System

Abstract

A system is provided.SOLUTION: This system includes a means for inputting user's attribute information and desired skill information, a means for collecting personal property raising program information provided from a plurality of educational institutions and enterprises, a means for vectorizing and clustering the collected personal property raising program information and the user's attribute information and desired skill information, and a means for specifying and providing a personal property raising program optimum to the user on the basis of a clustering result.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

[0002] Patent document 1 discloses a persona chatbot control method performed by at least one processor, the method including the steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to a description of the 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] Japanese Patent Publication No. 2022-180282 Summary of the Invention [Problem to be solved by the invention]

[0004] Modern companies and educational institutions lack individually optimized human resource development programs, and instead offer many general learning programs. This creates an issue where the effectiveness of individual skill development is not felt. For this reason, there is a demand for educational programs that provide high satisfaction tailored to the attributes and career paths of each user. [Means for solving the problem]

[0005] The present invention aims to solve the problem by the following means.

[0006] 1. A means for inputting user attribute information and desired skill information;

[0007] 2. A means of collecting information on human resource development programs offered by multiple educational institutions and companies;

[0008] 3. A means for vectorizing the collected human resource development program information, user attribute information, and desired skill information and clustering them;

[0009] 4. A means for identifying and providing the optimal human resource development program to the user based on the clustering results;

[0010] 5. A method is provided to select multiple optimal human resource development programs based on the clustering results and randomly select one of them to provide.

[0011] This makes it possible to automatically generate and provide educational programs that meet the needs and desires of individual users, providing a highly satisfying learning experience.In addition, by providing a means to return rewards to the human resources department and individuals based on the number of times users view the provided human resource development programs, it becomes possible to promote use of the system and measure its effectiveness.

[0012] "User attribute information" refers to basic information about the user, such as age, gender, occupation, and affiliation.

[0013] "Desired skill information" refers to information about specific knowledge or skills that the user wishes to acquire.

[0014] "Human resource development program information" refers to information about curricula and training programs provided by multiple educational institutions and companies to acquire specific knowledge and skills.

[0015] "Vectorization" refers to the process of converting text or data into a numerical vector format, allowing the text data to be treated as numerical data.

[0016] "Clustering" refers to a technique for classifying data into multiple groups based on similarity. In particular, in the present invention, grouping is performed based on the similarity between the user's desired skills and human resource development program information.

[0017] "Collection means" refers to the methods and technologies used to aggregate information from multiple external sources and organize it into a format that can be used within the system.

[0018] "Automatic generation" refers to the process of using algorithms or generative AI to mechanically create optimal educational programs based on input data.

[0019] "Means for providing" refers to the method or technology for presenting the generated educational program to the user and making it usable.

[0020] "Means of returning rewards based on the number of views" refers to a method for returning a certain amount of rewards or points to users or related departments depending on the number of times the users view the provided program. [Brief explanation of the drawings]

[0021] [Figure 1] 1 is a conceptual diagram showing an example of the configuration of a data processing system according to a first embodiment. [Figure 2] 1 is a conceptual diagram showing an example of main functions of a data processing device and a smart device according to a first embodiment. [Figure 3] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a second embodiment. [Figure 4] FIG. 10 is a conceptual diagram showing an example of main functions of a data processing device and smart glasses according to a second embodiment. [Figure 5] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a third embodiment. [Figure 6] FIG. 11 is a conceptual diagram showing an example of main functions of a data processing device and a headset-type terminal according to a third embodiment. [Figure 7]FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a fourth embodiment. [Figure 8] FIG. 10 is a conceptual diagram showing an example of main functions of a data processing device and a robot according to a fourth embodiment. [Figure 9] 1 shows an emotion map onto which multiple emotions are mapped. [Figure 10] 1 shows an emotion map onto which multiple emotions are mapped. [Figure 11] FIG. 3 is a sequence diagram showing a processing flow of the data processing system according to the first embodiment. [Figure 12] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system in Application Example 1. [Figure 13] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system according to the second embodiment when an emotion engine is combined. [Figure 14] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system in Application Example 2 when an emotion engine is combined. DETAILED DESCRIPTION OF THE INVENTION

[0022] An example of an embodiment of a system according to the technology of the present disclosure will be described below with reference to the accompanying drawings.

[0023] First, the terms used in the following description will be explained.

[0024] In the following embodiments, a coded processor (hereinafter simply referred to as a "processor") may be a single arithmetic device or a combination of multiple arithmetic devices. Furthermore, a processor may be a single type of arithmetic device or a combination of multiple types of arithmetic devices. Examples of arithmetic devices include a CPU (Central Processing Unit), a GPU (Graphics Processing Unit), a GPGPU (General-Purpose computing on Graphics Processing Units), and an APU (Accelerated Processing Unit).

[0025] In the following embodiments, a coded RAM (Random Access Memory) is a memory in which information is temporarily stored and is used as a working memory by a processor.

[0026] In the following embodiments, the coded storage is one or more non-volatile storage devices that store various programs, various parameters, etc. Examples of non-volatile storage devices include flash memory (SSD (Solid State Drive)), magnetic disks (e.g., hard disks), and magnetic tapes.

[0027] In the following embodiments, a communication I / F (Interface) with a symbol is an interface including a communication processor, 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), Bluetooth (registered trademark), etc.

[0028] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B." In other words, "A and / or B" means that it may be only A, only B, or a combination of A and B. Furthermore, in this specification, the same concept as "A and / or B" is also applied when three or more things are expressed connected by "and / or."

[0029] [First embodiment]

[0030] FIG. 1 shows an example of the configuration of a data processing system 10 according to the first embodiment.

[0031] 1, a 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.

[0032] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0033] The smart device 14 includes a computer 36, a reception device 38, an output device 40, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The reception device 38, the output device 40, and the camera 42 are also connected to the bus 52.

[0034] The reception device 38 includes a touch panel 38A, a microphone 38B, and the like, and receives user input. The touch panel 38A detects contact with an indicator (for example, a pen or a finger) to receive user input by the touch of the indicator. The microphone 38B detects the user's voice to receive user input by voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and the 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.

[0035] 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 of expression that the user 20 can perceive (for example, audio and / or text). The display 40A displays visible information such as text and images in accordance with instructions from the processor 46. The speaker 40B outputs audio in accordance with instructions from the processor 46. The camera 42 is a compact digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0036] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 control the exchange of various information between the processor 46 and the processor 28 via the network 54.

[0037] FIG. 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0038] 2, in the data processing device 12, a specific process 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" according to the technology of the present 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 process 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.

[0039] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0040] In the smart device 14, the processor 46 performs the reception output process. The storage 50 stores a reception output program 60. The reception output program 60 is used in conjunction with the 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 process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[0041] Next, a description will be given of the specific processing performed by the specific processing unit 290 of the data processing device 12. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0042] The present invention is a system that collects human resource development program information provided by multiple educational institutions and companies based on user attribute information and desired skill information, and automatically provides the most suitable educational program using generation AI. Specific embodiments of the present invention are described below.

[0043] First, the user accesses the input form using their own device. Here, they enter information such as age, gender, desired job type, and desired skills, and then submit it to the system. For example, let's say the user is a 25-year-old male who wants to be a data scientist and has the skills of Python and machine learning.

[0044] Next, the server vectorizes the human resource development program information collected from multiple educational institutions and companies based on the received user attribute information and desired skill information. Vectorization is the process of converting each piece of information into numerical data, which makes it a format that can be processed by a computer. For example, TF-IDF (Term Frequency-Inverse Document Frequency) can be used for this process.

[0045] The server then performs clustering processing on the vectorized data using the K-Means clustering algorithm. Clustering is a method for grouping similar data, and in this case, it identifies the cluster containing the human resource development program information that is most similar to the user's desired skills.

[0046] Based on the clustering results, the server selects the most suitable human resource development programs from the cluster and randomly selects one of them to provide to the user. For example, suppose the "Data Scientist Development Program" provided by Company A is selected. This program includes content such as Python, machine learning, and data analysis.

[0047] The selected program information is structured in JSON format or similar and sent to the user's device. The user's device displays the received information on its screen, allowing the user to check the content provided as the optimal program.

[0048] Furthermore, the system also has a mechanism whereby advertising revenues and other rewards are returned to the human resources department or individuals based on the number of times users view the presented programs. This mechanism makes it possible to promote system usage and measure the effectiveness of the programs.

[0049] Specifically, the more a user views a program, the greater the rewards the user and the company or educational institution that provided the program can receive. This makes it easier to evaluate the user's interest and the program's usefulness.

[0050] The above is a specific embodiment of the human resource development program automatic provision system according to the present invention. This system allows users to easily find the learning program that best suits them, enabling them to have an efficient and satisfying learning experience.

[0051] The processing flow will be explained below.

[0052] Step 1:

[0053] The user accesses the input form using their own terminal, where they enter their age, gender, desired job type, and desired skill information. Once the input is complete, the user clicks the "Submit" button to send the information to the server.

[0054] Step 2:

[0055] The server receives the request sent by the user and extracts the user's attribute information and desired skill information. For example, if the user is a 25-year-old male, his desired occupation is data scientist, and his desired skills are Python and machine learning, this information is passed to the server.

[0056] Step 3:

[0057] The server loads pre-collected information on human resource development programs provided by multiple educational institutions and companies, including detailed descriptions of each program and the skills that can be acquired.

[0058] Step 4:

[0059] The server vectorizes the user's desired skill information and the collected program information. Specifically, it uses TF-IDF vectorization technology to convert text data into numerical data. This makes it possible to compare the program information and the user's desired skill information numerically.

[0060] Step 5:

[0061] The server uses the vectorized data to perform clustering. It applies the K-Means clustering algorithm to classify the data into multiple clusters. A cluster is a collection of data grouped based on similarity.

[0062] Step 6:

[0063] Based on the clustering results, the server identifies the cluster that best matches the user's desired skill information. This cluster contains the program information that is closest to the user's preference.

[0064] Step 7:

[0065] The server selects multiple optimal programs from the program information in the cluster, then randomly selects one program from the list and prepares it to be provided to the user.

[0066] Step 8:

[0067] The server structures the selected program information in JSON format and sends it to the user's device, which then analyzes the information and displays it in a format that is easy for the user to understand.

[0068] Step 9:

[0069] The user views the program information provided on the device screen and confirms the content. For example, Company A's data scientist training program is selected and displayed on the device.

[0070] Step 10:

[0071] The server records the number of times a user views the program and operates a reward system based on that information. A system is implemented in which rewards are returned to the human resources department or individuals according to the number of times a user views the program and their learning outcomes. This allows the effectiveness of the program and frequency of use to be evaluated.

[0072] The above are the specific processing steps of the system for automatically providing a human resource development program according to the present invention. By performing specific operations at each step, it is possible to provide the user with an optimal educational program.

[0073] Example 1

[0074] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0075] In today's highly information-driven society, it is extremely important to find the best educational program to improve one's career aptitude and skills. However, selecting the most suitable program from the wide variety of programs offered by educational institutions and companies is difficult and requires time and effort. For this reason, there is a need for a system that allows users to easily and efficiently find the best educational program.

[0076] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[0077] In this invention, the server includes means for inputting user attribute information and desired skill information, means for collecting human resource development program information provided by multiple educational institutions and companies, means for vectorizing and clustering the collected human resource development program information and the user attribute information and desired skill information, means for identifying the human resource development program most suitable for the user based on the clustering results and transmitting the identified program to the user terminal as structured data, means for displaying the data received by the user terminal on a screen, and means for generating advertising revenue and rewards based on the user's browsing behavior. This enables users to efficiently and effectively find the educational program that best suits them.

[0078] "User" refers to a person who uses the system to search for and select the educational program that best suits them.

[0079] "Attribute information" is a general term for personal information such as a user's age, gender, desired occupation, etc.

[0080] "Desired skill information" is information about skills and qualifications that a user wishes to acquire.

[0081] The "means for inputting" is an interface that allows a user to input attribute information and desired skill information into the system.

[0082] "Means of collection" refers to the methods and functions for acquiring information on human resource development programs provided by multiple educational institutions and companies.

[0083] "Vectorization" is the process of converting collected information and user attribute information into numerical data.

[0084] "Clustering" is a data analysis technique that groups similar data based on vectorized data.

[0085] The "means for performing clustering" refers to an algorithm or mechanism for performing clustering using the collected human resource development program information and user information.

[0086] "Structured data" refers to information that has been organized into a format that makes it easier to manage, such as data in JSON or XML format.

[0087] The "transmitting means" is a communication means for transmitting the educational program information selected by the server to the user terminal.

[0088] The "means for displaying received data on a screen" refers to an interface that visually displays the received educational program information on the user terminal.

[0089] "Viewing behavior" refers to the act of a user viewing and using the educational program provided.

[0090] "Means for generating advertising revenue or rewards" means a mechanism for calculating and distributing advertising revenue or rewards based on user browsing behavior.

[0091] The present invention is a system that collects information on human resource development programs provided by multiple educational institutions and companies based on user attribute information and desired skill information, and automatically provides the most suitable educational program. Specific embodiments of the present invention are described below.

[0092] First, the user accesses the input form using their own device. Here, the user enters attribute information such as age, gender, desired job type, and desired skills, and submits it to the system. For example, a user might enter information such as, "I am a 25-year-old male who aspires to be a data scientist and would like to have skills in Python and machine learning."

[0093] The server converts the received user attribute information and desired skill information into numerical data (vectorization). This process uses TF-IDF (Term Frequency-Inverse Document Frequency), a natural language processing (NLP) method. By calculating the frequency of occurrence of each word and converting it into a vector as numerical data, subsequent clustering processing becomes possible.

[0094] The server then performs clustering on the vectorized data, using the K-Means clustering algorithm. This algorithm divides the data into multiple clusters and groups data points within each cluster so that they are similar to each other. This allows the server to identify the cluster containing the human resource development program that is most similar to the user's desired skills.

[0095] Based on the clustering results, the server selects the most suitable human resource development programs from the cluster and randomly selects one of them to provide to the user. For example, if the selected program is a "data scientist development program" that includes Python, machine learning, data analysis, etc.

[0096] Information about the selected program is sent to the user's device as structured data in JSON format or similar. The server sends this information using the HTTP protocol. The user's device parses the received JSON data and displays it on the screen. Specifically, the browser displays a message stating, "The program best suited for you is the 'Data Scientist Training Program.' It is provided by a certain company, and you will learn Python, machine learning, and data analysis."

[0097] In addition, the server collects user browsing behavior data and generates advertising revenue and rewards based on that data. For example, if a user views a provided program more often, the organization that provided the program can receive a larger reward. This information is sent to the advertisement display server and reward calculation system, and the reward is distributed appropriately.

[0098] A specific example is the following prompt for user input:

[0099] "I'm a 25-year-old male aspiring to become a data scientist. I'm looking for Python and machine learning skills. What educational program would be best for me?"

[0100] In this way, the system of the present invention can automatically provide the optimal human resource development program according to the user's needs, allowing the user to efficiently and effectively find the educational program that best suits them, thereby improving their learning experience.

[0101] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0102] Step 1:

[0103] The user inputs attribute information and desired skill information and sends it to the server.

[0104] The user accesses the input form using their own device and enters attribute information such as age, gender, desired job type, and desired skills. For example, they might enter information such as "I am a 25-year-old male, aiming to become a data scientist, and would like to have Python and machine learning skills." Once the information is complete, the user clicks the "Submit" button to send this information to the server. The input data is sent to the server in text format.

[0105] Step 2:

[0106] The server vectorizes the information it receives.

[0107] The server receives the attribute information and desired skill information sent by the user. The received data includes information such as "Age: 25," "Gender: Male," "Desired occupation: Data scientist," and "Desired skills: Python, machine learning." The server then converts this text information into numerical data (vectorizes it). Using TF-IDF (Term Frequency-Inverse Document Frequency), the server calculates the frequency of occurrence of each word and converts this into a vector as numerical data. For example, vector data such as "Python" being 0.8 and "machine learning" being 0.9 is generated.

[0108] Step 3:

[0109] The server clusters the vector data.

[0110] The server applies the K-Means clustering algorithm to the vectorized data. The input for clustering is the vector data of user information and collected program information. The server sets initial cluster centers and assigns each data point (program information) to the closest cluster center. It then recalculates the center of each cluster and obtains new cluster centers. This process is repeated until the cluster centers converge, and finally multiple clusters and their centers are identified. The output is a cluster number for each program.

[0111] Step 4:

[0112] The server selects the most suitable educational program and provides it to the user.

[0113] The server selects the most suitable educational program based on the clustering results. Based on the cluster number, it selects multiple programs within the cluster that are most similar to the user's attributes. It then randomly selects one of the selected candidate programs and converts the selection result into JSON format. For example, it generates JSON data such as "{"Program Name": "Data Scientist Training Program", "Provider": "Certain Educational Institution", "Skills": ["Python", "Machine Learning", "Data Analysis"]}".

[0114] Step 5:

[0115] The server sends the selected program information to the user terminal in JSON format.

[0116] The generated JSON data is sent to the user's device using the HTTP protocol. The server uses an appropriate security protocol (e.g., HTTPS) to ensure data integrity and confidentiality when sending the information. The information sent includes the program name, provider, related skills, etc.

[0117] Step 6:

[0118] The user terminal displays the received information on its screen.

[0119] The user's device analyzes the received JSON data and displays it on the screen. For example, a message might appear on the browser saying, "The program best suited to you is the 'Data Scientist Training Program.' It is provided by a certain educational institution, and the content you will learn is Python, machine learning, and data analysis." The user can review this information and investigate the program details in more detail if necessary.

[0120] Step 7:

[0121] The server collects user browsing behavior data and generates advertising revenue and rewards.

[0122] The server collects the number of times and duration that users view the provided programs. The collected data is sent to an advertising display server and a reward calculation system. For example, advertising revenue and rewards are returned to the organization that provided a program that is viewed frequently by users. Specifically, the system is equipped with an algorithm that calculates advertising revenue and distributes rewards based on that calculation.

[0123] (Application example 1)

[0124] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0125] Conventional human resource development programs have had issues with automatically providing optimal training programs based on user attribute information and desired skill information, and also with insufficient mechanisms for measuring the effectiveness of the programs provided and for reward return.In addition, there have been cases where appropriate programs for efficiently learning the specialized skills required, particularly for factory robot operators, have not been provided.

[0126] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[0127] In this invention, the server includes a means for inputting user attribute information and desired skill information, a means for collecting human resource development program information provided by multiple educational institutions and companies, a means for vectorizing and clustering the collected human resource development program information and the user attribute information and desired skill information, a means for identifying and providing the human resource development program optimal for the user based on the clustering results, and a means for transmitting the optimal program information to the user terminal in JSON format, which then receives and displays the information. This allows the system to efficiently provide the educational program most suited to the user's attribute information and preferences, and allows the user to easily view the optimal program content. Furthermore, rewards are provided based on the number of times the provided program is viewed, which promotes system usage and evaluates the program's practicality.

[0128] "User attribute information" is personal data such as the user's age, gender, desired occupation, etc.

[0129] "Desired skill information" is information about specific skills or knowledge that the user wishes to acquire.

[0130] An "educational institution" is an organization that provides education, such as a school, vocational school, or university.

[0131] "Enterprise" means a legal entity or organization engaged in commercial activities or providing products or services.

[0132] "Human resource development program information" is data on training and education programs provided by educational institutions and companies.

[0133] "Vectorization" is the process of converting data into a number-based format that can be processed by a computer.

[0134] "Clustering" is a data analysis technique that groups similar data points together.

[0135] "JSON format" is a data exchange format based on JavaScript object notation.

[0136] A "user terminal" is a device operated by a user, and includes a smartphone, a computer, and the like.

[0137] "Optimal program information" is information about the optimal educational program selected by the system based on the user's attribute information and desired skill information.

[0138] The "number of views" is an index showing how many times the user has viewed the provided information or program.

[0139] Rewarding is the process of providing rewards to individuals or departments for specific behaviors or results.

[0140] The present invention is a system that collects human resource development program information provided by multiple educational institutions and companies based on user attribute information and desired skill information, and automatically provides the optimal educational program using generation AI. Specific embodiments of the present invention are described below.

[0141] User Data Input

[0142] The user accesses the input form using a user device such as a smartphone or computer. At this time, the user enters their age, gender, desired job type, and desired skill information, and sends it to the server. For example, let's assume that the user is a 35-year-old male whose desired job type is robot operator and whose desired skill is robot programming.

[0143] Data Vectorization

[0144] The server vectorizes the received user attribute information, desired skill information, and collected educational program information using TF-IDF (Term Frequency-Inverse Document Frequency), which converts this information into numerical data that can be processed by a computer.

[0145] Clustering Process

[0146] The server performs clustering processing on the vectorized data by applying the K-Means clustering algorithm, which groups similar data and identifies the cluster containing the educational program information most similar to the user's desired skills.

[0147] Optimal program selection

[0148] Based on the clustering results, the server selects the most suitable educational programs from the cluster and randomly selects one of them to provide to the user. For example, if the "Robot Programming Skills Improvement Program" is selected, this program includes robot programming techniques from basic to advanced.

[0149] Providing program information

[0150] The selected program information is structured in JSON format and sent to the user's device. The user's device receives the information and displays it on the screen, allowing the user to easily check the optimal program content.

[0151] Reward based on number of views

[0152] Furthermore, the server will return rewards to the human resources department and individuals based on the number of times the user views the provided programs. This system will make it possible to promote system usage and measure the effectiveness of the programs.

[0153] Specific examples

[0154] For example, if the user is a 35-year-old man who wants to improve his skills as a "robot operator" and aims to learn "robot programming," the system will operate based on the following prompt sentence.

[0155] Prompt Sentence Examples

[0156] Prompt: "Please provide the best educational program for age: 35, gender: male, desired job: robot operator, desired skills: robot programming."

[0157] By inputting these prompts into a generative AI model, the model will provide an educational program that best suits the user's attributes and preferences.

[0158] The above is a specific embodiment of the human resource development program automatic provision system according to the present invention. This system allows users to easily find the most suitable educational program for them and efficiently acquire the necessary skills.

[0159] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0160] Step 1:

[0161] A user accesses the input form using a device such as a smartphone or computer, enters information such as age, gender, desired job type, and desired skill information, and submits it. The entered data is sent to the server, which collects the user's attribute information and desired skill information.

[0162] Step 2:

[0163] The server vectorizes the received user attribute information and desired skill information using TF-IDF. Specifically, the input data is converted into numerical data, which makes it possible for the computer to process it. The vectorized data becomes the input for the next step.

[0164] Step 3:

[0165] The server runs the K-Means clustering algorithm on the vectorized user data and the collected educational program information. This algorithm groups similar data points and identifies the cluster that contains the educational program information that is most similar to the user's desired skills. The clustering results are used as input for the next step.

[0166] Step 4:

[0167] The server selects multiple optimal educational programs based on the clustering results. At this time, it selects the program that best matches the user's attribute information and desired skill information. It then randomly selects one of the selected programs and creates data to provide to the user. This data becomes the input for the next step.

[0168] Step 5:

[0169] The server structures the selected educational program information in JSON format and sends it to the user's device. The user's device then analyzes the received JSON data and displays it on the screen, allowing the user to check the optimal program content.

[0170] Step 6:

[0171] The user's device sends the number of times the user has viewed the program to the server. The server then executes a process to return rewards to the human resources department and individuals based on this data. This reward return system is used to evaluate the usefulness of the program.

[0172] The above is a concrete flow of the program processing of the system based on the application example. This system enables users to efficiently search for advanced educational programs and acquire the necessary skills.

[0173] Furthermore, an emotion engine that estimates the user's emotion may be combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0174] The present invention is a system that collects human resource development program information provided by multiple educational institutions and companies based on user attribute information, desired skill information, and emotional information, and automatically provides the optimal educational program using a generation AI. Specific embodiments of the present invention are described below.

[0175] First, the user accesses the input form using their own device. Here, the user enters their age, gender, desired job type, and desired skill information. Once the input is complete, the user clicks the "Submit" button to send the information to the server. As an example, consider a case where the user is a 25-year-old male, whose desired job type is data scientist, and whose desired skills are Python and machine learning.

[0176] The server then receives the request from the user and extracts the user's attribute information and desired skill information. Furthermore, to recognize the user's emotions, the server uses facial recognition, voice analysis, and other techniques through an emotion engine built into the device to collect the user's emotional information. For example, it can recognize whether the user is highly motivated and excited about learning, or, conversely, whether they are feeling anxious or stressed.

[0177] The server loads pre-collected information on human resource development programs provided by multiple educational institutions and companies, including detailed descriptions of each program, the skills that can be acquired, and training methods.

[0178] The server vectorizes the user's desired skill information and the collected program information so that it can be handled as numerical data. This process uses TF-IDF (Term Frequency-Inverse Document Frequency) vectorization technology. The K-Means clustering algorithm is then applied to the vectorized data to classify the data into multiple clusters.

[0179] The server identifies the most suitable human resource development program for each user based on the clustering results and the user's emotional information. Specifically, clustering is performed that takes emotional information into account in order to select the learning program that best suits the user's emotional state. In this case, for example, a user who is highly motivated to learn may be offered a more difficult program, while a user who is feeling stressed may be offered more relaxing learning content.

[0180] Next, the server selects multiple optimal programs from the program information in the cluster, randomly selects one of them, and provides it to the user. For example, if Company A's "Data Scientist Training Program" is selected, the program content will consist of Python, machine learning, data analysis, etc.

[0181] The selected program information is structured in JSON format and sent to the user's device. The user's device analyzes the received information and displays it in a format that is easy for the user to understand. The user can then view the program information provided on their device screen and confirm its contents.

[0182] Finally, the system also includes a mechanism for returning advertising revenue and other rewards to the human resources department or individuals based on the user's emotional data and the number of times they have viewed the programs presented to them, making it possible to promote the use of the system and measure the effectiveness of the programs.

[0183] The above is a specific embodiment of the human resource development program automatic provision system based on the present invention. This system allows users to easily find the learning program that best suits them, and provides an efficient and satisfying learning experience that takes emotional information into account.

[0184] The processing flow will be explained below.

[0185] Step 1:

[0186] The user accesses the input form using their own terminal, where they enter their age, gender, desired job type, and desired skill information. Once the input is complete, the user clicks the "Submit" button to send the information to the server.

[0187] Step 2:

[0188] The device uses a built-in emotion engine to recognize the user's face and analyze their voice to collect real-time emotional information, for example, to sense whether the user is tense or relaxed.

[0189] Step 3:

[0190] The server receives the request from the user, extracts the user's attribute information and desired skill information, and also receives the user's emotional information collected from the terminal.

[0191] Step 4:

[0192] The server loads pre-collected information on human resource development programs provided by multiple educational institutions and companies, including detailed descriptions of each program, the skills that can be acquired, and training methods.

[0193] Step 5:

[0194] The server vectorizes the user's desired skill information and the collected program information, using TF-IDF (Term Frequency-Inverse Document Frequency) vectorization technology to convert text data into numerical data.

[0195] Step 6:

[0196] The server then applies the K-Means clustering algorithm to the vectorized data to perform clustering, which categorizes the data into multiple groups based on similarity.

[0197] Step 7:

[0198] The server identifies the most suitable human resource development program for each user based on the clustering results and the user's emotional information. For example, it selects a more difficult program for a user who is highly motivated to learn, and selects more relaxing learning content for a user who is feeling stressed.

[0199] Step 8:

[0200] The server selects multiple optimal programs from the identified cluster, randomly chooses one of them, and provides it to the user. For example, if Company A's "Data Scientist Training Program" is selected, the program content will include Python, machine learning, data analysis, etc.

[0201] Step 9:

[0202] The server structures the selected program information in JSON format and sends it to the user's device, which then analyzes the information and displays it in a format that is easy for the user to understand.

[0203] Step 10:

[0204] The user can view the provided program information on the device screen and check the content. For example, details of Company A's "Data Scientist Training Program" are displayed, and the user can check the content. The user can utilize the provided content as the optimal program according to their emotional state and continue their learning.

[0205] Step 11:

[0206] The server records the number of times a user views the app and their learning progress. Furthermore, a system is operated in which advertising revenues and other rewards are returned to the human resources department or individuals based on the collected emotional data and number of views. This makes it possible to promote system usage and measure the effectiveness of the program.

[0207] The above are the specific processing steps of the human resource development program automatic provision system based on the present invention. Specific operations are performed at each step to provide the user with an optimal educational program and realize an efficient and satisfying learning experience that takes emotional information into consideration.

[0208] Example 2

[0209] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0210] Existing human resource development program provision systems select the optimal program based only on the user's attribute information and desired skill information, which means that they are unable to provide appropriate programs that take into account the user's motivation to learn and emotional state.In addition, there are few mechanisms for rewarding the programs provided, which makes it difficult to promote system use and measure their effectiveness.

[0211] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[0212] In this invention, the server includes a means for inputting user attribute information and desired skill information, a means for collecting human resource development program information provided by multiple educational institutions and companies, a means for collecting user emotion information, a means for vectorizing and clustering the collected human resource development program information, user attribute information, and desired skill information, and a means for identifying and providing the optimal human resource development program for the user based on the clustering results and the user emotion information. This makes it possible to provide an optimal learning program that takes into account the user's motivation to learn and emotional state. Furthermore, by providing rewards based on the user's number of views and emotion information, it is possible to promote system usage and improve effectiveness measurement.

[0213] "Attribute information" is basic information for identifying an individual, such as the user's age, sex, occupation, etc.

[0214] "Desired skill information" is information related to specific knowledge or skills that the user wishes to acquire.

[0215] "Human resource development program information" refers to information about educational programs offered by educational institutions and companies to help people acquire skills and knowledge.

[0216] "Emotional information" is data that indicates the user's current emotional state, including, for example, motivation and stress level.

[0217] "Vectorization" is a technique for representing text data as numerical data, and is used to make data comparison and analysis easier.

[0218] "Clustering" is a data analysis technique that groups similar data together to clarify the relationships between the data.

[0219] A "clustering result" is a grouping result of data produced by a clustering algorithm.

[0220] The "optimal human resource development program" refers to the educational program that is most suitable for the user based on the user's attribute information, desired skill information, and emotional information.

[0221] "Random selection" refers to the process of randomly selecting one of a number of selected options.

[0222] The "number of views" is a numerical value indicating how often a user has viewed specific program information.

[0223] "Returning rewards" is the process of returning a portion of the benefits to individuals or departments based on system usage and performance.

[0224] This invention is a system that collects human resource development program information provided by multiple educational institutions and companies based on the user's attribute information, desired skill information, and emotional information, and automatically provides the most suitable educational program using generation AI.

[0225] First, the user accesses the input form using their own device. Here, the user enters their age, gender, desired job type, and desired skill information. Once the input is complete, the user clicks the "Submit" button to send the information to the server. As an example, consider a case where the user is a 25-year-old male, whose desired job type is data scientist, and whose desired skills are Python and machine learning.

[0226] The server receives the request from the user and extracts the user's attribute information and desired skill information. Furthermore, to recognize the user's emotions, the server uses facial recognition, voice analysis, and other methods to collect the user's emotional information through an emotion engine built into the device. For example, it can recognize whether the user is highly motivated and excited about learning, or, conversely, whether they are feeling anxious or stressed.

[0227] Next, the server loads previously collected information on human resource development programs provided by multiple educational institutions and companies. This information includes detailed descriptions of each program, the skills that can be acquired, and training methods. The server vectorizes the user's desired skill information and the collected program information so that it can be handled as numerical data. This process uses TF-IDF (Term Frequency-Inverse Document Frequency) vectorization technology.

[0228] The K-Means clustering algorithm is then applied to the vectorized data to classify the data into multiple clusters. The server then identifies the optimal human resource development program for the user based on the clustering results and the user's emotional information. Specifically, clustering is performed that takes emotional information into account in order to select the learning program that best suits the user's emotional state. In this process, for example, a more difficult program is offered to a user who is highly motivated to learn, while more relaxing learning content is offered to a user who is feeling stressed.

[0229] The server then selects multiple optimal programs from the program information in the cluster, randomly selecting one from the list and providing it to the user. For example, if a company's "data scientist training program" is selected, the program content will consist of Python, machine learning, data analysis, etc.

[0230] The provided program information is structured in JSON format and sent to the user's device. The user's device analyzes the received information and displays it in a format that is easy for the user to understand. The user can then view the provided program information on their device screen and confirm its contents.

[0231] Finally, the system also includes a mechanism for rewarding business divisions and individuals with advertising revenue based on users' emotional data and the number of views of the programs presented. This mechanism makes it possible to promote system usage and measure the effectiveness of the programs.

[0232] For example, consider the following prompt:

[0233] I am a 25-year-old male aspiring to become a data scientist. I would like to build a system that provides the optimal educational program for a user who wants to acquire Python and machine learning skills. Please also consider the user's emotional data. The user shows high motivation. I would like to suggest a recommended program.

[0234] This system allows users to have an efficient and satisfying learning experience.

[0235] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0236] Step 1:

[0237] The user accesses the system's input form using their own device and enters attribute information and desired skill information. Specifically, they enter their age, gender, desired job type, desired skills (e.g., Python and machine learning), etc. into the input form. Once they have completed the input, they click the "Submit" button.

[0238] Input: User's demographic information (age, gender, desired job type) and desired skill information.

[0239] Output: The request data that the user entered is sent to the server.

[0240] Step 2:

[0241] The server receives the request sent by the user and extracts various information, such as age, gender, desired job type, and desired skills, and analyzes the information to store in a database.

[0242] Input: Request data (user attribute information and desired skill information).

[0243] Output: The extracted attribute information and desired skill information are recorded.

[0244] Step 3:

[0245] The device uses a built-in emotion engine to collect the user's emotional information. It uses facial recognition and voice analysis technologies to analyze the user's current emotions (motivation, stress, etc.). The collected emotional information is then sent to a server.

[0246] Input: User's facial expression data and voice data.

[0247] Output: Parsed user emotion information.

[0248] Step 4:

[0249] The server loads pre-collected information on human resource development programs provided by multiple educational institutions and companies, including detailed descriptions of each program, the skills that can be acquired, and training methods.

[0250] Input: Human resource development program information provided by educational institutions and companies.

[0251] Output: Collected program information.

[0252] Step 5:

[0253] The server vectorizes the user's desired skill information and the collected program information using TF-IDF technology, allowing all data to be treated as numerical data.

[0254] Input: Desired skills information and program information.

[0255] Output: Vectorized data.

[0256] Step 6:

[0257] The server applies the K-Means clustering algorithm to the vectorized data, classifying it into multiple clusters, and selects the optimal human resource development program based on the clustering results.

[0258] Input: Vectorized data.

[0259] Output: Clustering results (cluster classification of each data).

[0260] Step 7:

[0261] The server then considers the clustering results and the user's emotional information to identify the most suitable educational program for the user. For example, it selects a more challenging program for a user who is highly motivated to learn, and a relaxing program for a user who is feeling stressed.

[0262] Input: Clustering results and user sentiment information.

[0263] Output: Optimal educational program.

[0264] Step 8:

[0265] The server selects multiple optimal programs from the program information in the identified cluster and randomly selects one of them. The selected program information is structured in JSON format and sent to the user's device.

[0266] Enter: optimal educational programs.

[0267] Output: Program information in JSON format.

[0268] Step 9:

[0269] The device analyzes the JSON formatted program information received from the server and displays it in a format that is easy for the user to understand. The user can then view and confirm the program information provided on the device screen.

[0270] Input: Program information in JSON format.

[0271] Output: Formatted program information.

[0272] Step 10:

[0273] The server records the user's emotional data and the number of times they have viewed the provided program. Based on this data, advertising revenue and other rewards are calculated and returned to business divisions and individuals.

[0274] Input: User sentiment data and number of views.

[0275] Output: Reward calculation results and refund processing.

[0276] (Application example 2)

[0277] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0278] Currently, the selection of educational programs aimed at users' skill acquisition and career advancement is not optimized to fully reflect their individual needs and emotional state. Furthermore, methods for automatically selecting the most appropriate program from among numerous programs are limited, creating a need for systems that can improve user satisfaction and learning outcomes. In particular, the adoption of real-time emotional analysis using smart devices to select more appropriate programs has not yet been achieved.

[0279] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 2 is realized by the following means.

[0280] In this invention, the server includes means for inputting user attribute information and desired skill information, means for collecting human resource development program information provided by multiple educational institutions and companies, means for vectorizing and clustering the collected human resource development program information, the user attribute information, and the desired skill information, means for identifying and providing the human resource development program optimal for the user based on the clustering results, means for acquiring user emotional information, means for selecting the optimal human resource development program taking the user emotional information into consideration, and means for implementing the above means using smart glasses. This enables real-time program selection taking the emotional information into consideration, making it possible to provide more appropriate educational programs to users.

[0281] "User attribute information" refers to basic personal information about a user, including the user's age, gender, occupation, and interests.

[0282] "Desired skill information" is information about specific skills or knowledge that a user wishes to learn or acquire.

[0283] "Human resource development program information" refers to data on education and training programs offered by multiple educational institutions and companies with the aim of acquiring skills and knowledge.

[0284] "Vectorization" is the process of converting text data or information into a format that is easy to handle as numerical data.

[0285] "Clustering" is a machine learning technique that classifies data into several groups or clusters.

[0286] "Emotion information" is data that indicates the user's emotional state, and includes, for example, motivation and stress level.

[0287] "Smart glasses" are wearable devices equipped with a camera and a display, and have the ability to acquire and display visual and audio information from the user.

[0288] This invention is a system that uses smart glasses to acquire user attribute information, desired skill information, and emotional information, and provides an optimal educational program. Specific embodiments of this system are described below.

[0289] System configuration

[0290] The system consists of smart glasses that acquire user input and emotional information, a server that processes the data, and a means of collecting information on human resource development programs provided by multiple educational institutions and companies. The smart glasses are equipped with a camera and microphone to recognize the user's face and perform voice analysis. The server processes the received data using a generative AI model and selects the optimal educational program using a clustering algorithm.

[0291] Specific usage

[0292] 1. Enter user information

[0293] The user puts on the smart glasses and accesses an input form, where they can enter their age, gender, desired job type, and desired skill information.

[0294] 2. Acquiring emotional information

[0295] When a user inputs information, the smart glasses' camera and microphone are used for facial recognition and voice analysis to obtain the user's emotional information, which is then analyzed using the emotion_recognition library.

[0296] 3. Collection of Program Information

[0297] The server collects information on human resource development programs provided by multiple educational institutions and companies via external APIs, including details of each program, the skills that can be acquired, and the training methods.

[0298] 4. Data Vectorization and Clustering

[0299] The server vectorizes the collected program information and user desired skill information using TF-IDF (Term Frequency-Inverse Document Frequency). It then applies the K-Means clustering algorithm to cluster the data. Based on the clustering results, the optimal program is identified.

[0300] 5. Program selection taking into account emotional information

[0301] The clustering results are combined with the acquired emotional information to select the program that best suits the user's emotional state. For example, a highly motivated user may be offered a more challenging program, while an anxious user may be offered a more relaxing program.

[0302] 6. Program Offering

[0303] The server structures the selected program information in JSON format and sends it to the smart glasses, which then analyzes the received information and visually displays it to the user.

[0304] Specific examples

[0305] Suppose a 25-year-old male user wants to become a data scientist and learn Python and machine learning. In this case, the smart glasses analyze the user's facial expressions and voice and determine that he is highly motivated.

[0306] Using a generative AI model, the server performs clustering and lists the most suitable programs from among the most difficult ones.

[0307] Based on the prompt, "A 25-year-old man who wants to become a data scientist wants to learn Python and machine learning. He seems highly motivated, so please recommend a high-level educational program," the server selects the most suitable program and provides it to the smart glasses.

[0308] Hardware and software used

[0309] Smart glasses: equipped with a camera and microphone to capture emotional information.

[0310] emotion_recognition library: Recognizes and analyzes emotions.

[0311] Server: Processes data, runs generative AI models, uses TF-IDF vectorization and K-Means clustering algorithms.

[0312] In this way, by taking emotional information into consideration and providing optimal educational programs through smart glasses, it is possible to provide users with an efficient and satisfying learning experience.

[0313] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0314] Step 1:

[0315] The user puts on the smart glasses and enters attribute information and desired skill information into an input form.

[0316] Input: User's age, gender, desired job type, desired skill information

[0317] How it works: The user enters this information in text format using the smart glasses interface.

[0318] Output: The user's attribute information and desired skill information are sent to the server as text data.

[0319] Step 2:

[0320] The camera and microphone of the smart glasses are used to acquire the user's emotional information.

[0321] Input: User's face image and voice data

[0322] How it works: The smart glasses use a camera to analyze the user's face and a microphone to collect audio. They then use the emotion_recognition library to analyze this data and identify the user's emotional information.

[0323] Output: The user's emotional information (e.g., motivation and stress level) is sent to the server as the analysis result.

[0324] Step 3:

[0325] The server collects human resource development program information through an external API.

[0326] Input: API for providing program information for educational institutions and companies

[0327] How it works: The server makes a request to an external API to retrieve relevant educational program information, including program details, skills learned, and teaching methods.

[0328] Output: Multiple educational program information is saved in JSON format on the server.

[0329] Step 4:

[0330] The program information and user desired skill information collected by the server are vectorized using TF-IDF, and clustering is performed using the K-Means clustering algorithm.

[0331] Input: User's desired skill information, educational program information

[0332] How it works: The server converts these text data into numerical vectors using TF-IDF vectorization techniques, then applies the K-Means clustering algorithm to classify the data into clusters.

[0333] Output: The clustering result is a set of programs suitable for multiple users.

[0334] Step 5:

[0335] The server considers emotional information and selects the most appropriate human resource development program.

[0336] Input: Clustering results, user emotion information

[0337] How it works: The server analyzes the clustering results and selects programs based on emotional information. For example, it selects a challenging program for a highly motivated user, and a relaxing program for a stressed user.

[0338] Output: One optimal educational program is selected.

[0339] Step 6:

[0340] The server transmits the optimal program information to the smart glasses and provides it to the user.

[0341] Input: Selected educational program information

[0342] How it works: The server structures program information in JSON format and sends it to the smart glasses. The smart glasses receive this information and display it in a user-friendly format.

[0343] Output: The optimal educational program is displayed on the user's smart glasses.

[0344] 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 a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0345] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0346] In the above embodiment, an example in which the specific process is performed by the data processing device 12 has been given, but the technology of the present disclosure is not limited to this, and the specific process may be performed by the smart device 14.

[0347] [Second embodiment]

[0348] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0349] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0350] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0351] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, and the camera 42 are also connected to the bus 52.

[0352] The microphone 238 receives instructions and the like from the user 20 by receiving voice uttered by the user 20. The microphone 238 captures the voice uttered by the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio in accordance with instructions from the processor 46.

[0353] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the surroundings of user 20 (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0354] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0355] Fig. 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Fig. 4, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0356] The specific processing program 56 is an example of a "program" according to the technology of the present 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.

[0357] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0358] In the smart glasses 214, the reception output process is performed by the processor 46. A reception output program 60 is stored in the storage 50. 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 process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[0359] Next, a description will be given of the identification process performed by the identification processing unit 290 of the data processing device 12. 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."

[0360] The present invention is a system that collects human resource development program information provided by multiple educational institutions and companies based on user attribute information and desired skill information, and automatically provides the most suitable educational program using generation AI. Specific embodiments of the present invention are described below.

[0361] First, the user accesses the input form using their own device. Here, they enter information such as age, gender, desired job type, and desired skills, and then submit it to the system. For example, let's say the user is a 25-year-old male who wants to be a data scientist and has the skills of Python and machine learning.

[0362] Next, the server vectorizes the human resource development program information collected from multiple educational institutions and companies based on the received user attribute information and desired skill information. Vectorization is the process of converting each piece of information into numerical data, which makes it a format that can be processed by a computer. For example, TF-IDF (Term Frequency-Inverse Document Frequency) can be used for this process.

[0363] The server then performs clustering processing on the vectorized data using the K-Means clustering algorithm. Clustering is a method for grouping similar data, and in this case, it identifies the cluster containing the human resource development program information that is most similar to the user's desired skills.

[0364] Based on the clustering results, the server selects the most suitable human resource development programs from the cluster and randomly selects one of them to provide to the user. For example, suppose the "Data Scientist Development Program" provided by Company A is selected. This program includes content such as Python, machine learning, and data analysis.

[0365] The selected program information is structured in JSON format or similar and sent to the user's device. The user's device displays the received information on its screen, allowing the user to check the content provided as the optimal program.

[0366] Furthermore, the system also has a mechanism whereby advertising revenues and other rewards are returned to the human resources department or individuals based on the number of times users view the presented programs. This mechanism makes it possible to promote system usage and measure the effectiveness of the programs.

[0367] Specifically, the more a user views a program, the greater the rewards the user and the company or educational institution that provided the program can receive. This makes it easier to evaluate the user's interest and the program's usefulness.

[0368] The above is a specific embodiment of the human resource development program automatic provision system according to the present invention. This system allows users to easily find the learning program that best suits them, enabling them to have an efficient and satisfying learning experience.

[0369] The processing flow will be explained below.

[0370] Step 1:

[0371] The user accesses the input form using their own terminal, where they enter their age, gender, desired job type, and desired skill information. Once the input is complete, the user clicks the "Submit" button to send the information to the server.

[0372] Step 2:

[0373] The server receives the request sent by the user and extracts the user's attribute information and desired skill information. For example, if the user is a 25-year-old male, his desired occupation is data scientist, and his desired skills are Python and machine learning, this information is passed to the server.

[0374] Step 3:

[0375] The server loads pre-collected information on human resource development programs provided by multiple educational institutions and companies, including detailed descriptions of each program and the skills that can be acquired.

[0376] Step 4:

[0377] The server vectorizes the user's desired skill information and the collected program information. Specifically, it uses TF-IDF vectorization technology to convert text data into numerical data. This makes it possible to compare the program information and the user's desired skill information numerically.

[0378] Step 5:

[0379] The server uses the vectorized data to perform clustering. It applies the K-Means clustering algorithm to classify the data into multiple clusters. A cluster is a collection of data grouped based on similarity.

[0380] Step 6:

[0381] Based on the clustering results, the server identifies the cluster that best matches the user's desired skill information. This cluster contains the program information that is closest to the user's preference.

[0382] Step 7:

[0383] The server selects multiple optimal programs from the program information in the cluster, then randomly selects one program from the list and prepares it to be provided to the user.

[0384] Step 8:

[0385] The server structures the selected program information in JSON format and sends it to the user's device, which then analyzes the information and displays it in a format that is easy for the user to understand.

[0386] Step 9:

[0387] The user views the program information provided on the device screen and confirms the content. For example, Company A's data scientist training program is selected and displayed on the device.

[0388] Step 10:

[0389] The server records the number of times a user views the program and operates a reward system based on that information. A system is implemented in which rewards are returned to the human resources department or individuals according to the number of times a user views the program and their learning outcomes. This allows the effectiveness of the program and frequency of use to be evaluated.

[0390] The above are the specific processing steps of the system for automatically providing a human resource development program according to the present invention. By performing specific operations at each step, it is possible to provide the user with an optimal educational program.

[0391] Example 1

[0392] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0393] In today's highly information-driven society, it is extremely important to find the best educational program to improve one's career aptitude and skills. However, selecting the most suitable program from the wide variety of programs offered by educational institutions and companies is difficult and requires time and effort. For this reason, there is a need for a system that allows users to easily and efficiently find the best educational program.

[0394] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[0395] In this invention, the server includes means for inputting user attribute information and desired skill information, means for collecting human resource development program information provided by multiple educational institutions and companies, means for vectorizing and clustering the collected human resource development program information and the user attribute information and desired skill information, means for identifying the human resource development program most suitable for the user based on the clustering results and transmitting the identified program to the user terminal as structured data, means for displaying the data received by the user terminal on a screen, and means for generating advertising revenue and rewards based on the user's browsing behavior. This enables users to efficiently and effectively find the educational program that best suits them.

[0396] "User" refers to a person who uses the system to search for and select the educational program that best suits them.

[0397] "Attribute information" is a general term for personal information such as a user's age, gender, desired occupation, etc.

[0398] "Desired skill information" is information about skills and qualifications that a user wishes to acquire.

[0399] The "means for inputting" is an interface that allows a user to input attribute information and desired skill information into the system.

[0400] "Means of collection" refers to the methods and functions for acquiring information on human resource development programs provided by multiple educational institutions and companies.

[0401] "Vectorization" is the process of converting collected information and user attribute information into numerical data.

[0402] "Clustering" is a data analysis technique that groups similar data based on vectorized data.

[0403] The "means for performing clustering" refers to an algorithm or mechanism for performing clustering using the collected human resource development program information and user information.

[0404] "Structured data" refers to information that has been organized into a format that makes it easier to manage, such as data in JSON or XML format.

[0405] The "transmitting means" is a communication means for transmitting the educational program information selected by the server to the user terminal.

[0406] The "means for displaying received data on a screen" refers to an interface that visually displays the received educational program information on the user terminal.

[0407] "Viewing behavior" refers to the act of a user viewing and using the educational program provided.

[0408] "Means for generating advertising revenue or rewards" means a mechanism for calculating and distributing advertising revenue or rewards based on user browsing behavior.

[0409] The present invention is a system that collects information on human resource development programs provided by multiple educational institutions and companies based on user attribute information and desired skill information, and automatically provides the most suitable educational program. Specific embodiments of the present invention are described below.

[0410] First, the user accesses the input form using their own device. Here, the user enters attribute information such as age, gender, desired job type, and desired skills, and submits it to the system. For example, a user might enter information such as, "I am a 25-year-old male who aspires to be a data scientist and would like to have skills in Python and machine learning."

[0411] The server converts the received user attribute information and desired skill information into numerical data (vectorization). This process uses TF-IDF (Term Frequency-Inverse Document Frequency), a natural language processing (NLP) method. By calculating the frequency of occurrence of each word and converting it into a vector as numerical data, subsequent clustering processing becomes possible.

[0412] The server then performs clustering on the vectorized data, using the K-Means clustering algorithm. This algorithm divides the data into multiple clusters and groups data points within each cluster so that they are similar to each other. This allows the server to identify the cluster containing the human resource development program that is most similar to the user's desired skills.

[0413] Based on the clustering results, the server selects the most suitable human resource development programs from the cluster and randomly selects one of them to provide to the user. For example, if the selected program is a "data scientist development program" that includes Python, machine learning, data analysis, etc.

[0414] Information about the selected program is sent to the user's device as structured data in JSON format or similar. The server sends this information using the HTTP protocol. The user's device parses the received JSON data and displays it on the screen. Specifically, the browser displays a message stating, "The program best suited for you is the 'Data Scientist Training Program.' It is provided by a certain company, and you will learn Python, machine learning, and data analysis."

[0415] In addition, the server collects user browsing behavior data and generates advertising revenue and rewards based on that data. For example, if a user views a provided program more often, the organization that provided the program can receive a larger reward. This information is sent to the advertisement display server and reward calculation system, and the reward is distributed appropriately.

[0416] A specific example is the following prompt for user input:

[0417] "I'm a 25-year-old male aspiring to become a data scientist. I'm looking for Python and machine learning skills. What educational program would be best for me?"

[0418] In this way, the system of the present invention can automatically provide the optimal human resource development program according to the user's needs, allowing the user to efficiently and effectively find the educational program that best suits them, thereby improving their learning experience.

[0419] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0420] Step 1:

[0421] The user inputs attribute information and desired skill information and sends it to the server.

[0422] The user accesses the input form using their own device and enters attribute information such as age, gender, desired job type, and desired skills. For example, they might enter information such as "I am a 25-year-old male, aiming to become a data scientist, and would like to have Python and machine learning skills." Once the information is complete, the user clicks the "Submit" button to send this information to the server. The input data is sent to the server in text format.

[0423] Step 2:

[0424] The server vectorizes the information it receives.

[0425] The server receives the attribute information and desired skill information sent by the user. The received data includes information such as "Age: 25," "Gender: Male," "Desired occupation: Data scientist," and "Desired skills: Python, machine learning." The server then converts this text information into numerical data (vectorizes it). Using TF-IDF (Term Frequency-Inverse Document Frequency), the server calculates the frequency of occurrence of each word and converts this into a vector as numerical data. For example, vector data such as "Python" being 0.8 and "machine learning" being 0.9 is generated.

[0426] Step 3:

[0427] The server clusters the vector data.

[0428] The server applies the K-Means clustering algorithm to the vectorized data. The input for clustering is the vector data of user information and collected program information. The server sets initial cluster centers and assigns each data point (program information) to the closest cluster center. It then recalculates the center of each cluster and obtains new cluster centers. This process is repeated until the cluster centers converge, and finally multiple clusters and their centers are identified. The output is a cluster number for each program.

[0429] Step 4:

[0430] The server selects the most suitable educational program and provides it to the user.

[0431] The server selects the most suitable educational program based on the clustering results. Based on the cluster number, it selects multiple programs within the cluster that are most similar to the user's attributes. It then randomly selects one of the selected candidate programs and converts the selection result into JSON format. For example, it generates JSON data such as "{"Program Name": "Data Scientist Training Program", "Provider": "Certain Educational Institution", "Skills": ["Python", "Machine Learning", "Data Analysis"]}".

[0432] Step 5:

[0433] The server sends the selected program information to the user terminal in JSON format.

[0434] The generated JSON data is sent to the user's device using the HTTP protocol. The server uses an appropriate security protocol (e.g., HTTPS) to ensure data integrity and confidentiality when sending the information. The information sent includes the program name, provider, related skills, etc.

[0435] Step 6:

[0436] The user terminal displays the received information on its screen.

[0437] The user's device analyzes the received JSON data and displays it on the screen. For example, a message might appear on the browser saying, "The program best suited to you is the 'Data Scientist Training Program.' It is provided by a certain educational institution, and the content you will learn is Python, machine learning, and data analysis." The user can review this information and investigate the program details in more detail if necessary.

[0438] Step 7:

[0439] The server collects user browsing behavior data and generates advertising revenue and rewards.

[0440] The server collects the number of times and duration that users view the provided programs. The collected data is sent to an advertising display server and a reward calculation system. For example, advertising revenue and rewards are returned to the organization that provided a program that is viewed frequently by users. Specifically, the system is equipped with an algorithm that calculates advertising revenue and distributes rewards based on that calculation.

[0441] (Application example 1)

[0442] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0443] Conventional human resource development programs have had issues with automatically providing optimal training programs based on user attribute information and desired skill information, and also with insufficient mechanisms for measuring the effectiveness of the programs provided and for reward return.In addition, there have been cases where appropriate programs for efficiently learning the specialized skills required, particularly for factory robot operators, have not been provided.

[0444] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[0445] In this invention, the server includes a means for inputting user attribute information and desired skill information, a means for collecting human resource development program information provided by multiple educational institutions and companies, a means for vectorizing and clustering the collected human resource development program information and the user attribute information and desired skill information, a means for identifying and providing the human resource development program optimal for the user based on the clustering results, and a means for transmitting the optimal program information to the user terminal in JSON format, which then receives and displays the information. This allows the system to efficiently provide the educational program most suited to the user's attribute information and preferences, and allows the user to easily view the optimal program content. Furthermore, rewards are provided based on the number of times the provided program is viewed, which promotes system usage and evaluates the program's practicality.

[0446] "User attribute information" is personal data such as the user's age, gender, desired occupation, etc.

[0447] "Desired skill information" is information about specific skills or knowledge that the user wishes to acquire.

[0448] An "educational institution" is an organization that provides education, such as a school, vocational school, or university.

[0449] "Enterprise" means a legal entity or organization engaged in commercial activities or providing products or services.

[0450] "Human resource development program information" is data on training and education programs provided by educational institutions and companies.

[0451] "Vectorization" is the process of converting data into a number-based format that can be processed by a computer.

[0452] "Clustering" is a data analysis technique that groups similar data points together.

[0453] "JSON format" is a data exchange format based on JavaScript object notation.

[0454] A "user terminal" is a device operated by a user, and includes a smartphone, a computer, and the like.

[0455] "Optimal program information" is information about the optimal educational program selected by the system based on the user's attribute information and desired skill information.

[0456] The "number of views" is an index showing how many times the user has viewed the provided information or program.

[0457] Rewarding is the process of providing rewards to individuals or departments for specific behaviors or results.

[0458] The present invention is a system that collects human resource development program information provided by multiple educational institutions and companies based on user attribute information and desired skill information, and automatically provides the optimal educational program using generation AI. Specific embodiments of the present invention are described below.

[0459] User Data Input

[0460] The user accesses the input form using a user device such as a smartphone or computer. At this time, the user enters their age, gender, desired job type, and desired skill information, and sends it to the server. For example, let's assume that the user is a 35-year-old male whose desired job type is robot operator and whose desired skill is robot programming.

[0461] Data Vectorization

[0462] The server vectorizes the received user attribute information, desired skill information, and collected educational program information using TF-IDF (Term Frequency-Inverse Document Frequency), which converts this information into numerical data that can be processed by a computer.

[0463] Clustering Process

[0464] The server performs clustering processing on the vectorized data by applying the K-Means clustering algorithm, which groups similar data and identifies the cluster containing the educational program information most similar to the user's desired skills.

[0465] Optimal program selection

[0466] Based on the clustering results, the server selects the most suitable educational programs from the cluster and randomly selects one of them to provide to the user. For example, if the "Robot Programming Skills Improvement Program" is selected, this program includes robot programming techniques from basic to advanced.

[0467] Providing program information

[0468] The selected program information is structured in JSON format and sent to the user's device. The user's device receives the information and displays it on the screen, allowing the user to easily check the optimal program content.

[0469] Reward based on number of views

[0470] Furthermore, the server will return rewards to the human resources department and individuals based on the number of times the user views the provided programs. This system will make it possible to promote system usage and measure the effectiveness of the programs.

[0471] Specific examples

[0472] For example, if the user is a 35-year-old man who wants to improve his skills as a "robot operator" and aims to learn "robot programming," the system will operate based on the following prompt sentence.

[0473] Prompt Sentence Examples

[0474] Prompt: "Please provide the best educational program for age: 35, gender: male, desired job: robot operator, desired skills: robot programming."

[0475] By inputting these prompts into a generative AI model, the model will provide an educational program that best suits the user's attributes and preferences.

[0476] The above is a specific embodiment of the human resource development program automatic provision system according to the present invention. This system allows users to easily find the most suitable educational program for them and efficiently acquire the necessary skills.

[0477] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0478] Step 1:

[0479] A user accesses the input form using a device such as a smartphone or computer, enters information such as age, gender, desired job type, and desired skill information, and submits it. The entered data is sent to the server, which collects the user's attribute information and desired skill information.

[0480] Step 2:

[0481] The server vectorizes the received user attribute information and desired skill information using TF-IDF. Specifically, the input data is converted into numerical data, which makes it possible for the computer to process it. The vectorized data becomes the input for the next step.

[0482] Step 3:

[0483] The server runs the K-Means clustering algorithm on the vectorized user data and the collected educational program information. This algorithm groups similar data points and identifies the cluster that contains the educational program information that is most similar to the user's desired skills. The clustering results are used as input for the next step.

[0484] Step 4:

[0485] The server selects multiple optimal educational programs based on the clustering results. At this time, it selects the program that best matches the user's attribute information and desired skill information. It then randomly selects one of the selected programs and creates data to provide to the user. This data becomes the input for the next step.

[0486] Step 5:

[0487] The server structures the selected educational program information in JSON format and sends it to the user's device. The user's device then analyzes the received JSON data and displays it on the screen, allowing the user to check the optimal program content.

[0488] Step 6:

[0489] The user's device sends the number of times the user has viewed the program to the server. The server then executes a process to return rewards to the human resources department and individuals based on this data. This reward return system is used to evaluate the usefulness of the program.

[0490] The above is a concrete flow of the program processing of the system based on the application example. This system enables users to efficiently search for advanced educational programs and acquire the necessary skills.

[0491] Furthermore, an emotion engine that estimates the user's emotion may be further combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59, and perform identification processing using the user's emotion.

[0492] The present invention is a system that collects human resource development program information provided by multiple educational institutions and companies based on user attribute information, desired skill information, and emotional information, and automatically provides the optimal educational program using a generation AI. Specific embodiments of the present invention are described below.

[0493] First, the user accesses the input form using their own device. Here, the user enters their age, gender, desired job type, and desired skill information. Once the input is complete, the user clicks the "Submit" button to send the information to the server. As an example, consider a case where the user is a 25-year-old male, whose desired job type is data scientist, and whose desired skills are Python and machine learning.

[0494] The server then receives the request from the user and extracts the user's attribute information and desired skill information. Furthermore, to recognize the user's emotions, the server uses facial recognition, voice analysis, and other techniques through an emotion engine built into the device to collect the user's emotional information. For example, it can recognize whether the user is highly motivated and excited about learning, or, conversely, whether they are feeling anxious or stressed.

[0495] The server loads pre-collected information on human resource development programs provided by multiple educational institutions and companies, including detailed descriptions of each program, the skills that can be acquired, and training methods.

[0496] The server vectorizes the user's desired skill information and the collected program information so that it can be handled as numerical data. This process uses TF-IDF (Term Frequency-Inverse Document Frequency) vectorization technology. The K-Means clustering algorithm is then applied to the vectorized data to classify the data into multiple clusters.

[0497] The server identifies the most suitable human resource development program for each user based on the clustering results and the user's emotional information. Specifically, clustering is performed that takes emotional information into account in order to select the learning program that best suits the user's emotional state. In this case, for example, a user who is highly motivated to learn may be offered a more difficult program, while a user who is feeling stressed may be offered more relaxing learning content.

[0498] Next, the server selects multiple optimal programs from the program information in the cluster, randomly selects one of them, and provides it to the user. For example, if Company A's "Data Scientist Training Program" is selected, the program content will consist of Python, machine learning, data analysis, etc.

[0499] The selected program information is structured in JSON format and sent to the user's device. The user's device analyzes the received information and displays it in a format that is easy for the user to understand. The user can then view the program information provided on their device screen and confirm its contents.

[0500] Finally, the system also includes a mechanism for returning advertising revenue and other rewards to the human resources department or individuals based on the user's emotional data and the number of times they have viewed the programs presented to them, making it possible to promote the use of the system and measure the effectiveness of the programs.

[0501] The above is a specific embodiment of the human resource development program automatic provision system based on the present invention. This system allows users to easily find the learning program that best suits them, and provides an efficient and satisfying learning experience that takes emotional information into account.

[0502] The processing flow will be explained below.

[0503] Step 1:

[0504] The user accesses the input form using their own terminal, where they enter their age, gender, desired job type, and desired skill information. Once the input is complete, the user clicks the "Submit" button to send the information to the server.

[0505] Step 2:

[0506] The device uses a built-in emotion engine to recognize the user's face and analyze their voice to collect real-time emotional information, for example, to sense whether the user is tense or relaxed.

[0507] Step 3:

[0508] The server receives the request from the user, extracts the user's attribute information and desired skill information, and also receives the user's emotional information collected from the terminal.

[0509] Step 4:

[0510] The server loads pre-collected information on human resource development programs provided by multiple educational institutions and companies, including detailed descriptions of each program, the skills that can be acquired, and training methods.

[0511] Step 5:

[0512] The server vectorizes the user's desired skill information and the collected program information, using TF-IDF (Term Frequency-Inverse Document Frequency) vectorization technology to convert text data into numerical data.

[0513] Step 6:

[0514] The server then applies the K-Means clustering algorithm to the vectorized data to perform clustering, which categorizes the data into multiple groups based on similarity.

[0515] Step 7:

[0516] The server identifies the most suitable human resource development program for each user based on the clustering results and the user's emotional information. For example, it selects a more difficult program for a user who is highly motivated to learn, and selects more relaxing learning content for a user who is feeling stressed.

[0517] Step 8:

[0518] The server selects multiple optimal programs from the identified cluster, randomly chooses one of them, and provides it to the user. For example, if Company A's "Data Scientist Training Program" is selected, the program content will include Python, machine learning, data analysis, etc.

[0519] Step 9:

[0520] The server structures the selected program information in JSON format and sends it to the user's device, which then analyzes the information and displays it in a format that is easy for the user to understand.

[0521] Step 10:

[0522] The user can view the provided program information on the device screen and check the content. For example, details of Company A's "Data Scientist Training Program" are displayed, and the user can check the content. The user can utilize the provided content as the optimal program according to their emotional state and continue their learning.

[0523] Step 11:

[0524] The server records the number of times a user views the app and their learning progress. Furthermore, a system is operated in which advertising revenues and other rewards are returned to the human resources department or individuals based on the collected emotional data and number of views. This makes it possible to promote system usage and measure the effectiveness of the program.

[0525] The above are the specific processing steps of the human resource development program automatic provision system based on the present invention. Specific operations are performed at each step to provide the user with an optimal educational program and realize an efficient and satisfying learning experience that takes emotional information into consideration.

[0526] Example 2

[0527] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0528] Existing human resource development program provision systems select the optimal program based only on the user's attribute information and desired skill information, which means that they are unable to provide appropriate programs that take into account the user's motivation to learn and emotional state.In addition, there are few mechanisms for rewarding the programs provided, which makes it difficult to promote system use and measure their effectiveness.

[0529] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[0530] In this invention, the server includes a means for inputting user attribute information and desired skill information, a means for collecting human resource development program information provided by multiple educational institutions and companies, a means for collecting user emotion information, a means for vectorizing and clustering the collected human resource development program information, user attribute information, and desired skill information, and a means for identifying and providing the optimal human resource development program for the user based on the clustering results and the user emotion information. This makes it possible to provide an optimal learning program that takes into account the user's motivation to learn and emotional state. Furthermore, by providing rewards based on the user's number of views and emotion information, it is possible to promote system usage and improve effectiveness measurement.

[0531] "Attribute information" is basic information for identifying an individual, such as the user's age, sex, occupation, etc.

[0532] "Desired skill information" is information related to specific knowledge or skills that the user wishes to acquire.

[0533] "Human resource development program information" refers to information about educational programs offered by educational institutions and companies to help people acquire skills and knowledge.

[0534] "Emotional information" is data that indicates the user's current emotional state, including, for example, motivation and stress level.

[0535] "Vectorization" is a technique for representing text data as numerical data, and is used to make data comparison and analysis easier.

[0536] "Clustering" is a data analysis technique that groups similar data together to clarify the relationships between the data.

[0537] A "clustering result" is a grouping result of data produced by a clustering algorithm.

[0538] The "optimal human resource development program" refers to the educational program that is most suitable for the user based on the user's attribute information, desired skill information, and emotional information.

[0539] "Random selection" refers to the process of randomly selecting one of a number of selected options.

[0540] The "number of views" is a numerical value indicating how often a user has viewed specific program information.

[0541] "Returning rewards" is the process of returning a portion of the benefits to individuals or departments based on system usage and performance.

[0542] This invention is a system that collects human resource development program information provided by multiple educational institutions and companies based on the user's attribute information, desired skill information, and emotional information, and automatically provides the most suitable educational program using generation AI.

[0543] First, the user accesses the input form using their own device. Here, the user enters their age, gender, desired job type, and desired skill information. Once the input is complete, the user clicks the "Submit" button to send the information to the server. As an example, consider a case where the user is a 25-year-old male, whose desired job type is data scientist, and whose desired skills are Python and machine learning.

[0544] The server receives the request from the user and extracts the user's attribute information and desired skill information. Furthermore, to recognize the user's emotions, the server uses facial recognition, voice analysis, and other methods to collect the user's emotional information through an emotion engine built into the device. For example, it can recognize whether the user is highly motivated and excited about learning, or, conversely, whether they are feeling anxious or stressed.

[0545] Next, the server loads previously collected information on human resource development programs provided by multiple educational institutions and companies. This information includes detailed descriptions of each program, the skills that can be acquired, and training methods. The server vectorizes the user's desired skill information and the collected program information so that it can be handled as numerical data. This process uses TF-IDF (Term Frequency-Inverse Document Frequency) vectorization technology.

[0546] The K-Means clustering algorithm is then applied to the vectorized data to classify the data into multiple clusters. The server then identifies the optimal human resource development program for the user based on the clustering results and the user's emotional information. Specifically, clustering is performed that takes emotional information into account in order to select the learning program that best suits the user's emotional state. In this process, for example, a more difficult program is offered to a user who is highly motivated to learn, while more relaxing learning content is offered to a user who is feeling stressed.

[0547] The server then selects multiple optimal programs from the program information in the cluster, randomly selecting one from the list and providing it to the user. For example, if a company's "data scientist training program" is selected, the program content will consist of Python, machine learning, data analysis, etc.

[0548] The provided program information is structured in JSON format and sent to the user's device. The user's device analyzes the received information and displays it in a format that is easy for the user to understand. The user can then view the provided program information on their device screen and confirm its contents.

[0549] Finally, the system also includes a mechanism for rewarding business divisions and individuals with advertising revenue based on users' emotional data and the number of views of the programs presented. This mechanism makes it possible to promote system usage and measure the effectiveness of the programs.

[0550] For example, consider the following prompt:

[0551] I am a 25-year-old male aspiring to become a data scientist. I would like to build a system that provides the optimal educational program for a user who wants to acquire Python and machine learning skills. Please also consider the user's emotional data. The user shows high motivation. I would like to suggest a recommended program.

[0552] This system allows users to have an efficient and satisfying learning experience.

[0553] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0554] Step 1:

[0555] The user accesses the system's input form using their own device and enters attribute information and desired skill information. Specifically, they enter their age, gender, desired job type, desired skills (e.g., Python and machine learning), etc. into the input form. Once they have completed the input, they click the "Submit" button.

[0556] Input: User's demographic information (age, gender, desired job type) and desired skill information.

[0557] Output: The request data that the user entered is sent to the server.

[0558] Step 2:

[0559] The server receives the request sent by the user and extracts various information, such as age, gender, desired job type, and desired skills, and analyzes the information to store in a database.

[0560] Input: Request data (user attribute information and desired skill information).

[0561] Output: The extracted attribute information and desired skill information are recorded.

[0562] Step 3:

[0563] The device uses a built-in emotion engine to collect the user's emotional information. It uses facial recognition and voice analysis technologies to analyze the user's current emotions (motivation, stress, etc.). The collected emotional information is then sent to a server.

[0564] Input: User's facial expression data and voice data.

[0565] Output: Parsed user emotion information.

[0566] Step 4:

[0567] The server loads pre-collected information on human resource development programs provided by multiple educational institutions and companies, including detailed descriptions of each program, the skills that can be acquired, and training methods.

[0568] Input: Human resource development program information provided by educational institutions and companies.

[0569] Output: Collected program information.

[0570] Step 5:

[0571] The server vectorizes the user's desired skill information and the collected program information using TF-IDF technology, allowing all data to be treated as numerical data.

[0572] Input: Desired skills information and program information.

[0573] Output: Vectorized data.

[0574] Step 6:

[0575] The server applies the K-Means clustering algorithm to the vectorized data, classifying it into multiple clusters, and selects the optimal human resource development program based on the clustering results.

[0576] Input: Vectorized data.

[0577] Output: Clustering results (cluster classification of each data).

[0578] Step 7:

[0579] The server then considers the clustering results and the user's emotional information to identify the most suitable educational program for the user. For example, it selects a more challenging program for a user who is highly motivated to learn, and a relaxing program for a user who is feeling stressed.

[0580] Input: Clustering results and user sentiment information.

[0581] Output: Optimal educational program.

[0582] Step 8:

[0583] The server selects multiple optimal programs from the program information in the identified cluster and randomly selects one of them. The selected program information is structured in JSON format and sent to the user's device.

[0584] Enter: optimal educational programs.

[0585] Output: Program information in JSON format.

[0586] Step 9:

[0587] The device analyzes the JSON formatted program information received from the server and displays it in a format that is easy for the user to understand. The user can then view and confirm the program information provided on the device screen.

[0588] Input: Program information in JSON format.

[0589] Output: Formatted program information.

[0590] Step 10:

[0591] The server records the user's emotional data and the number of times they have viewed the provided program. Based on this data, advertising revenue and other rewards are calculated and returned to business divisions and individuals.

[0592] Input: User sentiment data and number of views.

[0593] Output: Reward calculation results and refund processing.

[0594] (Application example 2)

[0595] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0596] Currently, the selection of educational programs aimed at users' skill acquisition and career advancement is not optimized to fully reflect their individual needs and emotional state. Furthermore, methods for automatically selecting the most appropriate program from among numerous programs are limited, creating a need for systems that can improve user satisfaction and learning outcomes. In particular, the adoption of real-time emotional analysis using smart devices to select more appropriate programs has not yet been achieved.

[0597] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 2 is realized by the following means.

[0598] In this invention, the server includes means for inputting user attribute information and desired skill information, means for collecting human resource development program information provided by multiple educational institutions and companies, means for vectorizing and clustering the collected human resource development program information, the user attribute information, and the desired skill information, means for identifying and providing the human resource development program optimal for the user based on the clustering results, means for acquiring user emotional information, means for selecting the optimal human resource development program taking the user emotional information into consideration, and means for implementing the above means using smart glasses. This enables real-time program selection taking the emotional information into consideration, making it possible to provide more appropriate educational programs to users.

[0599] "User attribute information" refers to basic personal information about a user, including the user's age, gender, occupation, and interests.

[0600] "Desired skill information" is information about specific skills or knowledge that a user wishes to learn or acquire.

[0601] "Human resource development program information" refers to data on education and training programs offered by multiple educational institutions and companies with the aim of acquiring skills and knowledge.

[0602] "Vectorization" is the process of converting text data or information into a format that is easy to handle as numerical data.

[0603] "Clustering" is a machine learning technique that classifies data into several groups or clusters.

[0604] "Emotion information" is data that indicates the user's emotional state, and includes, for example, motivation and stress level.

[0605] "Smart glasses" are wearable devices equipped with a camera and a display, and have the ability to acquire and display visual and audio information from the user.

[0606] This invention is a system that uses smart glasses to acquire user attribute information, desired skill information, and emotional information, and provides an optimal educational program. Specific embodiments of this system are described below.

[0607] System configuration

[0608] The system consists of smart glasses that acquire user input and emotional information, a server that processes the data, and a means of collecting information on human resource development programs provided by multiple educational institutions and companies. The smart glasses are equipped with a camera and microphone to recognize the user's face and perform voice analysis. The server processes the received data using a generative AI model and selects the optimal educational program using a clustering algorithm.

[0609] Specific usage

[0610] 1. Enter user information

[0611] The user puts on the smart glasses and accesses an input form, where they can enter their age, gender, desired job type, and desired skill information.

[0612] 2. Acquiring emotional information

[0613] When a user inputs information, the smart glasses' camera and microphone are used for facial recognition and voice analysis to obtain the user's emotional information, which is then analyzed using the emotion_recognition library.

[0614] 3. Collection of Program Information

[0615] The server collects information on human resource development programs provided by multiple educational institutions and companies via external APIs, including details of each program, the skills that can be acquired, and the training methods.

[0616] 4. Data Vectorization and Clustering

[0617] The server vectorizes the collected program information and user desired skill information using TF-IDF (Term Frequency-Inverse Document Frequency). It then applies the K-Means clustering algorithm to cluster the data. Based on the clustering results, the optimal program is identified.

[0618] 5. Program selection taking into account emotional information

[0619] The clustering results are combined with the acquired emotional information to select the program that best suits the user's emotional state. For example, a highly motivated user may be offered a more challenging program, while an anxious user may be offered a more relaxing program.

[0620] 6. Program Offering

[0621] The server structures the selected program information in JSON format and sends it to the smart glasses, which then analyzes the received information and visually displays it to the user.

[0622] Specific examples

[0623] Suppose a 25-year-old male user wants to become a data scientist and learn Python and machine learning. In this case, the smart glasses analyze the user's facial expressions and voice and determine that he is highly motivated.

[0624] Using a generative AI model, the server performs clustering and lists the most suitable programs from among the most difficult ones.

[0625] Based on the prompt, "A 25-year-old man who wants to become a data scientist wants to learn Python and machine learning. He seems highly motivated, so please recommend a high-level educational program," the server selects the most suitable program and provides it to the smart glasses.

[0626] Hardware and software used

[0627] Smart glasses: equipped with a camera and microphone to capture emotional information.

[0628] emotion_recognition library: Recognizes and analyzes emotions.

[0629] Server: Processes data, runs generative AI models, uses TF-IDF vectorization and K-Means clustering algorithms.

[0630] In this way, by taking emotional information into consideration and providing optimal educational programs through smart glasses, it is possible to provide users with an efficient and satisfying learning experience.

[0631] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0632] Step 1:

[0633] The user puts on the smart glasses and enters attribute information and desired skill information into an input form.

[0634] Input: User's age, gender, desired job type, desired skill information

[0635] How it works: The user enters this information in text format using the smart glasses interface.

[0636] Output: The user's attribute information and desired skill information are sent to the server as text data.

[0637] Step 2:

[0638] The camera and microphone of the smart glasses are used to acquire the user's emotional information.

[0639] Input: User's face image and voice data

[0640] How it works: The smart glasses use a camera to analyze the user's face and a microphone to collect audio. They then use the emotion_recognition library to analyze this data and identify the user's emotional information.

[0641] Output: The user's emotional information (e.g., motivation and stress level) is sent to the server as the analysis result.

[0642] Step 3:

[0643] The server collects human resource development program information through an external API.

[0644] Input: API for providing program information for educational institutions and companies

[0645] How it works: The server makes a request to an external API to retrieve relevant educational program information, including program details, skills learned, and teaching methods.

[0646] Output: Multiple educational program information is saved in JSON format on the server.

[0647] Step 4:

[0648] The program information and user desired skill information collected by the server are vectorized using TF-IDF, and clustering is performed using the K-Means clustering algorithm.

[0649] Input: User's desired skill information, educational program information

[0650] How it works: The server converts these text data into numerical vectors using TF-IDF vectorization techniques, then applies the K-Means clustering algorithm to classify the data into clusters.

[0651] Output: The clustering result is a set of programs suitable for multiple users.

[0652] Step 5:

[0653] The server considers emotional information and selects the most appropriate human resource development program.

[0654] Input: Clustering results, user emotion information

[0655] How it works: The server analyzes the clustering results and selects programs based on emotional information. For example, it selects a challenging program for a highly motivated user, and a relaxing program for a stressed user.

[0656] Output: One optimal educational program is selected.

[0657] Step 6:

[0658] The server transmits the optimal program information to the smart glasses and provides it to the user.

[0659] Input: Selected educational program information

[0660] How it works: The server structures program information in JSON format and sends it to the smart glasses. The smart glasses receive this information and display it in a user-friendly format.

[0661] Output: The optimal educational program is displayed on the user's smart glasses.

[0662] 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 a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0663] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0664] In the above embodiment, an example in which the specific processing is performed by the data processing device 12 has been given, but the technology of the present disclosure is not limited to this, and the specific processing may be performed by the smart glasses 214.

[0665] [Third embodiment]

[0666] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0667] 5, the data processing system 310 includes the data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0668] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0669] The headset type terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a display 343. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the display 343 are also connected to the bus 52.

[0670] The microphone 238 receives instructions and the like from the user 20 by receiving voice uttered by the user 20. The microphone 238 captures the voice uttered by the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio in accordance with instructions from the processor 46.

[0671] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the surroundings of user 20 (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0672] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0673] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset type terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0674] The specific processing program 56 is an example of a "program" according to the technology of the present 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.

[0675] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0676] In the headset type terminal 314, a reception output process is performed by the processor 46. A reception output program 60 is stored in the storage 50. 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 process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[0677] Next, a description will be given of the identification process performed by the identification processing unit 290 of the data processing device 12. 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."

[0678] The present invention is a system that collects human resource development program information provided by multiple educational institutions and companies based on user attribute information and desired skill information, and automatically provides the most suitable educational program using generation AI. Specific embodiments of the present invention are described below.

[0679] First, the user accesses the input form using their own device. Here, they enter information such as age, gender, desired job type, and desired skills, and then submit it to the system. For example, let's say the user is a 25-year-old male who wants to be a data scientist and has the skills of Python and machine learning.

[0680] Next, the server vectorizes the human resource development program information collected from multiple educational institutions and companies based on the received user attribute information and desired skill information. Vectorization is the process of converting each piece of information into numerical data, which makes it a format that can be processed by a computer. For example, TF-IDF (Term Frequency-Inverse Document Frequency) can be used for this process.

[0681] The server then performs clustering processing on the vectorized data using the K-Means clustering algorithm. Clustering is a method for grouping similar data, and in this case, it identifies the cluster containing the human resource development program information that is most similar to the user's desired skills.

[0682] Based on the clustering results, the server selects the most suitable human resource development programs from the cluster and randomly selects one of them to provide to the user. For example, suppose the "Data Scientist Development Program" provided by Company A is selected. This program includes content such as Python, machine learning, and data analysis.

[0683] The selected program information is structured in JSON format or similar and sent to the user's device. The user's device displays the received information on its screen, allowing the user to check the content provided as the optimal program.

[0684] Furthermore, the system also has a mechanism whereby advertising revenues and other rewards are returned to the human resources department or individuals based on the number of times users view the presented programs. This mechanism makes it possible to promote system usage and measure the effectiveness of the programs.

[0685] Specifically, the more a user views a program, the greater the rewards the user and the company or educational institution that provided the program can receive. This makes it easier to evaluate the user's interest and the program's usefulness.

[0686] The above is a specific embodiment of the human resource development program automatic provision system according to the present invention. This system allows users to easily find the learning program that best suits them, enabling them to have an efficient and satisfying learning experience.

[0687] The processing flow will be explained below.

[0688] Step 1:

[0689] The user accesses the input form using their own terminal, where they enter their age, gender, desired job type, and desired skill information. Once the input is complete, the user clicks the "Submit" button to send the information to the server.

[0690] Step 2:

[0691] The server receives the request sent by the user and extracts the user's attribute information and desired skill information. For example, if the user is a 25-year-old male, his desired occupation is data scientist, and his desired skills are Python and machine learning, this information is passed to the server.

[0692] Step 3:

[0693] The server loads pre-collected information on human resource development programs provided by multiple educational institutions and companies, including detailed descriptions of each program and the skills that can be acquired.

[0694] Step 4:

[0695] The server vectorizes the user's desired skill information and the collected program information. Specifically, it uses TF-IDF vectorization technology to convert text data into numerical data. This makes it possible to compare the program information and the user's desired skill information numerically.

[0696] Step 5:

[0697] The server uses the vectorized data to perform clustering. It applies the K-Means clustering algorithm to classify the data into multiple clusters. A cluster is a collection of data grouped based on similarity.

[0698] Step 6:

[0699] Based on the clustering results, the server identifies the cluster that best matches the user's desired skill information. This cluster contains the program information that is closest to the user's preference.

[0700] Step 7:

[0701] The server selects multiple optimal programs from the program information in the cluster, then randomly selects one program from the list and prepares it to be provided to the user.

[0702] Step 8:

[0703] The server structures the selected program information in JSON format and sends it to the user's device, which then analyzes the information and displays it in a format that is easy for the user to understand.

[0704] Step 9:

[0705] The user views the program information provided on the device screen and confirms the content. For example, Company A's data scientist training program is selected and displayed on the device.

[0706] Step 10:

[0707] The server records the number of times a user views the program and operates a reward system based on that information. A system is implemented in which rewards are returned to the human resources department or individuals according to the number of times a user views the program and their learning outcomes. This allows the effectiveness of the program and frequency of use to be evaluated.

[0708] The above are the specific processing steps of the system for automatically providing a human resource development program according to the present invention. By performing specific operations at each step, it is possible to provide the user with an optimal educational program.

[0709] Example 1

[0710] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[0711] In today's highly information-driven society, it is extremely important to find the best educational program to improve one's career aptitude and skills. However, selecting the most suitable program from the wide variety of programs offered by educational institutions and companies is difficult and requires time and effort. For this reason, there is a need for a system that allows users to easily and efficiently find the best educational program.

[0712] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[0713] In this invention, the server includes means for inputting user attribute information and desired skill information, means for collecting human resource development program information provided by multiple educational institutions and companies, means for vectorizing and clustering the collected human resource development program information and the user attribute information and desired skill information, means for identifying the human resource development program most suitable for the user based on the clustering results and transmitting the identified program to the user terminal as structured data, means for displaying the data received by the user terminal on a screen, and means for generating advertising revenue and rewards based on the user's browsing behavior. This enables users to efficiently and effectively find the educational program that best suits them.

[0714] "User" refers to a person who uses the system to search for and select the educational program that best suits them.

[0715] "Attribute information" is a general term for personal information such as a user's age, gender, desired occupation, etc.

[0716] "Desired skill information" is information about skills and qualifications that a user wishes to acquire.

[0717] The "means for inputting" is an interface that allows a user to input attribute information and desired skill information into the system.

[0718] "Means of collection" refers to the methods and functions for acquiring information on human resource development programs provided by multiple educational institutions and companies.

[0719] "Vectorization" is the process of converting collected information and user attribute information into numerical data.

[0720] "Clustering" is a data analysis technique that groups similar data based on vectorized data.

[0721] The "means for performing clustering" refers to an algorithm or mechanism for performing clustering using the collected human resource development program information and user information.

[0722] "Structured data" refers to information that has been organized into a format that makes it easier to manage, such as data in JSON or XML format.

[0723] The "transmitting means" is a communication means for transmitting the educational program information selected by the server to the user terminal.

[0724] The "means for displaying received data on a screen" refers to an interface that visually displays the received educational program information on the user terminal.

[0725] "Viewing behavior" refers to the act of a user viewing and using the educational program provided.

[0726] "Means for generating advertising revenue or rewards" means a mechanism for calculating and distributing advertising revenue or rewards based on user browsing behavior.

[0727] The present invention is a system that collects information on human resource development programs provided by multiple educational institutions and companies based on user attribute information and desired skill information, and automatically provides the most suitable educational program. Specific embodiments of the present invention are described below.

[0728] First, the user accesses the input form using their own device. Here, the user enters attribute information such as age, gender, desired job type, and desired skills, and submits it to the system. For example, a user might enter information such as, "I am a 25-year-old male who aspires to be a data scientist and would like to have skills in Python and machine learning."

[0729] The server converts the received user attribute information and desired skill information into numerical data (vectorization). This process uses TF-IDF (Term Frequency-Inverse Document Frequency), a natural language processing (NLP) method. By calculating the frequency of occurrence of each word and converting it into a vector as numerical data, subsequent clustering processing becomes possible.

[0730] The server then performs clustering on the vectorized data, using the K-Means clustering algorithm. This algorithm divides the data into multiple clusters and groups data points within each cluster so that they are similar to each other. This allows the server to identify the cluster containing the human resource development program that is most similar to the user's desired skills.

[0731] Based on the clustering results, the server selects the most suitable human resource development programs from the cluster and randomly selects one of them to provide to the user. For example, if the selected program is a "data scientist development program" that includes Python, machine learning, data analysis, etc.

[0732] Information about the selected program is sent to the user's device as structured data in JSON format or similar. The server sends this information using the HTTP protocol. The user's device parses the received JSON data and displays it on the screen. Specifically, the browser displays a message stating, "The program best suited for you is the 'Data Scientist Training Program.' It is provided by a certain company, and you will learn Python, machine learning, and data analysis."

[0733] In addition, the server collects user browsing behavior data and generates advertising revenue and rewards based on that data. For example, if a user views a provided program more often, the organization that provided the program can receive a larger reward. This information is sent to the advertisement display server and reward calculation system, and the reward is distributed appropriately.

[0734] A specific example is the following prompt for user input:

[0735] "I'm a 25-year-old male aspiring to become a data scientist. I'm looking for Python and machine learning skills. What educational program would be best for me?"

[0736] In this way, the system of the present invention can automatically provide the optimal human resource development program according to the user's needs, allowing the user to efficiently and effectively find the educational program that best suits them, thereby improving their learning experience.

[0737] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0738] Step 1:

[0739] The user inputs attribute information and desired skill information and sends it to the server.

[0740] The user accesses the input form using their own device and enters attribute information such as age, gender, desired job type, and desired skills. For example, they might enter information such as "I am a 25-year-old male, aiming to become a data scientist, and would like to have Python and machine learning skills." Once the information is complete, the user clicks the "Submit" button to send this information to the server. The input data is sent to the server in text format.

[0741] Step 2:

[0742] The server vectorizes the information it receives.

[0743] The server receives the attribute information and desired skill information sent by the user. The received data includes information such as "Age: 25," "Gender: Male," "Desired occupation: Data scientist," and "Desired skills: Python, machine learning." The server then converts this text information into numerical data (vectorizes it). Using TF-IDF (Term Frequency-Inverse Document Frequency), the server calculates the frequency of occurrence of each word and converts this into a vector as numerical data. For example, vector data such as "Python" being 0.8 and "machine learning" being 0.9 is generated.

[0744] Step 3:

[0745] The server clusters the vector data.

[0746] The server applies the K-Means clustering algorithm to the vectorized data. The input for clustering is the vector data of user information and collected program information. The server sets initial cluster centers and assigns each data point (program information) to the closest cluster center. It then recalculates the center of each cluster and obtains new cluster centers. This process is repeated until the cluster centers converge, and finally multiple clusters and their centers are identified. The output is a cluster number for each program.

[0747] Step 4:

[0748] The server selects the most suitable educational program and provides it to the user.

[0749] The server selects the most suitable educational program based on the clustering results. Based on the cluster number, it selects multiple programs within the cluster that are most similar to the user's attributes. It then randomly selects one of the selected candidate programs and converts the selection result into JSON format. For example, it generates JSON data such as "{"Program Name": "Data Scientist Training Program", "Provider": "Certain Educational Institution", "Skills": ["Python", "Machine Learning", "Data Analysis"]}".

[0750] Step 5:

[0751] The server sends the selected program information to the user terminal in JSON format.

[0752] The generated JSON data is sent to the user's device using the HTTP protocol. The server uses an appropriate security protocol (e.g., HTTPS) to ensure data integrity and confidentiality when sending the information. The information sent includes the program name, provider, related skills, etc.

[0753] Step 6:

[0754] The user terminal displays the received information on its screen.

[0755] The user's device analyzes the received JSON data and displays it on the screen. For example, a message might appear on the browser saying, "The program best suited to you is the 'Data Scientist Training Program.' It is provided by a certain educational institution, and the content you will learn is Python, machine learning, and data analysis." The user can review this information and investigate the program details in more detail if necessary.

[0756] Step 7:

[0757] The server collects user browsing behavior data and generates advertising revenue and rewards.

[0758] The server collects the number of times and duration that users view the provided programs. The collected data is sent to an advertising display server and a reward calculation system. For example, advertising revenue and rewards are returned to the organization that provided a program that is viewed frequently by users. Specifically, the system is equipped with an algorithm that calculates advertising revenue and distributes rewards based on that calculation.

[0759] (Application example 1)

[0760] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[0761] Conventional human resource development programs have had issues with automatically providing optimal training programs based on user attribute information and desired skill information, and also with insufficient mechanisms for measuring the effectiveness of the programs provided and for reward return.In addition, there have been cases where appropriate programs for efficiently learning the specialized skills required, particularly for factory robot operators, have not been provided.

[0762] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[0763] In this invention, the server includes a means for inputting user attribute information and desired skill information, a means for collecting human resource development program information provided by multiple educational institutions and companies, a means for vectorizing and clustering the collected human resource development program information and the user attribute information and desired skill information, a means for identifying and providing the human resource development program optimal for the user based on the clustering results, and a means for transmitting the optimal program information to the user terminal in JSON format, which then receives and displays the information. This allows the system to efficiently provide the educational program most suited to the user's attribute information and preferences, and allows the user to easily view the optimal program content. Furthermore, rewards are provided based on the number of times the provided program is viewed, which promotes system usage and evaluates the program's practicality.

[0764] "User attribute information" is personal data such as the user's age, gender, desired occupation, etc.

[0765] "Desired skill information" is information about specific skills or knowledge that the user wishes to acquire.

[0766] An "educational institution" is an organization that provides education, such as a school, vocational school, or university.

[0767] "Enterprise" means a legal entity or organization engaged in commercial activities or providing products or services.

[0768] "Human resource development program information" is data on training and education programs provided by educational institutions and companies.

[0769] "Vectorization" is the process of converting data into a number-based format that can be processed by a computer.

[0770] "Clustering" is a data analysis technique that groups similar data points together.

[0771] "JSON format" is a data exchange format based on JavaScript object notation.

[0772] A "user terminal" is a device operated by a user, and includes a smartphone, a computer, and the like.

[0773] "Optimal program information" is information about the optimal educational program selected by the system based on the user's attribute information and desired skill information.

[0774] The "number of views" is an index showing how many times the user has viewed the provided information or program.

[0775] Rewarding is the process of providing rewards to individuals or departments for specific behaviors or results.

[0776] The present invention is a system that collects human resource development program information provided by multiple educational institutions and companies based on user attribute information and desired skill information, and automatically provides the optimal educational program using generation AI. Specific embodiments of the present invention are described below.

[0777] User Data Input

[0778] The user accesses the input form using a user device such as a smartphone or computer. At this time, the user enters their age, gender, desired job type, and desired skill information, and sends it to the server. For example, let's assume that the user is a 35-year-old male whose desired job type is robot operator and whose desired skill is robot programming.

[0779] Data Vectorization

[0780] The server vectorizes the received user attribute information, desired skill information, and collected educational program information using TF-IDF (Term Frequency-Inverse Document Frequency), which converts this information into numerical data that can be processed by a computer.

[0781] Clustering Process

[0782] The server performs clustering processing on the vectorized data by applying the K-Means clustering algorithm, which groups similar data and identifies the cluster containing the educational program information most similar to the user's desired skills.

[0783] Optimal program selection

[0784] Based on the clustering results, the server selects the most suitable educational programs from the cluster and randomly selects one of them to provide to the user. For example, if the "Robot Programming Skills Improvement Program" is selected, this program includes robot programming techniques from basic to advanced.

[0785] Providing program information

[0786] The selected program information is structured in JSON format and sent to the user's device. The user's device receives the information and displays it on the screen, allowing the user to easily check the optimal program content.

[0787] Reward based on number of views

[0788] Furthermore, the server will return rewards to the human resources department and individuals based on the number of times the user views the provided programs. This system will make it possible to promote system usage and measure the effectiveness of the programs.

[0789] Specific examples

[0790] For example, if the user is a 35-year-old man who wants to improve his skills as a "robot operator" and aims to learn "robot programming," the system will operate based on the following prompt sentence.

[0791] Prompt Sentence Examples

[0792] Prompt: "Please provide the best educational program for age: 35, gender: male, desired job: robot operator, desired skills: robot programming."

[0793] By inputting these prompts into a generative AI model, the model will provide an educational program that best suits the user's attributes and preferences.

[0794] The above is a specific embodiment of the human resource development program automatic provision system according to the present invention. This system allows users to easily find the most suitable educational program for them and efficiently acquire the necessary skills.

[0795] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0796] Step 1:

[0797] A user accesses the input form using a device such as a smartphone or computer, enters information such as age, gender, desired job type, and desired skill information, and submits it. The entered data is sent to the server, which collects the user's attribute information and desired skill information.

[0798] Step 2:

[0799] The server vectorizes the received user attribute information and desired skill information using TF-IDF. Specifically, the input data is converted into numerical data, which makes it possible for the computer to process it. The vectorized data becomes the input for the next step.

[0800] Step 3:

[0801] The server runs the K-Means clustering algorithm on the vectorized user data and the collected educational program information. This algorithm groups similar data points and identifies the cluster that contains the educational program information that is most similar to the user's desired skills. The clustering results are used as input for the next step.

[0802] Step 4:

[0803] The server selects multiple optimal educational programs based on the clustering results. At this time, it selects the program that best matches the user's attribute information and desired skill information. It then randomly selects one of the selected programs and creates data to provide to the user. This data becomes the input for the next step.

[0804] Step 5:

[0805] The server structures the selected educational program information in JSON format and sends it to the user's device. The user's device then analyzes the received JSON data and displays it on the screen, allowing the user to check the optimal program content.

[0806] Step 6:

[0807] The user's device sends the number of times the user has viewed the program to the server. The server then executes a process to return rewards to the human resources department and individuals based on this data. This reward return system is used to evaluate the usefulness of the program.

[0808] The above is a concrete flow of the program processing of the system based on the application example. This system enables users to efficiently search for advanced educational programs and acquire the necessary skills.

[0809] Furthermore, an emotion engine that estimates the user's emotion may be further combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59, and perform identification processing using the user's emotion.

[0810] The present invention is a system that collects human resource development program information provided by multiple educational institutions and companies based on user attribute information, desired skill information, and emotional information, and automatically provides the optimal educational program using a generation AI. Specific embodiments of the present invention are described below.

[0811] First, the user accesses the input form using their own device. Here, the user enters their age, gender, desired job type, and desired skill information. Once the input is complete, the user clicks the "Submit" button to send the information to the server. As an example, consider a case where the user is a 25-year-old male, whose desired job type is data scientist, and whose desired skills are Python and machine learning.

[0812] The server then receives the request from the user and extracts the user's attribute information and desired skill information. Furthermore, to recognize the user's emotions, the server uses facial recognition, voice analysis, and other techniques through an emotion engine built into the device to collect the user's emotional information. For example, it can recognize whether the user is highly motivated and excited about learning, or, conversely, whether they are feeling anxious or stressed.

[0813] The server loads pre-collected information on human resource development programs provided by multiple educational institutions and companies, including detailed descriptions of each program, the skills that can be acquired, and training methods.

[0814] The server vectorizes the user's desired skill information and the collected program information so that it can be handled as numerical data. This process uses TF-IDF (Term Frequency-Inverse Document Frequency) vectorization technology. The K-Means clustering algorithm is then applied to the vectorized data to classify the data into multiple clusters.

[0815] The server identifies the most suitable human resource development program for each user based on the clustering results and the user's emotional information. Specifically, clustering is performed that takes emotional information into account in order to select the learning program that best suits the user's emotional state. In this case, for example, a user who is highly motivated to learn may be offered a more difficult program, while a user who is feeling stressed may be offered more relaxing learning content.

[0816] Next, the server selects multiple optimal programs from the program information in the cluster, randomly selects one of them, and provides it to the user. For example, if Company A's "Data Scientist Training Program" is selected, the program content will consist of Python, machine learning, data analysis, etc.

[0817] The selected program information is structured in JSON format and sent to the user's device. The user's device analyzes the received information and displays it in a format that is easy for the user to understand. The user can then view the program information provided on their device screen and confirm its contents.

[0818] Finally, the system also includes a mechanism for returning advertising revenue and other rewards to the human resources department or individuals based on the user's emotional data and the number of times they have viewed the programs presented to them, making it possible to promote the use of the system and measure the effectiveness of the programs.

[0819] The above is a specific embodiment of the human resource development program automatic provision system based on the present invention. This system allows users to easily find the learning program that best suits them, and provides an efficient and satisfying learning experience that takes emotional information into account.

[0820] The processing flow will be explained below.

[0821] Step 1:

[0822] The user accesses the input form using their own terminal, where they enter their age, gender, desired job type, and desired skill information. Once the input is complete, the user clicks the "Submit" button to send the information to the server.

[0823] Step 2:

[0824] The device uses a built-in emotion engine to recognize the user's face and analyze their voice to collect real-time emotional information, for example, to sense whether the user is tense or relaxed.

[0825] Step 3:

[0826] The server receives the request from the user, extracts the user's attribute information and desired skill information, and also receives the user's emotional information collected from the terminal.

[0827] Step 4:

[0828] The server loads pre-collected information on human resource development programs provided by multiple educational institutions and companies, including detailed descriptions of each program, the skills that can be acquired, and training methods.

[0829] Step 5:

[0830] The server vectorizes the user's desired skill information and the collected program information, using TF-IDF (Term Frequency-Inverse Document Frequency) vectorization technology to convert text data into numerical data.

[0831] Step 6:

[0832] The server then applies the K-Means clustering algorithm to the vectorized data to perform clustering, which categorizes the data into multiple groups based on similarity.

[0833] Step 7:

[0834] The server identifies the most suitable human resource development program for each user based on the clustering results and the user's emotional information. For example, it selects a more difficult program for a user who is highly motivated to learn, and selects more relaxing learning content for a user who is feeling stressed.

[0835] Step 8:

[0836] The server selects multiple optimal programs from the identified cluster, randomly chooses one of them, and provides it to the user. For example, if Company A's "Data Scientist Training Program" is selected, the program content will include Python, machine learning, data analysis, etc.

[0837] Step 9:

[0838] The server structures the selected program information in JSON format and sends it to the user's device, which then analyzes the information and displays it in a format that is easy for the user to understand.

[0839] Step 10:

[0840] The user can view the provided program information on the device screen and check the content. For example, details of Company A's "Data Scientist Training Program" are displayed, and the user can check the content. The user can utilize the provided content as the optimal program according to their emotional state and continue their learning.

[0841] Step 11:

[0842] The server records the number of times a user views the app and their learning progress. Furthermore, a system is operated in which advertising revenues and other rewards are returned to the human resources department or individuals based on the collected emotional data and number of views. This makes it possible to promote system usage and measure the effectiveness of the program.

[0843] The above are the specific processing steps of the human resource development program automatic provision system based on the present invention. Specific operations are performed at each step to provide the user with an optimal educational program and realize an efficient and satisfying learning experience that takes emotional information into consideration.

[0844] Example 2

[0845] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[0846] Existing human resource development program provision systems select the optimal program based only on the user's attribute information and desired skill information, which means that they are unable to provide appropriate programs that take into account the user's motivation to learn and emotional state.In addition, there are few mechanisms for rewarding the programs provided, which makes it difficult to promote system use and measure their effectiveness.

[0847] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[0848] In this invention, the server includes a means for inputting user attribute information and desired skill information, a means for collecting human resource development program information provided by multiple educational institutions and companies, a means for collecting user emotion information, a means for vectorizing and clustering the collected human resource development program information, user attribute information, and desired skill information, and a means for identifying and providing the optimal human resource development program for the user based on the clustering results and the user emotion information. This makes it possible to provide an optimal learning program that takes into account the user's motivation to learn and emotional state. Furthermore, by providing rewards based on the user's number of views and emotion information, it is possible to promote system usage and improve effectiveness measurement.

[0849] "Attribute information" is basic information for identifying an individual, such as the user's age, sex, occupation, etc.

[0850] "Desired skill information" is information related to specific knowledge or skills that the user wishes to acquire.

[0851] "Human resource development program information" refers to information about educational programs offered by educational institutions and companies to help people acquire skills and knowledge.

[0852] "Emotional information" is data that indicates the user's current emotional state, including, for example, motivation and stress level.

[0853] "Vectorization" is a technique for representing text data as numerical data, and is used to make data comparison and analysis easier.

[0854] "Clustering" is a data analysis technique that groups similar data together to clarify the relationships between the data.

[0855] A "clustering result" is a grouping result of data produced by a clustering algorithm.

[0856] The "optimal human resource development program" refers to the educational program that is most suitable for the user based on the user's attribute information, desired skill information, and emotional information.

[0857] "Random selection" refers to the process of randomly selecting one of a number of selected options.

[0858] The "number of views" is a numerical value indicating how often a user has viewed specific program information.

[0859] "Returning rewards" is the process of returning a portion of the benefits to individuals or departments based on system usage and performance.

[0860] This invention is a system that collects human resource development program information provided by multiple educational institutions and companies based on the user's attribute information, desired skill information, and emotional information, and automatically provides the most suitable educational program using generation AI.

[0861] First, the user accesses the input form using their own device. Here, the user enters their age, gender, desired job type, and desired skill information. Once the input is complete, the user clicks the "Submit" button to send the information to the server. As an example, consider a case where the user is a 25-year-old male, whose desired job type is data scientist, and whose desired skills are Python and machine learning.

[0862] The server receives the request from the user and extracts the user's attribute information and desired skill information. Furthermore, to recognize the user's emotions, the server uses facial recognition, voice analysis, and other methods to collect the user's emotional information through an emotion engine built into the device. For example, it can recognize whether the user is highly motivated and excited about learning, or, conversely, whether they are feeling anxious or stressed.

[0863] Next, the server loads previously collected information on human resource development programs provided by multiple educational institutions and companies. This information includes detailed descriptions of each program, the skills that can be acquired, and training methods. The server vectorizes the user's desired skill information and the collected program information so that it can be handled as numerical data. This process uses TF-IDF (Term Frequency-Inverse Document Frequency) vectorization technology.

[0864] The K-Means clustering algorithm is then applied to the vectorized data to classify the data into multiple clusters. The server then identifies the optimal human resource development program for the user based on the clustering results and the user's emotional information. Specifically, clustering is performed that takes emotional information into account in order to select the learning program that best suits the user's emotional state. In this process, for example, a more difficult program is offered to a user who is highly motivated to learn, while more relaxing learning content is offered to a user who is feeling stressed.

[0865] The server then selects multiple optimal programs from the program information in the cluster, randomly selecting one from the list and providing it to the user. For example, if a company's "data scientist training program" is selected, the program content will consist of Python, machine learning, data analysis, etc.

[0866] The provided program information is structured in JSON format and sent to the user's device. The user's device analyzes the received information and displays it in a format that is easy for the user to understand. The user can then view the provided program information on their device screen and confirm its contents.

[0867] Finally, the system also includes a mechanism for rewarding business divisions and individuals with advertising revenue based on users' emotional data and the number of views of the programs presented. This mechanism makes it possible to promote system usage and measure the effectiveness of the programs.

[0868] For example, consider the following prompt:

[0869] I am a 25-year-old male aspiring to become a data scientist. I would like to build a system that provides the optimal educational program for a user who wants to acquire Python and machine learning skills. Please also consider the user's emotional data. The user shows high motivation. I would like to suggest a recommended program.

[0870] This system allows users to have an efficient and satisfying learning experience.

[0871] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0872] Step 1:

[0873] The user accesses the system's input form using their own device and enters attribute information and desired skill information. Specifically, they enter their age, gender, desired job type, desired skills (e.g., Python and machine learning), etc. into the input form. Once they have completed the input, they click the "Submit" button.

[0874] Input: User's demographic information (age, gender, desired job type) and desired skill information.

[0875] Output: The request data that the user entered is sent to the server.

[0876] Step 2:

[0877] The server receives the request sent by the user and extracts various information, such as age, gender, desired job type, and desired skills, and analyzes the information to store in a database.

[0878] Input: Request data (user attribute information and desired skill information).

[0879] Output: The extracted attribute information and desired skill information are recorded.

[0880] Step 3:

[0881] The device uses a built-in emotion engine to collect the user's emotional information. It uses facial recognition and voice analysis technologies to analyze the user's current emotions (motivation, stress, etc.). The collected emotional information is then sent to a server.

[0882] Input: User's facial expression data and voice data.

[0883] Output: Parsed user emotion information.

[0884] Step 4:

[0885] The server loads pre-collected information on human resource development programs provided by multiple educational institutions and companies, including detailed descriptions of each program, the skills that can be acquired, and training methods.

[0886] Input: Human resource development program information provided by educational institutions and companies.

[0887] Output: Collected program information.

[0888] Step 5:

[0889] The server vectorizes the user's desired skill information and the collected program information using TF-IDF technology, allowing all data to be treated as numerical data.

[0890] Input: Desired skills information and program information.

[0891] Output: Vectorized data.

[0892] Step 6:

[0893] The server applies the K-Means clustering algorithm to the vectorized data, classifying it into multiple clusters, and selects the optimal human resource development program based on the clustering results.

[0894] Input: Vectorized data.

[0895] Output: Clustering results (cluster classification of each data).

[0896] Step 7:

[0897] The server then considers the clustering results and the user's emotional information to identify the most suitable educational program for the user. For example, it selects a more challenging program for a user who is highly motivated to learn, and a relaxing program for a user who is feeling stressed.

[0898] Input: Clustering results and user sentiment information.

[0899] Output: Optimal educational program.

[0900] Step 8:

[0901] The server selects multiple optimal programs from the program information in the identified cluster and randomly selects one of them. The selected program information is structured in JSON format and sent to the user's device.

[0902] Enter: optimal educational programs.

[0903] Output: Program information in JSON format.

[0904] Step 9:

[0905] The device analyzes the JSON formatted program information received from the server and displays it in a format that is easy for the user to understand. The user can then view and confirm the program information provided on the device screen.

[0906] Input: Program information in JSON format.

[0907] Output: Formatted program information.

[0908] Step 10:

[0909] The server records the user's emotional data and the number of times they have viewed the provided program. Based on this data, advertising revenue and other rewards are calculated and returned to business divisions and individuals.

[0910] Input: User sentiment data and number of views.

[0911] Output: Reward calculation results and refund processing.

[0912] (Application example 2)

[0913] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[0914] Currently, the selection of educational programs aimed at users' skill acquisition and career advancement is not optimized to fully reflect their individual needs and emotional state. Furthermore, methods for automatically selecting the most appropriate program from among numerous programs are limited, creating a need for systems that can improve user satisfaction and learning outcomes. In particular, the adoption of real-time emotional analysis using smart devices to select more appropriate programs has not yet been achieved.

[0915] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 2 is realized by the following means.

[0916] In this invention, the server includes means for inputting user attribute information and desired skill information, means for collecting human resource development program information provided by multiple educational institutions and companies, means for vectorizing and clustering the collected human resource development program information, the user attribute information, and the desired skill information, means for identifying and providing the human resource development program optimal for the user based on the clustering results, means for acquiring user emotional information, means for selecting the optimal human resource development program taking the user emotional information into consideration, and means for implementing the above means using smart glasses. This enables real-time program selection taking the emotional information into consideration, making it possible to provide more appropriate educational programs to users.

[0917] "User attribute information" refers to basic personal information about a user, including the user's age, gender, occupation, and interests.

[0918] "Desired skill information" is information about specific skills or knowledge that a user wishes to learn or acquire.

[0919] "Human resource development program information" refers to data on education and training programs offered by multiple educational institutions and companies with the aim of acquiring skills and knowledge.

[0920] "Vectorization" is the process of converting text data or information into a format that is easy to handle as numerical data.

[0921] "Clustering" is a machine learning technique that classifies data into several groups or clusters.

[0922] "Emotion information" is data that indicates the user's emotional state, and includes, for example, motivation and stress level.

[0923] "Smart glasses" are wearable devices equipped with a camera and a display, and have the ability to acquire and display visual and audio information from the user.

[0924] This invention is a system that uses smart glasses to acquire user attribute information, desired skill information, and emotional information, and provides an optimal educational program. Specific embodiments of this system are described below.

[0925] System configuration

[0926] The system consists of smart glasses that acquire user input and emotional information, a server that processes the data, and a means of collecting information on human resource development programs provided by multiple educational institutions and companies. The smart glasses are equipped with a camera and microphone to recognize the user's face and perform voice analysis. The server processes the received data using a generative AI model and selects the optimal educational program using a clustering algorithm.

[0927] Specific usage

[0928] 1. Enter user information

[0929] The user puts on the smart glasses and accesses an input form, where they can enter their age, gender, desired job type, and desired skill information.

[0930] 2. Acquiring emotional information

[0931] When a user inputs information, the smart glasses' camera and microphone are used for facial recognition and voice analysis to obtain the user's emotional information, which is then analyzed using the emotion_recognition library.

[0932] 3. Collection of Program Information

[0933] The server collects information on human resource development programs provided by multiple educational institutions and companies via external APIs, including details of each program, the skills that can be acquired, and the training methods.

[0934] 4. Data Vectorization and Clustering

[0935] The server vectorizes the collected program information and user desired skill information using TF-IDF (Term Frequency-Inverse Document Frequency). It then applies the K-Means clustering algorithm to cluster the data. Based on the clustering results, the optimal program is identified.

[0936] 5. Program selection taking into account emotional information

[0937] The clustering results are combined with the acquired emotional information to select the program that best suits the user's emotional state. For example, a highly motivated user may be offered a more challenging program, while an anxious user may be offered a more relaxing program.

[0938] 6. Program Offering

[0939] The server structures the selected program information in JSON format and sends it to the smart glasses, which then analyzes the received information and visually displays it to the user.

[0940] Specific examples

[0941] Suppose a 25-year-old male user wants to become a data scientist and learn Python and machine learning. In this case, the smart glasses analyze the user's facial expressions and voice and determine that he is highly motivated.

[0942] Using a generative AI model, the server performs clustering and lists the most suitable programs from among the most difficult ones.

[0943] Based on the prompt, "A 25-year-old man who wants to become a data scientist wants to learn Python and machine learning. He seems highly motivated, so please recommend a high-level educational program," the server selects the most suitable program and provides it to the smart glasses.

[0944] Hardware and software used

[0945] Smart glasses: equipped with a camera and microphone to capture emotional information.

[0946] emotion_recognition library: Recognizes and analyzes emotions.

[0947] Server: Processes data, runs generative AI models, uses TF-IDF vectorization and K-Means clustering algorithms.

[0948] In this way, by taking emotional information into consideration and providing optimal educational programs through smart glasses, it is possible to provide users with an efficient and satisfying learning experience.

[0949] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0950] Step 1:

[0951] The user puts on the smart glasses and enters attribute information and desired skill information into an input form.

[0952] Input: User's age, gender, desired job type, desired skill information

[0953] How it works: The user enters this information in text format using the smart glasses interface.

[0954] Output: The user's attribute information and desired skill information are sent to the server as text data.

[0955] Step 2:

[0956] The camera and microphone of the smart glasses are used to acquire the user's emotional information.

[0957] Input: User's face image and voice data

[0958] How it works: The smart glasses use a camera to analyze the user's face and a microphone to collect audio. They then use the emotion_recognition library to analyze this data and identify the user's emotional information.

[0959] Output: The user's emotional information (e.g., motivation and stress level) is sent to the server as the analysis result.

[0960] Step 3:

[0961] The server collects human resource development program information through an external API.

[0962] Input: API for providing program information for educational institutions and companies

[0963] How it works: The server makes a request to an external API to retrieve relevant educational program information, including program details, skills learned, and teaching methods.

[0964] Output: Multiple educational program information is saved in JSON format on the server.

[0965] Step 4:

[0966] The program information and user desired skill information collected by the server are vectorized using TF-IDF, and clustering is performed using the K-Means clustering algorithm.

[0967] Input: User's desired skill information, educational program information

[0968] How it works: The server converts these text data into numerical vectors using TF-IDF vectorization techniques, then applies the K-Means clustering algorithm to classify the data into clusters.

[0969] Output: The clustering result is a set of programs suitable for multiple users.

[0970] Step 5:

[0971] The server considers emotional information and selects the most appropriate human resource development program.

[0972] Input: Clustering results, user emotion information

[0973] How it works: The server analyzes the clustering results and selects programs based on emotional information. For example, it selects a challenging program for a highly motivated user, and a relaxing program for a stressed user.

[0974] Output: One optimal educational program is selected.

[0975] Step 6:

[0976] The server transmits the optimal program information to the smart glasses and provides it to the user.

[0977] Input: Selected educational program information

[0978] How it works: The server structures program information in JSON format and sends it to the smart glasses. The smart glasses receive this information and display it in a user-friendly format.

[0979] Output: The optimal educational program is displayed on the user's smart glasses.

[0980] The specific processing unit 290 transmits the result of the specific processing to the headset type terminal 314. In the headset type terminal 314, the control unit 46A causes the speaker 240 and the display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0981] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0982] 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 the present disclosure is not limited to this, and the specific processing may be performed by the headset type terminal 314.

[0983] [Fourth embodiment]

[0984] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0985] 7, a 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.

[0986] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0987] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a control target 443. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the control target 443 are also connected to the bus 52.

[0988] The microphone 238 receives instructions and the like from the user 20 by receiving voice uttered by the user 20. The microphone 238 captures the voice uttered by the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio in accordance with instructions from the processor 46.

[0989] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the surroundings of user 20 (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0990] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0991] The control object 443 includes a display device, LEDs in the eyes, and motors for driving 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 emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

[0992] Fig. 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Fig. 8, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0993] The specific processing program 56 is an example of a "program" according to the technology of the present 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.

[0994] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0995] In the robot 414, the processor 46 performs the reception output process. A reception output program 60 is stored in the storage 50. 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 process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[0996] Next, a description will be given of the specific processing performed by the specific processing unit 290 of the data processing device 12. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[0997] The present invention is a system that collects human resource development program information provided by multiple educational institutions and companies based on user attribute information and desired skill information, and automatically provides the most suitable educational program using generation AI. Specific embodiments of the present invention are described below.

[0998] First, the user accesses the input form using their own device. Here, they enter information such as age, gender, desired job type, and desired skills, and then submit it to the system. For example, let's say the user is a 25-year-old male who wants to be a data scientist and has the skills of Python and machine learning.

[0999] Next, the server vectorizes the human resource development program information collected from multiple educational institutions and companies based on the received user attribute information and desired skill information. Vectorization is the process of converting each piece of information into numerical data, which makes it a format that can be processed by a computer. For example, TF-IDF (Term Frequency-Inverse Document Frequency) can be used for this process.

[1000] The server then performs clustering processing on the vectorized data using the K-Means clustering algorithm. Clustering is a method for grouping similar data, and in this case, it identifies the cluster containing the human resource development program information that is most similar to the user's desired skills.

[1001] Based on the clustering results, the server selects the most suitable human resource development programs from the cluster and randomly selects one of them to provide to the user. For example, suppose the "Data Scientist Development Program" provided by Company A is selected. This program includes content such as Python, machine learning, and data analysis.

[1002] The selected program information is structured in JSON format or similar and sent to the user's device. The user's device displays the received information on its screen, allowing the user to check the content provided as the optimal program.

[1003] Furthermore, the system also has a mechanism whereby advertising revenues and other rewards are returned to the human resources department or individuals based on the number of times users view the presented programs. This mechanism makes it possible to promote system usage and measure the effectiveness of the programs.

[1004] Specifically, the more a user views a program, the greater the rewards the user and the company or educational institution that provided the program can receive. This makes it easier to evaluate the user's interest and the program's usefulness.

[1005] The above is a specific embodiment of the human resource development program automatic provision system according to the present invention. This system allows users to easily find the learning program that best suits them, enabling them to have an efficient and satisfying learning experience.

[1006] The processing flow will be explained below.

[1007] Step 1:

[1008] The user accesses the input form using their own terminal, where they enter their age, gender, desired job type, and desired skill information. Once the input is complete, the user clicks the "Submit" button to send the information to the server.

[1009] Step 2:

[1010] The server receives the request sent by the user and extracts the user's attribute information and desired skill information. For example, if the user is a 25-year-old male, his desired occupation is data scientist, and his desired skills are Python and machine learning, this information is passed to the server.

[1011] Step 3:

[1012] The server loads pre-collected information on human resource development programs provided by multiple educational institutions and companies, including detailed descriptions of each program and the skills that can be acquired.

[1013] Step 4:

[1014] The server vectorizes the user's desired skill information and the collected program information. Specifically, it uses TF-IDF vectorization technology to convert text data into numerical data. This makes it possible to compare the program information and the user's desired skill information numerically.

[1015] Step 5:

[1016] The server uses the vectorized data to perform clustering. It applies the K-Means clustering algorithm to classify the data into multiple clusters. A cluster is a collection of data grouped based on similarity.

[1017] Step 6:

[1018] Based on the clustering results, the server identifies the cluster that best matches the user's desired skill information. This cluster contains the program information that is closest to the user's preference.

[1019] Step 7:

[1020] The server selects multiple optimal programs from the program information in the cluster, then randomly selects one program from the list and prepares it to be provided to the user.

[1021] Step 8:

[1022] The server structures the selected program information in JSON format and sends it to the user's device, which then analyzes the information and displays it in a format that is easy for the user to understand.

[1023] Step 9:

[1024] The user views the program information provided on the device screen and confirms the content. For example, Company A's data scientist training program is selected and displayed on the device.

[1025] Step 10:

[1026] The server records the number of times a user views the program and operates a reward system based on that information. A system is implemented in which rewards are returned to the human resources department or individuals according to the number of times a user views the program and their learning outcomes. This allows the effectiveness of the program and frequency of use to be evaluated.

[1027] The above are the specific processing steps of the system for automatically providing a human resource development program according to the present invention. By performing specific operations at each step, it is possible to provide the user with an optimal educational program.

[1028] Example 1

[1029] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1030] In today's highly information-driven society, it is extremely important to find the best educational program to improve one's career aptitude and skills. However, selecting the most suitable program from the wide variety of programs offered by educational institutions and companies is difficult and requires time and effort. For this reason, there is a need for a system that allows users to easily and efficiently find the best educational program.

[1031] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[1032] In this invention, the server includes means for inputting user attribute information and desired skill information, means for collecting human resource development program information provided by multiple educational institutions and companies, means for vectorizing and clustering the collected human resource development program information and the user attribute information and desired skill information, means for identifying the human resource development program most suitable for the user based on the clustering results and transmitting the identified program to the user terminal as structured data, means for displaying the data received by the user terminal on a screen, and means for generating advertising revenue and rewards based on the user's browsing behavior. This enables users to efficiently and effectively find the educational program that best suits them.

[1033] "User" refers to a person who uses the system to search for and select the educational program that best suits them.

[1034] "Attribute information" is a general term for personal information such as a user's age, gender, desired occupation, etc.

[1035] "Desired skill information" is information about skills and qualifications that a user wishes to acquire.

[1036] The "means for inputting" is an interface that allows a user to input attribute information and desired skill information into the system.

[1037] "Means of collection" refers to the methods and functions for acquiring information on human resource development programs provided by multiple educational institutions and companies.

[1038] "Vectorization" is the process of converting collected information and user attribute information into numerical data.

[1039] "Clustering" is a data analysis technique that groups similar data based on vectorized data.

[1040] The "means for performing clustering" refers to an algorithm or mechanism for performing clustering using the collected human resource development program information and user information.

[1041] "Structured data" refers to information that has been organized into a format that makes it easier to manage, such as data in JSON or XML format.

[1042] The "transmitting means" is a communication means for transmitting the educational program information selected by the server to the user terminal.

[1043] The "means for displaying received data on a screen" refers to an interface that visually displays the received educational program information on the user terminal.

[1044] "Viewing behavior" refers to the act of a user viewing and using the educational program provided.

[1045] "Means for generating advertising revenue or rewards" means a mechanism for calculating and distributing advertising revenue or rewards based on user browsing behavior.

[1046] The present invention is a system that collects information on human resource development programs provided by multiple educational institutions and companies based on user attribute information and desired skill information, and automatically provides the most suitable educational program. Specific embodiments of the present invention are described below.

[1047] First, the user accesses the input form using their own device. Here, the user enters attribute information such as age, gender, desired job type, and desired skills, and submits it to the system. For example, a user might enter information such as, "I am a 25-year-old male who aspires to be a data scientist and would like to have skills in Python and machine learning."

[1048] The server converts the received user attribute information and desired skill information into numerical data (vectorization). This process uses TF-IDF (Term Frequency-Inverse Document Frequency), a natural language processing (NLP) method. By calculating the frequency of occurrence of each word and converting it into a vector as numerical data, subsequent clustering processing becomes possible.

[1049] The server then performs clustering on the vectorized data, using the K-Means clustering algorithm. This algorithm divides the data into multiple clusters and groups data points within each cluster so that they are similar to each other. This allows the server to identify the cluster containing the human resource development program that is most similar to the user's desired skills.

[1050] Based on the clustering results, the server selects the most suitable human resource development programs from the cluster and randomly selects one of them to provide to the user. For example, if the selected program is a "data scientist development program" that includes Python, machine learning, data analysis, etc.

[1051] Information about the selected program is sent to the user's device as structured data in JSON format or similar. The server sends this information using the HTTP protocol. The user's device parses the received JSON data and displays it on the screen. Specifically, the browser displays a message stating, "The program best suited for you is the 'Data Scientist Training Program.' It is provided by a certain company, and you will learn Python, machine learning, and data analysis."

[1052] In addition, the server collects user browsing behavior data and generates advertising revenue and rewards based on that data. For example, if a user views a provided program more often, the organization that provided the program can receive a larger reward. This information is sent to the advertisement display server and reward calculation system, and the reward is distributed appropriately.

[1053] A specific example is the following prompt for user input:

[1054] "I'm a 25-year-old male aspiring to become a data scientist. I'm looking for Python and machine learning skills. What educational program would be best for me?"

[1055] In this way, the system of the present invention can automatically provide the optimal human resource development program according to the user's needs, allowing the user to efficiently and effectively find the educational program that best suits them, thereby improving their learning experience.

[1056] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1057] Step 1:

[1058] The user inputs attribute information and desired skill information and sends it to the server.

[1059] The user accesses the input form using their own device and enters attribute information such as age, gender, desired job type, and desired skills. For example, they might enter information such as "I am a 25-year-old male, aiming to become a data scientist, and would like to have Python and machine learning skills." Once the information is complete, the user clicks the "Submit" button to send this information to the server. The input data is sent to the server in text format.

[1060] Step 2:

[1061] The server vectorizes the information it receives.

[1062] The server receives the attribute information and desired skill information sent by the user. The received data includes information such as "Age: 25," "Gender: Male," "Desired occupation: Data scientist," and "Desired skills: Python, machine learning." The server then converts this text information into numerical data (vectorizes it). Using TF-IDF (Term Frequency-Inverse Document Frequency), the server calculates the frequency of occurrence of each word and converts this into a vector as numerical data. For example, vector data such as "Python" being 0.8 and "machine learning" being 0.9 is generated.

[1063] Step 3:

[1064] The server clusters the vector data.

[1065] The server applies the K-Means clustering algorithm to the vectorized data. The input for clustering is the vector data of user information and collected program information. The server sets initial cluster centers and assigns each data point (program information) to the closest cluster center. It then recalculates the center of each cluster and obtains new cluster centers. This process is repeated until the cluster centers converge, and finally multiple clusters and their centers are identified. The output is a cluster number for each program.

[1066] Step 4:

[1067] The server selects the most suitable educational program and provides it to the user.

[1068] The server selects the most suitable educational program based on the clustering results. Based on the cluster number, it selects multiple programs within the cluster that are most similar to the user's attributes. It then randomly selects one of the selected candidate programs and converts the selection result into JSON format. For example, it generates JSON data such as "{"Program Name": "Data Scientist Training Program", "Provider": "Certain Educational Institution", "Skills": ["Python", "Machine Learning", "Data Analysis"]}".

[1069] Step 5:

[1070] The server sends the selected program information to the user terminal in JSON format.

[1071] The generated JSON data is sent to the user's device using the HTTP protocol. The server uses an appropriate security protocol (e.g., HTTPS) to ensure data integrity and confidentiality when sending the information. The information sent includes the program name, provider, related skills, etc.

[1072] Step 6:

[1073] The user terminal displays the received information on its screen.

[1074] The user's device analyzes the received JSON data and displays it on the screen. For example, a message might appear on the browser saying, "The program best suited to you is the 'Data Scientist Training Program.' It is provided by a certain educational institution, and the content you will learn is Python, machine learning, and data analysis." The user can review this information and investigate the program details in more detail if necessary.

[1075] Step 7:

[1076] The server collects user browsing behavior data and generates advertising revenue and rewards.

[1077] The server collects the number of times and duration that users view the provided programs. The collected data is sent to an advertising display server and a reward calculation system. For example, advertising revenue and rewards are returned to the organization that provided a program that is viewed frequently by users. Specifically, the system is equipped with an algorithm that calculates advertising revenue and distributes rewards based on that calculation.

[1078] (Application example 1)

[1079] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1080] Conventional human resource development programs have had issues with automatically providing optimal training programs based on user attribute information and desired skill information, and also with insufficient mechanisms for measuring the effectiveness of the programs provided and for reward return.In addition, there have been cases where appropriate programs for efficiently learning the specialized skills required, particularly for factory robot operators, have not been provided.

[1081] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[1082] In this invention, the server includes a means for inputting user attribute information and desired skill information, a means for collecting human resource development program information provided by multiple educational institutions and companies, a means for vectorizing and clustering the collected human resource development program information and the user attribute information and desired skill information, a means for identifying and providing the human resource development program optimal for the user based on the clustering results, and a means for transmitting the optimal program information to the user terminal in JSON format, which then receives and displays the information. This allows the system to efficiently provide the educational program most suited to the user's attribute information and preferences, and allows the user to easily view the optimal program content. Furthermore, rewards are provided based on the number of times the provided program is viewed, which promotes system usage and evaluates the program's practicality.

[1083] "User attribute information" is personal data such as the user's age, gender, desired occupation, etc.

[1084] "Desired skill information" is information about specific skills or knowledge that the user wishes to acquire.

[1085] An "educational institution" is an organization that provides education, such as a school, vocational school, or university.

[1086] "Enterprise" means a legal entity or organization engaged in commercial activities or providing products or services.

[1087] "Human resource development program information" is data on training and education programs provided by educational institutions and companies.

[1088] "Vectorization" is the process of converting data into a number-based format that can be processed by a computer.

[1089] "Clustering" is a data analysis technique that groups similar data points together.

[1090] "JSON format" is a data exchange format based on JavaScript object notation.

[1091] A "user terminal" is a device operated by a user, and includes a smartphone, a computer, and the like.

[1092] "Optimal program information" is information about the optimal educational program selected by the system based on the user's attribute information and desired skill information.

[1093] The "number of views" is an index showing how many times the user has viewed the provided information or program.

[1094] Rewarding is the process of providing rewards to individuals or departments for specific behaviors or results.

[1095] The present invention is a system that collects human resource development program information provided by multiple educational institutions and companies based on user attribute information and desired skill information, and automatically provides the optimal educational program using generation AI. Specific embodiments of the present invention are described below.

[1096] User Data Input

[1097] The user accesses the input form using a user device such as a smartphone or computer. At this time, the user enters their age, gender, desired job type, and desired skill information, and sends it to the server. For example, let's assume that the user is a 35-year-old male whose desired job type is robot operator and whose desired skill is robot programming.

[1098] Data Vectorization

[1099] The server vectorizes the received user attribute information, desired skill information, and collected educational program information using TF-IDF (Term Frequency-Inverse Document Frequency), which converts this information into numerical data that can be processed by a computer.

[1100] Clustering Process

[1101] The server performs clustering processing on the vectorized data by applying the K-Means clustering algorithm, which groups similar data and identifies the cluster containing the educational program information most similar to the user's desired skills.

[1102] Optimal program selection

[1103] Based on the clustering results, the server selects the most suitable educational programs from the cluster and randomly selects one of them to provide to the user. For example, if the "Robot Programming Skills Improvement Program" is selected, this program includes robot programming techniques from basic to advanced.

[1104] Providing program information

[1105] The selected program information is structured in JSON format and sent to the user's device. The user's device receives the information and displays it on the screen, allowing the user to easily check the optimal program content.

[1106] Reward based on number of views

[1107] Furthermore, the server will return rewards to the human resources department and individuals based on the number of times the user views the provided programs. This system will make it possible to promote system usage and measure the effectiveness of the programs.

[1108] Specific examples

[1109] For example, if the user is a 35-year-old man who wants to improve his skills as a "robot operator" and aims to learn "robot programming," the system will operate based on the following prompt sentence.

[1110] Prompt Sentence Examples

[1111] Prompt: "Please provide the best educational program for age: 35, gender: male, desired job: robot operator, desired skills: robot programming."

[1112] By inputting these prompts into a generative AI model, the model will provide an educational program that best suits the user's attributes and preferences.

[1113] The above is a specific embodiment of the human resource development program automatic provision system according to the present invention. This system allows users to easily find the most suitable educational program for them and efficiently acquire the necessary skills.

[1114] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1115] Step 1:

[1116] A user accesses the input form using a device such as a smartphone or computer, enters information such as age, gender, desired job type, and desired skill information, and submits it. The entered data is sent to the server, which collects the user's attribute information and desired skill information.

[1117] Step 2:

[1118] The server vectorizes the received user attribute information and desired skill information using TF-IDF. Specifically, the input data is converted into numerical data, which makes it possible for the computer to process it. The vectorized data becomes the input for the next step.

[1119] Step 3:

[1120] The server runs the K-Means clustering algorithm on the vectorized user data and the collected educational program information. This algorithm groups similar data points and identifies the cluster that contains the educational program information that is most similar to the user's desired skills. The clustering results are used as input for the next step.

[1121] Step 4:

[1122] The server selects multiple optimal educational programs based on the clustering results. At this time, it selects the program that best matches the user's attribute information and desired skill information. It then randomly selects one of the selected programs and creates data to provide to the user. This data becomes the input for the next step.

[1123] Step 5:

[1124] The server structures the selected educational program information in JSON format and sends it to the user's device. The user's device then analyzes the received JSON data and displays it on the screen, allowing the user to check the optimal program content.

[1125] Step 6:

[1126] The user's device sends the number of times the user has viewed the program to the server. The server then executes a process to return rewards to the human resources department and individuals based on this data. This reward return system is used to evaluate the usefulness of the program.

[1127] The above is a concrete flow of the program processing of the system based on the application example. This system enables users to efficiently search for advanced educational programs and acquire the necessary skills.

[1128] Furthermore, an emotion engine that estimates the user's emotion may be further combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59, and perform identification processing using the user's emotion.

[1129] The present invention is a system that collects human resource development program information provided by multiple educational institutions and companies based on user attribute information, desired skill information, and emotional information, and automatically provides the optimal educational program using a generation AI. Specific embodiments of the present invention are described below.

[1130] First, the user accesses the input form using their own device. Here, the user enters their age, gender, desired job type, and desired skill information. Once the input is complete, the user clicks the "Submit" button to send the information to the server. As an example, consider a case where the user is a 25-year-old male, whose desired job type is data scientist, and whose desired skills are Python and machine learning.

[1131] The server then receives the request from the user and extracts the user's attribute information and desired skill information. Furthermore, to recognize the user's emotions, the server uses facial recognition, voice analysis, and other techniques through an emotion engine built into the device to collect the user's emotional information. For example, it can recognize whether the user is highly motivated and excited about learning, or, conversely, whether they are feeling anxious or stressed.

[1132] The server loads pre-collected information on human resource development programs provided by multiple educational institutions and companies, including detailed descriptions of each program, the skills that can be acquired, and training methods.

[1133] The server vectorizes the user's desired skill information and the collected program information so that it can be handled as numerical data. This process uses TF-IDF (Term Frequency-Inverse Document Frequency) vectorization technology. The K-Means clustering algorithm is then applied to the vectorized data to classify the data into multiple clusters.

[1134] The server identifies the most suitable human resource development program for each user based on the clustering results and the user's emotional information. Specifically, clustering is performed that takes emotional information into account in order to select the learning program that best suits the user's emotional state. In this case, for example, a user who is highly motivated to learn may be offered a more difficult program, while a user who is feeling stressed may be offered more relaxing learning content.

[1135] Next, the server selects multiple optimal programs from the program information in the cluster, randomly selects one of them, and provides it to the user. For example, if Company A's "Data Scientist Training Program" is selected, the program content will consist of Python, machine learning, data analysis, etc.

[1136] The selected program information is structured in JSON format and sent to the user's device. The user's device analyzes the received information and displays it in a format that is easy for the user to understand. The user can then view the program information provided on their device screen and confirm its contents.

[1137] Finally, the system also includes a mechanism for returning advertising revenue and other rewards to the human resources department or individuals based on the user's emotional data and the number of times they have viewed the programs presented to them, making it possible to promote the use of the system and measure the effectiveness of the programs.

[1138] The above is a specific embodiment of the human resource development program automatic provision system based on the present invention. This system allows users to easily find the learning program that best suits them, and provides an efficient and satisfying learning experience that takes emotional information into account.

[1139] The processing flow will be explained below.

[1140] Step 1:

[1141] The user accesses the input form using their own terminal, where they enter their age, gender, desired job type, and desired skill information. Once the input is complete, the user clicks the "Submit" button to send the information to the server.

[1142] Step 2:

[1143] The device uses a built-in emotion engine to recognize the user's face and analyze their voice to collect real-time emotional information, for example, to sense whether the user is tense or relaxed.

[1144] Step 3:

[1145] The server receives the request from the user, extracts the user's attribute information and desired skill information, and also receives the user's emotional information collected from the terminal.

[1146] Step 4:

[1147] The server loads pre-collected information on human resource development programs provided by multiple educational institutions and companies, including detailed descriptions of each program, the skills that can be acquired, and training methods.

[1148] Step 5:

[1149] The server vectorizes the user's desired skill information and the collected program information, using TF-IDF (Term Frequency-Inverse Document Frequency) vectorization technology to convert text data into numerical data.

[1150] Step 6:

[1151] The server then applies the K-Means clustering algorithm to the vectorized data to perform clustering, which categorizes the data into multiple groups based on similarity.

[1152] Step 7:

[1153] The server identifies the most suitable human resource development program for each user based on the clustering results and the user's emotional information. For example, it selects a more difficult program for a user who is highly motivated to learn, and selects more relaxing learning content for a user who is feeling stressed.

[1154] Step 8:

[1155] The server selects multiple optimal programs from the identified cluster, randomly chooses one of them, and provides it to the user. For example, if Company A's "Data Scientist Training Program" is selected, the program content will include Python, machine learning, data analysis, etc.

[1156] Step 9:

[1157] The server structures the selected program information in JSON format and sends it to the user's device, which then analyzes the information and displays it in a format that is easy for the user to understand.

[1158] Step 10:

[1159] The user can view the provided program information on the device screen and check the content. For example, details of Company A's "Data Scientist Training Program" are displayed, and the user can check the content. The user can utilize the provided content as the optimal program according to their emotional state and continue their learning.

[1160] Step 11:

[1161] The server records the number of times a user views the app and their learning progress. Furthermore, a system is operated in which advertising revenues and other rewards are returned to the human resources department or individuals based on the collected emotional data and number of views. This makes it possible to promote system usage and measure the effectiveness of the program.

[1162] The above are the specific processing steps of the human resource development program automatic provision system based on the present invention. Specific operations are performed at each step to provide the user with an optimal educational program and realize an efficient and satisfying learning experience that takes emotional information into consideration.

[1163] Example 2

[1164] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1165] Existing human resource development program provision systems select the optimal program based only on the user's attribute information and desired skill information, which means that they are unable to provide appropriate programs that take into account the user's motivation to learn and emotional state.In addition, there are few mechanisms for rewarding the programs provided, which makes it difficult to promote system use and measure their effectiveness.

[1166] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[1167] In this invention, the server includes a means for inputting user attribute information and desired skill information, a means for collecting human resource development program information provided by multiple educational institutions and companies, a means for collecting user emotion information, a means for vectorizing and clustering the collected human resource development program information, user attribute information, and desired skill information, and a means for identifying and providing the optimal human resource development program for the user based on the clustering results and the user emotion information. This makes it possible to provide an optimal learning program that takes into account the user's motivation to learn and emotional state. Furthermore, by providing rewards based on the user's number of views and emotion information, it is possible to promote system usage and improve effectiveness measurement.

[1168] "Attribute information" is basic information for identifying an individual, such as the user's age, sex, occupation, etc.

[1169] "Desired skill information" is information related to specific knowledge or skills that the user wishes to acquire.

[1170] "Human resource development program information" refers to information about educational programs offered by educational institutions and companies to help people acquire skills and knowledge.

[1171] "Emotional information" is data that indicates the user's current emotional state, including, for example, motivation and stress level.

[1172] "Vectorization" is a technique for representing text data as numerical data, and is used to make data comparison and analysis easier.

[1173] "Clustering" is a data analysis technique that groups similar data together to clarify the relationships between the data.

[1174] A "clustering result" is a grouping result of data produced by a clustering algorithm.

[1175] The "optimal human resource development program" refers to the educational program that is most suitable for the user based on the user's attribute information, desired skill information, and emotional information.

[1176] "Random selection" refers to the process of randomly selecting one of a number of selected options.

[1177] The "number of views" is a numerical value indicating how often a user has viewed specific program information.

[1178] "Returning rewards" is the process of returning a portion of the benefits to individuals or departments based on system usage and performance.

[1179] This invention is a system that collects human resource development program information provided by multiple educational institutions and companies based on the user's attribute information, desired skill information, and emotional information, and automatically provides the most suitable educational program using generation AI.

[1180] First, the user accesses the input form using their own device. Here, the user enters their age, gender, desired job type, and desired skill information. Once the input is complete, the user clicks the "Submit" button to send the information to the server. As an example, consider a case where the user is a 25-year-old male, whose desired job type is data scientist, and whose desired skills are Python and machine learning.

[1181] The server receives the request from the user and extracts the user's attribute information and desired skill information. Furthermore, to recognize the user's emotions, the server uses facial recognition, voice analysis, and other methods to collect the user's emotional information through an emotion engine built into the device. For example, it can recognize whether the user is highly motivated and excited about learning, or, conversely, whether they are feeling anxious or stressed.

[1182] Next, the server loads previously collected information on human resource development programs provided by multiple educational institutions and companies. This information includes detailed descriptions of each program, the skills that can be acquired, and training methods. The server vectorizes the user's desired skill information and the collected program information so that it can be handled as numerical data. This process uses TF-IDF (Term Frequency-Inverse Document Frequency) vectorization technology.

[1183] The K-Means clustering algorithm is then applied to the vectorized data to classify the data into multiple clusters. The server then identifies the optimal human resource development program for the user based on the clustering results and the user's emotional information. Specifically, clustering is performed that takes emotional information into account in order to select the learning program that best suits the user's emotional state. In this process, for example, a more difficult program is offered to a user who is highly motivated to learn, while more relaxing learning content is offered to a user who is feeling stressed.

[1184] The server then selects multiple optimal programs from the program information in the cluster, randomly selecting one from the list and providing it to the user. For example, if a company's "data scientist training program" is selected, the program content will consist of Python, machine learning, data analysis, etc.

[1185] The provided program information is structured in JSON format and sent to the user's device. The user's device analyzes the received information and displays it in a format that is easy for the user to understand. The user can then view the provided program information on their device screen and confirm its contents.

[1186] Finally, the system also includes a mechanism for rewarding business divisions and individuals with advertising revenue based on users' emotional data and the number of views of the programs presented. This mechanism makes it possible to promote system usage and measure the effectiveness of the programs.

[1187] For example, consider the following prompt:

[1188] I am a 25-year-old male aspiring to become a data scientist. I would like to build a system that provides the optimal educational program for a user who wants to acquire Python and machine learning skills. Please also consider the user's emotional data. The user shows high motivation. I would like to suggest a recommended program.

[1189] This system allows users to have an efficient and satisfying learning experience.

[1190] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1191] Step 1:

[1192] The user accesses the system's input form using their own device and enters attribute information and desired skill information. Specifically, they enter their age, gender, desired job type, desired skills (e.g., Python and machine learning), etc. into the input form. Once they have completed the input, they click the "Submit" button.

[1193] Input: User's demographic information (age, gender, desired job type) and desired skill information.

[1194] Output: The request data that the user entered is sent to the server.

[1195] Step 2:

[1196] The server receives the request sent by the user and extracts various information, such as age, gender, desired job type, and desired skills, and analyzes the information to store in a database.

[1197] Input: Request data (user attribute information and desired skill information).

[1198] Output: The extracted attribute information and desired skill information are recorded.

[1199] Step 3:

[1200] The device uses a built-in emotion engine to collect the user's emotional information. It uses facial recognition and voice analysis technologies to analyze the user's current emotions (motivation, stress, etc.). The collected emotional information is then sent to a server.

[1201] Input: User's facial expression data and voice data.

[1202] Output: Parsed user emotion information.

[1203] Step 4:

[1204] The server loads pre-collected information on human resource development programs provided by multiple educational institutions and companies, including detailed descriptions of each program, the skills that can be acquired, and training methods.

[1205] Input: Human resource development program information provided by educational institutions and companies.

[1206] Output: Collected program information.

[1207] Step 5:

[1208] The server vectorizes the user's desired skill information and the collected program information using TF-IDF technology, allowing all data to be treated as numerical data.

[1209] Input: Desired skills information and program information.

[1210] Output: Vectorized data.

[1211] Step 6:

[1212] The server applies the K-Means clustering algorithm to the vectorized data, classifying it into multiple clusters, and selects the optimal human resource development program based on the clustering results.

[1213] Input: Vectorized data.

[1214] Output: Clustering results (cluster classification of each data).

[1215] Step 7:

[1216] The server then considers the clustering results and the user's emotional information to identify the most suitable educational program for the user. For example, it selects a more challenging program for a user who is highly motivated to learn, and a relaxing program for a user who is feeling stressed.

[1217] Input: Clustering results and user sentiment information.

[1218] Output: Optimal educational program.

[1219] Step 8:

[1220] The server selects multiple optimal programs from the program information in the identified cluster and randomly selects one of them. The selected program information is structured in JSON format and sent to the user's device.

[1221] Enter: optimal educational programs.

[1222] Output: Program information in JSON format.

[1223] Step 9:

[1224] The device analyzes the JSON formatted program information received from the server and displays it in a format that is easy for the user to understand. The user can then view and confirm the program information provided on the device screen.

[1225] Input: Program information in JSON format.

[1226] Output: Formatted program information.

[1227] Step 10:

[1228] The server records the user's emotional data and the number of times they have viewed the provided program. Based on this data, advertising revenue and other rewards are calculated and returned to business divisions and individuals.

[1229] Input: User sentiment data and number of views.

[1230] Output: Reward calculation results and refund processing.

[1231] (Application example 2)

[1232] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1233] Currently, the selection of educational programs aimed at users' skill acquisition and career advancement is not optimized to fully reflect their individual needs and emotional state. Furthermore, methods for automatically selecting the most appropriate program from among numerous programs are limited, creating a need for systems that can improve user satisfaction and learning outcomes. In particular, the adoption of real-time emotional analysis using smart devices to select more appropriate programs has not yet been achieved.

[1234] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 2 is realized by the following means.

[1235] In this invention, the server includes means for inputting user attribute information and desired skill information, means for collecting human resource development program information provided by multiple educational institutions and companies, means for vectorizing and clustering the collected human resource development program information, the user attribute information, and the desired skill information, means for identifying and providing the human resource development program optimal for the user based on the clustering results, means for acquiring user emotional information, means for selecting the optimal human resource development program taking the user emotional information into consideration, and means for implementing the above means using smart glasses. This enables real-time program selection taking the emotional information into consideration, making it possible to provide more appropriate educational programs to users.

[1236] "User attribute information" refers to basic personal information about a user, including the user's age, gender, occupation, and interests.

[1237] "Desired skill information" is information about specific skills or knowledge that a user wishes to learn or acquire.

[1238] "Human resource development program information" refers to data on education and training programs offered by multiple educational institutions and companies with the aim of acquiring skills and knowledge.

[1239] "Vectorization" is the process of converting text data or information into a format that is easy to handle as numerical data.

[1240] "Clustering" is a machine learning technique that classifies data into several groups or clusters.

[1241] "Emotion information" is data that indicates the user's emotional state, and includes, for example, motivation and stress level.

[1242] "Smart glasses" are wearable devices equipped with a camera and a display, and have the ability to acquire and display visual and audio information from the user.

[1243] This invention is a system that uses smart glasses to acquire user attribute information, desired skill information, and emotional information, and provides an optimal educational program. Specific embodiments of this system are described below.

[1244] System configuration

[1245] The system consists of smart glasses that acquire user input and emotional information, a server that processes the data, and a means of collecting information on human resource development programs provided by multiple educational institutions and companies. The smart glasses are equipped with a camera and microphone to recognize the user's face and perform voice analysis. The server processes the received data using a generative AI model and selects the optimal educational program using a clustering algorithm.

[1246] Specific usage

[1247] 1. Enter user information

[1248] The user puts on the smart glasses and accesses an input form, where they can enter their age, gender, desired job type, and desired skill information.

[1249] 2. Acquiring emotional information

[1250] When a user inputs information, the smart glasses' camera and microphone are used for facial recognition and voice analysis to obtain the user's emotional information, which is then analyzed using the emotion_recognition library.

[1251] 3. Collection of Program Information

[1252] The server collects information on human resource development programs provided by multiple educational institutions and companies via external APIs, including details of each program, the skills that can be acquired, and the training methods.

[1253] 4. Data Vectorization and Clustering

[1254] The server vectorizes the collected program information and user desired skill information using TF-IDF (Term Frequency-Inverse Document Frequency). It then applies the K-Means clustering algorithm to cluster the data. Based on the clustering results, the optimal program is identified.

[1255] 5. Program selection taking into account emotional information

[1256] The clustering results are combined with the acquired emotional information to select the program that best suits the user's emotional state. For example, a highly motivated user may be offered a more challenging program, while an anxious user may be offered a more relaxing program.

[1257] 6. Program Offering

[1258] The server structures the selected program information in JSON format and sends it to the smart glasses, which then analyzes the received information and visually displays it to the user.

[1259] Specific examples

[1260] Suppose a 25-year-old male user wants to become a data scientist and learn Python and machine learning. In this case, the smart glasses analyze the user's facial expressions and voice and determine that he is highly motivated.

[1261] Using a generative AI model, the server performs clustering and lists the most suitable programs from among the most difficult ones.

[1262] Based on the prompt, "A 25-year-old man who wants to become a data scientist wants to learn Python and machine learning. He seems highly motivated, so please recommend a high-level educational program," the server selects the most suitable program and provides it to the smart glasses.

[1263] Hardware and software used

[1264] Smart glasses: equipped with a camera and microphone to capture emotional information.

[1265] emotion_recognition library: Recognizes and analyzes emotions.

[1266] Server: Processes data, runs generative AI models, uses TF-IDF vectorization and K-Means clustering algorithms.

[1267] In this way, by taking emotional information into consideration and providing optimal educational programs through smart glasses, it is possible to provide users with an efficient and satisfying learning experience.

[1268] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1269] Step 1:

[1270] The user puts on the smart glasses and enters attribute information and desired skill information into an input form.

[1271] Input: User's age, gender, desired job type, desired skill information

[1272] How it works: The user enters this information in text format using the smart glasses interface.

[1273] Output: The user's attribute information and desired skill information are sent to the server as text data.

[1274] Step 2:

[1275] The camera and microphone of the smart glasses are used to acquire the user's emotional information.

[1276] Input: User's face image and voice data

[1277] How it works: The smart glasses use a camera to analyze the user's face and a microphone to collect audio. They then use the emotion_recognition library to analyze this data and identify the user's emotional information.

[1278] Output: The user's emotional information (e.g., motivation and stress level) is sent to the server as the analysis result.

[1279] Step 3:

[1280] The server collects human resource development program information through an external API.

[1281] Input: API for providing program information for educational institutions and companies

[1282] How it works: The server makes a request to an external API to retrieve relevant educational program information, including program details, skills learned, and teaching methods.

[1283] Output: Multiple educational program information is saved in JSON format on the server.

[1284] Step 4:

[1285] The program information and user desired skill information collected by the server are vectorized using TF-IDF, and clustering is performed using the K-Means clustering algorithm.

[1286] Input: User's desired skill information, educational program information

[1287] How it works: The server converts these text data into numerical vectors using TF-IDF vectorization techniques, then applies the K-Means clustering algorithm to classify the data into clusters.

[1288] Output: The clustering result is a set of programs suitable for multiple users.

[1289] Step 5:

[1290] The server considers emotional information and selects the most appropriate human resource development program.

[1291] Input: Clustering results, user emotion information

[1292] How it works: The server analyzes the clustering results and selects programs based on emotional information. For example, it selects a challenging program for a highly motivated user, and a relaxing program for a stressed user.

[1293] Output: One optimal educational program is selected.

[1294] Step 6:

[1295] The server transmits the optimal program information to the smart glasses and provides it to the user.

[1296] Input: Selected educational program information

[1297] How it works: The server structures program information in JSON format and sends it to the smart glasses. The smart glasses receive this information and display it in a user-friendly format.

[1298] Output: The optimal educational program is displayed on the user's smart glasses.

[1299] 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 control target 443 to output the result of the specific processing. The microphone 238 acquires voice indicating a user input regarding the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[1300] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[1301] 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 the present disclosure is not limited to this, and the specific processing may be performed by the robot 414.

[1302] The emotion identification model 59 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 an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[1303] FIG. 9 is a diagram illustrating an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. Emotions closer to the center of the concentric circles are more primitive. Emotions representing states and actions arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion includes both affect and mental states. Emotions generally generated from reactions occurring in the brain are arranged on the left side of the concentric circles. Emotions generally induced by situational judgment are arranged on the right side of the concentric circles. Emotions generally generated from reactions occurring in the brain and induced by situational judgment are arranged on the upper and lower sides of the concentric circles. Furthermore, the emotion of "pleasure" is arranged on the upper side of the concentric circles, and the emotion of "discomfort" is arranged on the lower side. In this way, in the emotion map 400, multiple emotions are mapped based on the structure by which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[1304] These emotions are distributed in the 3 o'clock direction on emotion map 400, and typically fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

[1305] The inside of emotion map 400 represents what is going on in the mind, and the outside of emotion map 400 represents behavior, so the further you go outside emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[1306] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is indicated, and when they approach the ideal, a state of pleasure is indicated. Emotions can also be created for robots, automobiles, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is indicated, and when they approach the ideal, a state of pleasure is indicated. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on Voice Emotion Recognition and Emotional Brain Physiological Signal Analysis Systems, Tokushima University, Doctoral Dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the "reaction" domain, where sensation is dominant. The right half of the emotion map lists emotions belonging to the "situation" domain, where situational awareness is dominant.

[1307] The emotion map defines two emotions that promote learning. One is a negative emotion on the situation side, around the middle of "repentance" or "reflection." In other words, this occurs 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 a positive emotion on the response side, around "desire." In other words, this occurs when the robot experiences positive feelings such as "I want more" or "I want to know more."

[1308] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple pieces of training data that are combinations of user input and emotion values ​​indicating each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions that are located close to each other have similar values, as in the emotion map 900 shown in FIG. 10. FIG. 10 shows an example in which multiple emotions, "relieved," "calm," and "reassuring," have similar emotion values.

[1309] The system according to the present disclosure has been described above mainly with respect to the functions of the data processing device 12, but the system according to the present disclosure is not necessarily implemented on a server. The system according to the present disclosure may be implemented as a general information processing system. The present disclosure may be implemented, for example, as a software program running on a personal computer or an application running on a smartphone, etc. The method according to the present disclosure may be provided to users in the form of SaaS (Software as a Service).

[1310] In the above embodiment, an example was given in which the specific processing is performed by one computer 22, but the technology of the present disclosure is not limited to this, and the specific processing may be distributed and performed by a plurality of computers including the computer 22. For example, the data generation model 58 may be provided in an external device of the data processing device 12, and data may be generated in the external device in accordance with input data.

[1311] In the above embodiment, an example in which the specific processing program 56 is stored in the storage 32 has been described, but the technology of the present disclosure is not limited to this. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-transitory storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-transitory storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes the specific processing in accordance with the specific processing program 56.

[1312] 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.

[1313] It is not necessary to store all 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 all of the specific processing program 56 in the storage 32; only a portion of the specific processing program 56 may be stored.

[1314] The hardware resource for executing a specific process can be any of the following processors: An example of a processor is a CPU, which is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. Another example of a processor is a dedicated electrical circuit, such as an FPGA (Field-Programmable Gate Array), a PLD (Programmable Logic Device), or an ASIC (Application Specific Integrated Circuit), which is a processor with a circuit configuration designed specifically for executing a specific process. Each processor has built-in or connected memory, and each processor uses the memory to execute the specific process.

[1315] The hardware resource that executes the specific processing may be configured with one of these various processors, or may be configured with 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). Also, the hardware resource that executes the specific processing may be a single processor.

[1316] As an example of a system configured with a single processor, first, one processor is configured by combining one or more CPUs and software, and this processor functions as a hardware resource that executes a specific process. Second, there is a system that uses a processor that realizes the functions of an entire system including multiple hardware resources that execute a specific process on a single IC chip, as typified by SoC (System-on-a-chip). In this way, a specific process is realized using one or more of the above-mentioned various processors as hardware resources.

[1317] Furthermore, the hardware structure of these various processors can be, more specifically, an electric circuit that combines circuit elements such as semiconductor devices. The specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps may be deleted, new steps may be added, or the processing order may be rearranged, without departing from the spirit of the invention.

[1318] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, to avoid confusion and facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[1319] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference.

[1320] The following is further disclosed regarding the above embodiment.

[1321] (Claim 1)

[1322] A means for inputting user attribute information and desired skill information;

[1323] A means of collecting information on human resource development programs offered by multiple educational institutions and companies, and

[1324] A means for vectorizing the collected human resource development program information, user attribute information, and desired skill information and performing clustering;

[1325] A means for identifying and providing the optimal human resource development program to the user based on the clustering results;

[1326] A system including:

[1327] (Claim 2)

[1328] The system according to claim 1, further comprising a means for selecting a plurality of optimal human resource development programs based on the clustering results, and randomly selecting and providing one of the programs.

[1329] (Claim 3)

[1330] 2. The system according to claim 1, further comprising means for returning rewards to the human resources department and individuals based on the number of times the user views the provided human resource development program.

[1331] "Example 1"

[1332] (Claim 1)

[1333] A means for inputting user attribute information and desired skill information;

[1334] A means of collecting information on human resource development programs offered by multiple educational institutions and companies, and

[1335] A means for vectorizing the collected human resource development program information, user attribute information, and desired skill information and performing clustering;

[1336] A means for identifying an optimal human resource development program for a user based on the clustering results and transmitting the identified program to a user terminal as structured data;

[1337] A means for displaying the received data on a screen of the user terminal;

[1338] means for generating advertising revenue or rewards based on user browsing behavior;

[1339] A system including:

[1340] (Claim 2)

[1341] The system according to claim 1, further comprising a means for selecting a plurality of optimal human resource development programs based on the clustering results, and randomly selecting and providing one of the programs.

[1342] (Claim 3)

[1343] 2. The system according to claim 1, further comprising means for returning rewards to the human resources department and individuals based on the number of times the user views the provided human resource development program.

[1344] "Application Example 1"

[1345] (Claim 1)

[1346] A means for inputting user attribute information and desired skill information;

[1347] A means of collecting information on human resource development programs offered by multiple educational institutions and companies, and

[1348] A means for vectorizing the collected human resource development program information, user attribute information, and desired skill information and performing clustering;

[1349] A means for identifying and providing the optimal human resource development program to the user based on the clustering results;

[1350] means for transmitting optimal program information in JSON format to a user terminal, and for the user terminal to receive and display the information;

[1351] A system including:

[1352] (Claim 2)

[1353] The system according to claim 1, further comprising a means for selecting a plurality of optimal human resource development programs based on the clustering results, and randomly selecting and providing one of the programs.

[1354] (Claim 3)

[1355] 2. The system according to claim 1, further comprising means for returning rewards to the human resources department and individuals based on the number of times the user views the provided human resource development program.

[1356] "Example 2: Combining Emotion Engines"

[1357] (Claim 1)

[1358] A means for inputting user attribute information and desired skill information;

[1359] A means of collecting information on human resource development programs offered by multiple educational institutions and companies, and

[1360] A means for collecting user emotion information;

[1361] A means for vectorizing the collected human resource development program information, user attribute information, and desired skill information and performing clustering;

[1362] A means for identifying and providing an optimal human resource development program for a user based on the clustering results and the user's emotional information;

[1363] A system including:

[1364] (Claim 2)

[1365] The system according to claim 1, further comprising means for selecting a plurality of optimal human resource development programs based on the clustering results and the user's emotional information, and randomly selecting and providing one of the programs.

[1366] (Claim 3)

[1367] 2. The system according to claim 1, further comprising means for returning rewards to business departments and individuals based on the number of times users view the provided human resource development program and sentiment information.

[1368] "Application example 2 when combining emotion engines"

[1369] (Claim 1)

[1370] A means for inputting user attribute information and desired skill information;

[1371] A means of collecting information on human resource development programs offered by multiple educational institutions and companies, and

[1372] A means for vectorizing the collected human resource development program information, user attribute information, and desired skill information and performing clustering;

[1373] A means for identifying and providing the optimal human resource development program to the user based on the clustering results;

[1374] A means for acquiring user emotion information;

[1375] A means for selecting an optimal human resource development program taking into consideration user emotional information;

[1376] A means for implementing the above means using smart glasses;

[1377] A system including:

[1378] (Claim 2)

[1379] The system according to claim 1, further comprising a means for selecting a plurality of optimal human resource development programs based on the clustering results, and randomly selecting and providing one of the programs.

[1380] (Claim 3)

[1381] 2. The system according to claim 1, further comprising means for returning rewards to the human resources department and individuals based on the number of times the user views the provided human resource development program. [Explanation of symbols]

[1382] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot< / url:> < / url:> < / url:> < / url:>

Claims

1. A means for inputting user attribute information and desired skill information; A means of collecting information on human resource development programs offered by multiple educational institutions and companies, and A means for vectorizing the collected human resource development program information, user attribute information, and desired skill information and performing clustering; A means for identifying and providing the optimal human resource development program to the user based on the clustering results; A system including:

2. The system according to claim 1, further comprising means for selecting a plurality of optimal human resource development programs based on the clustering results, and randomly selecting and providing one of the programs.

3. The system according to claim 1, further comprising means for returning rewards to the human resources department and individuals based on the number of times the user views the provided human resource development program.

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