system

Abstract

We provide the system. [Solution] A system designed to reduce the workload of members of educational institutions, Means for receiving candidate information, A means of storing and organizing the candidate information received, A means of matching candidates with educational institutions using information stored based on requests from educational institutions, Means for communicating the correspondence results, A means for receiving task information necessary to perform household support tasks and controlling the robot's movements to execute those tasks, A system that includes this.

Description

Technical Field

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

Background Art

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

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In educational institutions, there is a problem that the workload of teaching staff increases and the quality of education deteriorates. This problem is caused by the introduction of small-class teaching and the diversification of student support, which makes teaching staff overwhelmed by the increasing workload and unable to concentrate on their original educational activities. As a result, the physical and mental burden of teaching staff increases, leading to a decrease in job satisfaction and a reduction in the number of prospective teaching staff, and there is a risk that the overall function of educational institutions will decline.

Means for Solving the Problems

[0005] This invention provides an information processing system for reducing the workload of faculty and staff at educational institutions. This system receives and stores candidate information and, based on requests from educational institutions, matches the most suitable candidates with those institutions. Furthermore, by notifying the matching results and providing means for considering necessary skills and experience information, it enables the efficient dispatch of appropriate personnel, thereby reducing the workload of faculty and staff.

[0006] An "educational institution" is a facility or organization that specializes in education, and includes elementary schools, junior high schools, high schools, universities, and so on.

[0007] "Teachers and staff" refers to teachers and other personnel working at educational institutions, who are engaged in educational activities and administrative work.

[0008] "Workload" refers to additional work and responsibilities that teachers and staff members undertake in addition to their regular duties, and is a burden that can potentially lead to overwork.

[0009] "Candidate information" refers to personal information of individuals who have applied for a position, such as their name, skills, experience, and preferred work location.

[0010] "Request information" refers to information issued by educational institutions regarding specific skill and personnel needs, and is used in the matching process.

[0011] "Matching" refers to the process of selecting the most suitable personnel based on candidate information and requirements.

[0012] "Notification" refers to a means of communicating information to inform stakeholders of matching and other processing results.

[0013] "Skills" refer to the abilities and techniques necessary to perform a specific job or activity.

[0014] "Experience" refers to a practical history of job duties and records of past work performance.

[0015] An "information processing system" is a computer-based system designed to collect, store, analyze, and communicate data.

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.

Embodiment 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 provides an information processing system to reduce the workload of faculty and staff at educational institutions. Specifically, it is a mechanism for effectively dispatching personnel with IT skills and personnel who can alleviate administrative tasks to educational institutions. This system operates based on the following process.

[0038] System Overview

[0039] 1. User (Education Committee Representative):

[0040] Candidate information is entered into the system using a terminal. The information entered includes the candidate's name, skills, experience, and preferred work location.

[0041] Additionally, information requested by the educational institution where the worker will be dispatched is entered, including the required skills and the duration of the assignment.

[0042] 2. Server:

[0043] The received information is saved to a database, and its integrity is verified.

[0044] Tags are assigned based on skills, preferred work location, etc., to prepare for future searches and matching.

[0045] 3. Execute the matching algorithm:

[0046] The server selects the most suitable candidates based on stored candidate information and educational institution requests.

[0047] The matching algorithm prioritizes candidates based on their skill suitability, experience level, and preferences.

[0048] 4. Notification to the user:

[0049] The matching results are sent to the user's device, and adjustments or rematching are possible if necessary.

[0050] The notification will include specific details about the candidate or the educational institution where they will be placed.

[0051] Specific example

[0052] For example, in a situation where a junior high school in a certain city is seeking the dispatch of an administrative assistant with IT skills, this system would operate as follows:

[0053] The user (the person in charge at the Board of Education) registers candidate A, who has IT skills, in the system. Candidate A's experience includes a history of working at a junior high school.

[0054] Based on that information, the server extracts requests from junior high schools and sets priorities.

[0055] As a result, the server successfully matches candidate A with the middle school and notifies the user of the details. This information includes the assignment period and job details.

[0056] This system is expected to efficiently allocate appropriate personnel to educational institutions and reduce the workload of teachers and staff.

[0057] The following describes the processing flow.

[0058] Step 1:

[0059] The user (education board representative) logs into the system from their terminal and opens the candidate information input form. They enter detailed information such as the candidate's name, skills, experience, and desired work location, and then submit it to the server.

[0060] Step 2:

[0061] The server saves the received candidate information to the database. Once saved, it checks the integrity of the information and requests the user to correct any missing or inconsistent information.

[0062] Step 3:

[0063] The server retrieves request information from educational institutions and organizes requirements such as necessary skills, work location, and desired assignment period.

[0064] Step 4:

[0065] The server runs a matching algorithm based on candidate and request information. It selects the best candidate and educational institution combination, taking into account skill suitability, experience, and desired conditions.

[0066] Step 5:

[0067] The server generates matching results and sends them to the user's device. The notification includes detailed information about the candidate and the placement location.

[0068] Step 6:

[0069] Users can review the received matching results on their device and request adjustments or rematching from the server as needed. Once adjustments are complete, they can approve the final dispatch information.

[0070] Step 7:

[0071] The server notifies both the candidate and the educational institution of approved placement information and generates the necessary placement contracts and related documents. The notification includes the placement start date and details of the assigned duties.

[0072] (Example 1)

[0073] 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."

[0074] Traditional educational institutions are facing increasing workloads for faculty and staff, particularly those requiring IT skills and administrative tasks. Furthermore, the rapid and efficient deployment of appropriate personnel is difficult, resulting in delays in providing necessary support. Additionally, the selection of suitable candidates is becoming more complex, and the process may not function effectively.

[0075] 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.

[0076] In this invention, the server includes means for inputting candidate information, means for inputting request information, means for storing received information and verifying its integrity, means for tagging to improve search efficiency, means for performing matching based on skill suitability, and means for sending matching results to a terminal via notification. This makes it possible to respond quickly to the needs of educational institutions and to timely assign appropriate personnel while reducing their burden.

[0077] "Candidate information" refers to data about individuals applying for a specific job or role, including their name, skills, experience, and preferred work location.

[0078] "Request information" refers to detailed requirements such as the skills and personnel placement requirements sought by educational institutions and organizations, as well as the duration of assignment and work location.

[0079] "Tagging" is the process of assigning specific labels and keywords to candidate and request information, and is a technology used to streamline future searches and matching.

[0080] "Matching" is the process of selecting the most suitable personnel based on candidate information and requirements, and it is a series of activities carried out based on the compatibility of skills and experience.

[0081] A "terminal" refers to a type of computer equipment used for inputting and outputting information, such as personal computers and tablets.

[0082] A "server" is a computer system used to store and process data on a network and to provide various services.

[0083] This invention provides an information processing system to reduce the workload of faculty and staff at educational institutions. Specifically, it realizes a mechanism that utilizes information technology to quickly dispatch appropriate personnel to educational institutions. This system primarily functions through applications running on a server, accessed by users via terminals.

[0084] Users input candidate and request information from their terminals using a web browser. The entered information is sent to the server via an HTTP request. The server stores this information using a database management system (e.g., MySQL® or PostgreSQL) and performs a validation process to verify data integrity. Furthermore, it uses a programming language such as Python to tag the information. This process prepares the information for efficient searching.

[0085] The server executes a matching algorithm using pre-stored information. This algorithm is implemented using machine learning libraries such as Scikit-learn and selects the most suitable candidates based on their skills and experience. The selection results are sent from the server to the user's device via a notification system. Notifications are sent using email notifications via an SMTP server or push notifications via Firebase Cloud Messaging.

[0086] The following is a specific example. If a junior high school in a certain city is looking for an administrative assistant with IT skills, the user registers candidate A, who meets the needs, in the system via a terminal. Candidate A's past experience, including a history of working at a junior high school, is also registered. The server runs an algorithm based on candidate A's information, matches it with the junior high school's request information, and achieves an optimized match. The matching result is notified to the user.

[0087] An example of a prompt for the generating AI model is: "Please explain in detail the staffing system in educational institutions. Please also describe the specific processes and technologies used." Using this sentence, it is possible to obtain information about the system's overview and characteristics.

[0088] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0089] Step 1:

[0090] Users enter candidate information using their devices. Specifically, they enter data such as their name, skills, experience, and desired work location into a form in their web browser and submit it. The entered data is sent to the server via the HTTP protocol. This is how candidate information is collected.

[0091] Step 2:

[0092] Users enter request information from educational institutions on their devices. This is done by entering detailed information such as required skills, assignment period, and work location into a web form. The entered data is sent to the server via an HTTP request. This allows educational institution requests to be collected.

[0093] Step 3:

[0094] The server receives candidate and request information and stores it in a database. It uses a database management system such as MySQL to perform validation to verify the integrity of the information. This process checks whether the data is entered correctly and generates error messages as needed.

[0095] Step 4:

[0096] The server performs tagging based on the received information. Using a Python script, it assigns keywords and labels corresponding to each candidate and their request information. This improves the searchability of the information and streamlines future matching processes. The output of this step is the tagged data.

[0097] Step 5:

[0098] The server executes a matching algorithm using tagged information. The algorithm is implemented using machine learning libraries such as Scikit-learn and selects the most suitable candidates based on skill suitability and experience. Candidate information and request information are taken as input, and an optimized candidate list is generated as output.

[0099] Step 6:

[0100] The server notifies the user's terminal of the matching results. The results are sent via email or application notification using an SMTP server or push notification service. Based on the received results, the user can adjust the dispatch procedure or rematch.

[0101] (Application Example 1)

[0102] 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."

[0103] In recent years, the workload of teachers and staff in educational institutions has increased, making it difficult for them to dedicate themselves to teaching. Furthermore, the burden of general administrative tasks and miscellaneous duties is significant, raising concerns about a decline in the quality of education. It is necessary to address these issues and achieve efficient staffing and automation of tasks.

[0104] 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.

[0105] In this invention, the server includes means for receiving candidate information, means for storing and organizing the received candidate information, means for matching candidates with educational facilities using the stored information based on request information from educational institutions, means for transmitting the matching results, and means for receiving task information necessary to perform household support work and controlling robot movements for execution. This makes it possible to improve the work efficiency and reduce the workload of teachers and staff at educational institutions.

[0106] A "data processing system" is a computer-based system that receives, stores, processes, and outputs information to improve the efficiency of business operations.

[0107] "Candidate information" refers to the skills, work experience, and other relevant personal information of the person in question.

[0108] "Educational institutions" refer to organizations that include schools, cram schools, and other organizations and facilities that provide learning.

[0109] "Request information" refers to information that includes specific requirements such as the skills and job duties required by educational institutions, as well as dispatch conditions.

[0110] "Matching" refers to the process of comparing individual candidate information with the requirements of educational institutions to create the optimal combination.

[0111] "Means for controlling robot movements" refers to the software and hardware systems used to plan and send commands for robot movements.

[0112] The system for implementing this invention is configured to reduce the workload within educational institutions. The server receives candidate information based on requests from educational institutions, stores and organizes it. Furthermore, it performs a process to match the most suitable personnel with educational facilities based on the request information and candidate information. The matching results are notified to the user, who can then view the detailed information.

[0113] One of the key functions of this system is the ability to control the movements of robots and automate household chore support tasks within educational facilities. The server utilizes a cloud platform (e.g., Google Cloud Platform) to receive task information in real time and send commands to the robots. The robots are equipped with an AI computing module using the NVIDIA Jetson series, and operate autonomously while understanding the physical environment in conjunction with Lidar sensors.

[0114] As a concrete example, in one junior high school, robots assist with the preparation and cleanup of club activities. A server sends instructions to the robots based on the activity schedule, specifying what and how to prepare. Based on these instructions, the robots prepare the necessary materials and tools, and automatically clean up the tools after the activity is finished.

[0115] An example of a prompt statement that utilizes a generative AI model is, "Please provide detailed instructions on the steps to design a robot that will assist with daily tasks in educational institutions." Using this prompt statement allows for an efficient understanding of how the generative AI can be used in this system.

[0116] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0117] Step 1:

[0118] Users enter request information from educational institutions into a terminal. This information includes required skills, job duties, and dispatch conditions. The entered data is sent to a server. The server stores the received request information in a database. This data processing serves as the basis for subsequent searches and mappings.

[0119] Step 2:

[0120] The server receives candidate information. This information includes skills, work experience, and preferred work location. This information is stored in a database and tagged according to each attribute. This allows for the creation of data ready for matching with request information.

[0121] Step 3:

[0122] The server matches educational institution requests with candidate information. Using a skills matching algorithm, it selects the candidate best suited to the request and generates the matching results. This process involves data retrieval and scoring for prioritization, identifying the optimal candidate.

[0123] Step 4:

[0124] The results of the mapping are notified to the user. The user can receive the notification and review the information. The notification includes detailed information about the assignment location and specific job duties. This supports the smooth performance of duties by faculty and staff.

[0125] Step 5:

[0126] The server plans the robot's actions based on existing information. It uses Google Cloud Platform to collect task information in real time and sends it to the NVIDIA Jetson series robot. Based on the received commands, the robot performs household assistance tasks in the physical environment.

[0127] Step 6:

[0128] The generative AI model performs inference based on the prompt text. The prompt text used is "Please provide detailed steps for designing a robot to assist with daily tasks in educational institutions." This information will be used as reference data when the user designs and optimizes the robot and system.

[0129] 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.

[0130] This invention provides an information processing system incorporating an emotion engine to reduce the workload of faculty and staff in educational institutions. By introducing the emotion engine, it is possible to recognize the user's emotional state based on candidate information and request information entered by the user, thereby improving the efficiency and accuracy of system operation.

[0131] System Overview

[0132] 1. User (Education Committee Representative):

[0133] Candidate information and request information are entered into the system using a terminal. In addition to regular information, an emotion engine analyzes the inputter's emotional state in real time and identifies states such as stress and anxiety.

[0134] 2. Server:

[0135] The received information is stored in a database, and its integrity is checked. At the same time, user compliance is verified based on the emotional information recognized by the emotion engine, and the interface is adjusted as needed.

[0136] By taking emotional states into consideration, the input flow is flexibly changed to optimize the user experience.

[0137] 3. Matching process:

[0138] The matching algorithm is executed based on the emotional state provided by the emotion engine. If the user's dissatisfaction or stress level is high, the algorithm flexibly operates using special parameters to address it.

[0139] The matching results are designed to be prioritized based on data obtained from the emotion engine.

[0140] 4. Notifications and Feedback:

[0141] The server sends matching results, taking priority into account, to the user's device. The notification includes a message customized with sentiment information, and feedback is requested as appropriate.

[0142] Users provide feedback via their devices, and this information is sent to the server. The emotion engine analyzes the feedback to help with future processing and uses it as evaluation data.

[0143] Specific example

[0144] For example, suppose a user is in a situation where they need advanced IT skills and are entering candidate information to address a sudden labor shortage. If the emotion engine determines that the user is experiencing high levels of stress, the system will prioritize and quickly match them with suitable candidates to alleviate their stress.

[0145] This system is expected to enable the optimal allocation of personnel while taking their emotional state into consideration, thereby effectively reducing the workload of staff at educational institutions.

[0146] The following describes the processing flow.

[0147] Step 1:

[0148] The user (education board representative) logs into the terminal and accesses a screen to enter candidate and educational institution request information. The emotion engine analyzes the user's input actions and reactions, and assesses the user's level of excitement, stress, or anxiety in real time.

[0149] Step 2:

[0150] The server stores the received candidate and request information in a database. The emotion engine detects the user's emotional state, and if it determines, for example, that the stress level is high, it takes measures such as changing the interface's color scheme to a calmer tone.

[0151] Step 3:

[0152] The server analyzes request information from educational institutions and compares it with candidate information to perform the best possible matching. It then incorporates information from the emotion engine and adjusts priorities as much as possible to reduce stress.

[0153] Step 4:

[0154] The server notifies the user's device of the optimized matching results. The content of the notification is adjusted based on the user's emotional state. For example, it may include a message that provides reassurance.

[0155] Step 5:

[0156] The user reviews the notification and provides feedback. This feedback concerns the accuracy of the emotional state and matching precision based on the system's recognition results, and is sent from the device to the server.

[0157] Step 6:

[0158] The server receives feedback and uses it to improve the emotion engine and matching algorithms. The recorded data contributes to improving the system's performance in the next processing cycle.

[0159] (Example 2)

[0160] 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".

[0161] Faculty and staff at educational institutions bear a heavy workload, and appropriate personnel allocation is a particularly time-consuming and labor-intensive challenge. Traditional systems often failed to consider emotional factors when processing input information, resulting in inefficient matching. This leads to prolonged stressful situations and a decline in work efficiency and accuracy.

[0162] 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.

[0163] In this invention, the server includes an emotion analysis means for receiving candidate information and request information and analyzing the emotional state of the inputter; a means for storing the received information and checking the integrity of the information; and a means for executing a matching algorithm that takes the emotional state into consideration to achieve the optimal combination of candidate and educational organization. This reduces the workload of faculty and staff at educational institutions and enables rapid and accurate personnel placement that takes emotions into account.

[0164] An "educational institution" refers to an organization that provides education to students, and includes schools and universities.

[0165] "Teaching staff" refers to employees working at educational institutions, including teachers and administrative staff.

[0166] "Workload" refers to the burden imposed by the duties that teachers and staff at educational institutions must perform on a daily basis.

[0167] An "information processing system" refers to a system that automates the entire process of receiving, storing, analyzing, and outputting data.

[0168] "Candidate information" refers to data about a person who is suitable for a specific job, and includes skills, experience, qualifications, etc.

[0169] "Request information" refers to information that includes the requirements and conditions for personnel presented by educational institutions.

[0170] "Emotional state" refers to the psychological situation a user is currently in, and includes emotions such as stress and anxiety.

[0171] "Emotional analysis methods" refer to a technical process that analyzes a person's emotional state based on their input and actions.

[0172] "Consistency check" refers to the process of verifying whether the received information is accurate and free of contradictions.

[0173] A "matching algorithm" refers to a procedure or calculation method for optimally combining two or more elements based on specific conditions.

[0174] "Feedback" refers to actions, including evaluations and opinions, that system users provide regarding the system's operation.

[0175] A "notification message" refers to a portion of the information that a system sends to a user, specifically text or digital communication intended to inform them of a particular state or result.

[0176] This invention is an information processing system that reduces the workload of teachers and staff in educational institutions, and features the use of an emotion engine. The system is configured as follows:

[0177] Hardware and software configuration:

[0178] Users (educational institution faculty and staff): They use terminals (information devices such as PCs and tablets) to input candidate information and request information into the system. A web browser or dedicated application runs on the terminal, providing an interface for users to input information. The user's input data is sent to a cloud server in real time, and the emotional state is analyzed by an emotion engine.

[0179] Server: Based on a cloud platform, the server receives input data and analyzes the user's emotional state using an emotion engine. The emotion analysis engine quantifies the user's stress and anxiety using algorithms for voice analysis and input pattern analysis. The received data is stored in a database, and the integrity of the information is automatically checked.

[0180] Matching System: The server matches educational institution requests with candidate information based on data stored in the database. Using information analyzed by the emotion engine, the matching algorithm performs flexible calculations that take into account the user's emotional state. For example, if the user is experiencing high levels of stress, it prioritizes providing a quick matching result.

[0181] Specific example:

[0182] For example, if a position requiring information technology skills suddenly becomes vacant at an educational institution, the user responsible for filling the position enters candidate information via a terminal. The system analyzes the user's stress level in real time and applies special parameters to the algorithm to support rapid personnel placement. This system is expected to quickly reduce the workload of faculty and staff.

[0183] Example of a prompt:

[0184] "Please describe a system designed to reduce the workload of staff in educational institutions. Explain how it uses an emotion engine to optimize staffing based on the emotional state of users."

[0185] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0186] Step 1:

[0187] User (Educational institution faculty / staff): Uses a terminal to enter candidate and request information. This includes detailed conditions such as skills, experience, duration, and start date required for the job. Once this information is entered, the terminal sends it to the server. The server performs an initial reception of the received information and prepares for sentiment analysis.

[0188] Step 2:

[0189] Server: The server receives information sent from the terminal and then uses an emotion engine to analyze the user's emotional state. Specific data used for emotion analysis include voice, input speed, and typing rhythm, which are used to quantify stress and anxiety levels. In this process, input data is supplied to the emotion engine, and an indicator of the emotional state is generated as the output of the analysis and stored in a database.

[0190] Step 3:

[0191] Server: The server checks the integrity of the received data. It automatically verifies for any missing or inconsistent information and sends correction requests to the user if necessary. This process includes data processing such as format validation, duplicate removal, and range checking, resulting in a dataset with guaranteed integrity as output.

[0192] Step 4:

[0193] Server: Using consistent data, the server runs the matching algorithm. It takes into account the results of the emotion engine analysis and prioritizes a quick response, especially in cases of high stress. At this stage, it evaluates whether the candidate's skills and experience match the requirements of the educational institution and generates a candidate list as the matching output.

[0194] Step 5:

[0195] Server: Based on matching results and sentiment analysis, the server notifies the user's device of the results. The notification includes a customized message with words of encouragement and guidance tailored to the user's emotional state. The server generates the notification message as output and awaits user feedback.

[0196] Step 6:

[0197] User: Review the provided matching results and enter feedback on the terminal. Feedback includes evaluations and suggestions for improvement. The terminal sends this information to the server, which uses it to perform further sentiment analysis and update the database. The feedback output provides improvement data that will be useful for future processing of the system.

[0198] (Application Example 2)

[0199] 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".

[0200] This invention aims to solve the problems of stress and inefficient work processes faced by teachers and staff due to the increasing workload in the education sector. It also seeks to solve the problem of improving the experience of residents when using public services within a smart city and enabling better public services by providing individualized responses based on emotional states.

[0201] 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.

[0202] In this invention, the server includes a device for receiving information on the needs to be addressed from the user, a device for storing and organizing the received information, and a technology for analyzing the user's emotional state through a mechanism for evaluating their mental state. This makes it possible to improve the work efficiency of teachers and staff, as well as enhance the quality of citizen services within the smart city.

[0203] "Workload in the field of education" refers to the mental and physical burden that arises from the content and volume of work that teachers and staff in educational institutions face on a daily basis.

[0204] A "data processing system" is a system consisting of a series of electronic processes and devices for receiving, storing, organizing, analyzing, and notifying input information.

[0205] "User" refers to an individual or organization that operates data processing systems in educational institutions or smart cities.

[0206] "Supported information" is a general term for information necessary for improving business processes in the education sector and providing citizen services.

[0207] A "device" is a collection of mechanical or electronic components designed to perform a specific function.

[0208] A "mechanism for evaluating mental states" refers to technologies and algorithms designed to analyze and evaluate a user's emotions and mental state.

[0209] "Technology for analyzing emotional states" refers to technologies that identify a person's emotional state based on user input and other observable data.

[0210] A "smart city" is a city that aims to improve the quality of urban life by utilizing information and communication technology to operate urban functions efficiently and sustainably.

[0211] The system for realizing this application is configured as follows to improve the user experience in the education sector and smart cities: The server functions as the central data processing system, receiving support information provided by the user and storing it in a database. The server organizes the information and analyzes the user's emotional state using a mechanism to evaluate their mental state as needed.

[0212] The mechanism for evaluating mental states uses natural language processing libraries (e.g., NLTK or spaCy) to identify the user's emotional state based on text and voice input. Based on the analysis of the emotional state, the server dynamically adjusts the user interface to optimize the user experience. A specific example is providing users with priority access to options that allow for smoother use of public transportation on crowded holidays when using public services.

[0213] The terminal functions as a platform where the user receives feedback from the server and responds as needed. The feedback sent from the terminal to the server is used as important evaluation data in subsequent processes. By using generative AI models, such processes can generate personalized prompts based on the user's emotional state.

[0214] An example of a prompt message might be, "Think of ways to help residents use public transportation smoothly on crowded holidays." This allows the server to optimize the delivery of public services and reduce the burden on users.

[0215] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0216] Step 1:

[0217] The user enters information about the support they need using a terminal. The entered information is sent to the server in text format. This input includes information about requests to educational institutions and information about the use of citizen services. The server receives this information and stores it in a database.

[0218] Step 2:

[0219] The server organizes the stored information and converts it into an appropriate data structure. At this point, a mechanism for evaluating mental states is activated. The server uses a natural language processing library to analyze the text information received from the user and detect the emotional state. The input for the analysis is text data, and the output is the user's emotional state (e.g., stressed, high, medium, low).

[0220] Step 3:

[0221] Based on the analysis of the user's emotional state, the server adjusts the user interface. Specifically, it provides users with high stress levels with a simplified interface. The input for this step is the user's emotional state information, and the output is the customized user interface.

[0222] Step 4:

[0223] The server uses a context-aware AI model to generate appropriate prompts. These prompts provide advice and information tailored to the user's situation. The input for this step is the user's emotional state and request information, and the output is the prompt.

[0224] Step 5:

[0225] The generated prompt is sent to the terminal via a feedback mechanism. The user reviews the prompt and enters feedback as needed. The feedback information is then sent back to the server and stored in the database. In this step, the input is the generated prompt, and the output is the user's feedback information.

[0226] 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.

[0227] 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.

[0228] 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.

[0229] [Second Embodiment]

[0230] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0231] 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.

[0232] 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).

[0233] 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.

[0234] 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.

[0235] 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).

[0236] 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.

[0237] 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.

[0238] 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.

[0239] 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.

[0240] 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.

[0241] 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".

[0242] This invention provides an information processing system to reduce the workload of faculty and staff at educational institutions. Specifically, it is a mechanism for effectively dispatching personnel with IT skills and personnel who can alleviate administrative tasks to educational institutions. This system operates based on the following process.

[0243] System Overview

[0244] 1. User (Education Committee Representative):

[0245] Candidate information is entered into the system using a terminal. The information entered includes the candidate's name, skills, experience, and preferred work location.

[0246] Additionally, information requested by the educational institution where the worker will be dispatched is entered, including the required skills and the duration of the assignment.

[0247] 2. Server:

[0248] The received information is saved to a database, and its integrity is verified.

[0249] Tags are assigned based on skills, preferred work location, etc., to prepare for future searches and matching.

[0250] 3. Execute the matching algorithm:

[0251] The server selects the most suitable candidates based on stored candidate information and educational institution requests.

[0252] The matching algorithm prioritizes candidates based on their skill suitability, experience level, and preferences.

[0253] 4. Notification to the user:

[0254] The matching results are sent to the user's device, and adjustments or rematching are possible if necessary.

[0255] The notification will include specific details about the candidate or the educational institution where they will be placed.

[0256] Specific example

[0257] For example, in a situation where a junior high school in a certain city is seeking the dispatch of an administrative assistant with IT skills, this system would operate as follows:

[0258] The user (the person in charge at the Board of Education) registers candidate A, who has IT skills, in the system. Candidate A's experience includes a history of working at a junior high school.

[0259] Based on that information, the server extracts requests from junior high schools and sets priorities.

[0260] As a result, the server successfully matches candidate A with the middle school and notifies the user of the details. This information includes the assignment period and job details.

[0261] This system is expected to efficiently allocate appropriate personnel to educational institutions and reduce the workload of teachers and staff.

[0262] The following describes the processing flow.

[0263] Step 1:

[0264] The user (education board representative) logs into the system from their terminal and opens the candidate information input form. They enter detailed information such as the candidate's name, skills, experience, and desired work location, and then submit it to the server.

[0265] Step 2:

[0266] The server saves the received candidate information to the database. Once saved, it checks the integrity of the information and requests the user to correct any missing or inconsistent information.

[0267] Step 3:

[0268] The server retrieves request information from educational institutions and organizes requirements such as necessary skills, work location, and desired assignment period.

[0269] Step 4:

[0270] The server runs a matching algorithm based on candidate and request information. It selects the best candidate and educational institution combination, taking into account skill suitability, experience, and desired conditions.

[0271] Step 5:

[0272] The server generates matching results and sends them to the user's device. The notification includes detailed information about the candidate and the placement location.

[0273] Step 6:

[0274] Users can review the received matching results on their device and request adjustments or rematching from the server as needed. Once adjustments are complete, they can approve the final dispatch information.

[0275] Step 7:

[0276] The server notifies both the candidate and the educational institution of approved placement information and generates the necessary placement contracts and related documents. The notification includes the placement start date and details of the assigned duties.

[0277] (Example 1)

[0278] 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".

[0279] Traditional educational institutions are facing increasing workloads for faculty and staff, particularly those requiring IT skills and administrative tasks. Furthermore, the rapid and efficient deployment of appropriate personnel is difficult, resulting in delays in providing necessary support. Additionally, the selection of suitable candidates is becoming more complex, and the process may not function effectively.

[0280] 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.

[0281] In this invention, the server includes means for inputting candidate information, means for inputting request information, means for storing the received information and checking its integrity, means for tagging to improve search efficiency, means for performing matching based on skill suitability, and means for transmitting the matching result to the terminal by notification. As a result, it becomes possible to quickly respond to the needs of educational institutions, reduce the burden, and timely allocate appropriate personnel.

[0282] "Candidate information" refers to information about personnel applying for specific positions or roles, and is data including name, skills, experience, desired work location, etc.

[0283] "Request information" refers to detailed requirements including skills and personnel placement requirements sought by educational institutions or organizations, as well as dispatch periods and work locations, etc.

[0284] "Tagging" is a process of attaching specific labels or keywords to candidate information and request information, and is a technology used to improve the efficiency of future searches and matching.

[0285] "Matching" is a process of selecting the optimal personnel based on candidate information and request information, and is a series of activities performed based on the suitability of skills and experience.

[0286] "Terminal" is a part of computer equipment used for information input and output, and refers to a personal computer, tablet, etc.

[0287] "Server" is a computer system used to store and process data on a network and provide various services.

[0288] This invention provides an information processing system for reducing the workload of teaching staff in educational institutions. Specifically, it realizes a mechanism for quickly dispatching appropriate personnel to educational institutions by utilizing information technology. This system mainly functions through an application operating on the server when the user uses a terminal.

[0289] Users input candidate and request information from their terminals using a web browser. The entered information is sent to the server via an HTTP request. The server stores this information using a database management system (e.g., MySQL or PostgreSQL) and performs a validation process to verify data integrity. Furthermore, it uses a programming language such as Python to tag the information. This process prepares the information for efficient searching.

[0290] The server executes a matching algorithm using pre-stored information. This algorithm is implemented using machine learning libraries such as Scikit-learn and selects the most suitable candidates based on their skills and experience. The selection results are sent from the server to the user's device via a notification system. Notifications are sent using email notifications via an SMTP server or push notifications via Firebase Cloud Messaging.

[0291] The following is a specific example. If a junior high school in a certain city is looking for an administrative assistant with IT skills, the user registers candidate A, who meets the needs, in the system via a terminal. Candidate A's past experience, including a history of working at a junior high school, is also registered. The server runs an algorithm based on candidate A's information, matches it with the junior high school's request information, and achieves an optimized match. The matching result is notified to the user.

[0292] An example of a prompt for the generating AI model is: "Please explain in detail the staffing system in educational institutions. Please also describe the specific processes and technologies used." Using this sentence, it is possible to obtain information about the system's overview and characteristics.

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

[0294] Step 1:

[0295] Users enter candidate information using their devices. Specifically, they enter data such as their name, skills, experience, and desired work location into a form in their web browser and submit it. The entered data is sent to the server via the HTTP protocol. This is how candidate information is collected.

[0296] Step 2:

[0297] Users enter request information from educational institutions on their devices. This is done by entering detailed information such as required skills, assignment period, and work location into a web form. The entered data is sent to the server via an HTTP request. This allows educational institution requests to be collected.

[0298] Step 3:

[0299] The server receives candidate and request information and stores it in a database. It uses a database management system such as MySQL to perform validation to verify the integrity of the information. This process checks whether the data is entered correctly and generates error messages as needed.

[0300] Step 4:

[0301] The server performs tagging based on the received information. Using a Python script, it assigns keywords and labels corresponding to each candidate and their request information. This improves the searchability of the information and streamlines future matching processes. The output of this step is the tagged data.

[0302] Step 5:

[0303] The server executes a matching algorithm using the tagged information. The algorithm is implemented using a machine learning library such as Scikit-learn to select the most suitable personnel based on skill compatibility and experience. As input, candidate information and requirement information are provided, and as output, an optimized candidate list is generated.

[0304] Step 6:

[0305] The server notifies the user's terminal of the matching results. Using an SMTP server or a push notification service, the results are sent as email or application notifications. The user can adjust the dispatch procedure or perform rematching based on the received results.

[0306] (Application Example 1)

[0307] 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".

[0308] In recent years, in educational institutions, the workload of teaching staff has increased, resulting in difficulties in concentrating on educational activities. In addition, there are concerns that the burden of general administrative work and chores is also large, leading to a decline in the quality of education. It is necessary to solve these situations and achieve efficient personnel allocation and automation of work.

[0309] 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.

[0310] In this invention, the server includes means for receiving candidate information, means for storing and organizing the received candidate information, means for associating candidates with educational institutions using the stored information based on requirement information from educational institutions, means for transmitting the association results, and means for receiving task information necessary for performing housework support work and controlling robot operations for execution. This enables the improvement of the work efficiency and the reduction of the workload of teaching staff in educational institutions.

[0311] A "data processing system" is a computer-based system that receives, stores, processes, and outputs information to improve the efficiency of business operations.

[0312] "Candidate information" refers to the skills, work experience, and other relevant personal information of the person in question.

[0313] "Educational institutions" refer to organizations that include schools, cram schools, and other organizations and facilities that provide learning.

[0314] "Request information" refers to information that includes specific requirements such as the skills and job duties required by educational institutions, as well as dispatch conditions.

[0315] "Matching" refers to the process of comparing individual candidate information with the requirements of educational institutions to create the optimal combination.

[0316] "Means for controlling robot movements" refers to the software and hardware systems used to plan and send commands for robot movements.

[0317] The system for implementing this invention is configured to reduce the workload within educational institutions. The server receives candidate information based on requests from educational institutions, stores and organizes it. Furthermore, it performs a process to match the most suitable personnel with educational facilities based on the request information and candidate information. The matching results are notified to the user, who can then view the detailed information.

[0318] One of the key functions of this system is the ability to control the movements of robots and automate household chore support tasks within educational facilities. The server utilizes a cloud platform (e.g., Google Cloud Platform) to receive task information in real time and send commands to the robots. The robots are equipped with an AI computing module using the NVIDIA Jetson series, and operate autonomously while understanding the physical environment in conjunction with Lidar sensors.

[0319] As a concrete example, in one junior high school, robots assist with the preparation and cleanup of club activities. A server sends instructions to the robots based on the activity schedule, specifying what and how to prepare. Based on these instructions, the robots prepare the necessary materials and tools, and automatically clean up the tools after the activity is finished.

[0320] An example of a prompt statement that utilizes a generative AI model is, "Please provide detailed instructions on the steps to design a robot that will assist with daily tasks in educational institutions." Using this prompt statement allows for an efficient understanding of how the generative AI can be used in this system.

[0321] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0322] Step 1:

[0323] Users enter request information from educational institutions into a terminal. This information includes required skills, job duties, and dispatch conditions. The entered data is sent to a server. The server stores the received request information in a database. This data processing serves as the basis for subsequent searches and mappings.

[0324] Step 2:

[0325] The server receives candidate information. This information includes skills, work experience, and preferred work location. This information is stored in a database and tagged according to each attribute. This allows for the creation of data ready for matching with request information.

[0326] Step 3:

[0327] The server matches educational institution requests with candidate information. Using a skills matching algorithm, it selects the candidate best suited to the request and generates the matching results. This process involves data retrieval and scoring for prioritization, identifying the optimal candidate.

[0328] Step 4:

[0329] The results of the mapping are notified to the user. The user can receive the notification and review the information. The notification includes detailed information about the assignment location and specific job duties. This supports the smooth performance of duties by faculty and staff.

[0330] Step 5:

[0331] The server plans the robot's actions based on existing information. It uses Google Cloud Platform to collect task information in real time and sends it to the NVIDIA Jetson series robot. Based on the received commands, the robot performs household assistance tasks in the physical environment.

[0332] Step 6:

[0333] The generative AI model performs inference based on the prompt text. The prompt text used is "Please provide detailed steps for designing a robot to assist with daily tasks in educational institutions." This information will be used as reference data when the user designs and optimizes the robot and system.

[0334] 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.

[0335] This invention provides an information processing system incorporating an emotion engine to reduce the workload of faculty and staff in educational institutions. By introducing the emotion engine, it is possible to recognize the user's emotional state based on candidate information and request information entered by the user, thereby improving the efficiency and accuracy of system operation.

[0336] System Overview

[0337] 1. User (Education Committee Representative):

[0338] Candidate information and request information are entered into the system using a terminal. In addition to regular information, an emotion engine analyzes the inputter's emotional state in real time and identifies states such as stress and anxiety.

[0339] 2. Server:

[0340] The received information is stored in a database, and its integrity is checked. At the same time, user compliance is verified based on the emotional information recognized by the emotion engine, and the interface is adjusted as needed.

[0341] By taking emotional states into consideration, the input flow is flexibly changed to optimize the user experience.

[0342] 3. Matching process:

[0343] The matching algorithm is executed based on the emotional state provided by the emotion engine. If the user's dissatisfaction or stress level is high, the algorithm flexibly operates using special parameters to address it.

[0344] The matching results are designed to be prioritized based on data obtained from the emotion engine.

[0345] 4. Notifications and Feedback:

[0346] The server sends matching results, taking priority into account, to the user's device. The notification includes a message customized with sentiment information, and feedback is requested as appropriate.

[0347] Users provide feedback via their devices, and this information is sent to the server. The emotion engine analyzes the feedback to help with future processing and uses it as evaluation data.

[0348] Specific example

[0349] For example, suppose a user is in a situation where they need advanced IT skills and are entering candidate information to address a sudden labor shortage. If the emotion engine determines that the user is experiencing high levels of stress, the system will prioritize and quickly match them with suitable candidates to alleviate their stress.

[0350] This system is expected to enable the optimal allocation of personnel while taking their emotional state into consideration, thereby effectively reducing the workload of staff at educational institutions.

[0351] The following describes the processing flow.

[0352] Step 1:

[0353] The user (education board representative) logs into the terminal and accesses a screen to enter candidate and educational institution request information. The emotion engine analyzes the user's input actions and reactions, and assesses the user's level of excitement, stress, or anxiety in real time.

[0354] Step 2:

[0355] The server stores the received candidate and request information in a database. The emotion engine detects the user's emotional state, and if it determines, for example, that the stress level is high, it takes measures such as changing the interface's color scheme to a calmer tone.

[0356] Step 3:

[0357] The server analyzes request information from educational institutions and compares it with candidate information to perform the best possible matching. It then incorporates information from the emotion engine and adjusts priorities as much as possible to reduce stress.

[0358] Step 4:

[0359] The server notifies the user's device of the optimized matching results. The content of the notification is adjusted based on the user's emotional state. For example, it may include a message that provides reassurance.

[0360] Step 5:

[0361] The user reviews the notification and provides feedback. This feedback concerns the accuracy of the emotional state and matching precision based on the system's recognition results, and is sent from the device to the server.

[0362] Step 6:

[0363] The server receives feedback and uses it to improve the emotion engine and matching algorithms. The recorded data contributes to improving the system's performance in the next processing cycle.

[0364] (Example 2)

[0365] 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".

[0366] Faculty and staff at educational institutions bear a heavy workload, and appropriate personnel allocation is a particularly time-consuming and labor-intensive challenge. Traditional systems often failed to consider emotional factors when processing input information, resulting in inefficient matching. This leads to prolonged stressful situations and a decline in work efficiency and accuracy.

[0367] 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.

[0368] In this invention, the server includes an emotion analysis means for receiving candidate information and request information and analyzing the emotional state of the inputter; a means for storing the received information and checking the integrity of the information; and a means for executing a matching algorithm that takes the emotional state into consideration to achieve the optimal combination of candidate and educational organization. This reduces the workload of faculty and staff at educational institutions and enables rapid and accurate personnel placement that takes emotions into account.

[0369] An "educational institution" refers to an organization that provides education to students, and includes schools and universities.

[0370] "Teaching staff" refers to employees working at educational institutions, including teachers and administrative staff.

[0371] "Workload" refers to the burden imposed by the duties that teachers and staff at educational institutions must perform on a daily basis.

[0372] An "information processing system" refers to a system that automates the entire process of receiving, storing, analyzing, and outputting data.

[0373] "Candidate information" refers to data about a person who is suitable for a specific job, and includes skills, experience, qualifications, etc.

[0374] "Request information" refers to information that includes the requirements and conditions for personnel presented by educational institutions.

[0375] "Emotional state" refers to the psychological situation a user is currently in, and includes emotions such as stress and anxiety.

[0376] "Emotional analysis methods" refer to a technical process that analyzes a person's emotional state based on their input and actions.

[0377] "Consistency check" refers to the process of verifying whether the received information is accurate and free of contradictions.

[0378] A "matching algorithm" refers to a procedure or calculation method for optimally combining two or more elements based on specific conditions.

[0379] "Feedback" refers to actions, including evaluations and opinions, that system users provide regarding the system's operation.

[0380] A "notification message" refers to a portion of the information that a system sends to a user, specifically text or digital communication intended to inform them of a particular state or result.

[0381] This invention is an information processing system that reduces the workload of teachers and staff in educational institutions, and features the use of an emotion engine. The system is configured as follows:

[0382] Hardware and software configuration:

[0383] Users (educational institution faculty and staff): They use terminals (information devices such as PCs and tablets) to input candidate information and request information into the system. A web browser or dedicated application runs on the terminal, providing an interface for users to input information. The user's input data is sent to a cloud server in real time, and the emotional state is analyzed by an emotion engine.

[0384] Server: Based on a cloud platform, the server receives input data and analyzes the user's emotional state using an emotion engine. The emotion analysis engine quantifies the user's stress and anxiety using algorithms for voice analysis and input pattern analysis. The received data is stored in a database, and the integrity of the information is automatically checked.

[0385] Matching System: The server matches educational institution requests with candidate information based on data stored in the database. Using information analyzed by the emotion engine, the matching algorithm performs flexible calculations that take into account the user's emotional state. For example, if the user is experiencing high levels of stress, it prioritizes providing a quick matching result.

[0386] Specific example:

[0387] For example, if a position requiring information technology skills suddenly becomes vacant at an educational institution, the user responsible for filling the position enters candidate information via a terminal. The system analyzes the user's stress level in real time and applies special parameters to the algorithm to support rapid personnel placement. This system is expected to quickly reduce the workload of faculty and staff.

[0388] Example of a prompt:

[0389] "Please describe a system designed to reduce the workload of staff in educational institutions. Explain how it uses an emotion engine to optimize staffing based on the emotional state of users."

[0390] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0391] Step 1:

[0392] User (Educational institution faculty / staff): Uses a terminal to enter candidate and request information. This includes detailed conditions such as skills, experience, duration, and start date required for the job. Once this information is entered, the terminal sends it to the server. The server performs an initial reception of the received information and prepares for sentiment analysis.

[0393] Step 2:

[0394] Server: The server receives information sent from the terminal and then uses an emotion engine to analyze the user's emotional state. Specific data used for emotion analysis include voice, input speed, and typing rhythm, which are used to quantify stress and anxiety levels. In this process, input data is supplied to the emotion engine, and an indicator of the emotional state is generated as the output of the analysis and stored in a database.

[0395] Step 3:

[0396] Server: The server checks the integrity of the received data. It automatically verifies for any missing or inconsistent information and sends correction requests to the user if necessary. This process includes data processing such as format validation, duplicate removal, and range checking, resulting in a dataset with guaranteed integrity as output.

[0397] Step 4:

[0398] Server: Using consistent data, the server runs the matching algorithm. It takes into account the results of the emotion engine analysis and prioritizes a quick response, especially in cases of high stress. At this stage, it evaluates whether the candidate's skills and experience match the requirements of the educational institution and generates a candidate list as the matching output.

[0399] Step 5:

[0400] Server: Based on matching results and sentiment analysis, the server notifies the user's device of the results. The notification includes a customized message with words of encouragement and guidance tailored to the user's emotional state. The server generates the notification message as output and awaits user feedback.

[0401] Step 6:

[0402] User: Review the provided matching results and enter feedback on the terminal. Feedback includes evaluations and suggestions for improvement. The terminal sends this information to the server, which uses it to perform further sentiment analysis and update the database. The feedback output provides improvement data that will be useful for future processing of the system.

[0403] (Application Example 2)

[0404] 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."

[0405] This invention aims to solve the problems of stress and inefficient work processes faced by teachers and staff due to the increasing workload in the education sector. It also seeks to solve the problem of improving the experience of residents when using public services within a smart city and enabling better public services by providing individualized responses based on emotional states.

[0406] 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.

[0407] In this invention, the server includes a device for receiving information on the needs to be addressed from the user, a device for storing and organizing the received information, and a technology for analyzing the user's emotional state through a mechanism for evaluating their mental state. This makes it possible to improve the work efficiency of teachers and staff, as well as enhance the quality of citizen services within the smart city.

[0408] "Workload in the field of education" refers to the mental and physical burden that arises from the content and volume of work that teachers and staff in educational institutions face on a daily basis.

[0409] A "data processing system" is a system consisting of a series of electronic processes and devices for receiving, storing, organizing, analyzing, and notifying input information.

[0410] "User" refers to an individual or organization that operates data processing systems in educational institutions or smart cities.

[0411] "Supported information" is a general term for information necessary for improving business processes in the education sector and providing citizen services.

[0412] A "device" is a collection of mechanical or electronic components designed to perform a specific function.

[0413] A "mechanism for evaluating mental states" refers to technologies and algorithms designed to analyze and evaluate a user's emotions and mental state.

[0414] "Technology for analyzing emotional states" refers to technologies that identify a person's emotional state based on user input and other observable data.

[0415] A "smart city" is a city that aims to improve the quality of urban life by utilizing information and communication technology to operate urban functions efficiently and sustainably.

[0416] The system for realizing this application is configured as follows to improve the user experience in the education sector and smart cities: The server functions as the central data processing system, receiving support information provided by the user and storing it in a database. The server organizes the information and analyzes the user's emotional state using a mechanism to evaluate their mental state as needed.

[0417] The mechanism for evaluating mental states uses natural language processing libraries (e.g., NLTK or spaCy) to identify the user's emotional state based on text and voice input. Based on the analysis of the emotional state, the server dynamically adjusts the user interface to optimize the user experience. A specific example is providing users with priority access to options that allow for smoother use of public transportation on crowded holidays when using public services.

[0418] The terminal functions as a platform where the user receives feedback from the server and responds as needed. The feedback sent from the terminal to the server is used as important evaluation data in subsequent processes. By using generative AI models, such processes can generate personalized prompts based on the user's emotional state.

[0419] An example of a prompt message might be, "Think of ways to help residents use public transportation smoothly on crowded holidays." This allows the server to optimize the delivery of public services and reduce the burden on users.

[0420] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0421] Step 1:

[0422] The user enters information about the support they need using a terminal. The entered information is sent to the server in text format. This input includes information about requests to educational institutions and information about the use of citizen services. The server receives this information and stores it in a database.

[0423] Step 2:

[0424] The server organizes the stored information and converts it into an appropriate data structure. At this point, a mechanism for evaluating mental states is activated. The server uses a natural language processing library to analyze the text information received from the user and detect the emotional state. The input for the analysis is text data, and the output is the user's emotional state (e.g., stressed, high, medium, low).

[0425] Step 3:

[0426] Based on the analysis of the user's emotional state, the server adjusts the user interface. Specifically, it provides users with high stress levels with a simplified interface. The input for this step is the user's emotional state information, and the output is the customized user interface.

[0427] Step 4:

[0428] The server uses a context-aware AI model to generate appropriate prompts. These prompts provide advice and information tailored to the user's situation. The input for this step is the user's emotional state and request information, and the output is the prompt.

[0429] Step 5:

[0430] The generated prompt is sent to the terminal via a feedback mechanism. The user reviews the prompt and enters feedback as needed. The feedback information is then sent back to the server and stored in the database. In this step, the input is the generated prompt, and the output is the user's feedback information.

[0431] 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.

[0432] 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.

[0433] 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.

[0434] [Third Embodiment]

[0435] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0436] 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.

[0437] 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).

[0438] 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.

[0439] 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.

[0440] 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).

[0441] 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.

[0442] 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.

[0443] 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.

[0444] 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.

[0445] 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.

[0446] 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".

[0447] This invention provides an information processing system to reduce the workload of faculty and staff at educational institutions. Specifically, it is a mechanism for effectively dispatching personnel with IT skills and personnel who can alleviate administrative tasks to educational institutions. This system operates based on the following process.

[0448] System Overview

[0449] 1. User (Education Committee Representative):

[0450] Candidate information is entered into the system using a terminal. The information entered includes the candidate's name, skills, experience, and preferred work location.

[0451] Additionally, information requested by the educational institution where the worker will be dispatched is entered, including the required skills and the duration of the assignment.

[0452] 2. Server:

[0453] The received information is saved to a database, and its integrity is verified.

[0454] Tags are assigned based on skills, preferred work location, etc., to prepare for future searches and matching.

[0455] 3. Execute the matching algorithm:

[0456] The server selects the most suitable candidates based on stored candidate information and educational institution requests.

[0457] The matching algorithm prioritizes candidates based on their skill suitability, experience level, and preferences.

[0458] 4. Notification to the user:

[0459] The matching results are sent to the user's device, and adjustments or rematching are possible if necessary.

[0460] The notification will include specific details about the candidate or the educational institution where they will be placed.

[0461] Specific example

[0462] For example, in a situation where a junior high school in a certain city is seeking the dispatch of an administrative assistant with IT skills, this system would operate as follows:

[0463] The user (the person in charge at the Board of Education) registers candidate A, who has IT skills, in the system. Candidate A's experience includes a history of working at a junior high school.

[0464] Based on that information, the server extracts requests from junior high schools and sets priorities.

[0465] As a result, the server successfully matches candidate A with the middle school and notifies the user of the details. This information includes the assignment period and job details.

[0466] This system is expected to efficiently allocate appropriate personnel to educational institutions and reduce the workload of teachers and staff.

[0467] The following describes the processing flow.

[0468] Step 1:

[0469] The user (education board representative) logs into the system from their terminal and opens the candidate information input form. They enter detailed information such as the candidate's name, skills, experience, and desired work location, and then submit it to the server.

[0470] Step 2:

[0471] The server saves the received candidate information to the database. Once saved, it checks the integrity of the information and requests the user to correct any missing or inconsistent information.

[0472] Step 3:

[0473] The server retrieves request information from educational institutions and organizes requirements such as necessary skills, work location, and desired assignment period.

[0474] Step 4:

[0475] The server runs a matching algorithm based on candidate and request information. It selects the best candidate and educational institution combination, taking into account skill suitability, experience, and desired conditions.

[0476] Step 5:

[0477] The server generates matching results and sends them to the user's device. The notification includes detailed information about the candidate and the placement location.

[0478] Step 6:

[0479] Users can review the received matching results on their device and request adjustments or rematching from the server as needed. Once adjustments are complete, they can approve the final dispatch information.

[0480] Step 7:

[0481] The server notifies both the candidate and the educational institution of approved placement information and generates the necessary placement contracts and related documents. The notification includes the placement start date and details of the assigned duties.

[0482] (Example 1)

[0483] 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."

[0484] Traditional educational institutions are facing increasing workloads for faculty and staff, particularly those requiring IT skills and administrative tasks. Furthermore, the rapid and efficient deployment of appropriate personnel is difficult, resulting in delays in providing necessary support. Additionally, the selection of suitable candidates is becoming more complex, and the process may not function effectively.

[0485] 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.

[0486] In this invention, the server includes means for inputting candidate information, means for inputting request information, means for storing received information and verifying its integrity, means for tagging to improve search efficiency, means for performing matching based on skill suitability, and means for sending matching results to a terminal via notification. This makes it possible to respond quickly to the needs of educational institutions and to timely assign appropriate personnel while reducing their burden.

[0487] "Candidate information" refers to data about individuals applying for a specific job or role, including their name, skills, experience, and preferred work location.

[0488] "Request information" refers to detailed requirements such as the skills and personnel placement requirements sought by educational institutions and organizations, as well as the duration of assignment and work location.

[0489] "Tagging" is the process of assigning specific labels and keywords to candidate and request information, and is a technology used to streamline future searches and matching.

[0490] "Matching" is the process of selecting the most suitable personnel based on candidate information and requirements, and it is a series of activities carried out based on the compatibility of skills and experience.

[0491] A "terminal" refers to a type of computer equipment used for inputting and outputting information, such as personal computers and tablets.

[0492] A "server" is a computer system used to store and process data on a network and to provide various services.

[0493] This invention provides an information processing system to reduce the workload of faculty and staff at educational institutions. Specifically, it realizes a mechanism that utilizes information technology to quickly dispatch appropriate personnel to educational institutions. This system primarily functions through applications running on a server, accessed by users via terminals.

[0494] Users input candidate and request information from their terminals using a web browser. The entered information is sent to the server via an HTTP request. The server stores this information using a database management system (e.g., MySQL or PostgreSQL) and performs a validation process to verify data integrity. Furthermore, it uses a programming language such as Python to tag the information. This process prepares the information for efficient searching.

[0495] The server executes a matching algorithm using pre-stored information. This algorithm is implemented using machine learning libraries such as Scikit-learn and selects the most suitable candidates based on their skills and experience. The selection results are sent from the server to the user's device via a notification system. Notifications are sent using email notifications via an SMTP server or push notifications via Firebase Cloud Messaging.

[0496] The following is a specific example. If a junior high school in a certain city is looking for an administrative assistant with IT skills, the user registers candidate A, who meets the needs, in the system via a terminal. Candidate A's past experience, including a history of working at a junior high school, is also registered. The server runs an algorithm based on candidate A's information, matches it with the junior high school's request information, and achieves an optimized match. The matching result is notified to the user.

[0497] An example of a prompt for the generating AI model is: "Please explain in detail the staffing system in educational institutions. Please also describe the specific processes and technologies used." Using this sentence, it is possible to obtain information about the system's overview and characteristics.

[0498] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0499] Step 1:

[0500] Users enter candidate information using their devices. Specifically, they enter data such as their name, skills, experience, and desired work location into a form in their web browser and submit it. The entered data is sent to the server via the HTTP protocol. This is how candidate information is collected.

[0501] Step 2:

[0502] Users enter request information from educational institutions on their devices. This is done by entering detailed information such as required skills, assignment period, and work location into a web form. The entered data is sent to the server via an HTTP request. This allows educational institution requests to be collected.

[0503] Step 3:

[0504] The server receives candidate and request information and stores it in a database. It uses a database management system such as MySQL to perform validation to verify the integrity of the information. This process checks whether the data is entered correctly and generates error messages as needed.

[0505] Step 4:

[0506] The server performs tagging based on the received information. Using a Python script, it assigns keywords and labels corresponding to each candidate and their request information. This improves the searchability of the information and streamlines future matching processes. The output of this step is the tagged data.

[0507] Step 5:

[0508] The server executes a matching algorithm using tagged information. The algorithm is implemented using machine learning libraries such as Scikit-learn and selects the most suitable candidates based on skill suitability and experience. Candidate information and request information are taken as input, and an optimized candidate list is generated as output.

[0509] Step 6:

[0510] The server notifies the user's terminal of the matching results. The results are sent via email or application notification using an SMTP server or push notification service. Based on the received results, the user can adjust the dispatch procedure or rematch.

[0511] (Application Example 1)

[0512] 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."

[0513] In recent years, the workload of teachers and staff in educational institutions has increased, making it difficult for them to dedicate themselves to teaching. Furthermore, the burden of general administrative tasks and miscellaneous duties is significant, raising concerns about a decline in the quality of education. It is necessary to address these issues and achieve efficient staffing and automation of tasks.

[0514] 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.

[0515] In this invention, the server includes means for receiving candidate information, means for storing and organizing the received candidate information, means for matching candidates with educational facilities using the stored information based on request information from educational institutions, means for transmitting the matching results, and means for receiving task information necessary to perform household support work and controlling robot movements for execution. This makes it possible to improve the work efficiency and reduce the workload of teachers and staff at educational institutions.

[0516] A "data processing system" is a computer-based system that receives, stores, processes, and outputs information to improve the efficiency of business operations.

[0517] "Candidate information" refers to the skills, work experience, and other relevant personal information of the person in question.

[0518] "Educational institutions" refer to organizations that include schools, cram schools, and other organizations and facilities that provide learning.

[0519] "Request information" refers to information that includes specific requirements such as the skills and job duties required by educational institutions, as well as dispatch conditions.

[0520] "Matching" refers to the process of comparing individual candidate information with the requirements of educational institutions to create the optimal combination.

[0521] "Means for controlling robot movements" refers to the software and hardware systems used to plan and send commands for robot movements.

[0522] The system for implementing this invention is configured to reduce the workload within educational institutions. The server receives candidate information based on requests from educational institutions, stores and organizes it. Furthermore, it performs a process to match the most suitable personnel with educational facilities based on the request information and candidate information. The matching results are notified to the user, who can then view the detailed information.

[0523] One of the key functions of this system is the ability to control the movements of robots and automate household chore support tasks within educational facilities. The server utilizes a cloud platform (e.g., Google Cloud Platform) to receive task information in real time and send commands to the robots. The robots are equipped with an AI computing module using the NVIDIA Jetson series, and operate autonomously while understanding the physical environment in conjunction with Lidar sensors.

[0524] As a concrete example, in one junior high school, robots assist with the preparation and cleanup of club activities. A server sends instructions to the robots based on the activity schedule, specifying what and how to prepare. Based on these instructions, the robots prepare the necessary materials and tools, and automatically clean up the tools after the activity is finished.

[0525] An example of a prompt statement that utilizes a generative AI model is, "Please provide detailed instructions on the steps to design a robot that will assist with daily tasks in educational institutions." Using this prompt statement allows for an efficient understanding of how the generative AI can be used in this system.

[0526] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0527] Step 1:

[0528] Users enter request information from educational institutions into a terminal. This information includes required skills, job duties, and dispatch conditions. The entered data is sent to a server. The server stores the received request information in a database. This data processing serves as the basis for subsequent searches and mappings.

[0529] Step 2:

[0530] The server receives candidate information. This information includes skills, work experience, and preferred work location. This information is stored in a database and tagged according to each attribute. This allows for the creation of data ready for matching with request information.

[0531] Step 3:

[0532] The server matches educational institution requests with candidate information. Using a skills matching algorithm, it selects the candidate best suited to the request and generates the matching results. This process involves data retrieval and scoring for prioritization, identifying the optimal candidate.

[0533] Step 4:

[0534] The results of the mapping are notified to the user. The user can receive the notification and review the information. The notification includes detailed information about the assignment location and specific job duties. This supports the smooth performance of duties by faculty and staff.

[0535] Step 5:

[0536] The server plans the robot's actions based on existing information. It uses Google Cloud Platform to collect task information in real time and sends it to the NVIDIA Jetson series robot. Based on the received commands, the robot performs household assistance tasks in the physical environment.

[0537] Step 6:

[0538] The generative AI model performs inference based on the prompt text. The prompt text used is "Please provide detailed steps for designing a robot to assist with daily tasks in educational institutions." This information will be used as reference data when the user designs and optimizes the robot and system.

[0539] 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.

[0540] This invention provides an information processing system incorporating an emotion engine to reduce the workload of faculty and staff in educational institutions. By introducing the emotion engine, it is possible to recognize the user's emotional state based on candidate information and request information entered by the user, thereby improving the efficiency and accuracy of system operation.

[0541] System Overview

[0542] 1. User (Education Committee Representative):

[0543] Candidate information and request information are entered into the system using a terminal. In addition to regular information, an emotion engine analyzes the inputter's emotional state in real time and identifies states such as stress and anxiety.

[0544] 2. Server:

[0545] The received information is stored in a database, and its integrity is checked. At the same time, user compliance is verified based on the emotional information recognized by the emotion engine, and the interface is adjusted as needed.

[0546] By taking emotional states into consideration, the input flow is flexibly changed to optimize the user experience.

[0547] 3. Matching process:

[0548] The matching algorithm is executed based on the emotional state provided by the emotion engine. If the user's dissatisfaction or stress level is high, the algorithm flexibly operates using special parameters to address it.

[0549] The matching results are designed to be prioritized based on data obtained from the emotion engine.

[0550] 4. Notifications and Feedback:

[0551] The server sends matching results, taking priority into account, to the user's device. The notification includes a message customized with sentiment information, and feedback is requested as appropriate.

[0552] Users provide feedback via their devices, and this information is sent to the server. The emotion engine analyzes the feedback to help with future processing and uses it as evaluation data.

[0553] Specific example

[0554] For example, suppose a user is in a situation where they need advanced IT skills and are entering candidate information to address a sudden labor shortage. If the emotion engine determines that the user is experiencing high levels of stress, the system will prioritize and quickly match them with suitable candidates to alleviate their stress.

[0555] This system is expected to enable the optimal allocation of personnel while taking their emotional state into consideration, thereby effectively reducing the workload of staff at educational institutions.

[0556] The following describes the processing flow.

[0557] Step 1:

[0558] The user (education board representative) logs into the terminal and accesses a screen to enter candidate and educational institution request information. The emotion engine analyzes the user's input actions and reactions, and assesses the user's level of excitement, stress, or anxiety in real time.

[0559] Step 2:

[0560] The server stores the received candidate and request information in a database. The emotion engine detects the user's emotional state, and if it determines, for example, that the stress level is high, it takes measures such as changing the interface's color scheme to a calmer tone.

[0561] Step 3:

[0562] The server analyzes request information from educational institutions and compares it with candidate information to perform the best possible matching. It then incorporates information from the emotion engine and adjusts priorities as much as possible to reduce stress.

[0563] Step 4:

[0564] The server notifies the user's device of the optimized matching results. The content of the notification is adjusted based on the user's emotional state. For example, it may include a message that provides reassurance.

[0565] Step 5:

[0566] The user reviews the notification and provides feedback. This feedback concerns the accuracy of the emotional state and matching precision based on the system's recognition results, and is sent from the device to the server.

[0567] Step 6:

[0568] The server receives feedback and uses it to improve the emotion engine and matching algorithms. The recorded data contributes to improving the system's performance in the next processing cycle.

[0569] (Example 2)

[0570] 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."

[0571] Faculty and staff at educational institutions bear a heavy workload, and appropriate personnel allocation is a particularly time-consuming and labor-intensive challenge. Traditional systems often failed to consider emotional factors when processing input information, resulting in inefficient matching. This leads to prolonged stressful situations and a decline in work efficiency and accuracy.

[0572] 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.

[0573] In this invention, the server includes an emotion analysis means for receiving candidate information and request information and analyzing the emotional state of the inputter; a means for storing the received information and checking the integrity of the information; and a means for executing a matching algorithm that takes the emotional state into consideration to achieve the optimal combination of candidate and educational organization. This reduces the workload of faculty and staff at educational institutions and enables rapid and accurate personnel placement that takes emotions into account.

[0574] An "educational institution" refers to an organization that provides education to students, and includes schools and universities.

[0575] "Teaching staff" refers to employees working at educational institutions, including teachers and administrative staff.

[0576] "Workload" refers to the burden imposed by the duties that teachers and staff at educational institutions must perform on a daily basis.

[0577] An "information processing system" refers to a system that automates the entire process of receiving, storing, analyzing, and outputting data.

[0578] "Candidate information" refers to data about a person who is suitable for a specific job, and includes skills, experience, qualifications, etc.

[0579] "Request information" refers to information that includes the requirements and conditions for personnel presented by educational institutions.

[0580] "Emotional state" refers to the psychological situation a user is currently in, and includes emotions such as stress and anxiety.

[0581] "Emotional analysis methods" refer to a technical process that analyzes a person's emotional state based on their input and actions.

[0582] "Consistency check" refers to the process of verifying whether the received information is accurate and free of contradictions.

[0583] A "matching algorithm" refers to a procedure or calculation method for optimally combining two or more elements based on specific conditions.

[0584] "Feedback" refers to actions, including evaluations and opinions, that system users provide regarding the system's operation.

[0585] A "notification message" refers to a portion of the information that a system sends to a user, specifically text or digital communication intended to inform them of a particular state or result.

[0586] This invention is an information processing system that reduces the workload of teachers and staff in educational institutions, and features the use of an emotion engine. The system is configured as follows:

[0587] Hardware and software configuration:

[0588] Users (educational institution faculty and staff): They use terminals (information devices such as PCs and tablets) to input candidate information and request information into the system. A web browser or dedicated application runs on the terminal, providing an interface for users to input information. The user's input data is sent to a cloud server in real time, and the emotional state is analyzed by an emotion engine.

[0589] Server: Based on a cloud platform, the server receives input data and analyzes the user's emotional state using an emotion engine. The emotion analysis engine quantifies the user's stress and anxiety using algorithms for voice analysis and input pattern analysis. The received data is stored in a database, and the integrity of the information is automatically checked.

[0590] Matching System: The server matches educational institution requests with candidate information based on data stored in the database. Using information analyzed by the emotion engine, the matching algorithm performs flexible calculations that take into account the user's emotional state. For example, if the user is experiencing high levels of stress, it prioritizes providing a quick matching result.

[0591] Specific example:

[0592] For example, if a position requiring information technology skills suddenly becomes vacant at an educational institution, the user responsible for filling the position enters candidate information via a terminal. The system analyzes the user's stress level in real time and applies special parameters to the algorithm to support rapid personnel placement. This system is expected to quickly reduce the workload of faculty and staff.

[0593] Example of a prompt:

[0594] "Please describe a system designed to reduce the workload of staff in educational institutions. Explain how it uses an emotion engine to optimize staffing based on the emotional state of users."

[0595] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0596] Step 1:

[0597] User (Educational institution faculty / staff): Uses a terminal to enter candidate and request information. This includes detailed conditions such as skills, experience, duration, and start date required for the job. Once this information is entered, the terminal sends it to the server. The server performs an initial reception of the received information and prepares for sentiment analysis.

[0598] Step 2:

[0599] Server: The server receives information sent from the terminal and then uses an emotion engine to analyze the user's emotional state. Specific data used for emotion analysis include voice, input speed, and typing rhythm, which are used to quantify stress and anxiety levels. In this process, input data is supplied to the emotion engine, and an indicator of the emotional state is generated as the output of the analysis and stored in a database.

[0600] Step 3:

[0601] Server: The server checks the integrity of the received data. It automatically verifies for any missing or inconsistent information and sends correction requests to the user if necessary. This process includes data processing such as format validation, duplicate removal, and range checking, resulting in a dataset with guaranteed integrity as output.

[0602] Step 4:

[0603] Server: Using consistent data, the server runs the matching algorithm. It takes into account the results of the emotion engine analysis and prioritizes a quick response, especially in cases of high stress. At this stage, it evaluates whether the candidate's skills and experience match the requirements of the educational institution and generates a candidate list as the matching output.

[0604] Step 5:

[0605] Server: Based on matching results and sentiment analysis, the server notifies the user's device of the results. The notification includes a customized message with words of encouragement and guidance tailored to the user's emotional state. The server generates the notification message as output and awaits user feedback.

[0606] Step 6:

[0607] User: Review the provided matching results and enter feedback on the terminal. Feedback includes evaluations and suggestions for improvement. The terminal sends this information to the server, which uses it to perform further sentiment analysis and update the database. The feedback output provides improvement data that will be useful for future processing of the system.

[0608] (Application Example 2)

[0609] 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."

[0610] This invention aims to solve the problems of stress and inefficient work processes faced by teachers and staff due to the increasing workload in the education sector. It also seeks to solve the problem of improving the experience of residents when using public services within a smart city and enabling better public services by providing individualized responses based on emotional states.

[0611] 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.

[0612] In this invention, the server includes a device for receiving information on the needs to be addressed from the user, a device for storing and organizing the received information, and a technology for analyzing the user's emotional state through a mechanism for evaluating their mental state. This makes it possible to improve the work efficiency of teachers and staff, as well as enhance the quality of citizen services within the smart city.

[0613] "Workload in the field of education" refers to the mental and physical burden that arises from the content and volume of work that teachers and staff in educational institutions face on a daily basis.

[0614] A "data processing system" is a system consisting of a series of electronic processes and devices for receiving, storing, organizing, analyzing, and notifying input information.

[0615] "User" refers to an individual or organization that operates data processing systems in educational institutions or smart cities.

[0616] "Supported information" is a general term for information necessary for improving business processes in the education sector and providing citizen services.

[0617] A "device" is a collection of mechanical or electronic components designed to perform a specific function.

[0618] A "mechanism for evaluating mental states" refers to technologies and algorithms designed to analyze and evaluate a user's emotions and mental state.

[0619] "Technology for analyzing emotional states" refers to technologies that identify a person's emotional state based on user input and other observable data.

[0620] A "smart city" is a city that aims to improve the quality of urban life by utilizing information and communication technology to operate urban functions efficiently and sustainably.

[0621] The system for realizing this application is configured as follows to improve the user experience in the education sector and smart cities: The server functions as the central data processing system, receiving support information provided by the user and storing it in a database. The server organizes the information and analyzes the user's emotional state using a mechanism to evaluate their mental state as needed.

[0622] The mechanism for evaluating mental states uses natural language processing libraries (e.g., NLTK or spaCy) to identify the user's emotional state based on text and voice input. Based on the analysis of the emotional state, the server dynamically adjusts the user interface to optimize the user experience. A specific example is providing users with priority access to options that allow for smoother use of public transportation on crowded holidays when using public services.

[0623] The terminal functions as a platform where the user receives feedback from the server and responds as needed. The feedback sent from the terminal to the server is used as important evaluation data in subsequent processes. By using generative AI models, such processes can generate personalized prompts based on the user's emotional state.

[0624] An example of a prompt message might be, "Think of ways to help residents use public transportation smoothly on crowded holidays." This allows the server to optimize the delivery of public services and reduce the burden on users.

[0625] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0626] Step 1:

[0627] The user enters information about the support they need using a terminal. The entered information is sent to the server in text format. This input includes information about requests to educational institutions and information about the use of citizen services. The server receives this information and stores it in a database.

[0628] Step 2:

[0629] The server organizes the stored information and converts it into an appropriate data structure. At this point, a mechanism for evaluating mental states is activated. The server uses a natural language processing library to analyze the text information received from the user and detect the emotional state. The input for the analysis is text data, and the output is the user's emotional state (e.g., stressed, high, medium, low).

[0630] Step 3:

[0631] Based on the analysis of the user's emotional state, the server adjusts the user interface. Specifically, it provides users with high stress levels with a simplified interface. The input for this step is the user's emotional state information, and the output is the customized user interface.

[0632] Step 4:

[0633] The server uses a context-aware AI model to generate appropriate prompts. These prompts provide advice and information tailored to the user's situation. The input for this step is the user's emotional state and request information, and the output is the prompt.

[0634] Step 5:

[0635] The generated prompt is sent to the terminal via a feedback mechanism. The user reviews the prompt and enters feedback as needed. The feedback information is then sent back to the server and stored in the database. In this step, the input is the generated prompt, and the output is the user's feedback information.

[0636] 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.

[0637] 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.

[0638] 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.

[0639] [Fourth Embodiment]

[0640] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0641] 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.

[0642] 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).

[0643] 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.

[0644] 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.

[0645] 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).

[0646] 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.

[0647] 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.

[0648] 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.

[0649] 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.

[0650] 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.

[0651] 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.

[0652] 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".

[0653] This invention provides an information processing system to reduce the workload of faculty and staff at educational institutions. Specifically, it is a mechanism for effectively dispatching personnel with IT skills and personnel who can alleviate administrative tasks to educational institutions. This system operates based on the following process.

[0654] System Overview

[0655] 1. User (Education Committee Representative):

[0656] Candidate information is entered into the system using a terminal. The information entered includes the candidate's name, skills, experience, and preferred work location.

[0657] Additionally, information requested by the educational institution where the worker will be dispatched is entered, including the required skills and the duration of the assignment.

[0658] 2. Server:

[0659] The received information is saved to a database, and its integrity is verified.

[0660] Tags are assigned based on skills, preferred work location, etc., to prepare for future searches and matching.

[0661] 3. Execute the matching algorithm:

[0662] The server selects the most suitable candidates based on stored candidate information and educational institution requests.

[0663] The matching algorithm prioritizes candidates based on their skill suitability, experience level, and preferences.

[0664] 4. Notification to the user:

[0665] The matching results are sent to the user's device, and adjustments or rematching are possible if necessary.

[0666] The notification will include specific details about the candidate or the educational institution where they will be placed.

[0667] Specific example

[0668] For example, in a situation where a junior high school in a certain city is seeking the dispatch of an administrative assistant with IT skills, this system would operate as follows:

[0669] The user (the person in charge at the Board of Education) registers candidate A, who has IT skills, in the system. Candidate A's experience includes a history of working at a junior high school.

[0670] Based on that information, the server extracts requests from junior high schools and sets priorities.

[0671] As a result, the server successfully matches candidate A with the middle school and notifies the user of the details. This information includes the assignment period and job details.

[0672] This system is expected to efficiently allocate appropriate personnel to educational institutions and reduce the workload of teachers and staff.

[0673] The following describes the processing flow.

[0674] Step 1:

[0675] The user (education board representative) logs into the system from their terminal and opens the candidate information input form. They enter detailed information such as the candidate's name, skills, experience, and desired work location, and then submit it to the server.

[0676] Step 2:

[0677] The server saves the received candidate information to the database. Once saved, it checks the integrity of the information and requests the user to correct any missing or inconsistent information.

[0678] Step 3:

[0679] The server retrieves request information from educational institutions and organizes requirements such as necessary skills, work location, and desired assignment period.

[0680] Step 4:

[0681] The server runs a matching algorithm based on candidate and request information. It selects the best candidate and educational institution combination, taking into account skill suitability, experience, and desired conditions.

[0682] Step 5:

[0683] The server generates matching results and sends them to the user's device. The notification includes detailed information about the candidate and the placement location.

[0684] Step 6:

[0685] Users can review the received matching results on their device and request adjustments or rematching from the server as needed. Once adjustments are complete, they can approve the final dispatch information.

[0686] Step 7:

[0687] The server notifies both the candidate and the educational institution of approved placement information and generates the necessary placement contracts and related documents. The notification includes the placement start date and details of the assigned duties.

[0688] (Example 1)

[0689] 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".

[0690] Traditional educational institutions are facing increasing workloads for faculty and staff, particularly those requiring IT skills and administrative tasks. Furthermore, the rapid and efficient deployment of appropriate personnel is difficult, resulting in delays in providing necessary support. Additionally, the selection of suitable candidates is becoming more complex, and the process may not function effectively.

[0691] 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.

[0692] In this invention, the server includes means for inputting candidate information, means for inputting request information, means for storing received information and verifying its integrity, means for tagging to improve search efficiency, means for performing matching based on skill suitability, and means for sending matching results to a terminal via notification. This makes it possible to respond quickly to the needs of educational institutions and to timely assign appropriate personnel while reducing their burden.

[0693] "Candidate information" refers to data about individuals applying for a specific job or role, including their name, skills, experience, and preferred work location.

[0694] "Request information" refers to detailed requirements such as the skills and personnel placement requirements sought by educational institutions and organizations, as well as the duration of assignment and work location.

[0695] "Tagging" is the process of assigning specific labels and keywords to candidate and request information, and is a technology used to streamline future searches and matching.

[0696] "Matching" is the process of selecting the most suitable personnel based on candidate information and requirements, and it is a series of activities carried out based on the compatibility of skills and experience.

[0697] A "terminal" refers to a type of computer equipment used for inputting and outputting information, such as personal computers and tablets.

[0698] A "server" is a computer system used to store and process data on a network and to provide various services.

[0699] This invention provides an information processing system to reduce the workload of faculty and staff at educational institutions. Specifically, it realizes a mechanism that utilizes information technology to quickly dispatch appropriate personnel to educational institutions. This system primarily functions through applications running on a server, accessed by users via terminals.

[0700] Users input candidate and request information from their terminals using a web browser. The entered information is sent to the server via an HTTP request. The server stores this information using a database management system (e.g., MySQL or PostgreSQL) and performs a validation process to verify data integrity. Furthermore, it uses a programming language such as Python to tag the information. This process prepares the information for efficient searching.

[0701] The server executes a matching algorithm using pre-stored information. This algorithm is implemented using machine learning libraries such as Scikit-learn and selects the most suitable candidates based on their skills and experience. The selection results are sent from the server to the user's device via a notification system. Notifications are sent using email notifications via an SMTP server or push notifications via Firebase Cloud Messaging.

[0702] The following is a specific example. If a junior high school in a certain city is looking for an administrative assistant with IT skills, the user registers candidate A, who meets the needs, in the system via a terminal. Candidate A's past experience, including a history of working at a junior high school, is also registered. The server runs an algorithm based on candidate A's information, matches it with the junior high school's request information, and achieves an optimized match. The matching result is notified to the user.

[0703] An example of a prompt for the generating AI model is: "Please explain in detail the staffing system in educational institutions. Please also describe the specific processes and technologies used." Using this sentence, it is possible to obtain information about the system's overview and characteristics.

[0704] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0705] Step 1:

[0706] Users enter candidate information using their devices. Specifically, they enter data such as their name, skills, experience, and desired work location into a form in their web browser and submit it. The entered data is sent to the server via the HTTP protocol. This is how candidate information is collected.

[0707] Step 2:

[0708] Users enter request information from educational institutions on their devices. This is done by entering detailed information such as required skills, assignment period, and work location into a web form. The entered data is sent to the server via an HTTP request. This allows educational institution requests to be collected.

[0709] Step 3:

[0710] The server receives candidate and request information and stores it in a database. It uses a database management system such as MySQL to perform validation to verify the integrity of the information. This process checks whether the data is entered correctly and generates error messages as needed.

[0711] Step 4:

[0712] The server performs tagging based on the received information. Using a Python script, it assigns keywords and labels corresponding to each candidate and their request information. This improves the searchability of the information and streamlines future matching processes. The output of this step is the tagged data.

[0713] Step 5:

[0714] The server executes a matching algorithm using tagged information. The algorithm is implemented using machine learning libraries such as Scikit-learn and selects the most suitable candidates based on skill suitability and experience. Candidate information and request information are taken as input, and an optimized candidate list is generated as output.

[0715] Step 6:

[0716] The server notifies the user's terminal of the matching results. The results are sent via email or application notification using an SMTP server or push notification service. Based on the received results, the user can adjust the dispatch procedure or rematch.

[0717] (Application Example 1)

[0718] 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".

[0719] In recent years, the workload of teachers and staff in educational institutions has increased, making it difficult for them to dedicate themselves to teaching. Furthermore, the burden of general administrative tasks and miscellaneous duties is significant, raising concerns about a decline in the quality of education. It is necessary to address these issues and achieve efficient staffing and automation of tasks.

[0720] 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.

[0721] In this invention, the server includes means for receiving candidate information, means for storing and organizing the received candidate information, means for matching candidates with educational facilities using the stored information based on request information from educational institutions, means for transmitting the matching results, and means for receiving task information necessary to perform household support work and controlling robot movements for execution. This makes it possible to improve the work efficiency and reduce the workload of teachers and staff at educational institutions.

[0722] A "data processing system" is a computer-based system that receives, stores, processes, and outputs information to improve the efficiency of business operations.

[0723] "Candidate information" refers to the skills, work experience, and other relevant personal information of the person in question.

[0724] "Educational institutions" refer to organizations that include schools, cram schools, and other organizations and facilities that provide learning.

[0725] "Request information" refers to information that includes specific requirements such as the skills and job duties required by educational institutions, as well as dispatch conditions.

[0726] "Matching" refers to the process of comparing individual candidate information with the requirements of educational institutions to create the optimal combination.

[0727] "Means for controlling robot movements" refers to the software and hardware systems used to plan and send commands for robot movements.

[0728] The system for implementing this invention is configured to reduce the workload within educational institutions. The server receives candidate information based on requests from educational institutions, stores and organizes it. Furthermore, it performs a process to match the most suitable personnel with educational facilities based on the request information and candidate information. The matching results are notified to the user, who can then view the detailed information.

[0729] One of the key functions of this system is the ability to control the movements of robots and automate household chore support tasks within educational facilities. The server utilizes a cloud platform (e.g., Google Cloud Platform) to receive task information in real time and send commands to the robots. The robots are equipped with an AI computing module using the NVIDIA Jetson series, and operate autonomously while understanding the physical environment in conjunction with Lidar sensors.

[0730] As a concrete example, in one junior high school, robots assist with the preparation and cleanup of club activities. A server sends instructions to the robots based on the activity schedule, specifying what and how to prepare. Based on these instructions, the robots prepare the necessary materials and tools, and automatically clean up the tools after the activity is finished.

[0731] An example of a prompt statement that utilizes a generative AI model is, "Please provide detailed instructions on the steps to design a robot that will assist with daily tasks in educational institutions." Using this prompt statement allows for an efficient understanding of how the generative AI can be used in this system.

[0732] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0733] Step 1:

[0734] Users enter request information from educational institutions into a terminal. This information includes required skills, job duties, and dispatch conditions. The entered data is sent to a server. The server stores the received request information in a database. This data processing serves as the basis for subsequent searches and mappings.

[0735] Step 2:

[0736] The server receives candidate information. This information includes skills, work experience, and preferred work location. This information is stored in a database and tagged according to each attribute. This allows for the creation of data ready for matching with request information.

[0737] Step 3:

[0738] The server matches educational institution requests with candidate information. Using a skills matching algorithm, it selects the candidate best suited to the request and generates the matching results. This process involves data retrieval and scoring for prioritization, identifying the optimal candidate.

[0739] Step 4:

[0740] The results of the mapping are notified to the user. The user can receive the notification and review the information. The notification includes detailed information about the assignment location and specific job duties. This supports the smooth performance of duties by faculty and staff.

[0741] Step 5:

[0742] The server plans the robot's actions based on existing information. It uses Google Cloud Platform to collect task information in real time and sends it to the NVIDIA Jetson series robot. Based on the received commands, the robot performs household assistance tasks in the physical environment.

[0743] Step 6:

[0744] The generative AI model performs inference based on the prompt text. The prompt text used is "Please provide detailed steps for designing a robot to assist with daily tasks in educational institutions." This information will be used as reference data when the user designs and optimizes the robot and system.

[0745] 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.

[0746] This invention provides an information processing system incorporating an emotion engine to reduce the workload of faculty and staff in educational institutions. By introducing the emotion engine, it is possible to recognize the user's emotional state based on candidate information and request information entered by the user, thereby improving the efficiency and accuracy of system operation.

[0747] System Overview

[0748] 1. User (Education Committee Representative):

[0749] Candidate information and request information are entered into the system using a terminal. In addition to regular information, an emotion engine analyzes the inputter's emotional state in real time and identifies states such as stress and anxiety.

[0750] 2. Server:

[0751] The received information is stored in a database, and its integrity is checked. At the same time, user compliance is verified based on the emotional information recognized by the emotion engine, and the interface is adjusted as needed.

[0752] By taking emotional states into consideration, the input flow is flexibly changed to optimize the user experience.

[0753] 3. Matching process:

[0754] The matching algorithm is executed based on the emotional state provided by the emotion engine. If the user's dissatisfaction or stress level is high, the algorithm flexibly operates using special parameters to address it.

[0755] The matching results are designed to be prioritized based on data obtained from the emotion engine.

[0756] 4. Notifications and Feedback:

[0757] The server sends matching results, taking priority into account, to the user's device. The notification includes a message customized with sentiment information, and feedback is requested as appropriate.

[0758] Users provide feedback via their devices, and this information is sent to the server. The emotion engine analyzes the feedback to help with future processing and uses it as evaluation data.

[0759] Specific example

[0760] For example, suppose a user is in a situation where they need advanced IT skills and are entering candidate information to address a sudden labor shortage. If the emotion engine determines that the user is experiencing high levels of stress, the system will prioritize and quickly match them with suitable candidates to alleviate their stress.

[0761] This system is expected to enable the optimal allocation of personnel while taking their emotional state into consideration, thereby effectively reducing the workload of staff at educational institutions.

[0762] The following describes the processing flow.

[0763] Step 1:

[0764] The user (education board representative) logs into the terminal and accesses a screen to enter candidate and educational institution request information. The emotion engine analyzes the user's input actions and reactions, and assesses the user's level of excitement, stress, or anxiety in real time.

[0765] Step 2:

[0766] The server stores the received candidate and request information in a database. The emotion engine detects the user's emotional state, and if it determines, for example, that the stress level is high, it takes measures such as changing the interface's color scheme to a calmer tone.

[0767] Step 3:

[0768] The server analyzes request information from educational institutions and compares it with candidate information to perform the best possible matching. It then incorporates information from the emotion engine and adjusts priorities as much as possible to reduce stress.

[0769] Step 4:

[0770] The server notifies the user's device of the optimized matching results. The content of the notification is adjusted based on the user's emotional state. For example, it may include a message that provides reassurance.

[0771] Step 5:

[0772] The user reviews the notification and provides feedback. This feedback concerns the accuracy of the emotional state and matching precision based on the system's recognition results, and is sent from the device to the server.

[0773] Step 6:

[0774] The server receives feedback and uses it to improve the emotion engine and matching algorithms. The recorded data contributes to improving the system's performance in the next processing cycle.

[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] Faculty and staff at educational institutions bear a heavy workload, and appropriate personnel allocation is a particularly time-consuming and labor-intensive challenge. Traditional systems often failed to consider emotional factors when processing input information, resulting in inefficient matching. This leads to prolonged stressful situations and a decline in work efficiency and accuracy.

[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 an emotion analysis means for receiving candidate information and request information and analyzing the emotional state of the inputter; a means for storing the received information and checking the integrity of the information; and a means for executing a matching algorithm that takes the emotional state into consideration to achieve the optimal combination of candidate and educational organization. This reduces the workload of faculty and staff at educational institutions and enables rapid and accurate personnel placement that takes emotions into account.

[0780] An "educational institution" refers to an organization that provides education to students, and includes schools and universities.

[0781] "Teaching staff" refers to employees working at educational institutions, including teachers and administrative staff.

[0782] "Workload" refers to the burden imposed by the duties that teachers and staff at educational institutions must perform on a daily basis.

[0783] An "information processing system" refers to a system that automates the entire process of receiving, storing, analyzing, and outputting data.

[0784] "Candidate information" refers to data about a person who is suitable for a specific job, and includes skills, experience, qualifications, etc.

[0785] "Request information" refers to information that includes the requirements and conditions for personnel presented by educational institutions.

[0786] "Emotional state" refers to the psychological situation a user is currently in, and includes emotions such as stress and anxiety.

[0787] "Emotional analysis methods" refer to a technical process that analyzes a person's emotional state based on their input and actions.

[0788] "Consistency check" refers to the process of verifying whether the received information is accurate and free of contradictions.

[0789] A "matching algorithm" refers to a procedure or calculation method for optimally combining two or more elements based on specific conditions.

[0790] "Feedback" refers to actions, including evaluations and opinions, that system users provide regarding the system's operation.

[0791] A "notification message" refers to a portion of the information that a system sends to a user, specifically text or digital communication intended to inform them of a particular state or result.

[0792] This invention is an information processing system that reduces the workload of teachers and staff in educational institutions, and features the use of an emotion engine. The system is configured as follows:

[0793] Hardware and software configuration:

[0794] Users (educational institution faculty and staff): They use terminals (information devices such as PCs and tablets) to input candidate information and request information into the system. A web browser or dedicated application runs on the terminal, providing an interface for users to input information. The user's input data is sent to a cloud server in real time, and the emotional state is analyzed by an emotion engine.

[0795] Server: Based on a cloud platform, the server receives input data and analyzes the user's emotional state using an emotion engine. The emotion analysis engine quantifies the user's stress and anxiety using algorithms for voice analysis and input pattern analysis. The received data is stored in a database, and the integrity of the information is automatically checked.

[0796] Matching System: The server matches educational institution requests with candidate information based on data stored in the database. Using information analyzed by the emotion engine, the matching algorithm performs flexible calculations that take into account the user's emotional state. For example, if the user is experiencing high levels of stress, it prioritizes providing a quick matching result.

[0797] Specific example:

[0798] For example, if a position requiring information technology skills suddenly becomes vacant at an educational institution, the user responsible for filling the position enters candidate information via a terminal. The system analyzes the user's stress level in real time and applies special parameters to the algorithm to support rapid personnel placement. This system is expected to quickly reduce the workload of faculty and staff.

[0799] Example of a prompt:

[0800] "Please describe a system designed to reduce the workload of staff in educational institutions. Explain how it uses an emotion engine to optimize staffing based on the emotional state of users."

[0801] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0802] Step 1:

[0803] User (Educational institution faculty / staff): Uses a terminal to enter candidate and request information. This includes detailed conditions such as skills, experience, duration, and start date required for the job. Once this information is entered, the terminal sends it to the server. The server performs an initial reception of the received information and prepares for sentiment analysis.

[0804] Step 2:

[0805] Server: The server receives information sent from the terminal and then uses an emotion engine to analyze the user's emotional state. Specific data used for emotion analysis include voice, input speed, and typing rhythm, which are used to quantify stress and anxiety levels. In this process, input data is supplied to the emotion engine, and an indicator of the emotional state is generated as the output of the analysis and stored in a database.

[0806] Step 3:

[0807] Server: The server checks the integrity of the received data. It automatically verifies for any missing or inconsistent information and sends correction requests to the user if necessary. This process includes data processing such as format validation, duplicate removal, and range checking, resulting in a dataset with guaranteed integrity as output.

[0808] Step 4:

[0809] Server: Using consistent data, the server runs the matching algorithm. It takes into account the results of the emotion engine analysis and prioritizes a quick response, especially in cases of high stress. At this stage, it evaluates whether the candidate's skills and experience match the requirements of the educational institution and generates a candidate list as the matching output.

[0810] Step 5:

[0811] Server: Based on matching results and sentiment analysis, the server notifies the user's device of the results. The notification includes a customized message with words of encouragement and guidance tailored to the user's emotional state. The server generates the notification message as output and awaits user feedback.

[0812] Step 6:

[0813] User: Review the provided matching results and enter feedback on the terminal. Feedback includes evaluations and suggestions for improvement. The terminal sends this information to the server, which uses it to perform further sentiment analysis and update the database. The feedback output provides improvement data that will be useful for future processing of the system.

[0814] (Application Example 2)

[0815] 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".

[0816] This invention aims to solve the problems of stress and inefficient work processes faced by teachers and staff due to the increasing workload in the education sector. It also seeks to solve the problem of improving the experience of residents when using public services within a smart city and enabling better public services by providing individualized responses based on emotional states.

[0817] 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.

[0818] In this invention, the server includes a device for receiving information on the needs to be addressed from the user, a device for storing and organizing the received information, and a technology for analyzing the user's emotional state through a mechanism for evaluating their mental state. This makes it possible to improve the work efficiency of teachers and staff, as well as enhance the quality of citizen services within the smart city.

[0819] "Workload in the field of education" refers to the mental and physical burden that arises from the content and volume of work that teachers and staff in educational institutions face on a daily basis.

[0820] A "data processing system" is a system consisting of a series of electronic processes and devices for receiving, storing, organizing, analyzing, and notifying input information.

[0821] "User" refers to an individual or organization that operates data processing systems in educational institutions or smart cities.

[0822] "Supported information" is a general term for information necessary for improving business processes in the education sector and providing citizen services.

[0823] A "device" is a collection of mechanical or electronic components designed to perform a specific function.

[0824] A "mechanism for evaluating mental states" refers to technologies and algorithms designed to analyze and evaluate a user's emotions and mental state.

[0825] "Technology for analyzing emotional states" refers to technologies that identify a person's emotional state based on user input and other observable data.

[0826] A "smart city" is a city that aims to improve the quality of urban life by utilizing information and communication technology to operate urban functions efficiently and sustainably.

[0827] The system for realizing this application is configured as follows to improve the user experience in the education sector and smart cities: The server functions as the central data processing system, receiving support information provided by the user and storing it in a database. The server organizes the information and analyzes the user's emotional state using a mechanism to evaluate their mental state as needed.

[0828] The mechanism for evaluating mental states uses natural language processing libraries (e.g., NLTK or spaCy) to identify the user's emotional state based on text and voice input. Based on the analysis of the emotional state, the server dynamically adjusts the user interface to optimize the user experience. A specific example is providing users with priority access to options that allow for smoother use of public transportation on crowded holidays when using public services.

[0829] The terminal functions as a platform where the user receives feedback from the server and responds as needed. The feedback sent from the terminal to the server is used as important evaluation data in subsequent processes. By using generative AI models, such processes can generate personalized prompts based on the user's emotional state.

[0830] An example of a prompt message might be, "Think of ways to help residents use public transportation smoothly on crowded holidays." This allows the server to optimize the delivery of public services and reduce the burden on users.

[0831] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0832] Step 1:

[0833] The user enters information about the support they need using a terminal. The entered information is sent to the server in text format. This input includes information about requests to educational institutions and information about the use of citizen services. The server receives this information and stores it in a database.

[0834] Step 2:

[0835] The server organizes the stored information and converts it into an appropriate data structure. At this point, a mechanism for evaluating mental states is activated. The server uses a natural language processing library to analyze the text information received from the user and detect the emotional state. The input for the analysis is text data, and the output is the user's emotional state (e.g., stressed, high, medium, low).

[0836] Step 3:

[0837] Based on the analysis of the user's emotional state, the server adjusts the user interface. Specifically, it provides users with high stress levels with a simplified interface. The input for this step is the user's emotional state information, and the output is the customized user interface.

[0838] Step 4:

[0839] The server uses a context-aware AI model to generate appropriate prompts. These prompts provide advice and information tailored to the user's situation. The input for this step is the user's emotional state and request information, and the output is the prompt.

[0840] Step 5:

[0841] The generated prompt is sent to the terminal via a feedback mechanism. The user reviews the prompt and enters feedback as needed. The feedback information is then sent back to the server and stored in the database. In this step, the input is the generated prompt, and the output is the user's feedback information.

[0842] 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.

[0843] 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.

[0844] 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.

[0845] 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.

[0846] 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.

[0847] 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.

[0848] 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.

[0849] 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.

[0850] 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."

[0851] 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.

[0852] 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.

[0853] 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.

[0854] 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.

[0855] 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.

[0856] 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.

[0857] The following types of processors can be used as hardware resources to perform specific processing. Examples of processors include a CPU, a general-purpose processor that functions as a hardware resource to perform specific processing by executing software, i.e., a program. Other examples of processors include dedicated electrical circuits, such as FPGAs (Field-Programmable Gate Arrays), PLDs (Programmable Logic Devices), or ASICs (Application Specific Integrated Circuits), which have circuit configurations specifically designed to perform specific processing. All of these processors have built-in or connected memory, and all of them perform specific processing by using memory.

[0858] 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.

[0859] 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.

[0860] 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.

[0861] 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.

[0862] 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.

[0863] The following is further disclosed regarding the embodiments described above.

[0864] (Claim 1)

[0865] An information processing system for reducing the workload of teachers and staff at educational institutions,

[0866] Means for receiving candidate information,

[0867] A means of saving and organizing the candidate information received,

[0868] A means of matching candidates with educational institutions using information stored based on requests from educational institutions,

[0869] A means of notifying the matching results,

[0870] A system that includes this.

[0871] (Claim 2)

[0872] The system according to claim 1, comprising means for receiving and organizing skills and experience information as candidate profile information.

[0873] (Claim 3)

[0874] The system according to claim 1, comprising means for calculating matching priority and scoring based on request information from educational institutions.

[0875] "Example 1"

[0876] (Claim 1)

[0877] A means of entering candidate information,

[0878] A means of entering request information,

[0879] A means of saving the received information and verifying its integrity,

[0880] A method to improve search efficiency by tagging,

[0881] Means for performing matching based on skill suitability,

[0882] A means of sending matching results to the device via notification,

[0883] A system that includes this.

[0884] (Claim 2)

[0885] The system according to claim 1, comprising means for performing tagging using candidate information and request information to improve future search efficiency.

[0886] (Claim 3)

[0887] The system according to claim 1, comprising means for determining priority according to candidate information and request information using a matching algorithm.

[0888] "Application Example 1"

[0889] (Claim 1)

[0890] A data processing system for reducing the workload of members of educational institutions,

[0891] Means for receiving candidate information,

[0892] A means of storing and organizing the candidate information received,

[0893] A means of matching candidates with educational institutions using information stored based on requests from educational institutions,

[0894] Means for communicating the correspondence results,

[0895] A means for receiving task information necessary to perform household support tasks and controlling the robot's movements to execute those tasks,

[0896] A system that includes this.

[0897] (Claim 2)

[0898] The system according to claim 1, comprising means for receiving and organizing skills and work experience information as candidate resume information.

[0899] (Claim 3)

[0900] The system according to claim 1, comprising means for calculating and evaluating the priority of correspondence based on request information from educational facilities.

[0901] "Example 2 of combining an emotion engine"

[0902] (Claim 1)

[0903] An information processing system for reducing the workload of teachers and staff at educational institutions,

[0904] A sentiment analysis means that receives candidate information and request information and analyzes the emotional state of the inputter,

[0905] A means of storing the received information and checking the integrity of the information,

[0906] A means of executing a matching algorithm that takes emotional states into consideration to achieve the optimal combination of candidates and educational organizations,

[0907] A means of generating and notifying customized messages based on matching results and sentiment analysis,

[0908] A means of collecting user feedback and analyzing it for use in future processing,

[0909] A system that includes this.

[0910] (Claim 2)

[0911] The system according to claim 1, which includes means for optimizing priorities by considering stress levels through sentiment analysis, in addition to skills and experience information, as part of the candidate's profile information.

[0912] (Claim 3)

[0913] The system according to claim 1, comprising means for calculating matching priority based on educational institution request information and sentiment analysis information, scoring, and deriving combinations suitable for emotional states.

[0914] "Application example 2 when combining with an emotional engine"

[0915] (Claim 1)

[0916] A data processing system for reducing the workload in the education sector,

[0917] A device that receives information about the support recipients from users,

[0918] A device for storing and organizing the received information,

[0919] A device that optimizes the relationship between the recipient of support and the educational institution using information stored based on requests from the education sector,

[0920] A technology that analyzes the user's emotional state through a mechanism that evaluates mental state,

[0921] A technology that adjusts the user interface based on analyzed emotional information,

[0922] A device that notifies the optimization results,

[0923] A system that includes this.

[0924] (Claim 2)

[0925] The system according to claim 1, comprising a device that assists in the use of public services according to the emotional state.

[0926] (Claim 3)

[0927] The system according to claim 1, comprising a device that determines and evaluates optimization priorities based on emotional information. [Explanation of symbols]

[0928] 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 system designed to reduce the workload of members of educational institutions, Means for receiving candidate information, A means of storing and organizing the candidate information received, A means of matching candidates with educational institutions using information stored based on requests from educational institutions, Means for communicating the correspondence results, A means for receiving task information necessary to perform household support tasks and controlling the robot's movements to execute those tasks, A system that includes this.

2. The system according to claim 1, comprising means for receiving and organizing skills and work experience information as candidate resume information.

3. The system according to claim 1, comprising means for calculating and evaluating the priority of correspondence based on request information from educational facilities.

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