system

Abstract

Provide a system. 【Solution means】 Means for managing data held by educational institutions, Means for receiving requests from enterprises to obtain data from educational institutions, Means for analyzing the acquired data using data analysis techniques, Means for making improvement proposals to educational institutions based on the analysis results, Means for generating and publishing special lecture information based on the analysis results, Means for accepting reservations for lectures, Means for managing and publishing unused asset information of educational institutions, Means for accepting reservation requests for the use of unused assets, A system including the above.

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 educational institutions such as universities, digital transformation is lagging, and there is a problem that a lot of time and cost are spent on inefficient operations. In this situation, it is required to efficiently digitize operations and reduce costs and improve productivity. Also, in enterprises, there is a problem that there is a lack of effective means of data exchange with educational institutions, and the promotion of data utilization is lagging.

Means for Solving the Problems

[0005] This invention solves the above-mentioned problems by providing a system for efficiently managing and providing data held by educational institutions. Specifically, it provides means for managing data held by educational institutions and means for companies to request data acquisition on a server. Furthermore, it provides means for analyzing the acquired data using data analysis technology and makes improvement suggestions to educational institutions based on the analysis results. Based on the analysis results, it provides means for generating and publishing special lecture information and accepting reservations for lectures. It also provides means for managing and publishing information on unused assets of educational institutions and accepting reservations for the use of unused assets. This realizes a system that enables effective data utilization and resource optimization for educational institutions and companies.

[0006] An "educational institution" is an organization whose primary purpose is education, such as a university or vocational school, and is an institution that provides knowledge and develops human resources.

[0007] "Data" refers to information and figures collected, stored, and managed by educational institutions, and includes survey results, statistical information, and other education-related information.

[0008] A "business" is a legal entity or individual engaged in economic activity whose primary purpose is to produce and sell goods or services.

[0009] A "request" is a formal request or application made by an organization to obtain specific data from an educational institution.

[0010] A "server" is a computing system that provides information and services to multiple computers via a network.

[0011] "Data analysis technology" refers to a collection of analytical methods and algorithms used to extract useful information and gain insights from collected data.

[0012] A "special lecture" is a type of class that differs from regular classes, being planned and offered to students based on a specific theme.

[0013] A "reservation" is the act of making an agreement in advance to use and secure a specific service or asset.

[0014] "Unused assets" refer to physical resources such as classrooms and equipment that are not currently in use but are available. [Brief explanation of the drawing]

[0015] [Figure 1] This is a conceptual diagram showing an example of the configuration of a data processing system according to the first embodiment. [Figure 2] This is a conceptual diagram showing an example of the essential functions of a data processing device and a smart device according to the first embodiment. [Figure 3] This is a conceptual diagram showing an example of the configuration of a data processing system according to the second embodiment. [Figure 4] This is a conceptual diagram showing an example of the main functions of a data processing device and smart glasses according to the second embodiment. [Figure 5] This is a conceptual diagram showing an example of the configuration of a data processing system according to the third embodiment. [Figure 6] This is a conceptual diagram showing an example of the main functions of a data processing device and a headset-type terminal according to the third embodiment. [Figure 7] This is a conceptual diagram showing an example of the configuration of a data processing system according to the fourth embodiment. [Figure 8] This is a conceptual diagram showing an example of the main functions of a data processing device and a robot according to the fourth embodiment. [Figure 9] This shows an emotion map where multiple emotions are mapped. [Figure 10] This shows an emotion map where multiple emotions are mapped. [Figure 11] This is a sequence diagram showing the processing flow of the data processing system in Example 1. [Figure 12] This is a sequence diagram showing the processing flow of the data processing system in Application Example 1. [Figure 13]It is a sequence diagram showing the processing flow of the data processing system in Embodiment 2 when the emotion engine is combined. [Figure 14] It is a sequence diagram showing the processing flow of the data processing system in Application Example 2 when the emotion engine is combined.

Mode for Carrying Out the Invention

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

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

[0018] In the following embodiments, the numbered processor (hereinafter simply referred to as "processor") may be one arithmetic unit or a combination of multiple arithmetic units. Also, the processor may be one 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), APU (Accelerated Processing Unit), etc. [[ID=20=20]]

[0019] In the following embodiments, the numbered RAM (Random Access Memory) is a memory in which information is temporarily stored and is used as a work memory by the processor.

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

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

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

[0023] [First Embodiment]

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

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

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

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

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

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

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

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

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

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

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

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

[0036] This invention provides a system for efficiently managing data held by educational institutions and for facilitating smooth data exchange with companies. The system of this invention is implemented in the following forms to enable educational institutions and companies to share mutually beneficial information and to effectively utilize the assets of educational institutions.

[0037] First, users from educational institutions upload their own survey data and other relevant information to the system via their devices. This information is centrally managed by a server, and after the educational institution has configured its permissions, it grants access to companies. This allows company users to request the data they need. When a company user requests data, the server automatically notifies the educational institution of this request, and once the educational institution approves it, the data is provided to the company.

[0038] Furthermore, the server analyzes uploaded data using data analysis technologies, particularly machine learning algorithms. Based on the insights gained from this analysis, it provides educational institutions with suggestions for service improvements and support in planning special lectures. Educational institutions design special lectures and supplementary courses based on the recommendations provided by the server and offer them to students. Learners can use their devices to view information on special lectures and make reservations for lectures that interest them.

[0039] Furthermore, the system of this invention manages unused assets of educational institutions and facilitates their effective utilization in conjunction with companies. Users of educational institutions register unused classrooms and facilities and input the information into the server. This allows companies to reserve the desired time slots for using these assets via a terminal. The server manages the reservation information and ensures the appropriate use of the assets.

[0040] As a concrete example, by registering classrooms with some surplus capacity in the system, educational institutions can enable companies to use these classrooms for meetings and training sessions. Furthermore, companies can analyze survey data received from educational institutions to inform their marketing strategies and product development. This allows educational institutions to effectively utilize underutilized assets while also contributing to improving students' learning environments.

[0041] The following describes the processing flow.

[0042] Step 1:

[0043] Users (educational institutions) upload survey data to the system via their devices. The server receives the uploaded data, verifies its contents, and saves it to the database.

[0044] Step 2:

[0045] The server makes data authorized by educational institutions available for companies to view. Users (companies) access the system using their terminals, select the data they need, and submit requests.

[0046] Step 3:

[0047] The server receives a data request from the company and notifies the user (educational institution) of the request. Once the educational institution approves the request, the server provides the data to the company.

[0048] Step 4:

[0049] The server analyzes the acquired data using data analysis technology. This analysis utilizes machine learning algorithms to extract patterns and trends from the data.

[0050] Step 5:

[0051] The server uses the analysis results to notify users (educational institutions) of improvement suggestions. It also uses a generative AI to recommend content for special lectures.

[0052] Step 6:

[0053] Users (educational institutions) plan special lectures and enter detailed information about the lectures into the system. The server stores this information and makes it publicly available for users (students) to view.

[0054] Step 7:

[0055] Users (students) view publicly available lecture information through their terminals and reserve lectures they are interested in. The server records the reservation information in a database and notifies the student for confirmation.

[0056] Step 8:

[0057] Users (educational institutions) register information about unused assets in the system. The server stores this information and makes it accessible to other users (companies).

[0058] Step 9:

[0059] The user (company) selects the unused assets they need and reserves the desired usage time. The server manages the reservation details and notifies the user (company) of the reservation confirmation.

[0060] (Example 1)

[0061] 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."

[0062] With the advancement of information technology, information exchange between educational institutions and industrial organizations is increasing, but this presents challenges in data management, access control settings, and the effective utilization of underutilized assets. Furthermore, there is a need for rapid and effective information analysis and the subsequent feedback to educational institutions, as well as proposals for special lectures. A system is needed to efficiently carry out these processes.

[0063] 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.

[0064] In this invention, the server includes means for managing information assets held by educational institutions, means for receiving requests from industrial organizations to acquire information assets from educational institutions, and means for analyzing the acquired information using information analysis technology. This facilitates smooth information exchange between educational institutions and industrial organizations, enabling effective feedback and efficient utilization of assets based on the results of information analysis.

[0065] An "educational institution" refers to an organization or facility established to provide knowledge and skills, and includes institutions such as schools, universities, and vocational schools.

[0066] An "industrial organization" refers to a company or group that conducts commercial activities in a specific field, and includes organizations that conduct business through information and services.

[0067] "Information assets" refer to a collection of data and information held by educational institutions and industrial organizations, including survey data, research results, and learning materials.

[0068] "Information analysis technology" refers to techniques for analyzing data and deriving useful insights, and includes statistical analysis and machine learning.

[0069] "Unused equipment" refers to classrooms and facilities in educational institutions that are not currently in use, and are physical assets that can be used by other organizations or for other purposes.

[0070] A "generative AI model" refers to an artificial intelligence model designed to generate and analyze information from diverse data, and includes models based on machine learning and deep learning.

[0071] A "request" is the act of asking for specific information or services, including a formal request by an industrial organization to obtain information from an educational institution.

[0072] "Feedback" refers to the act of providing analysis results and suggestions for improvement of educational institutions, including providing information for improving the quality and processes of education.

[0073] This invention is a system for efficiently managing data from educational institutions and facilitating smooth information exchange with industrial organizations. The system centers around a server for managing information assets held by educational institutions and includes various means for acquiring and providing information according to the requests of industrial organizations.

[0074] The server uses a database system (e.g., MySQL® or PostgreSQL) to centrally manage the information assets of educational institutions. Information retrieval requests from industrial organizations are received via terminals through a dedicated portal site, and data is transferred using the secure SSL / TLS communication protocol. Furthermore, the server uses generative AI models that implement machine learning algorithms (e.g., TENSORFLOW® or Scikit-learn) to analyze the acquired information and provide educational institutions with useful insights.

[0075] As a concrete example, users from educational institutions upload survey data from a dedicated terminal, and users from industrial organizations request this data from their terminals. Based on the permissions set by the educational institution, the server automatically notifies the industrial organization of the request. Once the educational institution approves, the server provides the data to the industrial organization through a secure channel.

[0076] Examples of prompts include, "Please explain how companies can utilize survey data held by educational institutions," and "Please describe the reservation procedure for companies to use unused classrooms." Through these prompts, industry organizations can develop strategies to effectively utilize data from educational institutions.

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

[0078] Step 1:

[0079] Users (educational institutions) collect survey data and related information using terminals and upload the data to the server via a dedicated application. The input is a survey data file (e.g., a CSV file), which the server receives and stores in its database. During this process, data integrity is verified, and accuracy is ensured by checking for duplicates and missing data.

[0080] Step 2:

[0081] The server verifies the access permissions set by the educational institution based on the received data. Input includes the institution's policy information and access rights, and the server configures data permission settings based on this information. Output generates a list of data accessible to corporate users and any restrictions. The server provides access permission information to the educational institution through the management console.

[0082] Step 3:

[0083] The user (company) accesses a dedicated portal site to obtain the necessary data and enters their request details. This procedure requires the company to enter details such as the type and time period of the data they require. The server receives this request and sends a notification to the educational institution. The notification message contains the details of the request.

[0084] Step 4:

[0085] The user (educational institution) reviews the request received from the server and decides whether to approve or reject it. The input is request information from a company, and the educational institution selects a response. If approved, a notification is sent to the server, and the approval or rejection status is recorded as output.

[0086] Step 5:

[0087] The server will provide the requested data to the company once it receives approval from the educational institution. The approved data content will be the input, and the data will be securely transmitted using the SSL / TLS protocol as the output. After transmission, the server will notify both the educational institution and the company that the transmission is complete.

[0088] Step 6:

[0089] The server analyzes uploaded data using machine learning algorithms. Educational institution data is used as input, and the analysis process generates improvement suggestions and insights as output. The server provides this information to educational institutions to help improve their services and lectures.

[0090] Step 7:

[0091] Users (students) view special lecture information provided by the server using their terminals and reserve lectures they are interested in. Lecture information is the input. The reservation status of each student is updated as output. The server manages the reservation information and maintains appropriate lecture capacity and scheduling.

[0092] Step 8:

[0093] Users (educational institutions) register information about unused classrooms and facilities on the server. Facility information is provided as input, which the server receives and stores in its database. The server then makes the registered information public to industry organizations, allowing companies wishing to use the data to access it.

[0094] Step 9:

[0095] Users (companies) reserve their desired unused assets from a terminal and send the reservation information to the server. The input includes the desired date and time and asset details. The server accepts the reservation, adjusts the schedule and checks for duplicates, and sends a confirmation email to the company as output.

[0096] (Application Example 1)

[0097] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server," and the smart device 14 will be referred to as the "terminal."

[0098] There is a challenge in effectively utilizing the untapped resources within the education sector and providing an environment where businesses can efficiently acquire and reserve the information and resources they need. In conventional systems, managing information and untapped resources within the education sector is cumbersome, requiring significant effort and time for businesses to access them. This has resulted in a low utilization rate of educational resources and hindered the smooth provision of information needed by businesses.

[0099] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0100] In this invention, the server includes means for processing information held by the educational domain, means for receiving requests from entities to acquire information from the educational domain, means for processing acquired information using information processing technology, and means for entities to reserve unused resources in the educational domain using communication equipment. This enables efficient management of information and unused resources in the educational domain, and allows entities to acquire necessary information and reserve resources simply and quickly.

[0101] The "educational domain" refers to educational institutions, related organizations, and facilities, and is the place where the information and physical resources they possess are managed and provided.

[0102] A "business entity" refers to an organization such as a company or group, which is the entity that acquires and utilizes information and resources from the field of education.

[0103] "Information processing technology" refers to techniques for collecting, analyzing, and utilizing data, and includes methods such as machine learning algorithms.

[0104] "Unused resources" refer to physical assets such as classrooms and facilities that are not currently being used within the educational domain, and are targets for effective utilization.

[0105] "Communication equipment" refers to devices that send and receive information via a network connection, and includes smartphones and computers.

[0106] The system implementing this invention efficiently manages information and unused resources in the education sector, enabling organizations to utilize them. Specifically, a server and terminals work together to realize each function.

[0107] The server centrally manages information registered in the education domain. This information includes survey results, teaching material data, and classroom availability. A Python-based system runs on the server, and machine learning algorithms are used as information processing technology to analyze the data. This analysis allows for suggestions for service improvement in the education domain and the generation of information such as special lectures.

[0108] The user entities obtain necessary information from the education sector and reserve unused resources through terminals. These terminals include smartphones and computers, and these devices can connect to a server to retrieve information and check reservation status in real time.

[0109] For example, if a company is looking for space for employee training, they can use this system to easily reserve an available classroom in the education area. In this process, they can receive suggestions for the most suitable space based on insights gained from data analysis using a generative AI model, allowing them to efficiently achieve their objectives.

[0110] Example prompt for input to the generating AI model: "Analyze data from educational institutions and describe a system that allows unused classrooms to be utilized."

[0111] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0112] Step 1:

[0113] The server stores survey data and unused classroom information received from the education sector into a database. It receives information as input, checks for any anomalies in the database, and processes the data for conversion into a unified format for storage. The output is accurately stored information.

[0114] Step 2:

[0115] Using a terminal, the user accesses information in the educational field. The input is a request for the information the user seeks, and the terminal sends this request to the server. The server performs query processing to extract relevant information from the database and compiles the necessary information. The output is a list of the information the user searched for.

[0116] Step 3:

[0117] The server analyzes received data using machine learning algorithms. The input is information stored in a database, and the algorithms perform pattern recognition and anomaly detection on the data. This derives insights such as suggestions for improvements in the education field and recommendations for special lectures. The output is the insights and suggestions resulting from the analysis.

[0118] Step 4:

[0119] Users search for and reserve unused classrooms through their terminal. The input consists of search criteria for available classrooms; the terminal sends a request to the server, which filters the available classrooms based on the criteria. The output is a list of available classrooms.

[0120] Step 5:

[0121] When a user reserves a classroom, the server records the reservation details in the database. The input is the details of the classroom and the time slot to be reserved. The server accurately records this information and processes the data to update the reservation status. The output is a notification confirming a successful reservation.

[0122] 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.

[0123] This invention is a system that not only efficiently manages data held by educational institutions and facilitates smooth data exchange with companies, but also provides a more personalized learning experience by combining it with an emotion engine that recognizes user emotions. Specific embodiments are shown below.

[0124] First, users from educational institutions upload survey data and other relevant information to the system via their devices. This allows the server to centrally manage the data and enable corporate users to request data retrieval. When a user (corporate) requests data, the server notifies the educational institution of the request, and provides the data to the company only after receiving approval from the educational institution.

[0125] The server uses machine learning algorithms as a data analysis technique to analyze the acquired data. Based on the insights gained, it provides educational institutions with useful information for designing special lectures. It also utilizes the analysis results to generate the content of the special lectures, optimizing the lecture content according to predefined learning objectives.

[0126] Furthermore, by incorporating an emotion engine, the system can detect the user's (student's) emotional state in real time and optimize lectures and manage reservations accordingly. Specifically, if a user is experiencing stress or anxiety during a lecture, the emotion engine will detect this, and the server will adjust the lecture content. For example, it can improve learning effectiveness by providing more detailed explanations of difficult parts or adjusting the pace.

[0127] Educational institution users register unused classrooms and facilities through terminals, allowing companies to reserve them for use. The server manages the reservation information and adjusts it to ensure optimal asset utilization.

[0128] For example, when a company reserves an empty classroom to provide a learning environment and conducts a lecture, the system can acquire student emotional data in real time, allowing the instructor to check the students' level of understanding on the spot and adjust the lecture content as needed. In this way, the system of the present invention can effectively promote asset management for educational institutions and data utilization for companies while improving the quality of the learning experience.

[0129] The following describes the processing flow.

[0130] Step 1:

[0131] Users (educational institutions) upload survey data to the system via their devices. The server receives the data and stores it in a database.

[0132] Step 2:

[0133] The server configures its data access settings and prepares to receive data retrieval requests from corporate users. Corporate users access the system through their terminals and request the necessary data.

[0134] Step 3:

[0135] The server receives a data request from the company and notifies the user (educational institution) of the request's contents. Once the educational institution approves the data provision, the server sends the data to the company.

[0136] Step 4:

[0137] The server analyzes the received data using machine learning algorithms to generate useful insights from the data. This allows for the creation of improvement suggestions and recommendations for special lectures for educational institutions.

[0138] Step 5:

[0139] The emotion engine collects and analyzes user (student) emotional data, evaluating learning progress and emotional state in real time. The server dynamically optimizes the content of special lectures based on this information.

[0140] Step 6:

[0141] Users (educational institutions) use terminals to plan special lectures and register the lecture content and date / time on the server. The server makes this information public, allowing students to view and reserve it.

[0142] Step 7:

[0143] Users (students) can view the content of special lectures from their terminals, select the lectures they wish to attend, and make reservations. Reservation information is stored in a database by the server.

[0144] Step 8:

[0145] Users (educational institutions) register information about unused classrooms and facilities in the system via terminals. The server stores this information, allowing companies to make reservations through the system.

[0146] Step 9:

[0147] Users (companies) reserve the necessary classrooms and equipment from their terminals, and the server records the reservation information. At the same time, the server uses feedback from an emotion engine to suggest appropriate lecture plans to the companies.

[0148] (Example 2)

[0149] 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".

[0150] There is a need to streamline information management within educational institutions and provide information to businesses, as well as to offer individually optimized learning experiences based on students' emotional states. However, conventional systems have been inefficient in data management and analysis, and have struggled to optimize learning while considering students' emotions. This has led to problems such as hindering the smooth exchange of information between educational institutions and businesses, and the provision of optimal education to students.

[0151] 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.

[0152] In this invention, the server includes means for managing information held by educational institutions, means for receiving requests from companies to acquire information, and means for analyzing the information using data analysis technology. This enables efficient information exchange between educational institutions and companies, and further allows for learning optimization based on students' emotional states.

[0153] An "educational institution" is an institution that teaches academic subjects or skills, and refers to organizations such as universities, high schools, and vocational schools.

[0154] "Information" refers to data stored in digital format, such as surveys, grade data, and data related to course content, held by educational institutions.

[0155] A "company" is an organization that engages in commercial activities and refers to a legal entity that provides educational services or products through the acquisition of information.

[0156] A "request" refers to a formal request or inquiry made by an organization to obtain information from an educational institution.

[0157] "Data analysis technology" refers to a general term for techniques that process acquired information using statistical or machine learning methods to extract useful insights.

[0158] A "machine learning algorithm" is a type of data analysis technique that uses mathematical models to automatically learn rules and patterns from data and make predictions and classifications based on those results.

[0159] "Emotional state" refers to the psychological and physiological responses expressed by the user (student), including emotions such as excitement, stress, and relaxation.

[0160] "Unused assets" refer to physical resources, such as classrooms and equipment, owned by educational institutions that are not being used during specific time periods.

[0161] "Optimization" refers to the means of maximizing or streamlining the capabilities of a particular process or function based on given conditions.

[0162] This invention is a system that effectively manages information held by educational institutions, supports smooth information exchange with companies, and provides an individually optimized learning experience by analyzing the emotions of users (students).

[0163] The terminal provides an interface for educational institution users to upload survey data and related information to the system. This allows users to easily input information into the system and prepare it for centralized management. Specifically, users drag and drop data files onto a form and press an upload button.

[0164] The server stores uploaded information in a database and accepts information retrieval requests from corporate users. It also uses programming languages ​​such as Python and libraries like Scikit-learn and TensorFlow to analyze the data. Based on the resulting analysis, it provides improvement suggestions to educational institutions, contributing to the optimization of learning content.

[0165] The emotion engine is used to detect students' real-time emotional states. For example, it analyzes students' stress and relaxation levels through cameras and microphones, and suggests changes to provide users with the most suitable lecture content. Specifically, the server adjusts the content based on the student's stress level detected during the lecture.

[0166] Corporate users can reserve necessary classrooms and equipment based on publicly available information on unused assets at educational institutions. The reservation process is conducted through a dedicated web interface, and confirmation notifications are sent via email.

[0167] An example of a prompt message would be, "Generate optimal lecture content based on student sentiment data," when inputting this into the AI ​​generation model. In this way, the system streamlines information management and provision between educational institutions and companies, and provides students with an optimized learning environment.

[0168] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0169] Step 1:

[0170] Users (educational institution staff) upload survey data and related information to the system using a terminal. Input is in formats such as CSV or Excel files, and output is stored in the system's database. Specifically, users select files using a dedicated web interface and click the upload button. This operation transmits the data to the server via the network.

[0171] Step 2:

[0172] The server stores and centrally manages received data in a database. Input is data files sent by users, and output is structured database entries. Specifically, the server uses a database management system (DBMS) to verify data accuracy, format the data, and then save it.

[0173] Step 3:

[0174] Corporate users send requests to the server to retrieve information. The input is a request specifying the type and scope of information to be retrieved, and the output is a notification from the server. Specifically, the user selects the required dataset on the web portal and clicks the "Request Information Retrieval" button. This request is logged on the server side.

[0175] Step 4:

[0176] The server initiates the approval process by sending a notification to the educational institution regarding information requests from corporate users. The input is the request content from the corporate user, and the output is the approval notification. The server notifies the educational institution of the request content via email or a dedicated application.

[0177] Step 5:

[0178] The server processes approved information using data analysis techniques. Input data consists of information from approved educational institutions, and output is insights gained through analysis. The server uses programming languages ​​such as Python and R to apply machine learning algorithms, performing data cleansing, model training, and analysis results.

[0179] Step 6:

[0180] The emotion engine detects students' emotional states in real time and optimizes lecture content based on that. Input is student audio and video data acquired via terminals, and output is optimized lecture content and suggestions. Specifically, emotion analysis software uses deep learning technology to detect stress levels and interest levels, and generates lecture content recommendations provided by the server.

[0181] Step 7:

[0182] Users at educational institutions register information about unused assets in the system via terminals. Inputs are detailed information about available classrooms and equipment, while outputs are publicly available asset information. Specifically, users input classroom numbers, available hours, etc., via a management interface and save this information in the system.

[0183] Step 8:

[0184] Corporate users make reservations based on publicly available information on unused assets. Input is a reservation request for the required classroom or equipment, and output is a reservation confirmation from the server. Corporate users use the reservation system to reserve assets for specific dates and times, and reservation confirmations are sent via email.

[0185] (Application Example 2)

[0186] 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".

[0187] Traditional data management and exchange systems between educational institutions and businesses have the drawback of making it difficult to optimize learning experiences and work processes through real-time emotion recognition. There is a growing need to optimize asset utilization within educational institutions and to dynamically adjust lectures and tasks based on the emotional state of individual learners.

[0188] 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.

[0189] In this invention, the server includes means for managing information held by educational institutions, means for receiving requests from educational institutions for companies to obtain information, and means for analyzing the obtained information using data analysis techniques. This enables smooth data exchange between educational institutions and companies, and further enables adaptive adjustment of lectures and work processes based on emotional states.

[0190] "Educational institutions" refer to all organizations that provide education and training to learners, and include schools, universities, and vocational schools.

[0191] "Information" refers to data, materials, and knowledge held by educational institutions, and includes information on student performance data, lecture content, and educational resources.

[0192] "Company" refers to a for-profit organization or legal entity whose purpose is to acquire and utilize information from educational institutions, thereby improving services and products in the education market and related industries.

[0193] A "request" refers to the act or process by which a company requests information from an educational institution, and this is carried out through a system.

[0194] "Data analysis technology" refers to all techniques used to process information and extract meaningful results and insights, and includes machine learning and statistical models.

[0195] An "emotion engine" refers to a system or technology that recognizes a user's emotional state and provides information about it, enabling real-time data acquisition.

[0196] A "work process" refers to a series of activities performed in a factory or other work environment to achieve a specific objective.

[0197] The embodiments for carrying out the invention are described below.

[0198] This system has data management and analysis functions to provide information held by educational institutions to companies. The server primarily manages the educational institutions' information centrally and provides an interface for companies to appropriately access that information. Information is uploaded to the system via terminals and organized by the server.

[0199] Furthermore, the server uses machine learning algorithms to analyze information and provide improvement suggestions to educational institutions. Specifically, it analyzes student performance and learning trends based on collected data, generating insights that are useful for designing optimal lecture content and special lectures.

[0200] Emotion recognition utilizes an emotion engine. This engine assesses the user's (student or worker's) emotional state in real time, enabling dynamic adjustment of educational or work processes based on stress levels and comprehension. Physiological data from the user is collected and analyzed using smart glasses and other wearable devices.

[0201] For example, if a user is stressed by a difficult lecture, the server adjusts the pace of the lecture based on the analysis results of the emotion engine, providing information in a more easily understandable format. Similarly, in a factory setting, work processes are adjusted based on the emotional state of the workers to maintain a safe and effective working environment.

[0202] An example of a prompt using a generative AI model is: "Please suggest ways to optimize work processes based on the real-time emotional state of workers in order to improve factory productivity." This allows for further learning and operational efficiency through the integration of emotion recognition and data analysis.

[0203] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0204] Step 1:

[0205] The terminal uploads information from the educational institution to the system. This includes student learning data and lecture content. The input is educational institution information from the terminal, which the server receives and stores centrally in a database.

[0206] Step 2:

[0207] The server receives information retrieval requests from companies. These requests include requests for specific datasets or analysis results. The server analyzes these requests and filters and extracts the necessary information from the database. The output is a set of information based on the requests.

[0208] Step 3:

[0209] The server uses machine learning algorithms to analyze the collected information. Raw data from the database is used as input, and this data is processed to generate insights necessary for creating useful improvement suggestions and lecture content for educational institutions. The output is the analysis results.

[0210] Step 4:

[0211] The server uses an emotion engine to analyze emotional data from educational institutions in real time. This data includes physiological measurement data obtained from users (students and workers). Based on this, the server evaluates the emotional state and provides an estimated result of the emotional state as output.

[0212] Step 5:

[0213] The server adjusts the lecture content and work process based on the analysis results of the emotion engine. Specifically, if the emotional state is determined to be high stress, the pace of the lecture will be slowed or detailed explanations will be added. The input is the emotion analysis results, and the output is the adjusted lecture content or work process.

[0214] Step 6:

[0215] Users (companies, educators) review the analysis results and lecture adjustments generated by the server and provide feedback as needed. This optimizes the system. The input is the analysis results and adjustments, and the output is the user's feedback.

[0216] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0217] 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.

[0218] 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.

[0219] [Second Embodiment]

[0220] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0221] 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.

[0222] 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).

[0223] 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.

[0224] 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.

[0225] 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).

[0226] 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.

[0227] 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.

[0228] 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.

[0229] 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.

[0230] 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.

[0231] 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".

[0232] This invention provides a system for efficiently managing data held by educational institutions and for facilitating smooth data exchange with companies. The system of this invention is implemented in the following forms to enable educational institutions and companies to share mutually beneficial information and to effectively utilize the assets of educational institutions.

[0233] First, users from educational institutions upload their own survey data and other relevant information to the system via their devices. This information is centrally managed by a server, and after the educational institution has configured its permissions, it grants access to companies. This allows company users to request the data they need. When a company user requests data, the server automatically notifies the educational institution of this request, and once the educational institution approves it, the data is provided to the company.

[0234] Furthermore, the server analyzes uploaded data using data analysis technologies, particularly machine learning algorithms. Based on the insights gained from this analysis, it provides educational institutions with suggestions for service improvements and support in planning special lectures. Educational institutions design special lectures and supplementary courses based on the recommendations provided by the server and offer them to students. Learners can use their devices to view information on special lectures and make reservations for lectures that interest them.

[0235] Furthermore, the system of this invention manages unused assets of educational institutions and facilitates their effective utilization in conjunction with companies. Users of educational institutions register unused classrooms and facilities and input the information into the server. This allows companies to reserve the desired time slots for using these assets via a terminal. The server manages the reservation information and ensures the appropriate use of the assets.

[0236] As a concrete example, by registering classrooms with some surplus capacity in the system, educational institutions can enable companies to use these classrooms for meetings and training sessions. Furthermore, companies can analyze survey data received from educational institutions to inform their marketing strategies and product development. This allows educational institutions to effectively utilize underutilized assets while also contributing to improving students' learning environments.

[0237] The following describes the processing flow.

[0238] Step 1:

[0239] Users (educational institutions) upload survey data to the system via their devices. The server receives the uploaded data, verifies its contents, and saves it to the database.

[0240] Step 2:

[0241] The server makes data authorized by educational institutions available for companies to view. Users (companies) access the system using their terminals, select the data they need, and submit requests.

[0242] Step 3:

[0243] The server receives a data request from the company and notifies the user (educational institution) of the request. Once the educational institution approves the request, the server provides the data to the company.

[0244] Step 4:

[0245] The server analyzes the acquired data using data analysis technology. This analysis utilizes machine learning algorithms to extract patterns and trends from the data.

[0246] Step 5:

[0247] The server uses the analysis results to notify users (educational institutions) of improvement suggestions. It also uses a generative AI to recommend content for special lectures.

[0248] Step 6:

[0249] Users (educational institutions) plan special lectures and enter detailed information about the lectures into the system. The server stores this information and makes it publicly available for users (students) to view.

[0250] Step 7:

[0251] Users (students) view publicly available lecture information through their terminals and reserve lectures they are interested in. The server records the reservation information in a database and notifies the student for confirmation.

[0252] Step 8:

[0253] Users (educational institutions) register information about unused assets in the system. The server stores this information and makes it accessible to other users (companies).

[0254] Step 9:

[0255] The user (company) selects the unused assets they need and reserves the desired usage time. The server manages the reservation details and notifies the user (company) of the reservation confirmation.

[0256] (Example 1)

[0257] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server," and the smart glasses 214 will be referred to as the "terminal."

[0258] With the advancement of information technology, information exchange between educational institutions and industrial organizations is increasing, but this presents challenges in data management, access control settings, and the effective utilization of underutilized assets. Furthermore, there is a need for rapid and effective information analysis and the subsequent feedback to educational institutions, as well as proposals for special lectures. A system is needed to efficiently carry out these processes.

[0259] 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.

[0260] In this invention, the server includes means for managing information assets held by educational institutions, means for receiving requests from industrial organizations to acquire information assets from educational institutions, and means for analyzing the acquired information using information analysis technology. This facilitates smooth information exchange between educational institutions and industrial organizations, enabling effective feedback and efficient utilization of assets based on the results of information analysis.

[0261] An "educational institution" refers to an organization or facility established to provide knowledge and skills, and includes institutions such as schools, universities, and vocational schools.

[0262] An "industrial organization" refers to a company or group that conducts commercial activities in a specific field, and includes organizations that conduct business through information and services.

[0263] "Information assets" refer to a collection of data and information held by educational institutions and industrial organizations, including survey data, research results, and learning materials.

[0264] "Information analysis technology" refers to techniques for analyzing data and deriving useful insights, and includes statistical analysis and machine learning.

[0265] "Unused equipment" refers to classrooms and facilities in educational institutions that are not currently in use, and are physical assets that can be used by other organizations or for other purposes.

[0266] A "generative AI model" refers to an artificial intelligence model designed to generate and analyze information from diverse data, and includes models based on machine learning and deep learning.

[0267] A "request" is the act of asking for specific information or services, including a formal request by an industrial organization to obtain information from an educational institution.

[0268] "Feedback" refers to the act of providing analysis results and suggestions for improvement of educational institutions, including providing information for improving the quality and processes of education.

[0269] This invention is a system for efficiently managing data from educational institutions and facilitating smooth information exchange with industrial organizations. The system centers around a server for managing information assets held by educational institutions and includes various means for acquiring and providing information according to the requests of industrial organizations.

[0270] The server uses a database system (e.g., MySQL or PostgreSQL) to centrally manage the information assets of educational institutions. Information retrieval requests from industrial organizations are received via terminals through a dedicated portal site, and data is transferred using the secure SSL / TLS communication protocol. Furthermore, the server uses generative AI models that implement machine learning algorithms (e.g., TensorFlow or Scikit-learn) to analyze the acquired information and provide educational institutions with useful insights.

[0271] As a concrete example, users from educational institutions upload survey data from a dedicated terminal, and users from industrial organizations request this data from their terminals. Based on the permissions set by the educational institution, the server automatically notifies the industrial organization of the request. Once the educational institution approves, the server provides the data to the industrial organization through a secure channel.

[0272] Examples of prompts include, "Please explain how companies can utilize survey data held by educational institutions," and "Please describe the reservation procedure for companies to use unused classrooms." Through these prompts, industry organizations can develop strategies to effectively utilize data from educational institutions.

[0273] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0274] Step 1:

[0275] Users (educational institutions) collect survey data and related information using terminals and upload the data to the server via a dedicated application. The input is a survey data file (e.g., a CSV file), which the server receives and stores in its database. During this process, data integrity is verified, and accuracy is ensured by checking for duplicates and missing data.

[0276] Step 2:

[0277] The server verifies the access permissions set by the educational institution based on the received data. Input includes the institution's policy information and access rights, and the server configures data permission settings based on this information. Output generates a list of data accessible to corporate users and any restrictions. The server provides access permission information to the educational institution through the management console.

[0278] Step 3:

[0279] The user (company) accesses a dedicated portal site to obtain the necessary data and enters their request details. This procedure requires the company to enter details such as the type and time period of the data they require. The server receives this request and sends a notification to the educational institution. The notification message contains the details of the request.

[0280] Step 4:

[0281] The user (educational institution) reviews the request received from the server and decides whether to approve or reject it. The input is request information from a company, and the educational institution selects a response. If approved, a notification is sent to the server, and the approval or rejection status is recorded as output.

[0282] Step 5:

[0283] Once the server receives approval from the educational institution, it provides the requested data to the enterprise. As input, there is the approved data content, and as output, the data is securely transmitted using the SSL / TLS protocol. After transmission, the server notifies both the educational institution and the enterprise of the completion of the transmission.

[0284] Step 6:

[0285] The server analyzes the uploaded data using machine learning algorithms. As input, there is the data of the educational institution, and through the analysis process, improvement suggestions and insight information are generated as output. The server provides this information for the educational institution to use in improving services and lectures.

[0286] Step 7:

[0287] The user (student) browses the special lecture information provided by the server using a terminal and reserves the lectures of interest. As input, there is lecture information. As output, the reservation status of each student is updated. The server manages the reservation information and maintains the capacity and schedule appropriateness of the lectures.

[0288] Step 8: [[ID=I22]]

[0289] The user (educational institution) registers the information of unused classrooms and facilities with the server. As input, there is facility information, which the server receives and stores in the database. The server publishes the registration information to industrial organizations so that enterprises wishing to use it can refer to the data.

[0290] Step 9:

[0291] The user (enterprise) reserves the desired unused assets from the terminal and transmits the reservation information to the server. As input, it includes the desired date and time and details of the assets. The server accepts the reservation, performs schedule adjustment and duplicate check, and as output, sends a confirmation email to the enterprise.

[0292] (Application Example 1)

[0293] 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."

[0294] There is a challenge in effectively utilizing the untapped resources within the education sector and providing an environment where businesses can efficiently acquire and reserve the information and resources they need. In conventional systems, managing information and untapped resources within the education sector is cumbersome, requiring significant effort and time for businesses to access them. This has resulted in a low utilization rate of educational resources and hindered the smooth provision of information needed by businesses.

[0295] 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.

[0296] In this invention, the server includes means for processing information held by the educational domain, means for receiving requests from entities to acquire information from the educational domain, means for processing acquired information using information processing technology, and means for entities to reserve unused resources in the educational domain using communication equipment. This enables efficient management of information and unused resources in the educational domain, and allows entities to acquire necessary information and reserve resources simply and quickly.

[0297] The "educational domain" refers to educational institutions, related organizations, and facilities, and is the place where the information and physical resources they possess are managed and provided.

[0298] A "business entity" refers to an organization such as a company or group, which is the entity that acquires and utilizes information and resources from the field of education.

[0299] "Information processing technology" refers to techniques for collecting, analyzing, and utilizing data, and includes methods such as machine learning algorithms.

[0300] "Unused resources" refer to physical assets such as classrooms and facilities that are not currently being used within the educational domain, and are targets for effective utilization.

[0301] "Communication device" refers to a device that transmits and receives information through network connection, including smartphones, computers, etc.

[0302] The system for implementing this invention efficiently manages the information and unused resources in the education field, enabling business entities to utilize them. Specifically, the server and the terminal cooperate to operate and realize each function.

[0303] The server centrally manages the information registered in the education field. The information includes questionnaire results, teaching material data, classroom availability, etc. A Python-based system operates on the server, and machine learning algorithms as information processing technologies analyze the data. Through this analysis, proposals for service improvement can be made to the education field, and information such as special lectures can be generated.

[0304] The business entity, which is the user, acquires the necessary information from the education field and reserves unused resources through the terminal. Smartphones and computers are used as the terminal, and these devices can connect to the server to obtain information in real-time and check the reservation status.

[0305] As a specific example, when a company is looking for a space for employee training, this system can be used to easily reserve an empty classroom in the education field. At that time, based on the insights obtained from data analysis using the generated AI model, a proposal for the optimal space can be received, and the goal can be achieved efficiently.

[0306] <00岁00965>Example of a prompt sentence input to the generated AI model: "Please explain a system that can utilize unused classrooms by analyzing the data of educational institutions."

[0307] The flow of the specific process in Application Example 1 will be described using FIG. 12.

[0308] Step 1:

[0309] The server stores survey data and unused classroom information received from the education sector into a database. It receives information as input, checks for any anomalies in the database, and processes the data for conversion into a unified format for storage. The output is accurately stored information.

[0310] Step 2:

[0311] Using a terminal, the user accesses information in the educational field. The input is a request for the information the user seeks, and the terminal sends this request to the server. The server performs query processing to extract relevant information from the database and compiles the necessary information. The output is a list of the information the user searched for.

[0312] Step 3:

[0313] The server analyzes received data using machine learning algorithms. The input is information stored in a database, and the algorithms perform pattern recognition and anomaly detection on the data. This derives insights such as suggestions for improvements in the education field and recommendations for special lectures. The output is the insights and suggestions resulting from the analysis.

[0314] Step 4:

[0315] Users search for and reserve unused classrooms through their terminal. The input consists of search criteria for available classrooms; the terminal sends a request to the server, which filters the available classrooms based on the criteria. The output is a list of available classrooms.

[0316] Step 5:

[0317] When a user reserves a classroom, the server records the reservation details in the database. The input is the details of the classroom and the time slot to be reserved. The server accurately records this information and processes the data to update the reservation status. The output is a notification confirming a successful reservation.

[0318] 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.

[0319] This invention is a system that not only efficiently manages data held by educational institutions and facilitates smooth data exchange with companies, but also provides a more personalized learning experience by combining it with an emotion engine that recognizes user emotions. Specific embodiments are shown below.

[0320] First, users from educational institutions upload survey data and other relevant information to the system via their devices. This allows the server to centrally manage the data and enable corporate users to request data retrieval. When a user (corporate) requests data, the server notifies the educational institution of the request, and provides the data to the company only after receiving approval from the educational institution.

[0321] The server uses machine learning algorithms as a data analysis technique to analyze the acquired data. Based on the insights gained, it provides educational institutions with useful information for designing special lectures. It also utilizes the analysis results to generate the content of the special lectures, optimizing the lecture content according to predefined learning objectives.

[0322] Furthermore, by incorporating an emotion engine, the system can detect the user's (student's) emotional state in real time and optimize lectures and manage reservations accordingly. Specifically, if a user is experiencing stress or anxiety during a lecture, the emotion engine will detect this, and the server will adjust the lecture content. For example, it can improve learning effectiveness by providing more detailed explanations of difficult parts or adjusting the pace.

[0323] Educational institution users register unused classrooms and facilities through terminals, allowing companies to reserve them for use. The server manages the reservation information and adjusts it to ensure optimal asset utilization.

[0324] For example, when a company reserves an empty classroom to provide a learning environment and conducts a lecture, the system can acquire student emotional data in real time, allowing the instructor to check the students' level of understanding on the spot and adjust the lecture content as needed. In this way, the system of the present invention can effectively promote asset management for educational institutions and data utilization for companies while improving the quality of the learning experience.

[0325] The following describes the processing flow.

[0326] Step 1:

[0327] Users (educational institutions) upload survey data to the system via their devices. The server receives the data and stores it in a database.

[0328] Step 2:

[0329] The server configures its data access settings and prepares to receive data retrieval requests from corporate users. Corporate users access the system through their terminals and request the necessary data.

[0330] Step 3:

[0331] The server receives a data request from the company and notifies the user (educational institution) of the request's contents. Once the educational institution approves the data provision, the server sends the data to the company.

[0332] Step 4:

[0333] The server analyzes the received data using machine learning algorithms to generate useful insights from the data. This allows for the creation of improvement suggestions and recommendations for special lectures for educational institutions.

[0334] Step 5:

[0335] The emotion engine collects and analyzes user (student) emotional data, evaluating learning progress and emotional state in real time. The server dynamically optimizes the content of special lectures based on this information.

[0336] Step 6:

[0337] Users (educational institutions) use terminals to plan special lectures and register the lecture content and date / time on the server. The server makes this information public, allowing students to view and reserve it.

[0338] Step 7:

[0339] Users (students) can view the content of special lectures from their terminals, select the lectures they wish to attend, and make reservations. Reservation information is stored in a database by the server.

[0340] Step 8:

[0341] Users (educational institutions) register information about unused classrooms and facilities in the system via terminals. The server stores this information, allowing companies to make reservations through the system.

[0342] Step 9:

[0343] Users (companies) reserve the necessary classrooms and equipment from their terminals, and the server records the reservation information. At the same time, the server uses feedback from an emotion engine to suggest appropriate lecture plans to the companies.

[0344] (Example 2)

[0345] 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".

[0346] There is a need to streamline information management within educational institutions and provide information to businesses, as well as to offer individually optimized learning experiences based on students' emotional states. However, conventional systems have been inefficient in data management and analysis, and have struggled to optimize learning while considering students' emotions. This has led to problems such as hindering the smooth exchange of information between educational institutions and businesses, and the provision of optimal education to students.

[0347] 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.

[0348] In this invention, the server includes means for managing information held by educational institutions, means for receiving requests from companies to acquire information, and means for analyzing the information using data analysis technology. This enables efficient information exchange between educational institutions and companies, and further allows for learning optimization based on students' emotional states.

[0349] An "educational institution" is an institution that teaches academic subjects or skills, and refers to organizations such as universities, high schools, and vocational schools.

[0350] "Information" refers to data stored in digital format, such as surveys, grade data, and data related to course content, held by educational institutions.

[0351] A "company" is an organization that engages in commercial activities and refers to a legal entity that provides educational services or products through the acquisition of information.

[0352] A "request" refers to a formal request or inquiry made by an organization to obtain information from an educational institution.

[0353] "Data analysis technology" refers to a general term for techniques that process acquired information using statistical or machine learning methods to extract useful insights.

[0354] A "machine learning algorithm" is a type of data analysis technique that uses mathematical models to automatically learn rules and patterns from data and make predictions and classifications based on those results.

[0355] "Emotional state" refers to the psychological and physiological responses expressed by the user (student), including emotions such as excitement, stress, and relaxation.

[0356] "Unused assets" refer to physical resources, such as classrooms and equipment, owned by educational institutions that are not being used during specific time periods.

[0357] "Optimization" refers to the means of maximizing or streamlining the capabilities of a particular process or function based on given conditions.

[0358] This invention is a system that effectively manages information held by educational institutions, supports smooth information exchange with companies, and provides an individually optimized learning experience by analyzing the emotions of users (students).

[0359] The terminal provides an interface for educational institution users to upload survey data and related information to the system. This allows users to easily input information into the system and prepare it for centralized management. Specifically, users drag and drop data files onto a form and press an upload button.

[0360] The server stores uploaded information in a database and accepts information retrieval requests from corporate users. It also uses programming languages ​​such as Python and libraries like Scikit-learn and TensorFlow to analyze the data. Based on the resulting analysis, it provides improvement suggestions to educational institutions, contributing to the optimization of learning content.

[0361] The emotion engine is used to detect students' real-time emotional states. For example, it analyzes students' stress and relaxation levels through cameras and microphones, and suggests changes to provide users with the most suitable lecture content. Specifically, the server adjusts the content based on the student's stress level detected during the lecture.

[0362] Corporate users can reserve necessary classrooms and equipment based on publicly available information on unused assets at educational institutions. The reservation process is conducted through a dedicated web interface, and confirmation notifications are sent via email.

[0363] An example of a prompt message would be, "Generate optimal lecture content based on student sentiment data," when inputting this into the AI ​​generation model. In this way, the system streamlines information management and provision between educational institutions and companies, and provides students with an optimized learning environment.

[0364] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0365] Step 1:

[0366] Users (educational institution staff) upload survey data and related information to the system using a terminal. Input is in formats such as CSV or Excel files, and output is stored in the system's database. Specifically, users select files using a dedicated web interface and click the upload button. This operation transmits the data to the server via the network.

[0367] Step 2:

[0368] The server stores and centrally manages received data in a database. Input is data files sent by users, and output is structured database entries. Specifically, the server uses a database management system (DBMS) to verify data accuracy, format the data, and then save it.

[0369] Step 3:

[0370] Corporate users send requests to the server to retrieve information. The input is a request specifying the type and scope of information to be retrieved, and the output is a notification from the server. Specifically, the user selects the required dataset on the web portal and clicks the "Request Information Retrieval" button. This request is logged on the server side.

[0371] Step 4:

[0372] The server initiates the approval process by sending a notification to the educational institution regarding information requests from corporate users. The input is the request content from the corporate user, and the output is the approval notification. The server notifies the educational institution of the request content via email or a dedicated application.

[0373] Step 5:

[0374] The server processes approved information using data analysis techniques. Input data consists of information from approved educational institutions, and output is insights gained through analysis. The server uses programming languages ​​such as Python and R to apply machine learning algorithms, performing data cleansing, model training, and analysis results.

[0375] Step 6:

[0376] The emotion engine detects students' emotional states in real time and optimizes lecture content based on that. Input is student audio and video data acquired via terminals, and output is optimized lecture content and suggestions. Specifically, emotion analysis software uses deep learning technology to detect stress levels and interest levels, and generates lecture content recommendations provided by the server.

[0377] Step 7:

[0378] Users at educational institutions register information about unused assets in the system via terminals. Inputs are detailed information about available classrooms and equipment, while outputs are publicly available asset information. Specifically, users input classroom numbers, available hours, etc., via a management interface and save this information in the system.

[0379] Step 8:

[0380] Corporate users make reservations based on publicly available information on unused assets. Input is a reservation request for the required classroom or equipment, and output is a reservation confirmation from the server. Corporate users use the reservation system to reserve assets for specific dates and times, and reservation confirmations are sent via email.

[0381] (Application Example 2)

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

[0383] Traditional data management and exchange systems between educational institutions and businesses have the drawback of making it difficult to optimize learning experiences and work processes through real-time emotion recognition. There is a growing need to optimize asset utilization within educational institutions and to dynamically adjust lectures and tasks based on the emotional state of individual learners.

[0384] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0385] In this invention, the server includes means for managing information held by educational institutions, means for receiving requests from educational institutions for companies to obtain information, and means for analyzing the obtained information using data analysis techniques. This enables smooth data exchange between educational institutions and companies, and further enables adaptive adjustment of lectures and work processes based on emotional states.

[0386] "Educational institutions" refer to all organizations that provide education and training to learners, and include schools, universities, and vocational schools.

[0387] "Information" refers to data, materials, and knowledge held by educational institutions, and includes information on student performance data, lecture content, and educational resources.

[0388] "Company" refers to a for-profit organization or legal entity whose purpose is to acquire and utilize information from educational institutions, thereby improving services and products in the education market and related industries.

[0389] A "request" refers to the act or process by which a company requests information from an educational institution, and this is carried out through a system.

[0390] "Data analysis technology" refers to all techniques used to process information and extract meaningful results and insights, and includes machine learning and statistical models.

[0391] An "emotion engine" refers to a system or technology that recognizes a user's emotional state and provides information about it, enabling real-time data acquisition.

[0392] A "work process" refers to a series of activities performed in a factory or other work environment to achieve a specific objective.

[0393] The embodiments for carrying out the invention are described below.

[0394] This system has data management and analysis functions to provide information held by educational institutions to companies. The server primarily manages the educational institutions' information centrally and provides an interface for companies to appropriately access that information. Information is uploaded to the system via terminals and organized by the server.

[0395] Furthermore, the server uses machine learning algorithms to analyze information and provide improvement suggestions to educational institutions. Specifically, it analyzes student performance and learning trends based on collected data, generating insights that are useful for designing optimal lecture content and special lectures.

[0396] Emotion recognition utilizes an emotion engine. This engine assesses the user's (student or worker's) emotional state in real time, enabling dynamic adjustment of educational or work processes based on stress levels and comprehension. Physiological data from the user is collected and analyzed using smart glasses and other wearable devices.

[0397] For example, if a user is stressed by a difficult lecture, the server adjusts the pace of the lecture based on the analysis results of the emotion engine, providing information in a more easily understandable format. Similarly, in a factory setting, work processes are adjusted based on the emotional state of the workers to maintain a safe and effective working environment.

[0398] An example of a prompt using a generative AI model is: "Please suggest ways to optimize work processes based on the real-time emotional state of workers in order to improve factory productivity." This allows for further learning and operational efficiency through the integration of emotion recognition and data analysis.

[0399] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0400] Step 1:

[0401] The terminal uploads information from the educational institution to the system. This includes student learning data and lecture content. The input is educational institution information from the terminal, which the server receives and stores centrally in a database.

[0402] Step 2:

[0403] The server receives information retrieval requests from companies. These requests include requests for specific datasets or analysis results. The server analyzes these requests and filters and extracts the necessary information from the database. The output is a set of information based on the requests.

[0404] Step 3:

[0405] The server uses machine learning algorithms to analyze the collected information. Raw data from the database is used as input, and this data is processed to generate insights necessary for creating useful improvement suggestions and lecture content for educational institutions. The output is the analysis results.

[0406] Step 4:

[0407] The server uses an emotion engine to analyze emotional data from educational institutions in real time. This data includes physiological measurement data obtained from users (students and workers). Based on this, the server evaluates the emotional state and provides an estimated result of the emotional state as output.

[0408] Step 5:

[0409] The server adjusts the lecture content and work process based on the analysis results of the emotion engine. Specifically, if the emotional state is determined to be high stress, the pace of the lecture will be slowed or detailed explanations will be added. The input is the emotion analysis results, and the output is the adjusted lecture content or work process.

[0410] Step 6:

[0411] Users (companies, educators) review the analysis results and lecture adjustments generated by the server and provide feedback as needed. This optimizes the system. The input is the analysis results and adjustments, and the output is the user's feedback.

[0412] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0413] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). One example of data generation model 58 is ChatGPT (Internet search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0414] In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and the specific processing may also be performed by the smart glasses 214.

[0415] [Third Embodiment]

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

[0417] As shown in Figure 5, the data processing system 310 includes a data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0418] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0419] The headset terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a display 343. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and display 343 are also connected to the bus 52.

[0420] The microphone 238 receives voice signals from the user 20 and receives instructions from the user 20. The microphone 238 captures the voice signals from the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0421] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the area around the user 20 (for example, an imaging range defined by a field of view equivalent to the width of a typical healthy person's field of vision).

[0422] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0423] Figure 6 shows an example of the main functions of the data processing device 12 and the headset terminal 314. As shown in Figure 6, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0424] The specific processing program 56 is an example of a "program" relating to the technology of this disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0425] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0426] In the headset terminal 314, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0427] Next, the specific processing performed by the specific processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the headset terminal 314 will be referred to as the "terminal".

[0428] This invention provides a system for efficiently managing data held by educational institutions and for facilitating smooth data exchange with companies. The system of this invention is implemented in the following forms to enable educational institutions and companies to share mutually beneficial information and to effectively utilize the assets of educational institutions.

[0429] First, users from educational institutions upload their own survey data and other relevant information to the system via their devices. This information is centrally managed by a server, and after the educational institution has configured its permissions, it grants access to companies. This allows company users to request the data they need. When a company user requests data, the server automatically notifies the educational institution of this request, and once the educational institution approves it, the data is provided to the company.

[0430] Furthermore, the server analyzes uploaded data using data analysis technologies, particularly machine learning algorithms. Based on the insights gained from this analysis, it provides educational institutions with suggestions for service improvements and support in planning special lectures. Educational institutions design special lectures and supplementary courses based on the recommendations provided by the server and offer them to students. Learners can use their devices to view information on special lectures and make reservations for lectures that interest them.

[0431] Furthermore, the system of this invention manages unused assets of educational institutions and facilitates their effective utilization in conjunction with companies. Users of educational institutions register unused classrooms and facilities and input the information into the server. This allows companies to reserve the desired time slots for using these assets via a terminal. The server manages the reservation information and ensures the appropriate use of the assets.

[0432] As a concrete example, by registering classrooms with some surplus capacity in the system, educational institutions can enable companies to use these classrooms for meetings and training sessions. Furthermore, companies can analyze survey data received from educational institutions to inform their marketing strategies and product development. This allows educational institutions to effectively utilize underutilized assets while also contributing to improving students' learning environments.

[0433] The following describes the processing flow.

[0434] Step 1:

[0435] Users (educational institutions) upload survey data to the system via their devices. The server receives the uploaded data, verifies its contents, and saves it to the database.

[0436] Step 2:

[0437] The server makes data authorized by educational institutions available for companies to view. Users (companies) access the system using their terminals, select the data they need, and submit requests.

[0438] Step 3:

[0439] The server receives a data request from the company and notifies the user (educational institution) of the request. Once the educational institution approves the request, the server provides the data to the company.

[0440] Step 4:

[0441] The server analyzes the acquired data using data analysis technology. This analysis utilizes machine learning algorithms to extract patterns and trends from the data.

[0442] Step 5:

[0443] The server uses the analysis results to notify users (educational institutions) of improvement suggestions. It also uses a generative AI to recommend content for special lectures.

[0444] Step 6:

[0445] Users (educational institutions) plan special lectures and enter detailed information about the lectures into the system. The server stores this information and makes it publicly available for users (students) to view.

[0446] Step 7:

[0447] Users (students) view publicly available lecture information through their terminals and reserve lectures they are interested in. The server records the reservation information in a database and notifies the student for confirmation.

[0448] Step 8:

[0449] Users (educational institutions) register information about unused assets in the system. The server stores this information and makes it accessible to other users (companies).

[0450] Step 9:

[0451] The user (company) selects the unused assets they need and reserves the desired usage time. The server manages the reservation details and notifies the user (company) of the reservation confirmation.

[0452] (Example 1)

[0453] 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."

[0454] With the advancement of information technology, information exchange between educational institutions and industrial organizations is increasing, but this presents challenges in data management, access control settings, and the effective utilization of underutilized assets. Furthermore, there is a need for rapid and effective information analysis and the subsequent feedback to educational institutions, as well as proposals for special lectures. A system is needed to efficiently carry out these processes.

[0455] 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.

[0456] In this invention, the server includes means for managing information assets held by educational institutions, means for receiving requests from industrial organizations to acquire information assets from educational institutions, and means for analyzing the acquired information using information analysis technology. This facilitates smooth information exchange between educational institutions and industrial organizations, enabling effective feedback and efficient utilization of assets based on the results of information analysis.

[0457] An "educational institution" refers to an organization or facility established to provide knowledge and skills, and includes institutions such as schools, universities, and vocational schools.

[0458] An "industrial organization" refers to a company or group that conducts commercial activities in a specific field, and includes organizations that conduct business through information and services.

[0459] "Information assets" refer to a collection of data and information held by educational institutions and industrial organizations, including survey data, research results, and learning materials.

[0460] "Information analysis technology" refers to techniques for analyzing data and deriving useful insights, and includes statistical analysis and machine learning.

[0461] "Unused equipment" refers to classrooms and facilities in educational institutions that are not currently in use, and are physical assets that can be used by other organizations or for other purposes.

[0462] A "generative AI model" refers to an artificial intelligence model designed to generate and analyze information from diverse data, and includes models based on machine learning and deep learning.

[0463] A "request" is the act of asking for specific information or services, including a formal request by an industrial organization to obtain information from an educational institution.

[0464] "Feedback" refers to the act of providing analysis results and suggestions for improvement of educational institutions, including providing information for improving the quality and processes of education.

[0465] This invention is a system for efficiently managing data from educational institutions and facilitating smooth information exchange with industrial organizations. The system centers around a server for managing information assets held by educational institutions and includes various means for acquiring and providing information according to the requests of industrial organizations.

[0466] The server uses a database system (e.g., MySQL or PostgreSQL) to centrally manage the information assets of educational institutions. Information retrieval requests from industrial organizations are received via terminals through a dedicated portal site, and data is transferred using the secure SSL / TLS communication protocol. Furthermore, the server uses generative AI models that implement machine learning algorithms (e.g., TensorFlow or Scikit-learn) to analyze the acquired information and provide educational institutions with useful insights.

[0467] As a concrete example, users from educational institutions upload survey data from a dedicated terminal, and users from industrial organizations request this data from their terminals. Based on the permissions set by the educational institution, the server automatically notifies the industrial organization of the request. Once the educational institution approves, the server provides the data to the industrial organization through a secure channel.

[0468] Examples of prompts include, "Please explain how companies can utilize survey data held by educational institutions," and "Please describe the reservation procedure for companies to use unused classrooms." Through these prompts, industry organizations can develop strategies to effectively utilize data from educational institutions.

[0469] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0470] Step 1:

[0471] Users (educational institutions) collect survey data and related information using terminals and upload the data to the server via a dedicated application. The input is a survey data file (e.g., a CSV file), which the server receives and stores in its database. During this process, data integrity is verified, and accuracy is ensured by checking for duplicates and missing data.

[0472] Step 2:

[0473] The server verifies the access permissions set by the educational institution based on the received data. Input includes the institution's policy information and access rights, and the server configures data permission settings based on this information. Output generates a list of data accessible to corporate users and any restrictions. The server provides access permission information to the educational institution through the management console.

[0474] Step 3:

[0475] The user (company) accesses a dedicated portal site to obtain the necessary data and enters their request details. This procedure requires the company to enter details such as the type and time period of the data they require. The server receives this request and sends a notification to the educational institution. The notification message contains the details of the request.

[0476] Step 4:

[0477] The user (educational institution) reviews the request received from the server and decides whether to approve or reject it. The input is request information from a company, and the educational institution selects a response. If approved, a notification is sent to the server, and the approval or rejection status is recorded as output.

[0478] Step 5:

[0479] The server will provide the requested data to the company once it receives approval from the educational institution. The approved data content will be the input, and the data will be securely transmitted using the SSL / TLS protocol as the output. After transmission, the server will notify both the educational institution and the company that the transmission is complete.

[0480] Step 6:

[0481] The server analyzes uploaded data using machine learning algorithms. Educational institution data is used as input, and the analysis process generates improvement suggestions and insights as output. The server provides this information to educational institutions to help improve their services and lectures.

[0482] Step 7:

[0483] Users (students) view special lecture information provided by the server using their terminals and reserve lectures they are interested in. Lecture information is the input. The reservation status of each student is updated as output. The server manages the reservation information and maintains appropriate lecture capacity and scheduling.

[0484] Step 8:

[0485] Users (educational institutions) register information about unused classrooms and facilities on the server. Facility information is provided as input, which the server receives and stores in its database. The server then makes the registered information public to industry organizations, allowing companies wishing to use the data to access it.

[0486] Step 9:

[0487] Users (companies) reserve their desired unused assets from a terminal and send the reservation information to the server. The input includes the desired date and time and asset details. The server accepts the reservation, adjusts the schedule and checks for duplicates, and sends a confirmation email to the company as output.

[0488] (Application Example 1)

[0489] 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."

[0490] There is a challenge in effectively utilizing the untapped resources within the education sector and providing an environment where businesses can efficiently acquire and reserve the information and resources they need. In conventional systems, managing information and untapped resources within the education sector is cumbersome, requiring significant effort and time for businesses to access them. This has resulted in a low utilization rate of educational resources and hindered the smooth provision of information needed by businesses.

[0491] 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.

[0492] In this invention, the server includes means for processing information held by the educational domain, means for receiving requests from entities to acquire information from the educational domain, means for processing acquired information using information processing technology, and means for entities to reserve unused resources in the educational domain using communication equipment. This enables efficient management of information and unused resources in the educational domain, and allows entities to acquire necessary information and reserve resources simply and quickly.

[0493] The "educational domain" refers to educational institutions, related organizations, and facilities, and is the place where the information and physical resources they possess are managed and provided.

[0494] A "business entity" refers to an organization such as a company or group, which is the entity that acquires and utilizes information and resources from the field of education.

[0495] "Information processing technology" refers to techniques for collecting, analyzing, and utilizing data, and includes methods such as machine learning algorithms.

[0496] "Unused resources" refer to physical assets such as classrooms and facilities that are not currently being used within the educational domain, and are targets for effective utilization.

[0497] "Communication equipment" refers to devices that send and receive information via a network connection, and includes smartphones and computers.

[0498] The system implementing this invention efficiently manages information and unused resources in the education sector, enabling organizations to utilize them. Specifically, a server and terminals work together to realize each function.

[0499] The server centrally manages information registered in the education domain. This information includes survey results, teaching material data, and classroom availability. A Python-based system runs on the server, and machine learning algorithms are used as information processing technology to analyze the data. This analysis allows for suggestions for service improvement in the education domain and the generation of information such as special lectures.

[0500] The user entities obtain necessary information from the education sector and reserve unused resources through terminals. These terminals include smartphones and computers, and these devices can connect to a server to retrieve information and check reservation status in real time.

[0501] For example, if a company is looking for space for employee training, they can use this system to easily reserve an available classroom in the education area. In this process, they can receive suggestions for the most suitable space based on insights gained from data analysis using a generative AI model, allowing them to efficiently achieve their objectives.

[0502] Example prompt for input to the generating AI model: "Analyze data from educational institutions and describe a system that allows unused classrooms to be utilized."

[0503] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0504] Step 1:

[0505] The server stores survey data and unused classroom information received from the education sector into a database. It receives information as input, checks for any anomalies in the database, and processes the data for conversion into a unified format for storage. The output is accurately stored information.

[0506] Step 2:

[0507] Using a terminal, the user accesses information in the educational field. The input is a request for the information the user seeks, and the terminal sends this request to the server. The server performs query processing to extract relevant information from the database and compiles the necessary information. The output is a list of the information the user searched for.

[0508] Step 3:

[0509] The server analyzes received data using machine learning algorithms. The input is information stored in a database, and the algorithms perform pattern recognition and anomaly detection on the data. This derives insights such as suggestions for improvements in the education field and recommendations for special lectures. The output is the insights and suggestions resulting from the analysis.

[0510] Step 4:

[0511] Users search for and reserve unused classrooms through their terminal. The input consists of search criteria for available classrooms; the terminal sends a request to the server, which filters the available classrooms based on the criteria. The output is a list of available classrooms.

[0512] Step 5:

[0513] When a user reserves a classroom, the server records the reservation details in the database. The input is the details of the classroom and the time slot to be reserved. The server accurately records this information and processes the data to update the reservation status. The output is a notification confirming a successful reservation.

[0514] 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.

[0515] This invention is a system that not only efficiently manages data held by educational institutions and facilitates smooth data exchange with companies, but also provides a more personalized learning experience by combining it with an emotion engine that recognizes user emotions. Specific embodiments are shown below.

[0516] First, users from educational institutions upload survey data and other relevant information to the system via their devices. This allows the server to centrally manage the data and enable corporate users to request data retrieval. When a user (corporate) requests data, the server notifies the educational institution of the request, and provides the data to the company only after receiving approval from the educational institution.

[0517] The server uses machine learning algorithms as a data analysis technique to analyze the acquired data. Based on the insights gained, it provides educational institutions with useful information for designing special lectures. It also utilizes the analysis results to generate the content of the special lectures, optimizing the lecture content according to predefined learning objectives.

[0518] Furthermore, by incorporating an emotion engine, the system can detect the user's (student's) emotional state in real time and optimize lectures and manage reservations accordingly. Specifically, if a user is experiencing stress or anxiety during a lecture, the emotion engine will detect this, and the server will adjust the lecture content. For example, it can improve learning effectiveness by providing more detailed explanations of difficult parts or adjusting the pace.

[0519] Educational institution users register unused classrooms and facilities through terminals, allowing companies to reserve them for use. The server manages the reservation information and adjusts it to ensure optimal asset utilization.

[0520] For example, when a company reserves an empty classroom to provide a learning environment and conducts a lecture, the system can acquire student emotional data in real time, allowing the instructor to check the students' level of understanding on the spot and adjust the lecture content as needed. In this way, the system of the present invention can effectively promote asset management for educational institutions and data utilization for companies while improving the quality of the learning experience.

[0521] The following describes the processing flow.

[0522] Step 1:

[0523] Users (educational institutions) upload survey data to the system via their devices. The server receives the data and stores it in a database.

[0524] Step 2:

[0525] The server configures its data access settings and prepares to receive data retrieval requests from corporate users. Corporate users access the system through their terminals and request the necessary data.

[0526] Step 3:

[0527] The server receives a data request from the company and notifies the user (educational institution) of the request's contents. Once the educational institution approves the data provision, the server sends the data to the company.

[0528] Step 4:

[0529] The server analyzes the received data using machine learning algorithms to generate useful insights from the data. This allows for the creation of improvement suggestions and recommendations for special lectures for educational institutions.

[0530] Step 5:

[0531] The emotion engine collects and analyzes user (student) emotional data, evaluating learning progress and emotional state in real time. The server dynamically optimizes the content of special lectures based on this information.

[0532] Step 6:

[0533] Users (educational institutions) use terminals to plan special lectures and register the lecture content and date / time on the server. The server makes this information public, allowing students to view and reserve it.

[0534] Step 7:

[0535] Users (students) can view the content of special lectures from their terminals, select the lectures they wish to attend, and make reservations. Reservation information is stored in a database by the server.

[0536] Step 8:

[0537] Users (educational institutions) register information about unused classrooms and facilities in the system via terminals. The server stores this information, allowing companies to make reservations through the system.

[0538] Step 9:

[0539] Users (companies) reserve the necessary classrooms and equipment from their terminals, and the server records the reservation information. At the same time, the server uses feedback from an emotion engine to suggest appropriate lecture plans to the companies.

[0540] (Example 2)

[0541] 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."

[0542] There is a need to streamline information management within educational institutions and provide information to businesses, as well as to offer individually optimized learning experiences based on students' emotional states. However, conventional systems have been inefficient in data management and analysis, and have struggled to optimize learning while considering students' emotions. This has led to problems such as hindering the smooth exchange of information between educational institutions and businesses, and the provision of optimal education to students.

[0543] 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.

[0544] In this invention, the server includes means for managing information held by educational institutions, means for receiving requests from companies to acquire information, and means for analyzing the information using data analysis technology. This enables efficient information exchange between educational institutions and companies, and further allows for learning optimization based on students' emotional states.

[0545] An "educational institution" is an institution that teaches academic subjects or skills, and refers to organizations such as universities, high schools, and vocational schools.

[0546] "Information" refers to data stored in digital format, such as surveys, grade data, and data related to course content, held by educational institutions.

[0547] A "company" is an organization that engages in commercial activities and refers to a legal entity that provides educational services or products through the acquisition of information.

[0548] A "request" refers to a formal request or inquiry made by an organization to obtain information from an educational institution.

[0549] "Data analysis technology" refers to a general term for techniques that process acquired information using statistical or machine learning methods to extract useful insights.

[0550] A "machine learning algorithm" is a type of data analysis technique that uses mathematical models to automatically learn rules and patterns from data and make predictions and classifications based on those results.

[0551] "Emotional state" refers to the psychological and physiological responses expressed by the user (student), including emotions such as excitement, stress, and relaxation.

[0552] "Unused assets" refer to physical resources, such as classrooms and equipment, owned by educational institutions that are not being used during specific time periods.

[0553] "Optimization" refers to the means of maximizing or streamlining the capabilities of a particular process or function based on given conditions.

[0554] This invention is a system that effectively manages information held by educational institutions, supports smooth information exchange with companies, and provides an individually optimized learning experience by analyzing the emotions of users (students).

[0555] The terminal provides an interface for educational institution users to upload survey data and related information to the system. This allows users to easily input information into the system and prepare it for centralized management. Specifically, users drag and drop data files onto a form and press an upload button.

[0556] The server stores uploaded information in a database and accepts information retrieval requests from corporate users. It also uses programming languages ​​such as Python and libraries like Scikit-learn and TensorFlow to analyze the data. Based on the resulting analysis, it provides improvement suggestions to educational institutions, contributing to the optimization of learning content.

[0557] The emotion engine is used to detect students' real-time emotional states. For example, it analyzes students' stress and relaxation levels through cameras and microphones, and suggests changes to provide users with the most suitable lecture content. Specifically, the server adjusts the content based on the student's stress level detected during the lecture.

[0558] Corporate users can reserve necessary classrooms and equipment based on publicly available information on unused assets at educational institutions. The reservation process is conducted through a dedicated web interface, and confirmation notifications are sent via email.

[0559] An example of a prompt message would be, "Generate optimal lecture content based on student sentiment data," when inputting this into the AI ​​generation model. In this way, the system streamlines information management and provision between educational institutions and companies, and provides students with an optimized learning environment.

[0560] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0561] Step 1:

[0562] Users (educational institution staff) upload survey data and related information to the system using a terminal. Input is in formats such as CSV or Excel files, and output is stored in the system's database. Specifically, users select files using a dedicated web interface and click the upload button. This operation transmits the data to the server via the network.

[0563] Step 2:

[0564] The server stores and centrally manages received data in a database. Input is data files sent by users, and output is structured database entries. Specifically, the server uses a database management system (DBMS) to verify data accuracy, format the data, and then save it.

[0565] Step 3:

[0566] Corporate users send requests to the server to retrieve information. The input is a request specifying the type and scope of information to be retrieved, and the output is a notification from the server. Specifically, the user selects the required dataset on the web portal and clicks the "Request Information Retrieval" button. This request is logged on the server side.

[0567] Step 4:

[0568] The server initiates the approval process by sending a notification to the educational institution regarding information requests from corporate users. The input is the request content from the corporate user, and the output is the approval notification. The server notifies the educational institution of the request content via email or a dedicated application.

[0569] Step 5:

[0570] The server processes approved information using data analysis techniques. Input data consists of information from approved educational institutions, and output is insights gained through analysis. The server uses programming languages ​​such as Python and R to apply machine learning algorithms, performing data cleansing, model training, and analysis results.

[0571] Step 6:

[0572] The emotion engine detects students' emotional states in real time and optimizes lecture content based on that. Input is student audio and video data acquired via terminals, and output is optimized lecture content and suggestions. Specifically, emotion analysis software uses deep learning technology to detect stress levels and interest levels, and generates lecture content recommendations provided by the server.

[0573] Step 7:

[0574] Users at educational institutions register information about unused assets in the system via terminals. Inputs are detailed information about available classrooms and equipment, while outputs are publicly available asset information. Specifically, users input classroom numbers, available hours, etc., via a management interface and save this information in the system.

[0575] Step 8:

[0576] Corporate users make reservations based on publicly available information on unused assets. Input is a reservation request for the required classroom or equipment, and output is a reservation confirmation from the server. Corporate users use the reservation system to reserve assets for specific dates and times, and reservation confirmations are sent via email.

[0577] (Application Example 2)

[0578] 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."

[0579] Traditional data management and exchange systems between educational institutions and businesses have the drawback of making it difficult to optimize learning experiences and work processes through real-time emotion recognition. There is a growing need to optimize asset utilization within educational institutions and to dynamically adjust lectures and tasks based on the emotional state of individual learners.

[0580] 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.

[0581] In this invention, the server includes means for managing information held by educational institutions, means for receiving requests from educational institutions for companies to obtain information, and means for analyzing the obtained information using data analysis techniques. This enables smooth data exchange between educational institutions and companies, and further enables adaptive adjustment of lectures and work processes based on emotional states.

[0582] "Educational institutions" refer to all organizations that provide education and training to learners, and include schools, universities, and vocational schools.

[0583] "Information" refers to data, materials, and knowledge held by educational institutions, and includes information on student performance data, lecture content, and educational resources.

[0584] "Company" refers to a for-profit organization or legal entity whose purpose is to acquire and utilize information from educational institutions, thereby improving services and products in the education market and related industries.

[0585] A "request" refers to the act or process by which a company requests information from an educational institution, and this is carried out through a system.

[0586] "Data analysis technology" refers to all techniques used to process information and extract meaningful results and insights, and includes machine learning and statistical models.

[0587] An "emotion engine" refers to a system or technology that recognizes a user's emotional state and provides information about it, enabling real-time data acquisition.

[0588] A "work process" refers to a series of activities performed in a factory or other work environment to achieve a specific objective.

[0589] The embodiments for carrying out the invention are described below.

[0590] This system has data management and analysis functions to provide information held by educational institutions to companies. The server primarily manages the educational institutions' information centrally and provides an interface for companies to appropriately access that information. Information is uploaded to the system via terminals and organized by the server.

[0591] Furthermore, the server uses machine learning algorithms to analyze information and provide improvement suggestions to educational institutions. Specifically, it analyzes student performance and learning trends based on collected data, generating insights that are useful for designing optimal lecture content and special lectures.

[0592] Emotion recognition utilizes an emotion engine. This engine assesses the user's (student or worker's) emotional state in real time, enabling dynamic adjustment of educational or work processes based on stress levels and comprehension. Physiological data from the user is collected and analyzed using smart glasses and other wearable devices.

[0593] For example, if a user is stressed by a difficult lecture, the server adjusts the pace of the lecture based on the analysis results of the emotion engine, providing information in a more easily understandable format. Similarly, in a factory setting, work processes are adjusted based on the emotional state of the workers to maintain a safe and effective working environment.

[0594] An example of a prompt using a generative AI model is: "Please suggest ways to optimize work processes based on the real-time emotional state of workers in order to improve factory productivity." This allows for further learning and operational efficiency through the integration of emotion recognition and data analysis.

[0595] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0596] Step 1:

[0597] The terminal uploads information from the educational institution to the system. This includes student learning data and lecture content. The input is educational institution information from the terminal, which the server receives and stores centrally in a database.

[0598] Step 2:

[0599] The server receives information retrieval requests from companies. These requests include requests for specific datasets or analysis results. The server analyzes these requests and filters and extracts the necessary information from the database. The output is a set of information based on the requests.

[0600] Step 3:

[0601] The server uses machine learning algorithms to analyze the collected information. Raw data from the database is used as input, and this data is processed to generate insights necessary for creating useful improvement suggestions and lecture content for educational institutions. The output is the analysis results.

[0602] Step 4:

[0603] The server uses an emotion engine to analyze emotional data from educational institutions in real time. This data includes physiological measurement data obtained from users (students and workers). Based on this, the server evaluates the emotional state and provides an estimated result of the emotional state as output.

[0604] Step 5:

[0605] The server adjusts the lecture content and work process based on the analysis results of the emotion engine. Specifically, if the emotional state is determined to be high stress, the pace of the lecture will be slowed or detailed explanations will be added. The input is the emotion analysis results, and the output is the adjusted lecture content or work process.

[0606] Step 6:

[0607] Users (companies, educators) review the analysis results and lecture adjustments generated by the server and provide feedback as needed. This optimizes the system. The input is the analysis results and adjustments, and the output is the user's feedback.

[0608] The specific processing unit 290 transmits the result of the specific processing to the headset terminal 314. In the headset terminal 314, the control unit 46A causes the speaker 240 and display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0609] 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.

[0610] 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.

[0611] [Fourth Embodiment]

[0612] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0613] 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.

[0614] 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).

[0615] 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.

[0616] 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.

[0617] 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).

[0618] 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.

[0619] 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.

[0620] 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.

[0621] 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.

[0622] 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.

[0623] 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.

[0624] 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".

[0625] This invention provides a system for efficiently managing data held by educational institutions and for facilitating smooth data exchange with companies. The system of this invention is implemented in the following forms to enable educational institutions and companies to share mutually beneficial information and to effectively utilize the assets of educational institutions.

[0626] First, users from educational institutions upload their own survey data and other relevant information to the system via their devices. This information is centrally managed by a server, and after the educational institution has configured its permissions, it grants access to companies. This allows company users to request the data they need. When a company user requests data, the server automatically notifies the educational institution of this request, and once the educational institution approves it, the data is provided to the company.

[0627] Furthermore, the server analyzes uploaded data using data analysis technologies, particularly machine learning algorithms. Based on the insights gained from this analysis, it provides educational institutions with suggestions for service improvements and support in planning special lectures. Educational institutions design special lectures and supplementary courses based on the recommendations provided by the server and offer them to students. Learners can use their devices to view information on special lectures and make reservations for lectures that interest them.

[0628] Furthermore, the system of this invention manages unused assets of educational institutions and facilitates their effective utilization in conjunction with companies. Users of educational institutions register unused classrooms and facilities and input the information into the server. This allows companies to reserve the desired time slots for using these assets via a terminal. The server manages the reservation information and ensures the appropriate use of the assets.

[0629] As a concrete example, by registering classrooms with some surplus capacity in the system, educational institutions can enable companies to use these classrooms for meetings and training sessions. Furthermore, companies can analyze survey data received from educational institutions to inform their marketing strategies and product development. This allows educational institutions to effectively utilize underutilized assets while also contributing to improving students' learning environments.

[0630] The following describes the processing flow.

[0631] Step 1:

[0632] Users (educational institutions) upload survey data to the system via their devices. The server receives the uploaded data, verifies its contents, and saves it to the database.

[0633] Step 2:

[0634] The server makes data authorized by educational institutions available for companies to view. Users (companies) access the system using their terminals, select the data they need, and submit requests.

[0635] Step 3:

[0636] The server receives a data request from the company and notifies the user (educational institution) of the request. Once the educational institution approves the request, the server provides the data to the company.

[0637] Step 4:

[0638] The server analyzes the acquired data using data analysis technology. This analysis utilizes machine learning algorithms to extract patterns and trends from the data.

[0639] Step 5:

[0640] The server uses the analysis results to notify users (educational institutions) of improvement suggestions. It also uses a generative AI to recommend content for special lectures.

[0641] Step 6:

[0642] Users (educational institutions) plan special lectures and enter detailed information about the lectures into the system. The server stores this information and makes it publicly available for users (students) to view.

[0643] Step 7:

[0644] Users (students) view publicly available lecture information through their terminals and reserve lectures they are interested in. The server records the reservation information in a database and notifies the student for confirmation.

[0645] Step 8:

[0646] Users (educational institutions) register information about unused assets in the system. The server stores this information and makes it accessible to other users (companies).

[0647] Step 9:

[0648] The user (company) selects the unused assets they need and reserves the desired usage time. The server manages the reservation details and notifies the user (company) of the reservation confirmation.

[0649] (Example 1)

[0650] 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".

[0651] With the advancement of information technology, information exchange between educational institutions and industrial organizations is increasing, but this presents challenges in data management, access control settings, and the effective utilization of underutilized assets. Furthermore, there is a need for rapid and effective information analysis and the subsequent feedback to educational institutions, as well as proposals for special lectures. A system is needed to efficiently carry out these processes.

[0652] 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.

[0653] In this invention, the server includes means for managing information assets held by educational institutions, means for receiving requests from industrial organizations to acquire information assets from educational institutions, and means for analyzing the acquired information using information analysis technology. This facilitates smooth information exchange between educational institutions and industrial organizations, enabling effective feedback and efficient utilization of assets based on the results of information analysis.

[0654] An "educational institution" refers to an organization or facility established to provide knowledge and skills, and includes institutions such as schools, universities, and vocational schools.

[0655] An "industrial organization" refers to a company or group that conducts commercial activities in a specific field, and includes organizations that conduct business through information and services.

[0656] "Information assets" refer to a collection of data and information held by educational institutions and industrial organizations, including survey data, research results, and learning materials.

[0657] "Information analysis technology" refers to techniques for analyzing data and deriving useful insights, and includes statistical analysis and machine learning.

[0658] "Unused equipment" refers to classrooms and facilities in educational institutions that are not currently in use, and are physical assets that can be used by other organizations or for other purposes.

[0659] A "generative AI model" refers to an artificial intelligence model designed to generate and analyze information from diverse data, and includes models based on machine learning and deep learning.

[0660] A "request" is the act of asking for specific information or services, including a formal request by an industrial organization to obtain information from an educational institution.

[0661] "Feedback" refers to the act of providing analysis results and suggestions for improvement of educational institutions, including providing information for improving the quality and processes of education.

[0662] This invention is a system for efficiently managing data from educational institutions and facilitating smooth information exchange with industrial organizations. The system centers around a server for managing information assets held by educational institutions and includes various means for acquiring and providing information according to the requests of industrial organizations.

[0663] The server uses a database system (e.g., MySQL or PostgreSQL) to centrally manage the information assets of educational institutions. Information retrieval requests from industrial organizations are received via terminals through a dedicated portal site, and data is transferred using the secure SSL / TLS communication protocol. Furthermore, the server uses generative AI models that implement machine learning algorithms (e.g., TensorFlow or Scikit-learn) to analyze the acquired information and provide educational institutions with useful insights.

[0664] As a concrete example, users from educational institutions upload survey data from a dedicated terminal, and users from industrial organizations request this data from their terminals. Based on the permissions set by the educational institution, the server automatically notifies the industrial organization of the request. Once the educational institution approves, the server provides the data to the industrial organization through a secure channel.

[0665] Examples of prompts include, "Please explain how companies can utilize survey data held by educational institutions," and "Please describe the reservation procedure for companies to use unused classrooms." Through these prompts, industry organizations can develop strategies to effectively utilize data from educational institutions.

[0666] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0667] Step 1:

[0668] Users (educational institutions) collect survey data and related information using terminals and upload the data to the server via a dedicated application. The input is a survey data file (e.g., a CSV file), which the server receives and stores in its database. During this process, data integrity is verified, and accuracy is ensured by checking for duplicates and missing data.

[0669] Step 2:

[0670] The server verifies the access permissions set by the educational institution based on the received data. Input includes the institution's policy information and access rights, and the server configures data permission settings based on this information. Output generates a list of data accessible to corporate users and any restrictions. The server provides access permission information to the educational institution through the management console.

[0671] Step 3:

[0672] The user (company) accesses a dedicated portal site to obtain the necessary data and enters their request details. This procedure requires the company to enter details such as the type and time period of the data they require. The server receives this request and sends a notification to the educational institution. The notification message contains the details of the request.

[0673] Step 4:

[0674] The user (educational institution) reviews the request received from the server and decides whether to approve or reject it. The input is request information from a company, and the educational institution selects a response. If approved, a notification is sent to the server, and the approval or rejection status is recorded as output.

[0675] Step 5:

[0676] The server will provide the requested data to the company once it receives approval from the educational institution. The approved data content will be the input, and the data will be securely transmitted using the SSL / TLS protocol as the output. After transmission, the server will notify both the educational institution and the company that the transmission is complete.

[0677] Step 6:

[0678] The server analyzes uploaded data using machine learning algorithms. Educational institution data is used as input, and the analysis process generates improvement suggestions and insights as output. The server provides this information to educational institutions to help improve their services and lectures.

[0679] Step 7:

[0680] Users (students) view special lecture information provided by the server using their terminals and reserve lectures they are interested in. Lecture information is the input. The reservation status of each student is updated as output. The server manages the reservation information and maintains appropriate lecture capacity and scheduling.

[0681] Step 8:

[0682] Users (educational institutions) register information about unused classrooms and facilities on the server. Facility information is provided as input, which the server receives and stores in its database. The server then makes the registered information public to industry organizations, allowing companies wishing to use the data to access it.

[0683] Step 9:

[0684] Users (companies) reserve their desired unused assets from a terminal and send the reservation information to the server. The input includes the desired date and time and asset details. The server accepts the reservation, adjusts the schedule and checks for duplicates, and sends a confirmation email to the company as output.

[0685] (Application Example 1)

[0686] 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".

[0687] There is a challenge in effectively utilizing the untapped resources within the education sector and providing an environment where businesses can efficiently acquire and reserve the information and resources they need. In conventional systems, managing information and untapped resources within the education sector is cumbersome, requiring significant effort and time for businesses to access them. This has resulted in a low utilization rate of educational resources and hindered the smooth provision of information needed by businesses.

[0688] 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.

[0689] In this invention, the server includes means for processing information held by the educational domain, means for receiving requests from entities to acquire information from the educational domain, means for processing acquired information using information processing technology, and means for entities to reserve unused resources in the educational domain using communication equipment. This enables efficient management of information and unused resources in the educational domain, and allows entities to acquire necessary information and reserve resources simply and quickly.

[0690] The "educational domain" refers to educational institutions, related organizations, and facilities, and is the place where the information and physical resources they possess are managed and provided.

[0691] A "business entity" refers to an organization such as a company or group, which is the entity that acquires and utilizes information and resources from the field of education.

[0692] "Information processing technology" refers to techniques for collecting, analyzing, and utilizing data, and includes methods such as machine learning algorithms.

[0693] "Unused resources" refer to physical assets such as classrooms and facilities that are not currently being used within the educational domain, and are targets for effective utilization.

[0694] "Communication equipment" refers to devices that send and receive information via a network connection, and includes smartphones and computers.

[0695] The system implementing this invention efficiently manages information and unused resources in the education sector, enabling organizations to utilize them. Specifically, a server and terminals work together to realize each function.

[0696] The server centrally manages information registered in the education domain. This information includes survey results, teaching material data, and classroom availability. A Python-based system runs on the server, and machine learning algorithms are used as information processing technology to analyze the data. This analysis allows for suggestions for service improvement in the education domain and the generation of information such as special lectures.

[0697] The user entities obtain necessary information from the education sector and reserve unused resources through terminals. These terminals include smartphones and computers, and these devices can connect to a server to retrieve information and check reservation status in real time.

[0698] For example, if a company is looking for space for employee training, they can use this system to easily reserve an available classroom in the education area. In this process, they can receive suggestions for the most suitable space based on insights gained from data analysis using a generative AI model, allowing them to efficiently achieve their objectives.

[0699] Example prompt for input to the generating AI model: "Analyze data from educational institutions and describe a system that allows unused classrooms to be utilized."

[0700] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0701] Step 1:

[0702] The server stores survey data and unused classroom information received from the education sector into a database. It receives information as input, checks for any anomalies in the database, and processes the data for conversion into a unified format for storage. The output is accurately stored information.

[0703] Step 2:

[0704] Using a terminal, the user accesses information in the educational field. The input is a request for the information the user seeks, and the terminal sends this request to the server. The server performs query processing to extract relevant information from the database and compiles the necessary information. The output is a list of the information the user searched for.

[0705] Step 3:

[0706] The server analyzes received data using machine learning algorithms. The input is information stored in a database, and the algorithms perform pattern recognition and anomaly detection on the data. This derives insights such as suggestions for improvements in the education field and recommendations for special lectures. The output is the insights and suggestions resulting from the analysis.

[0707] Step 4:

[0708] Users search for and reserve unused classrooms through their terminal. The input consists of search criteria for available classrooms; the terminal sends a request to the server, which filters the available classrooms based on the criteria. The output is a list of available classrooms.

[0709] Step 5:

[0710] When a user reserves a classroom, the server records the reservation details in the database. The input is the details of the classroom and the time slot to be reserved. The server accurately records this information and processes the data to update the reservation status. The output is a notification confirming a successful reservation.

[0711] 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.

[0712] This invention is a system that not only efficiently manages data held by educational institutions and facilitates smooth data exchange with companies, but also provides a more personalized learning experience by combining it with an emotion engine that recognizes user emotions. Specific embodiments are shown below.

[0713] First, users from educational institutions upload survey data and other relevant information to the system via their devices. This allows the server to centrally manage the data and enable corporate users to request data retrieval. When a user (corporate) requests data, the server notifies the educational institution of the request, and provides the data to the company only after receiving approval from the educational institution.

[0714] The server uses machine learning algorithms as a data analysis technique to analyze the acquired data. Based on the insights gained, it provides educational institutions with useful information for designing special lectures. It also utilizes the analysis results to generate the content of the special lectures, optimizing the lecture content according to predefined learning objectives.

[0715] Furthermore, by incorporating an emotion engine, the system can detect the user's (student's) emotional state in real time and optimize lectures and manage reservations accordingly. Specifically, if a user is experiencing stress or anxiety during a lecture, the emotion engine will detect this, and the server will adjust the lecture content. For example, it can improve learning effectiveness by providing more detailed explanations of difficult parts or adjusting the pace.

[0716] Educational institution users register unused classrooms and facilities through terminals, allowing companies to reserve them for use. The server manages the reservation information and adjusts it to ensure optimal asset utilization.

[0717] For example, when a company reserves an empty classroom to provide a learning environment and conducts a lecture, the system can acquire student emotional data in real time, allowing the instructor to check the students' level of understanding on the spot and adjust the lecture content as needed. In this way, the system of the present invention can effectively promote asset management for educational institutions and data utilization for companies while improving the quality of the learning experience.

[0718] The following describes the processing flow.

[0719] Step 1:

[0720] Users (educational institutions) upload survey data to the system via their devices. The server receives the data and stores it in a database.

[0721] Step 2:

[0722] The server configures its data access settings and prepares to receive data retrieval requests from corporate users. Corporate users access the system through their terminals and request the necessary data.

[0723] Step 3:

[0724] The server receives a data request from the company and notifies the user (educational institution) of the request's contents. Once the educational institution approves the data provision, the server sends the data to the company.

[0725] Step 4:

[0726] The server analyzes the received data using machine learning algorithms to generate useful insights from the data. This allows for the creation of improvement suggestions and recommendations for special lectures for educational institutions.

[0727] Step 5:

[0728] The emotion engine collects and analyzes user (student) emotional data, evaluating learning progress and emotional state in real time. The server dynamically optimizes the content of special lectures based on this information.

[0729] Step 6:

[0730] Users (educational institutions) use terminals to plan special lectures and register the lecture content and date / time on the server. The server makes this information public, allowing students to view and reserve it.

[0731] Step 7:

[0732] Users (students) can view the content of special lectures from their terminals, select the lectures they wish to attend, and make reservations. Reservation information is stored in a database by the server.

[0733] Step 8:

[0734] Users (educational institutions) register information about unused classrooms and facilities in the system via terminals. The server stores this information, allowing companies to make reservations through the system.

[0735] Step 9:

[0736] Users (companies) reserve the necessary classrooms and equipment from their terminals, and the server records the reservation information. At the same time, the server uses feedback from an emotion engine to suggest appropriate lecture plans to the companies.

[0737] (Example 2)

[0738] 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".

[0739] There is a need to streamline information management within educational institutions and provide information to businesses, as well as to offer individually optimized learning experiences based on students' emotional states. However, conventional systems have been inefficient in data management and analysis, and have struggled to optimize learning while considering students' emotions. This has led to problems such as hindering the smooth exchange of information between educational institutions and businesses, and the provision of optimal education to students.

[0740] 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.

[0741] In this invention, the server includes means for managing information held by educational institutions, means for receiving requests from companies to acquire information, and means for analyzing the information using data analysis technology. This enables efficient information exchange between educational institutions and companies, and further allows for learning optimization based on students' emotional states.

[0742] An "educational institution" is an institution that teaches academic subjects or skills, and refers to organizations such as universities, high schools, and vocational schools.

[0743] "Information" refers to data stored in digital format, such as surveys, grade data, and data related to course content, held by educational institutions.

[0744] A "company" is an organization that engages in commercial activities and refers to a legal entity that provides educational services or products through the acquisition of information.

[0745] A "request" refers to a formal request or inquiry made by an organization to obtain information from an educational institution.

[0746] "Data analysis technology" refers to a general term for techniques that process acquired information using statistical or machine learning methods to extract useful insights.

[0747] A "machine learning algorithm" is a type of data analysis technique that uses mathematical models to automatically learn rules and patterns from data and make predictions and classifications based on those results.

[0748] "Emotional state" refers to the psychological and physiological responses expressed by the user (student), including emotions such as excitement, stress, and relaxation.

[0749] "Unused assets" refer to physical resources, such as classrooms and equipment, owned by educational institutions that are not being used during specific time periods.

[0750] "Optimization" refers to the means of maximizing or streamlining the capabilities of a particular process or function based on given conditions.

[0751] This invention is a system that effectively manages information held by educational institutions, supports smooth information exchange with companies, and provides an individually optimized learning experience by analyzing the emotions of users (students).

[0752] The terminal provides an interface for educational institution users to upload survey data and related information to the system. This allows users to easily input information into the system and prepare it for centralized management. Specifically, users drag and drop data files onto a form and press an upload button.

[0753] The server stores uploaded information in a database and accepts information retrieval requests from corporate users. It also uses programming languages ​​such as Python and libraries like Scikit-learn and TensorFlow to analyze the data. Based on the resulting analysis, it provides improvement suggestions to educational institutions, contributing to the optimization of learning content.

[0754] The emotion engine is used to detect students' real-time emotional states. For example, it analyzes students' stress and relaxation levels through cameras and microphones, and suggests changes to provide users with the most suitable lecture content. Specifically, the server adjusts the content based on the student's stress level detected during the lecture.

[0755] Corporate users can reserve necessary classrooms and equipment based on publicly available information on unused assets at educational institutions. The reservation process is conducted through a dedicated web interface, and confirmation notifications are sent via email.

[0756] An example of a prompt message would be, "Generate optimal lecture content based on student sentiment data," when inputting this into the AI ​​generation model. In this way, the system streamlines information management and provision between educational institutions and companies, and provides students with an optimized learning environment.

[0757] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0758] Step 1:

[0759] Users (educational institution staff) upload survey data and related information to the system using a terminal. Input is in formats such as CSV or Excel files, and output is stored in the system's database. Specifically, users select files using a dedicated web interface and click the upload button. This operation transmits the data to the server via the network.

[0760] Step 2:

[0761] The server stores and centrally manages received data in a database. Input is data files sent by users, and output is structured database entries. Specifically, the server uses a database management system (DBMS) to verify data accuracy, format the data, and then save it.

[0762] Step 3:

[0763] Corporate users send requests to the server to retrieve information. The input is a request specifying the type and scope of information to be retrieved, and the output is a notification from the server. Specifically, the user selects the required dataset on the web portal and clicks the "Request Information Retrieval" button. This request is logged on the server side.

[0764] Step 4:

[0765] The server initiates the approval process by sending a notification to the educational institution regarding information requests from corporate users. The input is the request content from the corporate user, and the output is the approval notification. The server notifies the educational institution of the request content via email or a dedicated application.

[0766] Step 5:

[0767] The server processes approved information using data analysis techniques. Input data consists of information from approved educational institutions, and output is insights gained through analysis. The server uses programming languages ​​such as Python and R to apply machine learning algorithms, performing data cleansing, model training, and analysis results.

[0768] Step 6:

[0769] The emotion engine detects students' emotional states in real time and optimizes lecture content based on that. Input is student audio and video data acquired via terminals, and output is optimized lecture content and suggestions. Specifically, emotion analysis software uses deep learning technology to detect stress levels and interest levels, and generates lecture content recommendations provided by the server.

[0770] Step 7:

[0771] Users at educational institutions register information about unused assets in the system via terminals. Inputs are detailed information about available classrooms and equipment, while outputs are publicly available asset information. Specifically, users input classroom numbers, available hours, etc., via a management interface and save this information in the system.

[0772] Step 8:

[0773] Corporate users make reservations based on publicly available information on unused assets. Input is a reservation request for the required classroom or equipment, and output is a reservation confirmation from the server. Corporate users use the reservation system to reserve assets for specific dates and times, and reservation confirmations are sent via email.

[0774] (Application Example 2)

[0775] 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".

[0776] Traditional data management and exchange systems between educational institutions and businesses have the drawback of making it difficult to optimize learning experiences and work processes through real-time emotion recognition. There is a growing need to optimize asset utilization within educational institutions and to dynamically adjust lectures and tasks based on the emotional state of individual learners.

[0777] 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.

[0778] In this invention, the server includes means for managing information held by educational institutions, means for receiving requests from educational institutions for companies to obtain information, and means for analyzing the obtained information using data analysis techniques. This enables smooth data exchange between educational institutions and companies, and further enables adaptive adjustment of lectures and work processes based on emotional states.

[0779] "Educational institutions" refer to all organizations that provide education and training to learners, and include schools, universities, and vocational schools.

[0780] "Information" refers to data, materials, and knowledge held by educational institutions, and includes information on student performance data, lecture content, and educational resources.

[0781] "Company" refers to a for-profit organization or legal entity whose purpose is to acquire and utilize information from educational institutions, thereby improving services and products in the education market and related industries.

[0782] A "request" refers to the act or process by which a company requests information from an educational institution, and this is carried out through a system.

[0783] "Data analysis technology" refers to all techniques used to process information and extract meaningful results and insights, and includes machine learning and statistical models.

[0784] An "emotion engine" refers to a system or technology that recognizes a user's emotional state and provides information about it, enabling real-time data acquisition.

[0785] A "work process" refers to a series of activities performed in a factory or other work environment to achieve a specific objective.

[0786] The embodiments for carrying out the invention are described below.

[0787] This system has data management and analysis functions to provide information held by educational institutions to companies. The server primarily manages the educational institutions' information centrally and provides an interface for companies to appropriately access that information. Information is uploaded to the system via terminals and organized by the server.

[0788] Furthermore, the server uses machine learning algorithms to analyze information and provide improvement suggestions to educational institutions. Specifically, it analyzes student performance and learning trends based on collected data, generating insights that are useful for designing optimal lecture content and special lectures.

[0789] Emotion recognition utilizes an emotion engine. This engine assesses the user's (student or worker's) emotional state in real time, enabling dynamic adjustment of educational or work processes based on stress levels and comprehension. Physiological data from the user is collected and analyzed using smart glasses and other wearable devices.

[0790] For example, if a user is stressed by a difficult lecture, the server adjusts the pace of the lecture based on the analysis results of the emotion engine, providing information in a more easily understandable format. Similarly, in a factory setting, work processes are adjusted based on the emotional state of the workers to maintain a safe and effective working environment.

[0791] An example of a prompt using a generative AI model is: "Please suggest ways to optimize work processes based on the real-time emotional state of workers in order to improve factory productivity." This allows for further learning and operational efficiency through the integration of emotion recognition and data analysis.

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

[0793] Step 1:

[0794] The terminal uploads information from the educational institution to the system. This includes student learning data and lecture content. The input is educational institution information from the terminal, which the server receives and stores centrally in a database.

[0795] Step 2:

[0796] The server receives information retrieval requests from companies. These requests include requests for specific datasets or analysis results. The server analyzes these requests and filters and extracts the necessary information from the database. The output is a set of information based on the requests.

[0797] Step 3:

[0798] The server uses machine learning algorithms to analyze the collected information. Raw data from the database is used as input, and this data is processed to generate insights necessary for creating useful improvement suggestions and lecture content for educational institutions. The output is the analysis results.

[0799] Step 4:

[0800] The server uses an emotion engine to analyze emotional data from educational institutions in real time. This data includes physiological measurement data obtained from users (students and workers). Based on this, the server evaluates the emotional state and provides an estimated result of the emotional state as output.

[0801] Step 5:

[0802] The server adjusts the lecture content and work process based on the analysis results of the emotion engine. Specifically, if the emotional state is determined to be high stress, the pace of the lecture will be slowed or detailed explanations will be added. The input is the emotion analysis results, and the output is the adjusted lecture content or work process.

[0803] Step 6:

[0804] Users (companies, educators) review the analysis results and lecture adjustments generated by the server and provide feedback as needed. This optimizes the system. The input is the analysis results and adjustments, and the output is the user's feedback.

[0805] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the controlled object 443 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0806] 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.

[0807] 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.

[0808] 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.

[0809] 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.

[0810] 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.

[0811] 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.

[0812] 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.

[0813] 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."

[0814] 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.

[0815] 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.

[0816] 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.

[0817] 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.

[0818] 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.

[0819] 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.

[0820] 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.

[0821] 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.

[0822] 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.

[0823] 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.

[0824] 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.

[0825] 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.

[0826] The following is further disclosed regarding the embodiments described above.

[0827] (Claim 1)

[0828] Means for managing data held by educational institutions,

[0829] A means by which companies can receive requests to obtain data from educational institutions,

[0830] A means of analyzing acquired data using data analysis techniques,

[0831] A means of making improvement suggestions to educational institutions based on the analysis results,

[0832] A means of generating and publishing special lecture information based on the analysis results,

[0833] The means of accepting reservations for lectures,

[0834] A means of managing and disclosing information on unused assets of educational institutions,

[0835] A means of accepting reservations for the use of unused assets,

[0836] A system that includes this.

[0837] (Claim 2)

[0838] The system according to claim 1, which uses a machine learning algorithm as a data analysis technique.

[0839] (Claim 3)

[0840] The system according to claim 1, which notifies educational institutions of data requests sent by companies and provides data only if the educational institutions approve its provision.

[0841] "Example 1"

[0842] (Claim 1)

[0843] Means for managing information assets held by educational institutions,

[0844] Means by which industrial organizations receive requests from educational institutions to acquire information assets,

[0845] A means of analyzing acquired information using information analysis technology,

[0846] A means of making improvement suggestions to educational institutions based on the analysis results,

[0847] A means of generating and publishing special lecture information based on the analysis results,

[0848] A means of accepting reservations for lecture participation,

[0849] A means of managing and disclosing information on unused equipment in educational institutions,

[0850] A means of accepting reservations for the use of unused equipment,

[0851] A means of providing information when a data request is notified to an educational institution and permission is granted,

[0852] A means of managing access permission settings to facilitate information exchange between educational institutions and industrial organizations,

[0853] A means of analyzing information and proposing improvements using machine learning algorithms,

[0854] A means for students to view lecture information and reserve their participation,

[0855] Means by which industrial organizations reserve unused equipment,

[0856] A system that includes this.

[0857] (Claim 2)

[0858] The system according to claim 1, which uses a generative AI model as an information analysis technique.

[0859] (Claim 3)

[0860] The system according to claim 1, which notifies an educational institution of an information request sent by an industrial organization and provides the information only if the educational institution approves its provision.

[0861] "Application Example 1"

[0862] (Claim 1)

[0863] The means of processing information in the field of education,

[0864] A means for an entity to receive requests for information from the education sector,

[0865] A means of processing acquired information using information processing technology,

[0866] A means of making improvement suggestions in the education field based on the processing results,

[0867] A means for generating and publishing special lesson information based on processing results,

[0868] A means of accepting reservations for classes,

[0869] A means of managing and publishing information on unused resources in the field of education,

[0870] A means of accepting reservations for the use of unused resources,

[0871] A means by which a business can reserve unused resources in the educational field using communication equipment,

[0872] A system that includes this.

[0873] (Claim 2)

[0874] The system according to claim 1, which uses a machine learning algorithm as an information processing technique.

[0875] (Claim 3)

[0876] The system according to claim 1, which notifies the education sector of a request for information transmitted by an entity and provides the information only if the education sector approves its provision.

[0877] "Example 2 of combining an emotion engine"

[0878] (Claim 1)

[0879] Means for managing information held by educational institutions,

[0880] Means by which companies can receive requests for information from educational institutions,

[0881] A means of analyzing acquired information using data analysis technology,

[0882] A means of making improvement suggestions to educational institutions based on the analysis results,

[0883] A means of generating and publishing special lecture information based on the analysis results,

[0884] A means of detecting students' emotional states and optimizing lecture content,

[0885] The means of accepting reservations for lectures,

[0886] A means of managing and disclosing information on unused assets of educational institutions,

[0887] A means of accepting reservations for the use of unused assets,

[0888] A system that includes this.

[0889] (Claim 2)

[0890] The system according to claim 1, which uses a machine learning algorithm as a data analysis technique.

[0891] (Claim 3)

[0892] The system according to claim 1, which notifies an educational institution of a request for information sent by a company and provides the information only if the educational institution approves its provision.

[0893] "Application example 2 when combining with an emotional engine"

[0894] (Claim 1)

[0895] Means for managing information held by educational institutions,

[0896] Means by which companies can receive requests for information from educational institutions,

[0897] A means of analyzing acquired information using data analysis technology,

[0898] A means of making improvement suggestions to educational institutions based on the analysis results,

[0899] A means of generating and publishing special lecture information based on the analysis results,

[0900] The means of accepting reservations for lectures,

[0901] A means of managing and disclosing information on unused assets of educational institutions,

[0902] A means of accepting reservations for the use of unused assets,

[0903] A means of incorporating an emotion engine that detects the emotional state of workers,

[0904] A means of dynamically adjusting the work process based on emotional state,

[0905] A system that includes this.

[0906] (Claim 2)

[0907] The system according to claim 1, which uses a machine learning algorithm as a data analysis technique.

[0908] (Claim 3)

[0909] The system according to claim 1, which notifies an educational institution of a request for information sent by a company and provides the information only if the educational institution approves its provision. [Explanation of Symbols]

[0910] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots< / url:> < / url:> < / url:> < / url:>

Claims

1. Means for managing data held by educational institutions, A means by which companies can receive requests to obtain data from educational institutions, A means of analyzing acquired data using data analysis techniques, A means of making improvement suggestions to educational institutions based on the analysis results, A means of generating and publishing special lecture information based on the analysis results, The means of accepting reservations for lectures, A means of managing and disclosing information on unused assets of educational institutions, A means of accepting reservations for the use of unused assets, A system that includes this.

2. The system according to claim 1, which uses a machine learning algorithm as a data analysis technique.

3. The system according to claim 1, which notifies educational institutions of data requests sent by companies and provides data only if the educational institutions approve its provision.

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