system

Abstract

We provide the system. [Solution] Methods for collecting data from educational institutions, A means of analyzing collected data and generating results for determining the priority of fund allocation, Based on the analysis results, a means to simulate fund allocation scenarios, A means of providing simulation results to users and collecting feedback, 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 persona chatbot control method performed by at least one processor, including steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to an explanation of a chatbot character, encoding the prompt, and inputting the encoded prompt into a language model to generate a chatbot utterance in response to the user utterance.

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] Funding allocation in education is often opaque, and uneven allocation not based on the current situation and achievements of each educational institution is often carried out. Also, the effective use of funds is not evaluated, and feedback necessary for improvement is not appropriately collected, which hinders the improvement of the quality of education.

Means for Solving the Problems

[0005] This invention provides a system that collects data from educational institutions, analyzes it, and determines priorities for funding allocation. Furthermore, it simulates funding allocation scenarios based on the analysis results and provides users with visual results, thereby increasing the transparency of allocation. In addition, continuous improvement can be achieved by accumulating feedback from users. This system makes it possible to streamline the allocation of educational funds and improve the quality of education.

[0006] An "educational institution" is an organization that conducts educational activities, such as a school or a university.

[0007] "Data" refers to numerical and textual information collected from educational institutions, including student grades, attendance rates, and teacher evaluations.

[0008] "Analysis" is the process of using collected data to derive information that will help achieve a specific objective.

[0009] "Funding allocation" refers to deciding how to distribute funds among educational institutions and projects.

[0010] Prioritization is the act of ranking multiple options based on their importance and urgency.

[0011] A "simulation" is a method of predicting results under various conditions by simulating and reproducing real-world situations.

[0012] "Users" refer to individuals or organizations that use the system, in this case being administrators or policymakers at educational institutions.

[0013] "Feedback" refers to opinions and evaluations provided by users, and is information that helps improve and optimize the system. [Brief explanation of the drawing]

[0014] [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 multiple emotions are mapped. [Figure 10] It shows an emotion map to which multiple emotions are mapped. [Figure 11] It is a sequence diagram showing the processing flow of the data processing system in Example 1. [Figure 12] It is a sequence diagram showing the processing flow of the data processing system in Application Example 1. [Figure 13] It is a sequence diagram showing the processing flow of the data processing system in Example 2 when an emotion engine is combined. [Figure 14] It is a sequence diagram showing the processing flow of the data processing system in Application Example 2 when an emotion engine is combined.

MODE FOR CARRYING OUT THE INVENTION

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

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

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

[0018] In the following embodiments, the numbered RAM (Random Access Memory) is a memory in which information is temporarily stored and is used as a work memory by the processor.

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

[0020] In the following embodiments, the numbered communication I / F (Interface) is an interface including a communication processor and an antenna, etc. The communication I / F controls communication between multiple computers. Examples of communication standards applied to the communication I / F include wireless communication standards including 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), or Bluetooth (registered trademark), etc.

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

[0022] [First Embodiment]

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

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

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

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

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

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

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

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

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

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

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

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

[0035] This invention is a system for optimizing funding allocation in educational institutions, thereby aiming to improve the quality of education. This system consists of information exchange and data processing among three parties: a server, a terminal, and a user.

[0036] The server collects necessary data from multiple educational institutions and uses it to evaluate overall performance. This evaluation includes a process of checking data quality and correcting for missing or incorrect data. The server then applies data analysis techniques to predict how much funding each educational institution needs and how that funding should be used. The information obtained through this process is used to prioritize funding allocation.

[0037] The terminal performs a simulation of fund allocation based on the analysis results received from the server. The simulation tries different allocation scenarios, and the results are displayed visually. The terminal presents the user with multiple scenarios and provides data for comparing and examining the effects of each.

[0038] Users review the simulation results displayed on their devices and select the optimal funding plan that aligns with their own criteria and strategic goals. Users also contribute to system improvement by sending feedback to the server based on the simulation results.

[0039] As a concrete example, the server collects student academic performance and teacher evaluation data from local elementary and junior high schools, and analyzes it to assess each school's funding needs. Based on these results, the terminal simulates how to allocate funds to designated projects and educational programs, making predictions aimed at maximizing educational outcomes. The user then uses this information to adjust and re-evaluate the funding allocation to achieve the best possible results.

[0040] Through the configuration described above, this invention supports the efficient and equitable allocation of educational funds.

[0041] The following describes the processing flow.

[0042] Step 1:

[0043] The server collects data from multiple educational institutions, such as student grades, attendance rates, teacher evaluations, and current budget usage, through APIs and database connections. This gathers the basic data necessary for funding allocation.

[0044] Step 2:

[0045] The server verifies the quality of the collected data. If inaccuracies or missing data are found, an automatic correction algorithm is applied to correct the data and generate a reliable dataset.

[0046] Step 3:

[0047] The server uses data analysis techniques to evaluate the funding needs and outcomes of each educational institution. Specifically, it uses machine learning models to identify factors that influence educational outcomes and performs predictive analysis.

[0048] Step 4:

[0049] The terminal receives analysis results from the server and runs a simulation of fund allocation. This involves setting up different fund allocation scenarios and performing calculations to predict the outcomes of each scenario.

[0050] Step 5:

[0051] The terminal visually displays the simulation results to the user. Based on the displayed graphs and indicators, the user compares and analyzes various scenarios and selects the optimal funding plan.

[0052] Step 6:

[0053] After the user reviews their chosen funding plan and makes a decision, they send feedback about the result from their terminal to the server. The server receives this feedback and uses it to improve the system.

[0054] (Example 1)

[0055] 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."

[0056] When optimizing funding allocation in educational institutions, traditional systems have suffered from data inaccuracies and missing information, making it difficult to determine appropriate priorities and create effective allocation scenarios. Furthermore, there was a lack of effective means to incorporate user feedback, limiting the flexibility of resource allocation. Solutions to these problems are needed.

[0057] 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.

[0058] In this invention, the server includes means for collecting information from educational institutions, means for processing the collected information and generating results for determining priorities for funding allocation, and means for testing funding allocation scenarios based on those priorities. This enables accurate information collection and analysis, as well as more efficient funding allocation trials. Furthermore, by incorporating user feedback and continuously improving the system, flexible and effective funding allocation becomes possible.

[0059] An "educational institution" refers to an organization that provides education, such as a school, university, or vocational school.

[0060] "Means of collecting information" refers to the methods and technologies used to obtain necessary data from educational institutions.

[0061] "Means of processing information" refer to the technologies and methods used to analyze and organize the content of collected data.

[0062] "Means for generating results to determine prioritizing fund allocation" refers to technologies that create information that provides guidance on how funds should be allocated, based on collected and processed data.

[0063] "Methods for testing funding allocation scenarios" refer to techniques that virtually test different funding allocation scenarios using computer simulations and analyze the results.

[0064] A "user" is a person who operates this system and makes decisions based on the analysis and simulation results.

[0065] "Means of collecting opinions" refer to methods and techniques for gathering feedback and suggestions from users and using them to improve and adjust the system.

[0066] This system is designed to optimize funding allocation within educational institutions and is configured to function through the mutual cooperation of three parties: the server, terminals, and users.

[0067] The server collects educational data from multiple educational institutions. This involves accessing each institution's database to retrieve student performance data and faculty evaluation data. Data collection can be done using API calls or SQL queries. Furthermore, Python libraries such as Pandas and NumPy are used for data preprocessing. This ensures data quality and corrects inaccurate or missing data.

[0068] The server then uses data analysis tools such as SciKit-Learn and TENSORFLOW® to analyze the funding needs of each educational institution and set priorities based on the results. This priority is calculated by utilizing information obtained by sending prompts to a generative AI model.

[0069] Meanwhile, the terminal performs a simulation of fund allocation based on the analysis results received from the server. The simulation on the terminal virtually tries out various allocation scenarios and utilizes simulation models built with R or Python. The results are presented to the user in a visually easy-to-understand format and are typically visualized using Tableau or Matplotlib.

[0070] Users review multiple simulation results presented through their devices and select the optimal funding allocation plan that aligns with their strategic goals and decision-making criteria. During this process, they are required to send prompts to the generated AI model, such as "To which project should funding be allocated to maximize educational outcomes?", prompting it to perform additional analysis.

[0071] As a concrete example, the system analyzes the funding needs of local schools based on student academic performance data and teacher evaluation data collected from those schools. Based on the results received via a terminal, it simulates scenarios for allocating funds to IT education projects and library expansion projects. From these results, users can implement the most effective allocation plan.

[0072] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0073] Step 1:

[0074] The server collects data from multiple educational institutions. Specifically, it accesses the databases of each institution and uses APIs and SQL queries to retrieve student grade information and faculty evaluation information. The input consists of various evaluation and grade data from the educational institutions' databases, and the output is a JSON file containing this data.

[0075] Step 2:

[0076] The server preprocesses the collected data. This process uses Pandas and NumPy to verify data quality and correct inaccurate values ​​and missing values. Specifically, it performs mean imputation or estimation from context for missing data. The input is the JSON data obtained in step 1, and the output is a clean dataset that has been quality checked and corrected.

[0077] Step 3:

[0078] The server performs data analysis. Using clean data, it analyzes the funding needs of each educational institution using analytical tools such as SciKit-Learn and TensorFlow. It applies machine learning algorithms to calculate funding allocation priorities. In this process, it sends prompts to a generative AI model to obtain results. The input is a corrected dataset, and the output is a priority list based on funding needs.

[0079] Step 4:

[0080] The terminal uses a priority list received from the server to perform a funding allocation simulation. It considers different allocation scenarios and runs simulation models created in R or Python. The results are visualized as graphs and heatmaps. The input is a priority list of funding needs, and the output is the predicted outcome for each scenario.

[0081] Step 5:

[0082] The user selects the optimal funding plan based on the simulation results displayed on the device. Multiple scenarios are compared, prompts are sent to the generating AI model for further analysis, and support is provided for the final decision. The input is the predicted outcomes of multiple scenarios, and the output is the selected funding scenario.

[0083] Step 6:

[0084] Users send feedback to the server regarding their selected funding allocation scenarios. This feedback is used for future analysis and system improvements. The input is the funding allocation scenario selected by the user, and the output is the recorded data used for the next analysis.

[0085] (Application Example 1)

[0086] 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."

[0087] Optimally allocating materials and personnel in manufacturing facilities is crucial for improving production efficiency and reducing costs. However, currently, it is difficult to quickly and accurately grasp the status of each facility and make appropriate allocations. Therefore, there is a need for methods to automate the optimal allocation of resources through real-time information gathering and analysis, thereby increasing production efficiency.

[0088] 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.

[0089] In this invention, the server includes means for collecting information from a manufacturing facility, means for analyzing the collected information and generating results for determining the priority of the allocation of materials and workers, and means for simulating allocation scenarios based on the analysis results. This enables real-time monitoring of the conditions within the manufacturing facility and the optimal and efficient allocation of resources.

[0090] A "manufacturing facility" refers to a place where work is done to create a product, and where the production process is carried out in an organized manner.

[0091] "Information" refers to data related to production management, such as the production status of manufacturing facilities, the operating status of equipment, the utilization status of materials, and the activity status of workers.

[0092] A "server" is a computer system that has the function of collecting and storing information, and processing and analyzing the collected data.

[0093] "Materials" refers to production elements such as raw materials and parts used in the manufacturing process of a product.

[0094] "Workers" refers to the human labor force that performs production activities in the manufacturing of products.

[0095] "Allocation" refers to the appropriate deployment and allocation of production resources such as materials and workers.

[0096] "Simulation" is a method of reproducing the actual manufacturing process on a computer and predicting the results of distribution scenarios in advance.

[0097] "Analysis results" refer to the output that includes conclusions and implications derived from data processed based on collected information.

[0098] The server builds a system that allows for real-time monitoring of production status based on information collected from manufacturing facilities. This system includes data obtained through an information collection infrastructure consisting of multiple sensors, and this data is processed and analyzed by the server. The server corrects for missing or inaccurate data and generates analysis results to determine the optimal allocation of materials and workers. This process utilizes programming languages ​​such as Python for data processing and libraries such as Pandas and NumPy for data analysis.

[0099] Based on these analysis results, the terminal simulates various allocation scenarios and presents the results visually to the user. The terminal requires a user interface that allows for flexible manipulation of the simulation results. Furthermore, the results of each scenario can be adjusted according to user feedback and on-site needs.

[0100] Based on the provided simulation results, users make decisions to optimize resource allocation within the manufacturing facility. User feedback is returned to the server and used for further system optimization. This feedback loop supports the continuous improvement of manufacturing efficiency.

[0101] As a concrete example, one factory was able to reduce material waste and improve production efficiency by 20% by introducing this system. An example of a prompt to be input into the generated AI model is: "Please propose a model to optimize resource allocation on the factory's production line. This model needs to collect on-site data and propose the optimal resource placement."

[0102] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0103] Step 1:

[0104] The server collects information from the manufacturing facility through sensors. This input includes information about the operating status of the production line, the inventory levels of materials, and the allocation of workers. The server stores the data in real time and processes it to verify the accuracy of the information.

[0105] Step 2:

[0106] The server corrects any missing or inaccurate information in the collected data. Based on this input data, it performs data cleaning, estimating and imputing missing values ​​and removing outliers to generate reliable data.

[0107] Step 3:

[0108] The server uses analytical methods to generate a model for determining the optimal allocation of materials and workers from corrected data. Using the corrected data as input, it applies the generated AI model to perform predictive analysis and outputs the optimal allocation result.

[0109] Step 4:

[0110] The terminal simulates various allocation scenarios based on the optimal allocation results received from the server. The simulation tries different allocation patterns and visually displays the results for each. It uses the optimal allocation results as input and provides the simulation results as output.

[0111] Step 5:

[0112] The user reviews the simulation results presented on the terminal and selects the resource allocation best suited to the on-site situation. User feedback is sent to the server based on the selected scenario. This feedback data is accumulated to improve the system's accuracy.

[0113] 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.

[0114] This invention is a system that optimizes the allocation of funds to educational institutions, and is characterized by its ability to promote the efficient use of educational funds by collecting and analyzing feedback that takes user emotions into consideration. The system consists of a server, terminals, and users, and incorporates an emotion engine.

[0115] The server collects data from educational institutions, including raw data such as student academic performance and faculty evaluations. The server receives this data, verifies its quality, and then uses machine learning algorithms to analyze the funding needs of each school and project, generating results. These analysis results form a crucial dataset for determining funding priorities.

[0116] The terminal simulates fund allocation scenarios based on analysis results received from the server. The terminal presents information through a visual interface to ensure users can easily understand the results. Furthermore, this process collects user feedback and analyzes the user's emotional state using an emotion engine. The emotion engine analyzes the user's input and emotions during interface operation, quantifying the quality of the feedback and satisfaction level.

[0117] Users can review the displayed fund allocation simulation results and compare multiple scenarios through the terminal interface. In the process of selecting the optimal fund allocation based on their own criteria, users can consider the results of the emotion engine's analysis. If emotions such as concern or dissatisfaction are identified, they can receive guidance on how to correct those points.

[0118] As a concrete example, suppose a middle school is considering launching a new educational program and several budget proposals are presented. A server collects and analyzes the relevant data, and a terminal presents the results relatively. The user considers different scenarios through simulations of the results, and the terminal analyzes the user's reactions during this process using an emotion engine. Based on the simulation results and emotion analysis, the user can select the optimal program.

[0119] Thus, by incorporating user emotions, this invention enables more humane and effective fund allocation, contributing to the improvement of the quality of education.

[0120] The following describes the processing flow.

[0121] Step 1:

[0122] The server collects data from educational institutions. Specifically, it uses APIs and database connections to obtain data such as student grades, attendance rates, teacher evaluations, and budget usage. This creates a detailed dataset necessary for funding allocation.

[0123] Step 2:

[0124] The system verifies the accuracy and completeness of the data collected by the server. If inaccurate or missing data is detected, an automatic correction algorithm is applied to supplement the data and improve its reliability.

[0125] Step 3:

[0126] The server uses machine learning algorithms to analyze the data. This analysis assesses the funding needs of educational institutions based on their performance. Furthermore, funding allocation priorities are determined based on each institution's performance indicators.

[0127] Step 4:

[0128] The terminal receives analysis results from the server and simulates fund allocation scenarios based on them. The terminal then presents the user with predicted results for different allocation scenarios through an intuitive visual interface.

[0129] Step 5:

[0130] Users view simulation results presented via their devices and make fund allocation choices based on them. Users compare various scenarios and identify the plan best suited to their own strategy and goals.

[0131] Step 6:

[0132] The device collects user feedback and simultaneously uses a built-in emotion engine to analyze the user's emotions during the process. This quantifies how the feedback is received and provides information that helps improve funding allocation plans.

[0133] Step 7:

[0134] The user considers the results of the sentiment analysis and sends feedback from their device to the server. The server uses this information to further optimize the system.

[0135] (Example 2)

[0136] 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".

[0137] A challenge in the allocation of educational funds is that funds are sometimes not used effectively or are allocated inappropriately. In addition, the lack of mechanisms to adequately reflect the emotions and feedback of stakeholders in the fund allocation process makes it difficult to optimize allocation.

[0138] 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.

[0139] In this invention, the server includes means for collecting information from educational institutions, means for verifying the quality of the collected information and correcting inaccurate or missing information, and means for identifying factors that affect educational outcomes, performing analysis using machine learning algorithms, and generating results for determining priorities for funding allocation. This makes it possible to allocate educational funds more effectively and in a way that takes user sentiment into account.

[0140] "Educational institutions" refer to organizations that provide education, such as schools, universities, and vocational schools.

[0141] "Information" refers to data obtained from educational institutions, such as students' academic performance and evaluations of faculty and staff.

[0142] "Quality verification" refers to the act of checking the completeness, consistency, and accuracy of collected information, and correcting any inaccuracies or missing information.

[0143] A "machine learning algorithm" refers to a mathematical model that allows a computer to learn patterns from data and automatically perform predictions and analyses.

[0144] A "funding allocation scenario" refers to a model that simulates in advance how educational funds will be allocated.

[0145] An "emotion engine" refers to a system that analyzes user feedback and emotions during operation, and quantifies and presents the emotional state.

[0146] A "user" refers to an individual or organization that operates a terminal to simulate and select fund allocations.

[0147] This invention provides a system for optimizing the allocation of educational funds. This system performs a series of processes including collecting information from educational institutions, verifying data quality, performing predictive analytics, simulating funding allocation scenarios, and analyzing user sentiment feedback.

[0148] The server collects student grades and faculty evaluation information from educational institutions. This information collection is performed by connecting to the educational institution's database and automatically retrieving the necessary information via an API. To verify the quality of the retrieved information, data cleaning software is used to detect and correct missing or outlier values.

[0149] The server also runs machine learning algorithms using verified data. These algorithms are used to identify factors influencing educational outcomes and predict the funding needs of each project. Specific implementations include regression models and clustering techniques, utilizing existing statistical analysis tools.

[0150] The terminal receives analysis results from the server and functions as a tool to simulate fund allocation scenarios. The simulation results are displayed on a visual interface and presented in a user-friendly format. The terminal incorporates an emotion engine to collect feedback and analyze the user's emotional state. This engine analyzes the emotional tone of the feedback entered by the user and quantifies the result as satisfaction.

[0151] Users can evaluate simulation results presented via their device and compare multiple scenarios. They can then leverage insights gained from sentiment analysis to make more appropriate financial allocation choices.

[0152] As a concrete example, consider a situation where a middle school is launching a new educational program and there are multiple budget proposals. A server gathers and analyzes relevant information, and a terminal presents relative scenarios to the user. The user can then compare the results based on this information, refer to sentiment feedback, and make a final decision.

[0153] Examples of prompts for a generative AI model:

[0154] "Please explain the optimal allocation method for educational funding, including how to integrate user feedback while taking their emotions into account."

[0155] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0156] Step 1:

[0157] The server collects information from educational institutions. The input to this process includes student performance data and faculty evaluation data. Specifically, the server accesses each educational institution's database via an API, retrieves the necessary information, and stores it in its own database. The output is a categorized raw dataset.

[0158] Step 2:

[0159] The server verifies the quality of the collected information. The input is the raw dataset obtained in Step 1. The server uses data quality control tools to impute missing values ​​and correct outliers. Specific actions include data integrity checks and simple statistical analysis. The output is a dataset with guaranteed quality.

[0160] Step 3:

[0161] The server runs machine learning algorithms using quality-verified data. The input is the dataset from Step 2. Here, the server uses regression models and clustering techniques to predict the funding needs of each project. Specifically, the server fits the model and outputs the funding allocation priority as a numerical value.

[0162] Step 4:

[0163] The terminal simulates funding allocation scenarios based on analysis results sent from the server. The input is the analysis results data from step 3. The terminal uses a simulation tool to visualize the impact of each funding allocation pattern. Specific actions include generating bar graphs and pie charts of budget allocation. The output is a visual simulation result that is easy for the user to understand.

[0164] Step 5:

[0165] The device uses the generated simulation results to collect feedback from users. The input consists of the simulation results and user responses. The device collects opinions and feedback using survey forms and direct input fields. The output is the compiled feedback data.

[0166] Step 6:

[0167] The device analyzes user feedback data using an emotion engine. The input is the feedback data obtained in step 5. Specifically, the device uses natural language processing to quantify the emotional tone and evaluate user satisfaction. The output is the quantified emotion evaluation data.

[0168] Step 7:

[0169] The user selects the optimal fund allocation based on the simulation results and sentiment analysis results presented on the terminal. The inputs are the simulation results and sentiment evaluation data. The user compares each displayed option and, using the sentiment analysis results as a reference, selects the optimal plan based on their own judgment. This process includes a selection process on the user interface. The output is the final selected fund allocation plan.

[0170] (Application Example 2)

[0171] 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".

[0172] Resource allocation in industry requires efficient and appropriate management. However, traditional systems fail to consider the emotional state of workers, leading to decreased productivity and dissatisfaction. Furthermore, there is a lack of objective means to evaluate the quality of feedback, making it difficult to contribute to improving the work environment.

[0173] 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.

[0174] In this invention, the server includes means for collecting information using a data aggregation device, means for analyzing the collected information and producing results for determining resource allocation priorities, and means for analyzing emotional states and quantifying the quality of feedback. This makes it possible to optimize resource allocation while taking into account the emotions of workers.

[0175] A "data aggregation device" is a device used to collect and integrate various types of information necessary for factory operations.

[0176] "Information analysis" is the process of using collected data to perform analyses tailored to a specific purpose and derive useful results.

[0177] "Resource allocation priority" is an indicator of importance used to indicate which processes or areas should be allocated limited resources to.

[0178] "Emotional state" refers to information obtained by identifying and capturing the psychological and emotional condition of a worker.

[0179] "Quantifying the quality of feedback" is a method of evaluating opinions and impressions obtained from workers as numerical data and objectively measuring their effectiveness and areas for improvement.

[0180] To realize this application, three elements—server, terminal, and user—play crucial roles. The server collects diverse information from the factory using a data aggregation device. The collected data is then analyzed using software to calculate resource allocation priorities. This provides a foundation for developing efficient production plans.

[0181] The terminal uses an emotion engine to evaluate the emotional state of workers based on analysis results provided by the server. This involves using software that analyzes user input and provides numerical evaluations to quantify the quality of feedback. This makes it possible to obtain concrete guidelines for improving the work environment.

[0182] Users can review production scenarios presented through the terminal's visual interface and contribute to process optimization. Because users can provide feedback based on their own experiences and feelings, they become a valuable source of information for effectively adjusting resource allocation.

[0183] As a concrete example, a manufacturer could use this system when launching a new product line to improve production efficiency. Improved resource allocation would increase worker satisfaction, resulting in smoother operation of the production line. When utilizing the generative AI model, the following example prompts are used:

[0184] "Develop a system that optimizes production schedules based on worker feedback to efficiently operate the new product line."

[0185] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0186] Step 1:

[0187] The server collects data from sensors and systems within the factory through an aggregation device. This data includes production status, machine operation information, and worker hours. The server then collects this data and prepares it for data analysis.

[0188] Step 2:

[0189] The server begins analyzing the collected data using software designed for this purpose. This process removes outliers and converts the data to the required format. The output is index data indicating resource allocation priorities. This clearly shows how much resources should be allocated to each process.

[0190] Step 3:

[0191] The terminal uses an emotion engine to evaluate the emotional state of workers based on resource allocation indicator data received from the server. Inputs include worker feedback and real-time emotional data. By analyzing this data, the quality of feedback and the workers' satisfaction with their environment are quantified.

[0192] Step 4:

[0193] Users review the production scenarios presented by the terminal through a visual interface and provide feedback based on their own criteria. This feedback is used to allocate resources in the next cycle, so users can input comments such as specific areas for improvement, and the system uses this feedback to aim for further efficiency improvements.

[0194] Step 5:

[0195] The device receives user feedback, re-evaluates it using an emotion engine, and monitors the progress of the feedback. Input includes newly added user comments and improvement requests. Based on this, it performs another analysis and generates data indicating the next improvement steps. The generated data is fed back to the server and reflected in the next data analysis.

[0196] 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.

[0197] 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.

[0198] 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.

[0199] [Second Embodiment]

[0200] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0201] 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.

[0202] 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).

[0203] 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.

[0204] 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.

[0205] 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).

[0206] 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.

[0207] 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.

[0208] 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.

[0209] 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.

[0210] 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.

[0211] 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".

[0212] This invention is a system for optimizing funding allocation in educational institutions, thereby aiming to improve the quality of education. This system consists of information exchange and data processing among three parties: a server, a terminal, and a user.

[0213] The server collects necessary data from multiple educational institutions and uses it to evaluate overall performance. This evaluation includes a process of checking data quality and correcting for missing or incorrect data. The server then applies data analysis techniques to predict how much funding each educational institution needs and how that funding should be used. The information obtained through this process is used to prioritize funding allocation.

[0214] The terminal performs a simulation of fund allocation based on the analysis results received from the server. The simulation tries different allocation scenarios, and the results are displayed visually. The terminal presents the user with multiple scenarios and provides data for comparing and examining the effects of each.

[0215] Users review the simulation results displayed on their devices and select the optimal funding plan that aligns with their own criteria and strategic goals. Users also contribute to system improvement by sending feedback to the server based on the simulation results.

[0216] As a concrete example, the server collects student academic performance and teacher evaluation data from local elementary and junior high schools, and analyzes it to assess each school's funding needs. Based on these results, the terminal simulates how to allocate funds to designated projects and educational programs, making predictions aimed at maximizing educational outcomes. The user then uses this information to adjust and re-evaluate the funding allocation to achieve the best possible results.

[0217] Through the configuration described above, this invention supports the efficient and equitable allocation of educational funds.

[0218] The following describes the processing flow.

[0219] Step 1:

[0220] The server collects data from multiple educational institutions, such as student grades, attendance rates, teacher evaluations, and current budget usage, through APIs and database connections. This gathers the basic data necessary for funding allocation.

[0221] Step 2:

[0222] The server verifies the quality of the collected data. If inaccuracies or missing data are found, an automatic correction algorithm is applied to correct the data and generate a reliable dataset.

[0223] Step 3:

[0224] The server uses data analysis techniques to evaluate the funding needs and outcomes of each educational institution. Specifically, it uses machine learning models to identify factors that influence educational outcomes and performs predictive analysis.

[0225] Step 4:

[0226] The terminal receives analysis results from the server and runs a simulation of fund allocation. This involves setting up different fund allocation scenarios and performing calculations to predict the outcomes of each scenario.

[0227] Step 5:

[0228] The terminal visually displays the simulation results to the user. Based on the displayed graphs and indicators, the user compares and analyzes various scenarios and selects the optimal funding plan.

[0229] Step 6:

[0230] After the user reviews their chosen funding plan and makes a decision, they send feedback about the result from their terminal to the server. The server receives this feedback and uses it to improve the system.

[0231] (Example 1)

[0232] 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".

[0233] When optimizing funding allocation in educational institutions, traditional systems have suffered from data inaccuracies and missing information, making it difficult to determine appropriate priorities and create effective allocation scenarios. Furthermore, there was a lack of effective means to incorporate user feedback, limiting the flexibility of resource allocation. Solutions to these problems are needed.

[0234] 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.

[0235] In this invention, the server includes means for collecting information from educational institutions, means for processing the collected information and generating results for determining priorities for funding allocation, and means for testing funding allocation scenarios based on those priorities. This enables accurate information collection and analysis, as well as more efficient funding allocation trials. Furthermore, by incorporating user feedback and continuously improving the system, flexible and effective funding allocation becomes possible.

[0236] An "educational institution" refers to an organization that provides education, such as a school, university, or vocational school.

[0237] "Means of collecting information" refers to the methods and technologies used to obtain necessary data from educational institutions.

[0238] "Means of processing information" refer to the technologies and methods used to analyze and organize the content of collected data.

[0239] "Means for generating results to determine prioritizing fund allocation" refers to technologies that create information that provides guidance on how funds should be allocated, based on collected and processed data.

[0240] "Methods for testing funding allocation scenarios" refer to techniques that virtually test different funding allocation scenarios using computer simulations and analyze the results.

[0241] A "user" is a person who operates this system and makes decisions based on the analysis and simulation results.

[0242] "Means of collecting opinions" refer to methods and techniques for gathering feedback and suggestions from users and using them to improve and adjust the system.

[0243] This system is designed to optimize funding allocation within educational institutions and is configured to function through the mutual cooperation of three parties: the server, terminals, and users.

[0244] The server collects educational data from multiple educational institutions. This involves accessing each institution's database to retrieve student performance data and faculty evaluation data. Data collection can be done using API calls or SQL queries. Furthermore, Python libraries such as Pandas and NumPy are used for data preprocessing. This ensures data quality and corrects inaccurate or missing data.

[0245] The server then uses data analysis tools such as SciKit-Learn and TensorFlow to analyze the funding needs of each educational institution and set priorities based on the results. This priority is calculated by utilizing information obtained by sending prompts to a generative AI model.

[0246] Meanwhile, the terminal performs a simulation of fund allocation based on the analysis results received from the server. The simulation on the terminal virtually tries out various allocation scenarios and utilizes simulation models built with R or Python. The results are presented to the user in a visually easy-to-understand format and are typically visualized using Tableau or Matplotlib.

[0247] Users review multiple simulation results presented through their devices and select the optimal funding allocation plan that aligns with their strategic goals and decision-making criteria. During this process, they are required to send prompts to the generated AI model, such as "To which project should funding be allocated to maximize educational outcomes?", prompting it to perform additional analysis.

[0248] As a concrete example, the system analyzes the funding needs of local schools based on student academic performance data and teacher evaluation data collected from those schools. Based on the results received via a terminal, it simulates scenarios for allocating funds to IT education projects and library expansion projects. From these results, users can implement the most effective allocation plan.

[0249] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0250] Step 1:

[0251] The server collects data from multiple educational institutions. Specifically, it accesses the databases of each institution and uses APIs and SQL queries to retrieve student grade information and faculty evaluation information. The input consists of various evaluation and grade data from the educational institutions' databases, and the output is a JSON file containing this data.

[0252] Step 2:

[0253] The server preprocesses the collected data. This process uses Pandas and NumPy to verify data quality and correct inaccurate values ​​and missing values. Specifically, it performs mean imputation or estimation from context for missing data. The input is the JSON data obtained in step 1, and the output is a clean dataset that has been quality checked and corrected.

[0254] Step 3:

[0255] The server performs data analysis. Using clean data, it analyzes the funding needs of each educational institution using analytical tools such as SciKit-Learn and TensorFlow. It applies machine learning algorithms to calculate funding allocation priorities. In this process, it sends prompts to a generative AI model to obtain results. The input is a corrected dataset, and the output is a priority list based on funding needs.

[0256] Step 4:

[0257] The terminal uses a priority list received from the server to perform a funding allocation simulation. It considers different allocation scenarios and runs simulation models created in R or Python. The results are visualized as graphs and heatmaps. The input is a priority list of funding needs, and the output is the predicted outcome for each scenario.

[0258] Step 5:

[0259] The user selects the optimal funding plan based on the simulation results displayed on the device. Multiple scenarios are compared, prompts are sent to the generating AI model for further analysis, and support is provided for the final decision. The input is the predicted outcomes of multiple scenarios, and the output is the selected funding scenario.

[0260] Step 6:

[0261] Users send feedback to the server regarding their selected funding allocation scenarios. This feedback is used for future analysis and system improvements. The input is the funding allocation scenario selected by the user, and the output is the recorded data used for the next analysis.

[0262] (Application Example 1)

[0263] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server," and the smart glasses 214 will be referred to as the "terminal."

[0264] Optimally allocating materials and personnel in manufacturing facilities is crucial for improving production efficiency and reducing costs. However, currently, it is difficult to quickly and accurately grasp the status of each facility and make appropriate allocations. Therefore, there is a need for methods to automate the optimal allocation of resources through real-time information gathering and analysis, thereby increasing production efficiency.

[0265] 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.

[0266] In this invention, the server includes means for collecting information from a manufacturing facility, means for analyzing the collected information and generating results for determining the priority of the allocation of materials and workers, and means for simulating allocation scenarios based on the analysis results. This enables real-time monitoring of the conditions within the manufacturing facility and the optimal and efficient allocation of resources.

[0267] A "manufacturing facility" refers to a place where work is done to create a product, and where the production process is carried out in an organized manner.

[0268] "Information" refers to data related to production management, such as the production status of manufacturing facilities, the operating status of equipment, the utilization status of materials, and the activity status of workers.

[0269] A "server" is a computer system that has the function of collecting and storing information, and processing and analyzing the collected data.

[0270] "Materials" refers to production elements such as raw materials and parts used in the manufacturing process of a product.

[0271] "Workers" refers to the human labor force that performs production activities in the manufacturing of products.

[0272] "Allocation" refers to the appropriate deployment and allocation of production resources such as materials and workers.

[0273] "Simulation" is a method of reproducing the actual manufacturing process on a computer and predicting the results of distribution scenarios in advance.

[0274] "Analysis results" refer to the output that includes conclusions and implications derived from data processed based on collected information.

[0275] The server builds a system that allows for real-time monitoring of production status based on information collected from manufacturing facilities. This system includes data obtained through an information collection infrastructure consisting of multiple sensors, and this data is processed and analyzed by the server. The server corrects for missing or inaccurate data and generates analysis results to determine the optimal allocation of materials and workers. This process utilizes programming languages ​​such as Python for data processing and libraries such as Pandas and NumPy for data analysis.

[0276] Based on these analysis results, the terminal simulates various allocation scenarios and presents the results visually to the user. The terminal requires a user interface that allows for flexible manipulation of the simulation results. Furthermore, the results of each scenario can be adjusted according to user feedback and on-site needs.

[0277] Based on the provided simulation results, users make decisions to optimize resource allocation within the manufacturing facility. User feedback is returned to the server and used for further system optimization. This feedback loop supports the continuous improvement of manufacturing efficiency.

[0278] As a concrete example, one factory was able to reduce material waste and improve production efficiency by 20% by introducing this system. An example of a prompt to be input into the generated AI model is: "Please propose a model to optimize resource allocation on the factory's production line. This model needs to collect on-site data and propose the optimal resource placement."

[0279] The flow of the specific process in Application Example 1 will be described using FIG. 12.

[0280] Step 1:

[0281] The server collects information from the manufacturing facility through sensors. This input relates to the operating status of the production line, the inventory of materials, and the deployment of workers. The server accumulates data in real time and performs processes to confirm the accuracy of the information based on these data.

[0282] Step 2:

[0283] If there are any deficiencies or inaccuracies in the collected information, the server performs corrections. Based on this input data, data cleaning is carried out to generate reliable data by estimating and complementing missing values and removing outliers.

[0284] Step 3:

[0285] The server uses an analysis method to generate a model for determining the optimal allocation of materials and workers from the corrected data. Using the corrected data as this input, the generated AI model is applied to perform predictive analysis and output the optimal allocation result.

[0286] Step 4:

[0287] The terminal simulates various allocation scenarios based on the optimal allocation result received from the server. In the simulation, different allocation patterns are tried, and the respective results are visually displayed. Using the optimal allocation result as the input and providing the simulation result as the output.

[0288] Step 5:

[0289] The user reviews the simulation results presented on the terminal and selects the resource allocation best suited to the on-site situation. User feedback is sent to the server based on the selected scenario. This feedback data is accumulated to improve the system's accuracy.

[0290] 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.

[0291] This invention is a system that optimizes the allocation of funds to educational institutions, and is characterized by its ability to promote the efficient use of educational funds by collecting and analyzing feedback that takes user emotions into consideration. The system consists of a server, terminals, and users, and incorporates an emotion engine.

[0292] The server collects data from educational institutions, including raw data such as student academic performance and faculty evaluations. The server receives this data, verifies its quality, and then uses machine learning algorithms to analyze the funding needs of each school and project, generating results. These analysis results form a crucial dataset for determining funding priorities.

[0293] The terminal simulates fund allocation scenarios based on analysis results received from the server. The terminal presents information through a visual interface to ensure users can easily understand the results. Furthermore, this process collects user feedback and analyzes the user's emotional state using an emotion engine. The emotion engine analyzes the user's input and emotions during interface operation, quantifying the quality of the feedback and satisfaction level.

[0294] Users can review the displayed fund allocation simulation results and compare multiple scenarios through the terminal interface. In the process of selecting the optimal fund allocation based on their own criteria, users can consider the results of the emotion engine's analysis. If emotions such as concern or dissatisfaction are identified, they can receive guidance on how to correct those points.

[0295] As a concrete example, suppose a middle school is considering launching a new educational program and several budget proposals are presented. A server collects and analyzes the relevant data, and a terminal presents the results relatively. The user considers different scenarios through simulations of the results, and the terminal analyzes the user's reactions during this process using an emotion engine. Based on the simulation results and emotion analysis, the user can select the optimal program.

[0296] Thus, by incorporating user emotions, this invention enables more humane and effective fund allocation, contributing to the improvement of the quality of education.

[0297] The following describes the processing flow.

[0298] Step 1:

[0299] The server collects data from educational institutions. Specifically, it uses APIs and database connections to obtain data such as student grades, attendance rates, teacher evaluations, and budget usage. This creates a detailed dataset necessary for funding allocation.

[0300] Step 2:

[0301] The system verifies the accuracy and completeness of the data collected by the server. If inaccurate or missing data is detected, an automatic correction algorithm is applied to supplement the data and improve its reliability.

[0302] Step 3:

[0303] The server analyzes data using machine learning algorithms. Based on this analysis, the funding needs based on the achievements of educational institutions are evaluated. Also, based on the performance indicators of each institution, the priority of funding allocation is determined.

[0304] Step 4:

[0305] The terminal receives the analysis results provided by the server and simulates a funding allocation scenario based on them. The terminal presents the prediction results of different allocation scenarios to the user through an intuitive visual interface.

[0306] Step 5:

[0307] The user browses the simulation results presented via the terminal and makes a selection for funding allocation based on them. The user compares different scenarios and identifies the plan that is optimal for their strategy and goals.

[0308] Step 6:

[0309] The terminal collects the user's feedback and at the same time uses the built-in emotion engine to analyze the user's emotions during the process. This quantifies how the feedback is received and provides information useful for improving the funding allocation plan.

[0310] Step 7:

[0311] The user considers the results of the emotion analysis and sends the feedback from the terminal to the server. The server utilizes this information to further optimize the system.

[0312] (Example 2)

[0313] Next, Example 2 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".

[0314] A challenge in the allocation of educational funds is that funds are sometimes not used effectively or are allocated inappropriately. In addition, the lack of mechanisms to adequately reflect the emotions and feedback of stakeholders in the fund allocation process makes it difficult to optimize allocation.

[0315] 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.

[0316] In this invention, the server includes means for collecting information from educational institutions, means for verifying the quality of the collected information and correcting inaccurate or missing information, and means for identifying factors that affect educational outcomes, performing analysis using machine learning algorithms, and generating results for determining priorities for funding allocation. This makes it possible to allocate educational funds more effectively and in a way that takes user sentiment into account.

[0317] "Educational institutions" refer to organizations that provide education, such as schools, universities, and vocational schools.

[0318] "Information" refers to data obtained from educational institutions, such as students' academic performance and evaluations of faculty and staff.

[0319] "Quality verification" refers to the act of checking the completeness, consistency, and accuracy of collected information, and correcting any inaccuracies or missing information.

[0320] A "machine learning algorithm" refers to a mathematical model that allows a computer to learn patterns from data and automatically perform predictions and analyses.

[0321] A "funding allocation scenario" refers to a model that simulates in advance how educational funds will be allocated.

[0322] An "emotion engine" refers to a system that analyzes user feedback and emotions during operation, and quantifies and presents the emotional state.

[0323] A "user" refers to an individual or organization that operates a terminal to simulate and select fund allocations.

[0324] This invention provides a system for optimizing the allocation of educational funds. This system performs a series of processes including collecting information from educational institutions, verifying data quality, performing predictive analytics, simulating funding allocation scenarios, and analyzing user sentiment feedback.

[0325] The server collects student grades and faculty evaluation information from educational institutions. This information collection is performed by connecting to the educational institution's database and automatically retrieving the necessary information via an API. To verify the quality of the retrieved information, data cleaning software is used to detect and correct missing or outlier values.

[0326] The server also runs machine learning algorithms using verified data. These algorithms are used to identify factors influencing educational outcomes and predict the funding needs of each project. Specific implementations include regression models and clustering techniques, utilizing existing statistical analysis tools.

[0327] The terminal receives analysis results from the server and functions as a tool to simulate fund allocation scenarios. The simulation results are displayed on a visual interface and presented in a user-friendly format. The terminal incorporates an emotion engine to collect feedback and analyze the user's emotional state. This engine analyzes the emotional tone of the feedback entered by the user and quantifies the result as satisfaction.

[0328] Users can evaluate simulation results presented via their device and compare multiple scenarios. They can then leverage insights gained from sentiment analysis to make more appropriate financial allocation choices.

[0329] As a concrete example, consider a situation where a middle school is launching a new educational program and there are multiple budget proposals. A server gathers and analyzes relevant information, and a terminal presents relative scenarios to the user. The user can then compare the results based on this information, refer to sentiment feedback, and make a final decision.

[0330] Examples of prompts for a generative AI model:

[0331] "Please explain the optimal allocation method for educational funding, including how to integrate user feedback while taking their emotions into account."

[0332] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0333] Step 1:

[0334] The server collects information from educational institutions. The input to this process includes student performance data and faculty evaluation data. Specifically, the server accesses each educational institution's database via an API, retrieves the necessary information, and stores it in its own database. The output is a categorized raw dataset.

[0335] Step 2:

[0336] The server verifies the quality of the collected information. The input is the raw dataset obtained in Step 1. The server uses data quality control tools to impute missing values ​​and correct outliers. Specific actions include data integrity checks and simple statistical analysis. The output is a dataset with guaranteed quality.

[0337] Step 3:

[0338] The server runs machine learning algorithms using quality-verified data. The input is the dataset from Step 2. Here, the server uses regression models and clustering techniques to predict the funding needs of each project. Specifically, the server fits the model and outputs the funding allocation priority as a numerical value.

[0339] Step 4:

[0340] The terminal simulates funding allocation scenarios based on analysis results sent from the server. The input is the analysis results data from step 3. The terminal uses a simulation tool to visualize the impact of each funding allocation pattern. Specific actions include generating bar graphs and pie charts of budget allocation. The output is a visual simulation result that is easy for the user to understand.

[0341] Step 5:

[0342] The device uses the generated simulation results to collect feedback from users. The input consists of the simulation results and user responses. The device collects opinions and feedback using survey forms and direct input fields. The output is the compiled feedback data.

[0343] Step 6:

[0344] The device analyzes user feedback data using an emotion engine. The input is the feedback data obtained in step 5. Specifically, the device uses natural language processing to quantify the emotional tone and evaluate user satisfaction. The output is the quantified emotion evaluation data.

[0345] Step 7:

[0346] The user selects the optimal fund allocation based on the simulation results and sentiment analysis results presented on the terminal. The inputs are the simulation results and sentiment evaluation data. The user compares each displayed option and, using the sentiment analysis results as a reference, selects the optimal plan based on their own judgment. This process includes a selection process on the user interface. The output is the final selected fund allocation plan.

[0347] (Application Example 2)

[0348] 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."

[0349] Resource allocation in industry requires efficient and appropriate management. However, traditional systems fail to consider the emotional state of workers, leading to decreased productivity and dissatisfaction. Furthermore, there is a lack of objective means to evaluate the quality of feedback, making it difficult to contribute to improving the work environment.

[0350] 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.

[0351] In this invention, the server includes means for collecting information using a data aggregation device, means for analyzing the collected information and producing results for determining resource allocation priorities, and means for analyzing emotional states and quantifying the quality of feedback. This makes it possible to optimize resource allocation while taking into account the emotions of workers.

[0352] A "data aggregation device" is a device used to collect and integrate various types of information necessary for factory operations.

[0353] "Information analysis" is the process of using collected data to perform analyses tailored to a specific purpose and derive useful results.

[0354] "Resource allocation priority" is an indicator of importance used to indicate which processes or areas should be allocated limited resources to.

[0355] "Emotional state" refers to information obtained by identifying and capturing the psychological and emotional condition of a worker.

[0356] "Quantifying the quality of feedback" is a method of evaluating opinions and impressions obtained from workers as numerical data and objectively measuring their effectiveness and areas for improvement.

[0357] To realize this application, three elements—server, terminal, and user—play crucial roles. The server collects diverse information from the factory using a data aggregation device. The collected data is then analyzed using software to calculate resource allocation priorities. This provides a foundation for developing efficient production plans.

[0358] The terminal uses an emotion engine to evaluate the emotional state of workers based on analysis results provided by the server. This involves using software that analyzes user input and provides numerical evaluations to quantify the quality of feedback. This makes it possible to obtain concrete guidelines for improving the work environment.

[0359] Users can review production scenarios presented through the terminal's visual interface and contribute to process optimization. Because users can provide feedback based on their own experiences and feelings, they become a valuable source of information for effectively adjusting resource allocation.

[0360] As a concrete example, a manufacturer could use this system when launching a new product line to improve production efficiency. Improved resource allocation would increase worker satisfaction, resulting in smoother operation of the production line. When utilizing the generative AI model, the following example prompts are used:

[0361] "Develop a system that optimizes production schedules based on worker feedback to efficiently operate the new product line."

[0362] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0363] Step 1:

[0364] The server collects data from sensors and systems within the factory through an aggregation device. This data includes production status, machine operation information, and worker hours. The server then collects this data and prepares it for data analysis.

[0365] Step 2:

[0366] The server begins analyzing the collected data using software designed for this purpose. This process removes outliers and converts the data to the required format. The output is index data indicating resource allocation priorities. This clearly shows how much resources should be allocated to each process.

[0367] Step 3:

[0368] The terminal uses an emotion engine to evaluate the emotional state of workers based on resource allocation indicator data received from the server. Inputs include worker feedback and real-time emotional data. By analyzing this data, the quality of feedback and the workers' satisfaction with their environment are quantified.

[0369] Step 4:

[0370] Users review the production scenarios presented by the terminal through a visual interface and provide feedback based on their own criteria. This feedback is used to allocate resources in the next cycle, so users can input comments such as specific areas for improvement, and the system uses this feedback to aim for further efficiency improvements.

[0371] Step 5:

[0372] The device receives user feedback, re-evaluates it using an emotion engine, and monitors the progress of the feedback. Input includes newly added user comments and improvement requests. Based on this, it performs another analysis and generates data indicating the next improvement steps. The generated data is fed back to the server and reflected in the next data analysis.

[0373] 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.

[0374] 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.

[0375] 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.

[0376] [Third Embodiment]

[0377] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0378] 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.

[0379] 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).

[0380] 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.

[0381] 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.

[0382] 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).

[0383] 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.

[0384] 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.

[0385] 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.

[0386] 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.

[0387] 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.

[0388] 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".

[0389] This invention is a system for optimizing funding allocation in educational institutions, thereby aiming to improve the quality of education. This system consists of information exchange and data processing among three parties: a server, a terminal, and a user.

[0390] The server collects necessary data from multiple educational institutions and uses it to evaluate overall performance. This evaluation includes a process of checking data quality and correcting for missing or incorrect data. The server then applies data analysis techniques to predict how much funding each educational institution needs and how that funding should be used. The information obtained through this process is used to prioritize funding allocation.

[0391] The terminal performs a simulation of fund allocation based on the analysis results received from the server. The simulation tries different allocation scenarios, and the results are displayed visually. The terminal presents the user with multiple scenarios and provides data for comparing and examining the effects of each.

[0392] Users review the simulation results displayed on their devices and select the optimal funding plan that aligns with their own criteria and strategic goals. Users also contribute to system improvement by sending feedback to the server based on the simulation results.

[0393] As a concrete example, the server collects student academic performance and teacher evaluation data from local elementary and junior high schools, and analyzes it to assess each school's funding needs. Based on these results, the terminal simulates how to allocate funds to designated projects and educational programs, making predictions aimed at maximizing educational outcomes. The user then uses this information to adjust and re-evaluate the funding allocation to achieve the best possible results.

[0394] Through the configuration described above, this invention supports the efficient and equitable allocation of educational funds.

[0395] The following describes the processing flow.

[0396] Step 1:

[0397] The server collects data from multiple educational institutions, such as student grades, attendance rates, teacher evaluations, and current budget usage, through APIs and database connections. This gathers the basic data necessary for funding allocation.

[0398] Step 2:

[0399] The server verifies the quality of the collected data. If inaccuracies or missing data are found, an automatic correction algorithm is applied to correct the data and generate a reliable dataset.

[0400] Step 3:

[0401] The server uses data analysis techniques to evaluate the funding needs and outcomes of each educational institution. Specifically, it uses machine learning models to identify factors that influence educational outcomes and performs predictive analysis.

[0402] Step 4:

[0403] The terminal receives analysis results from the server and runs a simulation of fund allocation. This involves setting up different fund allocation scenarios and performing calculations to predict the outcomes of each scenario.

[0404] Step 5:

[0405] The terminal visually displays the simulation results to the user. Based on the displayed graphs and indicators, the user compares and analyzes various scenarios and selects the optimal funding plan.

[0406] Step 6:

[0407] After the user reviews their chosen funding plan and makes a decision, they send feedback about the result from their terminal to the server. The server receives this feedback and uses it to improve the system.

[0408] (Example 1)

[0409] 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."

[0410] When optimizing funding allocation in educational institutions, traditional systems have suffered from data inaccuracies and missing information, making it difficult to determine appropriate priorities and create effective allocation scenarios. Furthermore, there was a lack of effective means to incorporate user feedback, limiting the flexibility of resource allocation. Solutions to these problems are needed.

[0411] 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.

[0412] In this invention, the server includes means for collecting information from educational institutions, means for processing the collected information and generating results for determining priorities for funding allocation, and means for testing funding allocation scenarios based on those priorities. This enables accurate information collection and analysis, as well as more efficient funding allocation trials. Furthermore, by incorporating user feedback and continuously improving the system, flexible and effective funding allocation becomes possible.

[0413] An "educational institution" refers to an organization that provides education, such as a school, university, or vocational school.

[0414] "Means of collecting information" refers to the methods and technologies used to obtain necessary data from educational institutions.

[0415] "Means of processing information" refer to the technologies and methods used to analyze and organize the content of collected data.

[0416] "Means for generating results to determine prioritizing fund allocation" refers to technologies that create information that provides guidance on how funds should be allocated, based on collected and processed data.

[0417] "Methods for testing funding allocation scenarios" refer to techniques that virtually test different funding allocation scenarios using computer simulations and analyze the results.

[0418] A "user" is a person who operates this system and makes decisions based on the analysis and simulation results.

[0419] "Means of collecting opinions" refer to methods and techniques for gathering feedback and suggestions from users and using them to improve and adjust the system.

[0420] This system is designed to optimize funding allocation within educational institutions and is configured to function through the mutual cooperation of three parties: the server, terminals, and users.

[0421] The server collects educational data from multiple educational institutions. This involves accessing each institution's database to retrieve student performance data and faculty evaluation data. Data collection can be done using API calls or SQL queries. Furthermore, Python libraries such as Pandas and NumPy are used for data preprocessing. This ensures data quality and corrects inaccurate or missing data.

[0422] The server then uses data analysis tools such as SciKit-Learn and TensorFlow to analyze the funding needs of each educational institution and set priorities based on the results. This priority is calculated by utilizing information obtained by sending prompts to a generative AI model.

[0423] Meanwhile, the terminal performs a simulation of fund allocation based on the analysis results received from the server. The simulation on the terminal virtually tries out various allocation scenarios and utilizes simulation models built with R or Python. The results are presented to the user in a visually easy-to-understand format and are typically visualized using Tableau or Matplotlib.

[0424] Users review multiple simulation results presented through their devices and select the optimal funding allocation plan that aligns with their strategic goals and decision-making criteria. During this process, they are required to send prompts to the generated AI model, such as "To which project should funding be allocated to maximize educational outcomes?", prompting it to perform additional analysis.

[0425] As a concrete example, the system analyzes the funding needs of local schools based on student academic performance data and teacher evaluation data collected from those schools. Based on the results received via a terminal, it simulates scenarios for allocating funds to IT education projects and library expansion projects. From these results, users can implement the most effective allocation plan.

[0426] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0427] Step 1:

[0428] The server collects data from multiple educational institutions. Specifically, it accesses the databases of each institution and uses APIs and SQL queries to retrieve student grade information and faculty evaluation information. The input consists of various evaluation and grade data from the educational institutions' databases, and the output is a JSON file containing this data.

[0429] Step 2:

[0430] The server preprocesses the collected data. This process uses Pandas and NumPy to verify data quality and correct inaccurate values ​​and missing values. Specifically, it performs mean imputation or estimation from context for missing data. The input is the JSON data obtained in step 1, and the output is a clean dataset that has been quality checked and corrected.

[0431] Step 3:

[0432] The server performs data analysis. Using clean data, it analyzes the funding needs of each educational institution using analytical tools such as SciKit-Learn and TensorFlow. It applies machine learning algorithms to calculate funding allocation priorities. In this process, it sends prompts to a generative AI model to obtain results. The input is a corrected dataset, and the output is a priority list based on funding needs.

[0433] Step 4:

[0434] The terminal uses a priority list received from the server to perform a funding allocation simulation. It considers different allocation scenarios and runs simulation models created in R or Python. The results are visualized as graphs and heatmaps. The input is a priority list of funding needs, and the output is the predicted outcome for each scenario.

[0435] Step 5:

[0436] The user selects the optimal funding plan based on the simulation results displayed on the device. Multiple scenarios are compared, prompts are sent to the generating AI model for further analysis, and support is provided for the final decision. The input is the predicted outcomes of multiple scenarios, and the output is the selected funding scenario.

[0437] Step 6:

[0438] Users send feedback to the server regarding their selected funding allocation scenarios. This feedback is used for future analysis and system improvements. The input is the funding allocation scenario selected by the user, and the output is the recorded data used for the next analysis.

[0439] (Application Example 1)

[0440] 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."

[0441] Optimally allocating materials and personnel in manufacturing facilities is crucial for improving production efficiency and reducing costs. However, currently, it is difficult to quickly and accurately grasp the status of each facility and make appropriate allocations. Therefore, there is a need for methods to automate the optimal allocation of resources through real-time information gathering and analysis, thereby increasing production efficiency.

[0442] 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.

[0443] In this invention, the server includes means for collecting information from a manufacturing facility, means for analyzing the collected information and generating results for determining the priority of the allocation of materials and workers, and means for simulating allocation scenarios based on the analysis results. This enables real-time monitoring of the conditions within the manufacturing facility and the optimal and efficient allocation of resources.

[0444] A "manufacturing facility" refers to a place where work is done to create a product, and where the production process is carried out in an organized manner.

[0445] "Information" refers to data related to production management, such as the production status of manufacturing facilities, the operating status of equipment, the utilization status of materials, and the activity status of workers.

[0446] A "server" is a computer system that has the function of collecting and storing information, and processing and analyzing the collected data.

[0447] "Materials" refers to production elements such as raw materials and parts used in the manufacturing process of a product.

[0448] "Workers" refers to the human labor force that performs production activities in the manufacturing of products.

[0449] "Allocation" refers to the appropriate deployment and allocation of production resources such as materials and workers.

[0450] "Simulation" is a method of reproducing the actual manufacturing process on a computer and predicting the results of distribution scenarios in advance.

[0451] "Analysis results" refer to the output that includes conclusions and implications derived from data processed based on collected information.

[0452] The server builds a system that allows for real-time monitoring of production status based on information collected from manufacturing facilities. This system includes data obtained through an information collection infrastructure consisting of multiple sensors, and this data is processed and analyzed by the server. The server corrects for missing or inaccurate data and generates analysis results to determine the optimal allocation of materials and workers. This process utilizes programming languages ​​such as Python for data processing and libraries such as Pandas and NumPy for data analysis.

[0453] Based on these analysis results, the terminal simulates various allocation scenarios and presents the results visually to the user. The terminal requires a user interface that allows for flexible manipulation of the simulation results. Furthermore, the results of each scenario can be adjusted according to user feedback and on-site needs.

[0454] Based on the provided simulation results, users make decisions to optimize resource allocation within the manufacturing facility. User feedback is returned to the server and used for further system optimization. This feedback loop supports the continuous improvement of manufacturing efficiency.

[0455] As a concrete example, one factory was able to reduce material waste and improve production efficiency by 20% by introducing this system. An example of a prompt to be input into the generated AI model is: "Please propose a model to optimize resource allocation on the factory's production line. This model needs to collect on-site data and propose the optimal resource placement."

[0456] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0457] Step 1:

[0458] The server collects information from the manufacturing facility through sensors. This input includes information about the operating status of the production line, the inventory levels of materials, and the allocation of workers. The server stores the data in real time and processes it to verify the accuracy of the information.

[0459] Step 2:

[0460] The server corrects any missing or inaccurate information in the collected data. Based on this input data, it performs data cleaning, estimating and imputing missing values ​​and removing outliers to generate reliable data.

[0461] Step 3:

[0462] The server uses analytical methods to generate a model for determining the optimal allocation of materials and workers from corrected data. Using the corrected data as input, it applies the generated AI model to perform predictive analysis and outputs the optimal allocation result.

[0463] Step 4:

[0464] The terminal simulates various allocation scenarios based on the optimal allocation results received from the server. The simulation tries different allocation patterns and visually displays the results for each. It uses the optimal allocation results as input and provides the simulation results as output.

[0465] Step 5:

[0466] The user reviews the simulation results presented on the terminal and selects the resource allocation best suited to the on-site situation. User feedback is sent to the server based on the selected scenario. This feedback data is accumulated to improve the system's accuracy.

[0467] 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.

[0468] This invention is a system that optimizes the allocation of funds to educational institutions, and is characterized by its ability to promote the efficient use of educational funds by collecting and analyzing feedback that takes user emotions into consideration. The system consists of a server, terminals, and users, and incorporates an emotion engine.

[0469] The server collects data from educational institutions, including raw data such as student academic performance and faculty evaluations. The server receives this data, verifies its quality, and then uses machine learning algorithms to analyze the funding needs of each school and project, generating results. These analysis results form a crucial dataset for determining funding priorities.

[0470] The terminal simulates fund allocation scenarios based on analysis results received from the server. The terminal presents information through a visual interface to ensure users can easily understand the results. Furthermore, this process collects user feedback and analyzes the user's emotional state using an emotion engine. The emotion engine analyzes the user's input and emotions during interface operation, quantifying the quality of the feedback and satisfaction level.

[0471] Users can review the displayed fund allocation simulation results and compare multiple scenarios through the terminal interface. In the process of selecting the optimal fund allocation based on their own criteria, users can consider the results of the emotion engine's analysis. If emotions such as concern or dissatisfaction are identified, they can receive guidance on how to correct those points.

[0472] As a concrete example, suppose a middle school is considering launching a new educational program and several budget proposals are presented. A server collects and analyzes the relevant data, and a terminal presents the results relatively. The user considers different scenarios through simulations of the results, and the terminal analyzes the user's reactions during this process using an emotion engine. Based on the simulation results and emotion analysis, the user can select the optimal program.

[0473] Thus, by incorporating user emotions, this invention enables more humane and effective fund allocation, contributing to the improvement of the quality of education.

[0474] The following describes the processing flow.

[0475] Step 1:

[0476] The server collects data from educational institutions. Specifically, it uses APIs and database connections to obtain data such as student grades, attendance rates, teacher evaluations, and budget usage. This creates a detailed dataset necessary for funding allocation.

[0477] Step 2:

[0478] The system verifies the accuracy and completeness of the data collected by the server. If inaccurate or missing data is detected, an automatic correction algorithm is applied to supplement the data and improve its reliability.

[0479] Step 3:

[0480] The server uses machine learning algorithms to analyze the data. This analysis assesses the funding needs of educational institutions based on their performance. Furthermore, funding allocation priorities are determined based on each institution's performance indicators.

[0481] Step 4:

[0482] The terminal receives analysis results from the server and simulates fund allocation scenarios based on them. The terminal then presents the user with predicted results for different allocation scenarios through an intuitive visual interface.

[0483] Step 5:

[0484] Users view simulation results presented via their devices and make fund allocation choices based on them. Users compare various scenarios and identify the plan best suited to their own strategy and goals.

[0485] Step 6:

[0486] The device collects user feedback and simultaneously uses a built-in emotion engine to analyze the user's emotions during the process. This quantifies how the feedback is received and provides information that helps improve funding allocation plans.

[0487] Step 7:

[0488] The user considers the results of the sentiment analysis and sends feedback from their device to the server. The server uses this information to further optimize the system.

[0489] (Example 2)

[0490] 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."

[0491] A challenge in the allocation of educational funds is that funds are sometimes not used effectively or are allocated inappropriately. In addition, the lack of mechanisms to adequately reflect the emotions and feedback of stakeholders in the fund allocation process makes it difficult to optimize allocation.

[0492] 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.

[0493] In this invention, the server includes means for collecting information from educational institutions, means for verifying the quality of the collected information and correcting inaccurate or missing information, and means for identifying factors that affect educational outcomes, performing analysis using machine learning algorithms, and generating results for determining priorities for funding allocation. This makes it possible to allocate educational funds more effectively and in a way that takes user sentiment into account.

[0494] "Educational institutions" refer to organizations that provide education, such as schools, universities, and vocational schools.

[0495] "Information" refers to data obtained from educational institutions, such as students' academic performance and evaluations of faculty and staff.

[0496] "Quality verification" refers to the act of checking the completeness, consistency, and accuracy of collected information, and correcting any inaccuracies or missing information.

[0497] A "machine learning algorithm" refers to a mathematical model that allows a computer to learn patterns from data and automatically perform predictions and analyses.

[0498] A "funding allocation scenario" refers to a model that simulates in advance how educational funds will be allocated.

[0499] An "emotion engine" refers to a system that analyzes user feedback and emotions during operation, and quantifies and presents the emotional state.

[0500] A "user" refers to an individual or organization that operates a terminal to simulate and select fund allocations.

[0501] This invention provides a system for optimizing the allocation of educational funds. This system performs a series of processes including collecting information from educational institutions, verifying data quality, performing predictive analytics, simulating funding allocation scenarios, and analyzing user sentiment feedback.

[0502] The server collects student grades and faculty evaluation information from educational institutions. This information collection is performed by connecting to the educational institution's database and automatically retrieving the necessary information via an API. To verify the quality of the retrieved information, data cleaning software is used to detect and correct missing or outlier values.

[0503] The server also runs machine learning algorithms using verified data. These algorithms are used to identify factors influencing educational outcomes and predict the funding needs of each project. Specific implementations include regression models and clustering techniques, utilizing existing statistical analysis tools.

[0504] The terminal receives analysis results from the server and functions as a tool to simulate fund allocation scenarios. The simulation results are displayed on a visual interface and presented in a user-friendly format. The terminal incorporates an emotion engine to collect feedback and analyze the user's emotional state. This engine analyzes the emotional tone of the feedback entered by the user and quantifies the result as satisfaction.

[0505] Users can evaluate simulation results presented via their device and compare multiple scenarios. They can then leverage insights gained from sentiment analysis to make more appropriate financial allocation choices.

[0506] As a concrete example, consider a situation where a middle school is launching a new educational program and there are multiple budget proposals. A server gathers and analyzes relevant information, and a terminal presents relative scenarios to the user. The user can then compare the results based on this information, refer to sentiment feedback, and make a final decision.

[0507] Examples of prompts for a generative AI model:

[0508] "Please explain the optimal allocation method for educational funding, including how to integrate user feedback while taking their emotions into account."

[0509] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0510] Step 1:

[0511] The server collects information from educational institutions. The input to this process includes student performance data and faculty evaluation data. Specifically, the server accesses each educational institution's database via an API, retrieves the necessary information, and stores it in its own database. The output is a categorized raw dataset.

[0512] Step 2:

[0513] The server verifies the quality of the collected information. The input is the raw dataset obtained in Step 1. The server uses data quality control tools to impute missing values ​​and correct outliers. Specific actions include data integrity checks and simple statistical analysis. The output is a dataset with guaranteed quality.

[0514] Step 3:

[0515] The server runs machine learning algorithms using quality-verified data. The input is the dataset from Step 2. Here, the server uses regression models and clustering techniques to predict the funding needs of each project. Specifically, the server fits the model and outputs the funding allocation priority as a numerical value.

[0516] Step 4:

[0517] The terminal simulates funding allocation scenarios based on analysis results sent from the server. The input is the analysis results data from step 3. The terminal uses a simulation tool to visualize the impact of each funding allocation pattern. Specific actions include generating bar graphs and pie charts of budget allocation. The output is a visual simulation result that is easy for the user to understand.

[0518] Step 5:

[0519] The device uses the generated simulation results to collect feedback from users. The input consists of the simulation results and user responses. The device collects opinions and feedback using survey forms and direct input fields. The output is the compiled feedback data.

[0520] Step 6:

[0521] The device analyzes user feedback data using an emotion engine. The input is the feedback data obtained in step 5. Specifically, the device uses natural language processing to quantify the emotional tone and evaluate user satisfaction. The output is the quantified emotion evaluation data.

[0522] Step 7:

[0523] The user selects the optimal fund allocation based on the simulation results and sentiment analysis results presented on the terminal. The inputs are the simulation results and sentiment evaluation data. The user compares each displayed option and, using the sentiment analysis results as a reference, selects the optimal plan based on their own judgment. This process includes a selection process on the user interface. The output is the final selected fund allocation plan.

[0524] (Application Example 2)

[0525] 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."

[0526] Resource allocation in industry requires efficient and appropriate management. However, traditional systems fail to consider the emotional state of workers, leading to decreased productivity and dissatisfaction. Furthermore, there is a lack of objective means to evaluate the quality of feedback, making it difficult to contribute to improving the work environment.

[0527] 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.

[0528] In this invention, the server includes means for collecting information using a data aggregation device, means for analyzing the collected information and producing results for determining resource allocation priorities, and means for analyzing emotional states and quantifying the quality of feedback. This makes it possible to optimize resource allocation while taking into account the emotions of workers.

[0529] A "data aggregation device" is a device used to collect and integrate various types of information necessary for factory operations.

[0530] "Information analysis" is the process of using collected data to perform analyses tailored to a specific purpose and derive useful results.

[0531] "Resource allocation priority" is an indicator of importance used to indicate which processes or areas should be allocated limited resources to.

[0532] "Emotional state" refers to information obtained by identifying and capturing the psychological and emotional condition of a worker.

[0533] "Quantifying the quality of feedback" is a method of evaluating opinions and impressions obtained from workers as numerical data and objectively measuring their effectiveness and areas for improvement.

[0534] To realize this application, three elements—server, terminal, and user—play crucial roles. The server collects diverse information from the factory using a data aggregation device. The collected data is then analyzed using software to calculate resource allocation priorities. This provides a foundation for developing efficient production plans.

[0535] The terminal uses an emotion engine to evaluate the emotional state of workers based on analysis results provided by the server. This involves using software that analyzes user input and provides numerical evaluations to quantify the quality of feedback. This makes it possible to obtain concrete guidelines for improving the work environment.

[0536] Users can review production scenarios presented through the terminal's visual interface and contribute to process optimization. Because users can provide feedback based on their own experiences and feelings, they become a valuable source of information for effectively adjusting resource allocation.

[0537] As a concrete example, a manufacturer could use this system when launching a new product line to improve production efficiency. Improved resource allocation would increase worker satisfaction, resulting in smoother operation of the production line. When utilizing the generative AI model, the following example prompts are used:

[0538] "Develop a system that optimizes production schedules based on worker feedback to efficiently operate the new product line."

[0539] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0540] Step 1:

[0541] The server collects data from sensors and systems within the factory through an aggregation device. This data includes production status, machine operation information, and worker hours. The server then collects this data and prepares it for data analysis.

[0542] Step 2:

[0543] The server begins analyzing the collected data using software designed for this purpose. This process removes outliers and converts the data to the required format. The output is index data indicating resource allocation priorities. This clearly shows how much resources should be allocated to each process.

[0544] Step 3:

[0545] The terminal uses an emotion engine to evaluate the emotional state of workers based on resource allocation indicator data received from the server. Inputs include worker feedback and real-time emotional data. By analyzing this data, the quality of feedback and the workers' satisfaction with their environment are quantified.

[0546] Step 4:

[0547] Users review the production scenarios presented by the terminal through a visual interface and provide feedback based on their own criteria. This feedback is used to allocate resources in the next cycle, so users can input comments such as specific areas for improvement, and the system uses this feedback to aim for further efficiency improvements.

[0548] Step 5:

[0549] The device receives user feedback, re-evaluates it using an emotion engine, and monitors the progress of the feedback. Input includes newly added user comments and improvement requests. Based on this, it performs another analysis and generates data indicating the next improvement steps. The generated data is fed back to the server and reflected in the next data analysis.

[0550] 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.

[0551] 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.

[0552] 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.

[0553] [Fourth Embodiment]

[0554] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0555] 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.

[0556] 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).

[0557] 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.

[0558] 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.

[0559] 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).

[0560] 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.

[0561] 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.

[0562] 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.

[0563] 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.

[0564] 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.

[0565] 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.

[0566] 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".

[0567] This invention is a system for optimizing funding allocation in educational institutions, thereby aiming to improve the quality of education. This system consists of information exchange and data processing among three parties: a server, a terminal, and a user.

[0568] The server collects necessary data from multiple educational institutions and uses it to evaluate overall performance. This evaluation includes a process of checking data quality and correcting for missing or incorrect data. The server then applies data analysis techniques to predict how much funding each educational institution needs and how that funding should be used. The information obtained through this process is used to prioritize funding allocation.

[0569] The terminal performs a simulation of fund allocation based on the analysis results received from the server. The simulation tries different allocation scenarios, and the results are displayed visually. The terminal presents the user with multiple scenarios and provides data for comparing and examining the effects of each.

[0570] Users review the simulation results displayed on their devices and select the optimal funding plan that aligns with their own criteria and strategic goals. Users also contribute to system improvement by sending feedback to the server based on the simulation results.

[0571] As a concrete example, the server collects student academic performance and teacher evaluation data from local elementary and junior high schools, and analyzes it to assess each school's funding needs. Based on these results, the terminal simulates how to allocate funds to designated projects and educational programs, making predictions aimed at maximizing educational outcomes. The user then uses this information to adjust and re-evaluate the funding allocation to achieve the best possible results.

[0572] Through the configuration described above, this invention supports the efficient and equitable allocation of educational funds.

[0573] The following describes the processing flow.

[0574] Step 1:

[0575] The server collects data from multiple educational institutions, such as student grades, attendance rates, teacher evaluations, and current budget usage, through APIs and database connections. This gathers the basic data necessary for funding allocation.

[0576] Step 2:

[0577] The server verifies the quality of the collected data. If inaccuracies or missing data are found, an automatic correction algorithm is applied to correct the data and generate a reliable dataset.

[0578] Step 3:

[0579] The server uses data analysis techniques to evaluate the funding needs and outcomes of each educational institution. Specifically, it uses machine learning models to identify factors that influence educational outcomes and performs predictive analysis.

[0580] Step 4:

[0581] The terminal receives analysis results from the server and runs a simulation of fund allocation. This involves setting up different fund allocation scenarios and performing calculations to predict the outcomes of each scenario.

[0582] Step 5:

[0583] The terminal visually displays the simulation results to the user. Based on the displayed graphs and indicators, the user compares and analyzes various scenarios and selects the optimal funding plan.

[0584] Step 6:

[0585] After the user reviews their chosen funding plan and makes a decision, they send feedback about the result from their terminal to the server. The server receives this feedback and uses it to improve the system.

[0586] (Example 1)

[0587] 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".

[0588] When optimizing funding allocation in educational institutions, traditional systems have suffered from data inaccuracies and missing information, making it difficult to determine appropriate priorities and create effective allocation scenarios. Furthermore, there was a lack of effective means to incorporate user feedback, limiting the flexibility of resource allocation. Solutions to these problems are needed.

[0589] 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.

[0590] In this invention, the server includes means for collecting information from educational institutions, means for processing the collected information and generating results for determining priorities for funding allocation, and means for testing funding allocation scenarios based on those priorities. This enables accurate information collection and analysis, as well as more efficient funding allocation trials. Furthermore, by incorporating user feedback and continuously improving the system, flexible and effective funding allocation becomes possible.

[0591] An "educational institution" refers to an organization that provides education, such as a school, university, or vocational school.

[0592] "Means of collecting information" refers to the methods and technologies used to obtain necessary data from educational institutions.

[0593] "Means of processing information" refer to the technologies and methods used to analyze and organize the content of collected data.

[0594] "Means for generating results to determine prioritizing fund allocation" refers to technologies that create information that provides guidance on how funds should be allocated, based on collected and processed data.

[0595] "Methods for testing funding allocation scenarios" refer to techniques that virtually test different funding allocation scenarios using computer simulations and analyze the results.

[0596] A "user" is a person who operates this system and makes decisions based on the analysis and simulation results.

[0597] "Means of collecting opinions" refer to methods and techniques for gathering feedback and suggestions from users and using them to improve and adjust the system.

[0598] This system is designed to optimize funding allocation within educational institutions and is configured to function through the mutual cooperation of three parties: the server, terminals, and users.

[0599] The server collects educational data from multiple educational institutions. This involves accessing each institution's database to retrieve student performance data and faculty evaluation data. Data collection can be done using API calls or SQL queries. Furthermore, Python libraries such as Pandas and NumPy are used for data preprocessing. This ensures data quality and corrects inaccurate or missing data.

[0600] The server then uses data analysis tools such as SciKit-Learn and TensorFlow to analyze the funding needs of each educational institution and set priorities based on the results. This priority is calculated by utilizing information obtained by sending prompts to a generative AI model.

[0601] Meanwhile, the terminal performs a simulation of fund allocation based on the analysis results received from the server. The simulation on the terminal virtually tries out various allocation scenarios and utilizes simulation models built with R or Python. The results are presented to the user in a visually easy-to-understand format and are typically visualized using Tableau or Matplotlib.

[0602] Users review multiple simulation results presented through their devices and select the optimal funding allocation plan that aligns with their strategic goals and decision-making criteria. During this process, they are required to send prompts to the generated AI model, such as "To which project should funding be allocated to maximize educational outcomes?", prompting it to perform additional analysis.

[0603] As a concrete example, the system analyzes the funding needs of local schools based on student academic performance data and teacher evaluation data collected from those schools. Based on the results received via a terminal, it simulates scenarios for allocating funds to IT education projects and library expansion projects. From these results, users can implement the most effective allocation plan.

[0604] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0605] Step 1:

[0606] The server collects data from multiple educational institutions. Specifically, it accesses the databases of each institution and uses APIs and SQL queries to retrieve student grade information and faculty evaluation information. The input consists of various evaluation and grade data from the educational institutions' databases, and the output is a JSON file containing this data.

[0607] Step 2:

[0608] The server preprocesses the collected data. This process uses Pandas and NumPy to verify data quality and correct inaccurate values ​​and missing values. Specifically, it performs mean imputation or estimation from context for missing data. The input is the JSON data obtained in step 1, and the output is a clean dataset that has been quality checked and corrected.

[0609] Step 3:

[0610] The server performs data analysis. Using clean data, it analyzes the funding needs of each educational institution using analytical tools such as SciKit-Learn and TensorFlow. It applies machine learning algorithms to calculate funding allocation priorities. In this process, it sends prompts to a generative AI model to obtain results. The input is a corrected dataset, and the output is a priority list based on funding needs.

[0611] Step 4:

[0612] The terminal uses a priority list received from the server to perform a funding allocation simulation. It considers different allocation scenarios and runs simulation models created in R or Python. The results are visualized as graphs and heatmaps. The input is a priority list of funding needs, and the output is the predicted outcome for each scenario.

[0613] Step 5:

[0614] The user selects the optimal funding plan based on the simulation results displayed on the device. Multiple scenarios are compared, prompts are sent to the generating AI model for further analysis, and support is provided for the final decision. The input is the predicted outcomes of multiple scenarios, and the output is the selected funding scenario.

[0615] Step 6:

[0616] Users send feedback to the server regarding their selected funding allocation scenarios. This feedback is used for future analysis and system improvements. The input is the funding allocation scenario selected by the user, and the output is the recorded data used for the next analysis.

[0617] (Application Example 1)

[0618] 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".

[0619] Optimally allocating materials and personnel in manufacturing facilities is crucial for improving production efficiency and reducing costs. However, currently, it is difficult to quickly and accurately grasp the status of each facility and make appropriate allocations. Therefore, there is a need for methods to automate the optimal allocation of resources through real-time information gathering and analysis, thereby increasing production efficiency.

[0620] 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.

[0621] In this invention, the server includes means for collecting information from a manufacturing facility, means for analyzing the collected information and generating results for determining the priority of the allocation of materials and workers, and means for simulating allocation scenarios based on the analysis results. This enables real-time monitoring of the conditions within the manufacturing facility and the optimal and efficient allocation of resources.

[0622] A "manufacturing facility" refers to a place where work is done to create a product, and where the production process is carried out in an organized manner.

[0623] "Information" refers to data related to production management, such as the production status of manufacturing facilities, the operating status of equipment, the utilization status of materials, and the activity status of workers.

[0624] A "server" is a computer system that has the function of collecting and storing information, and processing and analyzing the collected data.

[0625] "Materials" refers to production elements such as raw materials and parts used in the manufacturing process of a product.

[0626] "Workers" refers to the human labor force that performs production activities in the manufacturing of products.

[0627] "Allocation" refers to the appropriate deployment and allocation of production resources such as materials and workers.

[0628] "Simulation" is a method of reproducing the actual manufacturing process on a computer and predicting the results of distribution scenarios in advance.

[0629] "Analysis results" refer to the output that includes conclusions and implications derived from data processed based on collected information.

[0630] The server builds a system that allows for real-time monitoring of production status based on information collected from manufacturing facilities. This system includes data obtained through an information collection infrastructure consisting of multiple sensors, and this data is processed and analyzed by the server. The server corrects for missing or inaccurate data and generates analysis results to determine the optimal allocation of materials and workers. This process utilizes programming languages ​​such as Python for data processing and libraries such as Pandas and NumPy for data analysis.

[0631] Based on these analysis results, the terminal simulates various allocation scenarios and presents the results visually to the user. The terminal requires a user interface that allows for flexible manipulation of the simulation results. Furthermore, the results of each scenario can be adjusted according to user feedback and on-site needs.

[0632] Based on the provided simulation results, users make decisions to optimize resource allocation within the manufacturing facility. User feedback is returned to the server and used for further system optimization. This feedback loop supports the continuous improvement of manufacturing efficiency.

[0633] As a concrete example, one factory was able to reduce material waste and improve production efficiency by 20% by introducing this system. An example of a prompt to be input into the generated AI model is: "Please propose a model to optimize resource allocation on the factory's production line. This model needs to collect on-site data and propose the optimal resource placement."

[0634] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0635] Step 1:

[0636] The server collects information from the manufacturing facility through sensors. This input includes information about the operating status of the production line, the inventory levels of materials, and the allocation of workers. The server stores the data in real time and processes it to verify the accuracy of the information.

[0637] Step 2:

[0638] The server corrects any missing or inaccurate information in the collected data. Based on this input data, it performs data cleaning, estimating and imputing missing values ​​and removing outliers to generate reliable data.

[0639] Step 3:

[0640] The server uses analytical methods to generate a model for determining the optimal allocation of materials and workers from corrected data. Using the corrected data as input, it applies the generated AI model to perform predictive analysis and outputs the optimal allocation result.

[0641] Step 4:

[0642] The terminal simulates various allocation scenarios based on the optimal allocation results received from the server. The simulation tries different allocation patterns and visually displays the results for each. It uses the optimal allocation results as input and provides the simulation results as output.

[0643] Step 5:

[0644] The user reviews the simulation results presented on the terminal and selects the resource allocation best suited to the on-site situation. User feedback is sent to the server based on the selected scenario. This feedback data is accumulated to improve the system's accuracy.

[0645] 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.

[0646] This invention is a system that optimizes the allocation of funds to educational institutions, and is characterized by its ability to promote the efficient use of educational funds by collecting and analyzing feedback that takes user emotions into consideration. The system consists of a server, terminals, and users, and incorporates an emotion engine.

[0647] The server collects data from educational institutions, including raw data such as student academic performance and faculty evaluations. The server receives this data, verifies its quality, and then uses machine learning algorithms to analyze the funding needs of each school and project, generating results. These analysis results form a crucial dataset for determining funding priorities.

[0648] The terminal simulates fund allocation scenarios based on analysis results received from the server. The terminal presents information through a visual interface to ensure users can easily understand the results. Furthermore, this process collects user feedback and analyzes the user's emotional state using an emotion engine. The emotion engine analyzes the user's input and emotions during interface operation, quantifying the quality of the feedback and satisfaction level.

[0649] Users can review the displayed fund allocation simulation results and compare multiple scenarios through the terminal interface. In the process of selecting the optimal fund allocation based on their own criteria, users can consider the results of the emotion engine's analysis. If emotions such as concern or dissatisfaction are identified, they can receive guidance on how to correct those points.

[0650] As a concrete example, suppose a middle school is considering launching a new educational program and several budget proposals are presented. A server collects and analyzes the relevant data, and a terminal presents the results relatively. The user considers different scenarios through simulations of the results, and the terminal analyzes the user's reactions during this process using an emotion engine. Based on the simulation results and emotion analysis, the user can select the optimal program.

[0651] Thus, by incorporating user emotions, this invention enables more humane and effective fund allocation, contributing to the improvement of the quality of education.

[0652] The following describes the processing flow.

[0653] Step 1:

[0654] The server collects data from educational institutions. Specifically, it uses APIs and database connections to obtain data such as student grades, attendance rates, teacher evaluations, and budget usage. This creates a detailed dataset necessary for funding allocation.

[0655] Step 2:

[0656] The system verifies the accuracy and completeness of the data collected by the server. If inaccurate or missing data is detected, an automatic correction algorithm is applied to supplement the data and improve its reliability.

[0657] Step 3:

[0658] The server uses machine learning algorithms to analyze the data. This analysis assesses the funding needs of educational institutions based on their performance. Furthermore, funding allocation priorities are determined based on each institution's performance indicators.

[0659] Step 4:

[0660] The terminal receives analysis results from the server and simulates fund allocation scenarios based on them. The terminal then presents the user with predicted results for different allocation scenarios through an intuitive visual interface.

[0661] Step 5:

[0662] Users view simulation results presented via their devices and make fund allocation choices based on them. Users compare various scenarios and identify the plan best suited to their own strategy and goals.

[0663] Step 6:

[0664] The device collects user feedback and simultaneously uses a built-in emotion engine to analyze the user's emotions during the process. This quantifies how the feedback is received and provides information that helps improve funding allocation plans.

[0665] Step 7:

[0666] The user considers the results of the sentiment analysis and sends feedback from their device to the server. The server uses this information to further optimize the system.

[0667] (Example 2)

[0668] 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".

[0669] A challenge in the allocation of educational funds is that funds are sometimes not used effectively or are allocated inappropriately. In addition, the lack of mechanisms to adequately reflect the emotions and feedback of stakeholders in the fund allocation process makes it difficult to optimize allocation.

[0670] 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.

[0671] In this invention, the server includes means for collecting information from educational institutions, means for verifying the quality of the collected information and correcting inaccurate or missing information, and means for identifying factors that affect educational outcomes, performing analysis using machine learning algorithms, and generating results for determining priorities for funding allocation. This makes it possible to allocate educational funds more effectively and in a way that takes user sentiment into account.

[0672] "Educational institutions" refer to organizations that provide education, such as schools, universities, and vocational schools.

[0673] "Information" refers to data obtained from educational institutions, such as students' academic performance and evaluations of faculty and staff.

[0674] "Quality verification" refers to the act of checking the completeness, consistency, and accuracy of collected information, and correcting any inaccuracies or missing information.

[0675] A "machine learning algorithm" refers to a mathematical model that allows a computer to learn patterns from data and automatically perform predictions and analyses.

[0676] A "funding allocation scenario" refers to a model that simulates in advance how educational funds will be allocated.

[0677] An "emotion engine" refers to a system that analyzes user feedback and emotions during operation, and quantifies and presents the emotional state.

[0678] A "user" refers to an individual or organization that operates a terminal to simulate and select fund allocations.

[0679] This invention provides a system for optimizing the allocation of educational funds. This system performs a series of processes including collecting information from educational institutions, verifying data quality, performing predictive analytics, simulating funding allocation scenarios, and analyzing user sentiment feedback.

[0680] The server collects student grades and faculty evaluation information from educational institutions. This information collection is performed by connecting to the educational institution's database and automatically retrieving the necessary information via an API. To verify the quality of the retrieved information, data cleaning software is used to detect and correct missing or outlier values.

[0681] The server also runs machine learning algorithms using verified data. These algorithms are used to identify factors influencing educational outcomes and predict the funding needs of each project. Specific implementations include regression models and clustering techniques, utilizing existing statistical analysis tools.

[0682] The terminal receives analysis results from the server and functions as a tool to simulate fund allocation scenarios. The simulation results are displayed on a visual interface and presented in a user-friendly format. The terminal incorporates an emotion engine to collect feedback and analyze the user's emotional state. This engine analyzes the emotional tone of the feedback entered by the user and quantifies the result as satisfaction.

[0683] Users can evaluate simulation results presented via their device and compare multiple scenarios. They can then leverage insights gained from sentiment analysis to make more appropriate financial allocation choices.

[0684] As a concrete example, consider a situation where a middle school is launching a new educational program and there are multiple budget proposals. A server gathers and analyzes relevant information, and a terminal presents relative scenarios to the user. The user can then compare the results based on this information, refer to sentiment feedback, and make a final decision.

[0685] Examples of prompts for a generative AI model:

[0686] "Please explain the optimal allocation method for educational funding, including how to integrate user feedback while taking their emotions into account."

[0687] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0688] Step 1:

[0689] The server collects information from educational institutions. The input to this process includes student performance data and faculty evaluation data. Specifically, the server accesses each educational institution's database via an API, retrieves the necessary information, and stores it in its own database. The output is a categorized raw dataset.

[0690] Step 2:

[0691] The server verifies the quality of the collected information. The input is the raw dataset obtained in Step 1. The server uses data quality control tools to impute missing values ​​and correct outliers. Specific actions include data integrity checks and simple statistical analysis. The output is a dataset with guaranteed quality.

[0692] Step 3:

[0693] The server runs machine learning algorithms using quality-verified data. The input is the dataset from Step 2. Here, the server uses regression models and clustering techniques to predict the funding needs of each project. Specifically, the server fits the model and outputs the funding allocation priority as a numerical value.

[0694] Step 4:

[0695] The terminal simulates funding allocation scenarios based on analysis results sent from the server. The input is the analysis results data from step 3. The terminal uses a simulation tool to visualize the impact of each funding allocation pattern. Specific actions include generating bar graphs and pie charts of budget allocation. The output is a visual simulation result that is easy for the user to understand.

[0696] Step 5:

[0697] The device uses the generated simulation results to collect feedback from users. The input consists of the simulation results and user responses. The device collects opinions and feedback using survey forms and direct input fields. The output is the compiled feedback data.

[0698] Step 6:

[0699] The device analyzes user feedback data using an emotion engine. The input is the feedback data obtained in step 5. Specifically, the device uses natural language processing to quantify the emotional tone and evaluate user satisfaction. The output is the quantified emotion evaluation data.

[0700] Step 7:

[0701] The user selects the optimal fund allocation based on the simulation results and sentiment analysis results presented on the terminal. The inputs are the simulation results and sentiment evaluation data. The user compares each displayed option and, using the sentiment analysis results as a reference, selects the optimal plan based on their own judgment. This process includes a selection process on the user interface. The output is the final selected fund allocation plan.

[0702] (Application Example 2)

[0703] 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".

[0704] Resource allocation in industry requires efficient and appropriate management. However, traditional systems fail to consider the emotional state of workers, leading to decreased productivity and dissatisfaction. Furthermore, there is a lack of objective means to evaluate the quality of feedback, making it difficult to contribute to improving the work environment.

[0705] 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.

[0706] In this invention, the server includes means for collecting information using a data aggregation device, means for analyzing the collected information and producing results for determining resource allocation priorities, and means for analyzing emotional states and quantifying the quality of feedback. This makes it possible to optimize resource allocation while taking into account the emotions of workers.

[0707] A "data aggregation device" is a device used to collect and integrate various types of information necessary for factory operations.

[0708] "Information analysis" is the process of using collected data to perform analyses tailored to a specific purpose and derive useful results.

[0709] "Resource allocation priority" is an indicator of importance used to indicate which processes or areas should be allocated limited resources to.

[0710] "Emotional state" refers to information obtained by identifying and capturing the psychological and emotional condition of a worker.

[0711] "Quantifying the quality of feedback" is a method of evaluating opinions and impressions obtained from workers as numerical data and objectively measuring their effectiveness and areas for improvement.

[0712] To realize this application, three elements—server, terminal, and user—play crucial roles. The server collects diverse information from the factory using a data aggregation device. The collected data is then analyzed using software to calculate resource allocation priorities. This provides a foundation for developing efficient production plans.

[0713] The terminal uses an emotion engine to evaluate the emotional state of workers based on analysis results provided by the server. This involves using software that analyzes user input and provides numerical evaluations to quantify the quality of feedback. This makes it possible to obtain concrete guidelines for improving the work environment.

[0714] Users can review production scenarios presented through the terminal's visual interface and contribute to process optimization. Because users can provide feedback based on their own experiences and feelings, they become a valuable source of information for effectively adjusting resource allocation.

[0715] As a concrete example, a manufacturer could use this system when launching a new product line to improve production efficiency. Improved resource allocation would increase worker satisfaction, resulting in smoother operation of the production line. When utilizing the generative AI model, the following example prompts are used:

[0716] "Develop a system that optimizes production schedules based on worker feedback to efficiently operate the new product line."

[0717] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0718] Step 1:

[0719] The server collects data from sensors and systems within the factory through an aggregation device. This data includes production status, machine operation information, and worker hours. The server then collects this data and prepares it for data analysis.

[0720] Step 2:

[0721] The server begins analyzing the collected data using software designed for this purpose. This process removes outliers and converts the data to the required format. The output is index data indicating resource allocation priorities. This clearly shows how much resources should be allocated to each process.

[0722] Step 3:

[0723] The terminal uses an emotion engine to evaluate the emotional state of workers based on resource allocation indicator data received from the server. Inputs include worker feedback and real-time emotional data. By analyzing this data, the quality of feedback and the workers' satisfaction with their environment are quantified.

[0724] Step 4:

[0725] Users review the production scenarios presented by the terminal through a visual interface and provide feedback based on their own criteria. This feedback is used to allocate resources in the next cycle, so users can input comments such as specific areas for improvement, and the system uses this feedback to aim for further efficiency improvements.

[0726] Step 5:

[0727] The device receives user feedback, re-evaluates it using an emotion engine, and monitors the progress of the feedback. Input includes newly added user comments and improvement requests. Based on this, it performs another analysis and generates data indicating the next improvement steps. The generated data is fed back to the server and reflected in the next data analysis.

[0728] 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.

[0729] 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.

[0730] 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.

[0731] 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.

[0732] 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.

[0733] 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.

[0734] 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.

[0735] 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.

[0736] 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."

[0737] 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.

[0738] 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.

[0739] 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.

[0740] 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.

[0741] 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.

[0742] 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.

[0743] 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.

[0744] 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.

[0745] 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.

[0746] 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.

[0747] 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.

[0748] 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 as being incorporated by reference.

[0749] The following is further disclosed regarding the embodiments described above.

[0750] (Claim 1)

[0751] Methods for collecting data from educational institutions,

[0752] A means of analyzing collected data and generating results for determining the priority of fund allocation,

[0753] Based on the analysis results, a means to simulate fund allocation scenarios,

[0754] A means of providing simulation results to users and collecting feedback,

[0755] A system that includes this.

[0756] (Claim 2)

[0757] The system according to claim 1, further comprising means for verifying the quality of data from educational institutions and correcting inaccurate or missing data.

[0758] (Claim 3)

[0759] The system according to claim 1, further comprising means for identifying factors that influence educational outcomes and performing predictive analysis based on those factors.

[0760] "Example 1"

[0761] (Claim 1)

[0762] Means of collecting information from educational institutions,

[0763] A means for processing collected information and generating results for determining the priority of fund allocation,

[0764] A means of testing funding allocation scenarios based on priorities,

[0765] A means of providing trial results to users and collecting their opinions,

[0766] A system that includes this.

[0767] (Claim 2)

[0768] The system according to claim 1, further comprising means for verifying the accuracy of information on educational institutions and supplementing missing or inaccurate information.

[0769] (Claim 3)

[0770] The system according to claim 1, further comprising means for identifying factors that influence educational outcomes and performing predictive analysis based on that information.

[0771] "Application Example 1"

[0772] (Claim 1)

[0773] Means of collecting information from manufacturing facilities,

[0774] A means for analyzing collected information and generating results for determining the priority of the allocation of materials and workers,

[0775] Based on the analysis results, a means to simulate allocation scenarios,

[0776] A means of providing simulation results to users and collecting feedback,

[0777] A system that includes this.

[0778] (Claim 2)

[0779] The system according to claim 1, further comprising means for verifying the quality of information on a manufacturing facility and correcting inaccurate or missing information.

[0780] (Claim 3)

[0781] The system according to claim 1, further comprising means for identifying factors affecting manufacturing efficiency and performing predictive analysis based thereon.

[0782] "Example 2 of combining an emotion engine"

[0783] (Claim 1)

[0784] Means of collecting information from educational institutions,

[0785] Means for verifying the quality of collected information and correcting inaccurate or missing information,

[0786] A means to identify factors influencing educational outcomes, analyze them using machine learning algorithms, and generate results for determining prioritizing funding allocation,

[0787] Based on the analysis results, a means of simulating and visually presenting fund allocation scenarios on a terminal,

[0788] A means of providing simulation results to users, collecting feedback, and analyzing it using an emotion engine,

[0789] A system that includes this.

[0790] (Claim 2)

[0791] The system according to claim 1, further comprising means for analyzing the user's emotional state using the emotion engine described above and reflecting the results in the evaluation of fund allocation.

[0792] (Claim 3)

[0793] The system according to claim 1, further comprising means for supporting a process in which a user compares different funding allocation scenarios and selects the optimal scenario taking into account the results of sentiment analysis.

[0794] "Application example 2 when combining with an emotional engine"

[0795] (Claim 1)

[0796] A means for collecting information using a data aggregation device,

[0797] A means of analyzing the collected information and producing results to determine the priority of resource allocation,

[0798] Based on the analysis results, a means to simulate resource allocation scenarios,

[0799] A means of presenting the results of the mock exam to users and collecting their opinions,

[0800] A means to analyze emotional states and quantify the quality of feedback,

[0801] A system that includes this.

[0802] (Claim 2)

[0803] The system according to claim 1, further comprising means for optimizing resource allocation in consideration of emotional state.

[0804] (Claim 3)

[0805] The system according to claim 1, further comprising means for identifying factors affecting work efficiency and performing predictive analysis based on those factors. [Explanation of Symbols]

[0806] 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. Methods for collecting data from educational institutions, A means of analyzing collected data and generating results for determining the priority of fund allocation, Based on the analysis results, a means to simulate fund allocation scenarios, A means of providing simulation results to users and collecting feedback, A system that includes this.

2. The system according to claim 1, further comprising means for verifying the quality of data from educational institutions and correcting inaccurate or missing data.

3. The system according to claim 1, further comprising means for identifying factors that influence educational outcomes and performing predictive analysis based on those factors.

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