehensively aggregating learning progress and results and providing this information visualized in a dashboard format to both the company and the user, the growth process can be clearly understood.

[0233] This system allows users to utilize their free time between tasks and learn effectively at their own pace. Furthermore, companies can centrally manage the skill development of their entire workforce, fostering competitive talent. For example, if a user wants to improve their digital marketing skills, this system provides video learning materials and related quizzes on the latest digital marketing technologies, and the learning plan is adjusted according to the user's needs.

[0234] The following describes the processing flow.

[0235] Step 1:

[0236] The user enters their skill level, career goals, areas of interest, etc., on the device. The device formats this data and saves it to a database for the next processing step.

[0237] Step 2:

[0238] The terminal sends formatted user data to the server. The server receives the data, performs an integrity check, and prepares for analysis.

[0239] Step 3:

[0240] The server begins analysis based on the received user data. Using natural language processing and machine learning algorithms, it generates a personalized learning plan that matches the user's skill requirements and the company's goals.

[0241] Step 4:

[0242] The server generates a learning plan and sends it to the user's device for review. The device displays the received learning plan in its user interface and prompts the user to begin learning.

[0243] Step 5:

[0244] The user progresses through the learning process on their device, recording their progress and results. The device continuously sends this data to the server, which then monitors their progress.

[0245] Step 6:

[0246] The server analyzes progress data sent from the terminal to assess learning achievement and make adjustments for the next step. It automatically updates the learning plan as needed, providing an optimized learning experience.

[0247] Step 7:

[0248] The server aggregates user learning progress and displays it on a dashboard in a format that is easy for both the company and the user to understand. This allows users to track their own progress, and the company to grasp overall skill improvement.

[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] To effectively support employee skill development within companies and organizations, it is necessary to develop and continuously manage training plans tailored to individual needs. However, traditional methods have made it difficult to provide training content appropriate for each user in real time, to flexibly modify plans as progress is made, and to visually grasp the results.

[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 acquiring user ability information and purpose information, means for analyzing the user ability information and purpose information and generating an individualized educational plan, and means for providing access to educational materials using an application of an external educational platform. This enables the generation of an appropriate educational plan tailored to each user's needs and flexible updating of the plan according to progress.

[0254] "User" refers to an individual or member of an organization who uses the system to improve their own capabilities.

[0255] "Capability information" refers to data about the level of technology, knowledge, and skills that a user currently possesses.

[0256] "Purpose information" refers to information about the abilities, skills, or career goals that the user wishes to achieve in the future.

[0257] An "educational plan" refers to a plan that presents individualized learning content and schedules based on analyzed user ability and goal information.

[0258] A "display device" refers to a terminal or screen that users use to visually confirm information.

[0259] "Educational progress" refers to data that shows how far users have actually progressed in their learning based on their educational plan.

[0260] An "external education platform" refers to an external digital service that provides online learning content and materials accessible to users.

[0261] "Analysis" refers to the process of analyzing data based on information entered by users to generate appropriate educational plans.

[0262] This invention is a system designed to support efficient employee skill development within a company. The system functions primarily through interaction between servers, terminals, and users.

[0263] Users input their personal skills data and goal information using a terminal. Specifically, they use a GUI-based input form to enter their current skill level, desired skills, and career goals. The terminal structures this information in JSON format and sends it to the server using a secure protocol (e.g., HTTPS).

[0264] The server analyzes the received data. This analysis utilizes natural language processing techniques (e.g., spaCy) and machine learning algorithms (e.g., TensorFlow and PyTorch). Based on this analysis, the server generates personalized training plans, comparing them with company-specific skill requirements and up-to-date industry information. Furthermore, it leverages APIs from external online education platforms (e.g., Coursera and Udemy) to integrate supplementary video materials and quiz-style learning tasks into the training plans.

[0265] The device displays the generated educational plan on a dashboard, allowing users to begin learning at any time. Educational progress is recorded in real time, and data on the user's progress and understanding is sent back to the server. The server analyzes this data and dynamically updates the educational plan as needed. This ensures that users always have access to the most appropriate educational content.

[0266] For example, if a user wants to improve their digital marketing skills, the system will provide video tutorials and quizzes related to the latest digital marketing technologies. An example of a prompt that utilizes a generative AI model is, "Generate an optimal educational plan based on the user's current data science skills and goals." This prompt is designed to allow the AI ​​model to generate customized educational content tailored to the specific user's needs.

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

[0268] Step 1:

[0269] Users input their skill data and goal information using a terminal. Specifically, they fill in their skill level, desired skills, and career goals in an input form via a GUI. The entered information is converted into JSON format. At this stage, the input is text information, and the output is structured data.

[0270] Step 2:

[0271] The terminal sends structured JSON data to the server using a secure protocol (e.g., HTTPS). This communication is encrypted to ensure data confidentiality. The input here is data in JSON format, and the output is the completion of data transmission to the server.

[0272] Step 3:

[0273] The server analyzes the received data. Specifically, it uses natural language processing techniques (e.g., spaCy) to syntactically parse the input information and machine learning algorithms (e.g., TensorFlow) to identify skill gaps. The input consists of user ability data and goal information, and the output is the analysis results.

[0274] Step 4:

[0275] The server generates personalized learning plans based on the analysis results. These plans include learning materials collected using APIs from external online education platforms (e.g., Coursera and Udemy). The input is the analysis results, and the output is the learning plan.

[0276] Step 5:

[0277] The terminal displays the educational plan received from the server on the user's dashboard. The user uses this dashboard to review the educational plan and begin learning. The input is the educational plan, and the output is the information presented to the user.

[0278] Step 6:

[0279] The device records the user's progress as they advance through the learning process. Specifically, this includes the learning materials used, completed modules, and quiz accuracy rates. The progress data is recorded in JSON format. The input is user behavior data, and the output is real-time updated progress information.

[0280] Step 7:

[0281] The server analyzes progress data and dynamically updates the educational plan using a generated AI model as needed. If the analysis reveals delays in skill acquisition, additional materials are suggested. The input is progress data, and the output is the updated educational plan.

[0282] (Application Example 1)

[0283] Next, Application Example 1 will be described. In the following description, the data processing device 12 is referred to as a "server", and the smart glasses 214 are referred to as a "terminal".

[0284] In order for employees and individuals to efficiently improve specific skills, individualized learning plans and timely feedback are important. However, the current system has a problem in that it is difficult to effectively provide real-time learning support and feedback, resulting in a decrease in learning efficiency.

[0285] The specific processing by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0286] In this invention, the server includes means for acquiring the user's ability data and target information, means for generating an individualized learning plan, and means for implementing learning support via home appliances and providing guidance and feedback in real time. As a result, the user can effectively proceed with specific learning at their own pace while staying at home.

[0287] A "user" is an individual who inputs ability data and target information using the system and receives a learning plan.

[0288] "Ability data" is information regarding the user's current skill level and job ability.

[0289] "Target information" is information regarding the skills and career goals that the user wishes to achieve.

[0290] A "learning plan" is an individualized learning program generated based on the user's ability data and target information.

[0291] A "display device" is a device for presenting a learning plan and progress information to the user.

[0292] "Learning progress" refers to information about the progress and level of understanding that users have achieved based on their learning plan.

[0293] "Household automated devices" refer to devices installed in homes that provide learning support, such as robots and digital assistants.

[0294] "Real-time" is a concept that refers to processing or information presentation taking place in real time.

[0295] The system for implementing this invention functions primarily through interaction between a server, a home automated device, and a user. The user utilizes a home automated device installed in their home to input ability data and goal information via voice or touchscreen. This includes the user's current ability level, desired skills, and career goals. The home automated device immediately transmits the input data to the server, ensuring the data is securely structured.

[0296] The server uses Python to analyze the received data using natural language processing techniques such as Spacy and BERT. The analysis process utilizes machine learning algorithms (e.g., Scikit-learn and TensorFlow) to generate personalized learning plans optimized for each user, comparing them with company skill requirements and up-to-date industry information. These plans include videos, question-and-answer materials, and reading comprehension materials.

[0297] The generated learning plan is sent from the server to a home automated device, allowing the user to begin learning at their own pace. The home automated device records the user's learning progress in real time and provides timely feedback. The progress data is then sent back to the server, which dynamically updates the learning plan based on this data.

[0298] As a concrete example, consider a scenario where a user inputs "I want to learn digital marketing." A home automation device could play a video on "social media strategy" and present questions in a quiz format, such as "What are the elements of a successful campaign?" An example of a prompt for a generative AI model would be, "Please generate a plan for a user to learn a new cooking skill. Please include the cooking category, the technologies used (videos, quizzes), and progress tracking."

[0299] This system allows users to efficiently improve their skills at home and enhance the quality of their learning through real-time feedback.

[0300] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0301] Step 1:

[0302] Users input their ability data and goal information using home-based automated devices. This input includes their current skill level, desired skills, and career goals, via voice recognition or touchscreen. The input information is structured by the home-based automated device and then sent to a server.

[0303] Step 2:

[0304] The server analyzes the received capability data and target information using natural language processing with Spacy and BERT. First, the data is converted to text format, and then skill mapping is performed based on machine learning algorithms. This prepares the server to generate the most suitable skill plan for the user.

[0305] Step 3:

[0306] Based on the analyzed data, the server generates an individual learning plan using Scikit-learn or TensorFlow. The learning plan includes videos that attract the user's interest, teaching materials in quiz format, and related reading comprehension materials. These teaching materials are designed to be easy to learn in a short time.

[0307] Step 4:

[0308] The server sends the generated learning plan to the home automation device. The home automation device presents the received learning plan to the user as it is through the visual and audio interfaces. The user can decide on their own timing to start learning.

[0309] Step 5:

[0310] The user conducts learning activities based on the learning plan through the home automation device. The home automation device monitors the user's learning progress in real-time and sequentially records the learning comprehension and progress. This data is later sent to the server.

[0311] Step 6:

[0312] The server analyzes the received learning progress data and dynamically updates the learning plan as needed. This maintains the system to provide the user with optimal learning content based on the latest information. Also, the progress data is visually presented to the user and the administrator so that the specific growth situation can be grasped.

[0313] Furthermore, an emotion engine for estimating the user's emotion may be combined. That is, the specific processing unit 290 may estimate the user's emotion using the emotion identification model 59 and perform specific processing using the user's emotion.

[0314] This invention provides a system that supports employees within a company in engaging in individually optimized learning, and can further enhance the learning experience, particularly by incorporating an emotion engine. The system operates through interaction between the server, terminals, and users, with each component working in coordination.

[0315] Users input their skill level and goal information using a device. Based on this initial input, the device organizes the data and prepares it for transmission to the server. After transmission, the server generates a customized learning plan for each user based on the received data. The plan includes videos, question-and-answer format materials, and reading comprehension materials, and the selection of these materials takes into account not only the user's ability data but also their emotional state as recognized by the emotion engine.

[0316] The emotion engine analyzes the user's voice and facial expression data to recognize their current emotional state. This information is sent to the server, and the learning plan is modified accordingly. For example, if the emotion engine determines that the user is tired, the server can switch to shorter, easier-to-understand learning materials.

[0317] The generated learning plan is presented to the user via the device. The user proceeds with their learning based on the presented content. Learning progress and changes in the user's emotions are recorded in real time by the device and sent back to the server.

[0318] The server continuously analyzes this progress information and sentiment data, dynamically updating the learning plan to maximize learning effectiveness. It also provides enterprise administrators and users with a dashboard that visualizes progress and sentiment status, allowing for a clear understanding of individual growth and learning effectiveness.

[0319] As a concrete example, suppose a user wants to improve their presentation skills, which they find difficult. If the emotional engine detects stress while the user is learning using the materials, the system will suggest temporarily switching from impractical materials to materials on easily applicable relaxation techniques. In this way, it is possible to alleviate the user's psychological burden while effectively supporting skill improvement.

[0320] The following describes the processing flow.

[0321] Step 1:

[0322] The user uses the device to input their ability data and goal information. The device formats this data and prepares it for transmission.

[0323] Step 2:

[0324] The terminal transmits the user's ability data and goal information to the server. The server receives the data, verifies its integrity, and then stores it in an analysis database.

[0325] Step 3:

[0326] The server uses natural language processing and machine learning algorithms to analyze the user's learning needs based on the received data. Based on the analysis results, it generates a personalized learning plan.

[0327] Step 4:

[0328] The emotion engine uses the user's voice information and facial expression data to recognize their current emotional state. The device then sends this emotional data to the server.

[0329] Step 5:

[0330] The server dynamically adjusts the learning plan to reflect the user's emotional state. For example, if the user is feeling anxious, the server will change the content to prioritize easier-to-understand material.

[0331] Step 6:

[0332] The device displays the updated learning plan to the user. The user then proceeds with their studies according to the new learning materials.

[0333] Step 7:

[0334] The device records the user's learning progress and updated sentiment state in real time and periodically sends this information to the server.

[0335] Step 8:

[0336] The server analyzes the transmitted data, makes any necessary adjustments, and provides progress and sentiment data to companies and users through a dashboard that visualizes these metrics. This allows for objective verification of the learning process's effectiveness.

[0337] (Example 2)

[0338] 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".

[0339] Modern companies require employees to learn efficiently according to their individual skill levels and goals. However, providing an optimized training plan for each employee while maintaining a learning environment that takes their emotional state into consideration is challenging. In particular, traditional systems do not adequately address the impact of employees' emotional states on learning effectiveness. Solving this problem is essential.

[0340] 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.

[0341] In this invention, the server includes means for acquiring the user's personal data and goal information, means for analyzing the user's voice and facial expression data to recognize their emotional state, and means for adjusting the educational plan based on the recognized emotional state. This makes it possible for the individualized educational plan to be dynamically optimized according to the emotional state.

[0342] "Personal data" refers to information about individual users, such as their skill level and learning history.

[0343] "Goal information" refers to information that indicates the specific learning or skill goals that the user wants to achieve.

[0344] An "educational plan" is a collection of learning materials and tasks that are generated based on the individual needs and skill levels of the users.

[0345] "Emotional state" refers to the user's current emotions and psychological state, and is analyzed from voice and facial expression data.

[0346] A "display device" is an electronic device used by users to view educational plans, and includes computers, tablets, and other similar devices.

[0347] "Progress" refers to the rate at which users are progressing in their learning activities and their level of achievement.

[0348] "Visually displaying" means presenting data and information to users and administrators in a graphical manner.

[0349] "Multimedia teaching materials" refer to educational content that utilizes a variety of media formats, such as video, audio, and text.

[0350] "Interactive learning materials" are materials that allow users to directly participate in and progress through the learning process, and include quizzes and simulations.

[0351] "Emotion recognition technology" is a technology that identifies emotions by analyzing the user's voice and facial expressions.

[0352] This invention is a system for providing an individualized learning environment within a company. Users input their skill level and learning goals into a terminal. The terminal can be a computer or tablet, and this information can be entered through a dedicated application. After the user inputs the information, the terminal organizes it and prepares it for transmission to the server.

[0353] The server receives information transmitted from the terminal. Based on this information, the server uses a generative AI model to create an individualized educational plan. Specifically, a generative AI model implemented using programming languages ​​such as Python or Java analyzes the user's personal data and proposes an educational plan that combines multimedia and interactive learning materials.

[0354] The user provides voice and facial expression data through the device's camera and microphone. The device collects this data and analyzes the user's emotional state using emotion recognition technology. For example, OpenCV could be used for face recognition and Librosa for voice analysis.

[0355] The server has the ability to dynamically adjust the learning plan based on emotional data. If a user experiences stress during learning, the server can detect this and change the learning plan to make it more effective and efficient in a shorter amount of time. This allows users to continue learning at their own pace while reducing stress.

[0356] The device presents the user with a tailored learning plan. The user learns based on the provided materials and manages their own progress. The device records learning progress and changes in the user's emotions in real time and sends this information to the server. The server continuously analyzes this information and readjusts the learning plan to maximize learning effectiveness.

[0357] This system can integrate progress information and emotional state, providing a visually displayed dashboard for administrators and users. For example, suppose a user wants to improve their presentation skills. In this case, the system can provide learning materials incorporating relaxation techniques based on the user's emotional state, thereby reducing the learning burden and supporting effective skill improvement.

[0358] Examples of prompts for generative AI models:

[0359] "Please create an effective learning plan to improve presentation skills, especially including relaxation techniques that can help users when they feel stressed."

[0360] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0361] Step 1:

[0362] Users operate a terminal to input their skill level and learning goals. The input data includes specific goals, such as improving marketing skills or mastering specific analytical methods. The terminal structures this user data and prepares it for transmission to the server. The input skill information and goal data are organized into a dataset to identify the user's learning needs.

[0363] Step 2:

[0364] The terminal sends organized user data to the server. The server receives this data and uses a generative AI model to create an optimized educational plan for the user. Specifically, an AI algorithm written in Python evaluates and analyzes the data and selects learning materials that match the user's goals. The input is the user's personal data, and the output is a personalized educational plan that includes appropriate learning materials and programs.

[0365] Step 3:

[0366] The user provides voice and facial expressions during the learning process through the camera and microphone connected to the device. The device uses emotion recognition technology to analyze voice patterns and facial movements to determine the user's emotional state. For example, it receives data such as whether the user is laughing or frowning as input and evaluates the emotional response. The output is information about the user's current emotional state.

[0367] Step 4:

[0368] The server adjusts the learning plan based on the analyzed emotional data. For example, if the server determines that a user is experiencing stress during learning, it will switch to materials that are easier and more relaxing to learn from. This process dynamically optimizes the learning plan, using the input emotional data to maximize learning effectiveness.

[0369] Step 5:

[0370] The device presents the user with an optimized learning plan. Specifically, it displays the learning materials to be used via pop-up notifications and a dashboard. The user continues learning using these materials and acquires the necessary knowledge. During this process, responses to the learning materials are recorded again as input data on the device and sent to the server.

[0371] Step 6:

[0372] The server continuously analyzes collected learning and sentiment data and visualizes progress information in a dashboard format. Administrators and users can use this visualized dashboard to evaluate learning effectiveness and determine the next course of action. Inputs are the user's progress and sentiment state, while outputs include performance summaries and improvement suggestions.

[0373] (Application Example 2)

[0374] 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."

[0375] There is a lack of systems that consider the impact of emotional changes and stress on learning efficiency when employees engage in learning tailored to their individual abilities and goals. Therefore, recognizing employees' emotional states in real time and adjusting learning plans accordingly is essential for improving the learning experience.

[0376] 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.

[0377] In this invention, the server includes means for acquiring user ability data and goal information, means for generating an individualized learning plan, and means for analyzing the user's emotional state using an emotion engine and adjusting the learning plan. This makes it possible to provide learning optimized for each user and to flexibly change the learning plan based on the emotional state.

[0378] "Users" refer to employees or individuals who utilize the learning system and are entities that aim to improve their own abilities by receiving the system's services.

[0379] "Ability data" refers to information that indicates a user's current skill level and level of knowledge, and is an element that is used as a foundation for learning.

[0380] "Target information" refers to information that shows the specific learning objectives and outcomes that users aim to achieve.

[0381] A "learning plan" is a customized plan based on the user's ability data and goal information, designed to guide them through their learning content and progress.

[0382] An "emotion engine" is a technology that infers a user's emotional state by analyzing their voice and facial expression data.

[0383] A "presentation device" is a device or apparatus that allows users to visually confirm generated learning plans and learning materials.

[0384] "Emotional state" refers to the psychological condition and emotions of a user, and is a factor that affects the efficiency of learning.

[0385] "Relaxation techniques" are methods and techniques used to reduce stress in users and create an environment where they can concentrate on learning.

[0386] A "manager" is a person or role responsible for overseeing the operation of the entire learning system and understanding the learning progress and emotional state of users.

[0387] To implement this invention, it is necessary to build a system that effectively coordinates between the server, terminal, and user. The server acquires the user's ability data and goal information and generates an individualized learning plan. In generating the learning plan, natural language processing and machine learning are utilized to select learning materials appropriate to the user's current skill level.

[0388] The emotion engine analyzes the user's voice and facial expression data acquired through the device to infer the user's emotional state. This makes it possible to adjust the learning content in a timely manner according to the user's emotional changes. Specifically, if high stress is detected as a factor that reduces learning efficiency, the server will instruct the system to switch to learning materials that include relaxation techniques.

[0389] Users review the learning plan generated through the display device and proceed with their learning. Data on learning progress and emotional state is transmitted to the server in real time for continuous analysis. Based on this data, the server dynamically updates the learning plan and continues to provide the most suitable learning materials.

[0390] A concrete example is a personal assistant robot supporting English language learning. In this case, the robot would engage in everyday conversation with the user, and if it senses that the user is feeling stressed, it would instruct the user to switch to learning materials in a game format that allow for more relaxed learning. An example of a prompt when using a generative AI model would be, "Based on the user's current state, please suggest learning materials that will help them relax."

[0391] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0392] Step 1:

[0393] The terminal receives user ability data and goal information as input. The user enters their skill level and learning goals into the terminal. This data is then ready to be sent to the server.

[0394] Step 2:

[0395] The server uses natural language processing and machine learning to analyze the received ability data and goal information. This analysis generates a personalized learning plan for each user, which includes learning materials such as videos and practice problems.

[0396] Step 3:

[0397] The device collects the user's voice and facial expression data as input to the emotion engine. As the user progresses through the learning process, voice and facial expressions are automatically recorded, and the data is ready to be sent to the server.

[0398] Step 4:

[0399] The server uses an emotion engine to analyze the received audio and facial expression data and recognizes the user's emotional state as output. This analysis determines whether the user is experiencing stress, and the learning plan is adjusted accordingly if necessary.

[0400] Step 5:

[0401] The server sends the adjusted learning plan to the terminal and presents it to the user through a display device. The user can then continue learning based on the newly suggested learning materials.

[0402] Step 6:

[0403] The device records learning progress data and refreshed sentiment data in real time and periodically sends them to the server. This allows for centralized management of the user's progress.

[0404] Step 7:

[0405] The server analyzes the collected progress information and sentiment data to dynamically update the next learning plan. This continuous updating maximizes the learning effect.

[0406] 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.

[0407] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). One example of data generation model 58 is ChatGPT (Internet search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0408] 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.

[0409] [Third Embodiment]

[0410] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0411] 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.

[0412] 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).

[0413] 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.

[0414] 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.

[0415] 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).

[0416] 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.

[0417] 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.

[0418] 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.

[0419] 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.

[0420] 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.

[0421] 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".

[0422] Embodiments of the present invention are systems for efficiently supporting employee skill development within a company. The system functions primarily through interaction between servers, terminals, and users.

[0423] Users input their skill data and goal information on their device. This input includes their current skill level, skills they want to acquire in the future, and career goals. The device structures this data and sends it to the server.

[0424] The server analyzes the received data. This analysis utilizes natural language processing techniques and machine learning algorithms, generating a personalized learning plan for each user by comparing it with company-specific skill requirements and the latest industry information. The learning plan includes short, easy-to-learn videos, quiz-style materials, and reading materials to aid in deeper understanding.

[0425] The generated learning plan is sent from the server to the user's device and presented to the user so they can start learning at their preferred time. The device records the user's learning progress in real time and sends data such as achievement level and comprehension level back to the server.

[0426] The server analyzes the received learning progress data and dynamically updates the learning plan as needed. This ensures that each user continuously receives appropriate learning content based on the latest information. Furthermore, by comprehensively aggregating learning progress and results and providing this information visualized in a dashboard format to both the company and the user, the growth process can be clearly understood.

[0427] This system allows users to utilize their free time between tasks and learn effectively at their own pace. Furthermore, companies can centrally manage the skill development of their entire workforce, fostering competitive talent. For example, if a user wants to improve their digital marketing skills, this system provides video learning materials and related quizzes on the latest digital marketing technologies, and the learning plan is adjusted according to the user's needs.

[0428] The following describes the processing flow.

[0429] Step 1:

[0430] The user enters their skill level, career goals, areas of interest, etc., on the device. The device formats this data and saves it to a database for the next processing step.

[0431] Step 2:

[0432] The terminal sends formatted user data to the server. The server receives the data, performs an integrity check, and prepares for analysis.

[0433] Step 3:

[0434] The server begins analysis based on the received user data. Using natural language processing and machine learning algorithms, it generates a personalized learning plan that matches the user's skill requirements and the company's goals.

[0435] Step 4:

[0436] The server generates a learning plan and sends it to the user's device for review. The device displays the received learning plan in its user interface and prompts the user to begin learning.

[0437] Step 5:

[0438] The user progresses through the learning process on their device, recording their progress and results. The device continuously sends this data to the server, which then monitors their progress.

[0439] Step 6:

[0440] The server analyzes progress data sent from the terminal to assess learning achievement and make adjustments for the next step. It automatically updates the learning plan as needed, providing an optimized learning experience.

[0441] Step 7:

[0442] The server aggregates user learning progress and displays it on a dashboard in a format that is easy for both the company and the user to understand. This allows users to track their own progress, and the company to grasp overall skill improvement.

[0443] (Example 1)

[0444] 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."

[0445] To effectively support employee skill development within companies and organizations, it is necessary to develop and continuously manage training plans tailored to individual needs. However, traditional methods have made it difficult to provide training content appropriate for each user in real time, to flexibly modify plans as progress is made, and to visually grasp the results.

[0446] 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.

[0447] In this invention, the server includes means for acquiring user ability information and purpose information, means for analyzing the user ability information and purpose information and generating an individualized educational plan, and means for providing access to educational materials using an application of an external educational platform. This enables the generation of an appropriate educational plan tailored to each user's needs and flexible updating of the plan according to progress.

[0448] "User" refers to an individual or member of an organization who uses the system to improve their own capabilities.

[0449] "Capability information" refers to data about the level of technology, knowledge, and skills that a user currently possesses.

[0450] "Purpose information" refers to information about the abilities, skills, or career goals that the user wishes to achieve in the future.

[0451] An "educational plan" refers to a plan that presents individualized learning content and schedules based on analyzed user ability and goal information.

[0452] A "display device" refers to a terminal or screen that users use to visually confirm information.

[0453] "Educational progress" refers to data that shows how far users have actually progressed in their learning based on their educational plan.

[0454] An "external education platform" refers to an external digital service that provides online learning content and materials accessible to users.

[0455] "Analysis" refers to the process of analyzing data based on information entered by users to generate appropriate educational plans.

[0456] This invention is a system designed to support efficient employee skill development within a company. The system functions primarily through interaction between servers, terminals, and users.

[0457] Users input their personal skills data and goal information using a terminal. Specifically, they use a GUI-based input form to enter their current skill level, desired skills, and career goals. The terminal structures this information in JSON format and sends it to the server using a secure protocol (e.g., HTTPS).

[0458] The server analyzes the received data. This analysis utilizes natural language processing techniques (e.g., spaCy) and machine learning algorithms (e.g., TensorFlow and PyTorch). Based on this analysis, the server generates personalized training plans, comparing them with company-specific skill requirements and up-to-date industry information. Furthermore, it leverages APIs from external online education platforms (e.g., Coursera and Udemy) to integrate supplementary video materials and quiz-style learning tasks into the training plans.

[0459] The device displays the generated educational plan on a dashboard, allowing users to begin learning at any time. Educational progress is recorded in real time, and data on the user's progress and understanding is sent back to the server. The server analyzes this data and dynamically updates the educational plan as needed. This ensures that users always have access to the most appropriate educational content.

[0460] For example, if a user wants to improve their digital marketing skills, the system will provide video tutorials and quizzes related to the latest digital marketing technologies. An example of a prompt that utilizes a generative AI model is, "Generate an optimal educational plan based on the user's current data science skills and goals." This prompt is designed to allow the AI ​​model to generate customized educational content tailored to the specific user's needs.

[0461] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0462] Step 1:

[0463] Users input their skill data and goal information using a terminal. Specifically, they fill in their skill level, desired skills, and career goals in an input form via a GUI. The entered information is converted into JSON format. At this stage, the input is text information, and the output is structured data.

[0464] Step 2:

[0465] The terminal sends structured JSON data to the server using a secure protocol (e.g., HTTPS). This communication is encrypted to ensure data confidentiality. The input here is data in JSON format, and the output is the completion of data transmission to the server.

[0466] Step 3:

[0467] The server analyzes the received data. Specifically, it uses natural language processing techniques (e.g., spaCy) to syntactically parse the input information and machine learning algorithms (e.g., TensorFlow) to identify skill gaps. The input consists of user ability data and goal information, and the output is the analysis results.

[0468] Step 4:

[0469] The server generates personalized learning plans based on the analysis results. These plans include learning materials collected using APIs from external online education platforms (e.g., Coursera and Udemy). The input is the analysis results, and the output is the learning plan.

[0470] Step 5:

[0471] The terminal displays the educational plan received from the server on the user's dashboard. The user uses this dashboard to review the educational plan and begin learning. The input is the educational plan, and the output is the information presented to the user.

[0472] Step 6:

[0473] The device records the user's progress as they advance through the learning process. Specifically, this includes the learning materials used, completed modules, and quiz accuracy rates. The progress data is recorded in JSON format. The input is user behavior data, and the output is real-time updated progress information.

[0474] Step 7:

[0475] The server analyzes progress data and dynamically updates the educational plan using a generated AI model as needed. If the analysis reveals delays in skill acquisition, additional materials are suggested. The input is progress data, and the output is the updated educational plan.

[0476] (Application Example 1)

[0477] 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."

[0478] For employees and individuals to efficiently improve specific skills, personalized learning plans and timely feedback are crucial. However, current systems struggle to effectively provide real-time learning support and feedback, which leads to decreased learning efficiency.

[0479] 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.

[0480] In this invention, the server includes means for acquiring user ability data and goal information, means for generating personalized learning plans, and means for providing learning support via home-use automated devices and providing instruction and feedback in real time. This enables users to effectively progress through specific learning at their own pace while staying at home.

[0481] A "user" is an individual who uses the system to input ability data and goal information and receives a learning plan.

[0482] "Competency data" refers to information about the user's current skill level and job capabilities.

[0483] "Goal information" refers to information about the skills and career goals that the user wants to achieve.

[0484] A "learning plan" is a personalized learning program generated based on the user's ability data and goal information.

[0485] A "display device" is a device used to present learning plans and progress information to users.

[0486] "Learning progress" refers to information about the progress and level of understanding that users have achieved based on their learning plan.

[0487] "Household automated devices" refer to devices installed in homes that provide learning support, such as robots and digital assistants.

[0488] "Real-time" is a concept that refers to processing or information presentation taking place in real time.

[0489] The system for implementing this invention functions primarily through interaction between a server, a home automated device, and a user. The user utilizes a home automated device installed in their home to input ability data and goal information via voice or touchscreen. This includes the user's current ability level, desired skills, and career goals. The home automated device immediately transmits the input data to the server, ensuring the data is securely structured.

[0490] The server uses Python to analyze the received data using natural language processing techniques such as Spacy and BERT. The analysis process utilizes machine learning algorithms (e.g., Scikit-learn and TensorFlow) to generate personalized learning plans optimized for each user, comparing them with company skill requirements and up-to-date industry information. These plans include videos, question-and-answer materials, and reading comprehension materials.

[0491] The generated learning plan is sent from the server to a home automated device, allowing the user to begin learning at their own pace. The home automated device records the user's learning progress in real time and provides timely feedback. The progress data is then sent back to the server, which dynamically updates the learning plan based on this data.

[0492] As a concrete example, consider a scenario where a user inputs "I want to learn digital marketing." A home automation device could play a video on "social media strategy" and present questions in a quiz format, such as "What are the elements of a successful campaign?" An example of a prompt for a generative AI model would be, "Please generate a plan for a user to learn a new cooking skill. Please include the cooking category, the technologies used (videos, quizzes), and progress tracking."

[0493] This system allows users to efficiently improve their skills at home and enhance the quality of their learning through real-time feedback.

[0494] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0495] Step 1:

[0496] Users input their ability data and goal information using home-based automated devices. This input includes their current skill level, desired skills, and career goals, via voice recognition or touchscreen. The input information is structured by the home-based automated device and then sent to a server.

[0497] Step 2:

[0498] The server analyzes the received capability data and target information using natural language processing with Spacy and BERT. First, the data is converted to text format, and then skill mapping is performed based on machine learning algorithms. This prepares the server to generate the most suitable skill plan for the user.

[0499] Step 3:

[0500] The server generates individual learning plans using Scikit-learn and TensorFlow based on the analyzed data. These learning plans include engaging videos, quiz-style learning materials, and relevant reading materials. These materials are designed to be easy to learn in a short amount of time.

[0501] Step 4:

[0502] The server sends the generated learning plan to the home automated device. The home automated device then presents the received learning plan to the user through visual and audio interfaces. The user can decide when to start learning.

[0503] Step 5:

[0504] Users engage in learning activities based on their learning plan through home-based automated devices. These devices monitor the user's learning progress in real time, continuously recording their level of understanding and progress. This data is later transmitted to a server.

[0505] Step 6:

[0506] The server analyzes the received learning progress data and dynamically updates the learning plan as needed. This ensures that users receive the most up-to-date and optimal learning content. Furthermore, the progress data is visually presented to users and administrators, allowing them to understand their specific progress.

[0507] 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.

[0508] This invention provides a system that supports employees within a company in engaging in individually optimized learning, and can further enhance the learning experience, particularly by incorporating an emotion engine. The system operates through interaction between the server, terminals, and users, with each component working in coordination.

[0509] Users input their skill level and goal information using a device. Based on this initial input, the device organizes the data and prepares it for transmission to the server. After transmission, the server generates a customized learning plan for each user based on the received data. The plan includes videos, question-and-answer format materials, and reading comprehension materials, and the selection of these materials takes into account not only the user's ability data but also their emotional state as recognized by the emotion engine.

[0510] The emotion engine analyzes the user's voice and facial expression data to recognize their current emotional state. This information is sent to the server, and the learning plan is modified accordingly. For example, if the emotion engine determines that the user is tired, the server can switch to shorter, easier-to-understand learning materials.

[0511] The generated learning plan is presented to the user via the device. The user proceeds with their learning based on the presented content. Learning progress and changes in the user's emotions are recorded in real time by the device and sent back to the server.

[0512] The server continuously analyzes this progress information and sentiment data, dynamically updating the learning plan to maximize learning effectiveness. It also provides enterprise administrators and users with a dashboard that visualizes progress and sentiment status, allowing for a clear understanding of individual growth and learning effectiveness.

[0513] As a concrete example, suppose a user wants to improve their presentation skills, which they find difficult. If the emotional engine detects stress while the user is learning using the materials, the system will suggest temporarily switching from impractical materials to materials on easily applicable relaxation techniques. In this way, it is possible to alleviate the user's psychological burden while effectively supporting skill improvement.

[0514] The following describes the processing flow.

[0515] Step 1:

[0516] The user uses the device to input their ability data and goal information. The device formats this data and prepares it for transmission.

[0517] Step 2:

[0518] The terminal transmits the user's ability data and goal information to the server. The server receives the data, verifies its integrity, and then stores it in an analysis database.

[0519] Step 3:

[0520] The server uses natural language processing and machine learning algorithms to analyze the user's learning needs based on the received data. Based on the analysis results, it generates a personalized learning plan.

[0521] Step 4:

[0522] The emotion engine uses the user's voice information and facial expression data to recognize their current emotional state. The device then sends this emotional data to the server.

[0523] Step 5:

[0524] The server dynamically adjusts the learning plan to reflect the user's emotional state. For example, if the user is feeling anxious, the server will change the content to prioritize easier-to-understand material.

[0525] Step 6:

[0526] The device displays the updated learning plan to the user. The user then proceeds with their studies according to the new learning materials.

[0527] Step 7:

[0528] The device records the user's learning progress and updated sentiment state in real time and periodically sends this information to the server.

[0529] Step 8:

[0530] The server analyzes the transmitted data, makes any necessary adjustments, and provides progress and sentiment data to companies and users through a dashboard that visualizes these metrics. This allows for objective verification of the learning process's effectiveness.

[0531] (Example 2)

[0532] 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."

[0533] Modern companies require employees to learn efficiently according to their individual skill levels and goals. However, providing an optimized training plan for each employee while maintaining a learning environment that takes their emotional state into consideration is challenging. In particular, traditional systems do not adequately address the impact of employees' emotional states on learning effectiveness. Solving this problem is essential.

[0534] 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.

[0535] In this invention, the server includes means for acquiring the user's personal data and goal information, means for analyzing the user's voice and facial expression data to recognize their emotional state, and means for adjusting the educational plan based on the recognized emotional state. This makes it possible for the individualized educational plan to be dynamically optimized according to the emotional state.

[0536] "Personal data" refers to information about individual users, such as their skill level and learning history.

[0537] "Goal information" refers to information that indicates the specific learning or skill goals that the user wants to achieve.

[0538] An "educational plan" is a collection of learning materials and tasks that are generated based on the individual needs and skill levels of the users.

[0539] "Emotional state" refers to the user's current emotions and psychological state, and is analyzed from voice and facial expression data.

[0540] A "display device" is an electronic device used by users to view educational plans, and includes computers, tablets, and other similar devices.

[0541] "Progress" refers to the rate at which users are progressing in their learning activities and their level of achievement.

[0542] "Visually displaying" means presenting data and information to users and administrators in a graphical manner.

[0543] "Multimedia teaching materials" refer to educational content that utilizes a variety of media formats, such as video, audio, and text.

[0544] "Interactive learning materials" are materials that allow users to directly participate in and progress through the learning process, and include quizzes and simulations.

[0545] "Emotion recognition technology" is a technology that identifies emotions by analyzing the user's voice and facial expressions.

[0546] This invention is a system for providing an individualized learning environment within a company. Users input their skill level and learning goals into a terminal. The terminal can be a computer or tablet, and this information can be entered through a dedicated application. After the user inputs the information, the terminal organizes it and prepares it for transmission to the server.

[0547] The server receives information transmitted from the terminal. Based on this information, the server uses a generative AI model to create an individualized educational plan. Specifically, a generative AI model implemented using programming languages ​​such as Python or Java analyzes the user's personal data and proposes an educational plan that combines multimedia and interactive learning materials.

[0548] The user provides voice and facial expression data through the device's camera and microphone. The device collects this data and analyzes the user's emotional state using emotion recognition technology. For example, OpenCV could be used for face recognition and Librosa for voice analysis.

[0549] The server has the ability to dynamically adjust the learning plan based on emotional data. If a user experiences stress during learning, the server can detect this and change the learning plan to make it more effective and efficient in a shorter amount of time. This allows users to continue learning at their own pace while reducing stress.

[0550] The device presents the user with a tailored learning plan. The user learns based on the provided materials and manages their own progress. The device records learning progress and changes in the user's emotions in real time and sends this information to the server. The server continuously analyzes this information and readjusts the learning plan to maximize learning effectiveness.

[0551] This system can integrate progress information and emotional state, providing a visually displayed dashboard for administrators and users. For example, suppose a user wants to improve their presentation skills. In this case, the system can provide learning materials incorporating relaxation techniques based on the user's emotional state, thereby reducing the learning burden and supporting effective skill improvement.

[0552] Examples of prompts for generative AI models:

[0553] "Please create an effective learning plan to improve presentation skills, especially including relaxation techniques that can help users when they feel stressed."

[0554] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0555] Step 1:

[0556] Users operate a terminal to input their skill level and learning goals. The input data includes specific goals, such as improving marketing skills or mastering specific analytical methods. The terminal structures this user data and prepares it for transmission to the server. The input skill information and goal data are organized into a dataset to identify the user's learning needs.

[0557] Step 2:

[0558] The terminal sends organized user data to the server. The server receives this data and uses a generative AI model to create an optimized educational plan for the user. Specifically, an AI algorithm written in Python evaluates and analyzes the data and selects learning materials that match the user's goals. The input is the user's personal data, and the output is a personalized educational plan that includes appropriate learning materials and programs.

[0559] Step 3:

[0560] The user provides voice and facial expressions during the learning process through the camera and microphone connected to the device. The device uses emotion recognition technology to analyze voice patterns and facial movements to determine the user's emotional state. For example, it receives data such as whether the user is laughing or frowning as input and evaluates the emotional response. The output is information about the user's current emotional state.

[0561] Step 4:

[0562] The server adjusts the learning plan based on the analyzed emotional data. For example, if the server determines that a user is experiencing stress during learning, it will switch to materials that are easier and more relaxing to learn from. This process dynamically optimizes the learning plan, using the input emotional data to maximize learning effectiveness.

[0563] Step 5:

[0564] The device presents the user with an optimized learning plan. Specifically, it displays the learning materials to be used via pop-up notifications and a dashboard. The user continues learning using these materials and acquires the necessary knowledge. During this process, responses to the learning materials are recorded again as input data on the device and sent to the server.

[0565] Step 6:

[0566] The server continuously analyzes collected learning and sentiment data and visualizes progress information in a dashboard format. Administrators and users can use this visualized dashboard to evaluate learning effectiveness and determine the next course of action. Inputs are the user's progress and sentiment state, while outputs include performance summaries and improvement suggestions.

[0567] (Application Example 2)

[0568] 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."

[0569] There is a lack of systems that consider the impact of emotional changes and stress on learning efficiency when employees engage in learning tailored to their individual abilities and goals. Therefore, recognizing employees' emotional states in real time and adjusting learning plans accordingly is essential for improving the learning experience.

[0570] 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.

[0571] In this invention, the server includes means for acquiring user ability data and goal information, means for generating an individualized learning plan, and means for analyzing the user's emotional state using an emotion engine and adjusting the learning plan. This makes it possible to provide learning optimized for each user and to flexibly change the learning plan based on the emotional state.

[0572] "Users" refer to employees or individuals who utilize the learning system and are entities that aim to improve their own abilities by receiving the system's services.

[0573] "Ability data" refers to information that indicates a user's current skill level and level of knowledge, and is an element that is used as a foundation for learning.

[0574] "Target information" refers to information that shows the specific learning objectives and outcomes that users aim to achieve.

[0575] A "learning plan" is a customized plan based on the user's ability data and goal information, designed to guide them through their learning content and progress.

[0576] An "emotion engine" is a technology that infers a user's emotional state by analyzing their voice and facial expression data.

[0577] A "presentation device" is a device or apparatus that allows users to visually confirm generated learning plans and learning materials.

[0578] "Emotional state" refers to the psychological condition and emotions of a user, and is a factor that affects the efficiency of learning.

[0579] "Relaxation techniques" are methods and techniques used to reduce stress in users and create an environment where they can concentrate on learning.

[0580] A "manager" is a person or role responsible for overseeing the operation of the entire learning system and understanding the learning progress and emotional state of users.

[0581] To implement this invention, it is necessary to build a system that effectively coordinates between the server, terminal, and user. The server acquires the user's ability data and goal information and generates an individualized learning plan. In generating the learning plan, natural language processing and machine learning are utilized to select learning materials appropriate to the user's current skill level.

[0582] The emotion engine analyzes the user's voice and facial expression data acquired through the device to infer the user's emotional state. This makes it possible to adjust the learning content in a timely manner according to the user's emotional changes. Specifically, if high stress is detected as a factor that reduces learning efficiency, the server will instruct the system to switch to learning materials that include relaxation techniques.

[0583] Users review the learning plan generated through the display device and proceed with their learning. Data on learning progress and emotional state is transmitted to the server in real time for continuous analysis. Based on this data, the server dynamically updates the learning plan and continues to provide the most suitable learning materials.

[0584] A concrete example is a personal assistant robot supporting English language learning. In this case, the robot would engage in everyday conversation with the user, and if it senses that the user is feeling stressed, it would instruct the user to switch to learning materials in a game format that allow for more relaxed learning. An example of a prompt when using a generative AI model would be, "Based on the user's current state, please suggest learning materials that will help them relax."

[0585] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0586] Step 1:

[0587] The terminal receives user ability data and goal information as input. The user enters their skill level and learning goals into the terminal. This data is then ready to be sent to the server.

[0588] Step 2:

[0589] The server uses natural language processing and machine learning to analyze the received ability data and goal information. This analysis generates a personalized learning plan for each user, which includes learning materials such as videos and practice problems.

[0590] Step 3:

[0591] The device collects the user's voice and facial expression data as input to the emotion engine. As the user progresses through the learning process, voice and facial expressions are automatically recorded, and the data is ready to be sent to the server.

[0592] Step 4:

[0593] The server uses an emotion engine to analyze the received audio and facial expression data and recognizes the user's emotional state as output. This analysis determines whether the user is experiencing stress, and the learning plan is adjusted accordingly if necessary.

[0594] Step 5:

[0595] The server sends the adjusted learning plan to the terminal and presents it to the user through a display device. The user can then continue learning based on the newly suggested learning materials.

[0596] Step 6:

[0597] The device records learning progress data and refreshed sentiment data in real time and periodically sends them to the server. This allows for centralized management of the user's progress.

[0598] Step 7:

[0599] The server analyzes the collected progress information and sentiment data to dynamically update the next learning plan. This continuous updating maximizes the learning effect.

[0600] 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.

[0601] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). One example of data generation model 58 is ChatGPT (Internet search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0602] 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.

[0603] [Fourth Embodiment]

[0604] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0605] 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.

[0606] 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).

[0607] 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.

[0608] 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.

[0609] 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).

[0610] 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.

[0611] 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.

[0612] 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.

[0613] 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.

[0614] 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.

[0615] 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.

[0616] 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".

[0617] Embodiments of the present invention are systems for efficiently supporting employee skill development within a company. The system functions primarily through interaction between servers, terminals, and users.

[0618] Users input their skill data and goal information on their device. This input includes their current skill level, skills they want to acquire in the future, and career goals. The device structures this data and sends it to the server.

[0619] The server analyzes the received data. This analysis utilizes natural language processing techniques and machine learning algorithms, generating a personalized learning plan for each user by comparing it with company-specific skill requirements and the latest industry information. The learning plan includes short, easy-to-learn videos, quiz-style materials, and reading materials to aid in deeper understanding.

[0620] The generated learning plan is sent from the server to the user's device and presented to the user so they can start learning at their preferred time. The device records the user's learning progress in real time and sends data such as achievement level and comprehension level back to the server.

[0621] The server analyzes the received learning progress data and dynamically updates the learning plan as needed. This ensures that each user continuously receives appropriate learning content based on the latest information. Furthermore, by comprehensively aggregating learning progress and results and providing this information visualized in a dashboard format to both the company and the user, the growth process can be clearly understood.

[0622] This system allows users to utilize their free time between tasks and learn effectively at their own pace. Furthermore, companies can centrally manage the skill development of their entire workforce, fostering competitive talent. For example, if a user wants to improve their digital marketing skills, this system provides video learning materials and related quizzes on the latest digital marketing technologies, and the learning plan is adjusted according to the user's needs.

[0623] The following describes the processing flow.

[0624] Step 1:

[0625] The user enters their skill level, career goals, areas of interest, etc., on the device. The device formats this data and saves it to a database for the next processing step.

[0626] Step 2:

[0627] The terminal sends formatted user data to the server. The server receives the data, performs an integrity check, and prepares for analysis.

[0628] Step 3:

[0629] The server begins analysis based on the received user data. Using natural language processing and machine learning algorithms, it generates a personalized learning plan that matches the user's skill requirements and the company's goals.

[0630] Step 4:

[0631] The server generates a learning plan and sends it to the user's device for review. The device displays the received learning plan in its user interface and prompts the user to begin learning.

[0632] Step 5:

[0633] The user progresses through the learning process on their device, recording their progress and results. The device continuously sends this data to the server, which then monitors their progress.

[0634] Step 6:

[0635] The server analyzes progress data sent from the terminal to assess learning achievement and make adjustments for the next step. It automatically updates the learning plan as needed, providing an optimized learning experience.

[0636] Step 7:

[0637] The server aggregates user learning progress and displays it on a dashboard in a format that is easy for both the company and the user to understand. This allows users to track their own progress, and the company to grasp overall skill improvement.

[0638] (Example 1)

[0639] 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".

[0640] To effectively support employee skill development within companies and organizations, it is necessary to develop and continuously manage training plans tailored to individual needs. However, traditional methods have made it difficult to provide training content appropriate for each user in real time, to flexibly modify plans as progress is made, and to visually grasp the results.

[0641] 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.

[0642] In this invention, the server includes means for acquiring user ability information and purpose information, means for analyzing the user ability information and purpose information and generating an individualized educational plan, and means for providing access to educational materials using an application of an external educational platform. This enables the generation of an appropriate educational plan tailored to each user's needs and flexible updating of the plan according to progress.

[0643] "User" refers to an individual or member of an organization who uses the system to improve their own capabilities.

[0644] "Capability information" refers to data about the level of technology, knowledge, and skills that a user currently possesses.

[0645] "Purpose information" refers to information about the abilities, skills, or career goals that the user wishes to achieve in the future.

[0646] An "educational plan" refers to a plan that presents individualized learning content and schedules based on analyzed user ability and goal information.

[0647] A "display device" refers to a terminal or screen that users use to visually confirm information.

[0648] "Educational progress" refers to data that shows how far users have actually progressed in their learning based on their educational plan.

[0649] An "external education platform" refers to an external digital service that provides online learning content and materials accessible to users.

[0650] "Analysis" refers to the process of analyzing data based on information entered by users to generate appropriate educational plans.

[0651] This invention is a system designed to support efficient employee skill development within a company. The system functions primarily through interaction between servers, terminals, and users.

[0652] Users input their personal skills data and goal information using a terminal. Specifically, they use a GUI-based input form to enter their current skill level, desired skills, and career goals. The terminal structures this information in JSON format and sends it to the server using a secure protocol (e.g., HTTPS).

[0653] The server analyzes the received data. This analysis utilizes natural language processing techniques (e.g., spaCy) and machine learning algorithms (e.g., TensorFlow and PyTorch). Based on this analysis, the server generates personalized training plans, comparing them with company-specific skill requirements and up-to-date industry information. Furthermore, it leverages APIs from external online education platforms (e.g., Coursera and Udemy) to integrate supplementary video materials and quiz-style learning tasks into the training plans.

[0654] The device displays the generated educational plan on a dashboard, allowing users to begin learning at any time. Educational progress is recorded in real time, and data on the user's progress and understanding is sent back to the server. The server analyzes this data and dynamically updates the educational plan as needed. This ensures that users always have access to the most appropriate educational content.

[0655] For example, if a user wants to improve their digital marketing skills, the system will provide video tutorials and quizzes related to the latest digital marketing technologies. An example of a prompt that utilizes a generative AI model is, "Generate an optimal educational plan based on the user's current data science skills and goals." This prompt is designed to allow the AI ​​model to generate customized educational content tailored to the specific user's needs.

[0656] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0657] Step 1:

[0658] Users input their skill data and goal information using a terminal. Specifically, they fill in their skill level, desired skills, and career goals in an input form via a GUI. The entered information is converted into JSON format. At this stage, the input is text information, and the output is structured data.

[0659] Step 2:

[0660] The terminal sends structured JSON data to the server using a secure protocol (e.g., HTTPS). This communication is encrypted to ensure data confidentiality. The input here is data in JSON format, and the output is the completion of data transmission to the server.

[0661] Step 3:

[0662] The server analyzes the received data. Specifically, it uses natural language processing techniques (e.g., spaCy) to syntactically parse the input information and machine learning algorithms (e.g., TensorFlow) to identify skill gaps. The input consists of user ability data and goal information, and the output is the analysis results.

[0663] Step 4:

[0664] The server generates personalized learning plans based on the analysis results. These plans include learning materials collected using APIs from external online education platforms (e.g., Coursera and Udemy). The input is the analysis results, and the output is the learning plan.

[0665] Step 5:

[0666] The terminal displays the educational plan received from the server on the user's dashboard. The user uses this dashboard to review the educational plan and begin learning. The input is the educational plan, and the output is the information presented to the user.

[0667] Step 6:

[0668] The device records the user's progress as they advance through the learning process. Specifically, this includes the learning materials used, completed modules, and quiz accuracy rates. The progress data is recorded in JSON format. The input is user behavior data, and the output is real-time updated progress information.

[0669] Step 7:

[0670] The server analyzes progress data and dynamically updates the educational plan using a generated AI model as needed. If the analysis reveals delays in skill acquisition, additional materials are suggested. The input is progress data, and the output is the updated educational plan.

[0671] (Application Example 1)

[0672] 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".

[0673] For employees and individuals to efficiently improve specific skills, personalized learning plans and timely feedback are crucial. However, current systems struggle to effectively provide real-time learning support and feedback, which leads to decreased learning efficiency.

[0674] 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.

[0675] In this invention, the server includes means for acquiring user ability data and goal information, means for generating personalized learning plans, and means for providing learning support via home-use automated devices and providing instruction and feedback in real time. This enables users to effectively progress through specific learning at their own pace while staying at home.

[0676] A "user" is an individual who uses the system to input ability data and goal information and receives a learning plan.

[0677] "Competency data" refers to information about the user's current skill level and job capabilities.

[0678] "Goal information" refers to information about the skills and career goals that the user wants to achieve.

[0679] A "learning plan" is a personalized learning program generated based on the user's ability data and goal information.

[0680] A "display device" is a device used to present learning plans and progress information to users.

[0681] "Learning progress" refers to information about the progress and level of understanding that users have achieved based on their learning plan.

[0682] "Household automated devices" refer to devices installed in homes that provide learning support, such as robots and digital assistants.

[0683] "Real-time" is a concept that refers to processing or information presentation taking place in real time.

[0684] The system for implementing this invention functions primarily through interaction between a server, a home automated device, and a user. The user utilizes a home automated device installed in their home to input ability data and goal information via voice or touchscreen. This includes the user's current ability level, desired skills, and career goals. The home automated device immediately transmits the input data to the server, ensuring the data is securely structured.

[0685] The server uses Python to analyze the received data using natural language processing techniques such as Spacy and BERT. The analysis process utilizes machine learning algorithms (e.g., Scikit-learn and TensorFlow) to generate personalized learning plans optimized for each user, comparing them with company skill requirements and up-to-date industry information. These plans include videos, question-and-answer materials, and reading comprehension materials.

[0686] The generated learning plan is sent from the server to a home automated device, allowing the user to begin learning at their own pace. The home automated device records the user's learning progress in real time and provides timely feedback. The progress data is then sent back to the server, which dynamically updates the learning plan based on this data.

[0687] As a concrete example, consider a scenario where a user inputs "I want to learn digital marketing." A home automation device could play a video on "social media strategy" and present questions in a quiz format, such as "What are the elements of a successful campaign?" An example of a prompt for a generative AI model would be, "Please generate a plan for a user to learn a new cooking skill. Please include the cooking category, the technologies used (videos, quizzes), and progress tracking."

[0688] This system allows users to efficiently improve their skills at home and enhance the quality of their learning through real-time feedback.

[0689] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0690] Step 1:

[0691] Users input their ability data and goal information using home-based automated devices. This input includes their current skill level, desired skills, and career goals, via voice recognition or touchscreen. The input information is structured by the home-based automated device and then sent to a server.

[0692] Step 2:

[0693] The server analyzes the received capability data and target information using natural language processing with Spacy and BERT. First, the data is converted to text format, and then skill mapping is performed based on machine learning algorithms. This prepares the server to generate the most suitable skill plan for the user.

[0694] Step 3:

[0695] The server generates individual learning plans using Scikit-learn and TensorFlow based on the analyzed data. These learning plans include engaging videos, quiz-style learning materials, and relevant reading materials. These materials are designed to be easy to learn in a short amount of time.

[0696] Step 4:

[0697] The server sends the generated learning plan to the home automated device. The home automated device then presents the received learning plan to the user through visual and audio interfaces. The user can decide when to start learning.

[0698] Step 5:

[0699] Users engage in learning activities based on their learning plan through home-based automated devices. These devices monitor the user's learning progress in real time, continuously recording their level of understanding and progress. This data is later transmitted to a server.

[0700] Step 6:

[0701] The server analyzes the received learning progress data and dynamically updates the learning plan as needed. This ensures that users receive the most up-to-date and optimal learning content. Furthermore, the progress data is visually presented to users and administrators, allowing them to understand their specific progress.

[0702] 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.

[0703] This invention provides a system that supports employees within a company in engaging in individually optimized learning, and can further enhance the learning experience, particularly by incorporating an emotion engine. The system operates through interaction between the server, terminals, and users, with each component working in coordination.

[0704] Users input their skill level and goal information using a device. Based on this initial input, the device organizes the data and prepares it for transmission to the server. After transmission, the server generates a customized learning plan for each user based on the received data. The plan includes videos, question-and-answer format materials, and reading comprehension materials, and the selection of these materials takes into account not only the user's ability data but also their emotional state as recognized by the emotion engine.

[0705] The emotion engine analyzes the user's voice and facial expression data to recognize their current emotional state. This information is sent to the server, and the learning plan is modified accordingly. For example, if the emotion engine determines that the user is tired, the server can switch to shorter, easier-to-understand learning materials.

[0706] The generated learning plan is presented to the user via the device. The user proceeds with their learning based on the presented content. Learning progress and changes in the user's emotions are recorded in real time by the device and sent back to the server.

[0707] The server continuously analyzes this progress information and sentiment data, dynamically updating the learning plan to maximize learning effectiveness. It also provides enterprise administrators and users with a dashboard that visualizes progress and sentiment status, allowing for a clear understanding of individual growth and learning effectiveness.

[0708] As a concrete example, suppose a user wants to improve their presentation skills, which they find difficult. If the emotional engine detects stress while the user is learning using the materials, the system will suggest temporarily switching from impractical materials to materials on easily applicable relaxation techniques. In this way, it is possible to alleviate the user's psychological burden while effectively supporting skill improvement.

[0709] The following describes the processing flow.

[0710] Step 1:

[0711] The user uses the device to input their ability data and goal information. The device formats this data and prepares it for transmission.

[0712] Step 2:

[0713] The terminal transmits the user's ability data and goal information to the server. The server receives the data, verifies its integrity, and then stores it in an analysis database.

[0714] Step 3:

[0715] The server uses natural language processing and machine learning algorithms to analyze the user's learning needs based on the received data. Based on the analysis results, it generates a personalized learning plan.

[0716] Step 4:

[0717] The emotion engine uses the user's voice information and facial expression data to recognize their current emotional state. The device then sends this emotional data to the server.

[0718] Step 5:

[0719] The server dynamically adjusts the learning plan to reflect the user's emotional state. For example, if the user is feeling anxious, the server will change the content to prioritize easier-to-understand material.

[0720] Step 6:

[0721] The device displays the updated learning plan to the user. The user then proceeds with their studies according to the new learning materials.

[0722] Step 7:

[0723] The device records the user's learning progress and updated sentiment state in real time and periodically sends this information to the server.

[0724] Step 8:

[0725] The server analyzes the transmitted data, makes any necessary adjustments, and provides progress and sentiment data to companies and users through a dashboard that visualizes these metrics. This allows for objective verification of the learning process's effectiveness.

[0726] (Example 2)

[0727] 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".

[0728] Modern companies require employees to learn efficiently according to their individual skill levels and goals. However, providing an optimized training plan for each employee while maintaining a learning environment that takes their emotional state into consideration is challenging. In particular, traditional systems do not adequately address the impact of employees' emotional states on learning effectiveness. Solving this problem is essential.

[0729] 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.

[0730] In this invention, the server includes means for acquiring the user's personal data and goal information, means for analyzing the user's voice and facial expression data to recognize their emotional state, and means for adjusting the educational plan based on the recognized emotional state. This makes it possible for the individualized educational plan to be dynamically optimized according to the emotional state.

[0731] "Personal data" refers to information about individual users, such as their skill level and learning history.

[0732] "Goal information" refers to information that indicates the specific learning or skill goals that the user wants to achieve.

[0733] An "educational plan" is a collection of learning materials and tasks that are generated based on the individual needs and skill levels of the users.

[0734] "Emotional state" refers to the user's current emotions and psychological state, and is analyzed from voice and facial expression data.

[0735] A "display device" is an electronic device used by users to view educational plans, and includes computers, tablets, and other similar devices.

[0736] "Progress" refers to the rate at which users are progressing in their learning activities and their level of achievement.

[0737] "Visually displaying" means presenting data and information to users and administrators in a graphical manner.

[0738] "Multimedia teaching materials" refer to educational content that utilizes a variety of media formats, such as video, audio, and text.

[0739] "Interactive learning materials" are materials that allow users to directly participate in and progress through the learning process, and include quizzes and simulations.

[0740] "Emotion recognition technology" is a technology that identifies emotions by analyzing the user's voice and facial expressions.

[0741] This invention is a system for providing an individualized learning environment within a company. Users input their skill level and learning goals into a terminal. The terminal can be a computer or tablet, and this information can be entered through a dedicated application. After the user inputs the information, the terminal organizes it and prepares it for transmission to the server.

[0742] The server receives information transmitted from the terminal. Based on this information, the server uses a generative AI model to create an individualized educational plan. Specifically, a generative AI model implemented using programming languages ​​such as Python or Java analyzes the user's personal data and proposes an educational plan that combines multimedia and interactive learning materials.

[0743] The user provides voice and facial expression data through the device's camera and microphone. The device collects this data and analyzes the user's emotional state using emotion recognition technology. For example, OpenCV could be used for face recognition and Librosa for voice analysis.

[0744] The server has the ability to dynamically adjust the learning plan based on emotional data. If a user experiences stress during learning, the server can detect this and change the learning plan to make it more effective and efficient in a shorter amount of time. This allows users to continue learning at their own pace while reducing stress.

[0745] The device presents the user with a tailored learning plan. The user learns based on the provided materials and manages their own progress. The device records learning progress and changes in the user's emotions in real time and sends this information to the server. The server continuously analyzes this information and readjusts the learning plan to maximize learning effectiveness.

[0746] This system can integrate progress information and emotional state, providing a visually displayed dashboard for administrators and users. For example, suppose a user wants to improve their presentation skills. In this case, the system can provide learning materials incorporating relaxation techniques based on the user's emotional state, thereby reducing the learning burden and supporting effective skill improvement.

[0747] Examples of prompts for generative AI models:

[0748] "Please create an effective learning plan to improve presentation skills, especially including relaxation techniques that can help users when they feel stressed."

[0749] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0750] Step 1:

[0751] Users operate a terminal to input their skill level and learning goals. The input data includes specific goals, such as improving marketing skills or mastering specific analytical methods. The terminal structures this user data and prepares it for transmission to the server. The input skill information and goal data are organized into a dataset to identify the user's learning needs.

[0752] Step 2:

[0753] The terminal sends organized user data to the server. The server receives this data and uses a generative AI model to create an optimized educational plan for the user. Specifically, an AI algorithm written in Python evaluates and analyzes the data and selects learning materials that match the user's goals. The input is the user's personal data, and the output is a personalized educational plan that includes appropriate learning materials and programs.

[0754] Step 3:

[0755] The user provides voice and facial expressions during the learning process through the camera and microphone connected to the device. The device uses emotion recognition technology to analyze voice patterns and facial movements to determine the user's emotional state. For example, it receives data such as whether the user is laughing or frowning as input and evaluates the emotional response. The output is information about the user's current emotional state.

[0756] Step 4:

[0757] The server adjusts the learning plan based on the analyzed emotional data. For example, if the server determines that a user is experiencing stress during learning, it will switch to materials that are easier and more relaxing to learn from. This process dynamically optimizes the learning plan, using the input emotional data to maximize learning effectiveness.

[0758] Step 5:

[0759] The device presents the user with an optimized learning plan. Specifically, it displays the learning materials to be used via pop-up notifications and a dashboard. The user continues learning using these materials and acquires the necessary knowledge. During this process, responses to the learning materials are recorded again as input data on the device and sent to the server.

[0760] Step 6:

[0761] The server continuously analyzes collected learning and sentiment data and visualizes progress information in a dashboard format. Administrators and users can use this visualized dashboard to evaluate learning effectiveness and determine the next course of action. Inputs are the user's progress and sentiment state, while outputs include performance summaries and improvement suggestions.

[0762] (Application Example 2)

[0763] 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".

[0764] There is a lack of systems that consider the impact of emotional changes and stress on learning efficiency when employees engage in learning tailored to their individual abilities and goals. Therefore, recognizing employees' emotional states in real time and adjusting learning plans accordingly is essential for improving the learning experience.

[0765] 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.

[0766] In this invention, the server includes means for acquiring user ability data and goal information, means for generating an individualized learning plan, and means for analyzing the user's emotional state using an emotion engine and adjusting the learning plan. This makes it possible to provide learning optimized for each user and to flexibly change the learning plan based on the emotional state.

[0767] "Users" refer to employees or individuals who utilize the learning system and are entities that aim to improve their own abilities by receiving the system's services.

[0768] "Ability data" refers to information that indicates a user's current skill level and level of knowledge, and is an element that is used as a foundation for learning.

[0769] "Target information" refers to information that shows the specific learning objectives and outcomes that users aim to achieve.

[0770] A "learning plan" is a customized plan based on the user's ability data and goal information, designed to guide them through their learning content and progress.

[0771] An "emotion engine" is a technology that infers a user's emotional state by analyzing their voice and facial expression data.

[0772] A "presentation device" is a device or apparatus that allows users to visually confirm generated learning plans and learning materials.

[0773] "Emotional state" refers to the psychological condition and emotions of a user, and is a factor that affects the efficiency of learning.

[0774] "Relaxation techniques" are methods and techniques used to reduce stress in users and create an environment where they can concentrate on learning.

[0775] A "manager" is a person or role responsible for overseeing the operation of the entire learning system and understanding the learning progress and emotional state of users.

[0776] To implement this invention, it is necessary to build a system that effectively coordinates between the server, terminal, and user. The server acquires the user's ability data and goal information and generates an individualized learning plan. In generating the learning plan, natural language processing and machine learning are utilized to select learning materials appropriate to the user's current skill level.

[0777] The emotion engine analyzes the user's voice and facial expression data acquired through the device to infer the user's emotional state. This makes it possible to adjust the learning content in a timely manner according to the user's emotional changes. Specifically, if high stress is detected as a factor that reduces learning efficiency, the server will instruct the system to switch to learning materials that include relaxation techniques.

[0778] Users review the learning plan generated through the display device and proceed with their learning. Data on learning progress and emotional state is transmitted to the server in real time for continuous analysis. Based on this data, the server dynamically updates the learning plan and continues to provide the most suitable learning materials.

[0779] A concrete example is a personal assistant robot supporting English language learning. In this case, the robot would engage in everyday conversation with the user, and if it senses that the user is feeling stressed, it would instruct the user to switch to learning materials in a game format that allow for more relaxed learning. An example of a prompt when using a generative AI model would be, "Based on the user's current state, please suggest learning materials that will help them relax."

[0780] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0781] Step 1:

[0782] The terminal receives user ability data and goal information as input. The user enters their skill level and learning goals into the terminal. This data is then ready to be sent to the server.

[0783] Step 2:

[0784] The server uses natural language processing and machine learning to analyze the received ability data and goal information. This analysis generates a personalized learning plan for each user, which includes learning materials such as videos and practice problems.

[0785] Step 3:

[0786] The device collects the user's voice and facial expression data as input to the emotion engine. As the user progresses through the learning process, voice and facial expressions are automatically recorded, and the data is ready to be sent to the server.

[0787] Step 4:

[0788] The server uses an emotion engine to analyze the received audio and facial expression data and recognizes the user's emotional state as output. This analysis determines whether the user is experiencing stress, and the learning plan is adjusted accordingly if necessary.

[0789] Step 5:

[0790] The server sends the adjusted learning plan to the terminal and presents it to the user through a display device. The user can then continue learning based on the newly suggested learning materials.

[0791] Step 6:

[0792] The device records learning progress data and refreshed sentiment data in real time and periodically sends them to the server. This allows for centralized management of the user's progress.

[0793] Step 7:

[0794] The server analyzes the collected progress information and sentiment data to dynamically update the next learning plan. This continuous updating maximizes the learning effect.

[0795] 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.

[0796] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). One example of data generation model 58 is ChatGPT (Internet search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0797] 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.

[0798] 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.

[0799] 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.

[0800] 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.

[0801] 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.

[0802] 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.

[0803] 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."

[0804] 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.

[0805] 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.

[0806] 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.

[0807] 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.

[0808] 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.

[0809] 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.

[0810] 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.

[0811] 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.

[0812] 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.

[0813] 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.

[0814] 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.

[0815] 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.

[0816] The following is further disclosed regarding the embodiments described above.

[0817] (Claim 1)

[0818] Means for acquiring user ability data and goal information,

[0819] A means for analyzing the user's ability data and goal information and generating an individualized learning plan,

[0820] Means for presenting the learning plan on the user's display device,

[0821] A means for collecting the user's learning progress and dynamically updating the learning plan,

[0822] A means of aggregating learning progress and displaying it visually to administrators and users,

[0823] A system that includes this.

[0824] (Claim 2)

[0825] The system according to claim 1, characterized in that the learning plan includes video, question-based teaching materials, and reading comprehension materials.

[0826] (Claim 3)

[0827] The system according to claim 1, characterized in that the analysis uses natural language processing and machine learning algorithms.

[0828] "Example 1"

[0829] (Claim 1)

[0830] Means for obtaining user capability information and purpose information,

[0831] A means for analyzing the user's ability information and objective information and generating an individualized educational plan,

[0832] Means for displaying the aforementioned educational plan on the user's display device,

[0833] A means for collecting the educational progress of the aforementioned users and dynamically updating the educational plan,

[0834] A means of aggregating educational progress and displaying it visually to administrators and users,

[0835] A means of providing access to educational materials using the application of external educational platforms,

[0836] A system that includes this.

[0837] (Claim 2)

[0838] The system according to claim 1, characterized in that the aforementioned educational plan includes video materials, question-based teaching materials, and reading comprehension materials.

[0839] (Claim 3)

[0840] The system according to claim 1, characterized in that the analysis uses natural language processing techniques and machine learning methods.

[0841] "Application Example 1"

[0842] (Claim 1)

[0843] Means for acquiring user ability data and goal information,

[0844] A means for analyzing the user's ability data and goal information and generating an individualized learning plan,

[0845] Means for presenting the learning plan on the user's display device,

[0846] A means for collecting the user's learning progress and dynamically updating the learning plan,

[0847] A means of aggregating learning progress and displaying it visually to administrators and users,

[0848] A means of providing learning support using home-use automated devices and conducting instruction and feedback in real time,

[0849] A system that includes this.

[0850] (Claim 2)

[0851] The system according to claim 1, characterized in that the learning plan includes video, question-based teaching materials, and reading comprehension materials.

[0852] (Claim 3)

[0853] The system according to claim 1, characterized in that the analysis uses natural language processing and machine learning algorithms.

[0854] "Example 2 of combining an emotion engine"

[0855] (Claim 1)

[0856] Means for obtaining users' personal data and goal information,

[0857] A means for analyzing the user's personal data and goal information and generating an individualized educational plan,

[0858] A means of analyzing the user's voice and facial expression data to recognize their emotional state,

[0859] A means of adjusting educational plans based on perceived emotional states,

[0860] Means for displaying the aforementioned educational plan on the user's display device,

[0861] A means for collecting the educational progress of the aforementioned users and dynamically updating the educational plan,

[0862] A means of aggregating educational progress and emotional state and visually displaying it to administrators and users,

[0863] A system that includes this.

[0864] (Claim 2)

[0865] The system according to claim 1, characterized in that the aforementioned educational plan includes multimedia materials, interactive materials, and reading comprehension materials.

[0866] (Claim 3)

[0867] The system according to claim 1, characterized in that the analysis uses natural language processing, machine learning algorithms, and emotion recognition technology.

[0868] "Application example 2 when combining with an emotional engine"

[0869] (Claim 1)

[0870] Means for acquiring user ability data and goal information,

[0871] A means for analyzing the user's ability data and goal information and generating an individualized learning plan,

[0872] A means of analyzing the user's emotional state using an emotion engine and adjusting the learning plan accordingly,

[0873] Means for presenting the aforementioned learning plan to the user's display device,

[0874] A means for collecting the user's learning progress and emotional data and dynamically updating the learning plan,

[0875] A means of aggregating learning progress and emotional state and visually displaying them to administrators and users,

[0876] A system that includes this.

[0877] (Claim 2)

[0878] The system according to claim 1, characterized in that the learning plan includes visual learning materials, question-based learning materials, reading comprehension materials, and relaxation techniques corresponding to emotional states.

[0879] (Claim 3)

[0880] The system according to claim 1, characterized in that the analysis uses natural language processing, machine learning methods, and sentiment analysis techniques. [Explanation of symbols]

[0881] 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. Means for acquiring user ability data and goal information, A means for analyzing the user's ability data and goal information and generating an individualized learning plan, Means for presenting the learning plan on the user's display device, A means for collecting the user's learning progress and dynamically updating the learning plan, A means of aggregating learning progress and displaying it visually to administrators and users, A system that includes this.

2. The system according to claim 1, characterized in that the learning plan includes video, question-based teaching materials, and reading comprehension materials.

3. The system according to claim 1, characterized in that the analysis uses natural language processing and machine learning algorithms.

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