system

Abstract

Provide a system. 【Solution means】 A computing device for improving the operational efficiency of an educational institution, means for collecting schedule data on teachers and the educational environment, means for analyzing the schedule data and automatically generating a class schedule, means for obtaining budget data, analyzing it, and proposing a fund allocation, means for managing equipment utilization information and proposing an optimal equipment layout, means for optimizing placement using the skill information of staff, means for visualizing operation data and supporting management decisions, A system including.

Description

Technical Field

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

Background Art

[0002] Patent Document 1 discloses a persona chatbot control method performed by at least one processor, including steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to an explanation of a chatbot character, encoding the prompt, and inputting the encoded prompt into a language model to generate a chatbot utterance 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 the operation of educational institutions, it is necessary to efficiently perform a wide variety of tasks such as creating class schedules, budget management, efficient use of facilities, and appropriate allocation of staff. To achieve this, inefficiencies and errors in operations occur due to limitations in human resources, skill biases, and complex data management. To improve such a situation, an integrated system for improving and optimizing operations is required.

Means for Solving the Problems

[0005] This invention provides a computing device that streamlines and optimizes the operations of educational institutions. Specifically, it collects schedule data related to teachers and the educational environment, analyzes it, and automatically generates an optimal timetable. It also acquires and analyzes budget data and proposes funding allocation to support effective budget management. Furthermore, by managing equipment usage information, it proposes the optimal placement of equipment and optimizes placement based on staff skill information. By visualizing operational data, it supports management decisions and can significantly improve the operational efficiency of educational institutions.

[0006] An "educational institution" is an organization whose purpose is to provide education and academic instruction, and includes schools, universities, and other similar institutions.

[0007] "Business operations" refers to the entirety of the activities and management processes that an organization or institution undertakes to achieve its objectives.

[0008] A "computational device" is an electronic device that analyzes specific data and provides specific functions based on the results of that analysis.

[0009] "Schedule data" is a collection of information that describes the temporal arrangement of a particular event or activity.

[0010] A "timetable" is a table that shows the allocation of time and place for classes and activities offered over a specific period.

[0011] "Budget data" refers to a plan or record of the income and expenditure of funds over a specific period.

[0012] "Equipment usage information" refers to data regarding the usage status and condition of facilities and equipment.

[0013] "Employees" refer to people who are employed by a specific organization or institution and engage in work for that organization.

[0014] "Operational data" refers to information collected and analyzed for the efficient operation and management of an organization.

[0015] "Business judgment" refers to the decision-making process for determining the operating policies and strategies of an organization or enterprise.

Brief Description of Drawings

[0016] [Figure 1] It is a conceptual diagram showing an example of the configuration of a data processing system according to the first embodiment. [Figure 2] It is a conceptual diagram showing an example of the main functions of a data processing device and a smart device according to the first embodiment. [Figure 3] It is a conceptual diagram showing an example of the configuration of a data processing system according to the second embodiment. [Figure 4] It is a conceptual diagram showing an example of the main functions of a data processing device and smart glasses according to the second embodiment. [Figure 5] It is a conceptual diagram showing an example of the configuration of a data processing system according to the third embodiment. [Figure 6] It is a conceptual diagram showing an example of the main functions of a data processing device and a headset-type terminal according to the third embodiment. [Figure 7] It is a conceptual diagram showing an example of the configuration of a data processing system according to the fourth embodiment. [Figure 8] It is a conceptual diagram showing an example of the main functions of a data processing device and a robot according to the fourth embodiment. [Figure 9] It shows an emotion map to which a plurality of emotions are mapped. [Figure 10] It shows an emotion map to which a plurality of emotions are mapped. [Figure 11] It is a sequence diagram showing the processing flow of the data processing system in Example 1. [Figure 12] It is a sequence diagram showing the processing flow of the data processing system in Application Example 1. [Figure 13] It is a sequence diagram showing the processing flow of the data processing system in Example 2 when an emotion engine is combined. [Figure 14]It is a sequence diagram showing the processing flow of a data processing system in Application Example 2 when a sentiment engine is combined.

Embodiments for Carrying out the Invention

[0017] Hereinafter, an example of an embodiment of a system according to the technology of the present disclosure will be described with reference to the accompanying drawings.

[0018] First, the terms used in the following description will be explained.

[0019] In the following embodiments, a numbered processor (hereinafter simply referred to as "processor") may be a single arithmetic unit or a combination of multiple arithmetic units. Also, the processor may be a single type of arithmetic unit or a combination of multiple types of arithmetic units. Examples of arithmetic units include a CPU (Central Processing Unit), a GPU (Graphics Processing Unit), a GPGPU (General-Purpose computing on Graphics Processing Units), an APU (Accelerated Processing Unit), and the like.

[0020] In the following embodiments, a numbered RAM (Random Access Memory) is a memory in which information is temporarily stored and is used as a work memory by the processor.

[0021] In the following embodiments, a numbered storage is one or more non-volatile storage devices that store various programs and various parameters, etc. Examples of non-volatile storage devices include flash memory (SSD (Solid State Drive)), magnetic disks (e.g., hard disks), or magnetic tapes, etc.

[0022] 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).

[0023] 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."

[0024] [First Embodiment]

[0025] Figure 1 shows an example of the configuration of the data processing system 10 according to the first embodiment.

[0026] 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.

[0027] 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).

[0028] 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.

[0029] 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.

[0030] 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.

[0031] 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.

[0032] Figure 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0033] 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.

[0034] 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.

[0035] 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.

[0036] 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".

[0037] This invention is a system for streamlining the operations of educational institutions and is implemented as follows.

[0038] First, the terminal provides an interface for inputting schedule data for teachers and facilities at educational institutions. Here, information such as teachers' availability, class hours, and classroom reservation status is entered.

[0039] Next, the server analyzes the entered schedule data and automatically generates an optimal timetable based on it. This generated timetable is designed to equalize the workload of teachers and maximize classroom utilization.

[0040] The terminal also provides a function for managing budget data, allowing users to input annual budget plans and past expenditure history. The server uses this data to automatically propose an efficient budget allocation. This proposal includes specific adjustments for any shortfalls or surpluses.

[0041] Furthermore, terminals update the usage status of school facilities in real time, allowing the server to suggest the optimal placement of equipment. Examples include reviewing the placement of frequently used experimental equipment and AV devices.

[0042] Regarding staff allocation, the server analyzes each teacher's skill information and assigns the most suitable teacher to each class. In this process, the allocation is optimized by considering the teacher's past performance evaluations and area of ​​expertise.

[0043] Finally, the server aggregates operational data and provides a dashboard for visualization. Here, users can check the operational status in real time and use this information to inform management decisions. For example, it can display graphs showing fluctuations in attendance rates and budget utilization rates by year.

[0044] This allows for centralized management and increased efficiency of complex workflows within educational institutions. Users can then operate more effectively through this system.

[0045] The following describes the processing flow.

[0046] Step 1:

[0047] The terminal provides users with an interface for entering teachers' availability and classroom reservation status. Users enter teachers' schedules and preferred class times.

[0048] Step 2:

[0049] The server stores the schedule data entered from the terminal into a database and begins analysis. This analysis checks for schedule conflicts and classroom availability.

[0050] Step 3:

[0051] The server sets constraints based on the analysis results and executes an algorithm to generate an optimal timetable. This algorithm is designed to equalize the workload of each teacher and maximize classroom utilization.

[0052] Step 4:

[0053] The server proposes a generated timetable to the user, who then reviews it and makes corrections via their terminal if necessary.

[0054] Step 5:

[0055] Budget data is entered via a terminal. Users record their annual budget and expenditure history.

[0056] Step 6:

[0057] The server analyzes budget data and proposes efficient budget allocation. Based on past expenditure data, it estimates future expenditures and generates budget adjustment proposals accordingly.

[0058] Step 7:

[0059] To monitor the status of facilities within the school, the terminals collect facility usage information in real time and send it to the server.

[0060] Step 8:

[0061] The server analyzes equipment usage patterns and proposes optimal placement and maintenance schedules. These proposals concern the relocation or additional purchase of frequently used equipment.

[0062] Step 9:

[0063] The server analyzes staff skills and usage data to determine the optimal placement of teachers and staff. This placement takes into account each individual's abilities and workload.

[0064] Step 10:

[0065] All data is aggregated, and the server builds a dashboard for users. Users can use this to monitor operational status and make business decisions.

[0066] (Example 1)

[0067] 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."

[0068] The operations of educational institutions are extremely complex and multifaceted, encompassing tasks such as managing faculty and staff schedules and budgets, optimizing equipment utilization, and optimizing staff allocation. Therefore, it is essential to efficiently manage these various tasks, reduce the burden on faculty and staff, and ensure optimal resource utilization. However, managing all of these tasks manually is extremely difficult and can result in inefficiencies and errors. Consequently, a system is needed to comprehensively manage the operations of educational institutions in order to efficiently address these challenges.

[0069] 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.

[0070] In this invention, the server includes means for collecting timetable information relating to teachers and educational facilities, means for analyzing the timetable information and automatically structuring learning activities, and means for acquiring and processing resource allocation data and proposing usage plans. This enables centralized management of the operations of educational institutions, maximizing resource utilization and improving operational efficiency.

[0071] An "information processing device" is a device that includes hardware and software for collecting, analyzing, and managing data, and for improving the efficiency and optimization of specific tasks.

[0072] "Timetable information" refers to data related to the schedules of teachers and educational institutions, and includes information such as class times, locations, and instructors.

[0073] "Means for structuring learning activities" refers to a program or system that analyzes timetable information and automatically creates optimal lesson schedules and educational activities.

[0074] "Resource allocation data" refers to records of the current allocation and utilization plans of financial, physical, and human resources, and is used to propose optimal resource management strategies.

[0075] "Means for proposing resource utilization plans" refers to a system or algorithm that analyzes collected resource allocation data and automatically proposes plans for efficient future resource use.

[0076] "Resource usage information" refers to data on the usage history and current status of classrooms, equipment, and other resources used within educational institutions.

[0077] "Workforce allocation optimization" refers to a process that automatically determines the optimal job assignment by considering employee skills, performance evaluations, and workload.

[0078] "Operational information" refers to information that aggregates various data related to the operational activities of educational institutions and is used to aid in decision-making.

[0079] This invention constitutes an information processing device for efficiently managing the operations of educational institutions. Specifically, it is a system that uses servers and terminals to manage the schedules, budgets, facilities, and staffing of teachers and educational facilities.

[0080] First, users input teacher and facility timetable information using a device. These devices include personal computers and tablets, and the data is transmitted to a server via the internet. This timetable information includes class start and end times, classroom location, and assigned teacher.

[0081] Next, the server analyzes the timetable information and configures optimal learning activities. During this process, the server can process data using analytical tools such as Python or R, and can also utilize generative AI models. This helps to equalize the workload on teachers and optimize the use of classrooms and facilities.

[0082] Furthermore, users input resource allocation data through their devices. This includes annual budget plans and past usage history. The server processes this data and proposes efficient resource utilization plans for the future. This proposal may utilize machine learning algorithms powered by Google® Cloud Platform.

[0083] The server also analyzes resource usage information and suggests optimal equipment placement. Based on real-time equipment usage data updated from terminals, it proposes revising the placement of specific equipment and devices. For example, it might suggest moving or rearranging AV equipment or laboratory instruments based on their usage.

[0084] Regarding staff assignments, the server analyzes staff competency information and automatically determines the most suitable assignment for each class or task. Past performance data and skill information of staff members are considered to ensure optimal matching.

[0085] The server ultimately provides a visualized version of operational information. This allows users to view the educational institution's operations in real time on a dashboard and make effective critical decisions. This dashboard is built using visualization tools such as Tableau and Power BI, and key metrics such as attendance rates and budget utilization rates are displayed in graph format.

[0086] As a concrete example, consider a scenario for optimizing the timetable and budget for a summer intensive course at an educational institution. Examples of prompts include the following:

[0087] "Please propose an optimal timetable and budget management strategy for summer intensive courses at educational institutions. Considering teacher availability, classroom availability, and previous year's budget data, generate a report that supports efficient course management."

[0088] This invention enables educational institutions to improve operational efficiency and make effective use of resources, thereby contributing to an improvement in the quality of education.

[0089] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0090] Step 1:

[0091] Users input schedule information for educational institutions and teachers using their devices. This input saves the timetable information to a database. Specifically, users enter details such as class time, location, and teacher into a form on a web browser and click the submit button. The entered data is then sent to the server in JSON format and written to the database.

[0092] Step 2:

[0093] The server retrieves timetable information from the database, applies data analysis algorithms, and automatically constructs learning activities. The input is timetable information from the database, and the output is an optimized class schedule. Specifically, the server executes Python scripts to perform calculations to avoid schedule conflicts and equalize the workload of teachers. Using a generative AI model, it is possible to suggest even better schedules.

[0094] Step 3:

[0095] Users input resource allocation data from their terminals. This includes annual budget plans and past expenditure history. This information is sent to a server and stored in a database. Specifically, users input budget data using a dedicated application and upload the data to the server by performing a "submit" operation.

[0096] Step 4:

[0097] The server receives resource allocation data and uses machine learning algorithms to generate an efficient resource utilization plan. The input is resource allocation data, and the output is the proposed resource utilization plan. Specifically, the server predicts future budget allocations based on historical data, generates a report to determine the optimal allocation, and sends it to the terminal.

[0098] Step 5:

[0099] The terminal updates equipment usage information in real time. Users input the usage status of various equipment within the facility, and this information is sent to the server. Specifically, a QR code (registered trademark) reader or barcode scanner is used to scan equipment information and input the usage status.

[0100] Step 6:

[0101] The server analyzes the received equipment usage information and proposes the optimal equipment layout. The input is equipment usage information, and the output is the optimization proposal. Specifically, the server reviews the layout of frequently used equipment based on the received data, creates a document proposing relocation if necessary, and provides it to the terminal.

[0102] Step 7:

[0103] The server optimizes the allocation of staff to classes and tasks based on their competency information. The input is staff competency information, and the output is an optimal staff allocation plan. Specifically, the server analyzes existing staff data and automatically assigns the most suitable personnel to each subject and task.

[0104] Step 8:

[0105] The server visualizes operational information and provides a dashboard that users can check in real time. The input is operational information, and the output is visualized operational indicators. Specifically, the server uses visualization tools to graph attendance rates and budget usage, making this information easily accessible to users.

[0106] (Application Example 1)

[0107] 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."

[0108] Efficient operational management is a crucial challenge for modern educational institutions. In particular, tasks such as managing teacher schedules, creating timetables, budget management, equipment allocation, and appropriate staffing are complex, and performing these tasks manually is time-consuming, labor-intensive, and inefficient. Furthermore, real-time data updates and user-friendly operation are essential. However, currently, there is a lack of systems that comprehensively address these challenges, hindering the efficient operation of educational institutions.

[0109] 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.

[0110] In this invention, the server includes means for collecting planning data related to educators and educational settings, means for analyzing the planning data and automatically generating a teaching schedule, and means for acquiring and analyzing financial data and proposing funding allocations. This enables centralized management of educational institution operations and improves operational efficiency.

[0111] "Education-related personnel" refers to teachers, staff, or individuals engaged in related work at educational institutions.

[0112] "Educational setting" refers to the environment in which education is conducted and the conditions for it, including facilities such as classrooms and laboratories, and their usage.

[0113] "Planning data" refers to information related to the operational activities of an educational institution, such as its schedule, resource allocation, and budget.

[0114] A "teaching schedule" refers to a schedule that includes the timetable for classes and related activities.

[0115] "Financial data" refers to financial information, including budget plans and historical expenditure records, for educational institutions.

[0116] "Proposing a fund allocation" refers to the act of presenting an efficient budget allocation plan based on the analysis of financial data.

[0117] "Equipment usage information" refers to data on the usage status of equipment and supplies used within educational facilities.

[0118] "User interface" refers to the design of the operating screens and input devices that allow users to interact with an information system.

[0119] "Real-time data updates" refers to a state where information within a system is updated instantly without delay.

[0120] To realize this invention, an integrated system will be constructed using multiple terminals and a server to streamline the work of educators. The terminals will provide an operation screen for teachers and staff to input planning data. This includes entering schedules, reserving facilities, entering and confirming budgets, and updating equipment usage status. The server will aggregate this data and analyze it in real time. Specifically, it will analyze the planning data to generate an optimal teaching schedule and propose budget allocations based on past funding data. It will also analyze equipment usage information and propose optimal placement.

[0121] The hardware of this system includes a high-performance computer as the server and tablets or PCs as terminals. The software consists of a database management system and development tools for building the user interface. For example, the server retrieves information from the database and performs data analysis using Python or a similar programming language. The results are then visualized using a dashboard tool and delivered to terminals in real time.

[0122] As a concrete example, in one school, when creating the timetable for the new semester, teachers' free time is entered into a server. The server uses a generation AI model to distribute teachers' teaching schedules as evenly as possible and presents the resulting timetable to the principal. The principal checks via a terminal to see if any improvements are needed and then finalizes the schedule. Similarly, in budget management, the system proposes the optimal budget allocation based on past expenditure data, and the management team performs a final check.

[0123] An example of a prompt for a generative AI model is as follows: "Please input the available time slots of teachers at an educational institution and propose an optimal timetable. Please ensure that classroom utilization and teacher workload are evenly distributed."

[0124] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0125] Step 1:

[0126] Users use their devices to input data on the availability of educators and planning data related to lesson settings. This data includes teacher schedules, class times, and classroom booking status. This allows for a comprehensive overview of the entire educational institution's schedule.

[0127] Step 2:

[0128] The server receives planning data sent from the terminal and stores it in a database. Then, it analyzes this data using a generative AI model to generate an optimal teaching schedule. Based on the input data, it adjusts schedules to distribute the workload evenly among teachers and maximizes the availability of empty classrooms.

[0129] Step 3:

[0130] The server analyzes historically recorded financial data, including school annual budget plans and actual expenditure history. The server analyzes this data and uses a generative AI model to propose optimal future budget allocations. It calculates how much funding should be allocated to each area and outputs predictions.

[0131] Step 4:

[0132] The server monitors equipment usage and proposes the optimal placement of equipment. This is based on data on how frequently each piece of experimental equipment and AV device is used. Based on past and current usage, it suggests which equipment should be placed where for maximum efficiency and outputs a specific layout plan.

[0133] Step 5:

[0134] Users can review academic schedules, budgets, and equipment layout plans sent from the server on their devices. The user interface is updated in real time, allowing them to intuitively view this information and make corrections or approvals as needed.

[0135] Through the above process, the complex tasks involved in the operation of educational institutions are centralized, enabling efficient management.

[0136] 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.

[0137] This invention combines an emotion engine with a system that streamlines the operations of educational institutions, thereby recognizing user emotions and reflecting them in improvements to operations.

[0138] First, the terminal provides an interface for inputting teachers' availability, teaching preferences, and classroom reservation status. Users input the necessary data here, which forms the basis of the system. Budget, equipment usage, and staff skill information are also entered in the same way.

[0139] Next, the server analyzes the schedule and budget data obtained from the terminal and uses it for automatic timetable generation and budget allocation suggestions. In particular, at this stage, the emotion engine senses how the user is interacting with the system and analyzes their emotional state. For example, it determines how satisfied or dissatisfied the user is with the timetable generation suggestions.

[0140] Furthermore, the emotion engine revises automatically generated schedules and budget proposals to fine-tune the system's suggestions based on user feedback. For example, if a user is stressed by a particular placement suggestion, the server will focus on providing less burdensome alternatives based on emotional data.

[0141] The server aggregates operational data from educational institutions and related emotional states, then provides a visualized dashboard. This allows users to understand the health and efficiency of operations in real time, while also identifying areas for improvement based on emotional feedback.

[0142] In this way, the operation of educational institutions is carried out not only in terms of efficiency, but also in terms of considering human factors. This system can provide a more user-friendly environment and improve the overall quality of operations.

[0143] The following describes the processing flow.

[0144] Step 1:

[0145] The terminal provides users with an interface to input teacher schedules, class preferences, and classroom reservation status. Users enter this information, and the terminal sends it to a database.

[0146] Step 2:

[0147] The server receives schedule data sent from the terminal and stores it in the database. It then analyzes the received data and begins calculations to generate the optimal timetable.

[0148] Step 3:

[0149] Based on budget information entered by the user and past spending history, the server generates suggestions to optimize budget allocation. This includes comparing the current budget to the annual budget plan and analyzing the differences.

[0150] Step 4:

[0151] Equipment usage is updated in real time via terminals, and the server uses this information to calculate the optimal placement of equipment. This makes it possible to propose equipment solutions that support efficient school management.

[0152] Step 5:

[0153] Based on employee skill information, the server applies an algorithm to optimize employee placement. This ensures that each employee is placed in the right position for their skills.

[0154] Step 6:

[0155] When a user responds to these suggestions, the device sends user input and operation logs to the sentiment engine in real time.

[0156] Step 7:

[0157] The emotion engine installed on the server analyzes user actions and estimates the emotions the user feels towards the suggestions. Based on this analysis, the suggestions are fine-tuned.

[0158] Step 8:

[0159] The server presents the user with a final proposal that incorporates emotional metrics. At this stage, the proposal is optimized according to the user's emotional state.

[0160] Step 9:

[0161] Users can view all data through a dashboard and make decisions based on operational metrics, including emotional feedback. This makes it possible to support strategic management decisions.

[0162] (Example 2)

[0163] 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".

[0164] Operating in educational institutions involves a complex interplay of factors such as scheduling, budget allocation, and equipment utilization, making efficient management difficult. Furthermore, traditional management systems struggle to integrate and utilize this data for proposals and adjustments, particularly those that consider human factors. Therefore, there is a need for a system that reduces the burden on educators and improves operational efficiency.

[0165] 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.

[0166] In this invention, the server includes means for collecting time information and budget status related to education, means for acquiring and analyzing user emotional data using emotion analysis technology and reflecting it in the proposed content, and means for adjusting the generated educational schedule and funding allocation proposals based on the user's emotional feedback. This enables not only efficient operation but also flexible proposals and adjustments that take into account the user's emotions and satisfaction.

[0167] An "information processing device" is a combination of hardware and software used to collect, analyze, and provide suggestions to users based on data.

[0168] "Time information" refers to data related to time, such as the availability dates and times of faculty, staff, and facilities at educational institutions, as well as preferred class schedules.

[0169] "Budget status" refers to information that influences how educational institutions utilize their resources and how they allocate funds in the future.

[0170] "Emotional analysis technology" is an analytical method that recognizes the emotions of users and reflects them in improving the content of suggestions.

[0171] "Emotional feedback" refers to the emotional responses provided by users to the proposed content, such as satisfaction or dissatisfaction.

[0172] "Schedule" refers to the arrangement of class times and work schedules within educational institutions.

[0173] "Fund allocation" refers to plans and proposals for effectively allocating the budget to each item.

[0174] This invention is implemented by utilizing an information processing device to streamline the operations of educational institutions and to manage multiple elements in an integrated manner. Specific embodiments are shown below.

[0175] The terminal provides an interface for faculty and staff to input time information, budget status, and equipment usage information related to education. This allows users to easily register various types of data into the system. The terminal functions as a front-end for data entry, and the data is sent to the server.

[0176] The server functions as a center for analyzing received data, automatically generating educational schedules and proposing funding allocations. It utilizes a generative AI model to provide efficient and effective suggestions. Furthermore, the server employs sentiment analysis technology to collect user emotional feedback and incorporate it into the suggestions, seeking the optimal approach for each user. Natural language processing and machine learning techniques are used in this process.

[0177] As a concrete example, consider a case where a teacher requests three classes per week and seeks the most efficient use of equipment within their budget. The server automatically generates a proposal for the optimal schedule and funding allocation based on the input time information and budget data, and evaluates the user's response to it using sentiment analysis technology. Based on the satisfaction level, it then generates improvement suggestions.

[0178] As a concrete example of a prompt message for the generating AI model, a request such as, "Consider available classrooms on Monday and Wednesday afternoons and propose an efficient class schedule. Please make adjustments based on teacher satisfaction," can be set.

[0179] In this way, the present invention is a system that streamlines the operations of educational institutions while enabling a human-centered approach.

[0180] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0181] Step 1:

[0182] Users input faculty and staff availability, teaching preferences, equipment usage, and budget information through the terminal's interface. The entered data is organized by the terminal and sent to the server. This collects the basic data necessary for the operation of educational institutions.

[0183] Step 2:

[0184] The server analyzes time information and budget status received from the terminal. The generative AI model used here searches for efficient combinations of schedules and generates proposals for optimal class schedules and budget allocations. Based on the input data, the AI ​​model performs data calculations and presents the optimal schedule proposal as output.

[0185] Step 3:

[0186] The server uses sentiment analysis technology to analyze user reactions to the generated schedule and budget proposals. It acquires emotional feedback from users towards the system as data and analyzes it to evaluate satisfaction levels and dissatisfaction with the proposals.

[0187] Step 4:

[0188] The server fine-tunes the schedule and budget proposals based on the results of sentiment analysis. Specifically, it generates and presents alternatives and improvements to areas where the user expressed dissatisfaction. Data processing ensures that emotional feedback is reflected in the proposals.

[0189] Step 5:

[0190] The server aggregates the final operational data and provides a visualized dashboard. Through this dashboard, users can understand the efficiency and health of operations in real time and use this information to improve their work. The server integrates the analysis results and sentiment data to generate visual information as output and provides it to the user.

[0191] (Application Example 2)

[0192] 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".

[0193] Educational institutions need to improve operational efficiency while simultaneously reducing the emotional burden on teachers and administrators. However, conventional systems fail to consider emotional responses to schedule management and resource allocation, resulting in suboptimal suggestions and accumulated dissatisfaction. This invention aims to solve these problems.

[0194] 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.

[0195] In this invention, the server includes means for analyzing schedule data and automatically generating a timetable, means for recognizing emotional states and adjusting the generated suggestions based on user feedback, and means for visualizing operational data to assist in management decisions. This makes it possible to maximize the operational efficiency of educational institutions, reduce the burden on personnel, and provide users with a superior experience.

[0196] An "information processing system" is a computer-based system designed to collect, analyze, manage, and display data.

[0197] "Employee" is a term that refers to teachers and staff who perform duties at an educational institution.

[0198] "Educational facilities" refer to physical or virtual environments for conducting educational activities, including, for example, classrooms and online platforms.

[0199] A "timetable" is a table that lists the schedule of activities and events over a specific period in chronological order.

[0200] A "financial allocation proposal" refers to a specific proposal for the most effective allocation of available funds.

[0201] "Resource allocation" refers to a plan for optimizing the allocation of resources such as personnel, equipment, and time within an educational institution.

[0202] "Competency information" refers to information about the skills and expertise possessed by employees.

[0203] "Operational data" refers to various data related to the operational management of educational institutions, including schedules and resource utilization.

[0204] "Emotional state" refers to the psychological reactions and feedback that users experience while using the system.

[0205] "Feedback" refers to the reactions and opinions that users provide regarding the results of using the system and their suggestions.

[0206] The system for implementing the present invention mainly uses an information processing device (hereinafter referred to as "server") and terminal equipment. The server provides a mechanism to streamline the operation of educational institutions based on various data it receives. Users (teachers and administrators) input schedules, budgets, and facility information through terminals.

[0207] The server automatically generates a timetable based on the input schedule data. This is done using data analysis algorithms and calculations. The generated timetable can be adjusted according to the user's emotional state. This adjustment involves a software module called the emotion engine, which is achieved by analyzing user feedback.

[0208] The server also retrieves budget data and proposes financial allocations. This uses algorithms that analyze past usage trends. Furthermore, the server manages equipment usage information and presents optimal resource allocation plans. This process also takes into account employee competence information.

[0209] As a concrete example, if a teacher at an educational institution is dissatisfied with the class schedule, the server recognizes this emotional state and displays more appropriate alternatives for the user. This mechanism is made possible by using the Python language and an emotion analysis API.

[0210] Users can receive information processing in a visualized form, and a dashboard is provided to assist in management decisions. In this way, through an implementation that promotes operational improvements, educational institutions can achieve efficient and less burdensome operations.

[0211] An example of a prompt message might be: "Analyze the following feedback, assess the user's emotional state, and offer suggestions for stress reduction if necessary."

[0212] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0213] Step 1:

[0214] The terminal provides the user with an interface for various data inputs. Through this interface, the user inputs schedule information, budget data, equipment usage information, and staff competency information. The input in this step consists of various data provided by the user, while the output is an organized dataset stored in a database. The data is converted to an appropriate format and sent to the server.

[0215] Step 2:

[0216] The server generates a timetable based on the received schedule data. It processes the input data using a data analysis algorithm to create an appropriate timetable. The input here is the schedule data obtained in step 1, and the output is the generated timetable. The generated timetable is used to evaluate the sentiment engine.

[0217] Step 3:

[0218] The server uses an emotion engine to analyze the user's emotional state from their feedback. The input is the target feedback data, which the emotion engine processes to quantify the emotional state. The output is the emotional analysis result, providing a numerical evaluation board for a specific emotional state. Based on this result, the server instructs the user to make necessary adjustments.

[0219] Step 4:

[0220] The server optimizes the timetable and budget proposal based on the analyzed emotional state. The inputs are the timetable obtained in step 2 and the emotional state data from step 3. The output is an optimized proposal that reduces the user's burden. Specifically, this involves reorganizing the timetable and adjusting the budget allocation.

[0221] Step 5:

[0222] The server provides administrators and faculty with optimized suggestions in a visualized format. The input is the suggestion information generated in step 4, and the output is a visual dashboard. The dashboard is designed to be easily understood by users using a graphical user interface.

[0223] Step 6:

[0224] The user reviews the presented dashboard and makes operational decisions. The input is the dashboard information provided by the server, and the output is the improved decision-making process. Based on the information built on the dashboard, the user adopts specific operational improvement measures.

[0225] 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.

[0226] 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.

[0227] 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.

[0228] [Second Embodiment]

[0229] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0230] 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.

[0231] 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).

[0232] 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.

[0233] 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.

[0234] 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).

[0235] 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.

[0236] 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.

[0237] 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.

[0238] 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.

[0239] 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.

[0240] 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".

[0241] This invention is a system for streamlining the operations of educational institutions and is implemented as follows.

[0242] First, the terminal provides an interface for inputting schedule data for teachers and facilities at educational institutions. Here, information such as teachers' availability, class hours, and classroom reservation status is entered.

[0243] Next, the server analyzes the entered schedule data and automatically generates an optimal timetable based on it. This generated timetable is designed to equalize the workload of teachers and maximize classroom utilization.

[0244] The terminal also provides a function for managing budget data, allowing users to input annual budget plans and past expenditure history. The server uses this data to automatically propose an efficient budget allocation. This proposal includes specific adjustments for any shortfalls or surpluses.

[0245] Furthermore, terminals update the usage status of school facilities in real time, allowing the server to suggest the optimal placement of equipment. Examples include reviewing the placement of frequently used experimental equipment and AV devices.

[0246] Regarding staff allocation, the server analyzes each teacher's skill information and assigns the most suitable teacher to each class. In this process, the allocation is optimized by considering the teacher's past performance evaluations and area of ​​expertise.

[0247] Finally, the server aggregates operational data and provides a dashboard for visualization. Here, users can check the operational status in real time and use this information to inform management decisions. For example, it can display graphs showing fluctuations in attendance rates and budget utilization rates by year.

[0248] This allows for centralized management and increased efficiency of complex workflows within educational institutions. Users can then operate more effectively through this system.

[0249] The following describes the processing flow.

[0250] Step 1:

[0251] The terminal provides users with an interface for entering teachers' availability and classroom reservation status. Users enter teachers' schedules and preferred class times.

[0252] Step 2:

[0253] The server stores the schedule data entered from the terminal into a database and begins analysis. This analysis checks for schedule conflicts and classroom availability.

[0254] Step 3:

[0255] The server sets constraints based on the analysis results and executes an algorithm to generate an optimal timetable. This algorithm is designed to equalize the workload of each teacher and maximize classroom utilization.

[0256] Step 4:

[0257] The server proposes a generated timetable to the user, who then reviews it and makes corrections via their terminal if necessary.

[0258] Step 5:

[0259] Budget data is entered via a terminal. Users record their annual budget and expenditure history.

[0260] Step 6:

[0261] The server analyzes budget data and proposes efficient budget allocation. Based on past expenditure data, it estimates future expenditures and generates budget adjustment proposals accordingly.

[0262] Step 7:

[0263] To monitor the status of facilities within the school, the terminals collect facility usage information in real time and send it to the server.

[0264] Step 8:

[0265] The server analyzes equipment usage patterns and proposes optimal placement and maintenance schedules. These proposals concern the relocation or additional purchase of frequently used equipment.

[0266] Step 9:

[0267] The server analyzes staff skills and usage data to determine the optimal placement of teachers and staff. This placement takes into account each individual's abilities and workload.

[0268] Step 10:

[0269] All data is aggregated, and the server builds a dashboard for users. Users can use this to monitor operational status and make business decisions.

[0270] (Example 1)

[0271] 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."

[0272] The operations of educational institutions are extremely complex and multifaceted, encompassing tasks such as managing faculty and staff schedules and budgets, optimizing equipment utilization, and optimizing staff allocation. Therefore, it is essential to efficiently manage these various tasks, reduce the burden on faculty and staff, and ensure optimal resource utilization. However, managing all of these tasks manually is extremely difficult and can result in inefficiencies and errors. Consequently, a system is needed to comprehensively manage the operations of educational institutions in order to efficiently address these challenges.

[0273] 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.

[0274] In this invention, the server includes means for collecting timetable information relating to teachers and educational facilities, means for analyzing the timetable information and automatically structuring learning activities, and means for acquiring and processing resource allocation data and proposing usage plans. This enables centralized management of the operations of educational institutions, maximizing resource utilization and improving operational efficiency.

[0275] An "information processing device" is a device that includes hardware and software for collecting, analyzing, and managing data, and for improving the efficiency and optimization of specific tasks.

[0276] "Timetable information" refers to data related to the schedules of teachers and educational institutions, and includes information such as class times, locations, and instructors.

[0277] "Means for structuring learning activities" refers to a program or system that analyzes timetable information and automatically creates optimal lesson schedules and educational activities.

[0278] "Resource allocation data" refers to records of the current allocation and utilization plans of financial, physical, and human resources, and is used to propose optimal resource management strategies.

[0279] "Means for proposing resource utilization plans" refers to a system or algorithm that analyzes collected resource allocation data and automatically proposes plans for efficient future resource use.

[0280] "Resource usage information" refers to data on the usage history and current status of classrooms, equipment, and other resources used within educational institutions.

[0281] "Workforce allocation optimization" refers to a process that automatically determines the optimal job assignment by considering employee skills, performance evaluations, and workload.

[0282] "Operational information" refers to information that aggregates various data related to the operational activities of educational institutions and is used to aid in decision-making.

[0283] This invention constitutes an information processing device for efficiently managing the operations of educational institutions. Specifically, it is a system that uses servers and terminals to manage the schedules, budgets, facilities, and staffing of teachers and educational facilities.

[0284] First, the user uses the terminal to input the class schedule information of teachers and facilities. Personal computers and tablets are used as the terminal, and the data is sent to the server through the Internet. This class schedule information includes the start time and end time of classes, the location of classrooms, the teachers in charge, etc.

[0285] Next, the server analyzes the class schedule information and constructs optimal learning activities. At this time, the server can process the data using analysis tools such as Python and R, and it is also possible to use a generated AI model. This can equalize the burden on teachers and optimize the use of classrooms and facilities.

[0286] Furthermore, the user inputs resource allocation data through the terminal. This includes the annual budget plan and past usage history. The server processes this data and proposes an efficient resource utilization plan for the future. In this proposal, machine learning algorithms using Google Cloud Platform may also be used.

[0287] The server also analyzes the resource usage information and suggests the optimal placement of facilities. Based on the facility usage status data updated in real time from the terminal, it proposes to review the placement of specific equipment and facilities. For example, cases of moving or rearranging according to the usage status of AV equipment and experimental instruments can be considered.

[0288] Regarding the placement of staff, the server analyzes the staff's ability information and automatically determines the optimal placement for classes or tasks. The past evaluation data and skill information of the staff are considered, and optimal matching is performed.

[0289] Finally, the server visualizes and provides the operation information. As a result, the user can check the operation of the educational institution in real time on the dashboard and make important decisions effectively. This dashboard is constructed using visualization tools such as Tableau and PowerBI, and major indicators such as attendance rate and budget utilization rate are displayed in graph form.

[0290] As a concrete example, consider a scenario for optimizing the timetable and budget for a summer intensive course at an educational institution. Examples of prompts include the following:

[0291] "Please propose an optimal timetable and budget management strategy for summer intensive courses at educational institutions. Considering teacher availability, classroom availability, and previous year's budget data, generate a report that supports efficient course management."

[0292] This invention enables educational institutions to improve operational efficiency and make effective use of resources, thereby contributing to an improvement in the quality of education.

[0293] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0294] Step 1:

[0295] Users input schedule information for educational institutions and teachers using their devices. This input saves the timetable information to a database. Specifically, users enter details such as class time, location, and teacher into a form on a web browser and click the submit button. The entered data is then sent to the server in JSON format and written to the database.

[0296] Step 2:

[0297] The server retrieves timetable information from the database, applies data analysis algorithms, and automatically constructs learning activities. The input is timetable information from the database, and the output is an optimized class schedule. Specifically, the server executes Python scripts to perform calculations to avoid schedule conflicts and equalize the workload of teachers. Using a generative AI model, it is possible to suggest even better schedules.

[0298] Step 3:

[0299] Users input resource allocation data from their terminals. This includes annual budget plans and past expenditure history. This information is sent to a server and stored in a database. Specifically, users input budget data using a dedicated application and upload the data to the server by performing a "submit" operation.

[0300] Step 4:

[0301] The server receives resource allocation data and uses machine learning algorithms to generate an efficient resource utilization plan. The input is resource allocation data, and the output is the proposed resource utilization plan. Specifically, the server predicts future budget allocations based on historical data, generates a report to determine the optimal allocation, and sends it to the terminal.

[0302] Step 5:

[0303] The terminal updates equipment usage information in real time. Users input the usage status of various facilities within the facility, and this information is sent to the server. Specifically, the user scans equipment information using a QR code reader or barcode scanner and inputs the usage status.

[0304] Step 6:

[0305] The server analyzes the received equipment usage information and proposes the optimal equipment layout. The input is equipment usage information, and the output is the optimization proposal. Specifically, the server reviews the layout of frequently used equipment based on the received data, creates a document proposing relocation if necessary, and provides it to the terminal.

[0306] Step 7:

[0307] The server optimizes the allocation of staff to classes and tasks based on their competency information. The input is staff competency information, and the output is an optimal staff allocation plan. Specifically, the server analyzes existing staff data and automatically assigns the most suitable personnel to each subject and task.

[0308] Step 8:

[0309] The server visualizes the operation information and provides a dashboard that can be viewed by users in real time. The input is the operation information, and the output is the visualized operation metrics. As a specific operation, the server uses a visualization tool to graph the attendance rate and budget usage, enabling users to easily view this information.

[0310] (Application Example 1)

[0311] Next, Application Example 1 will be described. In the following description, the data processing device 12 is referred to as the "server", and the smart glasses 214 are referred to as the "terminal".

[0312] In modern educational institutions, efficiently conducting business operations is an important issue. In particular, teacher schedule management, timetable generation, budget management, equipment allocation, and proper staff placement are complex, and performing these tasks manually requires time and effort and is inefficient. Also, real-time data updates and user-friendly operations are required. However, currently, there is a lack of systems that comprehensively solve these problems, thus hindering the efficient operation of educational institutions.

[0313] The specific processing by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0314] In this invention, the server includes means for collecting planning data related to educational personnel and educational settings, means for analyzing the planning data and automatically generating a teaching schedule, and means for obtaining and analyzing fund data and proposing fund allocation. Thereby, the operations of educational institutions can be centrally managed, and the operation efficiency can be improved.

[0315] "Educational personnel" refers to teachers, staff, or persons engaged in related operations in educational institutions.

[0316] "Educational setting" refers to the environment in which education is conducted and the conditions for it, including facilities such as classrooms and laboratories, and their usage.

[0317] "Planning data" refers to information related to the operational activities of an educational institution, such as its schedule, resource allocation, and budget.

[0318] A "teaching schedule" refers to a schedule that includes the timetable for classes and related activities.

[0319] "Financial data" refers to financial information, including budget plans and historical expenditure records, for educational institutions.

[0320] "Proposing a fund allocation" refers to the act of presenting an efficient budget allocation plan based on the analysis of financial data.

[0321] "Equipment usage information" refers to data on the usage status of equipment and supplies used within educational facilities.

[0322] "User interface" refers to the design of the operating screens and input devices that allow users to interact with an information system.

[0323] "Real-time data updates" refers to a state where information within a system is updated instantly without delay.

[0324] To realize this invention, an integrated system will be constructed using multiple terminals and a server to streamline the work of educators. The terminals will provide an operation screen for teachers and staff to input planning data. This includes entering schedules, reserving facilities, entering and confirming budgets, and updating equipment usage status. The server will aggregate this data and analyze it in real time. Specifically, it will analyze the planning data to generate an optimal teaching schedule and propose budget allocations based on past funding data. It will also analyze equipment usage information and propose optimal placement.

[0325] The hardware of this system includes a high-performance computer as the server and tablets or PCs as terminals. The software consists of a database management system and development tools for building the user interface. For example, the server retrieves information from the database and performs data analysis using Python or a similar programming language. The results are then visualized using a dashboard tool and delivered to terminals in real time.

[0326] As a concrete example, in one school, when creating the timetable for the new semester, teachers' free time is entered into a server. The server uses a generation AI model to distribute teachers' teaching schedules as evenly as possible and presents the resulting timetable to the principal. The principal checks via a terminal to see if any improvements are needed and then finalizes the schedule. Similarly, in budget management, the system proposes the optimal budget allocation based on past expenditure data, and the management team performs a final check.

[0327] An example of a prompt for a generative AI model is as follows: "Please input the available time slots of teachers at an educational institution and propose an optimal timetable. Please ensure that classroom utilization and teacher workload are evenly distributed."

[0328] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0329] Step 1:

[0330] Users use their devices to input data on the availability of educators and planning data related to lesson settings. This data includes teacher schedules, class times, and classroom booking status. This allows for a comprehensive overview of the entire educational institution's schedule.

[0331] Step 2:

[0332] The server receives planning data sent from the terminal and stores it in a database. Then, it analyzes this data using a generative AI model to generate an optimal teaching schedule. Based on the input data, it adjusts schedules to distribute the workload evenly among teachers and maximizes the availability of empty classrooms.

[0333] Step 3:

[0334] The server analyzes historically recorded financial data, including school annual budget plans and actual expenditure history. The server analyzes this data and uses a generative AI model to propose optimal future budget allocations. It calculates how much funding should be allocated to each area and outputs predictions.

[0335] Step 4:

[0336] The server monitors equipment usage and proposes the optimal placement of equipment. This is based on data on how frequently each piece of experimental equipment and AV device is used. Based on past and current usage, it suggests which equipment should be placed where for maximum efficiency and outputs a specific layout plan.

[0337] Step 5:

[0338] Users can review academic schedules, budgets, and equipment layout plans sent from the server on their devices. The user interface is updated in real time, allowing them to intuitively view this information and make corrections or approvals as needed.

[0339] Through the above process, the complex tasks involved in the operation of educational institutions are centralized, enabling efficient management.

[0340] 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.

[0341] This invention combines an emotion engine with a system that streamlines the operations of educational institutions, thereby recognizing user emotions and reflecting them in improvements to operations.

[0342] First, the terminal provides an interface for inputting teachers' availability, teaching preferences, and classroom reservation status. Users input the necessary data here, which forms the basis of the system. Budget, equipment usage, and staff skill information are also entered in the same way.

[0343] Next, the server analyzes the schedule and budget data obtained from the terminal and uses it for automatic timetable generation and budget allocation suggestions. In particular, at this stage, the emotion engine senses how the user is interacting with the system and analyzes their emotional state. For example, it determines how satisfied or dissatisfied the user is with the timetable generation suggestions.

[0344] Furthermore, the emotion engine revises automatically generated schedules and budget proposals to fine-tune the system's suggestions based on user feedback. For example, if a user is stressed by a particular placement suggestion, the server will focus on providing less burdensome alternatives based on emotional data.

[0345] The server aggregates operational data from educational institutions and related emotional states, then provides a visualized dashboard. This allows users to understand the health and efficiency of operations in real time, while also identifying areas for improvement based on emotional feedback.

[0346] In this way, the operation of educational institutions is carried out not only in terms of efficiency, but also in terms of considering human factors. This system can provide a more user-friendly environment and improve the overall quality of operations.

[0347] The following describes the processing flow.

[0348] Step 1:

[0349] The terminal provides users with an interface to input teacher schedules, class preferences, and classroom reservation status. Users enter this information, and the terminal sends it to a database.

[0350] Step 2:

[0351] The server receives schedule data sent from the terminal and stores it in the database. It then analyzes the received data and begins calculations to generate the optimal timetable.

[0352] Step 3:

[0353] Based on budget information entered by the user and past spending history, the server generates suggestions to optimize budget allocation. This includes comparing the current budget to the annual budget plan and analyzing the differences.

[0354] Step 4:

[0355] Equipment usage is updated in real time via terminals, and the server uses this information to calculate the optimal placement of equipment. This makes it possible to propose equipment solutions that support efficient school management.

[0356] Step 5:

[0357] Based on employee skill information, the server applies an algorithm to optimize employee placement. This ensures that each employee is placed in the right position for their skills.

[0358] Step 6:

[0359] When a user responds to these suggestions, the device sends user input and operation logs to the sentiment engine in real time.

[0360] Step 7:

[0361] The emotion engine installed on the server analyzes user actions and estimates the emotions the user feels towards the suggestions. Based on this analysis, the suggestions are fine-tuned.

[0362] Step 8:

[0363] The server presents the user with a final proposal that incorporates emotional metrics. At this stage, the proposal is optimized according to the user's emotional state.

[0364] Step 9:

[0365] Users can view all data through a dashboard and make decisions based on operational metrics, including emotional feedback. This makes it possible to support strategic management decisions.

[0366] (Example 2)

[0367] 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".

[0368] Operating in educational institutions involves a complex interplay of factors such as scheduling, budget allocation, and equipment utilization, making efficient management difficult. Furthermore, traditional management systems struggle to integrate and utilize this data for proposals and adjustments, particularly those that consider human factors. Therefore, there is a need for a system that reduces the burden on educators and improves operational efficiency.

[0369] 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.

[0370] In this invention, the server includes means for collecting time information and budget status related to education, means for acquiring and analyzing user emotional data using emotion analysis technology and reflecting it in the proposed content, and means for adjusting the generated educational schedule and funding allocation proposals based on the user's emotional feedback. This enables not only efficient operation but also flexible proposals and adjustments that take into account the user's emotions and satisfaction.

[0371] An "information processing device" is a combination of hardware and software used to collect, analyze, and provide suggestions to users based on data.

[0372] "Time information" refers to data related to time, such as the availability dates and times of faculty, staff, and facilities at educational institutions, as well as preferred class schedules.

[0373] "Budget status" refers to information that influences how educational institutions utilize their resources and how they allocate funds in the future.

[0374] "Emotional analysis technology" is an analytical method that recognizes the emotions of users and reflects them in improving the content of suggestions.

[0375] "Emotional feedback" refers to the emotional responses provided by users to the proposed content, such as satisfaction or dissatisfaction.

[0376] "Schedule" refers to the arrangement of class times and work schedules within educational institutions.

[0377] "Fund allocation" refers to plans and proposals for effectively allocating the budget to each item.

[0378] This invention is implemented by utilizing an information processing device to streamline the operations of educational institutions and to manage multiple elements in an integrated manner. Specific embodiments are shown below.

[0379] The terminal provides an interface for faculty and staff to input time information, budget status, and equipment usage information related to education. This allows users to easily register various types of data into the system. The terminal functions as a front-end for data entry, and the data is sent to the server.

[0380] The server functions as a center for analyzing received data, automatically generating educational schedules and proposing funding allocations. It utilizes a generative AI model to provide efficient and effective suggestions. Furthermore, the server employs sentiment analysis technology to collect user emotional feedback and incorporate it into the suggestions, seeking the optimal approach for each user. Natural language processing and machine learning techniques are used in this process.

[0381] As a concrete example, consider a case where a teacher requests three classes per week and seeks the most efficient use of equipment within their budget. The server automatically generates a proposal for the optimal schedule and funding allocation based on the input time information and budget data, and evaluates the user's response to it using sentiment analysis technology. Based on the satisfaction level, it then generates improvement suggestions.

[0382] As a concrete example of a prompt message for the generating AI model, a request such as, "Consider available classrooms on Monday and Wednesday afternoons and propose an efficient class schedule. Please make adjustments based on teacher satisfaction," can be set.

[0383] In this way, the present invention is a system that streamlines the operations of educational institutions while enabling a human-centered approach.

[0384] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0385] Step 1:

[0386] Users input faculty and staff availability, teaching preferences, equipment usage, and budget information through the terminal's interface. The entered data is organized by the terminal and sent to the server. This collects the basic data necessary for the operation of educational institutions.

[0387] Step 2:

[0388] The server analyzes time information and budget status received from the terminal. The generative AI model used here searches for efficient combinations of schedules and generates proposals for optimal class schedules and budget allocations. Based on the input data, the AI ​​model performs data calculations and presents the optimal schedule proposal as output.

[0389] Step 3:

[0390] The server uses sentiment analysis technology to analyze user reactions to the generated schedule and budget proposals. It acquires emotional feedback from users towards the system as data and analyzes it to evaluate satisfaction levels and dissatisfaction with the proposals.

[0391] Step 4:

[0392] The server fine-tunes the schedule and budget proposals based on the results of sentiment analysis. Specifically, it generates and presents alternatives and improvements to areas where the user expressed dissatisfaction. Data processing ensures that emotional feedback is reflected in the proposals.

[0393] Step 5:

[0394] The server aggregates the final operational data and provides a visualized dashboard. Through this dashboard, users can understand the efficiency and health of operations in real time and use this information to improve their work. The server integrates the analysis results and sentiment data to generate visual information as output and provides it to the user.

[0395] (Application Example 2)

[0396] 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."

[0397] Educational institutions need to improve operational efficiency while simultaneously reducing the emotional burden on teachers and administrators. However, conventional systems fail to consider emotional responses to schedule management and resource allocation, resulting in suboptimal suggestions and accumulated dissatisfaction. This invention aims to solve these problems.

[0398] 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.

[0399] In this invention, the server includes means for analyzing schedule data and automatically generating a timetable, means for recognizing emotional states and adjusting the generated suggestions based on user feedback, and means for visualizing operational data to assist in management decisions. This makes it possible to maximize the operational efficiency of educational institutions, reduce the burden on personnel, and provide users with a superior experience.

[0400] An "information processing system" is a computer-based system designed to collect, analyze, manage, and display data.

[0401] "Employee" is a term that refers to teachers and staff who perform duties at an educational institution.

[0402] "Educational facilities" refer to physical or virtual environments for conducting educational activities, including, for example, classrooms and online platforms.

[0403] A "timetable" is a table that lists the schedule of activities and events over a specific period in chronological order.

[0404] A "financial allocation proposal" refers to a specific proposal for the most effective allocation of available funds.

[0405] "Resource allocation" refers to a plan for optimizing the allocation of resources such as personnel, equipment, and time within an educational institution.

[0406] "Competency information" refers to information about the skills and expertise possessed by employees.

[0407] "Operational data" refers to various data related to the operational management of educational institutions, including schedules and resource utilization.

[0408] "Emotional state" refers to the psychological reactions and feedback that users experience while using the system.

[0409] "Feedback" refers to the reactions and opinions that users provide regarding the results of using the system and their suggestions.

[0410] The system for implementing the present invention mainly uses an information processing device (hereinafter referred to as "server") and terminal equipment. The server provides a mechanism to streamline the operation of educational institutions based on various data it receives. Users (teachers and administrators) input schedules, budgets, and facility information through terminals.

[0411] The server automatically generates a timetable based on the input schedule data. This is done using data analysis algorithms and calculations. The generated timetable can be adjusted according to the user's emotional state. This adjustment involves a software module called the emotion engine, which is achieved by analyzing user feedback.

[0412] The server also retrieves budget data and proposes financial allocations. This uses algorithms that analyze past usage trends. Furthermore, the server manages equipment usage information and presents optimal resource allocation plans. This process also takes into account employee competence information.

[0413] As a concrete example, if a teacher at an educational institution is dissatisfied with the class schedule, the server recognizes this emotional state and displays more appropriate alternatives for the user. This mechanism is made possible by using the Python language and an emotion analysis API.

[0414] Users can receive information processing in a visualized form, and a dashboard is provided to assist in management decisions. In this way, through an implementation that promotes operational improvements, educational institutions can achieve efficient and less burdensome operations.

[0415] An example of a prompt message might be: "Analyze the following feedback, assess the user's emotional state, and offer suggestions for stress reduction if necessary."

[0416] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0417] Step 1:

[0418] The terminal provides the user with an interface for various data inputs. Through this interface, the user inputs schedule information, budget data, equipment usage information, and staff competency information. The input in this step consists of various data provided by the user, while the output is an organized dataset stored in a database. The data is converted to an appropriate format and sent to the server.

[0419] Step 2:

[0420] The server generates a timetable based on the received schedule data. It processes the input data using a data analysis algorithm to create an appropriate timetable. The input here is the schedule data obtained in step 1, and the output is the generated timetable. The generated timetable is used to evaluate the sentiment engine.

[0421] Step 3:

[0422] The server uses an emotion engine to analyze the user's emotional state from their feedback. The input is the target feedback data, which the emotion engine processes to quantify the emotional state. The output is the emotional analysis result, providing a numerical evaluation board for a specific emotional state. Based on this result, the server instructs the user to make necessary adjustments.

[0423] Step 4:

[0424] The server optimizes the timetable and budget proposal based on the analyzed emotional state. The inputs are the timetable obtained in step 2 and the emotional state data from step 3. The output is an optimized proposal that reduces the user's burden. Specifically, this involves reorganizing the timetable and adjusting the budget allocation.

[0425] Step 5:

[0426] The server provides administrators and faculty with optimized suggestions in a visualized format. The input is the suggestion information generated in step 4, and the output is a visual dashboard. The dashboard is designed to be easily understood by users using a graphical user interface.

[0427] Step 6:

[0428] The user reviews the presented dashboard and makes operational decisions. The input is the dashboard information provided by the server, and the output is the improved decision-making process. Based on the information built on the dashboard, the user adopts specific operational improvement measures.

[0429] 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.

[0430] 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.

[0431] 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.

[0432] [Third Embodiment]

[0433] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0434] 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.

[0435] 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).

[0436] 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.

[0437] 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.

[0438] 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).

[0439] 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.

[0440] 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.

[0441] 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.

[0442] 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.

[0443] 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.

[0444] 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".

[0445] This invention is a system for streamlining the operations of educational institutions and is implemented as follows.

[0446] First, the terminal provides an interface for inputting schedule data for teachers and facilities at educational institutions. Here, information such as teachers' availability, class hours, and classroom reservation status is entered.

[0447] Next, the server analyzes the entered schedule data and automatically generates an optimal timetable based on it. This generated timetable is designed to equalize the workload of teachers and maximize classroom utilization.

[0448] The terminal also provides a function for managing budget data, allowing users to input annual budget plans and past expenditure history. The server uses this data to automatically propose an efficient budget allocation. This proposal includes specific adjustments for any shortfalls or surpluses.

[0449] Furthermore, terminals update the usage status of school facilities in real time, allowing the server to suggest the optimal placement of equipment. Examples include reviewing the placement of frequently used experimental equipment and AV devices.

[0450] Regarding staff allocation, the server analyzes each teacher's skill information and assigns the most suitable teacher to each class. In this process, the allocation is optimized by considering the teacher's past performance evaluations and area of ​​expertise.

[0451] Finally, the server aggregates operational data and provides a dashboard for visualization. Here, users can check the operational status in real time and use this information to inform management decisions. For example, it can display graphs showing fluctuations in attendance rates and budget utilization rates by year.

[0452] This allows for centralized management and increased efficiency of complex workflows within educational institutions. Users can then operate more effectively through this system.

[0453] The following describes the processing flow.

[0454] Step 1:

[0455] The terminal provides users with an interface for entering teachers' availability and classroom reservation status. Users enter teachers' schedules and preferred class times.

[0456] Step 2:

[0457] The server stores the schedule data entered from the terminal into a database and begins analysis. This analysis checks for schedule conflicts and classroom availability.

[0458] Step 3:

[0459] The server sets constraints based on the analysis results and executes an algorithm to generate an optimal timetable. This algorithm is designed to equalize the workload of each teacher and maximize classroom utilization.

[0460] Step 4:

[0461] The server proposes a generated timetable to the user, who then reviews it and makes corrections via their terminal if necessary.

[0462] Step 5:

[0463] Budget data is entered via a terminal. Users record their annual budget and expenditure history.

[0464] Step 6:

[0465] The server analyzes budget data and proposes efficient budget allocation. Based on past expenditure data, it estimates future expenditures and generates budget adjustment proposals accordingly.

[0466] Step 7:

[0467] To monitor the status of facilities within the school, the terminals collect facility usage information in real time and send it to the server.

[0468] Step 8:

[0469] The server analyzes equipment usage patterns and proposes optimal placement and maintenance schedules. These proposals concern the relocation or additional purchase of frequently used equipment.

[0470] Step 9:

[0471] The server analyzes staff skills and usage data to determine the optimal placement of teachers and staff. This placement takes into account each individual's abilities and workload.

[0472] Step 10:

[0473] All data is aggregated, and the server builds a dashboard for users. Users can use this to monitor operational status and make business decisions.

[0474] (Example 1)

[0475] 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."

[0476] The operations of educational institutions are extremely complex and multifaceted, encompassing tasks such as managing faculty and staff schedules and budgets, optimizing equipment utilization, and optimizing staff allocation. Therefore, it is essential to efficiently manage these various tasks, reduce the burden on faculty and staff, and ensure optimal resource utilization. However, managing all of these tasks manually is extremely difficult and can result in inefficiencies and errors. Consequently, a system is needed to comprehensively manage the operations of educational institutions in order to efficiently address these challenges.

[0477] 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.

[0478] In this invention, the server includes means for collecting timetable information relating to teachers and educational facilities, means for analyzing the timetable information and automatically structuring learning activities, and means for acquiring and processing resource allocation data and proposing usage plans. This enables centralized management of the operations of educational institutions, maximizing resource utilization and improving operational efficiency.

[0479] An "information processing device" is a device that includes hardware and software for collecting, analyzing, and managing data, and for improving the efficiency and optimization of specific tasks.

[0480] "Timetable information" refers to data related to the schedules of teachers and educational institutions, and includes information such as class times, locations, and instructors.

[0481] "Means for structuring learning activities" refers to a program or system that analyzes timetable information and automatically creates optimal lesson schedules and educational activities.

[0482] "Resource allocation data" refers to records of the current allocation and utilization plans of financial, physical, and human resources, and is used to propose optimal resource management strategies.

[0483] "Means for proposing resource utilization plans" refers to a system or algorithm that analyzes collected resource allocation data and automatically proposes plans for efficient future resource use.

[0484] "Resource usage information" refers to data on the usage history and current status of classrooms, equipment, and other resources used within educational institutions.

[0485] "Workforce allocation optimization" refers to a process that automatically determines the optimal job assignment by considering employee skills, performance evaluations, and workload.

[0486] "Operational information" refers to information that aggregates various data related to the operational activities of educational institutions and is used to aid in decision-making.

[0487] This invention constitutes an information processing device for efficiently managing the operations of educational institutions. Specifically, it is a system that uses servers and terminals to manage the schedules, budgets, facilities, and staffing of teachers and educational facilities.

[0488] First, users input teacher and facility timetable information using a device. These devices include personal computers and tablets, and the data is transmitted to a server via the internet. This timetable information includes class start and end times, classroom location, and assigned teacher.

[0489] Next, the server analyzes the timetable information and configures optimal learning activities. During this process, the server can process data using analytical tools such as Python or R, and can also utilize generative AI models. This helps to equalize the workload on teachers and optimize the use of classrooms and facilities.

[0490] Furthermore, users input resource allocation data through their devices. This includes annual budget plans and past usage history. The server processes this data and proposes efficient resource utilization plans for the future. This proposal may utilize machine learning algorithms powered by Google Cloud Platform.

[0491] The server also analyzes resource usage information and suggests optimal equipment placement. Based on real-time equipment usage data updated from terminals, it proposes revising the placement of specific equipment and devices. For example, it might suggest moving or rearranging AV equipment or laboratory instruments based on their usage.

[0492] Regarding staff assignments, the server analyzes staff competency information and automatically determines the most suitable assignment for each class or task. Past performance data and skill information of staff members are considered to ensure optimal matching.

[0493] The server ultimately provides a visualized version of operational information. This allows users to view the educational institution's operations in real time on a dashboard and make effective critical decisions. This dashboard is built using visualization tools such as Tableau and Power BI, and key metrics such as attendance rates and budget utilization rates are displayed in graph format.

[0494] As a concrete example, consider a scenario for optimizing the timetable and budget for a summer intensive course at an educational institution. Examples of prompts include the following:

[0495] "Please propose an optimal timetable and budget management strategy for summer intensive courses at educational institutions. Considering teacher availability, classroom availability, and previous year's budget data, generate a report that supports efficient course management."

[0496] This invention enables educational institutions to improve operational efficiency and make effective use of resources, thereby contributing to an improvement in the quality of education.

[0497] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0498] Step 1:

[0499] Users input schedule information for educational institutions and teachers using their devices. This input saves the timetable information to a database. Specifically, users enter details such as class time, location, and teacher into a form on a web browser and click the submit button. The entered data is then sent to the server in JSON format and written to the database.

[0500] Step 2:

[0501] The server retrieves timetable information from the database, applies data analysis algorithms, and automatically constructs learning activities. The input is timetable information from the database, and the output is an optimized class schedule. Specifically, the server executes Python scripts to perform calculations to avoid schedule conflicts and equalize the workload of teachers. Using a generative AI model, it is possible to suggest even better schedules.

[0502] Step 3:

[0503] Users input resource allocation data from their terminals. This includes annual budget plans and past expenditure history. This information is sent to a server and stored in a database. Specifically, users input budget data using a dedicated application and upload the data to the server by performing a "submit" operation.

[0504] Step 4:

[0505] The server receives resource allocation data and uses machine learning algorithms to generate an efficient resource utilization plan. The input is resource allocation data, and the output is the proposed resource utilization plan. Specifically, the server predicts future budget allocations based on historical data, generates a report to determine the optimal allocation, and sends it to the terminal.

[0506] Step 5:

[0507] The terminal updates equipment usage information in real time. Users input the usage status of various facilities within the facility, and this information is sent to the server. Specifically, the user scans equipment information using a QR code reader or barcode scanner and inputs the usage status.

[0508] Step 6:

[0509] The server analyzes the received equipment usage information and proposes the optimal equipment layout. The input is equipment usage information, and the output is the optimization proposal. Specifically, the server reviews the layout of frequently used equipment based on the received data, creates a document proposing relocation if necessary, and provides it to the terminal.

[0510] Step 7:

[0511] The server optimizes the allocation of staff to classes and tasks based on their competency information. The input is staff competency information, and the output is an optimal staff allocation plan. Specifically, the server analyzes existing staff data and automatically assigns the most suitable personnel to each subject and task.

[0512] Step 8:

[0513] The server visualizes operational information and provides a dashboard that users can check in real time. The input is operational information, and the output is visualized operational indicators. Specifically, the server uses visualization tools to graph attendance rates and budget usage, making this information easily accessible to users.

[0514] (Application Example 1)

[0515] 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."

[0516] Efficient operational management is a crucial challenge for modern educational institutions. In particular, tasks such as managing teacher schedules, creating timetables, budget management, equipment allocation, and appropriate staffing are complex, and performing these tasks manually is time-consuming, labor-intensive, and inefficient. Furthermore, real-time data updates and user-friendly operation are essential. However, currently, there is a lack of systems that comprehensively address these challenges, hindering the efficient operation of educational institutions.

[0517] 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.

[0518] In this invention, the server includes means for collecting planning data related to educators and educational settings, means for analyzing the planning data and automatically generating a teaching schedule, and means for acquiring and analyzing financial data and proposing funding allocations. This enables centralized management of educational institution operations and improves operational efficiency.

[0519] "Education-related personnel" refers to teachers, staff, or individuals engaged in related work at educational institutions.

[0520] "Educational setting" refers to the environment in which education is conducted and the conditions for it, including facilities such as classrooms and laboratories, and their usage.

[0521] "Planning data" refers to information related to the operational activities of an educational institution, such as its schedule, resource allocation, and budget.

[0522] A "teaching schedule" refers to a schedule that includes the timetable for classes and related activities.

[0523] "Financial data" refers to financial information, including budget plans and historical expenditure records, for educational institutions.

[0524] "Proposing a fund allocation" refers to the act of presenting an efficient budget allocation plan based on the analysis of financial data.

[0525] "Equipment usage information" refers to data on the usage status of equipment and supplies used within educational facilities.

[0526] "User interface" refers to the design of the operating screens and input devices that allow users to interact with an information system.

[0527] "Real-time data updates" refers to a state where information within a system is updated instantly without delay.

[0528] To realize this invention, an integrated system will be constructed using multiple terminals and a server to streamline the work of educators. The terminals will provide an operation screen for teachers and staff to input planning data. This includes entering schedules, reserving facilities, entering and confirming budgets, and updating equipment usage status. The server will aggregate this data and analyze it in real time. Specifically, it will analyze the planning data to generate an optimal teaching schedule and propose budget allocations based on past funding data. It will also analyze equipment usage information and propose optimal placement.

[0529] The hardware of this system includes a high-performance computer as the server and tablets or PCs as terminals. The software consists of a database management system and development tools for building the user interface. For example, the server retrieves information from the database and performs data analysis using Python or a similar programming language. The results are then visualized using a dashboard tool and delivered to terminals in real time.

[0530] As a concrete example, in one school, when creating the timetable for the new semester, teachers' free time is entered into a server. The server uses a generation AI model to distribute teachers' teaching schedules as evenly as possible and presents the resulting timetable to the principal. The principal checks via a terminal to see if any improvements are needed and then finalizes the schedule. Similarly, in budget management, the system proposes the optimal budget allocation based on past expenditure data, and the management team performs a final check.

[0531] An example of a prompt for a generative AI model is as follows: "Please input the available time slots of teachers at an educational institution and propose an optimal timetable. Please ensure that classroom utilization and teacher workload are evenly distributed."

[0532] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0533] Step 1:

[0534] Users use their devices to input data on the availability of educators and planning data related to lesson settings. This data includes teacher schedules, class times, and classroom booking status. This allows for a comprehensive overview of the entire educational institution's schedule.

[0535] Step 2:

[0536] The server receives planning data sent from the terminal and stores it in a database. Then, it analyzes this data using a generative AI model to generate an optimal teaching schedule. Based on the input data, it adjusts schedules to distribute the workload evenly among teachers and maximizes the availability of empty classrooms.

[0537] Step 3:

[0538] The server analyzes historically recorded financial data, including school annual budget plans and actual expenditure history. The server analyzes this data and uses a generative AI model to propose optimal future budget allocations. It calculates how much funding should be allocated to each area and outputs predictions.

[0539] Step 4:

[0540] The server monitors equipment usage and proposes the optimal placement of equipment. This is based on data on how frequently each piece of experimental equipment and AV device is used. Based on past and current usage, it suggests which equipment should be placed where for maximum efficiency and outputs a specific layout plan.

[0541] Step 5:

[0542] Users can review academic schedules, budgets, and equipment layout plans sent from the server on their devices. The user interface is updated in real time, allowing them to intuitively view this information and make corrections or approvals as needed.

[0543] Through the above process, the complex tasks involved in the operation of educational institutions are centralized, enabling efficient management.

[0544] 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.

[0545] This invention combines an emotion engine with a system that streamlines the operations of educational institutions, thereby recognizing user emotions and reflecting them in improvements to operations.

[0546] First, the terminal provides an interface for inputting teachers' availability, teaching preferences, and classroom reservation status. Users input the necessary data here, which forms the basis of the system. Budget, equipment usage, and staff skill information are also entered in the same way.

[0547] Next, the server analyzes the schedule and budget data obtained from the terminal and uses it for automatic timetable generation and budget allocation suggestions. In particular, at this stage, the emotion engine senses how the user is interacting with the system and analyzes their emotional state. For example, it determines how satisfied or dissatisfied the user is with the timetable generation suggestions.

[0548] Furthermore, the emotion engine revises automatically generated schedules and budget proposals to fine-tune the system's suggestions based on user feedback. For example, if a user is stressed by a particular placement suggestion, the server will focus on providing less burdensome alternatives based on emotional data.

[0549] The server aggregates operational data from educational institutions and related emotional states, then provides a visualized dashboard. This allows users to understand the health and efficiency of operations in real time, while also identifying areas for improvement based on emotional feedback.

[0550] In this way, the operation of educational institutions is carried out not only in terms of efficiency, but also in terms of considering human factors. This system can provide a more user-friendly environment and improve the overall quality of operations.

[0551] The following describes the processing flow.

[0552] Step 1:

[0553] The terminal provides users with an interface to input teacher schedules, class preferences, and classroom reservation status. Users enter this information, and the terminal sends it to a database.

[0554] Step 2:

[0555] The server receives schedule data sent from the terminal and stores it in the database. It then analyzes the received data and begins calculations to generate the optimal timetable.

[0556] Step 3:

[0557] Based on budget information entered by the user and past spending history, the server generates suggestions to optimize budget allocation. This includes comparing the current budget to the annual budget plan and analyzing the differences.

[0558] Step 4:

[0559] Equipment usage is updated in real time via terminals, and the server uses this information to calculate the optimal placement of equipment. This makes it possible to propose equipment solutions that support efficient school management.

[0560] Step 5:

[0561] Based on employee skill information, the server applies an algorithm to optimize employee placement. This ensures that each employee is placed in the right position for their skills.

[0562] Step 6:

[0563] When a user responds to these suggestions, the device sends user input and operation logs to the sentiment engine in real time.

[0564] Step 7:

[0565] The emotion engine installed on the server analyzes user actions and estimates the emotions the user feels towards the suggestions. Based on this analysis, the suggestions are fine-tuned.

[0566] Step 8:

[0567] The server presents the user with a final proposal that incorporates emotional metrics. At this stage, the proposal is optimized according to the user's emotional state.

[0568] Step 9:

[0569] Users can view all data through a dashboard and make decisions based on operational metrics, including emotional feedback. This makes it possible to support strategic management decisions.

[0570] (Example 2)

[0571] 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."

[0572] Operating in educational institutions involves a complex interplay of factors such as scheduling, budget allocation, and equipment utilization, making efficient management difficult. Furthermore, traditional management systems struggle to integrate and utilize this data for proposals and adjustments, particularly those that consider human factors. Therefore, there is a need for a system that reduces the burden on educators and improves operational efficiency.

[0573] 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.

[0574] In this invention, the server includes means for collecting time information and budget status related to education, means for acquiring and analyzing user emotional data using emotion analysis technology and reflecting it in the proposed content, and means for adjusting the generated educational schedule and funding allocation proposals based on the user's emotional feedback. This enables not only efficient operation but also flexible proposals and adjustments that take into account the user's emotions and satisfaction.

[0575] An "information processing device" is a combination of hardware and software used to collect, analyze, and provide suggestions to users based on data.

[0576] "Time information" refers to data related to time, such as the availability dates and times of faculty, staff, and facilities at educational institutions, as well as preferred class schedules.

[0577] "Budget status" refers to information that influences how educational institutions utilize their resources and how they allocate funds in the future.

[0578] "Emotional analysis technology" is an analytical method that recognizes the emotions of users and reflects them in improving the content of suggestions.

[0579] "Emotional feedback" refers to the emotional responses provided by users to the proposed content, such as satisfaction or dissatisfaction.

[0580] "Schedule" refers to the arrangement of class times and work schedules within educational institutions.

[0581] "Fund allocation" refers to plans and proposals for effectively allocating the budget to each item.

[0582] This invention is implemented by utilizing an information processing device to streamline the operations of educational institutions and to manage multiple elements in an integrated manner. Specific embodiments are shown below.

[0583] The terminal provides an interface for faculty and staff to input time information, budget status, and equipment usage information related to education. This allows users to easily register various types of data into the system. The terminal functions as a front-end for data entry, and the data is sent to the server.

[0584] The server functions as a center for analyzing received data, automatically generating educational schedules and proposing funding allocations. It utilizes a generative AI model to provide efficient and effective suggestions. Furthermore, the server employs sentiment analysis technology to collect user emotional feedback and incorporate it into the suggestions, seeking the optimal approach for each user. Natural language processing and machine learning techniques are used in this process.

[0585] As a concrete example, consider a case where a teacher requests three classes per week and seeks the most efficient use of equipment within their budget. The server automatically generates a proposal for the optimal schedule and funding allocation based on the input time information and budget data, and evaluates the user's response to it using sentiment analysis technology. Based on the satisfaction level, it then generates improvement suggestions.

[0586] As a concrete example of a prompt message for the generating AI model, a request such as, "Consider available classrooms on Monday and Wednesday afternoons and propose an efficient class schedule. Please make adjustments based on teacher satisfaction," can be set.

[0587] In this way, the present invention is a system that streamlines the operations of educational institutions while enabling a human-centered approach.

[0588] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0589] Step 1:

[0590] Users input faculty and staff availability, teaching preferences, equipment usage, and budget information through the terminal's interface. The entered data is organized by the terminal and sent to the server. This collects the basic data necessary for the operation of educational institutions.

[0591] Step 2:

[0592] The server analyzes time information and budget status received from the terminal. The generative AI model used here searches for efficient combinations of schedules and generates proposals for optimal class schedules and budget allocations. Based on the input data, the AI ​​model performs data calculations and presents the optimal schedule proposal as output.

[0593] Step 3:

[0594] The server uses sentiment analysis technology to analyze user reactions to the generated schedule and budget proposals. It acquires emotional feedback from users towards the system as data and analyzes it to evaluate satisfaction levels and dissatisfaction with the proposals.

[0595] Step 4:

[0596] The server fine-tunes the schedule and budget proposals based on the results of sentiment analysis. Specifically, it generates and presents alternatives and improvements to areas where the user expressed dissatisfaction. Data processing ensures that emotional feedback is reflected in the proposals.

[0597] Step 5:

[0598] The server aggregates the final operational data and provides a visualized dashboard. Through this dashboard, users can understand the efficiency and health of operations in real time and use this information to improve their work. The server integrates the analysis results and sentiment data to generate visual information as output and provides it to the user.

[0599] (Application Example 2)

[0600] 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."

[0601] Educational institutions need to improve operational efficiency while simultaneously reducing the emotional burden on teachers and administrators. However, conventional systems fail to consider emotional responses to schedule management and resource allocation, resulting in suboptimal suggestions and accumulated dissatisfaction. This invention aims to solve these problems.

[0602] 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.

[0603] In this invention, the server includes means for analyzing schedule data and automatically generating a timetable, means for recognizing emotional states and adjusting the generated suggestions based on user feedback, and means for visualizing operational data to assist in management decisions. This makes it possible to maximize the operational efficiency of educational institutions, reduce the burden on personnel, and provide users with a superior experience.

[0604] An "information processing system" is a computer-based system designed to collect, analyze, manage, and display data.

[0605] "Employee" is a term that refers to teachers and staff who perform duties at an educational institution.

[0606] "Educational facilities" refer to physical or virtual environments for conducting educational activities, including, for example, classrooms and online platforms.

[0607] A "timetable" is a table that lists the schedule of activities and events over a specific period in chronological order.

[0608] A "financial allocation proposal" refers to a specific proposal for the most effective allocation of available funds.

[0609] "Resource allocation" refers to a plan for optimizing the allocation of resources such as personnel, equipment, and time within an educational institution.

[0610] "Competency information" refers to information about the skills and expertise possessed by employees.

[0611] "Operational data" refers to various data related to the operational management of educational institutions, including schedules and resource utilization.

[0612] "Emotional state" refers to the psychological reactions and feedback that users experience while using the system.

[0613] "Feedback" refers to the reactions and opinions that users provide regarding the results of using the system and their suggestions.

[0614] The system for implementing the present invention mainly uses an information processing device (hereinafter referred to as "server") and terminal equipment. The server provides a mechanism to streamline the operation of educational institutions based on various data it receives. Users (teachers and administrators) input schedules, budgets, and facility information through terminals.

[0615] The server automatically generates a timetable based on the input schedule data. This is done using data analysis algorithms and calculations. The generated timetable can be adjusted according to the user's emotional state. This adjustment involves a software module called the emotion engine, which is achieved by analyzing user feedback.

[0616] The server also retrieves budget data and proposes financial allocations. This uses algorithms that analyze past usage trends. Furthermore, the server manages equipment usage information and presents optimal resource allocation plans. This process also takes into account employee competence information.

[0617] As a concrete example, if a teacher at an educational institution is dissatisfied with the class schedule, the server recognizes this emotional state and displays more appropriate alternatives for the user. This mechanism is made possible by using the Python language and an emotion analysis API.

[0618] Users can receive information processing in a visualized form, and a dashboard is provided to assist in management decisions. In this way, through an implementation that promotes operational improvements, educational institutions can achieve efficient and less burdensome operations.

[0619] An example of a prompt message might be: "Analyze the following feedback, assess the user's emotional state, and offer suggestions for stress reduction if necessary."

[0620] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0621] Step 1:

[0622] The terminal provides the user with an interface for various data inputs. Through this interface, the user inputs schedule information, budget data, equipment usage information, and staff competency information. The input in this step consists of various data provided by the user, while the output is an organized dataset stored in a database. The data is converted to an appropriate format and sent to the server.

[0623] Step 2:

[0624] The server generates a timetable based on the received schedule data. It processes the input data using a data analysis algorithm to create an appropriate timetable. The input here is the schedule data obtained in step 1, and the output is the generated timetable. The generated timetable is used to evaluate the sentiment engine.

[0625] Step 3:

[0626] The server uses an emotion engine to analyze the user's emotional state from their feedback. The input is the target feedback data, which the emotion engine processes to quantify the emotional state. The output is the emotional analysis result, providing a numerical evaluation board for a specific emotional state. Based on this result, the server instructs the user to make necessary adjustments.

[0627] Step 4:

[0628] The server optimizes the timetable and budget proposal based on the analyzed emotional state. The inputs are the timetable obtained in step 2 and the emotional state data from step 3. The output is an optimized proposal that reduces the user's burden. Specifically, this involves reorganizing the timetable and adjusting the budget allocation.

[0629] Step 5:

[0630] The server provides administrators and faculty with optimized suggestions in a visualized format. The input is the suggestion information generated in step 4, and the output is a visual dashboard. The dashboard is designed to be easily understood by users using a graphical user interface.

[0631] Step 6:

[0632] The user reviews the presented dashboard and makes operational decisions. The input is the dashboard information provided by the server, and the output is the improved decision-making process. Based on the information built on the dashboard, the user adopts specific operational improvement measures.

[0633] 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.

[0634] 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.

[0635] 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.

[0636] [Fourth Embodiment]

[0637] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0638] 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.

[0639] 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).

[0640] 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.

[0641] 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.

[0642] 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).

[0643] 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.

[0644] 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.

[0645] 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.

[0646] 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.

[0647] 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.

[0648] 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.

[0649] 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".

[0650] This invention is a system for streamlining the operations of educational institutions and is implemented as follows.

[0651] First, the terminal provides an interface for inputting schedule data for teachers and facilities at educational institutions. Here, information such as teachers' availability, class hours, and classroom reservation status is entered.

[0652] Next, the server analyzes the entered schedule data and automatically generates an optimal timetable based on it. This generated timetable is designed to equalize the workload of teachers and maximize classroom utilization.

[0653] The terminal also provides a function for managing budget data, allowing users to input annual budget plans and past expenditure history. The server uses this data to automatically propose an efficient budget allocation. This proposal includes specific adjustments for any shortfalls or surpluses.

[0654] Furthermore, terminals update the usage status of school facilities in real time, allowing the server to suggest the optimal placement of equipment. Examples include reviewing the placement of frequently used experimental equipment and AV devices.

[0655] Regarding staff allocation, the server analyzes each teacher's skill information and assigns the most suitable teacher to each class. In this process, the allocation is optimized by considering the teacher's past performance evaluations and area of ​​expertise.

[0656] Finally, the server aggregates operational data and provides a dashboard for visualization. Here, users can check the operational status in real time and use this information to inform management decisions. For example, it can display graphs showing fluctuations in attendance rates and budget utilization rates by year.

[0657] This allows for centralized management and increased efficiency of complex workflows within educational institutions. Users can then operate more effectively through this system.

[0658] The following describes the processing flow.

[0659] Step 1:

[0660] The terminal provides users with an interface for entering teachers' availability and classroom reservation status. Users enter teachers' schedules and preferred class times.

[0661] Step 2:

[0662] The server stores the schedule data entered from the terminal into a database and begins analysis. This analysis checks for schedule conflicts and classroom availability.

[0663] Step 3:

[0664] The server sets constraints based on the analysis results and executes an algorithm to generate an optimal timetable. This algorithm is designed to equalize the workload of each teacher and maximize classroom utilization.

[0665] Step 4:

[0666] The server proposes a generated timetable to the user, who then reviews it and makes corrections via their terminal if necessary.

[0667] Step 5:

[0668] Budget data is entered via a terminal. Users record their annual budget and expenditure history.

[0669] Step 6:

[0670] The server analyzes budget data and proposes efficient budget allocation. Based on past expenditure data, it estimates future expenditures and generates budget adjustment proposals accordingly.

[0671] Step 7:

[0672] To monitor the status of facilities within the school, the terminals collect facility usage information in real time and send it to the server.

[0673] Step 8:

[0674] The server analyzes equipment usage patterns and proposes optimal placement and maintenance schedules. These proposals concern the relocation or additional purchase of frequently used equipment.

[0675] Step 9:

[0676] The server analyzes staff skills and usage data to determine the optimal placement of teachers and staff. This placement takes into account each individual's abilities and workload.

[0677] Step 10:

[0678] All data is aggregated, and the server builds a dashboard for users. Users can use this to monitor operational status and make business decisions.

[0679] (Example 1)

[0680] 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".

[0681] The operations of educational institutions are extremely complex and multifaceted, encompassing tasks such as managing faculty and staff schedules and budgets, optimizing equipment utilization, and optimizing staff allocation. Therefore, it is essential to efficiently manage these various tasks, reduce the burden on faculty and staff, and ensure optimal resource utilization. However, managing all of these tasks manually is extremely difficult and can result in inefficiencies and errors. Consequently, a system is needed to comprehensively manage the operations of educational institutions in order to efficiently address these challenges.

[0682] 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.

[0683] In this invention, the server includes means for collecting timetable information relating to teachers and educational facilities, means for analyzing the timetable information and automatically structuring learning activities, and means for acquiring and processing resource allocation data and proposing usage plans. This enables centralized management of the operations of educational institutions, maximizing resource utilization and improving operational efficiency.

[0684] An "information processing device" is a device that includes hardware and software for collecting, analyzing, and managing data, and for improving the efficiency and optimization of specific tasks.

[0685] "Timetable information" refers to data related to the schedules of teachers and educational institutions, and includes information such as class times, locations, and instructors.

[0686] "Means for structuring learning activities" refers to a program or system that analyzes timetable information and automatically creates optimal lesson schedules and educational activities.

[0687] "Resource allocation data" refers to records of the current allocation and utilization plans of financial, physical, and human resources, and is used to propose optimal resource management strategies.

[0688] "Means for proposing resource utilization plans" refers to a system or algorithm that analyzes collected resource allocation data and automatically proposes plans for efficient future resource use.

[0689] "Resource usage information" refers to data on the usage history and current status of classrooms, equipment, and other resources used within educational institutions.

[0690] "Workforce allocation optimization" refers to a process that automatically determines the optimal job assignment by considering employee skills, performance evaluations, and workload.

[0691] "Operational information" refers to information that aggregates various data related to the operational activities of educational institutions and is used to aid in decision-making.

[0692] This invention constitutes an information processing device for efficiently managing the operations of educational institutions. Specifically, it is a system that uses servers and terminals to manage the schedules, budgets, facilities, and staffing of teachers and educational facilities.

[0693] First, users input teacher and facility timetable information using a device. These devices include personal computers and tablets, and the data is transmitted to a server via the internet. This timetable information includes class start and end times, classroom location, and assigned teacher.

[0694] Next, the server analyzes the timetable information and configures optimal learning activities. During this process, the server can process data using analytical tools such as Python or R, and can also utilize generative AI models. This helps to equalize the workload on teachers and optimize the use of classrooms and facilities.

[0695] Furthermore, users input resource allocation data through their devices. This includes annual budget plans and past usage history. The server processes this data and proposes efficient resource utilization plans for the future. This proposal may utilize machine learning algorithms powered by Google Cloud Platform.

[0696] The server also analyzes resource usage information and suggests optimal equipment placement. Based on real-time equipment usage data updated from terminals, it proposes revising the placement of specific equipment and devices. For example, it might suggest moving or rearranging AV equipment or laboratory instruments based on their usage.

[0697] Regarding staff assignments, the server analyzes staff competency information and automatically determines the most suitable assignment for each class or task. Past performance data and skill information of staff members are considered to ensure optimal matching.

[0698] The server ultimately provides a visualized version of operational information. This allows users to view the educational institution's operations in real time on a dashboard and make effective critical decisions. This dashboard is built using visualization tools such as Tableau and Power BI, and key metrics such as attendance rates and budget utilization rates are displayed in graph format.

[0699] As a concrete example, consider a scenario for optimizing the timetable and budget for a summer intensive course at an educational institution. Examples of prompts include the following:

[0700] "Please propose an optimal timetable and budget management strategy for summer intensive courses at educational institutions. Considering teacher availability, classroom availability, and previous year's budget data, generate a report that supports efficient course management."

[0701] This invention enables educational institutions to improve operational efficiency and make effective use of resources, thereby contributing to an improvement in the quality of education.

[0702] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0703] Step 1:

[0704] Users input schedule information for educational institutions and teachers using their devices. This input saves the timetable information to a database. Specifically, users enter details such as class time, location, and teacher into a form on a web browser and click the submit button. The entered data is then sent to the server in JSON format and written to the database.

[0705] Step 2:

[0706] The server retrieves timetable information from the database, applies data analysis algorithms, and automatically constructs learning activities. The input is timetable information from the database, and the output is an optimized class schedule. Specifically, the server executes Python scripts to perform calculations to avoid schedule conflicts and equalize the workload of teachers. Using a generative AI model, it is possible to suggest even better schedules.

[0707] Step 3:

[0708] Users input resource allocation data from their terminals. This includes annual budget plans and past expenditure history. This information is sent to a server and stored in a database. Specifically, users input budget data using a dedicated application and upload the data to the server by performing a "submit" operation.

[0709] Step 4:

[0710] The server receives resource allocation data and uses machine learning algorithms to generate an efficient resource utilization plan. The input is resource allocation data, and the output is the proposed resource utilization plan. Specifically, the server predicts future budget allocations based on historical data, generates a report to determine the optimal allocation, and sends it to the terminal.

[0711] Step 5:

[0712] The terminal updates equipment usage information in real time. Users input the usage status of various facilities within the facility, and this information is sent to the server. Specifically, the user scans equipment information using a QR code reader or barcode scanner and inputs the usage status.

[0713] Step 6:

[0714] The server analyzes the received equipment usage information and proposes the optimal equipment layout. The input is equipment usage information, and the output is the optimization proposal. Specifically, the server reviews the layout of frequently used equipment based on the received data, creates a document proposing relocation if necessary, and provides it to the terminal.

[0715] Step 7:

[0716] The server optimizes the allocation of staff to classes and tasks based on their competency information. The input is staff competency information, and the output is an optimal staff allocation plan. Specifically, the server analyzes existing staff data and automatically assigns the most suitable personnel to each subject and task.

[0717] Step 8:

[0718] The server visualizes operational information and provides a dashboard that users can check in real time. The input is operational information, and the output is visualized operational indicators. Specifically, the server uses visualization tools to graph attendance rates and budget usage, making this information easily accessible to users.

[0719] (Application Example 1)

[0720] 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".

[0721] Efficient operational management is a crucial challenge for modern educational institutions. In particular, tasks such as managing teacher schedules, creating timetables, budget management, equipment allocation, and appropriate staffing are complex, and performing these tasks manually is time-consuming, labor-intensive, and inefficient. Furthermore, real-time data updates and user-friendly operation are essential. However, currently, there is a lack of systems that comprehensively address these challenges, hindering the efficient operation of educational institutions.

[0722] 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.

[0723] In this invention, the server includes means for collecting planning data related to educators and educational settings, means for analyzing the planning data and automatically generating a teaching schedule, and means for acquiring and analyzing financial data and proposing funding allocations. This enables centralized management of educational institution operations and improves operational efficiency.

[0724] "Education-related personnel" refers to teachers, staff, or individuals engaged in related work at educational institutions.

[0725] "Educational setting" refers to the environment in which education is conducted and the conditions for it, including facilities such as classrooms and laboratories, and their usage.

[0726] "Planning data" refers to information related to the operational activities of an educational institution, such as its schedule, resource allocation, and budget.

[0727] A "teaching schedule" refers to a schedule that includes the timetable for classes and related activities.

[0728] "Financial data" refers to financial information, including budget plans and historical expenditure records, for educational institutions.

[0729] "Proposing a fund allocation" refers to the act of presenting an efficient budget allocation plan based on the analysis of financial data.

[0730] "Equipment usage information" refers to data on the usage status of equipment and supplies used within educational facilities.

[0731] "User interface" refers to the design of the operating screens and input devices that allow users to interact with an information system.

[0732] "Real-time data updates" refers to a state where information within a system is updated instantly without delay.

[0733] To realize this invention, an integrated system will be constructed using multiple terminals and a server to streamline the work of educators. The terminals will provide an operation screen for teachers and staff to input planning data. This includes entering schedules, reserving facilities, entering and confirming budgets, and updating equipment usage status. The server will aggregate this data and analyze it in real time. Specifically, it will analyze the planning data to generate an optimal teaching schedule and propose budget allocations based on past funding data. It will also analyze equipment usage information and propose optimal placement.

[0734] The hardware of this system includes a high-performance computer as the server and tablets or PCs as terminals. The software consists of a database management system and development tools for building the user interface. For example, the server retrieves information from the database and performs data analysis using Python or a similar programming language. The results are then visualized using a dashboard tool and delivered to terminals in real time.

[0735] As a concrete example, in one school, when creating the timetable for the new semester, teachers' free time is entered into a server. The server uses a generation AI model to distribute teachers' teaching schedules as evenly as possible and presents the resulting timetable to the principal. The principal checks via a terminal to see if any improvements are needed and then finalizes the schedule. Similarly, in budget management, the system proposes the optimal budget allocation based on past expenditure data, and the management team performs a final check.

[0736] An example of a prompt for a generative AI model is as follows: "Please input the available time slots of teachers at an educational institution and propose an optimal timetable. Please ensure that classroom utilization and teacher workload are evenly distributed."

[0737] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0738] Step 1:

[0739] Users use their devices to input data on the availability of educators and planning data related to lesson settings. This data includes teacher schedules, class times, and classroom booking status. This allows for a comprehensive overview of the entire educational institution's schedule.

[0740] Step 2:

[0741] The server receives planning data sent from the terminal and stores it in a database. Then, it analyzes this data using a generative AI model to generate an optimal teaching schedule. Based on the input data, it adjusts schedules to distribute the workload evenly among teachers and maximizes the availability of empty classrooms.

[0742] Step 3:

[0743] The server analyzes historically recorded financial data, including school annual budget plans and actual expenditure history. The server analyzes this data and uses a generative AI model to propose optimal future budget allocations. It calculates how much funding should be allocated to each area and outputs predictions.

[0744] Step 4:

[0745] The server monitors equipment usage and proposes the optimal placement of equipment. This is based on data on how frequently each piece of experimental equipment and AV device is used. Based on past and current usage, it suggests which equipment should be placed where for maximum efficiency and outputs a specific layout plan.

[0746] Step 5:

[0747] Users can review academic schedules, budgets, and equipment layout plans sent from the server on their devices. The user interface is updated in real time, allowing them to intuitively view this information and make corrections or approvals as needed.

[0748] Through the above process, the complex tasks involved in the operation of educational institutions are centralized, enabling efficient management.

[0749] 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.

[0750] This invention combines an emotion engine with a system that streamlines the operations of educational institutions, thereby recognizing user emotions and reflecting them in improvements to operations.

[0751] First, the terminal provides an interface for inputting teachers' availability, teaching preferences, and classroom reservation status. Users input the necessary data here, which forms the basis of the system. Budget, equipment usage, and staff skill information are also entered in the same way.

[0752] Next, the server analyzes the schedule and budget data obtained from the terminal and uses it for automatic timetable generation and budget allocation suggestions. In particular, at this stage, the emotion engine senses how the user is interacting with the system and analyzes their emotional state. For example, it determines how satisfied or dissatisfied the user is with the timetable generation suggestions.

[0753] Furthermore, the emotion engine revises automatically generated schedules and budget proposals to fine-tune the system's suggestions based on user feedback. For example, if a user is stressed by a particular placement suggestion, the server will focus on providing less burdensome alternatives based on emotional data.

[0754] The server aggregates operational data from educational institutions and related emotional states, then provides a visualized dashboard. This allows users to understand the health and efficiency of operations in real time, while also identifying areas for improvement based on emotional feedback.

[0755] In this way, the operation of educational institutions is carried out not only in terms of efficiency, but also in terms of considering human factors. This system can provide a more user-friendly environment and improve the overall quality of operations.

[0756] The following describes the processing flow.

[0757] Step 1:

[0758] The terminal provides users with an interface to input teacher schedules, class preferences, and classroom reservation status. Users enter this information, and the terminal sends it to a database.

[0759] Step 2:

[0760] The server receives schedule data sent from the terminal and stores it in the database. It then analyzes the received data and begins calculations to generate the optimal timetable.

[0761] Step 3:

[0762] Based on budget information entered by the user and past spending history, the server generates suggestions to optimize budget allocation. This includes comparing the current budget to the annual budget plan and analyzing the differences.

[0763] Step 4:

[0764] Equipment usage is updated in real time via terminals, and the server uses this information to calculate the optimal placement of equipment. This makes it possible to propose equipment solutions that support efficient school management.

[0765] Step 5:

[0766] Based on employee skill information, the server applies an algorithm to optimize employee placement. This ensures that each employee is placed in the right position for their skills.

[0767] Step 6:

[0768] When a user responds to these suggestions, the device sends user input and operation logs to the sentiment engine in real time.

[0769] Step 7:

[0770] The emotion engine installed on the server analyzes user actions and estimates the emotions the user feels towards the suggestions. Based on this analysis, the suggestions are fine-tuned.

[0771] Step 8:

[0772] The server presents the user with a final proposal that incorporates emotional metrics. At this stage, the proposal is optimized according to the user's emotional state.

[0773] Step 9:

[0774] Users can view all data through a dashboard and make decisions based on operational metrics, including emotional feedback. This makes it possible to support strategic management decisions.

[0775] (Example 2)

[0776] 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".

[0777] Operating in educational institutions involves a complex interplay of factors such as scheduling, budget allocation, and equipment utilization, making efficient management difficult. Furthermore, traditional management systems struggle to integrate and utilize this data for proposals and adjustments, particularly those that consider human factors. Therefore, there is a need for a system that reduces the burden on educators and improves operational efficiency.

[0778] 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.

[0779] In this invention, the server includes means for collecting time information and budget status related to education, means for acquiring and analyzing user emotional data using emotion analysis technology and reflecting it in the proposed content, and means for adjusting the generated educational schedule and funding allocation proposals based on the user's emotional feedback. This enables not only efficient operation but also flexible proposals and adjustments that take into account the user's emotions and satisfaction.

[0780] An "information processing device" is a combination of hardware and software used to collect, analyze, and provide suggestions to users based on data.

[0781] "Time information" refers to data related to time, such as the availability dates and times of faculty, staff, and facilities at educational institutions, as well as preferred class schedules.

[0782] "Budget status" refers to information that influences how educational institutions utilize their resources and how they allocate funds in the future.

[0783] "Emotional analysis technology" is an analytical method that recognizes the emotions of users and reflects them in improving the content of suggestions.

[0784] "Emotional feedback" refers to the emotional responses provided by users to the proposed content, such as satisfaction or dissatisfaction.

[0785] "Schedule" refers to the arrangement of class times and work schedules within educational institutions.

[0786] "Fund allocation" refers to plans and proposals for effectively allocating the budget to each item.

[0787] This invention is implemented by utilizing an information processing device to streamline the operations of educational institutions and to manage multiple elements in an integrated manner. Specific embodiments are shown below.

[0788] The terminal provides an interface for faculty and staff to input time information, budget status, and equipment usage information related to education. This allows users to easily register various types of data into the system. The terminal functions as a front-end for data entry, and the data is sent to the server.

[0789] The server functions as a center for analyzing received data, automatically generating educational schedules and proposing funding allocations. It utilizes a generative AI model to provide efficient and effective suggestions. Furthermore, the server employs sentiment analysis technology to collect user emotional feedback and incorporate it into the suggestions, seeking the optimal approach for each user. Natural language processing and machine learning techniques are used in this process.

[0790] As a concrete example, consider a case where a teacher requests three classes per week and seeks the most efficient use of equipment within their budget. The server automatically generates a proposal for the optimal schedule and funding allocation based on the input time information and budget data, and evaluates the user's response to it using sentiment analysis technology. Based on the satisfaction level, it then generates improvement suggestions.

[0791] As a concrete example of a prompt message for the generating AI model, a request such as, "Consider available classrooms on Monday and Wednesday afternoons and propose an efficient class schedule. Please make adjustments based on teacher satisfaction," can be set.

[0792] In this way, the present invention is a system that streamlines the operations of educational institutions while enabling a human-centered approach.

[0793] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0794] Step 1:

[0795] Users input faculty and staff availability, teaching preferences, equipment usage, and budget information through the terminal's interface. The entered data is organized by the terminal and sent to the server. This collects the basic data necessary for the operation of educational institutions.

[0796] Step 2:

[0797] The server analyzes time information and budget status received from the terminal. The generative AI model used here searches for efficient combinations of schedules and generates proposals for optimal class schedules and budget allocations. Based on the input data, the AI ​​model performs data calculations and presents the optimal schedule proposal as output.

[0798] Step 3:

[0799] The server uses sentiment analysis technology to analyze user reactions to the generated schedule and budget proposals. It acquires emotional feedback from users towards the system as data and analyzes it to evaluate satisfaction levels and dissatisfaction with the proposals.

[0800] Step 4:

[0801] The server fine-tunes the schedule and budget proposals based on the results of sentiment analysis. Specifically, it generates and presents alternatives and improvements to areas where the user expressed dissatisfaction. Data processing ensures that emotional feedback is reflected in the proposals.

[0802] Step 5:

[0803] The server aggregates the final operational data and provides a visualized dashboard. Through this dashboard, users can understand the efficiency and health of operations in real time and use this information to improve their work. The server integrates the analysis results and sentiment data to generate visual information as output and provides it to the user.

[0804] (Application Example 2)

[0805] 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".

[0806] Educational institutions need to improve operational efficiency while simultaneously reducing the emotional burden on teachers and administrators. However, conventional systems fail to consider emotional responses to schedule management and resource allocation, resulting in suboptimal suggestions and accumulated dissatisfaction. This invention aims to solve these problems.

[0807] 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.

[0808] In this invention, the server includes means for analyzing schedule data and automatically generating a timetable, means for recognizing emotional states and adjusting the generated suggestions based on user feedback, and means for visualizing operational data to assist in management decisions. This makes it possible to maximize the operational efficiency of educational institutions, reduce the burden on personnel, and provide users with a superior experience.

[0809] An "information processing system" is a computer-based system designed to collect, analyze, manage, and display data.

[0810] "Employee" is a term that refers to teachers and staff who perform duties at an educational institution.

[0811] "Educational facilities" refer to physical or virtual environments for conducting educational activities, including, for example, classrooms and online platforms.

[0812] A "timetable" is a table that lists the schedule of activities and events over a specific period in chronological order.

[0813] A "financial allocation proposal" refers to a specific proposal for the most effective allocation of available funds.

[0814] "Resource allocation" refers to a plan for optimizing the allocation of resources such as personnel, equipment, and time within an educational institution.

[0815] "Competency information" refers to information about the skills and expertise possessed by employees.

[0816] "Operational data" refers to various data related to the operational management of educational institutions, including schedules and resource utilization.

[0817] "Emotional state" refers to the psychological reactions and feedback that users experience while using the system.

[0818] "Feedback" refers to the reactions and opinions that users provide regarding the results of using the system and their suggestions.

[0819] The system for implementing the present invention mainly uses an information processing device (hereinafter referred to as "server") and terminal equipment. The server provides a mechanism to streamline the operation of educational institutions based on various data it receives. Users (teachers and administrators) input schedules, budgets, and facility information through terminals.

[0820] The server automatically generates a timetable based on the input schedule data. This is done using data analysis algorithms and calculations. The generated timetable can be adjusted according to the user's emotional state. This adjustment involves a software module called the emotion engine, which is achieved by analyzing user feedback.

[0821] The server also retrieves budget data and proposes financial allocations. This uses algorithms that analyze past usage trends. Furthermore, the server manages equipment usage information and presents optimal resource allocation plans. This process also takes into account employee competence information.

[0822] As a concrete example, if a teacher at an educational institution is dissatisfied with the class schedule, the server recognizes this emotional state and displays more appropriate alternatives for the user. This mechanism is made possible by using the Python language and an emotion analysis API.

[0823] Users can receive information processing in a visualized form, and a dashboard is provided to assist in management decisions. In this way, through an implementation that promotes operational improvements, educational institutions can achieve efficient and less burdensome operations.

[0824] An example of a prompt message might be: "Analyze the following feedback, assess the user's emotional state, and offer suggestions for stress reduction if necessary."

[0825] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0826] Step 1:

[0827] The terminal provides the user with an interface for various data inputs. Through this interface, the user inputs schedule information, budget data, equipment usage information, and staff competency information. The input in this step consists of various data provided by the user, while the output is an organized dataset stored in a database. The data is converted to an appropriate format and sent to the server.

[0828] Step 2:

[0829] The server generates a timetable based on the received schedule data. It processes the input data using a data analysis algorithm to create an appropriate timetable. The input here is the schedule data obtained in step 1, and the output is the generated timetable. The generated timetable is used to evaluate the sentiment engine.

[0830] Step 3:

[0831] The server uses an emotion engine to analyze the user's emotional state from their feedback. The input is the target feedback data, which the emotion engine processes to quantify the emotional state. The output is the emotional analysis result, providing a numerical evaluation board for a specific emotional state. Based on this result, the server instructs the user to make necessary adjustments.

[0832] Step 4:

[0833] The server optimizes the timetable and budget proposal based on the analyzed emotional state. The inputs are the timetable obtained in step 2 and the emotional state data from step 3. The output is an optimized proposal that reduces the user's burden. Specifically, this involves reorganizing the timetable and adjusting the budget allocation.

[0834] Step 5:

[0835] The server provides administrators and faculty with optimized suggestions in a visualized format. The input is the suggestion information generated in step 4, and the output is a visual dashboard. The dashboard is designed to be easily understood by users using a graphical user interface.

[0836] Step 6:

[0837] The user reviews the presented dashboard and makes operational decisions. The input is the dashboard information provided by the server, and the output is the improved decision-making process. Based on the information built on the dashboard, the user adopts specific operational improvement measures.

[0838] 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.

[0839] 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.

[0840] 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.

[0841] 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.

[0842] 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.

[0843] 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.

[0844] 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.

[0845] 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.

[0846] 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."

[0847] 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.

[0848] 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.

[0849] 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.

[0850] 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.

[0851] 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.

[0852] 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.

[0853] 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 this memory.

[0854] 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.

[0855] 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.

[0856] 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.

[0857] 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.

[0858] 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.

[0859] The following is further disclosed regarding the embodiments described above.

[0860] (Claim 1)

[0861] A computing device for streamlining the operations of educational institutions,

[0862] A means of collecting schedule data related to teachers and the educational environment,

[0863] A means for analyzing the aforementioned schedule data and automatically generating a timetable,

[0864] A means of acquiring budget data, analyzing it, and proposing fund allocation,

[0865] A means of managing equipment usage information and proposing the optimal equipment layout,

[0866] A means of optimizing staffing using employee skill information,

[0867] A means to visualize operational data and support management decisions,

[0868] A system that includes this.

[0869] (Claim 2)

[0870] The system according to claim 1, wherein the means for automatically generating the timetable uses an algorithm that equalizes the burden on educators.

[0871] (Claim 3)

[0872] The system according to claim 1, wherein the means for making the budget allocation proposal uses an algorithm that analyzes past budget usage trend data to predict future budgets.

[0873] "Example 1"

[0874] (Claim 1)

[0875] An information processing device,

[0876] Means for collecting timetable information regarding teachers and educational facilities,

[0877] A means for analyzing the aforementioned timetable information and automatically constructing learning activities,

[0878] A means of acquiring resource allocation data, processing it, and proposing usage plans,

[0879] A means of managing resource usage information and suggesting efficient resource allocation,

[0880] A means of optimizing labor force allocation using employee competency information,

[0881] A means to visualize operational information and support decision-making,

[0882] A system that includes this.

[0883] (Claim 2)

[0884] The system according to claim 1, wherein the means for automatically structuring the learning activities uses a calculation method that equalizes the burden on educators.

[0885] (Claim 3)

[0886] The system according to claim 1, wherein the means for proposing the aforementioned usage plan uses a calculation method that analyzes past resource usage records and predicts future resource management.

[0887] "Application Example 1"

[0888] (Claim 1)

[0889] A data processing device for streamlining the operations of educational institutions,

[0890] Means for collecting planning data on educators and educational settings,

[0891] A means for analyzing the aforementioned planning data and automatically generating a teaching schedule,

[0892] A means of acquiring and analyzing financial data to propose financial allocation,

[0893] A means of managing equipment usage information and proposing the optimal equipment layout,

[0894] A means of optimizing staffing using employee skills information,

[0895] A means of visualizing operational information and supporting management decisions,

[0896] It provides a user-friendly interface and a means to update data in real time.

[0897] A system that includes this.

[0898] (Claim 2)

[0899] The system according to claim 1, wherein the means for automatically generating the teaching schedule uses an optimization method that equalizes the burden on educators.

[0900] (Claim 3)

[0901] The system according to claim 1, wherein the means for making the aforementioned fund allocation proposal uses a method that analyzes past fund usage trend data to predict future fund allocation.

[0902] "Example 2 of combining an emotion engine"

[0903] (Claim 1)

[0904] An information processing device for streamlining the operational tasks of educational institutions,

[0905] Means for collecting time information and budget status related to education,

[0906] A means for analyzing the collected information and automatically generating a schedule of educational activities,

[0907] A means of analyzing financial data to propose fund allocation,

[0908] A means of managing equipment usage and proposing appropriate equipment placement,

[0909] Means for optimizing the allocation of human resources while considering their capabilities,

[0910] A means of visualizing operational data to support operational decision-making,

[0911] A means of acquiring and analyzing user emotional data using emotion analysis technology and reflecting it in the proposed content,

[0912] A means of adjusting the generated educational schedule and funding allocation proposals based on user emotional feedback,

[0913] A system that includes this.

[0914] (Claim 2)

[0915] The system according to claim 1, wherein the means for automatically generating the schedule of the aforementioned educational activities uses a mathematical model for equalizing the workload of educators.

[0916] (Claim 3)

[0917] The system according to claim 1, wherein the means for proposing the allocation of funds uses a model that analyzes previous fund usage data to predict future fund allocation.

[0918] "Application example 2 when combining with an emotional engine"

[0919] (Claim 1)

[0920] An information processing system for streamlining the operations of educational institutions,

[0921] A means of collecting schedule data related to employees and educational facilities,

[0922] A means for analyzing the aforementioned schedule data and automatically generating a timetable,

[0923] A means of acquiring budget data, analyzing it, and proposing financial allocations,

[0924] A means of managing equipment usage information and proposing the optimal resource allocation,

[0925] A means of optimizing staffing using employee competency information,

[0926] A means to visualize operational data and assist in management decisions,

[0927] A means of recognizing emotional states and adjusting the suggestions generated based on user feedback,

[0928] A system that includes this.

[0929] (Claim 2)

[0930] The system according to claim 1, wherein the means for automatically generating the timetable uses an algorithm that equalizes the burden on educators.

[0931] (Claim 3)

[0932] The system according to claim 1, wherein the means for making the financial allocation proposal uses an algorithm that analyzes past financial usage trend data to predict future financials. [Explanation of Symbols]

[0933] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots< / url:> < / url:> < / url:> < / url:>

Claims

1. A computing device for streamlining the operations of educational institutions, A means of collecting schedule data related to teachers and the educational environment, A means for analyzing the aforementioned schedule data and automatically generating a timetable, A means of acquiring budget data, analyzing it, and proposing fund allocation, A means of managing equipment usage information and proposing the optimal equipment layout, A means of optimizing staffing using employee skill information, A means to visualize operational data and support management decisions, A system that includes this.

2. The system according to claim 1, wherein the means for automatically generating the timetable uses an algorithm that equalizes the burden on educators.

3. The system according to claim 1, wherein the means for making the budget allocation proposal uses an algorithm that analyzes past budget usage trend data to predict future budgets.

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