system

Abstract

We provide the system. [Solution] A learning platform provided by an information processing device, An artificial intelligence agent that analyzes data related to tasks entered by learners, A feedback function that provides the learner with the analysis results generated by the aforementioned artificial intelligence agent, A database that stores learner input data and data generated by the artificial intelligence agent, and uses it for improvement, A system that includes this.

Description

Technical Field

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

Background Art

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

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In recent years, many companies have been trying to introduce artificial intelligence and digital transformation, but there are problems that the cultivation of human resources and the application to existing businesses are not sufficient. In particular, there is a lack of practical educational means to bridge the gap when employees effectively learn new technologies and apply them to actual work. In addition, there is a need for a support system to efficiently utilize the know-how accumulated within the organization and industry-specific knowledge to create new value.

Means for Solving the Problems

[0005] This invention provides a means for constructing a learning platform provided by an information processing device, and for an artificial intelligence agent to analyze work-related data input by learners through this platform. Furthermore, by providing the analysis results generated by the artificial intelligence agent as feedback to the learner, it enables the learner to utilize that knowledge in their work. In addition, the accumulated input data and analysis result data are stored in a database, which is used to support the improvement of the artificial intelligence agent and the optimization of business processes. This makes it possible to realize efficient human resource development and technology application within an organization and promote sustainable innovation.

[0006] An "information processing device" is a computing device that processes data and provides various information services.

[0007] A "learning platform" is a digital environment designed for learners to acquire knowledge and skills online.

[0008] A "learner" is an individual who learns specific knowledge or skills through an online platform.

[0009] "Data related to work" refers to data that includes information about the learner's work content and processes, as well as issues and suggestions for improvement.

[0010] An "artificial intelligence agent" is a program that analyzes data and automatically generates useful information and suggestions for learners.

[0011] The "feedback function" is a mechanism for providing learners with analysis results and suggestions generated by the artificial intelligence agent.

[0012] A "database" is a system that systematically stores digital data in a format that allows for quick access for a specific purpose. [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] This is a conceptual diagram showing an example of the configuration of a data processing system according to the second embodiment. [Figure 4] This is a conceptual diagram showing an example of the main functions of a data processing device and smart glasses according to the second embodiment. [Figure 5] This is a conceptual diagram showing an example of the configuration of a data processing system according to the third embodiment. [Figure 6] This is a conceptual diagram showing an example of the main functions of a data processing device and a headset-type terminal according to the third embodiment. [Figure 7] This is a conceptual diagram showing an example of the configuration of a data processing system according to the fourth embodiment. [Figure 8] This is a conceptual diagram showing an example of the main functions of a data processing device and a robot according to the fourth embodiment. [Figure 9] This shows an emotion map where multiple emotions are mapped. [Figure 10] This shows an emotion map where multiple emotions are mapped. [Figure 11] This is a sequence diagram showing the processing flow of the data processing system in Example 1. [Figure 12] This is a sequence diagram showing the processing flow of the data processing system in Application Example 1. [Figure 13] This is a sequence diagram showing the processing flow of the data processing system in Example 2, which incorporates an emotion engine. [Figure 14] This is a sequence diagram showing the processing flow of the data processing system in Application Example 2, which combines an emotion engine. [Modes for carrying out the invention]

[0014] An example of an embodiment of the system according to the technology of the present disclosure will be described below with reference to the accompanying drawings.

[0015] First, the terms 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, etc. Examples of non-volatile storage devices include flash memory (SSD (Solid State Drive)), magnetic disks (e.g., hard disks), or magnetic tapes, etc.

[0019] In the following embodiments, the signed communication interface (I / F) is an interface that includes a communication processor and an antenna, etc. The communication interface manages communication between multiple computers. Examples of communication standards applicable to the communication interface include wireless communication standards such as 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), or Bluetooth (registered trademark).

[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] This invention is a system that enables learners to effectively acquire knowledge and skills related to AI and digital transformation through an online learning platform using an information processing device, and to apply them to actual work. This system aims not only to have an artificial intelligence agent analyze the learner's input data and provide feedback to the learner using the results, but also to accumulate knowledge within the organization and promote further business improvement based on that knowledge.

[0035] Overview of program processing

[0036] The server hosts an online learning platform where learners can log in, providing a wealth of AI and digital transformation-related learning materials and exercises. These materials are individually customized based on the learner's skill level and progress.

[0037] Users access the provided platform and work on exercises. Within the exercises, users input data and information related to their work, specific challenges, and improvement suggestions.

[0038] The terminal provides a user-friendly interface and sends user input to the server. It also receives feedback and suggestions from the server and displays them to the user.

[0039] An artificial intelligence agent analyzes user input data sent to the server in real time. This analysis utilizes natural language processing and machine learning algorithms to understand the meaning of the data, generating solutions and suggestions for specific challenges the user faces in their work.

[0040] The server provides the user with analysis results and suggestions generated by the artificial intelligence agent as feedback. Based on this feedback, the user can improve their business processes.

[0041] Specific examples

[0042] For example, when considering the introduction of a system to improve the efficiency of inventory management in a manufacturing company, the user inputs the current inventory management flow and challenges into the system. The server analyzes this data through an artificial intelligence agent and generates suggestions for optimal inventory management methods and new tools to be introduced. Based on this, the user formulates specific inventory management improvement measures and applies them to their work. In this process, the accumulated data becomes the foundation for more refined learning and feedback from subsequent users.

[0043] In this way, the present invention provides a practical form that supports the use of AI throughout an entire company and improves operational efficiency.

[0044] The following describes the processing flow.

[0045] Step 1:

[0046] The server launches the online learning platform that learners access, and prepares the necessary learning materials and exercises. It also authenticates user login information and verifies access rights.

[0047] Step 2:

[0048] Users log in to the online platform using their devices and view the provided learning materials and exercises. Based on their skill level and progress, they access individually recommended content aligned with their learning objectives.

[0049] Step 3:

[0050] Users input specific challenges and information related to their work into the platform as answers to practice problems. This may include diagrams of business processes and detailed descriptions of the challenges.

[0051] Step 4:

[0052] The terminal formats the data entered by the user and sends it to the server. The information is transferred in a secure format and stored in the server's database.

[0053] Step 5:

[0054] The server uses an artificial intelligence agent to analyze user input data and extract insights that can help improve business operations. This process utilizes machine learning algorithms and natural language processing techniques.

[0055] Step 6:

[0056] The server sends the analysis results and improvement suggestions generated by the artificial intelligence agent to the user as feedback. The feedback is displayed to the user via the terminal.

[0057] Step 7:

[0058] Users develop action plans to improve their business processes based on the feedback they receive. They can request re-entry or additional feedback for the next steps as needed.

[0059] Step 8:

[0060] The server stores user feedback and improvement results in a database, which will be used to improve future AI agents. This also strengthens the knowledge base within the organization.

[0061] (Example 1)

[0062] 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."

[0063] In today's learning environment, learners are required to effectively acquire practical skills directly applicable to their work and to use those skills to improve work processes. However, traditional learning platforms have struggled to provide personalized learning materials tailored to the needs of individual learners, as well as to offer concrete feedback and suggestions useful for actual work. Furthermore, they lacked mechanisms to accumulate learner input information and utilize it for the next step in their learning.

[0064] 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.

[0065] In this invention, the server includes an educational platform provided by an information processing device, an intelligent agent that analyzes work-related information entered by the learner, and an evaluation means that provides the learner with the analysis results generated by the intelligent agent. This enables effective skill acquisition and work improvement suggestions based on the learner's work-related information.

[0066] An "information processing device" is a system consisting of a combination of hardware and software for storing, processing, and acquiring data.

[0067] An "educational platform" is an online-based information management system that allows learners to access and participate in learning materials and related learning activities.

[0068] A "learner" refers to an individual user who utilizes an educational platform to acquire knowledge and skills.

[0069] An "intelligent agent" refers to a program or algorithm that can analyze input data from learners and generate feedback and suggestions tailored to business needs.

[0070] "Evaluation means" refers to a mechanism that provides learners with analysis results generated by an intelligent agent, thereby improving the quality and effectiveness of the learning process.

[0071] "Information storage means" refers to a database or storage device used to store learner input information and intelligent agent generation information for subsequent learning and improvement.

[0072] "Methods for providing learning materials" refers to a system or function for selecting and providing learning materials and practice problems according to the learner's skill level and progress.

[0073] "Consultation tools" refer to functions that use data analyzed by intelligent agents to suggest improvement measures for business processes to learners and support their implementation.

[0074] This invention is a system that uses an online education platform powered by an information processing device to enable learners to effectively acquire knowledge and skills related to AI and digital transformation and apply them to their work. The server provides learners with individually customized educational materials. This process utilizes a learning management system and database management software. Specifically, a content management system (CMS) is used as the software.

[0075] The server uses a front-end framework to build a user interface that makes it easy for learners to access the platform. Technologies such as React and Vue.js are used for this purpose. Through this interface, users work on exercises and input tasks and data related to their work. The entered information is sent to the server.

[0076] The data provided by the user is analyzed by an intelligent agent. This analysis utilizes natural language processing (NLP) algorithms and machine learning models, leveraging Python libraries such as NLTK and TENSORFLOW®. This analysis generates specific improvement strategies and suggestions for the challenges the learner faces in their work.

[0077] As a concrete example, consider using this system to improve the efficiency of inventory management in the manufacturing industry. The user follows the prompt, "Please enter details of your current inventory management flow and its challenges," and inputs the current state of operations and points they wish to improve. The intelligent agent analyzes the entered information, proposes the optimal inventory management method, and helps the user improve their business processes based on that. In this way, the present invention provides an effective technology that supports the use of AI and operational efficiency throughout the enterprise.

[0078] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0079] Step 1:

[0080] Users enter their credentials to log in to the online education platform. This input data is sent to an authentication server for verification of the credentials' validity. If authentication is successful, users can access a personalized dashboard. As output, users will have access to the following learning materials and progress information.

[0081] Step 2:

[0082] The server recommends appropriate learning materials and exercises based on the user's learning history and skill level. To do this, the server retrieves user information from an internal database and uses a learning management system to select the most suitable materials. As output, the selected materials are displayed on the user's dashboard.

[0083] Step 3:

[0084] Users input their answers to the provided exercises. This requires them to describe data and specific challenges related to their own work. The input data is transmitted to the server in real time, preparing it for the next analysis step.

[0085] Step 4:

[0086] The server passes the data received from the user to an artificial intelligence agent for analysis. Specifically, it uses natural language processing (NLP) and machine learning algorithms to understand the context of the data and extract necessary information. In this step, the intelligent agent uses libraries such as Python's NLTK and TensorFlow to perform data analysis and insight generation. As output, suggestions useful for improving business operations are generated.

[0087] Step 5:

[0088] The terminal displays suggestions and feedback received from the server in a user-friendly format. Interface technologies such as React and Vue.js are used for this display to help users easily grasp the received data. As output, users review the suggestions and consider specific actions for improving their work processes.

[0089] Step 6:

[0090] Based on suggestions from the server, users implement specific measures to improve their own work processes. During this process, they utilize the feedback they receive and apply it to their actual work. As a result, work efficiency improves, and learner growth is promoted.

[0091] (Application Example 1)

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

[0093] In modern urban environments, there is a need for systems that can efficiently solve the various problems residents face in their daily lives. However, existing systems have struggled to provide real-time improvement suggestions tailored to the individual problems residents encounter. This invention aims to solve this problem.

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

[0095] In this invention, the server includes a learning platform provided by an information processing device, an artificial intelligence agent means for analyzing information related to tasks input by the learner, and a feedback means for providing the learner with the analysis results generated by the artificial intelligence agent. This makes it possible to accurately analyze problems that residents face in their daily lives and to provide accurate improvement suggestions regarding urban management in real time.

[0096] An "information processing device" is a device designed to efficiently manage and analyze data.

[0097] A "learning platform" is an online platform used by learners to acquire the skills and knowledge they need.

[0098] An "artificial intelligence agent" is a program that uses artificial intelligence technology to analyze input information and generate appropriate feedback and suggestions.

[0099] "Analysis results" refer to the conclusions and suggestions obtained after an artificial intelligence agent analyzes the input information.

[0100] "Feedback means" refers to methods or functions for providing analysis results to the user.

[0101] The "data storage unit" is a function that stores learner input information and information generated by artificial intelligence, for later reference and performance improvement.

[0102] "Means" refers to the methods or techniques used to achieve a specific objective.

[0103] "Residents" refers to people who live in a specific area and are the entities that need to solve problems in the urban environment.

[0104] "City management" refers to a series of activities aimed at the efficient and smooth operation and management of a city.

[0105] An "improvement suggestion" refers to specific advice or methods for resolving current problems and achieving a better situation.

[0106] A system for realizing this invention includes a learning platform provided by an information processing device, an artificial intelligence agent, a feedback means, a data storage unit, and means for suggesting improvements to urban management to support residents in solving problems.

[0107] The server hosts the learning infrastructure and provides an interface for learners and residents to input questions. The terminal uses React Native to build the frontend and implement a user-friendly interface, and is responsible for sending user input to the server.

[0108] When a user inputs problems or suggestions related to their daily life into a server via their device, the data is analyzed in real time by an artificial intelligence agent. This analysis utilizes Python's natural language processing libraries (nltk and spaCy) and machine learning frameworks (TensorFlow). The AI ​​agent generates analysis results and derives optimal improvement suggestions.

[0109] The analysis results are provided to the user as feedback generated by the server. Based on this feedback, residents can gain concrete ideas for improving their urban life more efficiently.

[0110] For example, if a resident inputs "Traffic congestion in this area is terrible every morning," the system will suggest alternative routes or adjust traffic signals. In this way, it provides concrete solutions to the problems residents face.

[0111] An example of a prompt using a generative AI model is, "Please come up with suggestions for improving local events. Please provide ideas for events that are easy for local residents to participate in." By using this prompt, the system helps to automatically generate effective event plans.

[0112] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0113] Step 1:

[0114] Users input problems related to their daily lives using a terminal. The entered data is sent to the server through a user-friendly interface. During this process, users are guided to fill in the appropriate fields according to their needs.

[0115] Step 2:

[0116] The server passes the input data to the artificial intelligence agent in real time. The data is analyzed using Python's natural language processing libraries (nltk and spaCy). The purpose of the analysis is to accurately grasp the meaning of the input sentence and identify specific problems and needs. The output includes a list of problems and categorized requests.

[0117] Step 3:

[0118] The artificial intelligence agent generates optimal improvement suggestions using a machine learning framework (TensorFlow) based on the analysis results. This step involves referencing historical data and statistical models to consider solutions to the derived problems. The output is a list of specific solutions and advice.

[0119] Step 4:

[0120] The server provides the generated suggestions to the user as feedback. This feedback is sent back from the server to the terminal and displayed on the user's interface. The user can review the suggestions and obtain specific steps to take action. This feedback may also include the expected effects of the suggested improvements and any additional resources needed.

[0121] Step 5:

[0122] Through the terminal, users can input additional questions or comments on the feedback provided, starting a new cycle. This allows the suggestions to be more individualized or adjusted as needed. The output is a newly adjusted suggestion.

[0123] An example of a prompt message would be: "Please think of suggestions for improving local events. Please come up with ideas for events that are easy for local residents to participate in."

[0124] 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.

[0125] This invention relates to a system for online learning platforms that can recognize learners' emotions and provide feedback based on those emotions. This system allows learners to learn effectively through individually adapted learning materials and receive a combination of analysis results from an artificial intelligence agent and emotional feedback.

[0126] Overview of program processing

[0127] The server hosts the online learning platform and provides learners with appropriate learning materials and exercises. During this process, it handles learner login authentication and access verification.

[0128] Users access the learning platform through their devices and work on necessary learning materials and exercises. During the learning process, users are required to input information about their emotions and work-related matters.

[0129] The terminal is responsible for receiving user input data and sending it to the server. It also provides feedback and displays learning materials through the user interface.

[0130] The emotion engine analyzes data input from the user and recognizes the learner's emotional state in real time. Based on this emotional data, the AI ​​agent adjusts the analysis results.

[0131] The server integrates emotional data obtained by the emotion engine with learner work data and reflects this in the analysis results generated by the artificial intelligence agent. This ensures that feedback is provided in a way that is adapted to the learner's emotional state.

[0132] The database accumulates learner input data, sentiment data, and analysis results, contributing to improved feedback accuracy and platform improvements in subsequent sessions.

[0133] Specific examples

[0134] For example, suppose a learner uses a platform to learn new sales techniques. When working on exercises, the user inputs their current sales strategies and their feelings about the results. The emotion engine analyzes this, and if it recognizes that the user's emotional state is, for example, "anxious," the AI ​​agent adjusts the feedback to make the suggestions more positive and focus on solutions. This process allows the user to receive suggestions tailored to their individual emotional state and try new techniques with confidence.

[0135] In this way, by utilizing emotion recognition, it is possible to implement a system that provides a learning experience optimized for learners and supports its application in business.

[0136] The following describes the processing flow.

[0137] Step 1:

[0138] Users log in to the online learning platform using their devices and access the provided learning materials and practice problems. Authentication is performed using a username and password during login.

[0139] Step 2:

[0140] The server presents the most suitable learning materials and exercises based on the logged-in user's skill level and progress. This allows users to effectively engage with learning content tailored to their needs.

[0141] Step 3:

[0142] When answering practice questions, users input work-related information and their emotional state (e.g., satisfaction, anxiety, excitement) into the terminal. This information is used to customize the learning experience.

[0143] Step 4:

[0144] The terminal transmits the entered work information and emotional data to the server. The transmitted data is encrypted to protect privacy.

[0145] Step 5:

[0146] To analyze the received data, the server first uses an emotion engine to analyze emotional data and identify the user's emotional state. In this step, natural language processing techniques are used to determine text-based emotional information.

[0147] Step 6:

[0148] The server analyzes business-related data through an artificial intelligence agent and generates optimal business improvement suggestions. During this process, the content and tone of the suggestions are adjusted based on the results of the emotion engine.

[0149] Step 7:

[0150] The server transmits the generated feedback and suggestions for business improvement to the user via the terminal. These suggestions include personalized responses that take into account the user's emotional state.

[0151] Step 8:

[0152] Based on the feedback received, users develop improvement strategies for their own work. By providing emotionally resonant suggestions, users are expected to take more proactive and positive actions.

[0153] Step 9:

[0154] The server stores all user input data and analysis results in a database, which is used for the continuous improvement of the AI ​​model. This data contributes to improving the accuracy of feedback for future users.

[0155] (Example 2)

[0156] 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".

[0157] Traditional education systems have a problem in that they do not take into account the emotional state of learners and optimize the learning process accordingly, resulting in insufficient improvements in learning efficiency and work processes. In particular, when learners are experiencing anxiety or stress, these emotions are left unaddressed, leading to a decrease in motivation to learn and making it difficult to achieve optimal educational results.

[0158] 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.

[0159] In this invention, the server includes means for constructing an educational platform provided by an information processing device, artificial intelligence means for identifying emotional data input by learners in real time and adjusting the analysis results, and feedback function means for providing feedback optimized to the learner's emotional state. This enables individualized optimization according to the learner's emotional state, leading to improved learning efficiency and emotion-based improvements to business processes.

[0160] An "information processing device" refers to a machine equipped with hardware and software capable of handling electronic data and performing various calculations and analyses based on that data.

[0161] An "educational platform" refers to a system that provides an online environment for learners to access and engage in learning through educational materials and practice exercises.

[0162] "Emotional state" refers to data that indicates the psychological state, such as anxiety, joy, and stress, that learners experience in a particular situation.

[0163] "Artificial intelligence methods" refer to technologies that use machine learning and natural language processing to analyze input data and generate judgments and feedback.

[0164] "Feedback function means" refers to a function that provides learners with information based on analysis results to improve and support the learning process.

[0165] "Memory storage" refers to data storage that saves learner input data and analysis results to be used for future improvement.

[0166] This invention is a system for providing a learning environment that responds to the emotional state of learners, based on an educational platform provided by an information processing device.

[0167] The server manages the educational platform and provides learners with learning materials and exercises optimized for their needs. Using artificial intelligence (AI) tools, the server analyzes the emotional states entered by learners through their devices in real time and adjusts feedback based on the results. Specific examples of AI tools include emotion analysis engines and machine learning algorithms.

[0168] The terminal is a device that allows learners to access the platform and work on learning materials and exercises via a user interface. The terminal not only displays feedback and learning materials from the server, but also prompts learners to input their emotional state and work-related data, which is then transmitted to the server.

[0169] Users, or learners, need to input their emotional state and learning progress while using a device. This information is analyzed by a server, and feedback tailored to the learner's needs is provided.

[0170] This allows the learning experience to be individually optimized based on the learner's emotional state, thereby improving learning efficiency. For example, if a learner inputs an emotion such as "anxiety," the emotion analysis engine will analyze this data and generate feedback such as, "Let's try some specific steps to reduce anxiety," provided by an AI model. In this process, an example of a prompt might be, "Learner's emotional data has been entered. Please adjust the feedback and generate specific suggestions to improve the learning content." This method allows learners to acquire new knowledge and skills with confidence.

[0171] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0172] Step 1:

[0173] The user logs into the educational platform using a terminal. The input here is the user's authentication information. The terminal sends the user's entered authentication information to the server. The server compares it against the database to verify the validity of the login. The output of this process is the authentication result, granting the user access to the platform.

[0174] Step 2:

[0175] After confirming that the user has logged in, the server selects appropriate learning materials and exercises on the educational platform. The server uses the user's learning history data and progress information as input and analyzes this data using a generative AI model. The output of this process is a set of learning materials tailored to the user. The terminal then displays these materials on the user interface.

[0176] Step 3:

[0177] Users input their emotional state using a terminal while working through the provided learning materials and exercises. Emotional data and work-related information are used as input data and sent from the terminal to the server. The server transmits this input data to an emotion analysis engine, which then analyzes the user's emotional state. The output of this process is data concerning the user's specific emotional state.

[0178] Step 4:

[0179] The server holds emotional data generated by an emotion analysis engine and uses a generative AI model to generate user-appropriate feedback. In this process, the AI ​​model uses emotional data and learning history data as input and performs data calculations. The output is feedback adjusted according to the user's emotional state. This feedback includes appropriate solutions and encouragement for the learner.

[0180] Step 5:

[0181] The device receives optimized feedback from the server and presents it to the user through the user interface. The outputted feedback supports the user's learning experience and is individually optimized based on their emotional state. The user reviews this feedback and uses it to improve the learning process.

[0182] Step 6:

[0183] The server stores all input data, sentiment data, and generated feedback in a database. This record is used to improve the system in the future and enhance the accuracy of the feedback. As output, this data helps to customize the learning process for subsequent sessions.

[0184] (Application Example 2)

[0185] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as the "server," and the smart device 14 will be referred to as the "terminal."

[0186] Traditional online learning platforms have struggled to provide feedback that takes into account learners' emotions and motivations. Therefore, there is a need for technology that can accurately grasp learners' emotional states and provide feedback tailored to their individual learning needs.

[0187] 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.

[0188] In this invention, the server includes a learning platform provided by an information processing device, an emotion analysis means that recognizes the learner's emotional state and dynamically adjusts the feedback content based on it, and a feedback function that provides the learner with the analysis results generated by an artificial intelligence agent. This makes it possible to provide feedback that is tailored to the learner's emotional state.

[0189] An "information processing device" is an electronic device used to process and manage data, and it plays a role in providing an online platform.

[0190] A "learning platform" refers to an electronic environment or system that allows users to access learning content and progress in their studies.

[0191] An "artificial intelligence agent" is software equipped with the ability to analyze data and make autonomous decisions, and its role is to analyze the learner's work data.

[0192] A "feedback function" is a mechanism that returns the results of analysis and observation to learners, in order to support their learning process.

[0193] A "storage device" is a device that stores information and makes it available for retrieval as needed, functioning as a database.

[0194] "Emotional analysis tools" are techniques or methods for recognizing and analyzing a learner's emotional state, and for adjusting feedback in real time.

[0195] This invention realizes a system for online learning platforms that analyzes learners' emotions and provides feedback based on those emotions. The system mainly consists of a server, terminals, and users.

[0196] The server hosts the learning platform and runs artificial intelligence agents and emotion analysis tools for analyzing learners' emotions. Emotion analysis typically uses an "emotion analysis engine," which leverages image processing and speech recognition technologies to read emotions from the learner's facial expressions and voice. Specifically, it can utilize existing services such as Google® Cloud Vision API and Google Text-to-Speech. This allows the server to quantitatively evaluate the learner's emotional state and provide a personalized learning experience through feedback functions.

[0197] The terminal is a device operated by the learner to access the platform. Through the terminal, the learner works on exercises, and the data entered during this process is immediately sent to the server. The terminal also functions as the primary means of displaying feedback to the learner. In this case, visual or audible feedback is provided in real time through the interface.

[0198] For example, if the emotion analysis engine detects that a learner is struggling while solving a math problem, such as by their facial expression becoming strained, the server will send an encouraging message to the device, such as, "How about taking a short break?" An example of a prompt message when a generative AI model is used might be, "Generate an appropriate encouraging message for when the learner is stuck."

[0199] This system also includes a memory device that stores learner-specific data, accumulating past learning progress and emotional data to improve the accuracy of future feedback. This ensures that learners always receive an optimized learning experience that takes their emotions into consideration.

[0200] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0201] Step 1:

[0202] The server authenticates users accessing the learning platform. Users enter their login information, and the server authenticates them by referring to the database. If authentication is successful, learning content is provided to the user.

[0203] Step 2:

[0204] Users work on learning materials and exercises provided through their devices. The devices send input data from users during their learning (answers, feedback, self-reports on emotions, etc.) to the server.

[0205] Step 3:

[0206] The server receives the transmitted input data and analyzes the user's emotional state in real time using emotion analysis tools. During this process, the emotion analysis engine extracts emotions from the user's facial expressions and voice using data acquired from the camera and microphone. This identifies the user's emotional state (e.g., anxiety, concentration, joy).

[0207] Step 4:

[0208] The artificial intelligence agent generates appropriate feedback using the user's learning progress data along with the analyzed emotional state. Utilizing a generative AI model, it can, for example, generate positive feedback based on a prompt such as "Generate words of encouragement for when the user is feeling anxious."

[0209] Step 5:

[0210] The device presents feedback received from the server to the user through a user interface. Specifically, users can choose between voice and text feedback, further supporting their learning activities.

[0211] Step 6:

[0212] The server stores user input data, sentiment data obtained through analysis, and generated feedback in its storage device. This information will be used to improve the accuracy of feedback in future learning sessions.

[0213] Step 7:

[0214] Based on the feedback received, the user proceeds to the next learning process. The server continuously tracks subsequent progress and learning outcomes, and adjusts the learning content and methods as needed.

[0215] 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.

[0216] 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.

[0217] 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.

[0218] [Second Embodiment]

[0219] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0220] 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.

[0221] 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).

[0222] 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.

[0223] 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.

[0224] 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).

[0225] 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.

[0226] 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.

[0227] 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.

[0228] 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.

[0229] 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.

[0230] 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".

[0231] This invention is a system that enables learners to effectively acquire knowledge and skills related to AI and digital transformation through an online learning platform using an information processing device, and to apply them to actual work. This system aims not only to have an artificial intelligence agent analyze the learner's input data and provide feedback to the learner using the results, but also to accumulate knowledge within the organization and promote further business improvement based on that knowledge.

[0232] Overview of program processing

[0233] The server hosts an online learning platform where learners can log in, providing a wealth of AI and digital transformation-related learning materials and exercises. These materials are individually customized based on the learner's skill level and progress.

[0234] Users access the provided platform and work on exercises. Within the exercises, users input data and information related to their work, specific challenges, and improvement suggestions.

[0235] The terminal provides a user-friendly interface and sends user input to the server. It also receives feedback and suggestions from the server and displays them to the user.

[0236] An artificial intelligence agent analyzes user input data sent to the server in real time. This analysis utilizes natural language processing and machine learning algorithms to understand the meaning of the data, generating solutions and suggestions for specific challenges the user faces in their work.

[0237] The server provides the user with analysis results and suggestions generated by the artificial intelligence agent as feedback. Based on this feedback, the user can improve their business processes.

[0238] Specific examples

[0239] For example, when considering the introduction of a system to improve the efficiency of inventory management in a manufacturing company, the user inputs the current inventory management flow and challenges into the system. The server analyzes this data through an artificial intelligence agent and generates suggestions for optimal inventory management methods and new tools to be introduced. Based on this, the user formulates specific inventory management improvement measures and applies them to their operations. In this process, the accumulated data becomes the foundation for more refined learning and feedback from subsequent users.

[0240] In this way, the present invention provides a practical form that supports the use of AI throughout an entire company and improves operational efficiency.

[0241] The following describes the processing flow.

[0242] Step 1:

[0243] The server launches the online learning platform that learners access, and prepares the necessary learning materials and exercises. It also authenticates user login information and verifies access rights.

[0244] Step 2:

[0245] Users log in to the online platform using their devices and view the provided learning materials and exercises. Based on their skill level and progress, they access individually recommended content aligned with their learning objectives.

[0246] Step 3:

[0247] Users input specific challenges and information related to their work into the platform as answers to practice problems. This may include diagrams of business processes and detailed descriptions of the challenges.

[0248] Step 4:

[0249] The terminal formats the data entered by the user and sends it to the server. The information is transferred in a secure format and stored in the server's database.

[0250] Step 5:

[0251] The server uses an artificial intelligence agent to analyze user input data and extract insights that can help improve business operations. This process utilizes machine learning algorithms and natural language processing techniques.

[0252] Step 6:

[0253] The server sends the analysis results and improvement suggestions generated by the artificial intelligence agent to the user as feedback. The feedback is displayed to the user via the terminal.

[0254] Step 7:

[0255] Users develop action plans to improve their business processes based on the feedback they receive. They can request re-entry or additional feedback for the next steps as needed.

[0256] Step 8:

[0257] The server stores user feedback and improvement results in a database, which will be used to improve future AI agents. This also strengthens the knowledge base within the organization.

[0258] (Example 1)

[0259] 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."

[0260] In today's learning environment, learners are required to effectively acquire practical skills directly applicable to their work and to use those skills to improve work processes. However, traditional learning platforms have struggled to provide personalized learning materials tailored to the needs of individual learners, as well as to offer concrete feedback and suggestions useful for actual work. Furthermore, they lacked mechanisms to accumulate learner input information and utilize it for the next step in their learning.

[0261] 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.

[0262] In this invention, the server includes an educational platform provided by an information processing device, an intelligent agent that analyzes work-related information entered by the learner, and an evaluation means that provides the learner with the analysis results generated by the intelligent agent. This enables effective skill acquisition and work improvement suggestions based on the learner's work-related information.

[0263] An "information processing device" is a system consisting of a combination of hardware and software for storing, processing, and acquiring data.

[0264] An "educational platform" is an online-based information management system that allows learners to access and participate in learning materials and related learning activities.

[0265] A "learner" refers to an individual user who utilizes an educational platform to acquire knowledge and skills.

[0266] An "intelligent agent" refers to a program or algorithm that can analyze input data from learners and generate feedback and suggestions tailored to business needs.

[0267] "Evaluation means" refers to a mechanism that provides learners with analysis results generated by an intelligent agent, thereby improving the quality and effectiveness of the learning process.

[0268] "Information storage means" refers to a database or storage device used to store learner input information and intelligent agent generation information for subsequent learning and improvement.

[0269] "Methods for providing learning materials" refers to a system or function for selecting and providing learning materials and practice problems according to the learner's skill level and progress.

[0270] "Consultation tools" refer to functions that, based on data analyzed by intelligent agents, present learners with solutions for improving business processes and support their implementation.

[0271] This invention is a system that uses an online education platform powered by an information processing device to enable learners to effectively acquire knowledge and skills related to AI and digital transformation and apply them to their work. The server provides learners with individually customized educational materials. This process utilizes a learning management system and database management software. Specifically, a content management system (CMS) is used as the software.

[0272] The server uses a front-end framework to build a user interface that makes it easy for learners to access the platform. Technologies such as React and Vue.js are used for this purpose. Through this interface, users work on exercises and input tasks and data related to their work. The entered information is sent to the server.

[0273] The data provided by the user is analyzed by an intelligent agent. Natural language processing (NLP) algorithms and machine learning models are used for the analysis, utilizing Python libraries such as NLTK and TensorFlow. This analysis generates specific improvement measures and suggestions for the challenges the learner faces in their work.

[0274] As a concrete example, consider using this system to improve the efficiency of inventory management in the manufacturing industry. The user follows the prompt, "Please enter details of your current inventory management flow and its challenges," and inputs the current state of operations and points they wish to improve. The intelligent agent analyzes the entered information, proposes the optimal inventory management method, and helps the user improve their business processes based on that. In this way, the present invention provides an effective technology that supports the use of AI and operational efficiency throughout the enterprise.

[0275] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0276] Step 1:

[0277] The user inputs their qualification information to log in to the online education platform. This input data is sent to the authentication server to verify whether the qualification information is correct. If the authentication is successful, the user can access a personalized dashboard. As output, the user can access the following learning materials and progress information.

[0278] Step 2:

[0279] Based on the user's learning history and skill level, the server recommends appropriate teaching materials and practice questions. For this purpose, the server retrieves user information from the internal database and selects the optimal materials using a learning management system. As output, the selected teaching materials are displayed on the user's dashboard.

[0280] Step 3:

[0281] The user inputs answers to the provided practice questions. Here, it is required to describe data and specific issues related to the user's own business. The input data is sent to the server in real time and prepared for the next analysis step.

[0282] Step 4:

[0283] The server passes the data received from the user to an artificial intelligence agent for analysis. Specifically, natural language processing (NLP) and machine learning algorithms are used to understand the context of the data and extract the necessary information. In this step, the intelligent agent uses libraries such as NLTK and TensorFlow in Python for data analysis and insight generation. As output, proposals useful for business improvement are generated.

[0284] Step 5:

[0285] The terminal displays the proposals and feedback received from the server on the screen in a format that is easy for the user to understand. This display uses interface technologies such as React and Vue.js to assist the user in easily grasping the received data. As an output, the user checks the proposal content and considers specific actions for business improvement.

[0286] Step 6:

[0287] Based on the proposals from the server, the user implements specific measures to improve their business process. In this process, the user utilizes the feedback obtained and applies it to the actual business. As a result, business efficiency is improved and the growth of the learner is promoted.

[0288] (Application Example 1)

[0289] Next, Application Example 1 will be described. In the following description, the data processing device 12 is referred to as the "server", and the smart glasses 214 are referred to as the "terminal".

[0290] In a modern urban environment, there is a demand for a system that can efficiently solve various problems that residents face in their daily lives. However, it has been difficult for previous systems to provide improvement proposals in real time that address the individual problems faced by residents. The purpose of this invention is to solve such problems.

[0291] 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.

[0292] In this invention, the server includes a learning platform provided by an information processing device, an artificial intelligence agent means for analyzing information related to the business input by the learner, and a feedback means for providing the analysis result generated by the artificial intelligence agent to the learner. Thereby, it becomes possible to accurately analyze the problems that residents face in their daily lives and provide accurate improvement proposals regarding urban operation in real time.

[0293] An "information processing device" is a device designed to efficiently manage and analyze data.

[0294] A "learning platform" is an online platform used by learners to acquire the skills and knowledge they need.

[0295] An "artificial intelligence agent" is a program that uses artificial intelligence technology to analyze input information and generate appropriate feedback and suggestions.

[0296] "Analysis results" refer to the conclusions and suggestions obtained after an artificial intelligence agent analyzes the input information.

[0297] "Feedback means" refers to methods or functions for providing analysis results to the user.

[0298] The "data storage unit" is a function that stores learner input information and information generated by artificial intelligence, for later reference and performance improvement.

[0299] "Means" refers to the methods or techniques used to achieve a specific objective.

[0300] "Residents" refers to people who live in a specific area and are the entities that need to solve problems in the urban environment.

[0301] "City management" refers to a series of activities aimed at the efficient and smooth operation and management of a city.

[0302] An "improvement suggestion" refers to specific advice or methods for resolving current problems and achieving a better situation.

[0303] A system for realizing this invention includes a learning platform provided by an information processing device, an artificial intelligence agent, a feedback means, a data storage unit, and means for suggesting improvements to urban management to support residents in solving problems.

[0304] The server hosts the learning platform and provides an interface for learners and residents to input problems. The terminal uses React Native to build the front end to realize a user-friendly interface and plays the role of sending user input to the server.

[0305] When a user inputs problems or suggestions related to daily life into the server via the terminal, the data is analyzed in real time by an artificial intelligence agent. For this analysis, natural language processing libraries in Python (such as nltk and spaCy) and machine learning frameworks (TensorFlow) are used. The artificial intelligence agent generates analysis results and derives optimal improvement suggestions.

[0306] The analysis results are provided to the user as feedback generated by the server. Based on this feedback, residents can obtain specific ideas to more efficiently improve their life in the city.

[0307] As a specific example, when a resident inputs "Every morning, the traffic jam in this area is severe", the system proposes alternative routes or adjustments to traffic signals. In this way, specific solutions to the problems faced by residents are presented.

[0308] Examples of prompt texts using the generated AI model include "Please consider improvement suggestions for local events. Please come up with ideas for events that are easy for local residents to participate in." By using this prompt, the system supports the automatic generation of effective event plans.

[0309] The flow of specific processing in Application Example 1 will be described using FIG. 12.

[0310] Step 1:

[0311] Users input problems related to their daily lives using a terminal. The entered data is sent to the server through a user-friendly interface. During this process, users are guided to fill in the appropriate fields according to their needs.

[0312] Step 2:

[0313] The server passes the input data to the artificial intelligence agent in real time. The data is analyzed using Python's natural language processing libraries (nltk and spaCy). The purpose of the analysis is to accurately grasp the meaning of the input sentence and identify specific problems and needs. The output includes a list of problems and categorized requests.

[0314] Step 3:

[0315] The artificial intelligence agent generates optimal improvement suggestions using a machine learning framework (TensorFlow) based on the analysis results. This step involves referencing historical data and statistical models to consider solutions to the derived problems. The output is a list of specific solutions and advice.

[0316] Step 4:

[0317] The server provides the generated suggestions to the user as feedback. This feedback is sent back from the server to the terminal and displayed on the user's interface. The user can review the suggestions and obtain specific steps to take action. This feedback may also include the expected effects of the suggested improvements and any additional resources needed.

[0318] Step 5:

[0319] Through the terminal, users can input additional questions or comments on the feedback provided, starting a new cycle. This allows the suggestions to be more individualized or adjusted as needed. The output is a newly adjusted suggestion.

[0320] An example of a prompt message would be: "Please come up with suggestions for improving local events. Please provide ideas for events that are easy for local residents to participate in."

[0321] 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.

[0322] This invention relates to a system for online learning platforms that can recognize learners' emotions and provide feedback based on those emotions. This system allows learners to learn effectively through individually adapted learning materials and receive a combination of analysis results from an artificial intelligence agent and emotional feedback.

[0323] Overview of program processing

[0324] The server hosts the online learning platform and provides learners with appropriate learning materials and exercises. During this process, it handles learner login authentication and access verification.

[0325] Users access the learning platform through their devices and work on necessary materials and exercises. During the learning process, users are required to input information about their emotions and work-related matters.

[0326] The terminal is responsible for receiving user input data and sending it to the server. It also provides feedback and displays learning materials through the user interface.

[0327] The emotion engine analyzes data input from the user and recognizes the learner's emotional state in real time. Based on this emotional data, the AI ​​agent adjusts the analysis results.

[0328] The server integrates emotional data obtained by the emotion engine with learner work data and reflects this in the analysis results generated by the artificial intelligence agent. This ensures that feedback is provided in a way that is adapted to the learner's emotional state.

[0329] The database accumulates learner input data, sentiment data, and analysis results, contributing to improved feedback accuracy and platform improvements in subsequent sessions.

[0330] Specific examples

[0331] For example, suppose a learner uses a platform to learn new sales techniques. When working on exercises, the user inputs their current sales strategies and their feelings about the results. The emotion engine analyzes this, and if it recognizes that the user's emotional state is, for example, "anxious," the AI ​​agent adjusts the feedback to make the suggestions more positive and focus on solutions. This process allows the user to receive suggestions tailored to their individual emotional state and try new techniques with confidence.

[0332] In this way, by utilizing emotion recognition, it is possible to implement a system that provides a learning experience optimized for learners and supports its application in business.

[0333] The following describes the processing flow.

[0334] Step 1:

[0335] Users log in to the online learning platform using their devices and access the provided learning materials and practice problems. Authentication is performed using a username and password during login.

[0336] Step 2:

[0337] The server presents the most suitable learning materials and exercises based on the logged-in user's skill level and progress. This allows users to effectively engage with learning content tailored to their needs.

[0338] Step 3:

[0339] When answering practice questions, users input work-related information and their emotional state (e.g., satisfaction, anxiety, excitement) into the terminal. This information is used to customize the learning experience.

[0340] Step 4:

[0341] The terminal transmits the entered work information and emotional data to the server. The transmitted data is encrypted to protect privacy.

[0342] Step 5:

[0343] To analyze the received data, the server first uses an emotion engine to analyze the emotional data and identify the user's emotional state. In this step, natural language processing techniques are used to determine text-based emotional information.

[0344] Step 6:

[0345] The server analyzes business-related data through an artificial intelligence agent and generates optimal business improvement suggestions. During this process, the content and tone of the suggestions are adjusted based on the results of the emotion engine.

[0346] Step 7:

[0347] The server transmits the generated feedback and suggestions for business improvement to the user via the terminal. These suggestions include personalized responses that take into account the user's emotional state.

[0348] Step 8:

[0349] Based on the feedback received, users develop improvement strategies for their own work. By providing emotionally resonant suggestions, users are expected to take more proactive and positive actions.

[0350] Step 9:

[0351] The server stores all user input data and analysis results in a database, which is used for the continuous improvement of the AI ​​model. This data contributes to improving the accuracy of feedback for future users.

[0352] (Example 2)

[0353] 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".

[0354] Traditional education systems have a problem in that they do not take into account the emotional state of learners and optimize the learning process accordingly, resulting in insufficient improvements in learning efficiency and work processes. In particular, when learners are experiencing anxiety or stress, these emotions are left unaddressed, leading to a decrease in motivation to learn and making it difficult to achieve optimal educational results.

[0355] 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.

[0356] In this invention, the server includes means for constructing an educational platform provided by an information processing device, artificial intelligence means for identifying emotional data input by learners in real time and adjusting the analysis results, and feedback function means for providing feedback optimized to the learner's emotional state. This enables individualized optimization according to the learner's emotional state, leading to improved learning efficiency and emotion-based improvements to business processes.

[0357] An "information processing device" refers to a machine equipped with hardware and software capable of handling electronic data and performing various calculations and analyses based on that data.

[0358] An "educational platform" refers to a system that provides an online environment for learners to access and engage in learning through educational materials and practice exercises.

[0359] "Emotional state" refers to data that indicates the psychological state, such as anxiety, joy, and stress, that learners experience in a particular situation.

[0360] "Artificial intelligence methods" refer to technologies that use machine learning and natural language processing to analyze input data and generate judgments and feedback.

[0361] "Feedback function means" refers to a function that provides learners with information based on analysis results to improve and support the learning process.

[0362] "Memory storage" refers to data storage that saves learner input data and analysis results to be used for future improvement.

[0363] This invention is a system for providing a learning environment that responds to the emotional state of learners, based on an educational platform provided by an information processing device.

[0364] The server manages the educational platform and provides learners with learning materials and exercises optimized for their needs. Using artificial intelligence (AI) tools, the server analyzes the emotional states entered by learners through their devices in real time and adjusts feedback based on the results. Specific examples of AI tools include emotion analysis engines and machine learning algorithms.

[0365] The terminal is a device that allows learners to access the platform and work on learning materials and exercises via a user interface. The terminal not only displays feedback and learning materials from the server, but also prompts learners to input their emotional state and work-related data, which is then transmitted to the server.

[0366] Users, or learners, need to input their emotional state and learning progress while using a device. This information is analyzed by a server, and feedback tailored to the learner's needs is provided.

[0367] This allows the learning experience to be individually optimized based on the learner's emotional state, thereby improving learning efficiency. For example, if a learner inputs an emotion such as "anxiety," the emotion analysis engine will analyze this data and generate feedback such as, "Let's try some specific steps to reduce anxiety," provided by an AI model. In this process, an example of a prompt might be, "Learner's emotional data has been entered. Please adjust the feedback and generate specific suggestions to improve the learning content." This method allows learners to acquire new knowledge and skills with confidence.

[0368] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0369] Step 1:

[0370] The user logs into the educational platform using a terminal. The input here is the user's authentication information. The terminal sends the user's entered authentication information to the server. The server compares it against the database to verify the validity of the login. The output of this process is the authentication result, granting the user access to the platform.

[0371] Step 2:

[0372] After confirming that the user has logged in, the server selects appropriate learning materials and exercises on the educational platform. The server uses the user's learning history data and progress information as input and analyzes this data using a generative AI model. The output of this process is a set of learning materials tailored to the user. The terminal then displays these materials on the user interface.

[0373] Step 3:

[0374] Users input their emotional state using a terminal while working through the provided learning materials and exercises. Emotional data and work-related information are used as input data and sent from the terminal to the server. The server transmits this input data to an emotion analysis engine, which then analyzes the user's emotional state. The output of this process is data concerning the user's specific emotional state.

[0375] Step 4:

[0376] The server holds emotional data generated by an emotion analysis engine and uses a generative AI model to generate user-appropriate feedback. In this process, the AI ​​model uses emotional data and learning history data as input and performs data calculations. The output is feedback adjusted according to the user's emotional state. This feedback includes appropriate solutions and encouragement for the learner.

[0377] Step 5:

[0378] The device receives optimized feedback from the server and presents it to the user through the user interface. The outputted feedback supports the user's learning experience and is individually optimized based on their emotional state. The user reviews this feedback and uses it to improve the learning process.

[0379] Step 6:

[0380] The server stores all input data, sentiment data, and generated feedback in a database. This record is used to improve the system in the future and enhance the accuracy of the feedback. As output, this data helps to customize the learning process for subsequent sessions.

[0381] (Application Example 2)

[0382] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as the "server" and the smart glasses 214 as the "terminal".

[0383] Traditional online learning platforms have struggled to provide feedback that takes into account learners' emotions and motivations. Therefore, there is a need for technology that can accurately grasp learners' emotional states and provide feedback tailored to their individual learning needs.

[0384] 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.

[0385] In this invention, the server includes a learning platform provided by an information processing device, an emotion analysis means that recognizes the learner's emotional state and dynamically adjusts the feedback content based on it, and a feedback function that provides the learner with the analysis results generated by an artificial intelligence agent. This makes it possible to provide feedback that is tailored to the learner's emotional state.

[0386] An "information processing device" is an electronic device used to process and manage data, and it plays a role in providing an online platform.

[0387] A "learning platform" refers to an electronic environment or system that allows users to access learning content and progress in their studies.

[0388] An "artificial intelligence agent" is software equipped with the ability to analyze data and make autonomous decisions, and its role is to analyze the learner's work data.

[0389] A "feedback function" is a mechanism that returns the results of analysis and observation to learners, in order to support their learning process.

[0390] A "storage device" is a device that stores information and makes it available for retrieval as needed, functioning as a database.

[0391] "Emotional analysis tools" are techniques or methods for recognizing and analyzing a learner's emotional state, and for adjusting feedback in real time.

[0392] This invention realizes a system for online learning platforms that analyzes learners' emotions and provides feedback based on those emotions. The system mainly consists of a server, terminals, and users.

[0393] The server hosts the learning platform and runs artificial intelligence agents and emotion analysis tools for analyzing learners' emotions. Emotion analysis typically uses an "emotion analysis engine," which leverages image processing and speech recognition technologies to read emotions from the learner's facial expressions and voice. Specifically, existing services such as Google Cloud Vision API and Google Text-to-Speech can be utilized. This allows the server to quantitatively evaluate the learner's emotional state and provide a personalized learning experience through feedback functions.

[0394] The terminal is a device operated by the learner to access the platform. Through the terminal, the learner works on exercises, and the data entered during this process is immediately sent to the server. The terminal also functions as the primary means of displaying feedback to the learner. In this case, visual or audible feedback is provided in real time through the interface.

[0395] For example, if the emotion analysis engine detects that a learner is struggling while solving a math problem, such as by their facial expression becoming strained, the server will send an encouraging message to the device, such as, "How about taking a short break?" An example of a prompt message when a generative AI model is used might be, "Generate an appropriate encouraging message for when the learner is stuck."

[0396] This system also includes a memory device that stores learner-specific data, accumulating past learning progress and emotional data to improve the accuracy of future feedback. This ensures that learners always receive an optimized learning experience that takes their emotions into consideration.

[0397] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0398] Step 1:

[0399] The server authenticates users accessing the learning platform. Users enter their login information, and the server authenticates them by referring to the database. If authentication is successful, learning content is provided to the user.

[0400] Step 2:

[0401] Users work on learning materials and exercises provided through their devices. The devices send input data from users during their learning (answers, feedback, self-reports on emotions, etc.) to the server.

[0402] Step 3:

[0403] The server receives the transmitted input data and analyzes the user's emotional state in real time using emotion analysis tools. During this process, the emotion analysis engine extracts emotions from the user's facial expressions and voice using data acquired from the camera and microphone. This identifies the user's emotional state (e.g., anxiety, concentration, joy).

[0404] Step 4:

[0405] The artificial intelligence agent generates appropriate feedback using the user's learning progress data along with the analyzed emotional state. Utilizing a generative AI model, it can, for example, generate positive feedback based on a prompt such as "Generate words of encouragement for when the user is feeling anxious."

[0406] Step 5:

[0407] The device presents feedback received from the server to the user through a user interface. Specifically, users can choose between voice and text feedback, further supporting their learning activities.

[0408] Step 6:

[0409] The server stores user input data, sentiment data obtained through analysis, and generated feedback in its storage device. This information will be used to improve the accuracy of feedback in future learning sessions.

[0410] Step 7:

[0411] Based on the feedback received, the user proceeds to the next learning process. The server continuously tracks subsequent progress and learning outcomes, and adjusts the learning content and methods as needed.

[0412] 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.

[0413] 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.

[0414] 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.

[0415] [Third Embodiment]

[0416] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0417] 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.

[0418] 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).

[0419] 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.

[0420] 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.

[0421] 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).

[0422] 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.

[0423] 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.

[0424] 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.

[0425] 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.

[0426] 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.

[0427] 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".

[0428] This invention is a system that enables learners to effectively acquire knowledge and skills related to AI and digital transformation through an online learning platform using an information processing device, and to apply them to actual work. This system aims not only to have an artificial intelligence agent analyze the learner's input data and provide feedback to the learner using the results, but also to accumulate knowledge within the organization and promote further business improvement based on that knowledge.

[0429] Overview of program processing

[0430] The server hosts an online learning platform where learners can log in, providing a wealth of AI and digital transformation-related learning materials and exercises. These materials are individually customized based on the learner's skill level and progress.

[0431] Users access the provided platform and work on exercises. Within the exercises, users input data and information related to their work, specific challenges, and improvement suggestions.

[0432] The terminal provides a user-friendly interface and sends user input to the server. It also receives feedback and suggestions from the server and displays them to the user.

[0433] An artificial intelligence agent analyzes user input data sent to the server in real time. This analysis utilizes natural language processing and machine learning algorithms to understand the meaning of the data, generating solutions and suggestions for specific challenges the user faces in their work.

[0434] The server provides the user with analysis results and suggestions generated by the artificial intelligence agent as feedback. Based on this feedback, the user can improve their business processes.

[0435] Specific examples

[0436] For example, when considering the introduction of a system to improve the efficiency of inventory management in a manufacturing company, the user inputs the current inventory management flow and challenges into the system. The server analyzes this data through an artificial intelligence agent and generates suggestions for optimal inventory management methods and new tools to be introduced. Based on this, the user formulates specific inventory management improvement measures and applies them to their operations. In this process, the accumulated data becomes the foundation for more refined learning and feedback from subsequent users.

[0437] In this way, the present invention provides a practical form that supports the use of AI throughout an entire company and improves operational efficiency.

[0438] The following describes the processing flow.

[0439] Step 1:

[0440] The server launches the online learning platform that learners access, and prepares the necessary learning materials and exercises. It also authenticates user login information and verifies access rights.

[0441] Step 2:

[0442] Users log in to the online platform using their devices and view the provided learning materials and exercises. Based on their skill level and progress, they access individually recommended content aligned with their learning objectives.

[0443] Step 3:

[0444] Users input specific challenges and information related to their work into the platform as answers to practice problems. This may include diagrams of business processes and detailed descriptions of the challenges.

[0445] Step 4:

[0446] The terminal formats the data entered by the user and sends it to the server. The information is transferred in a secure format and stored in the server's database.

[0447] Step 5:

[0448] The server uses an artificial intelligence agent to analyze user input data and extract insights that can help improve business operations. This process utilizes machine learning algorithms and natural language processing techniques.

[0449] Step 6:

[0450] The server sends the analysis results and improvement suggestions generated by the artificial intelligence agent to the user as feedback. The feedback is displayed to the user via the terminal.

[0451] Step 7:

[0452] Users develop action plans to improve their business processes based on the feedback they receive. They can request re-entry or additional feedback for the next steps as needed.

[0453] Step 8:

[0454] The server stores user feedback and improvement results in a database, which will be used to improve future AI agents. This also strengthens the knowledge base within the organization.

[0455] (Example 1)

[0456] 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."

[0457] In today's learning environment, learners are required to effectively acquire practical skills directly applicable to their work and to use those skills to improve work processes. However, traditional learning platforms have struggled to provide personalized learning materials tailored to the needs of individual learners, as well as to offer concrete feedback and suggestions useful for actual work. Furthermore, they lacked mechanisms to accumulate learner input information and utilize it for the next step in their learning.

[0458] 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.

[0459] In this invention, the server includes an educational platform provided by an information processing device, an intelligent agent that analyzes work-related information entered by the learner, and an evaluation means that provides the learner with the analysis results generated by the intelligent agent. This enables effective skill acquisition and work improvement suggestions based on the learner's work-related information.

[0460] An "information processing device" is a system consisting of a combination of hardware and software for storing, processing, and acquiring data.

[0461] An "educational platform" is an online-based information management system that allows learners to access and participate in learning materials and related learning activities.

[0462] A "learner" refers to an individual user who utilizes an educational platform to acquire knowledge and skills.

[0463] An "intelligent agent" refers to a program or algorithm that can analyze input data from learners and generate feedback and suggestions tailored to business needs.

[0464] "Evaluation means" refers to a mechanism that provides learners with analysis results generated by an intelligent agent, thereby improving the quality and effectiveness of the learning process.

[0465] "Information storage means" refers to a database or storage device used to store learner input information and intelligent agent generation information for subsequent learning and improvement.

[0466] "Methods for providing learning materials" refers to a system or function for selecting and providing learning materials and practice problems according to the learner's skill level and progress.

[0467] "Consultation tools" refer to functions that use data analyzed by intelligent agents to suggest improvement measures for business processes to learners and support their implementation.

[0468] This invention is a system that uses an online education platform powered by an information processing device to enable learners to effectively acquire knowledge and skills related to AI and digital transformation and apply them to their work. The server provides learners with individually customized educational materials. This process utilizes a learning management system and database management software. Specifically, a content management system (CMS) is used as the software.

[0469] The server uses a front-end framework to build a user interface that makes it easy for learners to access the platform. Technologies such as React and Vue.js are used for this purpose. Through this interface, users work on exercises and input tasks and data related to their work. The entered information is sent to the server.

[0470] The data provided by the user is analyzed by an intelligent agent. Natural language processing (NLP) algorithms and machine learning models are used for the analysis, utilizing Python libraries such as NLTK and TensorFlow. This analysis generates specific improvement measures and suggestions for the challenges the learner faces in their work.

[0471] As a concrete example, consider using this system to improve the efficiency of inventory management in the manufacturing industry. The user follows the prompt, "Please enter details of your current inventory management flow and its challenges," and inputs the current state of operations and points they wish to improve. The intelligent agent analyzes the entered information, proposes the optimal inventory management method, and helps the user improve their business processes based on that. In this way, the present invention provides an effective technology that supports the use of AI and operational efficiency throughout the enterprise.

[0472] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0473] Step 1:

[0474] Users enter their credentials to log in to the online education platform. This input data is sent to an authentication server for verification of the credentials' validity. If authentication is successful, users can access a personalized dashboard. As output, users will have access to the following learning materials and progress information.

[0475] Step 2:

[0476] The server recommends appropriate learning materials and exercises based on the user's learning history and skill level. To do this, the server retrieves user information from an internal database and uses a learning management system to select the most suitable materials. As output, the selected materials are displayed on the user's dashboard.

[0477] Step 3:

[0478] Users input their answers to the provided exercises. Here, they are required to describe data and specific challenges related to their own work. The input data is transmitted to the server in real time and prepared for the next analysis step.

[0479] Step 4:

[0480] The server passes the data received from the user to an artificial intelligence agent for analysis. Specifically, it uses natural language processing (NLP) and machine learning algorithms to understand the context of the data and extract necessary information. In this step, the intelligent agent uses libraries such as Python's NLTK and TensorFlow to perform data analysis and insight generation. As output, suggestions that can help improve business operations are generated.

[0481] Step 5:

[0482] The terminal displays suggestions and feedback received from the server in a user-friendly format. Interface technologies such as React and Vue.js are used for this display to help users easily grasp the received data. As output, users review the suggestions and consider specific actions for improving their work processes.

[0483] Step 6:

[0484] Based on suggestions from the server, users implement specific measures to improve their own work processes. During this process, they utilize the feedback they receive and apply it to their actual work. As a result, work efficiency improves, and learner growth is promoted.

[0485] (Application Example 1)

[0486] 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."

[0487] In modern urban environments, there is a need for systems that can efficiently solve the various problems residents face in their daily lives. However, existing systems have struggled to provide real-time improvement suggestions tailored to the individual problems residents encounter. This invention aims to solve this problem.

[0488] 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.

[0489] In this invention, the server includes a learning platform provided by an information processing device, an artificial intelligence agent means for analyzing information related to tasks input by the learner, and a feedback means for providing the learner with the analysis results generated by the artificial intelligence agent. This makes it possible to accurately analyze problems that residents face in their daily lives and to provide accurate improvement suggestions regarding urban management in real time.

[0490] An "information processing device" is a device designed to efficiently manage and analyze data.

[0491] A "learning platform" is an online platform used by learners to acquire the skills and knowledge they need.

[0492] An "artificial intelligence agent" is a program that uses artificial intelligence technology to analyze input information and generate appropriate feedback and suggestions.

[0493] "Analysis results" refer to the conclusions and suggestions obtained after an artificial intelligence agent analyzes the input information.

[0494] "Feedback means" refers to methods or functions for providing analysis results to the user.

[0495] The "data storage unit" is a function that stores learner input information and information generated by artificial intelligence, for later reference and performance improvement.

[0496] "Means" refers to the methods or techniques used to achieve a specific objective.

[0497] "Residents" refers to people who live in a specific area and are the entities that need to solve problems in the urban environment.

[0498] "City management" refers to a series of activities aimed at the efficient and smooth operation and management of a city.

[0499] An "improvement suggestion" refers to specific advice or methods for resolving current problems and achieving a better situation.

[0500] A system for realizing this invention includes a learning platform provided by an information processing device, an artificial intelligence agent, a feedback means, a data storage unit, and means for suggesting improvements to urban management to support residents in solving problems.

[0501] The server hosts the learning infrastructure and provides an interface for learners and residents to input questions. The terminal uses React Native to build the frontend and implement a user-friendly interface, and is responsible for sending user input to the server.

[0502] When a user inputs problems or suggestions related to their daily life into a server via their device, the data is analyzed in real time by an artificial intelligence agent. This analysis utilizes Python's natural language processing libraries (nltk and spaCy) and machine learning frameworks (TensorFlow). The AI ​​agent generates analysis results and derives optimal improvement suggestions.

[0503] The analysis results are provided to the user as feedback generated by the server. Based on this feedback, residents can gain concrete ideas for improving their urban life more efficiently.

[0504] For example, if a resident inputs "Traffic congestion in this area is terrible every morning," the system will suggest alternative routes or adjust traffic signals. In this way, it provides concrete solutions to the problems residents face.

[0505] An example of a prompt using a generative AI model is, "Please come up with suggestions for improving local events. Please provide ideas for events that are easy for local residents to participate in." By using this prompt, the system helps to automatically generate effective event plans.

[0506] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0507] Step 1:

[0508] Users input problems related to their daily lives using a terminal. The entered data is sent to the server through a user-friendly interface. During this process, users are guided to fill in the appropriate fields according to their needs.

[0509] Step 2:

[0510] The server passes the input data to the artificial intelligence agent in real time. The data is analyzed using Python's natural language processing libraries (nltk and spaCy). The purpose of the analysis is to accurately grasp the meaning of the input sentence and identify specific problems and needs. The output includes a list of problems and categorized requests.

[0511] Step 3:

[0512] The artificial intelligence agent generates optimal improvement suggestions using a machine learning framework (TensorFlow) based on the analysis results. This step involves referencing historical data and statistical models to consider solutions to the derived problems. The output is a list of specific solutions and advice.

[0513] Step 4:

[0514] The server provides the generated suggestions to the user as feedback. This feedback is sent back from the server to the terminal and displayed on the user's interface. The user can review the suggestions and obtain specific steps to take action. This feedback may also include the expected effects of the suggested improvements and any additional resources needed.

[0515] Step 5:

[0516] Through the terminal, users can input additional questions or comments on the feedback provided, starting a new cycle. This allows the suggestions to be more individualized or adjusted as needed. The output is a newly adjusted suggestion.

[0517] An example of a prompt message would be: "Please come up with suggestions for improving local events. Please provide ideas for events that are easy for local residents to participate in."

[0518] 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.

[0519] This invention relates to a system for online learning platforms that can recognize learners' emotions and provide feedback based on those emotions. This system allows learners to learn effectively through individually adapted learning materials and receive a combination of analysis results from an artificial intelligence agent and emotional feedback.

[0520] Overview of program processing

[0521] The server hosts the online learning platform and provides learners with appropriate learning materials and exercises. During this process, it handles learner login authentication and access verification.

[0522] Users access the learning platform through their devices and work on necessary materials and exercises. During the learning process, users are required to input information about their emotions and work-related matters.

[0523] The terminal is responsible for receiving user input data and sending it to the server. It also provides feedback and displays learning materials through the user interface.

[0524] The emotion engine analyzes data input from the user and recognizes the learner's emotional state in real time. Based on this emotional data, the AI ​​agent adjusts the analysis results.

[0525] The server integrates emotional data obtained by the emotion engine with learner work data and reflects this in the analysis results generated by the artificial intelligence agent. This ensures that feedback is provided in a way that is adapted to the learner's emotional state.

[0526] The database accumulates learner input data, sentiment data, and analysis results, contributing to improved feedback accuracy and platform improvements in subsequent sessions.

[0527] Specific examples

[0528] For example, suppose a learner uses a platform to learn new sales techniques. When working on exercises, the user inputs their current sales strategies and their feelings about the results. The emotion engine analyzes this, and if it recognizes that the user's emotional state is, for example, "anxious," the AI ​​agent adjusts the feedback to make the suggestions more positive and focus on solutions. This process allows the user to receive suggestions tailored to their individual emotional state and try new techniques with confidence.

[0529] In this way, by utilizing emotion recognition, it is possible to implement a system that provides a learning experience optimized for learners and supports its application in business.

[0530] The following describes the processing flow.

[0531] Step 1:

[0532] Users log in to the online learning platform using their devices and access the provided learning materials and practice problems. Authentication is performed using a username and password during login.

[0533] Step 2:

[0534] The server presents the most suitable learning materials and exercises based on the logged-in user's skill level and progress. This allows users to effectively engage with learning content tailored to their needs.

[0535] Step 3:

[0536] When answering practice questions, users input work-related information and their emotional state (e.g., satisfaction, anxiety, excitement) into the terminal. This information is used to customize the learning experience.

[0537] Step 4:

[0538] The terminal transmits the entered work information and emotional data to the server. The transmitted data is encrypted to protect privacy.

[0539] Step 5:

[0540] To analyze the received data, the server first uses an emotion engine to analyze the emotional data and identify the user's emotional state. In this step, natural language processing techniques are used to determine text-based emotional information.

[0541] Step 6:

[0542] The server analyzes business-related data through an artificial intelligence agent and generates optimal business improvement suggestions. During this process, the content and tone of the suggestions are adjusted based on the results of the emotion engine.

[0543] Step 7:

[0544] The server transmits the generated feedback and suggestions for business improvement to the user via the terminal. These suggestions include personalized responses that take into account the user's emotional state.

[0545] Step 8:

[0546] Based on the feedback received, users develop improvement strategies for their own work. By providing emotionally resonant suggestions, users are expected to take more proactive and positive actions.

[0547] Step 9:

[0548] The server stores all user input data and analysis results in a database, which is used for the continuous improvement of the AI ​​model. This data contributes to improving the accuracy of feedback for future users.

[0549] (Example 2)

[0550] 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."

[0551] Traditional education systems have a problem in that they do not take into account the emotional state of learners and optimize the learning process accordingly, resulting in insufficient improvements in learning efficiency and work processes. In particular, when learners are experiencing anxiety or stress, these emotions are left unaddressed, leading to a decrease in motivation to learn and making it difficult to achieve optimal educational results.

[0552] 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.

[0553] In this invention, the server includes means for constructing an educational platform provided by an information processing device, artificial intelligence means for identifying emotional data input by learners in real time and adjusting the analysis results, and feedback function means for providing feedback optimized to the learner's emotional state. This enables individualized optimization according to the learner's emotional state, leading to improved learning efficiency and emotion-based improvements to business processes.

[0554] An "information processing device" refers to a machine equipped with hardware and software capable of handling electronic data and performing various calculations and analyses based on that data.

[0555] An "educational platform" refers to a system that provides an online environment for learners to access and engage in learning through educational materials and practice exercises.

[0556] "Emotional state" refers to data that indicates the psychological state, such as anxiety, joy, and stress, that learners experience in a particular situation.

[0557] "Artificial intelligence methods" refer to technologies that use machine learning and natural language processing to analyze input data and generate judgments and feedback.

[0558] "Feedback function means" refers to a function that provides learners with information based on analysis results to improve and support the learning process.

[0559] "Memory storage" refers to data storage that saves learner input data and analysis results to be used for future improvement.

[0560] This invention is a system for providing a learning environment that responds to the emotional state of learners, based on an educational platform provided by an information processing device.

[0561] The server manages the educational platform and provides learners with learning materials and exercises optimized for their needs. Using artificial intelligence (AI) tools, the server analyzes the emotional states entered by learners through their devices in real time and adjusts feedback based on the results. Specific examples of AI tools include emotion analysis engines and machine learning algorithms.

[0562] The terminal is a device that allows learners to access the platform and work on learning materials and exercises via a user interface. The terminal not only displays feedback and learning materials from the server, but also prompts learners to input their emotional state and work-related data, which is then transmitted to the server.

[0563] Users, or learners, need to input their emotional state and learning progress while using a device. This information is analyzed by a server, and feedback tailored to the learner's needs is provided.

[0564] This allows the learning experience to be individually optimized based on the learner's emotional state, thereby improving learning efficiency. For example, if a learner inputs an emotion such as "anxiety," the emotion analysis engine will analyze this data and generate feedback such as, "Let's try some specific steps to reduce anxiety," provided by an AI model. In this process, an example of a prompt might be, "Learner's emotional data has been entered. Please adjust the feedback and generate specific suggestions to improve the learning content." This method allows learners to acquire new knowledge and skills with confidence.

[0565] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0566] Step 1:

[0567] The user logs into the educational platform using a terminal. The input here is the user's authentication information. The terminal sends the user's entered authentication information to the server. The server compares it against the database to verify the validity of the login. The output of this process is the authentication result, granting the user access to the platform.

[0568] Step 2:

[0569] After confirming that the user has logged in, the server selects appropriate learning materials and exercises on the educational platform. The server uses the user's learning history data and progress information as input and analyzes this data using a generative AI model. The output of this process is a set of learning materials tailored to the user. The terminal then displays these materials on the user interface.

[0570] Step 3:

[0571] Users input their emotional state using a terminal while working through the provided learning materials and exercises. Emotional data and work-related information are used as input data and sent from the terminal to the server. The server transmits this input data to an emotion analysis engine, which then analyzes the user's emotional state. The output of this process is data concerning the user's specific emotional state.

[0572] Step 4:

[0573] The server holds emotional data generated by an emotion analysis engine and uses a generative AI model to generate user-appropriate feedback. In this process, the AI ​​model uses emotional data and learning history data as input and performs data calculations. The output is feedback adjusted according to the user's emotional state. This feedback includes appropriate solutions and encouragement for the learner.

[0574] Step 5:

[0575] The device receives optimized feedback from the server and presents it to the user through the user interface. The outputted feedback supports the user's learning experience and is individually optimized based on their emotional state. The user reviews this feedback and uses it to improve the learning process.

[0576] Step 6:

[0577] The server stores all input data, sentiment data, and generated feedback in a database. This record is used to improve the system in the future and enhance the accuracy of the feedback. As output, this data helps to customize the learning process for subsequent sessions.

[0578] (Application Example 2)

[0579] 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."

[0580] Traditional online learning platforms have struggled to provide feedback that takes into account learners' emotions and motivations. Therefore, there is a need for technology that can accurately grasp learners' emotional states and provide feedback tailored to their individual learning needs.

[0581] 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.

[0582] In this invention, the server includes a learning platform provided by an information processing device, an emotion analysis means that recognizes the learner's emotional state and dynamically adjusts the feedback content based on it, and a feedback function that provides the learner with the analysis results generated by an artificial intelligence agent. This makes it possible to provide feedback that is tailored to the learner's emotional state.

[0583] An "information processing device" is an electronic device used to process and manage data, and it plays a role in providing an online platform.

[0584] A "learning platform" refers to an electronic environment or system that allows users to access learning content and progress in their studies.

[0585] An "artificial intelligence agent" is software equipped with the ability to analyze data and make autonomous decisions, and its role is to analyze the learner's work data.

[0586] A "feedback function" is a mechanism that returns the results of analysis and observation to learners, in order to support their learning process.

[0587] A "storage device" is a device that stores information and makes it available for retrieval as needed, functioning as a database.

[0588] "Emotional analysis tools" are techniques or methods for recognizing and analyzing a learner's emotional state, and for adjusting feedback in real time.

[0589] This invention realizes a system for online learning platforms that analyzes learners' emotions and provides feedback based on those emotions. The system mainly consists of a server, terminals, and users.

[0590] The server hosts the learning platform and runs artificial intelligence agents and emotion analysis tools for analyzing learners' emotions. Emotion analysis typically uses an "emotion analysis engine," which leverages image processing and speech recognition technologies to read emotions from the learner's facial expressions and voice. Specifically, existing services such as Google Cloud Vision API and Google Text-to-Speech can be utilized. This allows the server to quantitatively evaluate the learner's emotional state and provide a personalized learning experience through feedback functions.

[0591] The terminal is a device operated by the learner to access the platform. Through the terminal, the learner works on exercises, and the data entered during this process is immediately sent to the server. The terminal also functions as the primary means of displaying feedback to the learner. In this case, visual or audible feedback is provided in real time through the interface.

[0592] For example, if the emotion analysis engine detects that a learner is struggling while solving a math problem, such as by their facial expression becoming strained, the server will send an encouraging message to the device, such as, "How about taking a short break?" An example of a prompt message when a generative AI model is used might be, "Generate an appropriate encouraging message for when the learner is stuck."

[0593] This system also includes a memory device that stores learner-specific data, accumulating past learning progress and emotional data to improve the accuracy of future feedback. This ensures that learners always receive an optimized learning experience that takes their emotions into consideration.

[0594] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0595] Step 1:

[0596] The server authenticates users accessing the learning platform. Users enter their login information, and the server authenticates them by referring to the database. If authentication is successful, learning content is provided to the user.

[0597] Step 2:

[0598] Users work on learning materials and exercises provided through their devices. The devices send input data from users during their learning (answers, feedback, self-reports on emotions, etc.) to the server.

[0599] Step 3:

[0600] The server receives the transmitted input data and analyzes the user's emotional state in real time using emotion analysis tools. During this process, the emotion analysis engine extracts emotions from the user's facial expressions and voice using data acquired from the camera and microphone. This identifies the user's emotional state (e.g., anxiety, concentration, joy).

[0601] Step 4:

[0602] The artificial intelligence agent generates appropriate feedback using the user's learning progress data along with the analyzed emotional state. Utilizing a generative AI model, it can, for example, generate positive feedback based on a prompt such as "Generate words of encouragement for when the user is feeling anxious."

[0603] Step 5:

[0604] The device presents feedback received from the server to the user through a user interface. Specifically, users can choose between voice and text feedback, further supporting their learning activities.

[0605] Step 6:

[0606] The server stores user input data, sentiment data obtained through analysis, and generated feedback in its storage device. This information will be used to improve the accuracy of feedback in future learning sessions.

[0607] Step 7:

[0608] Based on the feedback received, the user proceeds to the next learning process. The server continuously tracks subsequent progress and learning outcomes, and adjusts the learning content and methods as needed.

[0609] 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.

[0610] 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.

[0611] 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.

[0612] [Fourth Embodiment]

[0613] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0614] 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.

[0615] 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).

[0616] 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.

[0617] 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.

[0618] 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).

[0619] 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.

[0620] 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.

[0621] 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.

[0622] 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.

[0623] 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.

[0624] 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.

[0625] 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".

[0626] This invention is a system that enables learners to effectively acquire knowledge and skills related to AI and digital transformation through an online learning platform using an information processing device, and to apply them to actual work. This system aims not only to have an artificial intelligence agent analyze the learner's input data and provide feedback to the learner using the results, but also to accumulate knowledge within the organization and promote further business improvement based on that knowledge.

[0627] Overview of program processing

[0628] The server hosts an online learning platform where learners can log in, providing a wealth of AI and digital transformation-related learning materials and exercises. These materials are individually customized based on the learner's skill level and progress.

[0629] Users access the provided platform and work on exercises. Within the exercises, users input data and information related to their work, specific challenges, and improvement suggestions.

[0630] The terminal provides a user-friendly interface and sends user input to the server. It also receives feedback and suggestions from the server and displays them to the user.

[0631] An artificial intelligence agent analyzes user input data sent to the server in real time. This analysis utilizes natural language processing and machine learning algorithms to understand the meaning of the data, generating solutions and suggestions for specific challenges the user faces in their work.

[0632] The server provides the user with analysis results and suggestions generated by the artificial intelligence agent as feedback. Based on this feedback, the user can improve their business processes.

[0633] Specific examples

[0634] For example, when considering the introduction of a system to improve the efficiency of inventory management in a manufacturing company, the user inputs the current inventory management flow and challenges into the system. The server analyzes this data through an artificial intelligence agent and generates suggestions for optimal inventory management methods and new tools to be introduced. Based on this, the user formulates specific inventory management improvement measures and applies them to their work. In this process, the accumulated data becomes the foundation for more refined learning and feedback from subsequent users.

[0635] In this way, the present invention provides a practical form that supports the use of AI throughout an entire company and improves operational efficiency.

[0636] The following describes the processing flow.

[0637] Step 1:

[0638] The server launches the online learning platform that learners access, and prepares the necessary learning materials and exercises. It also authenticates user login information and verifies access rights.

[0639] Step 2:

[0640] Users log in to the online platform using their devices and view the provided learning materials and exercises. Based on their skill level and progress, they access individually recommended content aligned with their learning objectives.

[0641] Step 3:

[0642] Users input specific challenges and information related to their work into the platform as answers to practice problems. This may include diagrams of business processes and detailed descriptions of the challenges.

[0643] Step 4:

[0644] The terminal formats the data entered by the user and sends it to the server. The information is transferred in a secure format and stored in the server's database.

[0645] Step 5:

[0646] The server uses an artificial intelligence agent to analyze user input data and extract insights that can help improve business operations. This process utilizes machine learning algorithms and natural language processing techniques.

[0647] Step 6:

[0648] The server sends the analysis results and improvement suggestions generated by the artificial intelligence agent to the user as feedback. The feedback is displayed to the user via the terminal.

[0649] Step 7:

[0650] Users develop action plans to improve their business processes based on the feedback they receive. They can request re-entry or additional feedback for the next steps as needed.

[0651] Step 8:

[0652] The server stores user feedback and improvement results in a database, which will be used to improve future AI agents. This also strengthens the knowledge base within the organization.

[0653] (Example 1)

[0654] 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".

[0655] In today's learning environment, learners are required to effectively acquire practical skills directly applicable to their work and to use those skills to improve work processes. However, traditional learning platforms have struggled to provide personalized learning materials tailored to the needs of individual learners, as well as to offer concrete feedback and suggestions useful for actual work. Furthermore, they lacked mechanisms to accumulate learner input information and utilize it for the next step in their learning.

[0656] 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.

[0657] In this invention, the server includes an educational platform provided by an information processing device, an intelligent agent that analyzes work-related information entered by the learner, and an evaluation means that provides the learner with the analysis results generated by the intelligent agent. This enables effective skill acquisition and work improvement suggestions based on the learner's work-related information.

[0658] An "information processing device" is a system consisting of a combination of hardware and software for storing, processing, and acquiring data.

[0659] An "educational platform" is an online-based information management system that allows learners to access and participate in learning materials and related learning activities.

[0660] A "learner" refers to an individual user who utilizes an educational platform to acquire knowledge and skills.

[0661] An "intelligent agent" refers to a program or algorithm that can analyze input data from learners and generate feedback and suggestions tailored to business needs.

[0662] "Evaluation means" refers to a mechanism that provides learners with analysis results generated by an intelligent agent, thereby improving the quality and effectiveness of the learning process.

[0663] "Information storage means" refers to a database or storage device used to store learner input information and intelligent agent generation information for subsequent learning and improvement.

[0664] "Methods for providing learning materials" refers to a system or function for selecting and providing learning materials and practice problems according to the learner's skill level and progress.

[0665] "Consultation tools" refer to functions that, based on data analyzed by intelligent agents, present learners with solutions for improving business processes and support their implementation.

[0666] This invention is a system that uses an online education platform powered by an information processing device to enable learners to effectively acquire knowledge and skills related to AI and digital transformation and apply them to their work. The server provides learners with individually customized educational materials. This process utilizes a learning management system and database management software. Specifically, a content management system (CMS) is used as the software.

[0667] The server uses a front-end framework to build a user interface that makes it easy for learners to access the platform. Technologies such as React and Vue.js are used for this purpose. Through this interface, users work on exercises and input tasks and data related to their work. The entered information is sent to the server.

[0668] The data provided by the user is analyzed by an intelligent agent. Natural language processing (NLP) algorithms and machine learning models are used for the analysis, utilizing Python libraries such as NLTK and TensorFlow. This analysis generates specific improvement measures and suggestions for the challenges the learner faces in their work.

[0669] As a concrete example, consider using this system to improve the efficiency of inventory management in the manufacturing industry. The user follows the prompt, "Please enter details of your current inventory management flow and its challenges," and inputs the current state of operations and points they wish to improve. The intelligent agent analyzes the entered information, proposes the optimal inventory management method, and helps the user improve their business processes based on that. In this way, the present invention provides an effective technology that supports the use of AI and operational efficiency throughout the enterprise.

[0670] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0671] Step 1:

[0672] Users enter their credentials to log in to the online education platform. This input data is sent to an authentication server for verification of the credentials' validity. If authentication is successful, users can access a personalized dashboard. As output, users will have access to the following learning materials and progress information.

[0673] Step 2:

[0674] The server recommends appropriate learning materials and exercises based on the user's learning history and skill level. To do this, the server retrieves user information from an internal database and uses a learning management system to select the most suitable materials. As output, the selected materials are displayed on the user's dashboard.

[0675] Step 3:

[0676] Users input their answers to the provided exercises. Here, they are required to describe data and specific challenges related to their own work. The input data is transmitted to the server in real time and prepared for the next analysis step.

[0677] Step 4:

[0678] The server passes the data received from the user to an artificial intelligence agent for analysis. Specifically, it uses natural language processing (NLP) and machine learning algorithms to understand the context of the data and extract necessary information. In this step, the intelligent agent uses libraries such as Python's NLTK and TensorFlow to perform data analysis and insight generation. As output, suggestions useful for improving business operations are generated.

[0679] Step 5:

[0680] The terminal displays suggestions and feedback received from the server in a user-friendly format. Interface technologies such as React and Vue.js are used for this display to help users easily grasp the received data. As output, users review the suggestions and consider specific actions for improving their work processes.

[0681] Step 6:

[0682] Based on suggestions from the server, users implement specific measures to improve their own work processes. During this process, they utilize the feedback they receive and apply it to their actual work. As a result, work efficiency improves, and learner growth is promoted.

[0683] (Application Example 1)

[0684] 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".

[0685] In modern urban environments, there is a need for systems that can efficiently solve the various problems residents face in their daily lives. However, existing systems have struggled to provide real-time improvement suggestions tailored to the individual problems residents encounter. This invention aims to solve this problem.

[0686] 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.

[0687] In this invention, the server includes a learning platform provided by an information processing device, an artificial intelligence agent means for analyzing information related to tasks input by the learner, and a feedback means for providing the learner with the analysis results generated by the artificial intelligence agent. This makes it possible to accurately analyze problems that residents face in their daily lives and to provide accurate improvement suggestions regarding urban management in real time.

[0688] An "information processing device" is a device designed to efficiently manage and analyze data.

[0689] A "learning platform" is an online platform used by learners to acquire the skills and knowledge they need.

[0690] An "artificial intelligence agent" is a program that uses artificial intelligence technology to analyze input information and generate appropriate feedback and suggestions.

[0691] "Analysis results" refer to the conclusions and suggestions obtained after an artificial intelligence agent analyzes the input information.

[0692] "Feedback means" refers to methods or functions for providing analysis results to the user.

[0693] The "data storage unit" is a function that stores learner input information and information generated by artificial intelligence, for later reference and performance improvement.

[0694] "Means" refers to the methods or techniques used to achieve a specific objective.

[0695] "Residents" refers to people who live in a specific area and are the entities that need to solve problems in the urban environment.

[0696] "City management" refers to a series of activities aimed at the efficient and smooth operation and management of a city.

[0697] An "improvement suggestion" refers to specific advice or methods for resolving current problems and achieving a better situation.

[0698] A system for realizing this invention includes a learning platform provided by an information processing device, an artificial intelligence agent, feedback means, a data storage unit, and means for suggesting improvements to urban management to support residents in solving problems.

[0699] The server hosts the learning infrastructure and provides an interface for learners and residents to input questions. The terminal uses React Native to build the frontend and implement a user-friendly interface, and is responsible for sending user input to the server.

[0700] When a user inputs problems or suggestions related to their daily life into a server via their device, the data is analyzed in real time by an artificial intelligence agent. This analysis utilizes Python's natural language processing libraries (nltk and spaCy) and machine learning frameworks (TensorFlow). The AI ​​agent generates analysis results and derives optimal improvement suggestions.

[0701] The analysis results are provided to the user as feedback generated by the server. Based on this feedback, residents can gain concrete ideas for improving their urban life more efficiently.

[0702] For example, if a resident inputs "Traffic congestion in this area is terrible every morning," the system will suggest alternative routes or adjust traffic signals. In this way, it provides concrete solutions to the problems residents face.

[0703] An example of a prompt using a generative AI model is, "Please come up with suggestions for improving local events. Please provide ideas for events that are easy for local residents to participate in." By using this prompt, the system helps to automatically generate effective event plans.

[0704] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0705] Step 1:

[0706] Users input problems related to their daily lives using a terminal. The entered data is sent to the server through a user-friendly interface. During this process, users are guided to fill in the appropriate fields according to their needs.

[0707] Step 2:

[0708] The server passes the input data to the artificial intelligence agent in real time. The data is analyzed using Python's natural language processing libraries (nltk and spaCy). The purpose of the analysis is to accurately grasp the meaning of the input sentence and identify specific problems and needs. The output includes a list of problems and categorized requests.

[0709] Step 3:

[0710] The artificial intelligence agent generates optimal improvement suggestions using a machine learning framework (TensorFlow) based on the analysis results. This step involves referencing historical data and statistical models to consider solutions to the derived problems. The output is a list of specific solutions and advice.

[0711] Step 4:

[0712] The server provides the generated suggestions to the user as feedback. This feedback is sent back from the server to the terminal and displayed on the user's interface. The user can review the suggestions and obtain specific steps to take action. This feedback may also include the expected effects of the suggested improvements and any additional resources needed.

[0713] Step 5:

[0714] Through the terminal, users can input additional questions or comments on the feedback provided, starting a new cycle. This allows the suggestions to be more individualized or adjusted as needed. The output is a newly adjusted suggestion.

[0715] An example of a prompt message would be: "Please come up with suggestions for improving local events. Please provide ideas for events that are easy for local residents to participate in."

[0716] 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.

[0717] This invention relates to a system for online learning platforms that can recognize learners' emotions and provide feedback based on those emotions. This system allows learners to learn effectively through individually adapted learning materials and receive a combination of analysis results from an artificial intelligence agent and emotional feedback.

[0718] Overview of program processing

[0719] The server hosts the online learning platform and provides learners with appropriate learning materials and exercises. During this process, it handles learner login authentication and access verification.

[0720] Users access the learning platform through their devices and work on necessary materials and exercises. During the learning process, users are required to input information about their emotions and work-related matters.

[0721] The terminal is responsible for receiving user input data and sending it to the server. It also provides feedback and displays learning materials through the user interface.

[0722] The emotion engine analyzes data input from the user and recognizes the learner's emotional state in real time. Based on this emotional data, the AI ​​agent adjusts the analysis results.

[0723] The server integrates emotional data obtained by the emotion engine with learner work data and reflects this in the analysis results generated by the artificial intelligence agent. This ensures that feedback is provided in a way that is adapted to the learner's emotional state.

[0724] The database accumulates learner input data, sentiment data, and analysis results, contributing to improved feedback accuracy and platform improvements in subsequent sessions.

[0725] Specific examples

[0726] For example, suppose a learner uses a platform to learn new sales techniques. When working on exercises, the user inputs their current sales strategies and their feelings about the results. The emotion engine analyzes this, and if it recognizes that the user's emotional state is, for example, "anxious," the AI ​​agent adjusts the feedback to make the suggestions more positive and focus on solutions. This process allows the user to receive suggestions tailored to their individual emotional state and try new techniques with confidence.

[0727] In this way, by utilizing emotion recognition, it is possible to implement a system that provides a learning experience optimized for learners and supports its application in business.

[0728] The following describes the processing flow.

[0729] Step 1:

[0730] Users log in to the online learning platform using their devices and access the provided learning materials and practice problems. Authentication is performed using a username and password during login.

[0731] Step 2:

[0732] The server presents the most suitable learning materials and exercises based on the logged-in user's skill level and progress. This allows users to effectively engage with learning content tailored to their needs.

[0733] Step 3:

[0734] When answering practice questions, users input work-related information and their emotional state (e.g., satisfaction, anxiety, excitement) into the terminal. This information is used to customize the learning experience.

[0735] Step 4:

[0736] The terminal transmits the entered work information and emotional data to the server. The transmitted data is encrypted to protect privacy.

[0737] Step 5:

[0738] To analyze the received data, the server first uses an emotion engine to analyze the emotional data and identify the user's emotional state. In this step, natural language processing techniques are used to determine text-based emotional information.

[0739] Step 6:

[0740] The server analyzes business-related data through an artificial intelligence agent and generates optimal business improvement suggestions. During this process, the content and tone of the suggestions are adjusted based on the results of the emotion engine.

[0741] Step 7:

[0742] The server transmits the generated feedback and suggestions for business improvement to the user via the terminal. These suggestions include personalized responses that take into account the user's emotional state.

[0743] Step 8:

[0744] Based on the feedback received, users develop improvement strategies for their own work. By providing emotionally resonant suggestions, users are expected to take more proactive and positive actions.

[0745] Step 9:

[0746] The server stores all user input data and analysis results in a database, which is used for the continuous improvement of the AI ​​model. This data contributes to improving the accuracy of feedback for future users.

[0747] (Example 2)

[0748] 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".

[0749] Traditional education systems have a problem in that they do not take into account the emotional state of learners and optimize the learning process accordingly, resulting in insufficient improvements in learning efficiency and work processes. In particular, when learners are experiencing anxiety or stress, these emotions are left unaddressed, leading to a decrease in motivation to learn and making it difficult to achieve optimal educational results.

[0750] 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.

[0751] In this invention, the server includes means for constructing an educational platform provided by an information processing device, artificial intelligence means for identifying emotional data input by learners in real time and adjusting the analysis results, and feedback function means for providing feedback optimized to the learner's emotional state. This enables individualized optimization according to the learner's emotional state, leading to improved learning efficiency and emotion-based improvements to business processes.

[0752] An "information processing device" refers to a machine equipped with hardware and software capable of handling electronic data and performing various calculations and analyses based on that data.

[0753] An "educational platform" refers to a system that provides an online environment for learners to access and engage in learning through educational materials and practice exercises.

[0754] "Emotional state" refers to data that indicates the psychological state, such as anxiety, joy, and stress, that learners experience in a particular situation.

[0755] "Artificial intelligence methods" refer to technologies that use machine learning and natural language processing to analyze input data and generate judgments and feedback.

[0756] "Feedback function means" refers to a function that provides learners with information based on analysis results to improve and support the learning process.

[0757] "Memory storage" refers to data storage that saves learner input data and analysis results to be used for future improvement.

[0758] This invention is a system for providing a learning environment that responds to the emotional state of learners, based on an educational platform provided by an information processing device.

[0759] The server manages the educational platform and provides learners with learning materials and exercises optimized for their needs. Using artificial intelligence (AI) tools, the server analyzes the emotional states entered by learners through their devices in real time and adjusts feedback based on the results. Specific examples of AI tools include emotion analysis engines and machine learning algorithms.

[0760] The terminal is a device that allows learners to access the platform and work on learning materials and exercises via a user interface. The terminal not only displays feedback and learning materials from the server, but also prompts learners to input their emotional state and work-related data, which is then transmitted to the server.

[0761] Users, or learners, need to input their emotional state and learning progress while using a device. This information is analyzed by a server, and feedback tailored to the learner's needs is provided.

[0762] This allows the learning experience to be individually optimized based on the learner's emotional state, thereby improving learning efficiency. For example, if a learner inputs an emotion such as "anxiety," the emotion analysis engine will analyze this data and generate feedback such as, "Let's try some specific steps to reduce anxiety," provided by an AI model. In this process, an example of a prompt might be, "Learner's emotional data has been entered. Please adjust the feedback and generate specific suggestions to improve the learning content." This method allows learners to acquire new knowledge and skills with confidence.

[0763] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0764] Step 1:

[0765] The user logs into the educational platform using a terminal. The input here is the user's authentication information. The terminal sends the user's entered authentication information to the server. The server compares it against the database to verify the validity of the login. The output of this process is the authentication result, granting the user access to the platform.

[0766] Step 2:

[0767] After confirming that the user has logged in, the server selects appropriate learning materials and exercises on the educational platform. The server uses the user's learning history data and progress information as input and analyzes this data using a generative AI model. The output of this process is a set of learning materials tailored to the user. The terminal then displays these materials on the user interface.

[0768] Step 3:

[0769] Users input their emotional state using a terminal while working through the provided learning materials and exercises. Emotional data and work-related information are used as input data and sent from the terminal to the server. The server transmits this input data to an emotion analysis engine, which then analyzes the user's emotional state. The output of this process is data concerning the user's specific emotional state.

[0770] Step 4:

[0771] The server holds emotional data generated by an emotion analysis engine and uses a generative AI model to generate user-appropriate feedback. In this process, the AI ​​model uses emotional data and learning history data as input and performs data calculations. The output is feedback adjusted according to the user's emotional state. This feedback includes appropriate solutions and encouragement for the learner.

[0772] Step 5:

[0773] The device receives optimized feedback from the server and presents it to the user through the user interface. The outputted feedback supports the user's learning experience and is individually optimized based on their emotional state. The user reviews this feedback and uses it to improve the learning process.

[0774] Step 6:

[0775] The server stores all input data, sentiment data, and generated feedback in a database. This record is used to improve the system in the future and enhance the accuracy of the feedback. As output, this data helps to customize the learning process for subsequent sessions.

[0776] (Application Example 2)

[0777] 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".

[0778] Traditional online learning platforms have struggled to provide feedback that takes into account learners' emotions and motivations. Therefore, there is a need for technology that can accurately grasp learners' emotional states and provide feedback tailored to their individual learning needs.

[0779] 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.

[0780] In this invention, the server includes a learning platform provided by an information processing device, an emotion analysis means that recognizes the learner's emotional state and dynamically adjusts the feedback content based on it, and a feedback function that provides the learner with the analysis results generated by an artificial intelligence agent. This makes it possible to provide feedback that is tailored to the learner's emotional state.

[0781] An "information processing device" is an electronic device used to process and manage data, and it plays a role in providing an online platform.

[0782] A "learning platform" refers to an electronic environment or system that allows users to access learning content and progress in their studies.

[0783] An "artificial intelligence agent" is software equipped with the ability to analyze data and make autonomous decisions, and its role is to analyze the learner's work data.

[0784] A "feedback function" is a mechanism that returns the results of analysis and observation to learners, in order to support their learning process.

[0785] A "storage device" is a device that stores information and makes it available for retrieval as needed, functioning as a database.

[0786] "Emotional analysis tools" are techniques or methods for recognizing and analyzing a learner's emotional state, and for adjusting feedback in real time.

[0787] This invention realizes a system for online learning platforms that analyzes learners' emotions and provides feedback based on those emotions. The system mainly consists of a server, terminals, and users.

[0788] The server hosts the learning platform and runs artificial intelligence agents and emotion analysis tools for analyzing learners' emotions. Emotion analysis typically uses an "emotion analysis engine," which leverages image processing and speech recognition technologies to read emotions from the learner's facial expressions and voice. Specifically, existing services such as Google Cloud Vision API and Google Text-to-Speech can be utilized. This allows the server to quantitatively evaluate the learner's emotional state and provide a personalized learning experience through feedback functions.

[0789] The terminal is a device operated by the learner to access the platform. Through the terminal, the learner works on exercises, and the data entered during this process is immediately sent to the server. The terminal also functions as the primary means of displaying feedback to the learner. In this case, visual or audible feedback is provided in real time through the interface.

[0790] For example, if the emotion analysis engine detects that a learner is struggling while solving a math problem, such as by their facial expression becoming strained, the server will send an encouraging message to the device, such as, "How about taking a short break?" An example of a prompt message when a generative AI model is used might be, "Generate an appropriate encouraging message for when the learner is stuck."

[0791] This system also includes a memory device that stores learner-specific data, accumulating past learning progress and emotional data to improve the accuracy of future feedback. This ensures that learners always receive an optimized learning experience that takes their emotions into consideration.

[0792] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0793] Step 1:

[0794] The server authenticates users accessing the learning platform. Users enter their login information, and the server authenticates them by referring to the database. If authentication is successful, learning content is provided to the user.

[0795] Step 2:

[0796] Users work on learning materials and exercises provided through their devices. The devices send input data from users during their learning (answers, feedback, self-reports on emotions, etc.) to the server.

[0797] Step 3:

[0798] The server receives the transmitted input data and analyzes the user's emotional state in real time using emotion analysis tools. During this process, the emotion analysis engine extracts emotions from the user's facial expressions and voice using data acquired from the camera and microphone. This identifies the user's emotional state (e.g., anxiety, concentration, joy).

[0799] Step 4:

[0800] The artificial intelligence agent generates appropriate feedback using the user's learning progress data along with the analyzed emotional state. Utilizing a generative AI model, it can, for example, generate positive feedback based on a prompt such as "Generate words of encouragement for when the user is feeling anxious."

[0801] Step 5:

[0802] The device presents feedback received from the server to the user through a user interface. Specifically, users can choose between voice and text feedback, further supporting their learning activities.

[0803] Step 6:

[0804] The server stores user input data, sentiment data obtained through analysis, and generated feedback in its storage device. This information will be used to improve the accuracy of feedback in future learning sessions.

[0805] Step 7:

[0806] Based on the feedback received, the user proceeds to the next learning process. The server continuously tracks subsequent progress and learning outcomes, and adjusts the learning content and methods as needed.

[0807] 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.

[0808] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). An 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.

[0809] 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 robot 414.

[0810] 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.

[0811] 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.

[0812] 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.

[0813] 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.

[0814] 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.

[0815] 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."

[0816] 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.

[0817] 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.

[0818] 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.

[0819] 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.

[0820] 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.

[0821] 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.

[0822] 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.

[0823] 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.

[0824] 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.

[0825] 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.

[0826] 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.

[0827] 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.

[0828] The following is further disclosed regarding the embodiments described above.

[0829] (Claim 1)

[0830] A learning platform provided by an information processing device,

[0831] An artificial intelligence agent that analyzes data related to tasks entered by learners,

[0832] A feedback function that provides the learner with the analysis results generated by the aforementioned artificial intelligence agent,

[0833] A database that stores learner input data and data generated by the artificial intelligence agent, and uses it for improvement,

[0834] A system that includes this.

[0835] (Claim 2)

[0836] The system according to claim 1, which uses the results of an analysis by an artificial intelligence agent to make suggestions for improving the learner's work process.

[0837] (Claim 3)

[0838] The system according to claim 1, which provides a learning platform in an information processing device that presents individually recommended learning materials based on the learner's level and progress.

[0839] "Example 1"

[0840] (Claim 1)

[0841] Educational platforms provided by information processing equipment,

[0842] An intelligent agent that analyzes information related to the tasks entered by the learner,

[0843] An evaluation means that provides the learner with the analysis results generated by the intelligent agent,

[0844] An information storage means for accumulating learner input information and information generated by an intelligent agent, and using it for improvement,

[0845] A means of providing learning materials that allows learners to access individually customized materials and exercises,

[0846] A consultation method that proposes improvement measures for business processes to learners based on data analyzed by an intelligent agent,

[0847] A system that includes this.

[0848] (Claim 2)

[0849] The system according to claim 1, which uses the results of analysis by an intelligent agent to propose ways to streamline the learner's work processes.

[0850] (Claim 3)

[0851] The system according to claim 1, which presents individually recommended educational materials based on the learner's abilities and progress on an educational platform provided by an information processing device.

[0852] "Application Example 1"

[0853] (Claim 1)

[0854] A learning platform provided by an information processing device,

[0855] An artificial intelligence agent that analyzes information related to the tasks entered by the learner,

[0856] A feedback means that provides the analysis results generated by the artificial intelligence agent to the learner,

[0857] A data storage unit that stores learner input information and information generated by the artificial intelligence agent and uses it to improve performance,

[0858] A means for residents to input problems they face in their daily lives and make suggestions for improvements to urban management,

[0859] A system that includes this.

[0860] (Claim 2)

[0861] The system according to claim 1, which makes suggestions to improve the learner's work process based on the results of analysis by an artificial intelligence agent.

[0862] (Claim 3)

[0863] The system according to claim 1, which presents individually selected learning materials based on the learner's level and progress in a learning platform provided by an information processing device.

[0864] "Example 2 of combining an emotion engine"

[0865] (Claim 1)

[0866] Educational platforms provided by information processing equipment,

[0867] An artificial intelligence means that identifies the emotional state input by the learner and adjusts the analysis results based on this,

[0868] A feedback function means that presents the analysis results and emotional data generated by the artificial intelligence means to the learner,

[0869] A memory system that stores learner input data, emotional data, and data generated by artificial intelligence tools, and uses them for improvement.

[0870] A system that includes this.

[0871] (Claim 2)

[0872] The system according to claim 1, which uses feedback generated by an artificial intelligence means based on emotional data to propose improvements to a learner's work process according to their emotional state.

[0873] (Claim 3)

[0874] The system according to claim 1, which presents individually recommended learning materials based on the learner's level, progress, and emotional state, on an educational platform provided by an information processing device.

[0875] "Application example 2 when combining with an emotional engine"

[0876] (Claim 1)

[0877] A learning platform provided by an information processing device,

[0878] An artificial intelligence agent that analyzes data related to tasks entered by learners,

[0879] A feedback function that provides the learner with the analysis results generated by the aforementioned artificial intelligence agent,

[0880] A memory device that stores learner input data and data generated by the artificial intelligence agent, and uses it for improvement,

[0881] An emotion analysis means that recognizes the learner's emotional state and dynamically adjusts the content of the feedback based on that state,

[0882] A system that includes this.

[0883] (Claim 2)

[0884] The system according to claim 1, which uses the results of analysis by an artificial intelligence agent and the emotional state of the learner obtained by an emotion analysis means to make suggestions for improving the learner's work process.

[0885] (Claim 3)

[0886] The system according to claim 1, which presents individually recommended learning materials based on the learner's level, progress, and emotional state, on a learning platform provided by an information processing device. [Explanation of symbols]

[0887] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots< / url:> < / url:> < / url:> < / url:>

Claims

1. A learning platform provided by an information processing device, An artificial intelligence agent that analyzes data related to tasks entered by learners, A feedback function that provides the learner with the analysis results generated by the aforementioned artificial intelligence agent, A database that stores learner input data and data generated by the artificial intelligence agent, and uses it for improvement, A system that includes this.

2. The system according to claim 1, which uses the results of an analysis by an artificial intelligence agent to make suggestions for improving the learner's work process.

3. The system according to claim 1, which presents individually recommended learning materials based on the learner's level and progress in a learning platform provided by an information processing device.

Images