system

Abstract

We provide the system. [Solution] An information processing device that analyzes user registration information and automatically screens the suitability of membership, A recommendation device that analyzes the user's learning history and recommends relevant educational content, An access control device for a company to access the learning history of the aforementioned user and conduct recruitment activities, A system that includes this.

Description

Technical Field

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

Background Art

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

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In conventional educational platforms, there is a problem that practical learning content required by learners cannot be obtained due to economic constraints. In addition, since companies lack means to efficiently discover excellent talents, a system for solving this problem is demanded.

Means for Solving the Problems

[0005] The present invention provides an information processing device that analyzes user registration information and automates the membership screening. Further, it includes a recommendation device that automatically recommends relevant educational content based on the user's learning history. Furthermore, by integrating an access control device for companies to easily access the user's learning history and streamline the scouting activities, these problems are solved.

[0006] An "information processing device" is a device that analyzes data input by a user and performs processing and decisions based on specific criteria.

[0007] "Review" is the process of evaluating a user's registration information and determining whether it meets the criteria.

[0008] "Learning history" refers to data that records what learning activities a user has engaged in in the past.

[0009] A "recommendation device" is a device that automatically selects and presents appropriate educational content based on the user's interests and needs.

[0010] An "access control device" is a device that manages access to specific user data and provides data only to those with appropriate permissions.

[0011] "Recruitment activities" refer to the process by which companies identify talented individuals and make contact with them with the aim of hiring them.

[0012] A "content generation device" is a device that creates and provides educational content tailored to the user's needs and history.

[0013] A "matching processing device" is a device that analyzes the needs of users and companies and automatically selects the most suitable partner. [Brief explanation of the drawing]

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

Embodiments for Carrying Out the Invention

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

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

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

[0018] In the following embodiments, signed RAM (Random Access Memory) is a memory that temporarily stores information and is used as work memory by the processor.

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

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

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

[0022] [First Embodiment]

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

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

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

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

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

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

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

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

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

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

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

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

[0035] The system of this invention integrates an information processing device, a recommendation device, an access control device, a content generation device, and a matching processing device to effectively connect students and companies. The following describes how each device functions with specific examples.

[0036] Users (students) first enter their personal information, educational background, and areas of interest through the platform's registration form. The device then sends this information to the server. The information processing device on the server uses the received data to screen the user's application. This is done using AI and is processed automatically based on specific criteria. Users who pass the screening receive account information from the server and gain access to the platform.

[0037] When a user uses the platform, the server constantly records and updates their learning history. Based on this, the recommendation system on the server operates and lists and suggests company-provided educational content that is suitable for the user. For example, if a user expresses interest in machine learning, relevant practical courses and materials will be presented.

[0038] Furthermore, companies can access servers via terminals and conduct recruitment activities based on students' academic histories. An access control device manages this process, and companies view the data under appropriate permissions. For example, if a company is looking for students with specific skills, a list of students with academic histories matching those skills will be provided.

[0039] Companies can generate special educational content. Through content generation devices, companies can create and deliver customized courses based on users' learning histories.

[0040] Finally, the matching processing device analyzes the profiles of users and companies and performs optimal matching. As a result, users and companies can contact each other, providing opportunities for effective communication. Thus, the present invention provides students with the opportunity to receive high-quality education free of charge, while simultaneously enabling companies to efficiently discover talented individuals.

[0041] The following describes the processing flow.

[0042] Step 1:

[0043] Users access the platform's registration page using their devices and enter their personal information, educational background, and areas of interest.

[0044] Step 2:

[0045] The terminal sends the entered data to the server.

[0046] Step 3:

[0047] The server analyzes the received data using an information processing device and uses AI to assess the suitability of the applicant for membership.

[0048] Step 4:

[0049] Based on the review results, the server sends account information via email to approved users and grants them access to the platform.

[0050] Step 5:

[0051] After the user logs in, the server retrieves their learning history and creates a user profile.

[0052] Step 6:

[0053] The recommendation system installed on the server selects relevant educational content based on the user's learning history and interests, and presents it to the user as a recommendation list.

[0054] Step 7:

[0055] The user reviews the presented content list, selects the content that interests them, and begins learning.

[0056] Step 8:

[0057] The server records the user's learning progress and reflects it in their learning history.

[0058] Step 9:

[0059] The corporate terminal accesses the server and requests to access the user's learning history.

[0060] Step 10:

[0061] The server uses access control devices to provide appropriate user data to companies, supporting their recruitment activities.

[0062] Step 11:

[0063] The company terminal sends recruitment messages to selected users based on the information it has acquired.

[0064] Step 12:

[0065] Companies use content generation equipment to create specialized educational content tailored to user needs and deliver it to users via servers.

[0066] Step 13:

[0067] The matching processing unit operates, comparing and analyzing user and company profiles to achieve optimal matching.

[0068] Step 14:

[0069] The server notifies users and companies of the matching results, facilitating communication between them.

[0070] (Example 1)

[0071] 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."

[0072] Traditional systems connecting students and companies have struggled to efficiently provide the right talent and educational opportunities. Furthermore, finding students who match the skills and fields companies require is time-consuming and labor-intensive, and students also face challenges in finding suitable educational content.

[0073] 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.

[0074] In this invention, the server includes a calculation means for analyzing user registration information and automatically assessing the suitability of membership; a means for recording and updating the user's learning history; a recommendation means for analyzing the user's learning history and recommending relevant educational resources; and an access control means for organizations to access the user's learning history and conduct recruitment activities. This makes it possible to provide individual users with optimal educational opportunities and to efficiently discover and collaborate with personnel who match the skills required by companies.

[0075] A "user" refers to an individual who registers with the system and seeks educational or employment opportunities.

[0076] "Registration information" refers to data such as personal information, educational background, and areas of interest that users enter into the system.

[0077] A "computational device" refers to an information processing system used to analyze user registration information and conduct membership screening.

[0078] "Learning history" refers to data that records the knowledge, skills, and educational content a user has acquired in the past.

[0079] A "recommendation system" refers to a system that has the function of suggesting relevant educational resources to users based on their learning history.

[0080] "Organization" refers to a corporation or group that provides educational content to users or seeks personnel.

[0081] "Access control measures" refer to systems that have the function of managing so that an organization can access users' learning history under appropriate permissions.

[0082] A "resource generation method" refers to a system that has the function of creating and providing educational resources offered by an organization based on the user's learning history.

[0083] "Analysis tools" refer to systems equipped with information processing functions to analyze the requirements of users and organizations and perform optimal matching.

[0084] The system of this invention utilizes computer and network technologies to effectively connect students and companies. It primarily involves the coordinated functioning of a computing device, recommendation system, access control system, resource generation system, and analysis system. Its specific configuration is described below.

[0085] Users access the platform via an internet-enabled device and enter personal information, educational background, and areas of interest into a registration form. This information is encrypted and transmitted to the server. The server, using an information processing system that acts as a computing device, analyzes the user's registration information and automatically performs an admission screening according to pre-set criteria. Users who pass the screening are sent account information used by the server via email and granted access to the platform.

[0086] As a user progresses through their learning on the platform, the device automatically generates a learning history and sends it to the server. This learning history is updated in real time and stored in a database on the server. Based on this information, the recommendation system on the server operates and suggests appropriate educational resources to the user. This system, which utilizes a generative AI model, recommends content tailored to the user's interests and skills. For example, if the user enters a prompt such as, "What educational resources would be suitable if the user is interested in machine learning?", relevant online courses and materials will be suggested.

[0087] Companies can use these terminals to access servers and view students' learning histories. Access control measures manage the company's data access rights and prevent the leakage of user information. Based on this information, companies can find candidates with specific skills and conduct recruitment activities.

[0088] Furthermore, companies can use resource generation tools to provide special educational resources based on users' learning histories. These resource generation tools create educational courses based on company-specific data and provide them to users via a server.

[0089] Ultimately, the server uses analytical tools to perform optimal matching based on user and company profiles. This enables effective communication between users and companies, allowing them to efficiently meet each other's needs.

[0090] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0091] Step 1:

[0092] Users access the platform using their devices and provide input data such as personal information, educational background, and areas of interest through a registration form. The device transmits this information to the server via a secure channel. The server verifies the format integrity of the received data before storing it in a database. At this stage, the entered registration information is reflected in the database stored on the server as output.

[0093] Step 2:

[0094] The server uses computing power to automatically screen the user's eligibility for membership based on their registration information. An AI algorithm is used to compare the user information against pre-defined criteria. This process generates a screening result, determining whether or not the user can access an account. If approved, the user receives a confirmation email containing their account information.

[0095] Step 3:

[0096] Users access educational content on the platform using their devices. This usage history is sent from the device to the server, which stores it in a database as learning history. Here, user behavior data is input, and the updated learning history is saved as output. The learning history is updated in real time as time passes.

[0097] Step 4:

[0098] The server uses recommendation tools to suggest relevant educational resources based on the user's most recent learning history. A generative AI model is used for this process, identifying the most suitable educational content from the input learning history and listing it as output. For example, if a user expresses interest in data science, links to relevant online courses will be generated.

[0099] Step 5:

[0100] The organization accesses the server via a terminal and displays a list of users based on specific learning histories through access control mechanisms. In this process, the input is the search criteria specified by the organization, and the learning histories of users matching these criteria are provided as output. The organization uses this information to conduct recruitment activities.

[0101] Step 6:

[0102] Using resource generation tools, organizations create special educational resources based on users' learning histories. This includes customization based on user characteristics. User history data is input, and the educational resources generated based on this data are provided to the user as output.

[0103] Step 7:

[0104] The server uses analytical tools to perform optimal matching based on user and organization profiles. User and organization request data is processed as input, and a suitable matching result is generated as output. This result is notified to the user and organization, enabling two-way communication.

[0105] (Application Example 1)

[0106] 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."

[0107] In today's educational environment, ensuring students access optimal educational resources based on their interests and backgrounds, and enabling organizations to efficiently recruit suitable talent, are crucial challenges in promoting education and employment. Furthermore, there is a need for technologies that enhance learning effectiveness by increasing the visualization and interactivity of educational resources.

[0108] 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.

[0109] In this invention, the server includes information analysis means for analyzing user information and automatically evaluating suitability; suggestion means for analyzing user history information and recommending corresponding educational materials; access control means for organizations to access user history information and conduct talent scouting activities; and visualization means for displaying learning materials using augmented reality technology. As a result, students can receive educational resources tailored to their interests, organizations can efficiently scout for talent, and the quality of education can be improved using augmented reality.

[0110] "User information" refers to basic data that can identify an individual, including information such as an individual's characteristics, hobbies, and history.

[0111] "Information analysis means" refers to technical elements used to process collected user information and determine its appropriateness based on specific criteria.

[0112] "History information" refers to a data set that shows a record of a user's learning activities and related experiences.

[0113] The "recommendation mechanism" is a function that recommends highly relevant educational resources and materials based on analyzed historical information.

[0114] An "organization" refers to a business entity or legal entity that conducts educational or recruitment activities for students or users.

[0115] "Access control means" refers to system elements that manage how an organization can securely and appropriately access users' historical information.

[0116] Augmented reality technology is a technique that overlays virtual information onto the real world environment and is used to enrich the user experience.

[0117] "Visualization means" refers to functions that visually display digital information, and in particular, play a role in providing information through augmented reality technology.

[0118] The system for realizing this invention functions through the cooperation of users, terminals, and servers. The underlying hardware of the system includes mobile terminals such as smartphones and tablets, and servers connected to them. These devices operate in combination with information analysis means, suggestion means, access control means, and visualization means.

[0119] The server first receives user registration information and automatically evaluates its suitability using data analysis tools. This process utilizes software such as Python and TENSORFLOW® to perform advanced data analysis.

[0120] Next, the user's history information is analyzed by the server, and educational content matching their interests is recommended using a suggestion method. This recommendation information is then displayed on the device by an application developed in Swift.

[0121] When an organization conducts activities based on user information, server access control measures are applied, enabling secure information sharing. Security protocols are strictly managed throughout this process.

[0122] Furthermore, augmented reality technology is employed as a visualization tool, using ARKit to overlay educational content onto the real world. This feature allows users to gain a visually rich learning experience. For example, a user interested in history can recreate ancient cultural heritage sites in 3D via their smartphone, enabling intuitive learning.

[0123] Generative AI models are also used in situations where educational tasks are suggested based on user behavior data. For example, more sophisticated suggestions can be made by using prompts like the following: "Suggest learning tasks based on the user's interests and provide the user with the most suitable educational content."

[0124] In this way, users can obtain the most suitable educational resources that match their interests and learning history, and organizations can efficiently capture their target talent.

[0125] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0126] Step 1:

[0127] The server receives registration information sent from the user via their device. This information includes personal data and areas of interest. The server uses information analysis tools, performing analysis using Python and TensorFlow. As a result of the analysis, a suitability assessment of the user is generated, and if deemed suitable, account information is generated.

[0128] Step 2:

[0129] The device displays account information provided by the server to the user. Based on this information, the user can log in to the platform and begin using it. The user's activity history is continuously transmitted from the device to the server and stored as historical information.

[0130] Step 3:

[0131] The server processes data using suggestion tools based on the user's history information and performs data calculations to recommend highly relevant educational content. This process uses AI algorithms to analyze the user's interests. As output, a list of specific educational materials is generated and sent to the terminal.

[0132] Step 4:

[0133] The device displays a list of educational materials received from the server to the user. The user can select content of interest from this list and begin learning through AR technology, which is provided as a visualization tool. ARKit is used to overlay virtual information onto the real world.

[0134] Step 5:

[0135] Organizations can access user history information using access control measures. The server implements appropriate security protocols and manages the organization to securely access the information it needs. As a result of this access, organizations can obtain information useful for talent acquisition activities.

[0136] Step 6:

[0137] The server uses a generative AI model to suggest new learning tasks to the user. Based on the prompt, it analyzes the user's behavioral data and outputs optimal advice for the next step. For example, a prompt such as "Suggest learning tasks based on the user's interests and provide the user with the most suitable educational content" might be used.

[0138] 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.

[0139] This invention is a system that improves the user's learning experience while simultaneously optimizing corporate talent scouting activities. To achieve this, it integrates an information processing device, a recommendation device, an access control device, a content generation device, a matching processing device, and an emotion engine.

[0140] Users first access the platform from their device and enter personal information, educational background, and areas of interest. The server analyzes this information using an AI-powered information processing device to assess the user's suitability for membership. If approved, the server provides the user with account information.

[0141] When a user uses the platform, the server records and updates their learning history, and based on this, a recommendation system selects relevant educational content. Furthermore, an emotion engine analyzes the user's emotions at that time based on their input data and learning behavior, and the server recommends content that promotes a positive learning experience. For example, if a user is feeling stressed about a particular task, the server will present learning materials with adjusted difficulty levels or content that promotes relaxation.

[0142] Corporate terminals can access users' learning history via an access control device on the server, enabling recruitment activities. Furthermore, by analyzing users' emotional data using an emotion engine, companies can take a more effective approach. For example, if a user provides a lot of positive feedback, companies can proactively recruit that user.

[0143] Furthermore, companies can create customized educational content based on users' emotions using content generation devices and deliver it through servers. This approach can increase user motivation and improve learning efficiency.

[0144] Finally, the matching processing unit selects the optimal partner by considering the user's interests and emotional state, as well as the company's requirements. As a result, the server notifies the user and the company of the matching results, facilitating smooth communication between them.

[0145] This system allows students to have a learning experience tailored to their own emotions, and enables companies to more accurately identify top talent.

[0146] The following describes the processing flow.

[0147] Step 1:

[0148] Users access the platform using their devices and enter the personal information required to create an account.

[0149] Step 2:

[0150] The terminal sends the entered user information to the server.

[0151] Step 3:

[0152] The server's information processing device uses AI to analyze the received user registration information and automatically screens the suitability of the applicant for membership.

[0153] Step 4:

[0154] Based on the review results, the server sends the user account information along with membership approval.

[0155] Step 5:

[0156] The server periodically records the user's learning history and stores it in a database. This includes the content learned, progress, and results.

[0157] Step 6:

[0158] The recommendation system selects highly relevant educational content based on the user's learning history and presents it to the user as a recommendation list.

[0159] Step 7:

[0160] The user selects content presented by the server and begins learning. During learning, the user's actions are monitored in real time by the server.

[0161] Step 8:

[0162] The emotion engine activates and analyzes the user's emotions based on their learning behavior patterns and input data.

[0163] Step 9:

[0164] Based on the output of the emotion engine, the server recommends content and adjusts the difficulty level according to the user's emotions, providing a personalized learning experience.

[0165] Step 10:

[0166] Corporate terminals request access permission to the server to retrieve user learning history and sentiment data.

[0167] Step 11:

[0168] The server provides the necessary data to the company via an access control device. The company then uses this information to conduct recruitment activities.

[0169] Step 12:

[0170] Companies use content generation devices to create specially customized educational content based on user sentiment data.

[0171] Step 13:

[0172] The server provides users with content created by the content generation device, aiming to improve the learning experience.

[0173] Step 14:

[0174] The matching processing unit compares user data with company requirements to perform the optimal match. The server notifies both the user and the company of the results, supporting communication between them.

[0175] (Example 2)

[0176] 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".

[0177] Traditional educational platforms have faced challenges in enriching users' learning experiences and supporting companies' efficient talent acquisition activities. In particular, they lacked mechanisms to dynamically deliver educational content based on individual users' emotional states and learning histories, and to facilitate appropriate matching with companies.

[0178] 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.

[0179] In this invention, the server includes data processing means for analyzing user registration information and assessing suitability for membership, recommendation means for analyzing the user's learning history and recommending educational materials, permission control means for companies to conduct talent discovery activities, sentiment analysis means for evaluating the user's emotional state based on user behavior data, and matching processing means for performing matching processing based on the user's interests and emotional state. This makes it possible to provide users with personalized learning experiences and to realize appropriate talent discovery approaches for companies.

[0180] "Data processing means" refers to a device or function for analyzing user registration information and automatically screening the suitability of an applicant for membership.

[0181] "Recommendation method" refers to a device or function that analyzes a user's learning history and selects and recommends relevant educational materials.

[0182] "Permission control means" refers to a device or function for managing and controlling an enterprise's access to a user's learning history and its activities related to talent discovery.

[0183] "Emotional analysis means" refers to a device or function used to evaluate a user's emotional state based on their behavioral data and to select educational materials.

[0184] A "matching processing means" is a device or function that matches a user's interests and emotional state with a company's requirements to achieve the optimal response.

[0185] This invention is an integrated system aimed at improving the user's learning experience and optimizing talent acquisition activities by companies. This system provides efficient and effective support to both the server, user terminals, and company terminals by performing data processing between them.

[0186] Users access the platform using their devices and enter their personal information, educational background, and areas of interest. The server analyzes this data using data processing tools to assess the user's suitability for membership. If successful, the server issues account information to the user. The data processing tools utilize commonly used database management systems and machine learning algorithms.

[0187] Subsequently, once the user begins learning, the server continuously records the learning history and selects relevant educational materials using recommendation tools. These recommendation tools utilize a digital library system for managing educational materials and a recommendation engine to analyze the user's interests. Furthermore, sentiment analysis tools evaluate the user's emotional state using behavioral data obtained from the user's device. Sentiment analysis tools employ sentiment recognition algorithms and natural language processing.

[0188] Companies can access users' learning histories via terminals and through permission control mechanisms on the server to find the best talent for their needs. Furthermore, matching mechanisms enable rapid matching between company requirements and user profiles. This facilitates smooth communication and talent scouting activities.

[0189] As a concrete example, here is an example of a prompt message for an AI model:

[0190] Example of a prompt:

[0191] "When users are stressed while learning a new programming language, how can we make their learning experience more positive?"

[0192] By utilizing this prompt, the generative AI model can suggest advice and content that is suitable for reducing user stress.

[0193] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0194] Step 1:

[0195] Users access the platform from their devices and enter personal information, educational background, and areas of interest. Based on this input data, the server performs user authentication and a membership eligibility check. The input data is analyzed by data processing tools to determine whether the user meets the membership criteria. The output is the generation of account information as a result of the eligibility check. Specifically, the user enters the required information into an online form and clicks the submit button.

[0196] Step 2:

[0197] The server records and updates the user's learning history by collecting user activity data. Using this data as input, the recommendation system selects appropriate educational materials. Through data analysis, the selection engine identifies content based on the user's learning patterns and interests. The output is a list of educational materials optimized for the user. Specifically, the learning log is stored in a database on the server, and this data is analyzed as needed to suggest new content.

[0198] Step 3:

[0199] Based on behavioral data obtained from the user's device, the emotion analysis system evaluates the emotional state. Input consists of user action data and sensor information, and the analysis engine uses emotion recognition algorithms to determine the user's psychological state. Output is data representing the user's emotional state. Specifically, the emotion engine analyzes the user's voice tone and input speed, and quantifies stress and excitement levels.

[0200] Step 4:

[0201] Corporate terminals access users' learning history using the server's permission control mechanisms. The input is the company's search criteria, and the output is a list of user profiles that match the criteria. Through access control, the company accesses the necessary data when needed. Specifically, a company recruiter sets the search criteria via a dashboard and retrieves the list.

[0202] Step 5:

[0203] The matching process matches users' interests and emotional states with company requirements to select appropriate partners. The input is user and company profile data, and the output is a highly suitable matching result. Specifically, an AI algorithm integrates the data from both parties and presents matching candidates.

[0204] Step 6:

[0205] By inputting prompts into a generative AI model, the system generates solutions based on the user's specific conditions. The input might be a prompt such as, "If a user is feeling stressed while learning a new programming language, how can we make their learning experience more positive?" The output is the generated content or advice. The specific operation involves the AI-generated solutions being delivered to the user from the server, along with suggestions for new learning materials and methods.

[0206] (Application Example 2)

[0207] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as a "server" and the smart device 14 as a "terminal".

[0208] Traditional learning platforms lack the means to optimize the user's learning experience, particularly by failing to provide personalized content that takes into account the user's emotional state. Furthermore, corporate recruitment activities are based solely on user learning data, without considering the user's emotions or motivations, making optimal matching difficult.

[0209] 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.

[0210] In this invention, the server includes data processing means for analyzing user registration information and automatically assessing the suitability of membership; recommendation means for analyzing the user's learning history and recommending relevant educational content; sentiment analysis means for analyzing the user's emotional state and adjusting the difficulty level of the educational content; and control means for companies to access the user's learning history and sentiment analysis results and conduct recruitment activities. This improves the individual learning experience of users while enabling companies to conduct effective recruitment activities based on sentiment data.

[0211] "User registration information" refers to data that platform users provide, including their personal information, educational background, and areas of interest.

[0212] A "data processing device" is a device that analyzes user registration information and automatically performs appropriate membership screening for the platform.

[0213] "Recommendation methods" refer to a function that selects and presents relevant educational content to users based on their learning history.

[0214] An "emotion analysis tool" is a mechanism that detects the user's emotional state and adjusts the difficulty level of educational content accordingly.

[0215] A "control device" is a device that manages the access necessary for a company to conduct recruitment activities based on the user's learning history and sentiment analysis results.

[0216] This invention is a system designed to improve the user's learning experience and to optimize a company's talent scouting activities. The following describes specific embodiments for realizing this system.

[0217] The server is equipped with a data processing device that processes user input data. This device utilizes an AI model built using Python to automatically assess the suitability of users for membership by analyzing their registration information. Specifically, it registers users' interests and educational background information in a database and evaluates their suitability.

[0218] As a recommendation mechanism, the server-side system incorporates a system for managing educational content. This system records the user's learning history and uses this data to provide relevant content through Firebase. This allows users to experience more effective learning.

[0219] The emotion analysis method uses Google Cloud's facial recognition API and other tools to evaluate the user's emotional state. This analysis helps understand the user's situation and allows for flexible adjustment of the learning content's difficulty level. This adjustment reduces user stress and improves learning efficiency.

[0220] Companies can access users' learning history and emotional data using control mechanisms. This functionality is implemented by companies through database management systems such as Firebase. This allows companies to conduct efficient recruitment activities based on users' learning and emotional tendencies.

[0221] For example, if a user is working on a learning content for an extended period and the system detects signs of stress, the system will recommend easier content with adjusted difficulty levels. Furthermore, the company will receive an analysis report indicating that the user is persistent but also sensitive to stress.

[0222] An example of a prompt for a generative AI model is: "Generate optimal educational content with adjusted difficulty levels based on the user's learning history and emotional state. Also, create a report for businesses based on the user's learning patterns and emotional analysis results."

[0223] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0224] Step 1:

[0225] Users access the system using a terminal and enter registration information such as personal information, educational background, and areas of interest. The server stores this information in a database and analyzes it using an AI model implemented in Python to determine eligibility for membership. The user is then notified whether their membership has been approved or denied.

[0226] Step 2:

[0227] Once a user's membership is approved, they begin using the learning platform. As they progress through their learning via their device, their learning history is sent to the server in real time. The server uses this data to manage learning progress via a Firebase database and recommend relevant educational content. The output is provided to the user as a list of recommended content.

[0228] Step 3:

[0229] When the user is learning, facial expression data is acquired using the camera of the terminal. This data is sent to the server and the emotional state is analyzed by Google Cloud's facial recognition API. Through this analysis, the current emotional state of the user is grasped, and the server adjusts the content again based on the result. The output is that the adjusted content is presented to the user.

[0230] Step 4:

[0231] The company accesses the user's learning history and emotional analysis results through the server and via the control means. Based on these data, the company identifies users who are considered to be the most suitable talents for the company and conducts scouting activities. The output is sent from the company as scouting information to the target users.

[0232] Step 5:

[0233] Based on the user's learning data and emotional data, the server uses a generative AI model to generate a report for the company. This report is based on the user's learning patterns and emotional tendencies and is distributed to the company immediately after generation. The output is provided to the company as a detailed report.

[0234] The specific processing unit 290 sends 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 voice indicating user input for the result of the specific processing. The control unit 46A sends the voice data indicating the 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 voice data.

[0235] 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.

[0236] 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.

[0237] [Second Embodiment]

[0238] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0239] 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.

[0240] 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).

[0241] 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.

[0242] 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.

[0243] 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).

[0244] 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.

[0245] 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.

[0246] 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.

[0247] 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.

[0248] 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.

[0249] 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".

[0250] The system of this invention integrates an information processing device, a recommendation device, an access control device, a content generation device, and a matching processing device to effectively connect students and companies. The following describes how each device functions with specific examples.

[0251] Users (students) first enter their personal information, educational background, and areas of interest through the platform's registration form. The device then sends this information to the server. The information processing device on the server uses the received data to screen the user's application. This is done using AI and is processed automatically based on specific criteria. Users who pass the screening receive account information from the server and gain access to the platform.

[0252] When a user uses the platform, the server constantly records and updates their learning history. Based on this, the recommendation system on the server operates and lists and suggests company-provided educational content that is suitable for the user. For example, if a user expresses interest in machine learning, relevant practical courses and materials will be presented.

[0253] Furthermore, companies can access servers via terminals and conduct recruitment activities based on students' academic histories. An access control device manages this process, and companies view the data under appropriate permissions. For example, if a company is looking for students with specific skills, a list of students with academic histories matching those skills will be provided.

[0254] Companies can generate special educational content. Through content generation devices, companies can create and deliver customized courses based on users' learning histories.

[0255] Finally, the matching processing device analyzes the profiles of users and companies and performs optimal matching. As a result, users and companies can contact each other, providing opportunities for effective communication. Thus, the present invention provides students with the opportunity to receive high-quality education free of charge, while simultaneously enabling companies to efficiently discover talented individuals.

[0256] The following describes the processing flow.

[0257] Step 1:

[0258] Users access the platform's registration page using their devices and enter their personal information, educational background, and areas of interest.

[0259] Step 2:

[0260] The terminal sends the entered data to the server.

[0261] Step 3:

[0262] The server analyzes the received data using an information processing device and uses AI to assess the suitability of the applicant for membership.

[0263] Step 4:

[0264] Based on the review results, the server sends account information via email to approved users and grants them access to the platform.

[0265] Step 5:

[0266] After the user logs in, the server retrieves their learning history and creates a user profile.

[0267] Step 6:

[0268] The recommendation system installed on the server selects relevant educational content based on the user's learning history and interests, and presents it to the user as a recommendation list.

[0269] Step 7:

[0270] The user reviews the presented content list, selects the content that interests them, and begins learning.

[0271] Step 8:

[0272] The server records the user's learning progress and reflects it in their learning history.

[0273] Step 9:

[0274] The enterprise terminal accesses the server and requests to refer to the user's learning history.

[0275] Step 10:

[0276] The server uses the access control device to provide appropriate user data to the enterprise and assist the enterprise's scouting activities.

[0277] Step 11:

[0278] The enterprise terminal transmits the selected scout message to the selected user based on the acquired information.

[0279] Step 12:

[0280] The enterprise uses the content generation device to create special educational content according to the user's needs and provides it to the user via the server.

[0281] Step 13:

[0282] The matching processing device operates to compare and analyze the profiles of the user and the enterprise to achieve optimal matching.

[0283] Step 14:

[0284] The server notifies the matching results to the user and the enterprise and promotes communication between the two.

[0285] (Example 1)

[0286] Next, 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".

[0287] Traditional systems connecting students and companies have struggled to efficiently provide the right talent and educational opportunities. Furthermore, finding students who match the skills and fields companies require is time-consuming and labor-intensive, and students also face challenges in finding suitable educational content.

[0288] 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.

[0289] In this invention, the server includes a calculation means for analyzing user registration information and automatically assessing the suitability of membership; a means for recording and updating the user's learning history; a recommendation means for analyzing the user's learning history and recommending relevant educational resources; and an access control means for organizations to access the user's learning history and conduct recruitment activities. This makes it possible to provide individual users with optimal educational opportunities and to efficiently discover and collaborate with personnel who match the skills required by companies.

[0290] A "user" refers to an individual who registers with the system and seeks educational or employment opportunities.

[0291] "Registration information" refers to data such as personal information, educational background, and areas of interest that users enter into the system.

[0292] A "computational device" refers to an information processing system used to analyze user registration information and conduct membership screening.

[0293] "Learning history" refers to data that records the knowledge, skills, and educational content a user has acquired in the past.

[0294] A "recommendation system" refers to a system that has the function of suggesting relevant educational resources to users based on their learning history.

[0295] "Organization" refers to a corporation or group that provides educational content to users or seeks personnel.

[0296] "Access control measures" refer to systems that have the function of managing so that an organization can access users' learning history under appropriate permissions.

[0297] A "resource generation method" refers to a system that has the function of creating and providing educational resources offered by an organization based on the user's learning history.

[0298] "Analysis tools" refer to systems equipped with information processing functions to analyze the requirements of users and organizations and perform optimal matching.

[0299] The system of this invention utilizes computer and network technologies to effectively connect students and companies. It primarily involves the coordinated functioning of a computing device, recommendation system, access control system, resource generation system, and analysis system. Its specific configuration is described below.

[0300] Users access the platform via an internet-enabled device and enter personal information, educational background, and areas of interest into a registration form. This information is encrypted and transmitted to the server. The server, using an information processing system that acts as a computing device, analyzes the user's registration information and automatically performs an admission screening according to pre-set criteria. Users who pass the screening are sent account information used by the server via email and granted access to the platform.

[0301] As a user progresses through their learning on the platform, the device automatically generates a learning history and sends it to the server. This learning history is updated in real time and stored in a database on the server. Based on this information, the recommendation system on the server operates and suggests appropriate educational resources to the user. This system, which utilizes a generative AI model, recommends content tailored to the user's interests and skills. For example, if the user enters a prompt such as, "What educational resources would be suitable if the user is interested in machine learning?", relevant online courses and materials will be suggested.

[0302] The enterprise can access the server using its terminal and view the learning histories of students. The access control means manages the enterprise's data access rights and plays a role in preventing the leakage of user information. Based on this information, the enterprise searches for candidates with specific skills and conducts recruitment activities.

[0303] Furthermore, the enterprise can use the resource generation means to provide special educational resources based on the learning histories of users. This resource generation means creates educational courses based on the enterprise's exclusive data and provides them to users through the server.

[0304] Finally, the server uses the analysis means to perform optimal matching based on the profiles of users and enterprises. As a result, effective communication can be carried out between users and enterprises, and it becomes possible to efficiently meet each other's needs.

[0305] The flow of the specific process in Example 1 will be described using FIG. 11.

[0306] Step 1:

[0307] The user accesses the platform using the terminal and provides input data such as personal information, educational background, and areas of interest in the registration form. The terminal sends this information to the server through a secure channel. At the server, after verifying the format consistency of the received data, it is stored in the database. At this stage, the input registration information is reflected in the database where the data saved by the server as output.

[0308] Step 2:

[0309] The server automatically reviews the eligibility for membership based on the user's registration information using a computing device. Using an AI algorithm, the user information is compared with pre-set criteria. As the output of this process, a review result is generated and the availability of the account is determined. If qualified, a confirmation email containing the account information is sent to the user.

[0310] Step 3:

[0311] Users access educational content on the platform using their devices. This usage history is sent from the device to the server, which stores it in a database as learning history. Here, user behavior data is input, and the updated learning history is saved as output. The learning history is updated in real time as time passes.

[0312] Step 4:

[0313] The server uses recommendation tools to suggest relevant educational resources based on the user's most recent learning history. A generative AI model is used for this process, identifying the most suitable educational content from the input learning history and listing it as output. For example, if a user expresses interest in data science, links to relevant online courses will be generated.

[0314] Step 5:

[0315] The organization accesses the server via a terminal and displays a list of users based on specific learning histories through access control mechanisms. In this process, the input is the search criteria specified by the organization, and the learning histories of users matching these criteria are provided as output. The organization uses this information to conduct recruitment activities.

[0316] Step 6:

[0317] Using resource generation tools, organizations create special educational resources based on users' learning histories. This includes customization based on user characteristics. User history data is input, and the educational resources generated based on this data are provided to the user as output.

[0318] Step 7:

[0319] The server uses analytical tools to perform optimal matching based on user and organization profiles. User and organization request data is processed as input, and a suitable matching result is generated as output. This result is notified to the user and organization, enabling two-way communication.

[0320] (Application Example 1)

[0321] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server," and the smart glasses 214 will be referred to as the "terminal."

[0322] In today's educational environment, ensuring students access optimal educational resources based on their interests and backgrounds, and enabling organizations to efficiently recruit suitable talent, are crucial challenges in promoting education and employment. Furthermore, there is a need for technologies that enhance learning effectiveness by increasing the visualization and interactivity of educational resources.

[0323] 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.

[0324] In this invention, the server includes information analysis means for analyzing user information and automatically evaluating suitability; suggestion means for analyzing user history information and recommending corresponding educational materials; access control means for organizations to access user history information and conduct talent scouting activities; and visualization means for displaying learning materials using augmented reality technology. As a result, students can receive educational resources tailored to their interests, organizations can efficiently scout for talent, and the quality of education can be improved using augmented reality.

[0325] "User information" refers to basic data that can identify an individual, including information such as an individual's characteristics, hobbies, and history.

[0326] "Information analysis means" refers to technical elements used to process collected user information and determine its appropriateness based on specific criteria.

[0327] "History information" refers to a data set that shows a record of a user's learning activities and related experiences.

[0328] The "recommendation mechanism" is a function that recommends highly relevant educational resources and materials based on analyzed historical information.

[0329] An "organization" refers to a business entity or legal entity that conducts educational or recruitment activities for students or users.

[0330] "Access control means" refers to system elements that manage how an organization can securely and appropriately access users' historical information.

[0331] Augmented reality technology is a technique that overlays virtual information onto the real world environment and is used to enrich the user experience.

[0332] "Visualization means" refers to functions that visually display digital information, and in particular, play a role in providing information through augmented reality technology.

[0333] The system for realizing this invention functions through the cooperation of users, terminals, and servers. The underlying hardware of the system includes mobile terminals such as smartphones and tablets, and servers connected to them. These devices operate in combination with information analysis means, suggestion means, access control means, and visualization means.

[0334] The server first receives user registration information and automatically evaluates its suitability using data analysis tools. This process utilizes software such as Python and TensorFlow to perform advanced data analysis.

[0335] Next, the user's history information is analyzed by the server, and educational content matching their interests is recommended using a suggestion method. This recommendation information is then displayed on the device by an application developed in Swift.

[0336] When an organization conducts activities based on user information, server access control measures are applied, enabling secure information sharing. Security protocols are strictly managed throughout this process.

[0337] Furthermore, augmented reality technology is employed as a visualization tool, using ARKit to overlay educational content onto the real world. This feature allows users to gain a visually rich learning experience. For example, a user interested in history can recreate ancient cultural heritage sites in 3D via their smartphone, enabling intuitive learning.

[0338] Generative AI models are also used in situations where educational tasks are suggested based on user behavior data. For example, more sophisticated suggestions can be made by using prompts like the following: "Suggest learning tasks based on the user's interests and provide the user with the most suitable educational content."

[0339] In this way, users can obtain the most suitable educational resources that match their interests and learning history, and organizations can efficiently capture their target talent.

[0340] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0341] Step 1:

[0342] The server receives registration information sent from the user via their device. This information includes personal data and areas of interest. The server uses information analysis tools, performing analysis using Python and TensorFlow. As a result of the analysis, a suitability assessment of the user is generated, and if deemed suitable, account information is generated.

[0343] Step 2:

[0344] The device displays account information provided by the server to the user. Based on this information, the user can log in to the platform and begin using it. The user's activity history is continuously transmitted from the device to the server and stored as historical information.

[0345] Step 3:

[0346] The server processes data using suggestion tools based on the user's history information and performs data calculations to recommend highly relevant educational content. This process uses AI algorithms to analyze the user's interests. As output, a list of specific educational materials is generated and sent to the terminal.

[0347] Step 4:

[0348] The device displays a list of educational materials received from the server to the user. The user can select content of interest from this list and begin learning through AR technology, which is provided as a visualization tool. ARKit is used to overlay virtual information onto the real world.

[0349] Step 5:

[0350] Organizations can access user history information using access control measures. The server implements appropriate security protocols and manages the organization to securely access the information it needs. As a result of this access, organizations can obtain information useful for talent acquisition activities.

[0351] Step 6:

[0352] The server uses a generative AI model to suggest new learning tasks to the user. Based on the prompt, it analyzes the user's behavioral data and outputs optimal advice for the next step. For example, a prompt such as "Suggest learning tasks based on the user's interests and provide the user with the most suitable educational content" might be used.

[0353] 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.

[0354] This invention is a system that improves the user's learning experience while simultaneously optimizing corporate talent scouting activities. To achieve this, it integrates an information processing device, a recommendation device, an access control device, a content generation device, a matching processing device, and an emotion engine.

[0355] Users first access the platform from their device and enter personal information, educational background, and areas of interest. The server analyzes this information using an AI-powered information processing device to assess the user's suitability for membership. If approved, the server provides the user with account information.

[0356] When a user uses the platform, the server records and updates their learning history, and based on this, a recommendation system selects relevant educational content. Furthermore, an emotion engine analyzes the user's emotions at that time based on their input data and learning behavior, and the server recommends content that promotes a positive learning experience. For example, if a user is feeling stressed about a particular task, the server will present learning materials with adjusted difficulty levels or content that promotes relaxation.

[0357] Corporate terminals can access users' learning history via an access control device on the server, enabling recruitment activities. Furthermore, by analyzing users' emotional data using an emotion engine, companies can take a more effective approach. For example, if a user provides a lot of positive feedback, companies can proactively recruit that user.

[0358] Furthermore, companies can create customized educational content based on users' emotions using content generation devices and deliver it through servers. This approach can increase user motivation and improve learning efficiency.

[0359] Finally, the matching processing unit selects the optimal partner by considering the user's interests and emotional state, as well as the company's requirements. As a result, the server notifies the user and the company of the matching results, facilitating smooth communication between them.

[0360] This system allows students to have a learning experience tailored to their own emotions, and enables companies to more accurately identify top talent.

[0361] The following describes the processing flow.

[0362] Step 1:

[0363] Users access the platform using their devices and enter the personal information required to create an account.

[0364] Step 2:

[0365] The terminal sends the entered user information to the server.

[0366] Step 3:

[0367] The server's information processing device uses AI to analyze the received user registration information and automatically screens the suitability of the applicant for membership.

[0368] Step 4:

[0369] Based on the review results, the server sends the user account information along with membership approval.

[0370] Step 5:

[0371] The server periodically records the user's learning history and stores it in a database. This includes the content learned, progress, and results.

[0372] Step 6:

[0373] The recommendation system selects highly relevant educational content based on the user's learning history and presents it to the user as a recommendation list.

[0374] Step 7:

[0375] The user selects content presented by the server and begins learning. During learning, the user's actions are monitored in real time by the server.

[0376] Step 8:

[0377] The emotion engine activates and analyzes the user's emotions based on their learning behavior patterns and input data.

[0378] Step 9:

[0379] Based on the output of the emotion engine, the server recommends content and adjusts the difficulty level according to the user's emotions, providing a personalized learning experience.

[0380] Step 10:

[0381] Corporate terminals request access permission to the server to retrieve user learning history and sentiment data.

[0382] Step 11:

[0383] The server provides the necessary data to the company via an access control device. The company then uses this information to conduct recruitment activities.

[0384] Step 12:

[0385] Companies use content generation devices to create specially customized educational content based on user sentiment data.

[0386] Step 13:

[0387] The server provides users with content created by the content generation device, aiming to improve the learning experience.

[0388] Step 14:

[0389] The matching processing unit compares user data with company requirements to perform the optimal match. The server notifies both the user and the company of the results, supporting communication between them.

[0390] (Example 2)

[0391] 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".

[0392] Traditional educational platforms have faced challenges in enriching users' learning experiences and supporting companies' efficient talent acquisition activities. In particular, they lacked mechanisms to dynamically deliver educational content based on individual users' emotional states and learning histories, and to facilitate appropriate matching with companies.

[0393] 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.

[0394] In this invention, the server includes data processing means for analyzing user registration information and assessing suitability for membership, recommendation means for analyzing the user's learning history and recommending educational materials, permission control means for companies to conduct talent discovery activities, sentiment analysis means for evaluating the user's emotional state based on user behavior data, and matching processing means for performing matching processing based on the user's interests and emotional state. This makes it possible to provide users with personalized learning experiences and to realize appropriate talent discovery approaches for companies.

[0395] "Data processing means" refers to a device or function for analyzing user registration information and automatically screening the suitability of an applicant for membership.

[0396] "Recommendation method" refers to a device or function that analyzes a user's learning history and selects and recommends relevant educational materials.

[0397] "Permission control means" refers to a device or function for managing and controlling an enterprise's access to a user's learning history and its activities related to talent discovery.

[0398] "Emotional analysis means" refers to a device or function used to evaluate a user's emotional state based on their behavioral data and to select educational materials.

[0399] A "matching processing means" is a device or function that matches a user's interests and emotional state with a company's requirements to achieve the optimal response.

[0400] This invention is an integrated system aimed at improving the user's learning experience and optimizing talent acquisition activities by companies. This system provides efficient and effective support to both the server, user terminals, and company terminals by performing data processing between them.

[0401] Users access the platform using their devices and enter their personal information, educational background, and areas of interest. The server analyzes this data using data processing tools to assess the user's suitability for membership. If successful, the server issues account information to the user. The data processing tools utilize commonly used database management systems and machine learning algorithms.

[0402] Subsequently, once the user begins learning, the server continuously records the learning history and selects relevant educational materials using recommendation tools. These recommendation tools utilize a digital library system for managing educational materials and a recommendation engine to analyze the user's interests. Furthermore, sentiment analysis tools evaluate the user's emotional state using behavioral data obtained from the user's device. Sentiment analysis tools employ sentiment recognition algorithms and natural language processing.

[0403] Companies can access users' learning histories via terminals and through permission control mechanisms on the server to find the best talent for their needs. Furthermore, matching mechanisms enable rapid matching between company requirements and user profiles. This facilitates smooth communication and talent scouting activities.

[0404] As a concrete example, here is an example of a prompt message for an AI model:

[0405] Example of a prompt:

[0406] "When users are stressed while learning a new programming language, how can we make their learning experience more positive?"

[0407] By utilizing this prompt, the generative AI model can suggest advice and content that is suitable for reducing user stress.

[0408] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0409] Step 1:

[0410] Users access the platform from their devices and enter personal information, educational background, and areas of interest. Based on this input data, the server performs user authentication and a membership eligibility check. The input data is analyzed by data processing tools to determine whether the user meets the membership criteria. The output is the generation of account information as a result of the eligibility check. Specifically, the user enters the required information into an online form and clicks the submit button.

[0411] Step 2:

[0412] The server records and updates the user's learning history by collecting user activity data. Using this data as input, the recommendation system selects appropriate educational materials. Through data analysis, the selection engine identifies content based on the user's learning patterns and interests. The output is a list of educational materials optimized for the user. Specifically, the learning log is stored in a database on the server, and this data is analyzed as needed to suggest new content.

[0413] Step 3:

[0414] Based on behavioral data obtained from the user's device, the emotion analysis system evaluates the emotional state. Input consists of user action data and sensor information, and the analysis engine uses emotion recognition algorithms to determine the user's psychological state. Output is data representing the user's emotional state. Specifically, the emotion engine analyzes the user's voice tone and input speed, and quantifies stress and excitement levels.

[0415] Step 4:

[0416] Corporate terminals access users' learning history using the server's permission control mechanisms. The input is the company's search criteria, and the output is a list of user profiles that match the criteria. Through access control, the company accesses the necessary data when needed. Specifically, a company recruiter sets the search criteria via a dashboard and retrieves the list.

[0417] Step 5:

[0418] The matching process matches users' interests and emotional states with company requirements to select appropriate partners. The input is user and company profile data, and the output is a highly suitable matching result. Specifically, an AI algorithm integrates the data from both parties and presents matching candidates.

[0419] Step 6:

[0420] By inputting prompts into a generative AI model, the system generates solutions based on the user's specific conditions. The input might be a prompt such as, "If a user is feeling stressed while learning a new programming language, how can we make their learning experience more positive?" The output is the generated content or advice. The specific operation involves the AI-generated solutions being delivered to the user from the server, along with suggestions for new learning materials and methods.

[0421] (Application Example 2)

[0422] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as the "server," and the smart glasses 214 will be referred to as the "terminal."

[0423] Traditional learning platforms lack the means to optimize the user's learning experience, particularly by failing to provide personalized content that takes into account the user's emotional state. Furthermore, corporate recruitment activities are based solely on user learning data, without considering the user's emotions or motivations, making optimal matching difficult.

[0424] 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.

[0425] In this invention, the server includes data processing means for analyzing user registration information and automatically assessing the suitability of membership; recommendation means for analyzing the user's learning history and recommending relevant educational content; sentiment analysis means for analyzing the user's emotional state and adjusting the difficulty level of the educational content; and control means for companies to access the user's learning history and sentiment analysis results and conduct recruitment activities. This improves the individual learning experience of users while enabling companies to conduct effective recruitment activities based on sentiment data.

[0426] "User registration information" refers to data that platform users provide, including their personal information, educational background, and areas of interest.

[0427] A "data processing device" is a device that analyzes user registration information and automatically performs appropriate membership screening for the platform.

[0428] "Recommendation methods" refer to a function that selects and presents relevant educational content to users based on their learning history.

[0429] An "emotion analysis tool" is a mechanism that detects the user's emotional state and adjusts the difficulty level of educational content accordingly.

[0430] A "control device" is a device that manages the access necessary for a company to conduct recruitment activities based on the user's learning history and sentiment analysis results.

[0431] This invention is a system designed to improve the user's learning experience and to optimize a company's talent scouting activities. The following describes specific embodiments for realizing this system.

[0432] The server is equipped with a data processing device that processes user input data. This device utilizes an AI model built using Python to automatically assess the suitability of users for membership by analyzing their registration information. Specifically, it registers users' interests and educational background information in a database and evaluates their suitability.

[0433] As a recommendation mechanism, the server-side system incorporates a system for managing educational content. This system records the user's learning history and uses this data to provide relevant content through Firebase. This allows users to experience more effective learning.

[0434] The emotion analysis method uses Google Cloud's facial recognition API and other tools to evaluate the user's emotional state. This analysis helps understand the user's situation and allows for flexible adjustment of the learning content's difficulty level. This adjustment reduces user stress and improves learning efficiency.

[0435] Companies can access users' learning history and emotional data using control mechanisms. This functionality is implemented by companies through database management systems such as Firebase. This allows companies to conduct efficient recruitment activities based on users' learning and emotional tendencies.

[0436] For example, if a user is working on a learning content for an extended period and the system detects signs of stress, the system will recommend easier content with adjusted difficulty levels. Furthermore, the company will receive an analysis report indicating that the user is persistent but also sensitive to stress.

[0437] An example of a prompt for a generative AI model is: "Generate optimal educational content with adjusted difficulty levels based on the user's learning history and emotional state. Also, create a report for businesses based on the user's learning patterns and emotional analysis results."

[0438] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0439] Step 1:

[0440] Users access the system using a terminal and enter registration information such as personal information, educational background, and areas of interest. The server stores this information in a database and analyzes it using an AI model implemented in Python to determine eligibility for membership. The user is then notified whether their membership has been approved or denied.

[0441] Step 2:

[0442] Once a user's membership is approved, they begin using the learning platform. As they progress through their learning via their device, their learning history is sent to the server in real time. The server uses this data to manage learning progress via a Firebase database and recommend relevant educational content. The output is provided to the user as a list of recommended content.

[0443] Step 3:

[0444] When the user is learning, the camera of the terminal is used to acquire facial expression data. This data is sent to the server and the emotional state is analyzed by Google Cloud's face recognition API. Through this analysis, the current emotional state of the user is grasped, and the server adjusts the content again based on the result. The output is that the adjusted content is presented to the user.

[0445] Step 4:

[0446] The enterprise accesses the user's learning history and emotional analysis results through the server and via the control means. Based on these data, the enterprise identifies users who seem to be the most suitable talents for the company and conducts scouting activities. The output is sent from the enterprise as scouting information to the target users.

[0447] Step 5:

[0448] Based on the user's learning data and emotional data, the server uses a generative AI model to generate a report for the enterprise. This report is based on the user's learning patterns and emotional tendencies and is delivered to the enterprise immediately after generation. The output is provided to the enterprise as a detailed report.

[0449] 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 voice indicating user input for the result of the specific processing. The control unit 46A transmits the voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[0450] The data generation model 58 is a so-called generative AI (Artificial Intelligence). As an example of the data generation model 58, there 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.

[0451] 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.

[0452] [Third Embodiment]

[0453] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0454] 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.

[0455] 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).

[0456] 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.

[0457] 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.

[0458] 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).

[0459] 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.

[0460] 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.

[0461] 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.

[0462] 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.

[0463] 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.

[0464] 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".

[0465] The system of this invention integrates an information processing device, a recommendation device, an access control device, a content generation device, and a matching processing device to effectively connect students and companies. The following describes how each device functions with specific examples.

[0466] Users (students) first enter their personal information, educational background, and areas of interest through the platform's registration form. The device then sends this information to the server. The information processing device on the server uses the received data to screen the user's application. This is done using AI and is processed automatically based on specific criteria. Users who pass the screening receive account information from the server and gain access to the platform.

[0467] When a user uses the platform, the server constantly records and updates their learning history. Based on this, the recommendation system on the server operates and lists and suggests company-provided educational content that is suitable for the user. For example, if a user expresses interest in machine learning, relevant practical courses and materials will be presented.

[0468] Furthermore, companies can access servers via terminals and conduct recruitment activities based on students' academic histories. An access control device manages this process, and companies view the data under appropriate permissions. For example, if a company is looking for students with specific skills, a list of students with academic histories matching those skills will be provided.

[0469] Companies can generate special educational content. Through content generation devices, companies can create and deliver customized courses based on users' learning histories.

[0470] Finally, the matching processing device analyzes the profiles of users and companies and performs optimal matching. As a result, users and companies can contact each other, providing opportunities for effective communication. Thus, the present invention provides students with the opportunity to receive high-quality education free of charge, while simultaneously enabling companies to efficiently discover talented individuals.

[0471] The following describes the processing flow.

[0472] Step 1:

[0473] Users access the platform's registration page using their devices and enter their personal information, educational background, and areas of interest.

[0474] Step 2:

[0475] The terminal sends the entered data to the server.

[0476] Step 3:

[0477] The server analyzes the received data using an information processing device and uses AI to assess the suitability of the applicant for membership.

[0478] Step 4:

[0479] Based on the review results, the server sends account information via email to approved users and grants them access to the platform.

[0480] Step 5:

[0481] After the user logs in, the server retrieves their learning history and creates a user profile.

[0482] Step 6:

[0483] The recommendation system installed on the server selects relevant educational content based on the user's learning history and interests, and presents it to the user as a recommendation list.

[0484] Step 7:

[0485] The user reviews the presented content list, selects the content that interests them, and begins learning.

[0486] Step 8:

[0487] The server records the user's learning progress and reflects it in their learning history.

[0488] Step 9:

[0489] The corporate terminal accesses the server and requests to access the user's learning history.

[0490] Step 10:

[0491] The server uses access control devices to provide appropriate user data to companies, supporting their recruitment activities.

[0492] Step 11:

[0493] The company terminal sends recruitment messages to selected users based on the information it has acquired.

[0494] Step 12:

[0495] Companies use content generation equipment to create specialized educational content tailored to user needs and deliver it to users via servers.

[0496] Step 13:

[0497] The matching processing unit operates, comparing and analyzing user and company profiles to achieve optimal matching.

[0498] Step 14:

[0499] The server notifies users and companies of the matching results, facilitating communication between them.

[0500] (Example 1)

[0501] 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."

[0502] Traditional systems connecting students and companies have struggled to efficiently provide the right talent and educational opportunities. Furthermore, finding students who match the skills and fields companies require is time-consuming and labor-intensive, and students also face challenges in finding suitable educational content.

[0503] 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.

[0504] In this invention, the server includes a calculation means for analyzing user registration information and automatically assessing the suitability of membership; a means for recording and updating the user's learning history; a recommendation means for analyzing the user's learning history and recommending relevant educational resources; and an access control means for organizations to access the user's learning history and conduct recruitment activities. This makes it possible to provide individual users with optimal educational opportunities and to efficiently discover and collaborate with personnel who match the skills required by companies.

[0505] A "user" refers to an individual who registers with the system and seeks educational or employment opportunities.

[0506] "Registration information" refers to data such as personal information, educational background, and areas of interest that users enter into the system.

[0507] A "computational device" refers to an information processing system used to analyze user registration information and conduct membership screening.

[0508] "Learning history" refers to data that records the knowledge, skills, and educational content a user has acquired in the past.

[0509] A "recommendation system" refers to a system that has the function of suggesting relevant educational resources to users based on their learning history.

[0510] "Organization" refers to a corporation or group that provides educational content to users or seeks personnel.

[0511] "Access control measures" refer to systems that have the function of managing so that an organization can access users' learning history under appropriate permissions.

[0512] A "resource generation method" refers to a system that has the function of creating and providing educational resources offered by an organization based on the user's learning history.

[0513] "Analysis tools" refer to systems equipped with information processing functions to analyze the requirements of users and organizations and perform optimal matching.

[0514] The system of this invention utilizes computer and network technologies to effectively connect students and companies. It primarily involves the coordinated functioning of a computing device, recommendation system, access control system, resource generation system, and analysis system. Its specific configuration is described below.

[0515] Users access the platform via an internet-enabled device and enter personal information, educational background, and areas of interest into a registration form. This information is encrypted and transmitted to the server. The server, using an information processing system that acts as a computing device, analyzes the user's registration information and automatically performs an admission screening according to pre-set criteria. Users who pass the screening are sent account information used by the server via email and granted access to the platform.

[0516] As a user progresses through their learning on the platform, the device automatically generates a learning history and sends it to the server. This learning history is updated in real time and stored in a database on the server. Based on this information, the recommendation system on the server operates and suggests appropriate educational resources to the user. This system, which utilizes a generative AI model, recommends content tailored to the user's interests and skills. For example, if the user enters a prompt such as, "What educational resources would be suitable if the user is interested in machine learning?", relevant online courses and materials will be suggested.

[0517] Companies can use these terminals to access servers and view students' learning histories. Access control measures manage the company's data access rights and prevent the leakage of user information. Based on this information, companies can find candidates with specific skills and conduct recruitment activities.

[0518] Furthermore, companies can use resource generation tools to provide special educational resources based on users' learning histories. These resource generation tools create educational courses based on company-specific data and provide them to users via a server.

[0519] Ultimately, the server uses analytical tools to perform optimal matching based on user and company profiles. This enables effective communication between users and companies, allowing them to efficiently meet each other's needs.

[0520] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0521] Step 1:

[0522] Users access the platform using their devices and provide input data such as personal information, educational background, and areas of interest through a registration form. The device transmits this information to the server via a secure channel. The server verifies the format integrity of the received data before storing it in a database. At this stage, the entered registration information is reflected in the database stored on the server as output.

[0523] Step 2:

[0524] The server uses computing power to automatically screen the user's eligibility for membership based on their registration information. An AI algorithm is used to compare the user information against pre-defined criteria. This process generates a screening result, determining whether or not the user can access an account. If approved, the user receives a confirmation email containing their account information.

[0525] Step 3:

[0526] Users access educational content on the platform using their devices. This usage history is sent from the device to the server, which stores it in a database as learning history. Here, user behavior data is input, and the updated learning history is saved as output. The learning history is updated in real time as time passes.

[0527] Step 4:

[0528] The server uses recommendation tools to suggest relevant educational resources based on the user's most recent learning history. A generative AI model is used for this process, identifying the most suitable educational content from the input learning history and listing it as output. For example, if a user expresses interest in data science, links to relevant online courses will be generated.

[0529] Step 5:

[0530] The organization accesses the server via a terminal and displays a list of users based on specific learning histories through access control mechanisms. In this process, the input is the search criteria specified by the organization, and the learning histories of users matching these criteria are provided as output. The organization uses this information to conduct recruitment activities.

[0531] Step 6:

[0532] Using resource generation tools, organizations create special educational resources based on users' learning histories. This includes customization based on user characteristics. User history data is input, and the educational resources generated based on this data are provided to the user as output.

[0533] Step 7:

[0534] The server uses analytical tools to perform optimal matching based on user and organization profiles. User and organization request data is processed as input, and a suitable matching result is generated as output. This result is notified to the user and organization, enabling two-way communication.

[0535] (Application Example 1)

[0536] 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."

[0537] In today's educational environment, ensuring students access optimal educational resources based on their interests and backgrounds, and enabling organizations to efficiently recruit suitable talent, are crucial challenges in promoting education and employment. Furthermore, there is a need for technologies that enhance learning effectiveness by increasing the visualization and interactivity of educational resources.

[0538] 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.

[0539] In this invention, the server includes information analysis means for analyzing user information and automatically evaluating suitability; suggestion means for analyzing user history information and recommending corresponding educational materials; access control means for organizations to access user history information and conduct talent scouting activities; and visualization means for displaying learning materials using augmented reality technology. As a result, students can receive educational resources tailored to their interests, organizations can efficiently scout for talent, and the quality of education can be improved using augmented reality.

[0540] "User information" refers to basic data that can identify an individual, including information such as an individual's characteristics, hobbies, and history.

[0541] "Information analysis means" refers to technical elements used to process collected user information and determine its appropriateness based on specific criteria.

[0542] "History information" refers to a data set that shows a record of a user's learning activities and related experiences.

[0543] The "recommendation mechanism" is a function that recommends highly relevant educational resources and materials based on analyzed historical information.

[0544] An "organization" refers to a business entity or legal entity that conducts educational or recruitment activities for students or users.

[0545] "Access control means" refers to system elements that manage how an organization can securely and appropriately access users' historical information.

[0546] Augmented reality technology is a technique that overlays virtual information onto the real world environment and is used to enrich the user experience.

[0547] "Visualization means" refers to functions that visually display digital information, and in particular, play a role in providing information through augmented reality technology.

[0548] The system for realizing this invention functions through the cooperation of users, terminals, and servers. The underlying hardware of the system includes mobile terminals such as smartphones and tablets, and servers connected to them. These devices operate in combination with information analysis means, suggestion means, access control means, and visualization means.

[0549] The server first receives user registration information and automatically evaluates its suitability using data analysis tools. This process utilizes software such as Python and TensorFlow to perform advanced data analysis.

[0550] Next, the user's history information is analyzed by the server, and educational content matching their interests is recommended using a suggestion method. This recommendation information is then displayed on the device by an application developed in Swift.

[0551] When an organization conducts activities based on user information, server access control measures are applied, enabling secure information sharing. Security protocols are strictly managed throughout this process.

[0552] Furthermore, augmented reality technology is employed as a visualization tool, using ARKit to overlay educational content onto the real world. This feature allows users to gain a visually rich learning experience. For example, a user interested in history can recreate ancient cultural heritage sites in 3D via their smartphone, enabling intuitive learning.

[0553] Generative AI models are also used in situations where educational tasks are suggested based on user behavior data. For example, more sophisticated suggestions can be made by using prompts like the following: "Suggest learning tasks based on the user's interests and provide the user with the most suitable educational content."

[0554] In this way, users can obtain the most suitable educational resources that match their interests and learning history, and organizations can efficiently capture their target talent.

[0555] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0556] Step 1:

[0557] The server receives registration information sent from the user via their device. This information includes personal data and areas of interest. The server uses information analysis tools, performing analysis using Python and TensorFlow. As a result of the analysis, a suitability assessment of the user is generated, and if deemed suitable, account information is generated.

[0558] Step 2:

[0559] The device displays account information provided by the server to the user. Based on this information, the user can log in to the platform and begin using it. The user's activity history is continuously transmitted from the device to the server and stored as historical information.

[0560] Step 3:

[0561] The server processes data using suggestion tools based on the user's history information and performs data calculations to recommend highly relevant educational content. This process uses AI algorithms to analyze the user's interests. As output, a list of specific educational materials is generated and sent to the terminal.

[0562] Step 4:

[0563] The device displays a list of educational materials received from the server to the user. The user can select content of interest from this list and begin learning through AR technology, which is provided as a visualization tool. ARKit is used to overlay virtual information onto the real world.

[0564] Step 5:

[0565] Organizations can access user history information using access control measures. The server implements appropriate security protocols and manages the organization to securely access the information it needs. As a result of this access, organizations can obtain information useful for talent acquisition activities.

[0566] Step 6:

[0567] The server uses a generative AI model to suggest new learning tasks to the user. Based on the prompt, it analyzes the user's behavioral data and outputs optimal advice for the next step. For example, a prompt such as "Suggest learning tasks based on the user's interests and provide the user with the most suitable educational content" might be used.

[0568] 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.

[0569] This invention is a system that improves the user's learning experience while simultaneously optimizing corporate talent scouting activities. To achieve this, it integrates an information processing device, a recommendation device, an access control device, a content generation device, a matching processing device, and an emotion engine.

[0570] Users first access the platform from their device and enter personal information, educational background, and areas of interest. The server analyzes this information using an AI-powered information processing device to assess the user's suitability for membership. If approved, the server provides the user with account information.

[0571] When a user uses the platform, the server records and updates their learning history, and based on this, a recommendation system selects relevant educational content. Furthermore, an emotion engine analyzes the user's emotions at that time based on their input data and learning behavior, and the server recommends content that promotes a positive learning experience. For example, if a user is feeling stressed about a particular task, the server will present learning materials with adjusted difficulty levels or content that promotes relaxation.

[0572] Corporate terminals can access users' learning history via an access control device on the server, enabling recruitment activities. Furthermore, by analyzing users' emotional data using an emotion engine, companies can take a more effective approach. For example, if a user provides a lot of positive feedback, companies can proactively recruit that user.

[0573] Furthermore, companies can create customized educational content based on users' emotions using content generation devices and deliver it through servers. This approach can increase user motivation and improve learning efficiency.

[0574] Finally, the matching processing unit selects the optimal partner by considering the user's interests and emotional state, as well as the company's requirements. As a result, the server notifies the user and the company of the matching results, facilitating smooth communication between them.

[0575] This system allows students to have a learning experience tailored to their own emotions, and enables companies to more accurately identify top talent.

[0576] The following describes the processing flow.

[0577] Step 1:

[0578] Users access the platform using their devices and enter the personal information required to create an account.

[0579] Step 2:

[0580] The terminal sends the entered user information to the server.

[0581] Step 3:

[0582] The server's information processing device uses AI to analyze the received user registration information and automatically screens the suitability of the applicant for membership.

[0583] Step 4:

[0584] Based on the review results, the server sends the user account information along with membership approval.

[0585] Step 5:

[0586] The server periodically records the user's learning history and stores it in a database. This includes the content learned, progress, and results.

[0587] Step 6:

[0588] The recommendation system selects highly relevant educational content based on the user's learning history and presents it to the user as a recommendation list.

[0589] Step 7:

[0590] The user selects content presented by the server and begins learning. During learning, the user's actions are monitored in real time by the server.

[0591] Step 8:

[0592] The emotion engine activates and analyzes the user's emotions based on their learning behavior patterns and input data.

[0593] Step 9:

[0594] Based on the output of the emotion engine, the server recommends content and adjusts the difficulty level according to the user's emotions, providing a personalized learning experience.

[0595] Step 10:

[0596] Corporate terminals request access permission to the server to retrieve user learning history and sentiment data.

[0597] Step 11:

[0598] The server provides the necessary data to the company via an access control device. The company then uses this information to conduct recruitment activities.

[0599] Step 12:

[0600] Companies use content generation devices to create specially customized educational content based on user sentiment data.

[0601] Step 13:

[0602] The server provides users with content created by the content generation device, aiming to improve the learning experience.

[0603] Step 14:

[0604] The matching processing unit compares user data with company requirements to perform the optimal match. The server notifies both the user and the company of the results, supporting communication between them.

[0605] (Example 2)

[0606] 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."

[0607] Traditional educational platforms have faced challenges in enriching users' learning experiences and supporting companies' efficient talent acquisition activities. In particular, they lacked mechanisms to dynamically deliver educational content based on individual users' emotional states and learning histories, and to facilitate appropriate matching with companies.

[0608] 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.

[0609] In this invention, the server includes data processing means for analyzing user registration information and assessing suitability for membership, recommendation means for analyzing the user's learning history and recommending educational materials, permission control means for companies to conduct talent discovery activities, sentiment analysis means for evaluating the user's emotional state based on user behavior data, and matching processing means for performing matching processing based on the user's interests and emotional state. This makes it possible to provide users with personalized learning experiences and to realize appropriate talent discovery approaches for companies.

[0610] "Data processing means" refers to a device or function for analyzing user registration information and automatically screening the suitability of an applicant for membership.

[0611] "Recommendation method" refers to a device or function that analyzes a user's learning history and selects and recommends relevant educational materials.

[0612] "Permission control means" refers to a device or function for managing and controlling an enterprise's access to a user's learning history and its activities related to talent discovery.

[0613] "Emotional analysis means" refers to a device or function used to evaluate a user's emotional state based on their behavioral data and to select educational materials.

[0614] A "matching processing means" is a device or function that matches a user's interests and emotional state with a company's requirements to achieve the optimal response.

[0615] This invention is an integrated system aimed at improving the user's learning experience and optimizing talent acquisition activities by companies. This system provides efficient and effective support to both the server, user terminals, and company terminals by performing data processing between them.

[0616] Users access the platform using their devices and enter their personal information, educational background, and areas of interest. The server analyzes this data using data processing tools to assess the user's suitability for membership. If successful, the server issues account information to the user. The data processing tools utilize commonly used database management systems and machine learning algorithms.

[0617] Subsequently, once the user begins learning, the server continuously records the learning history and selects relevant educational materials using recommendation tools. These recommendation tools utilize a digital library system for managing educational materials and a recommendation engine to analyze the user's interests. Furthermore, sentiment analysis tools evaluate the user's emotional state using behavioral data obtained from the user's device. Sentiment analysis tools employ sentiment recognition algorithms and natural language processing.

[0618] Companies can access users' learning histories via terminals and through permission control mechanisms on the server to find the best talent for their needs. Furthermore, matching mechanisms enable rapid matching between company requirements and user profiles. This facilitates smooth communication and talent scouting activities.

[0619] As a concrete example, here is an example of a prompt message for an AI model:

[0620] Example of a prompt:

[0621] "When users are stressed while learning a new programming language, how can we make their learning experience more positive?"

[0622] By utilizing this prompt, the generative AI model can suggest advice and content that is suitable for reducing user stress.

[0623] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0624] Step 1:

[0625] Users access the platform from their devices and enter personal information, educational background, and areas of interest. Based on this input data, the server performs user authentication and a membership eligibility check. The input data is analyzed by data processing tools to determine whether the user meets the membership criteria. The output is the generation of account information as a result of the eligibility check. Specifically, the user enters the required information into an online form and clicks the submit button.

[0626] Step 2:

[0627] The server records and updates the user's learning history by collecting user activity data. Using this data as input, the recommendation system selects appropriate educational materials. Through data analysis, the selection engine identifies content based on the user's learning patterns and interests. The output is a list of educational materials optimized for the user. Specifically, the learning log is stored in a database on the server, and this data is analyzed as needed to suggest new content.

[0628] Step 3:

[0629] Based on behavioral data obtained from the user's device, the emotion analysis system evaluates the emotional state. Input consists of user action data and sensor information, and the analysis engine uses emotion recognition algorithms to determine the user's psychological state. Output is data representing the user's emotional state. Specifically, the emotion engine analyzes the user's voice tone and input speed, and quantifies stress and excitement levels.

[0630] Step 4:

[0631] Corporate terminals access users' learning history using the server's permission control mechanisms. The input is the company's search criteria, and the output is a list of user profiles that match the criteria. Through access control, the company accesses the necessary data when needed. Specifically, a company recruiter sets the search criteria via a dashboard and retrieves the list.

[0632] Step 5:

[0633] The matching process matches users' interests and emotional states with company requirements to select appropriate partners. The input is user and company profile data, and the output is a highly suitable matching result. Specifically, an AI algorithm integrates the data from both parties and presents matching candidates.

[0634] Step 6:

[0635] By inputting prompts into a generative AI model, the system generates solutions based on the user's specific conditions. The input might be a prompt such as, "If a user is feeling stressed while learning a new programming language, how can we make their learning experience more positive?" The output is the generated content or advice. The specific operation involves the AI-generated solutions being delivered to the user from the server, along with suggestions for new learning materials and methods.

[0636] (Application Example 2)

[0637] 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."

[0638] Traditional learning platforms lack the means to optimize the user's learning experience, particularly by failing to provide personalized content that takes into account the user's emotional state. Furthermore, corporate recruitment activities are based solely on user learning data, without considering the user's emotions or motivations, making optimal matching difficult.

[0639] 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.

[0640] In this invention, the server includes data processing means for analyzing user registration information and automatically assessing the suitability of membership; recommendation means for analyzing the user's learning history and recommending relevant educational content; sentiment analysis means for analyzing the user's emotional state and adjusting the difficulty level of the educational content; and control means for companies to access the user's learning history and sentiment analysis results and conduct recruitment activities. This improves the individual learning experience of users while enabling companies to conduct effective recruitment activities based on sentiment data.

[0641] "User registration information" refers to data that platform users provide, including their personal information, educational background, and areas of interest.

[0642] A "data processing device" is a device that analyzes user registration information and automatically performs appropriate membership screening for the platform.

[0643] "Recommendation methods" refer to a function that selects and presents relevant educational content to users based on their learning history.

[0644] An "emotion analysis tool" is a mechanism that detects the user's emotional state and adjusts the difficulty level of educational content accordingly.

[0645] A "control device" is a device that manages the access necessary for a company to conduct recruitment activities based on the user's learning history and sentiment analysis results.

[0646] This invention is a system designed to improve the user's learning experience and to optimize a company's talent scouting activities. The following describes specific embodiments for realizing this system.

[0647] The server is equipped with a data processing device that processes user input data. This device utilizes an AI model built using Python to automatically assess the suitability of users for membership by analyzing their registration information. Specifically, it registers users' interests and educational background information in a database and evaluates their suitability.

[0648] As a recommendation mechanism, the server-side system incorporates a system for managing educational content. This system records the user's learning history and uses this data to provide relevant content through Firebase. This allows users to experience more effective learning.

[0649] The emotion analysis method uses Google Cloud's facial recognition API and other tools to evaluate the user's emotional state. This analysis helps understand the user's situation and allows for flexible adjustment of the learning content's difficulty level. This adjustment reduces user stress and improves learning efficiency.

[0650] Companies can access users' learning history and emotional data using control mechanisms. This functionality is implemented by companies through database management systems such as Firebase. This allows companies to conduct efficient recruitment activities based on users' learning and emotional tendencies.

[0651] For example, if a user is working on a learning content for an extended period and the system detects signs of stress, the system will recommend easier content with adjusted difficulty levels. Furthermore, the company will receive an analysis report indicating that the user is persistent but also sensitive to stress.

[0652] An example of a prompt for a generative AI model is: "Generate optimal educational content with adjusted difficulty levels based on the user's learning history and emotional state. Also, create a report for businesses based on the user's learning patterns and emotional analysis results."

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

[0654] Step 1:

[0655] Users access the system using a terminal and enter registration information such as personal information, educational background, and areas of interest. The server stores this information in a database and analyzes it using an AI model implemented in Python to determine eligibility for membership. The user is then notified whether their membership has been approved or denied.

[0656] Step 2:

[0657] Once a user's membership is approved, they begin using the learning platform. As they progress through their learning via their device, their learning history is sent to the server in real time. The server uses this data to manage learning progress via a Firebase database and recommend relevant educational content. The output is provided to the user as a list of recommended content.

[0658] Step 3:

[0659] When the user is learning, facial expression data is acquired using the camera of the terminal. This data is sent to the server and the emotional state is analyzed by Google Cloud's facial recognition API. Through this analysis, the user's current emotional state is grasped, and the server adjusts the content again based on the result. The output is that the adjusted content is presented to the user.

[0660] Step 4:

[0661] The company accesses the user's learning history and emotional analysis results through the server and the control means. Based on these data, the company identifies users who seem to be the most suitable talents for the company and conducts scouting activities. The output is sent from the company as scouting information to the target users.

[0662] Step 5:

[0663] Based on the user's learning data and emotional data, the server uses a generative AI model to generate a report for the company. This report is based on the user's learning patterns and emotional tendencies and is delivered to the company immediately after generation. The output is provided to the company as a detailed report.

[0664] The specific processing unit 290 sends the result of the specific processing to the headset-type terminal 314. In the headset-type terminal 314, the control unit 46A causes the speaker 240 and the display 343 to output the result of the specific processing. The microphone 238 acquires voice indicating user input for the result of the specific processing. The control unit 46A sends the voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[0665] 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.

[0666] 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.

[0667] [Fourth Embodiment]

[0668] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0669] 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.

[0670] 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).

[0671] 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.

[0672] 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.

[0673] 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).

[0674] 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.

[0675] 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.

[0676] 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.

[0677] 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.

[0678] 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.

[0679] 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.

[0680] 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".

[0681] The system of this invention integrates an information processing device, a recommendation device, an access control device, a content generation device, and a matching processing device to effectively connect students and companies. The following describes how each device functions with specific examples.

[0682] Users (students) first enter their personal information, educational background, and areas of interest through the platform's registration form. The device then sends this information to the server. The information processing device on the server uses the received data to screen the user's application. This is done using AI and is processed automatically based on specific criteria. Users who pass the screening receive account information from the server and gain access to the platform.

[0683] When a user uses the platform, the server constantly records and updates their learning history. Based on this, the recommendation system on the server operates and lists and suggests company-provided educational content that is suitable for the user. For example, if a user expresses interest in machine learning, relevant practical courses and materials will be presented.

[0684] Furthermore, companies can access servers via terminals and conduct recruitment activities based on students' academic histories. An access control device manages this process, and companies view the data under appropriate permissions. For example, if a company is looking for students with specific skills, a list of students with academic histories matching those skills will be provided.

[0685] Companies can generate special educational content. Through content generation devices, companies can create and deliver customized courses based on users' learning histories.

[0686] Finally, the matching processing device analyzes the profiles of users and companies and performs optimal matching. As a result, users and companies can contact each other, providing opportunities for effective communication. Thus, the present invention provides students with the opportunity to receive high-quality education free of charge, while simultaneously enabling companies to efficiently discover talented individuals.

[0687] The following describes the processing flow.

[0688] Step 1:

[0689] Users access the platform's registration page using their devices and enter their personal information, educational background, and areas of interest.

[0690] Step 2:

[0691] The terminal sends the entered data to the server.

[0692] Step 3:

[0693] The server analyzes the received data using an information processing device and uses AI to assess the suitability of the applicant for membership.

[0694] Step 4:

[0695] Based on the review results, the server sends account information via email to approved users and grants them access to the platform.

[0696] Step 5:

[0697] After the user logs in, the server retrieves their learning history and creates a user profile.

[0698] Step 6:

[0699] The recommendation system installed on the server selects relevant educational content based on the user's learning history and interests, and presents it to the user as a recommendation list.

[0700] Step 7:

[0701] The user reviews the presented content list, selects the content that interests them, and begins learning.

[0702] Step 8:

[0703] The server records the user's learning progress and reflects it in their learning history.

[0704] Step 9:

[0705] The corporate terminal accesses the server and requests to access the user's learning history.

[0706] Step 10:

[0707] The server uses access control devices to provide appropriate user data to companies, supporting their recruitment activities.

[0708] Step 11:

[0709] The company terminal sends recruitment messages to selected users based on the information it has acquired.

[0710] Step 12:

[0711] Companies use content generation equipment to create specialized educational content tailored to user needs and deliver it to users via servers.

[0712] Step 13:

[0713] The matching processing unit operates, comparing and analyzing user and company profiles to achieve optimal matching.

[0714] Step 14:

[0715] The server notifies users and companies of the matching results, facilitating communication between them.

[0716] (Example 1)

[0717] 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".

[0718] Traditional systems connecting students and companies have struggled to efficiently provide the right talent and educational opportunities. Furthermore, finding students who match the skills and fields companies require is time-consuming and labor-intensive, and students also face challenges in finding suitable educational content.

[0719] 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.

[0720] In this invention, the server includes a calculation means for analyzing user registration information and automatically assessing the suitability of membership; a means for recording and updating the user's learning history; a recommendation means for analyzing the user's learning history and recommending relevant educational resources; and an access control means for organizations to access the user's learning history and conduct recruitment activities. This makes it possible to provide individual users with optimal educational opportunities and to efficiently discover and collaborate with personnel who match the skills required by companies.

[0721] A "user" refers to an individual who registers with the system and seeks educational or employment opportunities.

[0722] "Registration information" refers to data such as personal information, educational background, and areas of interest that users enter into the system.

[0723] A "computational device" refers to an information processing system used to analyze user registration information and conduct membership screening.

[0724] "Learning history" refers to data that records the knowledge, skills, and educational content a user has acquired in the past.

[0725] A "recommendation system" refers to a system that has the function of suggesting relevant educational resources to users based on their learning history.

[0726] "Organization" refers to a corporation or group that provides educational content to users or seeks personnel.

[0727] "Access control measures" refer to systems that have the function of managing so that an organization can access users' learning history under appropriate permissions.

[0728] A "resource generation method" refers to a system that has the function of creating and providing educational resources offered by an organization based on the user's learning history.

[0729] "Analysis tools" refer to systems equipped with information processing functions to analyze the requirements of users and organizations and perform optimal matching.

[0730] The system of this invention utilizes computer and network technologies to effectively connect students and companies. It primarily involves the coordinated functioning of a computing device, recommendation system, access control system, resource generation system, and analysis system. Its specific configuration is described below.

[0731] Users access the platform via an internet-enabled device and enter personal information, educational background, and areas of interest into a registration form. This information is encrypted and transmitted to the server. The server, using an information processing system that acts as a computing device, analyzes the user's registration information and automatically performs an admission screening according to pre-set criteria. Users who pass the screening are sent account information used by the server via email and granted access to the platform.

[0732] As a user progresses through their learning on the platform, the device automatically generates a learning history and sends it to the server. This learning history is updated in real time and stored in a database on the server. Based on this information, the recommendation system on the server operates and suggests appropriate educational resources to the user. This system, which utilizes a generative AI model, recommends content tailored to the user's interests and skills. For example, if the user enters a prompt such as, "What educational resources would be suitable if the user is interested in machine learning?", relevant online courses and materials will be suggested.

[0733] Companies can use these terminals to access servers and view students' learning histories. Access control measures manage the company's data access rights and prevent the leakage of user information. Based on this information, companies can find candidates with specific skills and conduct recruitment activities.

[0734] Furthermore, companies can use resource generation tools to provide special educational resources based on users' learning histories. These resource generation tools create educational courses based on company-specific data and provide them to users via a server.

[0735] Ultimately, the server uses analytical tools to perform optimal matching based on user and company profiles. This enables effective communication between users and companies, allowing them to efficiently meet each other's needs.

[0736] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0737] Step 1:

[0738] Users access the platform using their devices and provide input data such as personal information, educational background, and areas of interest through a registration form. The device transmits this information to the server via a secure channel. The server verifies the format integrity of the received data before storing it in a database. At this stage, the entered registration information is reflected in the database stored on the server as output.

[0739] Step 2:

[0740] The server uses computing power to automatically screen the user's eligibility for membership based on their registration information. An AI algorithm is used to compare the user information against pre-defined criteria. This process generates a screening result, determining whether or not the user can access an account. If approved, the user receives a confirmation email containing their account information.

[0741] Step 3:

[0742] Users access educational content on the platform using their devices. This usage history is sent from the device to the server, which stores it in a database as learning history. Here, user behavior data is input, and the updated learning history is saved as output. The learning history is updated in real time as time passes.

[0743] Step 4:

[0744] The server uses recommendation tools to suggest relevant educational resources based on the user's most recent learning history. A generative AI model is used for this process, identifying the most suitable educational content from the input learning history and listing it as output. For example, if a user expresses interest in data science, links to relevant online courses will be generated.

[0745] Step 5:

[0746] The organization accesses the server via a terminal and displays a list of users based on specific learning histories through access control mechanisms. In this process, the input is the search criteria specified by the organization, and the learning histories of users matching these criteria are provided as output. The organization uses this information to conduct recruitment activities.

[0747] Step 6:

[0748] Using resource generation tools, organizations create special educational resources based on users' learning histories. This includes customization based on user characteristics. User history data is input, and the educational resources generated based on this data are provided to the user as output.

[0749] Step 7:

[0750] The server uses analytical tools to perform optimal matching based on user and organization profiles. User and organization request data is processed as input, and a suitable matching result is generated as output. This result is notified to the user and organization, enabling two-way communication.

[0751] (Application Example 1)

[0752] 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".

[0753] In today's educational environment, ensuring students access optimal educational resources based on their interests and backgrounds, and enabling organizations to efficiently recruit suitable talent, are crucial challenges in promoting education and employment. Furthermore, there is a need for technologies that enhance learning effectiveness by increasing the visualization and interactivity of educational resources.

[0754] 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.

[0755] In this invention, the server includes information analysis means for analyzing user information and automatically evaluating suitability; suggestion means for analyzing user history information and recommending corresponding educational materials; access control means for organizations to access user history information and conduct talent scouting activities; and visualization means for displaying learning materials using augmented reality technology. As a result, students can receive educational resources tailored to their interests, organizations can efficiently scout for talent, and the quality of education can be improved using augmented reality.

[0756] "User information" refers to basic data that can identify an individual, including information such as an individual's characteristics, hobbies, and history.

[0757] "Information analysis means" refers to technical elements used to process collected user information and determine its appropriateness based on specific criteria.

[0758] "History information" refers to a data set that shows a record of a user's learning activities and related experiences.

[0759] The "recommendation mechanism" is a function that recommends highly relevant educational resources and materials based on analyzed historical information.

[0760] An "organization" refers to a business entity or legal entity that conducts educational or recruitment activities for students or users.

[0761] "Access control means" refers to system elements that manage how an organization can securely and appropriately access users' historical information.

[0762] Augmented reality technology is a technique that overlays virtual information onto the real world environment and is used to enrich the user experience.

[0763] "Visualization means" refers to functions that visually display digital information, and in particular, play a role in providing information through augmented reality technology.

[0764] The system for realizing this invention functions through the cooperation of users, terminals, and servers. The underlying hardware of the system includes mobile terminals such as smartphones and tablets, and servers connected to them. These devices operate in combination with information analysis means, suggestion means, access control means, and visualization means.

[0765] The server first receives user registration information and automatically evaluates its suitability using data analysis tools. This process utilizes software such as Python and TensorFlow to perform advanced data analysis.

[0766] Next, the user's history information is analyzed by the server, and educational content matching their interests is recommended using a suggestion method. This recommendation information is then displayed on the device by an application developed in Swift.

[0767] When an organization conducts activities based on user information, server access control measures are applied, enabling secure information sharing. Security protocols are strictly managed throughout this process.

[0768] Furthermore, augmented reality technology is employed as a visualization tool, using ARKit to overlay educational content onto the real world. This feature allows users to gain a visually rich learning experience. For example, a user interested in history can recreate ancient cultural heritage sites in 3D via their smartphone, enabling intuitive learning.

[0769] Generative AI models are also used in situations where educational tasks are suggested based on user behavior data. For example, more sophisticated suggestions can be made by using prompts like the following: "Suggest learning tasks based on the user's interests and provide the user with the most suitable educational content."

[0770] In this way, users can obtain the most suitable educational resources that match their interests and learning history, and organizations can efficiently capture their target talent.

[0771] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0772] Step 1:

[0773] The server receives registration information sent from the user via their device. This information includes personal data and areas of interest. The server uses information analysis tools, performing analysis using Python and TensorFlow. As a result of the analysis, a suitability assessment of the user is generated, and if deemed suitable, account information is generated.

[0774] Step 2:

[0775] The device displays account information provided by the server to the user. Based on this information, the user can log in to the platform and begin using it. The user's activity history is continuously transmitted from the device to the server and stored as historical information.

[0776] Step 3:

[0777] The server processes data using suggestion tools based on the user's history information and performs data calculations to recommend highly relevant educational content. This process uses AI algorithms to analyze the user's interests. As output, a list of specific educational materials is generated and sent to the terminal.

[0778] Step 4:

[0779] The device displays a list of educational materials received from the server to the user. The user can select content of interest from this list and begin learning through AR technology, which is provided as a visualization tool. ARKit is used to overlay virtual information onto the real world.

[0780] Step 5:

[0781] Organizations can access user history information using access control measures. The server implements appropriate security protocols and manages the organization to securely access the information it needs. As a result of this access, organizations can obtain information useful for talent acquisition activities.

[0782] Step 6:

[0783] The server uses a generative AI model to suggest new learning tasks to the user. Based on the prompt, it analyzes the user's behavioral data and outputs optimal advice for the next step. For example, a prompt such as "Suggest learning tasks based on the user's interests and provide the user with the most suitable educational content" might be used.

[0784] 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.

[0785] This invention is a system that improves the user's learning experience while simultaneously optimizing corporate talent scouting activities. To achieve this, it integrates an information processing device, a recommendation device, an access control device, a content generation device, a matching processing device, and an emotion engine.

[0786] Users first access the platform from their device and enter personal information, educational background, and areas of interest. The server analyzes this information using an AI-powered information processing device to assess the user's suitability for membership. If approved, the server provides the user with account information.

[0787] When a user uses the platform, the server records and updates their learning history, and based on this, a recommendation system selects relevant educational content. Furthermore, an emotion engine analyzes the user's emotions at that time based on their input data and learning behavior, and the server recommends content that promotes a positive learning experience. For example, if a user is feeling stressed about a particular task, the server will present learning materials with adjusted difficulty levels or content that promotes relaxation.

[0788] Corporate terminals can access users' learning history via an access control device on the server, enabling recruitment activities. Furthermore, by analyzing users' emotional data using an emotion engine, companies can take a more effective approach. For example, if a user provides a lot of positive feedback, companies can proactively recruit that user.

[0789] Furthermore, companies can create customized educational content based on users' emotions using content generation devices and deliver it through servers. This approach can increase user motivation and improve learning efficiency.

[0790] Finally, the matching processing unit selects the optimal partner by considering the user's interests and emotional state, as well as the company's requirements. As a result, the server notifies the user and the company of the matching results, facilitating smooth communication between them.

[0791] This system allows students to have a learning experience tailored to their own emotions, and enables companies to more accurately identify top talent.

[0792] The following describes the processing flow.

[0793] Step 1:

[0794] Users access the platform using their devices and enter the personal information required to create an account.

[0795] Step 2:

[0796] The terminal sends the entered user information to the server.

[0797] Step 3:

[0798] The server's information processing device uses AI to analyze the received user registration information and automatically screens the suitability of the applicant for membership.

[0799] Step 4:

[0800] Based on the review results, the server sends the user account information along with membership approval.

[0801] Step 5:

[0802] The server periodically records the user's learning history and stores it in a database. This includes the content learned, progress, and results.

[0803] Step 6:

[0804] The recommendation system selects highly relevant educational content based on the user's learning history and presents it to the user as a recommendation list.

[0805] Step 7:

[0806] The user selects content presented by the server and begins learning. During learning, the user's actions are monitored in real time by the server.

[0807] Step 8:

[0808] The emotion engine activates and analyzes the user's emotions based on their learning behavior patterns and input data.

[0809] Step 9:

[0810] Based on the output of the emotion engine, the server recommends content and adjusts the difficulty level according to the user's emotions, providing a personalized learning experience.

[0811] Step 10:

[0812] Corporate terminals request access permission to the server to retrieve user learning history and sentiment data.

[0813] Step 11:

[0814] The server provides the necessary data to the company via an access control device. The company then uses this information to conduct recruitment activities.

[0815] Step 12:

[0816] Companies use content generation devices to create specially customized educational content based on user sentiment data.

[0817] Step 13:

[0818] The server provides users with content created by the content generation device, aiming to improve the learning experience.

[0819] Step 14:

[0820] The matching processing unit compares user data with company requirements to perform the optimal match. The server notifies both the user and the company of the results, supporting communication between them.

[0821] (Example 2)

[0822] 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".

[0823] Traditional educational platforms have faced challenges in enriching users' learning experiences and supporting companies' efficient talent acquisition activities. In particular, they lacked mechanisms to dynamically deliver educational content based on individual users' emotional states and learning histories, and to facilitate appropriate matching with companies.

[0824] 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.

[0825] In this invention, the server includes data processing means for analyzing user registration information and assessing suitability for membership, recommendation means for analyzing the user's learning history and recommending educational materials, permission control means for companies to conduct talent discovery activities, sentiment analysis means for evaluating the user's emotional state based on user behavior data, and matching processing means for performing matching processing based on the user's interests and emotional state. This makes it possible to provide users with personalized learning experiences and to realize appropriate talent discovery approaches for companies.

[0826] "Data processing means" refers to a device or function for analyzing user registration information and automatically screening the suitability of an applicant for membership.

[0827] "Recommendation method" refers to a device or function that analyzes a user's learning history and selects and recommends relevant educational materials.

[0828] "Permission control means" refers to a device or function for managing and controlling an enterprise's access to a user's learning history and its activities related to talent discovery.

[0829] "Emotional analysis means" refers to a device or function used to evaluate a user's emotional state based on their behavioral data and to select educational materials.

[0830] A "matching processing means" is a device or function that matches a user's interests and emotional state with a company's requirements to achieve the optimal response.

[0831] This invention is an integrated system aimed at improving the user's learning experience and optimizing talent acquisition activities by companies. This system provides efficient and effective support to both the server, user terminals, and company terminals by performing data processing between them.

[0832] Users access the platform using their devices and enter their personal information, educational background, and areas of interest. The server analyzes this data using data processing tools to assess the user's suitability for membership. If successful, the server issues account information to the user. The data processing tools utilize commonly used database management systems and machine learning algorithms.

[0833] Subsequently, once the user begins learning, the server continuously records the learning history and selects relevant educational materials using recommendation tools. These recommendation tools utilize a digital library system for managing educational materials and a recommendation engine to analyze the user's interests. Furthermore, sentiment analysis tools evaluate the user's emotional state using behavioral data obtained from the user's device. Sentiment analysis tools employ sentiment recognition algorithms and natural language processing.

[0834] Companies can access users' learning histories via terminals and through permission control mechanisms on the server to find the best talent for their needs. Furthermore, matching mechanisms enable rapid matching between company requirements and user profiles. This facilitates smooth communication and talent scouting activities.

[0835] As a concrete example, here is an example of a prompt message for an AI model:

[0836] Example of a prompt:

[0837] "When users are stressed while learning a new programming language, how can we make their learning experience more positive?"

[0838] By utilizing this prompt, the generative AI model can suggest advice and content that is suitable for reducing user stress.

[0839] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0840] Step 1:

[0841] Users access the platform from their devices and enter personal information, educational background, and areas of interest. Based on this input data, the server performs user authentication and a membership eligibility check. The input data is analyzed by data processing tools to determine whether the user meets the membership criteria. The output is the generation of account information as a result of the eligibility check. Specifically, the user enters the required information into an online form and clicks the submit button.

[0842] Step 2:

[0843] The server records and updates the user's learning history by collecting user activity data. Using this data as input, the recommendation system selects appropriate educational materials. Through data analysis, the selection engine identifies content based on the user's learning patterns and interests. The output is a list of educational materials optimized for the user. Specifically, the learning log is stored in a database on the server, and this data is analyzed as needed to suggest new content.

[0844] Step 3:

[0845] Based on behavioral data obtained from the user's device, the emotion analysis system evaluates the emotional state. Input consists of user action data and sensor information, and the analysis engine uses emotion recognition algorithms to determine the user's psychological state. Output is data representing the user's emotional state. Specifically, the emotion engine analyzes the user's voice tone and input speed, and quantifies stress and excitement levels.

[0846] Step 4:

[0847] Corporate terminals access users' learning history using the server's permission control mechanisms. The input is the company's search criteria, and the output is a list of user profiles that match the criteria. Through access control, the company accesses the necessary data when needed. Specifically, a company recruiter sets the search criteria via a dashboard and retrieves the list.

[0848] Step 5:

[0849] The matching process matches users' interests and emotional states with company requirements to select appropriate partners. The input is user and company profile data, and the output is a highly suitable matching result. Specifically, an AI algorithm integrates the data from both parties and presents matching candidates.

[0850] Step 6:

[0851] By inputting prompts into a generative AI model, the system generates solutions based on the user's specific conditions. The input might be a prompt such as, "If a user is feeling stressed while learning a new programming language, how can we make their learning experience more positive?" The output is the generated content or advice. The specific operation involves the AI-generated solutions being delivered to the user from the server, along with suggestions for new learning materials and methods.

[0852] (Application Example 2)

[0853] 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".

[0854] Traditional learning platforms lack the means to optimize the user's learning experience, particularly by failing to provide personalized content that takes into account the user's emotional state. Furthermore, corporate recruitment activities are based solely on user learning data, without considering the user's emotions or motivations, making optimal matching difficult.

[0855] 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.

[0856] In this invention, the server includes data processing means for analyzing user registration information and automatically assessing the suitability of membership; recommendation means for analyzing the user's learning history and recommending relevant educational content; sentiment analysis means for analyzing the user's emotional state and adjusting the difficulty level of the educational content; and control means for companies to access the user's learning history and sentiment analysis results and conduct recruitment activities. This improves the individual learning experience of users while enabling companies to conduct effective recruitment activities based on sentiment data.

[0857] "User registration information" refers to data that platform users provide, including their personal information, educational background, and areas of interest.

[0858] A "data processing device" is a device that analyzes user registration information and automatically performs appropriate membership screening for the platform.

[0859] "Recommendation methods" refer to a function that selects and presents relevant educational content to users based on their learning history.

[0860] An "emotion analysis tool" is a mechanism that detects the user's emotional state and adjusts the difficulty level of educational content accordingly.

[0861] A "control device" is a device that manages the access necessary for a company to conduct recruitment activities based on the user's learning history and sentiment analysis results.

[0862] This invention is a system designed to improve the user's learning experience and to optimize a company's talent scouting activities. The following describes specific embodiments for realizing this system.

[0863] The server is equipped with a data processing device that processes user input data. This device utilizes an AI model built using Python to automatically assess the suitability of users for membership by analyzing their registration information. Specifically, it registers users' interests and educational background information in a database and evaluates their suitability.

[0864] As a recommendation mechanism, the server-side system incorporates a system for managing educational content. This system records the user's learning history and uses this data to provide relevant content through Firebase. This allows users to experience more effective learning.

[0865] The emotion analysis method uses Google Cloud's facial recognition API and other tools to evaluate the user's emotional state. This analysis helps understand the user's situation and allows for flexible adjustment of the learning content's difficulty level. This adjustment reduces user stress and improves learning efficiency.

[0866] Companies can access users' learning history and emotional data using control mechanisms. This functionality is implemented by companies through database management systems such as Firebase. This allows companies to conduct efficient recruitment activities based on users' learning and emotional tendencies.

[0867] For example, if a user is working on a learning content for an extended period and the system detects signs of stress, the system will recommend easier content with adjusted difficulty levels. Furthermore, the company will receive an analysis report indicating that the user is persistent but also sensitive to stress.

[0868] An example of a prompt for a generative AI model is: "Generate optimal educational content with adjusted difficulty levels based on the user's learning history and emotional state. Also, create a report for businesses based on the user's learning patterns and emotional analysis results."

[0869] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0870] Step 1:

[0871] Users access the system using a terminal and enter registration information such as personal information, educational background, and areas of interest. The server stores this information in a database and analyzes it using an AI model implemented in Python to determine eligibility for membership. The user is then notified whether their membership has been approved or denied.

[0872] Step 2:

[0873] Once a user's membership is approved, they begin using the learning platform. As they progress through their learning via their device, their learning history is sent to the server in real time. The server uses this data to manage learning progress via a Firebase database and recommend relevant educational content. The output is provided to the user as a list of recommended content.

[0874] Step 3:

[0875] While the user is learning, the device's camera is used to capture facial expression data. This data is sent to a server, where the emotional state is analyzed by Google Cloud's facial recognition API. This analysis reveals the user's current emotional state, and the server adjusts the content accordingly. The output is the user being presented with the adjusted content.

[0876] Step 4:

[0877] Companies access users' learning history and sentiment analysis results via servers and control systems. Based on this data, companies identify users who they believe are the best fit for their company and conduct recruitment activities. The output is sent by companies as recruitment information to the target users.

[0878] Step 5:

[0879] Based on the user's learning data and sentiment data, the server uses the generative AI model to generate a corporate report. This report is based on the user's learning patterns and sentiment trends and is delivered to the company immediately after generation. The output is provided to the company as a detailed report.

[0880] The specific processing unit 290 sends the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the control target 443 to output the result of the specific processing. The microphone 238 acquires voice indicating user input with respect to the result of the specific processing. The control unit 46A sends voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[0881] The data generation model 58 is a so-called generative AI (Artificial Intelligence). As an example of the data generation model 58, there 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.

[0882] In the above embodiment, an example was given in which the specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and the specific processing may also be performed by the robot 414.

[0883] 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.

[0884] 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.

[0885] 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.

[0886] 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.

[0887] 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.

[0888] 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."

[0889] 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.

[0890] 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.

[0891] 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.

[0892] 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.

[0893] 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.

[0894] 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.

[0895] 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.

[0896] 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.

[0897] 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.

[0898] 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.

[0899] 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.

[0900] 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.

[0901] The following is further disclosed regarding the embodiments described above.

[0902] (Claim 1)

[0903] An information processing device that analyzes user registration information and automatically screens the suitability of membership,

[0904] A recommendation device that analyzes the user's learning history and recommends relevant educational content,

[0905] An access control device for a company to access the learning history of the aforementioned user and conduct recruitment activities,

[0906] A system that includes this.

[0907] (Claim 2)

[0908] The system according to claim 1, comprising a content generation device that customizes and provides special educational content provided by a company based on the user's learning history.

[0909] (Claim 3)

[0910] The system according to claim 1, comprising a matching processing device that compares the requirements of the user and the company and performs optimal matching.

[0911] "Example 1"

[0912] (Claim 1)

[0913] A computing device that analyzes user registration information and automatically screens the suitability of membership,

[0914] A means for recording and updating the user's learning history,

[0915] A recommendation system that analyzes the user's learning history and recommends relevant educational resources,

[0916] Access control means for an organization to access the user's learning history and conduct recruitment activities,

[0917] A system that includes this.

[0918] (Claim 2)

[0919] The system according to claim 1, comprising a resource generation means for customizing and providing special educational resources provided by an organization based on the user's learning history.

[0920] (Claim 3)

[0921] The system according to claim 1, comprising an analysis means for comparing the requirements of the user and the organization and performing an optimal match.

[0922] "Application Example 1"

[0923] (Claim 1)

[0924] An information analysis means that analyzes user information and automatically evaluates suitability,

[0925] A suggestion means that analyzes the user's history information and recommends corresponding educational materials,

[0926] Access control means for an organization to access the user's history information and conduct talent scouting activities,

[0927] A visualization means for displaying learning materials using augmented reality technology,

[0928] A system that includes this.

[0929] (Claim 2)

[0930] The system according to claim 1, comprising a material generation means for adjusting and providing specific educational materials provided by the organization based on the user's history information.

[0931] (Claim 3)

[0932] The system according to claim 1, further comprising a matching processing means for comparing the requirements of the user and the organization and making the optimal combination.

[0933] "Example 2 of combining an emotion engine"

[0934] (Claim 1)

[0935] A data processing method that analyzes user registration information and automatically screens the suitability of membership,

[0936] A recommendation system that analyzes the user's learning history and recommends relevant educational materials,

[0937] A permission control means for a company to access the learning history of the aforementioned user and conduct talent discovery activities,

[0938] A sentiment analysis tool that analyzes user behavior data, evaluates their emotional state, and selects appropriate educational materials,

[0939] A matching processing means that realizes the optimal response based on the user's interests, emotional state, and the company's requirements,

[0940] ...

[0941] A system that includes this.

[0942] (Claim 2)

[0943] The system according to claim 1, comprising a material generation means for customizing and providing special educational materials provided by a company based on the user's learning history and emotional state.

[0944] (Claim 3)

[0945] The system according to claim 1, further comprising an adjustment function for optimizing the learning experience, taking into account the emotional state of the user.

[0946] "Application example 2 when combining with an emotional engine"

[0947] (Claim 1)

[0948] A data processing device that analyzes user registration information and automatically screens the suitability of membership,

[0949] A recommendation method that analyzes the user's learning history and recommends relevant educational content,

[0950] An emotion analysis means for analyzing the emotional state of the user and adjusting the difficulty level of the educational content,

[0951] A control means for a company to access the user's learning history and sentiment analysis results and conduct recruitment activities,

[0952] A system that includes this.

[0953] (Claim 2)

[0954] The system according to claim 1, comprising a content generation means for customizing and providing special educational content provided by a company based on the user's learning history and sentiment analysis results.

[0955] (Claim 3)

[0956] The system according to claim 1, further comprising a matching processing means that compares the requirements and emotional states of the user and the company and performs the optimal matching. [Explanation of Symbols]

[0957] 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. An information processing device that analyzes user registration information and automatically screens the suitability of membership, A recommendation device that analyzes the user's learning history and recommends relevant educational content, An access control device for a company to access the learning history of the aforementioned user and conduct recruitment activities, A system that includes this.

2. The system according to claim 1, comprising a content generation device that customizes and provides special educational content provided by a company based on the user's learning history.

3. The system according to claim 1, further comprising a matching processing device that compares the requirements of the user and the company and performs optimal matching.

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