System

Abstract

To provide a system.SOLUTION: A system comprises: means for a user to input job type information; means for comparing the user's skill information with the input job type information, and using generative AI to suggest skills that the user lacks; means for using the generative AI to generate a study plan for the user based on the lacking skills; means for receiving progress information on the study plan for the user; and means for using the generative AI to update the study plan based on the progress information.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] Current vocational training and skill-upgrading education programs generally have generic content, making it difficult to address the specific needs of individual employees or the specific skills and qualifications required for the job they are aiming for. In addition, managing the progress of self-study and updating learning plans is left up to each employee, making it difficult to efficiently improve skills. [Means for solving the problem]

[0005] By utilizing generative AI, employees can input information about other jobs they aspire to, and the AI ​​will check the employee information, identify differences between their own and the job, and suggest necessary skills and qualifications. Furthermore, by providing study plans and internal company information and managing progress, it becomes possible for each employee to efficiently improve their skills. [Brief explanation of the drawings]

[0006] [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. 2 is a sequence diagram showing a flow of processing in the data processing system according to the first embodiment of the first form example. [Figure 12] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system in Application Example 1 of Embodiment 1. [Figure 13] FIG. 10 is a sequence diagram showing a processing flow of a data processing system in a second embodiment of the second form example. [Figure 14] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system in Application Example 2 of Embodiment Example 2. [Figure 15] FIG. 10 is a sequence diagram showing the flow of processing in a data processing system according to a third embodiment of the third embodiment. [Figure 16] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system in Application Example 3 of Embodiment 3. [Figure 17] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system in the first embodiment of the first form example when an emotion engine is combined. [Figure 18] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system in Application Example 1 of Form Example 1 when an emotion engine is combined. [Figure 19] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system in the second embodiment of the second form example when an emotion engine is combined. [Figure 20] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system in Application Example 2 of Form Example 2 when an emotion engine is combined. [Figure 21] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system in the third embodiment of the third form example when an emotion engine is combined. [Figure 22] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system in Application Example 3 of Form Example 3 when an emotion engine is combined. DETAILED DESCRIPTION OF THE INVENTION

[0007] 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.

[0008] First, the terms used in the following description will be explained.

[0009] 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, the 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), an APU (Accelerated Processing Unit), or a TPU (TENSOR PROCESSING UNIT (registered trademark)).

[0010] 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.

[0011] 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.

[0012] 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.

[0013] 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."

[0014] [First embodiment]

[0015] FIG. 1 shows an example of the configuration of a data processing system 10 according to the first embodiment.

[0016] 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.

[0017] 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).

[0018] 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.

[0019] 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.

[0020] 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.

[0021] 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.

[0022] FIG. 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0023] 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.

[0024] 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.

[0025] 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.

[0026] Next, the specific processing by the specific processing unit 290 of the data processing device 12 will be described.

[0027] "Example 1"

[0028] One embodiment of the present invention is an in-house reskilling promotion system that utilizes generative AI. This system provides an interface where employees can input information about the job they are aiming for. Based on this input information and the employee's current skills and qualifications, the generative AI calculates the difference in the required skills and qualifications. Based on this difference, the system then generates an individual study plan and provides it to the employee. The system also periodically checks the employee's learning progress and updates the study plan as necessary.

[0029] "Example 2"

[0030] As a specific example, if an employee wants to become a project manager, they enter "project manager" as the desired job title into the system. The generative AI calculates the difference between the employee's current skills (e.g., programming skills and communication skills) and the skills required for a project manager (e.g., project management skills and leadership skills). Based on this difference, the system generates a study plan (e.g., taking an online course on project management, reading a book on leadership, etc.) and provides it to the employee.

[0031] "Example 3"

[0032] Furthermore, the generative AI periodically monitors employees' learning progress. For example, if an employee completes an online project management course, the generative AI receives that information and updates their study plan. In this way, the system helps employees improve their skills efficiently.

[0033] The processing flow of each embodiment will be described below.

[0034] "Example 1"

[0035] Step 1: Employees enter information about the job they are aiming for through the system interface.

[0036] Step 2: The generative AI calculates the difference in required skills and qualifications based on the employee's current skills and qualifications and the input job information.

[0037] Step 3: The generative AI generates an individual study plan based on the calculated differences and provides it to the employee.

[0038] Step 4: Generative AI periodically checks employees' learning progress and updates their study plans as needed.

[0039] "Example 2"

[0040] Step 1: An employee enters "Project Manager" as the job title they are aiming for in the system.

[0041] Step 2: The generative AI calculates the difference between an employee's current skills (e.g., programming skills and communication skills) and the skills required for a project manager (e.g., project management skills and leadership skills).

[0042] Step 3: Generative AI generates a study plan based on these differences (e.g., taking an online course in project management, reading a book on leadership, etc.) and provides it to the employee.

[0043] Step 4: The generative AI periodically checks the employee's learning progress. For example, if an employee completes an online course on project management, it receives that information and updates the employee's study plan.

[0044] Step 1: Generative AI periodically checks employees' learning progress.

[0045] Step 2: The generative AI receives the employee's learning progress information and updates the study plan as needed.

[0046] Step 3: In this way, the system helps employees improve their skills efficiently.

[0047] Example 1

[0048] Next, a description will be given of Example 1 of Form 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."

[0049] With conventional reskilling systems, it was difficult to accurately grasp the gap in skills and qualifications required for the job an employee was aiming for and to create an individual study plan. In addition, efficient reskilling could not be achieved because learning progress management and study plan updates were done manually. This led to problems such as delays in employee skill development and a decline in the company's overall competitiveness.

[0050] 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.

[0051] In this invention, the server includes a means for inputting information about the job type the employee is aiming for, a means for the generative AI model to calculate the gap in required skills and qualifications based on the employee's current skills and qualification information, a means for generating an individual study plan based on the calculated gap information, a means for providing the generated study plan to the employee, and a means for periodically checking the employee's learning progress and updating the study plan as necessary. This allows the employee to accurately understand the gap in skills and qualifications required for the job they are aiming for, enabling efficient reskilling.

[0052] The "means for inputting information about the job type that an employee aims for" is a function that provides an interface for inputting information about the job type that an employee has set as his or her career goal.

[0053] A "generative AI model" is an artificial intelligence model that calculates the difference in skills and qualifications required for a desired job based on an employee's current skills and qualifications.

[0054] "Means for calculating the difference in required skills and qualifications" refers to a function that uses a generative AI model to calculate the difference between an employee's current skills and qualifications and the skills and qualifications required for the desired job.

[0055] The "means for generating individual study plans" is a function that automatically creates a customized study plan for each employee based on the calculated difference information.

[0056] The "means of providing study plans to employees" is a function that presents the generated study plans to employees and provides guidelines for advancing their studies.

[0057] "Means for regularly checking learning progress" refers to a function that periodically checks the progress of employees as they proceed with their studies according to their study plan.

[0058] "Means for updating study plans" refers to the function of amending and updating existing study plans as needed according to the employee's learning progress.

[0059] This invention relates to an in-house reskilling promotion system that utilizes a generative AI model. This system involves inputting information about the job type an employee is aiming for, and the generative AI model calculates the gap in skills and qualifications required based on the employee's current skills and qualifications, and then generates and provides an individual study plan. It also includes a function to regularly check the employee's learning progress and update the study plan as necessary.

[0060] Hardware and software used

[0061] The system uses the following hardware and software:

[0062] Server: Built using the Python (registered trademark) Flask framework. The database uses MySQL (registered trademark).

[0063] Generative AI model: For example, use GPT-4 (registered trademark) from OpenAI (registered trademark).

[0064] Terminal: Web browser used by employees. The interface is built using HTML, CSS, and JavaScript (registered trademark).

[0065] Explanation of program processing

[0066] Server Processing

[0067] The server provides an interface for employees to input their desired job information. After employees input their job information, the server receives the information and stores it in a database. The server then retrieves the employee's current skills and qualifications from the database and sends prompts to the generative AI model.

[0068] Processing generative AI models

[0069] Based on the prompt received, the generative AI model calculates the difference between the employee's current skills and the skills required for the desired job. This difference information is returned to the server in JSON format.

[0070] Server processing (study plan generation)

[0071] The server generates an individual study plan based on the differential information returned by the generative AI model. This study plan is automatically generated using a Python script and provided to the employee.

[0072] Terminal handling

[0073] The employee's device displays the study plan provided by the server, and the employee proceeds with their studies according to this plan.

[0074] Server processing (checking learning progress)

[0075] The server provides an interface for employees to periodically check their learning progress. When employees enter their progress information, the server stores it in a database and updates their study plans as needed.

[0076] Specific examples

[0077] For example, suppose Employee A wants to become a "data scientist." Employee A enters the job title "data scientist" through the interface. The server retrieves Employee A's current skill information (e.g., basic knowledge of Python, basic statistics, etc.) from the database and sends the following prompt to the generative AI model:

[0078] Current employee skills: Basic knowledge of Python, basic statistics

[0079] Job title: Data scientist

[0080] Calculate the skill gap you need and generate a study plan.

[0081] The generative AI model calculates the difference in required skills and returns it to the server. Based on this difference information, the server generates a study plan for employee A, such as "Take an online course to learn the basics of machine learning," and provides it to employee A. Employee A studies according to this study plan and enters their progress. The server checks employee A's progress and updates the study plan as necessary.

[0082] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0083] Step 1:

[0084] The user enters job information

[0085] Users access the interface provided through a web browser. This interface is built using HTML, CSS, and JavaScript. Users enter information about the job they are aiming for into the input form and click the "Submit" button. The input data is the job information they are aiming for.

[0086] Step 2:

[0087] The server receives the input information and stores it in a database

[0088] The server receives the job type information submitted by the user. The received information is processed using the Python Flask framework. The server saves this information in a MySQL database. The input data is the job type information submitted by the user, and the output data is the job type information saved in the database.

[0089] Step 3:

[0090] The server retrieves the user's current skills and credentials from a database

[0091] The server retrieves the user's current skills and credentials from the database using a query, which is executed using a SQL statement, where the input data is the user's ID and the output data is the user's current skills and credentials.

[0092] Step 4:

[0093] The server sends a prompt to the generative AI model

[0094] The server generates a prompt based on the user's current skill information and desired job type. The generated prompt is sent to a generative AI model (e.g., OpenAI's GPT-4) using an HTTP request. The input data is the user's current skill information and desired job type, and the output data is the generated prompt.

[0095] Step 5:

[0096] Generative AI model calculates gaps in required skills and qualifications

[0097] Based on the received prompt, the generative AI model calculates the difference between the user's current skills and the skills required for the desired job. This difference information is returned to the server in JSON format. The input data is the prompt, and the output data is the difference information between skills and qualifications.

[0098] Step 6:

[0099] The server generates a study plan based on the difference information

[0100] The server generates an individualized study plan based on the difference information returned from the generative AI model. This study plan is automatically generated using a Python script. The input data is the difference information for skills and qualifications, and the output data is the generated study plan.

[0101] Step 7:

[0102] The server provides the user with a study plan

[0103] The server provides the generated study plan to the user, who can then view it through a web browser. The input data is the generated study plan, and the output data is the study plan provided to the user.

[0104] Step 8:

[0105] User enters learning progress

[0106] The user accesses an interface for inputting learning progress. The user enters progress information into the input form and clicks the "Submit" button. The input data is the learning progress information, and the output data is the progress information sent to the server.

[0107] Step 9:

[0108] The server checks your learning progress and updates your study plan as needed

[0109] The server receives the learning progress information sent by the user and stores it in a database. The server periodically checks this progress information and updates the study plan as necessary. For example, if a user completes "Fundamentals of Machine Learning," it adds "Applications of Machine Learning" to the study plan as the next step. The input data is the learning progress information, and the output data is the updated study plan.

[0110] (Application example 1)

[0111] Next, a description will be given of Application Example 1 of Embodiment 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."

[0112] With conventional reskilling systems, it was difficult to accurately grasp the gap in skills and qualifications required for the job an employee was aiming for and provide an individual study plan. Furthermore, the system lacked the functionality to regularly check learning progress and update study plans as needed, making effective reskilling impossible. In particular, factory robot operators require specialized skills and qualifications, making it difficult for conventional systems to handle this.

[0113] 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.

[0114] In this invention, the server includes: a means for inputting information about other jobs that employees aspire to using generative AI; a means for the generative AI to check employee information, identify differences between the employee's current skills and qualifications, and suggest necessary skills and qualifications; a means for providing study plans and internal company information and managing progress; a means for inputting information about the job that a factory robot operator aspires to; a means for the generative AI to calculate the difference in required skills and qualifications based on the operator's current skills and qualifications; a means for generating an individual study plan based on the difference and providing it to the operator; and a means for periodically checking learning progress and updating the study plan as needed. This makes it possible to accurately grasp the skills and qualifications required for the jobs that employees and factory robot operators aspire to and provide individual study plans. Furthermore, by periodically checking learning progress and updating the study plan as needed, effective reskilling can be achieved.

[0115] "Generative AI" is an artificial intelligence technology that generates new information and content based on input data.

[0116] An "employee" is an individual employed by a business or organization.

[0117] "Occupation information" is detailed information about a specific occupation or position.

[0118] "Skills" are the abilities and techniques required to carry out specific tasks or work.

[0119] A "qualification" is a certification or certification required to perform a particular occupation or job.

[0120] A "difference" is a difference or deficiency that exists between the current state and the desired state.

[0121] A "study plan" is a plan that outlines the study content and schedule necessary to achieve a specific goal.

[0122] "Progress management" is the process of regularly checking the progress of a plan and making adjustments as necessary.

[0123] A "factory robot" is a mechanical device used to perform automated tasks in a factory.

[0124] An "operator" is an individual whose role is to operate and manage a machine or system.

[0125] The system for implementing this invention utilizes generative AI to input information about the job type that an employee or factory robot operator is aiming for, calculates the gap in required skills and qualifications, and provides an individual study plan. A specific embodiment of this system is described below.

[0126] 1. System Program

[0127] The server uses generative AI to provide a means for employees and operators to input information about the job they are aiming for. When a user inputs the job information using a smartphone or computer, the server receives the information and sends it to the generative AI.

[0128] The generative AI calculates the difference in skills and qualifications required for the desired job based on the user's current skills and qualifications. This difference information is sent to the server and displayed to the user.

[0129] The server then uses generative AI to generate a personalized study plan based on the differences, which is then displayed on the user's smartphone or computer.

[0130] Furthermore, the server periodically checks the user's learning progress and updates the study plan as necessary, allowing the user to always proceed with their studies based on the most up-to-date study plan.

[0131] 2. Hardware and Software Used

[0132] Hardware: smartphones, PCs, servers

[0133] Software: Python, OpenAI API

[0134] 3. Data processing and calculation

[0135] The server receives the job information and current skills and qualifications entered by the user and sends them to the generative AI. The generative AI calculates the difference in skills and qualifications based on this information and generates an individual study plan. The generated study plan is provided to the user via the server.

[0136] 4. Specific Examples

[0137] For example, if a factory robot operator aims to become a "robot control engineer," they would input "basic programming" and "machine operation" as their current skills. The generative AI would then calculate "advanced programming," "robot control," and "data analysis" as the skills required for the desired job, and calculate the difference.

[0138] Prompt Sentence Examples

[0139] Prompts to create a study plan to close skills gaps:

[0140] Create a study plan to fill the following skill gaps: Advanced Programming, Robotics, Data Analysis

[0141] Prompts to update your study plan based on your progress:

[0142] Your current learning progress is as follows: Completed basic programming. Please update your study plan accordingly.

[0143] In this way, users can achieve effective reskilling through systems that utilize generative AI.

[0144] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0145] Step 1:

[0146] The user inputs the desired job information using a smartphone or computer. The input job information is sent from the device to the server. The server receives this information and sends it to the generative AI.

[0147] Step 2:

[0148] The server provides an interface for users to input their current skills and credentials. Users input their skills and credentials and send them to the server from their device. The server receives this information and sends it to the generative AI.

[0149] Step 3:

[0150] The generative AI calculates the difference in the required skills and qualifications based on the desired job information received from the server and the current skill and qualification information. Specifically, it compares the list of skills and qualifications required for the desired job with the user's current list of skills and qualifications and calculates the difference. This difference information is sent from the generative AI to the server.

[0151] Step 4:

[0152] The server generates an individual study plan based on the difference information received from the generative AI. It sends a prompt to the generative AI to create a study plan to fill in the gaps, and receives the generated study plan. The server then sends this study plan to the user's device.

[0153] Step 5:

[0154] The user studies based on the study plan received from the server. The user periodically reports their learning progress from their device to the server. The server receives this progress information and sends it to the generative AI.

[0155] Step 6:

[0156] The generative AI updates the study plan as needed based on the learning progress information received from the server. Specifically, it adjusts the content and schedule of the study plan taking into account the current progress. The updated study plan is then sent from the generative AI to the server.

[0157] Step 7:

[0158] The server sends the updated study plan received from the generative AI to the user's device, and the user continues studying based on the updated study plan.

[0159] In this way, users can achieve effective reskilling through systems that utilize generative AI.

[0160] Example 2

[0161] Next, a description will be given of Example 2 of Form 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."

[0162] With conventional skill improvement systems, it was difficult to efficiently identify the skills and qualifications required for the job an employee was aiming for and to create a specific study plan based on that. Progress management and updating of study plans also had to be done manually, which was time-consuming and labor-intensive. This could delay employee skill development and affect productivity across the company.

[0163] 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.

[0164] In this invention, the server includes a means for inputting information about the job type the employee is aiming for, a means for the generative AI model to acquire the employee's current skill data, a means for the generative AI model to acquire the skill set required for the desired job, a means for the generative AI model to calculate the difference between the employee's current skills and the required skills, a means for the generative AI model to generate a study plan based on the difference, and a means for the generative AI model to provide the generated study plan to the employee. This allows employees to efficiently grasp the skills and qualifications required for the desired job and automatically create a specific study plan based on that. Progress management and study plan updates can also be performed automatically, significantly reducing time and effort.

[0165] An "employee" is an individual who belongs to a company or organization and performs a specific job or role.

[0166] "Targeted occupation information" is information about the occupation that the employee wants to have in the future.

[0167] A "generative AI model" is a model that uses artificial intelligence techniques to analyze data and automatically perform specific tasks.

[0168] "Current skills data" refers to information about the skills and qualifications that employees currently possess.

[0169] A "required skill set" is the set of skills and qualifications required to work in a particular occupation.

[0170] A "difference" is a gap or shortfall between current skill data and the required skill set.

[0171] A "study plan" is a specific learning plan for an employee to acquire the skills and qualifications necessary for the job they are aiming for.

[0172] Progress management is the process of monitoring and, if necessary, adjusting the progress of employees as they acquire skills and qualifications based on their study plans.

[0173] This invention is a system that efficiently identifies the skills and qualifications required for the job an employee is aiming for and automatically creates a specific study plan based on that. A specific embodiment of this system will be described below.

[0174] Hardware and software used

[0175] This system uses the following hardware and software:

[0176] Server: Provides the database management system (DBMS) and computational resources to run generative AI models.

[0177] Terminal: The device (PC, smartphone, tablet, etc.) through which a user accesses the system.

[0178] Generative AI model: An artificial intelligence model that analyzes employee skill data and generates study plans.

[0179] System processing flow

[0180] 1. Enter the job information the user is aiming for

[0181] A user accesses the system interface using a terminal and inputs the job type information they are aiming for, for example, "project manager."

[0182] 2. The server retrieves the employee's current skill data.

[0183] The server retrieves current skill data from the user's profile information. This data includes programming skills, communication skills, etc. For example, the server retrieves information such as "Programming skills: High" and "Communication skills: Medium" from the database.

[0184] 3. Acquire the skill set necessary for the job the server is aiming for

[0185] The server retrieves the skill set required for the desired job (project manager) from the database. For example, it retrieves information such as "Project management skills: High" and "Leadership skills: High."

[0186] 4. The server calculates the difference between the current skill and the required skill.

[0187] The server compares the current skills with the required skills and calculates the difference. For example, "Project management skills: Low" and "Leadership skills: Low" are calculated as the difference.

[0188] 5. The generative AI model generates a study plan based on the differences

[0189] The generative AI model generates a study plan based on the calculated differences, suggesting specific actions such as "take an online course in project management" or "read a book on leadership."

[0190] 6. The server provides the generated study plan to the user.

[0191] The server displays the generated study plan on the user's interface, allowing the user to improve their skills according to the plan provided.

[0192] Examples and prompts

[0193] Examples:

[0194] If an employee wants to become a project manager, they can input the following prompt into the generative AI model:

[0195] Example prompt sentence:

[0196] An employee is aiming to become a project manager. Their current skill set is as follows:

[0197] Programming skills: High

[0198] Communication Skills: Medium

[0199] Project Management Skills: Low

[0200] Leadership Skills: Low

[0201] The skill set required for a project manager is:

[0202] Programming skills: Medium

[0203] Communication Skills: High

[0204] Project Management Skills: High

[0205] Leadership Skills: High

[0206] Generate a study plan based on the difference between your current skills and the skills you need.

[0207] By inputting this prompt into a generative AI model, a specific study plan is generated, suggesting actions such as "take an online course in project management" or "read a book on leadership."

[0208] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0209] Step 1:

[0210] Enter the job information the user is aiming for

[0211] A user uses a terminal to access the system interface and inputs information about the job type they are aiming for. For example, they might input "project manager." This input data is sent to the server. The input data is the "job type information" and the output is the job type information sent to the server.

[0212] Step 2:

[0213] The server retrieves the employee's current skill data.

[0214] The server obtains current skill data from the user's profile information. This data includes programming skills, communication skills, etc. For example, it obtains information such as "Programming skills: High" and "Communication skills: Medium" from the database. The input data is the user's ID, and the output is the current skill data.

[0215] Step 3:

[0216] Acquire the skill set required for the job the server is aiming for

[0217] The server retrieves the skill set required for the desired job (project manager) from the database. For example, it retrieves information such as "Project management skills: high" and "Leadership skills: high." The input data is the desired job information, and the output is the required skill set.

[0218] Step 4:

[0219] The server calculates the difference between the current skill and the required skill.

[0220] The server compares the current skills with the required skills and calculates the difference. For example, "Project management skills: Low" and "Leadership skills: Low" are calculated as the difference. The input data is the current skill data and the required skill set, and the output is the skill difference.

[0221] Step 5:

[0222] A generative AI model generates a study plan based on the differences

[0223] The generative AI model generates a study plan based on the calculated differences. For example, it suggests specific actions such as "take an online course in project management" or "read a book on leadership." The input data is the skill differences, and the output is a study plan.

[0224] Step 6:

[0225] The server provides the generated study plan to the user.

[0226] The server displays the generated study plan on the user's interface, and the user can improve their skills according to the provided plan. The input data is the study plan, and the output is the study plan provided to the user.

[0227] (Application example 2)

[0228] Next, a description will be given of Application Example 2 of Form 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."

[0229] With conventional employee training systems, it was difficult to efficiently calculate the gap between the skills required for an employee's desired job and their current skills, and to provide an appropriate study plan. Furthermore, there was a lack of systems specifically designed to improve the skills of robot operators, making it difficult to provide specific study plans for operators to reach their desired skill set. This delayed the improvement of the skills of employees and robot operators, hindering productivity improvements.

[0230] 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.

[0231] In this invention, the server includes: a means for utilizing generative AI to input information about other jobs that employees aspire to; a means for the generative AI to check employee information, identify differences between the current skill set and the employee's own, and suggest necessary skills and qualifications; a means for providing study plans and internal company information and managing progress; a means for inputting the skill set that the robot operator is aiming for; a means for inputting the current skill set or conducting a skill test; a means for the generative AI to calculate differences between the current skill set and the required skills and generate a study plan; and a means for providing the generated study plan to the operator. This makes it possible to efficiently provide specific study plans for employees and robot operators to achieve their desired skill sets.

[0232] "Generative AI" is an artificial intelligence technology that generates new information and content based on input data.

[0233] An "employee" is an individual who belongs to a company or organization and performs work.

[0234] "Occupation information" is detailed information about a specific occupation or position.

[0235] "Skills" are the knowledge and abilities required to carry out a specific job or task.

[0236] A "qualification" is a certification or certificate required to perform a particular occupation or job.

[0237] A "study plan" is a specific study schedule and method for acquiring the desired skills or qualifications.

[0238] "Progress management" is the process of monitoring progress against a plan and making adjustments as necessary.

[0239] A "robot operator" is an engineer who operates and manages robots in factories and on production lines.

[0240] A "skill set" is a set of skills and abilities required for a particular occupation or position.

[0241] A "skills test" is an exam or test used to assess an individual's skills or abilities.

[0242] A "difference" is the difference or gap between the current state and the target state.

[0243] A "means for generating a study plan" is a method or system for creating a specific study plan for acquiring the required skills or qualifications.

[0244] "Means for providing to operator" refers to a method or system for transmitting the generated study plan to the robot operator.

[0245] The system for implementing this invention utilizes generative AI to help employees and robot operators improve their skills. Specific embodiments are described below.

[0246] System Configuration

[0247] The system consists of the following main components:

[0248] 1. Server: Executes generative AI models and processes data.

[0249] 2. Terminals: Devices used by employees and robot operators (PCs, smartphones, tablets, etc.).

[0250] 3. Database: Stores employee and robot operator skill information, target skill sets, study plans, etc.

[0251] Hardware / Software used

[0252] Hardware: PCs, servers, smartphones, tablets

[0253] Software: Python, OpenAI API, database management system (e.g., MySQL, PostgreSQL)

[0254] Data processing / data calculation

[0255] 1. Data entry: Employees and robot operators enter their current and desired skill sets via a terminal.

[0256] 2. Prompt generation: The server sends a prompt to the generative AI model (OpenAI's GPT-3 (registered trademark)) based on the input skill information.

[0257] 3. Calculating the difference and generating a study plan: The generative AI model calculates the skill difference and generates a study plan.

[0258] 4. Result output: The generated study plan is displayed on the terminal and provided to employees or robot operators.

[0259] Specific examples

[0260] For example, a robot operator inputs "basic programming skills, simple machine maintenance" as his current skills and "advanced programming skills, complex machine maintenance, troubleshooting" as his desired skills. Based on this information, the server sends the following prompt to the generative AI model:

[0261] Prompt Sentence Examples

[0262] Current skills: Basic programming skills, simple machine maintenance

[0263] Skills to aim for: Advanced programming skills, complex machine maintenance, troubleshooting

[0264] Calculate the skill gap you need and generate a study plan.

[0265] The generative AI model calculates skill gaps based on this prompt and generates a specific study plan, such as "take an online course to acquire advanced programming skills," "receive hands-on training in complex machine maintenance," or "read reference books on troubleshooting."

[0266] In this way, it is possible to efficiently provide employees and robot operators with specific study plans to reach their desired skill sets.

[0267] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0268] Step 1:

[0269] The user uses a terminal to input the current skill set and the desired skill set.

[0270] Input: Current skill set, desired skill set

[0271] Output: Input skill information

[0272] Specific operation: The user enters their current skill set, such as "basic programming skills, simple machine maintenance," and their desired skill set, such as "advanced programming skills, complex machine maintenance, troubleshooting," into an input form on the terminal.

[0273] Step 2:

[0274] The terminal transmits the input skill information to the server.

[0275] Input: Skill information entered by the user

[0276] Output: Skill information sent to the server

[0277] Specific operation: The terminal transmits the skill information input by the user to the server as a data packet.

[0278] Step 3:

[0279] The server generates a prompt sentence based on the received skill information.

[0280] Input: Received skill information

[0281] Output: Generated prompt statement

[0282] Specific operation: The server generates a prompt in the form of "Current skills: Basic programming skills, simple machine maintenance. Target skills: Advanced programming skills, complex machine maintenance, troubleshooting. Calculate the required skill difference and generate a study plan."

[0283] Step 4:

[0284] The server sends the generated prompt to the generative AI model.

[0285] Input: Generated prompt text

[0286] Output: The prompt sent to the generative AI model

[0287] Specific operation: The server sends the generated prompt text to the generative AI model via the OpenAI API.

[0288] Step 5:

[0289] The generative AI model calculates skill differences based on prompt statements and generates a study plan.

[0290] Input: prompt statement

[0291] Output: Skill Diff and Study Plan

[0292] Specific operation: The generative AI model analyzes the prompt, calculates the difference between the current skill and the desired skill, and generates a specific study plan to fill that gap.

[0293] Step 6:

[0294] The server receives the generated study plan and transmits it to the terminal.

[0295] Input: Generated study plan

[0296] Output: Study plan sent to device

[0297] Specific operation: The server sends the study plan received from the generative AI model to the terminal as a data packet.

[0298] Step 7:

[0299] The terminal displays the received study plan to the user.

[0300] Input: Received study plan

[0301] Output: The study plan displayed to the user

[0302] Specific operation: The device displays the received study plan on the screen so that the user can check it.

[0303] Example 3

[0304] Next, a third embodiment of the third embodiment will be described. 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."

[0305] Previous employee skill improvement support systems made it difficult to efficiently manage employees' learning progress and provide appropriate feedback. Furthermore, they lacked the functionality to automatically calculate the gap in skills and qualifications required for the job an employee was aiming for and update their study plan, which could delay employee skill improvement. This led to problems such as a decline in productivity and competitiveness across the company.

[0306] The specific processing by the specific processing unit 290 of the data processing device 12 in the third embodiment is realized by the following means.

[0307] In this invention, the server includes a means for collecting employee learning progress data, a means for inputting the collected data into a generative AI model, a means for the generative AI model to analyze the learning progress data and generate feedback, a means for updating a study plan based on the generated feedback, and a means for displaying the updated study plan to the employee. This makes it possible to efficiently manage an employee's learning progress and provide appropriate feedback. Furthermore, by automatically calculating the gap in skills and qualifications required for the employee's desired job and updating the study plan, it is possible to quickly support the employee's skill development.

[0308] "Generative AI" is an artificial intelligence technology that generates new information and suggestions based on data.

[0309] "Employee information" refers to data about individuals, such as an employee's skills, qualifications, and learning history.

[0310] The "difference" is the gap between an employee's current skills and qualifications and those required for the job they are aiming for.

[0311] A "study plan" is a plan that outlines the content and schedule for employees to study in order to improve their skills.

[0312] "Internal information" refers to information and resources shared within a company, such as training programs and educational materials.

[0313] Progress management is the process of tracking employees' progress in learning and skill development and making adjustments and providing support as needed.

[0314] "Learning progress data" is data that indicates how far an employee has progressed in their learning.

[0315] A "generative AI model" is an artificial intelligence model that analyzes data and generates new information and suggestions.

[0316] "Feedback" refers to advice or suggestions provided by a generative AI model based on its analysis results.

[0317] An "updated study plan" is a study plan that has been updated to its latest state based on feedback from the generative AI model.

[0318] A "display means" is a method or device for visually presenting the updated study plan and feedback to the employee.

[0319] This invention is a system for efficiently supporting employee skill development. This system utilizes a generative AI model to manage employees' learning progress and provide appropriate feedback to promote employee skill development.

[0320] Hardware and software used

[0321] This system uses the following hardware and software:

[0322] Server: Collects learning progress data, inputs data into the generative AI model, generates feedback, and updates study plans.

[0323] Device: Display the updated study plan to the employee.

[0324] Generative AI models: For example, use generative AI models such as OpenAI's GPT-4.

[0325] Data processing and calculation

[0326] 1. The server collects employee learning progress data. For example, if an employee completes an online course on project management, the completion data is sent to the server.

[0327] 2. The server inputs the collected data into the generative AI model. The data is converted into an appropriate format and sent to the generative AI model.

[0328] 3. The generative AI model analyzes the input learning progress data and determines the employee's current skill level and what they should learn next.

[0329] 4. The generative AI model generates feedback based on the analysis results, providing specific advice such as, "The next thing you should learn is risk management and team leadership."

[0330] 5. The server receives feedback from the generative AI model and updates the employee's study plan, for example adding "Risk Management 101" and "Team Leadership 101" to the employee's study schedule.

[0331] 6. The device displays the updated study plan to the employee, who can then use it to take the next step in their studies.

[0332] Specific examples

[0333] For example, if employee A completes an online course on project management, the following prompt sentences can be input into the generative AI model:

[0334] Example prompt sentence:

[0335] Employee A has completed an online course on project management. What would you suggest they learn next?

[0336] The specific operation flow:

[0337] 1. The server receives data that Employee A has completed an online course on project management.

[0338] 2. The server inputs this data into a generative AI model (e.g., GPT-4).

[0339] 3. The generative AI model analyzes Employee A's learning progress and generates feedback such as, "The next things you should learn are risk management and team leadership."

[0340] 4. The server receives this feedback and updates Employee A's study plan by adding "Risk Management 101" and "Team Leadership 101."

[0341] 5. The device displays the updated study plan to Employee A, who can then proceed to the next step in his / her studies.

[0342] In this way, the system helps employees improve their skills efficiently. The flow of the specific process in the third embodiment will be described with reference to FIG.

[0343] Step 1:

[0344] The server collects employee learning progress data. For example, when an employee completes an online project management course, the completion data is sent to the server. The input is learning progress data from the LMS (Learning Management System), and the output is the collected learning progress data. Specifically, the server obtains data from the LMS through an API.

[0345] Step 2:

[0346] The server inputs the collected data into the generative AI model. The input is the collected learning progress data, and the output is the data sent to the generative AI model. Specifically, the server converts the data into JSON format and sends it to the generative AI model. For example, it generates JSON data like the one below.

[0347] json

[0348] {

[0349] "employee_id": "12345",

[0350] "course_completed": "Project Management 101",

[0351] "completion_date": "2023-10-01"

[0352] }

[0353] Step 3:

[0354] The generative AI model analyzes the input learning progress data. The input is JSON data sent from the server, and the output is the analysis results. Specifically, the generative AI model evaluates the employee's learning history and skill level, and generates feedback such as, "The next things you should learn are risk management and team leadership."

[0355] Step 4:

[0356] The generative AI model generates feedback based on the analysis results. The input is the analysis results, and the output is the feedback. Specifically, the generative AI model uses natural language generation technology to provide specific advice. For example, it generates feedback such as, "The next thing you should learn is risk management and team leadership."

[0357] Step 5:

[0358] The server receives feedback from the generative AI model and updates the employee's study plan. The input is the feedback from the generative AI model, and the output is the updated study plan. Specifically, the server adds "Risk Management 101" and "Team Leadership 101" to the employee's study schedule.

[0359] Step 6:

[0360] The terminal displays the updated study plan to the employee. The input is the updated study plan sent from the server, and the output is the study plan displayed to the employee. Specifically, the terminal sends a push notification to the employee's smartphone, displaying the message "The next course to study has been updated."

[0361] In this way, the system helps employees improve their skills efficiently.

[0362] (Application example 3)

[0363] Next, a description will be given of Application Example 3 of Form Example 3. 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."

[0364] With conventional employee skill improvement support systems, it was difficult to efficiently manage employee learning progress and provide appropriate study plans. Furthermore, there was a lack of means to manage the learning progress of factory robots and update study plans as needed, making it difficult to efficiently improve the robots' skills. This resulted in delays in improving the skills of employees and robots, leading to problems such as reduced productivity.

[0365] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 3 is realized by the following means.

[0366] In this invention, the server includes means for utilizing generative AI to input information on other jobs that employees aspire to, means for the generative AI to check employee information, identify differences with the employee's own information, and suggest necessary skills and qualifications, means for providing study plans and internal company information and managing progress, means for managing the robot's learning progress and updating the study plan as needed, means for saving and reading the robot's learning progress data, and means for generating a study plan for the robot. This makes it possible to efficiently manage the learning progress of employees and robots and provide appropriate study plans.

[0367] "Generative AI" is an artificial intelligence technology that generates new information and content based on data.

[0368] "Employee information" refers to data such as an employee's skills, qualifications, work history, and learning progress.

[0369] "Difference" refers to the gap or difference that exists between the current state and the target state.

[0370] A "study plan" is a plan that outlines the study content and schedule required to acquire specific skills or qualifications.

[0371] "Internal information" refers to information and data shared within a company, including materials and resources that can help employees improve their skills.

[0372] "Progress management" is the process of monitoring the progress of learning or work and making adjustments or providing support as needed.

[0373] A "robot" refers to an automated mechanical device that performs tasks in factories and on production lines.

[0374] "Learning progress" is data that indicates the current level of achievement or progress toward a specific learning goal.

[0375] A "learning plan" is a plan that indicates the learning content and schedule required for a robot to acquire new work procedures and skills.

[0376] "Storage" is the process of recording data so that it can be accessed at a later time.

[0377] "Loading" is the process of retrieving stored data and making it available for use again.

[0378] The system for implementing this invention utilizes generative AI to manage the learning progress of employees and robots and update study plans as needed. A specific embodiment of this system is described below.

[0379] System Configuration

[0380] The system consists of the following major components:

[0381] 1. Server: Runs the generative AI and stores and processes data.

[0382] 2. Devices: Computers and smartphones used by employees and managers.

[0383] 3. Robot: An automated mechanical device that performs tasks in a factory or on a production line.

[0384] Hardware and software used

[0385] Hardware: Servers, terminals (PCs, smartphones), factory robots

[0386] Software: Python, JSON, generative AI models

[0387] Data processing and calculation

[0388] The server processes and calculates data using the following means:

[0389] 1. Enter and confirm employee information: Enter information about other jobs that employees aspire to, and the generative AI will confirm the employee information.

[0390] 2. Calculating the difference: Generative AI automatically calculates the difference in skills and qualifications required for the desired job based on the employee's current skills and qualifications.

[0391] 3. Providing study plans and progress management: Generative AI provides study plans and internal company information and manages progress.

[0392] 4. Managing the robot's learning progress: Generative AI manages the robot's learning progress and updates the study plan as needed.

[0393] 5. Save and load data: Save and load the robot's learning progress data.

[0394] Specific examples

[0395] For example, if an employee completes an online course in "Project Management," the generative AI will receive that information and update their study plan. Or, if a factory robot completes 50% of its "Assembly Line Training," the study plan will be updated to read "In Progress: 50%."

[0396] Prompt Sentence Examples

[0397] Below is an example of a prompt sentence to input to the generative AI model.

[0398] Robot ID: robot_001

[0399] Course Name: Assembly Line Training

[0400] Progress: 50%

[0401] This system makes it possible to efficiently manage the learning progress of employees and robots and provide appropriate study plans.

[0402] The flow of the specific processing in Application Example 3 will be described with reference to FIG.

[0403] Step 1:

[0404] The user uses the terminal to input employee information and information about the desired occupation.

[0405] Input: Employee skills, qualifications, work history, learning progress, desired job information

[0406] Output: Employee information and desired job information sent to the server

[0407] Specific operation: The user enters the necessary information into the input form on the terminal and presses the send button. The terminal then sends this information to the server.

[0408] Step 2:

[0409] The server uses generative AI to check employee information and calculate the difference in skills and qualifications required for the desired job.

[0410] Input: Employee information, desired job information

[0411] Output: Difference in required skills and qualifications

[0412] Specific operation: The server inputs the received employee information and desired job information into the generative AI and calculates the difference. The generative AI compares the employee's current skills with the skills required for the desired job and calculates the difference.

[0413] Step 3:

[0414] The server uses generative AI to generate a study plan suitable for the employee and sends it to the device.

[0415] Input: Difference in required skills and qualifications

[0416] Output:Study plan

[0417] Specific operation: The server uses generative AI to generate a study plan based on the differential data. The generated study plan is sent to the device and displayed to the user.

[0418] Step 4:

[0419] The user uses the device to study based on the study plan.

[0420] Input: Study plan

[0421] Output: Learning progress data

[0422] Specific operation: The user studies according to the study plan displayed on the device and enters their progress into the device. The device then sends the learning progress data to the server.

[0423] Step 5:

[0424] The server uses generative AI to check learning progress data and update study plans as needed.

[0425] Input: Learning progress data

[0426] Output: Updated study plan

[0427] Specific operation: The server inputs the received learning progress data into the generative AI, checks the progress, updates the study plan as needed, and sends the updated study plan to the device.

[0428] Step 6:

[0429] The robot records its learning progress and sends it to the server.

[0430] Input: Robot learning progress data

[0431] Output: Learning progress data sent to the server

[0432] Specific operation: The robot records its own learning progress and periodically sends it to the server.

[0433] Step 7:

[0434] The server stores the robot's learning progress data and updates the study plan as needed.

[0435] Input: Robot learning progress data

[0436] Output: Updated robot study plan

[0437] Specific operation: The server stores the received learning progress data of the robot, checks the progress using generative AI, updates the study plan as needed, and sends it to the robot.

[0438] 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.

[0439] "Example 1"

[0440] In one embodiment of the present invention, the generative AI includes an emotion engine. The emotion engine recognizes an employee's emotional state and makes skill-up suggestions that take that emotional state into account. Specifically, if an employee is feeling stressed, the emotion engine communicates that information to the generative AI. Based on that information, the generative AI makes skill-up suggestions to the employee to reduce stress, such as suggesting online courses for stress management training or relaxation techniques.

[0441] "Example 2"

[0442] The emotion engine also automatically calculates the difference in required skills and qualifications based on the employee's emotional state. For example, if an employee is feeling happy, the emotion engine conveys that information to the generative AI. Based on that information, the generative AI makes suggestions for improving skills that will bring joy to the employee. For example, if an employee is interested in programming, the generative AI will make suggestions for improving programming skills.

[0443] "Example 3"

[0444] Furthermore, the emotion engine takes into account the employee's emotional state to manage their progress and update their study plan as needed. For example, if an employee is feeling frustrated, the emotion engine relays that information to the generative AI, which then uses that information to update the employee's study plan and provide new learning resources to reduce frustration.

[0445] The processing flow of each embodiment will be described below.

[0446] "Example 1"

[0447] Step 1: The emotion engine recognizes the employee's emotional state.

[0448] Step 2: The emotion engine communicates its emotional state to the generative AI.

[0449] Step 3: Based on this information, the generative AI makes suggestions to employees about improving their skills to reduce stress.

[0450] Step 4: The generative AI suggests online courses, for example, for stress management training or relaxation techniques.

[0451] "Example 2"

[0452] Step 1: The emotion engine recognizes the employee's emotional state.

[0453] Step 2: The emotion engine communicates this information to the generative AI.

[0454] Step 3: Based on this information, the generative AI makes suggestions for skill development that will delight employees.

[0455] Step 4: For example, if an employee is interested in programming, the generative AI will suggest ways to improve their programming skills.

[0456] "Example 3"

[0457] Step 1: The emotion engine recognizes the employee's emotional state.

[0458] Step 2: The emotion engine communicates this information to the generative AI.

[0459] Step 3: Generative AI uses this information to update employees' study plans and provide new learning resources to reduce frustration.

[0460] Example 1

[0461] Next, a description will be given of Example 1 of Form 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."

[0462] With conventional in-house reskilling systems, it was difficult to accurately grasp the gap in skills and qualifications required for the job an employee was aiming for and provide an individual study plan. Furthermore, since suggestions for improving skills were not made taking into account the emotional state of the employee, there was a problem of decreased motivation to study. Furthermore, management of learning progress was often done manually, making it impossible to achieve efficient reskilling.

[0463] 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.

[0464] In this invention, the server includes: a means for inputting information about the job type an employee is aiming for; a means for a generative AI to calculate the gap in skills and qualifications required for the desired job based on the employee's current skill and qualification information; a means for the generative AI to generate an individual study plan based on the calculated gap and provide it to the employee; a means for periodically checking the employee's learning progress and updating the study plan as necessary; a means for an emotion engine to recognize the employee's emotional state and communicate that information to the generative AI; and a means for the generative AI to make skill improvement suggestions taking the employee's emotional state into account. This makes it possible to accurately grasp the gap in skills and qualifications required for the employee's desired job and provide an individual study plan. Furthermore, because skill improvement suggestions are made taking the employee's emotional state into account, learning motivation is improved and efficient reskilling is achieved.

[0465] The "means for inputting information about the job type that an employee is aiming for" refers to a device or software that provides an interface for employees to input information about the job type that they are aiming for as their career goal.

[0466] "Generative AI" refers to artificial intelligence technology that generates necessary information based on input data. Specifically, it has the ability to calculate differences in employees' skills and qualifications and generate study plans.

[0467] "Employee current skills and qualifications" refers to data about the skills and qualifications that employees currently possess, including past work experience and qualifications they have obtained.

[0468] A "means for calculating the difference in skills and qualifications" is a device or software that compares the skills and qualifications required for the job an employee is aiming for with the employee's current skills and qualifications and calculates the difference.

[0469] "Means for generating individual study plans and providing them to employees" refers to a device or software that creates an optimal study plan for each employee based on the calculated differences in skills and qualifications, and provides it to the employee.

[0470] "Means for regularly checking employees' learning progress and updating study plans as necessary" refers to devices or software that regularly monitor employees' learning status and update their study plans as appropriate based on their progress.

[0471] An "emotion engine" is a device or software that recognizes the emotional state of employees and conveys that information to generative AI.

[0472] The "means for making suggestions for skill improvement taking into consideration emotional state" is a device or software for suggesting appropriate methods for skill improvement to employees based on the emotional state of the employees obtained from the emotion engine.

[0473] This invention relates to an in-house reskilling promotion system that utilizes generative AI. This system provides an interface where employees can input information about the job they are aiming for, and the generative AI calculates the difference in the required skills and qualifications based on that information and the employee's current skills and qualifications. It then generates an individual study plan based on this difference and provides it to the employee. It also regularly checks the employee's learning progress and updates the study plan as necessary. The generative AI, which includes an emotion engine, recognizes the employee's emotional state and makes suggestions for skill improvement that take that emotional state into account.

[0474] Hardware and software used

[0475] Hardware: Servers, devices (PCs, tablets, smartphones)

[0476] Software: Generative AI models (e.g., OpenAI GPT-4), emotion engines, database management systems (e.g., MySQL), web interfaces (e.g., React)

[0477] Specific operation of the system

[0478] 1. The user (employee) uses a terminal to access the web interface, enters the desired job information into the input form, and clicks the "Submit" button.

[0479] 2. The server receives the job information sent by the user and stores it in a database, typically using a database management system such as MySQL.

[0480] 3. The server accesses the database to retrieve the user's current skills and credentials, which are then passed to the generative AI.

[0481] 4. Generative AI (e.g., OpenAI GPT-4) calculates the difference in required skills and qualifications based on the user's desired job type and current skills and qualifications. For example, if the user is aiming to become a "project manager," the generative AI will determine that "the user lacks knowledge of project management."

[0482] 5. The generative AI generates an individual study plan based on the calculated differences. For example, it generates a study plan such as "take an online course in project management." The generated study plan is sent to the server.

[0483] 6. The server provides the generated study plan to the user, who can then view the study plan on a web interface via their device.

[0484] 7. The user follows the provided study plan, for example, enrolls in an online course and begins studying.

[0485] 8. The server periodically checks the user's learning progress and updates the study plan accordingly.

[0486] 9. The emotion engine recognizes the user's emotional state and conveys that information to the generative AI. For example, if the user is feeling stressed, it conveys that information to the generative AI.

[0487] 10. Generative AI will take into account emotional state and provide users with skill-building suggestions to reduce stress, such as suggesting online courses for stress management training or relaxation techniques.

[0488] Specific examples

[0489] For example, let's say a user wants to become a "project manager." The user types "project manager" into their device. The server receives this information and stores it in a database. The server then retrieves the user's current skills and qualifications from the database. Based on this information, the generative AI calculates the difference in skills and qualifications required to become a project manager. For example, it may determine that "knowledge of project management is lacking." Based on this difference, the generative AI generates a study plan such as "take an online course in project management" and sends it to the server. The server provides this study plan to the user.

[0490] Prompt Sentence Examples

[0491] "What is the difference between the skills and qualifications required to become a project manager? My current skills are: programming, data analysis, and basic project management."

[0492] In this way, an internal reskilling promotion system that utilizes generative AI efficiently supports employees in improving their skills.

[0493] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0494] Step 1:

[0495] The user enters job type information.

[0496] Input: Job type information the user is aiming for (e.g., project manager)

[0497] How it works: A user accesses the web interface using a device (PC, tablet, smartphone), enters information about the job they are seeking into the input form, and clicks the "Submit" button.

[0498] Output: The desired job information is sent to the server.

[0499] Step 2:

[0500] The server receives the input information and stores it in a database.

[0501] Input: Job information submitted by the user

[0502] How it works: The server receives the job information submitted by the user and stores it in a database, typically using a database management system such as MySQL.

[0503] Output: Job information is saved in the database.

[0504] Step 3:

[0505] The server retrieves the user's current skills and credentials from a database.

[0506] Input: User job title information stored in the database

[0507] How it works: The server accesses a database to retrieve the user's current skills and credentials.

[0508] Output: The acquired skills and credentials are passed to the generative AI.

[0509] Step 4:

[0510] Generative AI calculates the differences in skills and qualifications.

[0511] Input: User's desired job type and current skills and qualifications

[0512] How it works: Generative AI (e.g., OpenAI GPT-4) calculates the difference in required skills and qualifications based on the user's desired job type and current skills and qualifications. For example, if the user is aiming to become a "project manager," the generative AI will determine that "the user lacks knowledge of project management."

[0513] Output: Calculated skill or qualification difference

[0514] Step 5:

[0515] The generative AI generates an individual study plan and sends it to the server.

[0516] Input: Calculated skill or qualification difference

[0517] How it works: The generative AI generates an individual study plan based on the calculated differences. For example, it generates a study plan such as "take an online course in project management." The generated study plan is sent to the server.

[0518] Output: Generated study plan

[0519] Step 6:

[0520] The server provides the study plan to the user.

[0521] Input: Generated study plan

[0522] Operation: The server provides the generated study plan to the user, who can then view it on a web interface via their device.

[0523] Output: User can check the study plan

[0524] Step 7:

[0525] The user progresses through their studies according to the study plan.

[0526] Input: Provided study plan

[0527] Action: The user follows the provided study plan, for example, enrolls in an online course and begins studying.

[0528] Output: Learning progress

[0529] Step 8:

[0530] The server periodically checks the learning progress and updates the study plan as needed.

[0531] Input: User's learning progress

[0532] How it works: The server periodically checks the user's learning progress and updates the study plan accordingly.

[0533] Output: Updated study plan

[0534] Step 9:

[0535] The emotion engine recognizes the user's emotional state and conveys it to the generative AI.

[0536] Input: User's emotional state

[0537] How it works: The emotion engine recognizes the user's emotional state and, for example, if the user is feeling stressed, it conveys that information to the generative AI.

[0538] Output: Emotional state information

[0539] Step 10:

[0540] Generative AI takes into account your emotional state and makes suggestions for improving your skills.

[0541] Input: Emotional state information

[0542] How it works: Generative AI takes into account emotional state and makes suggestions to the user to improve their skills to reduce stress, such as suggesting online courses to learn stress management training or relaxation techniques.

[0543] Output: Suggestions for skill development

[0544] (Application example 1)

[0545] Next, a description will be given of Application Example 1 of Embodiment 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."

[0546] Conventional reskilling systems are primarily targeted at human employees and do not address the skill development and stress management of factory robots. This poses a challenge, making it difficult to efficiently develop the skills and optimize the performance of factory robots. Furthermore, there are no systems available that evaluate robot performance and make suggestions to reduce stress. This could potentially lead to a decline in the productivity of robots in factories.

[0547] 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.

[0548] In this invention, the server includes: a means for utilizing generative AI to input information on other jobs that employees aspire to; a means for the generative AI to check employee information, compare it with its own and suggest necessary skills and qualifications; a means for providing study plans and internal company information and managing progress; a means for inputting information on the work and tasks that the robot aims to do; a means for comparing the robot's current skills and abilities with the required skills and abilities to calculate the difference; a means for generating an individual training plan and providing it to the robot; a means for periodically checking the robot's operation data and updating the training plan as necessary; and a means for evaluating the robot's operation performance and making suggestions to reduce stress. This enables efficient skill development and performance optimization of factory robots.

[0549] "Generative AI" is a type of artificial intelligence that generates new information and suggestions based on input data.

[0550] An "employee" is a person who belongs to a company or organization and performs work for that company or organization.

[0551] "Job information" refers to detailed information about a specific job or role, including required skills, qualifications, and job duties.

[0552] "Skills" refer to the abilities and techniques required to carry out a specific job or task.

[0553] "Qualification" refers to the certification or certificate required to perform a particular job or function.

[0554] A "study plan" refers to a plan that specifically outlines the study content and study schedule necessary to achieve a goal.

[0555] "Internal information" refers to information shared within a company or organization, including data and materials related to business operations.

[0556] "Progress management" refers to the process of monitoring the progress of planned work or learning and making adjustments or improvements as needed.

[0557] A "robot" refers to a mechanical device that performs tasks automatically according to programmed instructions.

[0558] "Task information" refers to detailed information about a specific task or job, including necessary procedures and conditions.

[0559] "Ability" refers to the knowledge, skills, physical strength, etc. required to carry out a specific task or work.

[0560] A "training plan" refers to a plan that specifies the training content and schedule for improving specific skills and abilities.

[0561] "Operation data" refers to data related to the operation of a robot or machine, including the speed and accuracy of the operation, error information, etc.

[0562] "Motion performance" refers to an indicator that evaluates how efficiently and accurately a robot or machine can perform a specific task.

[0563] "Stress reduction" refers to methods or means for reducing mental or physical strain.

[0564] The system for carrying out this invention utilizes generative AI to promote skill development for employees and robots. A specific embodiment of this system will be described below.

[0565] System configuration

[0566] The server includes the following means:

[0567] 1. A means for inputting job information: A means for inputting information about other jobs that employees aspire to, as well as information about the work and tasks that the robot aims to perform.

[0568] 2. Skill difference calculation method: A method for generative AI to calculate the difference between the current skills and abilities of employees or robots and the skills and abilities required for the desired job type or task.

[0569] 3. Training plan generation means: A means for generating individual study and training plans and providing them to employees and robots.

[0570] 4. Progress management method: A method for regularly checking the learning progress and operation data of employees and robots, and updating the plan as necessary.

[0571] 5. Performance evaluation tools: A tool to evaluate the robot's operational performance and provide suggestions to reduce stress.

[0572] Hardware and software used

[0573] Hardware: Servers, employee devices (PCs, smartphones), robots

[0574] Software: Generative AI model, emotion engine, database management system

[0575] Data processing and calculation

[0576] The server receives job and task information input from employees and robots, and uses a generative AI model to calculate the required skills and qualification gaps. Based on this, it generates individual study and training plans and provides them to employees and robots. It also uses progress management tools to regularly check learning progress and behavior data and update plans as needed. It also uses an emotion engine to evaluate the robot's behavioral performance and make suggestions to reduce stress.

[0577] Specific examples

[0578] For example, in a scenario where a robot aiming to perform welding tasks needs to generate a training plan to close the gap between its current and desired skills and adjust the plan based on performance data, the server might input the following prompt into the generative AI model:

[0579] If a robot is aiming to perform welding tasks, calculate the gap between current and desired skills, generate a personalized training plan, adjust the training plan based on the robot's performance data, and provide suggestions for stress reduction if stress levels are high.

[0580] Based on this prompt, the generative AI model can provide specific training plans and stress reduction suggestions to help the robot improve its skills.

[0581] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0582] Step 1:

[0583] The user uses the terminal to input information about other jobs that employees aspire to, as well as information about the work and tasks that the robot aims to perform.

[0584] Input: Job type information, task information

[0585] Output: Entered job type information, task information

[0586] Step 2:

[0587] The server uses a generative AI model to calculate the difference between the current skills and abilities of employees and robots and the skills and abilities required for the desired job or task, based on the input job and task information.

[0588] Input: Job information, task information, current skills and abilities

[0589] Output: Difference in skills and abilities

[0590] Specific operation: The server retrieves the current skills and abilities of employees and robots from the database and inputs them into the generative AI model. The generative AI model calculates the difference in the required skills and abilities and returns it to the server.

[0591] Step 3:

[0592] The server generates individual study and training plans based on differences in skills and abilities and provides them to employees and robots.

[0593] Input: Difference in skills or abilities

[0594] Output: Study plan, training plan

[0595] Specific operation: The server uses a generative AI model to generate specific study and training plans to fill gaps in skills and abilities, and sends them to the device.

[0596] Step 4:

[0597] The server uses a progress management means to periodically check the learning progress and operation data of employees and robots, and updates the plan as necessary.

[0598] Input: Learning progress, behavior data

[0599] Output: Updated study plan, training plan

[0600] Specific behavior: The server periodically collects learning progress and behavior data from the device and robot, reevaluates the plan using the generative AI model, and updates it as necessary.

[0601] Step 5:

[0602] The server uses an emotion engine to evaluate the robot's performance and make suggestions to reduce stress.

[0603] Input: Operational performance data

[0604] Output: Stress reduction suggestions

[0605] Specific Actions: The server collects the robot's performance data and uses an emotion engine to assess its stress level. If the stress level is high, the server uses a generative AI model to generate specific suggestions for stress reduction and provides them to the robot.

[0606] Example 2

[0607] Next, a description will be given of Example 2 of Form 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."

[0608] Conventional career support systems have difficulty calculating the difference between a user's current skills and the skills required for the desired job, and providing an appropriate learning plan. Furthermore, because the system does not consider the user's emotional state when proposing ways to improve skills, it is difficult to maintain the user's motivation. This prevents users from effectively improving their skills, and the system provides insufficient support for achieving career goals.

[0609] 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.

[0610] In this invention, the server utilizes generative artificial intelligence and includes: means for inputting information about the job type the user is aiming for; means for the generative artificial intelligence to check the user information, identify differences between the user's own information and the information and suggest necessary skills and qualifications; means for providing study plans and organizational information and managing progress; means for analyzing the user's emotional state using an emotion analysis engine; and means for the generative artificial intelligence to suggest skill development based on the emotional state. This makes it possible to accurately calculate the difference between the user's current skills and the skills required for the desired job and provide an appropriate study plan. Furthermore, by suggesting skill development that takes the user's emotional state into consideration, it is possible to maintain the user's motivation and support effective skill development.

[0611] "Generative AI" is an AI technology that calculates the difference in required skills and qualifications based on information input by the user, and generates appropriate study plans and suggestions.

[0612] "Users" are individuals who use the system to advance their careers.

[0613] "Occupation information" is information about the occupation or position that the user is aiming for.

[0614] "Skills" are the knowledge and techniques required to perform a specific job or role.

[0615] A "qualification" is a certification or certificate required to perform a particular job or role.

[0616] A "study plan" is a plan that indicates the specific study content and methods for acquiring the skills and qualifications necessary for the job the user is aiming for.

[0617] "In-organization information" refers to learning resources and support information provided within the organization to which the user belongs.

[0618] "Progress management" is the process of monitoring the progress of users' learning and skill development, and making adjustments and providing support as needed.

[0619] An "emotion analysis engine" is a technology that analyzes a user's emotional state and makes appropriate suggestions based on that information.

[0620] "Emotional state" refers to the emotional state, such as happiness, sadness, or excitement, that a user feels while using the system.

[0621] This invention is a system that utilizes generative artificial intelligence and a sentiment analysis engine to propose skills and qualifications required for the job a user is aiming for and provide a study plan. Specific embodiments of this system are described below.

[0622] The server utilizes generative artificial intelligence to provide a means for users to input information about the job they are aiming for. Users access the system's web interface and input the job they are aiming for (e.g., project manager). For example, a user might input "project manager."

[0623] Next, the server obtains the user's current skill data. This data is collected from the user's past work history and self-evaluation sheets. For example, the user's skill data might be "Programming skills: intermediate, communication skills: advanced."

[0624] The server uses generative artificial intelligence (e.g., OpenAI's GPT-4) to calculate the difference between the user's current skills and the skills required for the desired job. For example, if the skills required for a project manager are "Project management skills: advanced, leadership skills: intermediate," the server calculates the difference between these skills and the user's current skills.

[0625] The server then generates a learning plan based on the skill gap, making specific suggestions such as "take an online course on project management" or "read a book on leadership." The generated learning plan is then displayed on the user's device.

[0626] Furthermore, the server uses an emotion analysis engine (e.g., emotion recognition software) to analyze the user's emotional state in real time. For example, if the user feels joy while using the system, that information will be detected by the emotion analysis engine.

[0627] The server transmits the emotional state information obtained from the emotion analysis engine to the generative AI. Based on this information, the generative AI makes suggestions for skill improvement that will bring joy to the user. For example, if a user is interested in programming, it will make suggestions for improving programming skills.

[0628] As a concrete example, consider the case where a user is aiming to become a project manager. The user enters "project manager" as the desired occupation into the system. The server obtains the user's current skill data and calculates the skill difference using a generative artificial intelligence model. For example, if the user's current skills are "programming skills: intermediate, communication skills: advanced" and the skills required for a project manager are "project management skills: advanced, leadership skills: intermediate," the server will generate a study plan such as "take an online course in project management" and "read books on leadership."

[0629] Example prompt sentence:

[0630] Calculate the skill gap required for the user to become a project manager and propose a study plan. The user's current skills are "Programming Skills: Intermediate, Communication Skills: Advanced." The skills required for a project manager are "Project Management Skills: Advanced, Leadership Skills: Intermediate."

[0631] In this way, a system is realized in which the server, terminal, and user work together to support the user's career advancement.

[0632] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0633] Step 1:

[0634] The user inputs the job type he or she is aiming for.

[0635] The user accesses the system's web interface and enters the desired job title (e.g., project manager). The entered job title information is sent to the server. Specifically, the user enters "project manager" in the search bar and clicks the submit button. Input: desired job title information, Output: job title information sent to the server.

[0636] Step 2:

[0637] The server retrieves the user's current skill data.

[0638] The server queries and retrieves the user's skill data from the database. This data is collected from the user's past work history and self-assessment sheets. Specifically, the server retrieves the data using an SQL query such as "SELECT FROM skills WHERE user_id = '12345'". Input: User ID, Output: User's current skill data.

[0639] Step 3:

[0640] The server calculates the skill difference using a generative AI model.

[0641] The server sends a prompt to a generative AI model (e.g., OpenAI's GPT-4) and calculates the difference between the user's current skills and the skills required for the desired job. Specifically, the server sends the prompt, "The user's current skills are 'Programming skills: intermediate, communication skills: advanced'. The skills required for a project manager are 'Project management skills: advanced, leadership skills: intermediate'. Please calculate the skill difference." to the generative AI model. Input: User's current skill data, skill information required for the desired job, Output: Skill difference.

[0642] Step 4:

[0643] The server generates a study plan and provides it to the user.

[0644] The server generates a learning plan based on the skill difference. Specifically, the server generates specific suggestions such as "take an online course on project management" or "read a book on leadership" and displays them on the user's dashboard. Input: skill difference, output: learning plan.

[0645] Step 5:

[0646] The server uses an emotion analysis engine to analyze the user's emotional state.

[0647] The server uses an emotion analysis engine (e.g., emotion recognition software) to analyze the user's emotional state in real time. Specifically, the server sends the user's facial expression and voice data to the emotion analysis engine, which then analyzes the user's emotional state. Input: User's facial expression and voice data, Output: Information on the user's emotional state.

[0648] Step 6:

[0649] The server makes suggestions for skill improvement based on the emotional state.

[0650] The server transmits the emotional state information obtained from the emotion analysis engine to the generative AI. Based on this information, the generative AI makes suggestions for skill improvement that will bring joy to the user. Specifically, the server generates suggestions such as "Take an online course on the latest programming language to improve your programming skills" and provides them to the user. Input: Emotional state information, Output: Skill improvement suggestions based on emotional state.

[0651] (Application example 2)

[0652] Next, a description will be given of Application Example 2 of Form 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."

[0653] Conventional skill improvement support systems can calculate the difference between an employee's current skills and the skills required for the desired job and provide a study plan, but they lack suggestions for improving motivation that take into account the employee's emotional state.In addition, there is no system specialized in supporting the skill improvement of factory workers, making it difficult to efficiently improve workers' skills.

[0654] 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.

[0655] In this invention, the server includes means for utilizing generative AI to input information on other jobs that employees aspire to, means for the generative AI to check employee information, identify differences with the employee's own information, and suggest necessary skills and qualifications, means for providing study plans and internal company information and managing progress, means for analyzing the emotional state of employees using an emotion engine and making suggestions to increase motivation, and means for supporting the skill development of factory workers. This makes it possible to make suggestions to increase motivation that take into account the emotional state of employees, thereby enabling efficient skill development of factory workers.

[0656] "Generative AI" refers to artificial intelligence that generates new information and suggestions based on input data.

[0657] "Employee information" refers to information about individuals, such as an employee's skills, qualifications, work history, and desired job type.

[0658] The "difference" refers to the gap between an employee's current skills and qualifications and those required for the job they are aiming for.

[0659] A "study plan" is a specific learning plan for an employee to acquire the skills and qualifications necessary for the job they are aiming for.

[0660] "Internal information" refers to internal company resources and information available to employees.

[0661] "Progress management" is the process of monitoring employees' progress in learning and skill development and making adjustments and providing support as needed.

[0662] The "emotion engine" is a system that analyzes employees' emotional state and makes appropriate suggestions and feedback based on that information.

[0663] "Motivational suggestions" are specific suggestions that take into account employees' emotional state to improve their motivation to learn and work.

[0664] "Factory workers" means employees working in a factory.

[0665] "Upskilling support measures" are methods and tools that help workers acquire new skills and improve existing skills.

[0666] A system for implementing the present invention includes a generative AI, an emotion engine, and various means for supporting the skill development of factory workers. Specific embodiments are described below.

[0667] System Configuration

[0668] The system consists of the following main components:

[0669] 1. Generative AI: Using the OpenAI API, calculate the difference between an employee's current skills and the skills required for the desired job and generate a study plan.

[0670] 2. Emotion Engine: Uses an emotion recognition library to analyze employees' emotional state and provide motivational suggestions.

[0671] 3. User Interface: An interface for factory workers to input information and review the generated study plans and suggestions.

[0672] Hardware and Software

[0673] Hardware: Factory robots

[0674] Software: Python, OpenAI API, emotion recognition library

[0675] Data processing and calculation

[0676] 1. Enter employee information: The user enters the job type they are aiming for and their current skills.

[0677] 2. Calculating skill gaps: Generative AI calculates the gap between an employee's current skills and the skills required for the desired job.

[0678] 3. Study plan generation: The generative AI generates a specific study plan based on the calculated differences.

[0679] 4. Emotional state analysis: The emotion engine analyzes the emotional state of employees and provides that information to the generative AI.

[0680] 5. Motivation suggestions: Based on information from the emotion engine, the generative AI makes suggestions to increase employee motivation.

[0681] Specific examples

[0682] For example, if Worker A is aiming to become a "project manager," the system calculates the difference between his current skills (programming: 3, communication: 4) and his desired skills (project management: 5, leadership: 4) and generates a study plan. If the emotion engine analyzes Worker A's emotional state as "joy," the generative AI will suggest improving his programming skills.

[0683] Prompt Sentence Examples

[0684] Generate a study plan based on the following skill gaps: {'Project Management': 5, 'Leadership': 4}

[0685] In this way, it becomes possible to propose ways to improve motivation that take into account the emotional state of employees, thereby enabling efficient skill development for factory workers.

[0686] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0687] Step 1:

[0688] The user inputs the desired job type and current skill information. The user uses a terminal to input the desired job type (e.g., project manager) and current skills (e.g., programming: 3, communication: 4). The input data is sent to the server.

[0689] Step 2:

[0690] The server uses generative AI to calculate the difference between the user's current skills and the skills required for the desired job. The server compares the input current skill information with the skill information required for the desired job and calculates the difference. For example, if project management skill level 5 is required and the user's current skill level is 3, the difference will be 2.

[0691] Step 3:

[0692] The server uses a generative AI to generate a study plan based on the calculated difference. The server sends the difference information to the generative AI as a prompt to generate a study plan. For example, the server sends the prompt "Please generate a study plan based on the following skill gaps: {'Project management': 2}" to the generative AI. The generative AI generates an appropriate study plan (e.g., take an online course in project management) and returns it to the server.

[0693] Step 4:

[0694] The server uses the emotion engine to analyze the user's emotional state. The server inputs the user's emotional data (e.g., facial expressions, tone of voice, etc.) into the emotion engine and analyzes the emotional state (e.g., joy, sadness). The emotion engine returns the analysis results to the server.

[0695] Step 5:

[0696] The server uses a generative AI to make suggestions to increase motivation based on the user's emotional state. The server inputs the analysis results from the emotion engine into the generative AI, which then generates suggestions to increase motivation. For example, if the user is feeling "joy," the generative AI will generate a suggestion such as, "Let's aim to improve your programming skills!"

[0697] Step 6:

[0698] The server provides the generated study plan and motivation suggestions to the user. The server sends the generated study plan and motivation suggestions to the terminal so that the user can check them. The user can check the study plan and motivation suggestions through the terminal and put them into practice.

[0699] In this way, it becomes possible to propose ways to improve motivation that take into account the emotional state of the user, thereby enabling efficient skill improvement for factory workers.

[0700] Example 3

[0701] Next, a third embodiment of the third embodiment will be described. 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."

[0702] With conventional employee skill improvement support systems, it was difficult to grasp employees' learning progress and emotional state in real time and flexibly update their study plans based on that information. As a result, employees were unable to improve their skills efficiently, leading to problems such as a decline in motivation to learn and increased frustration.

[0703] The specific processing by the specific processing unit 290 of the data processing device 12 in the third embodiment is realized by the following means.

[0704] In this invention, the server includes: means for utilizing generative AI to input information on other jobs that the employee aspires to; means for the generative AI to check the employee information, determine differences with the employee's own information, and suggest necessary skills and qualifications; means for providing study plans and internal company information and managing progress; means for receiving the employee's learning progress information and inputting it into the generative AI model; means for the generative AI model to update the study plan based on the learning progress; means for the terminal to analyze the employee's emotional state and send the emotional information to the server; and means for the generative AI model to re-update the study plan based on the emotional information. This makes it possible to grasp the employee's learning progress and emotional state in real time and flexibly update the study plan based on that information.

[0705] "Generative AI" is an artificial intelligence technology that generates new information and suggestions based on input data.

[0706] "Employee information" refers to information that includes data such as an employee's skills, qualifications, learning progress, and emotional state.

[0707] A "study plan" is a plan that shows the learning content and schedule required for employees to improve their skills.

[0708] "Progress management" is the process of monitoring employees' learning progress and updating their study plans as needed.

[0709] "Emotional state" is information that indicates an employee's current emotional or psychological state.

[0710] The "emotion engine" is a technology that analyzes employees' facial expressions, tone of voice, etc. to determine their emotional state.

[0711] A "server" is a computer system that receives, stores, and processes data.

[0712] A "terminal" is a device used by a user, such as a PC, tablet, or smartphone.

[0713] A "prompt" is an instruction given to a generative AI to generate specific information or suggestions.

[0714] This invention is a system that grasps an employee's learning progress and emotional state in real time and flexibly updates the study plan based on that. A specific embodiment of this system will be described below.

[0715] Hardware and software used

[0716] Hardware: Servers, devices (PCs, tablets, smartphones)

[0717] Software: Generative AI models (e.g., GPT-4), emotion engines

[0718] System configuration

[0719] 1. Checking user learning progress

[0720] A user takes an online course in project management.

[0721] When you complete a course, the device records the completion information.

[0722] As a specific operation, the user clicks the "Done" button.

[0723] 2. Receipt of learning progress information by the server

[0724] The terminal transmits the user's learning progress information to the server.

[0725] The server receives this information and stores it in a database.

[0726] Specifically, the terminal sends an HTTP request to the server, and the server processes the request.

[0727] 3. Updating study plans with generative AI models

[0728] The server inputs the received learning progress information into the generative AI model.

[0729] The generative AI model generates a new study plan based on the user's progress.

[0730] Specifically, the server sends an API request to the generating AI model and receives the generated study plan.

[0731] 4. Checking emotional state via device

[0732] While the user is learning, the device uses the camera and microphone to analyze their emotional state.

[0733] The emotion engine analyzes the user's facial expressions and tone of voice to determine their emotional state.

[0734] Specifically, the device calls the emotion engine's API and obtains the analysis results.

[0735] 5. Sending emotional information to the server

[0736] The device transmits the analyzed emotion information to the server.

[0737] The server receives this information and stores it in a database.

[0738] Specifically, the terminal sends an HTTP request to the server, and the server processes the request.

[0739] 6. Re-updating study plans using generative AI models

[0740] The server inputs the emotional information into the generative AI model.

[0741] The generative AI model then updates the user's study plan based on the emotional information.

[0742] Specifically, the server sends an API request to the generative AI model and receives the generated new study plan.

[0743] Specific examples

[0744] Prompt Sentence Examples

[0745] "User completed an online course on project management. What courses should they take next?"

[0746] "User is frustrated. Can you suggest learning resources to help alleviate their frustration?"

[0747] This system monitors the user's learning progress and emotional state in real time and can flexibly update the study plan based on the results. This allows employees to efficiently improve their skills. The flow of the specific processing in Example 3 will be explained using Figure 21.

[0748] Step 1:

[0749] A user takes and completes an online course on project management.

[0750] Input: A user takes an online course and clicks the Finish button.

[0751] Specific operation: When the user clicks the "Done" button, the device records the completion information.

[0752] Output: Learning progress information is logged to the terminal.

[0753] Step 2:

[0754] The terminal transmits the user's learning progress information to the server.

[0755] Input: Learning progress information recorded on the device.

[0756] Specific operation: The device sends an HTTP request to the server and transmits learning progress information.

[0757] Output: The server receives the learning progress information and stores it in a database.

[0758] Step 3:

[0759] The learning progress information received by the server is input into the generative AI model.

[0760] Input: Learning progress information stored on the server.

[0761] Specific operation: The server sends an API request to the generated AI model and inputs learning progress information.

[0762] Output: The generative AI model generates a new study plan.

[0763] Step 4:

[0764] A generative AI model generates a new study plan based on the user's progress.

[0765] Input: Learning progress information.

[0766] How it works: A generative AI model analyzes learning progress information and suggests next courses and resources to learn.

[0767] Output: New study plan.

[0768] Step 5:

[0769] The device analyzes the user's emotional state.

[0770] Input: The user's facial expression and tone of voice.

[0771] Specific operation: The device uses the camera and microphone to call the emotion engine's API and analyze the emotional state.

[0772] Output: Emotional state analysis result.

[0773] Step 6:

[0774] The device transmits the analyzed emotion information to the server.

[0775] Input: Emotional state analysis results.

[0776] Specific operation: The device sends an HTTP request to the server and transmits emotion information.

[0777] Output: The server receives the emotion information and stores it in a database.

[0778] Step 7:

[0779] The server inputs the emotional information into the generative AI model.

[0780] Input: Emotion information stored on the server.

[0781] Specific operation: The server sends an API request to the generated AI model and inputs emotion information.

[0782] Output: The generative AI model updates the study plan again based on the emotional information.

[0783] Step 8:

[0784] The generative AI model then updates the user's study plan based on the emotional information.

[0785] Input: Emotion information.

[0786] Specific behavior: The generative AI model analyzes emotional information and suggests new learning resources to reduce frustration.

[0787] Output: Updated study plan.

[0788] (Application example 3)

[0789] Next, a description will be given of Application Example 3 of Form Example 3. 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."

[0790] Conventional employee skill improvement systems manage progress without taking into account the emotional state of employees, which often leads to frustration and reduced learning efficiency. Furthermore, when factory robot operators learn new operating procedures and safety protocols, they often lack adequate support, which can slow their learning progress. To solve these problems, a system is needed that takes into account the emotional state of employees and operators and provides appropriate learning resources.

[0791] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 3 is realized by the following means.

[0792] In this invention, the server includes means for utilizing generative AI to input information on other jobs that employees aspire to, means for the generative AI to check employee information, identify differences with the employee's own, and suggest necessary skills and qualifications, means for providing study plans and internal company information and managing progress, means for monitoring the employee's emotional state using an emotion engine, means for the generative AI to update the study plan based on information from the emotion engine and provide appropriate learning resources, means for periodically checking the operator's learning progress and emotional state, and means for updating the study plan when the operator feels frustrated. This makes it possible to efficiently improve the skills of employees and operators while taking into account their emotional state.

[0793] "Generative AI" is artificial intelligence that generates new data and plans based on input information.

[0794] "Employee information" refers to information that includes data such as an employee's skills, qualifications, work history, and learning progress.

[0795] A "diff" refers to the difference that exists between the current state and the desired state.

[0796] A "study plan" is a plan that includes the study schedule and resources for employees to acquire the skills and qualifications they are aiming for.

[0797] "Internal information" refers to information such as learning resources and training programs provided within a company.

[0798] Progress management is the process of tracking employees' learning and skill development progress and making adjustments as needed.

[0799] The "Emotion Engine" is a system that analyzes employees' emotional states and provides that information.

[0800] "Monitoring" refers to the continuous observation and recording of a particular condition or progress.

[0801] "Learning resources" refers to the learning materials and tools employees use to acquire skills and qualifications.

[0802] "Operator" refers to the person who operates factory robots and machines.

[0803] "Frustration" refers to the stress and dissatisfaction felt when faced with a situation that makes it difficult to achieve a goal.

[0804] A system for implementing this invention includes a generative AI, an emotion engine, a learning progress tracker, and emotion monitoring hardware. The specific configuration and operation of the system are described below.

[0805] The server utilizes generative AI to provide a means for employees to input information about other jobs they aspire to. When an employee inputs the job information they are aiming for, the generative AI checks the employee information and automatically calculates the difference between their current skills and qualifications and the skills and qualifications required for the desired job. This allows employees to clearly understand the skills and qualifications they need.

[0806] The server then provides study plans and internal company information, and provides a means for progress management. The generative AI periodically checks employees' learning progress and updates study plans as necessary, allowing employees to improve their skills efficiently.

[0807] Furthermore, the server provides a means to monitor employees' emotional states using an emotion engine. The emotion engine analyzes employees' emotional states using cameras and sensors and transmits that information to the generative AI. The generative AI updates study plans based on the information from the emotion engine and provides appropriate learning resources. For example, if an employee is feeling frustrated, the generative AI uses that information to provide new learning resources to alleviate the frustration.

[0808] As a concrete example, consider a factory robot operator learning how to operate a new machine. If the operator is frustrated, the emotion engine detects this information and the generative AI updates the operator's learning plan, for example by providing a simpler instructional video or interactive simulation.

[0809] An example of a prompt is as follows:

[0810] "Operators are experiencing frustration while learning new machine operating procedures. Please update operator learning plans and provide new learning resources to reduce frustration."

[0811] In this way, the server can efficiently improve the skills of employees and operators while taking into account their emotional state.

[0812] The flow of the specific processing in Application Example 3 will be described with reference to FIG.

[0813] Step 1:

[0814] The user uses the terminal to input information about the job type he or she is aiming for.

[0815] Input: Information about the job you are aiming for

[0816] Output: The desired job information is sent to the server

[0817] Specific operation: The user enters the desired job information into the input form on the terminal and presses the send button. The server receives this information.

[0818] Step 2:

[0819] The server uses generative AI to check employee information and calculate the difference between current skills and qualifications and those required for the desired job.

[0820] Input: Employee information, desired job type information

[0821] Output: Difference in skills and qualifications

[0822] Specific operation: The server retrieves employee skills and qualifications from the employee information database, compares them with the skills and qualifications required for the job the generative AI is aiming for, and calculates the difference.

[0823] Step 3:

[0824] The server uses generative AI to create a study plan and provides it to the device.

[0825] Input: Difference in skills and qualifications

[0826] Output:Study plan

[0827] Specific operation: Based on the differences in skills and qualifications, the generative AI creates a study plan for employees to acquire the necessary skills and qualifications and sends it to the device.

[0828] Step 4:

[0829] The server uses an emotion engine to monitor the emotional state of employees.

[0830] Input: Employee emotion data (obtained from cameras and sensors)

[0831] Output: Emotional state information

[0832] Specific operation: The emotion engine analyzes data obtained from cameras and sensors to determine the employee's emotional state, which is then sent to the server.

[0833] Step 5:

[0834] Based on the information from the emotion engine, the server uses generative AI to update the study plan and provide appropriate learning resources.

[0835] Input: Emotional state information, current study plan

[0836] Output: Updated study plan, learning resources

[0837] How it works: Based on emotional state information, generative AI updates study plans and provides appropriate learning resources (e.g., brief instructional videos or interactive simulations) if employees are feeling frustrated.

[0838] Step 6:

[0839] The server periodically checks the operator's learning progress and emotional state and updates the learning plan as needed.

[0840] Input: learning progress data, emotional state information

[0841] Output: Updated lesson plan

[0842] Specific operation: The server periodically checks the learning progress data and emotional state information, and the generative AI updates the learning plan as needed. If the operator is feeling frustrated, it provides appropriate learning resources.

[0843] 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.

[0844] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search engine).<URL: https: / / openai.com / blog / chatgpt> ) 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.

[0845] Another example of generative AI is Gemini (registered trademark) (Internet search engine). <url: https: gemini.google.com ?hl="ja">) are mentioned.

[0846] 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.

[0847] [Second embodiment]

[0848] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0849] 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.

[0850] 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).

[0851] 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.

[0852] 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.

[0853] 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).

[0854] 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.

[0855] 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.

[0856] 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.

[0857] 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.

[0858] In the smart glasses 214, 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.

[0859] Next, the specific processing by the specific processing unit 290 of the data processing device 12 will be described.

[0860] "Example 1"

[0861] One embodiment of the present invention is an in-house reskilling promotion system that utilizes generative AI. This system provides an interface where employees can input information about the job they are aiming for. Based on this input information and the employee's current skills and qualifications, the generative AI calculates the difference in the required skills and qualifications. Based on this difference, the system then generates an individual study plan and provides it to the employee. The system also periodically checks the employee's learning progress and updates the study plan as necessary.

[0862] "Example 2"

[0863] As a specific example, if an employee wants to become a project manager, they enter "project manager" as the desired job title into the system. The generative AI calculates the difference between the employee's current skills (e.g., programming skills and communication skills) and the skills required for a project manager (e.g., project management skills and leadership skills). Based on this difference, the system generates a study plan (e.g., taking an online course on project management, reading books on leadership, etc.) and provides it to the employee.

[0864] "Example 3"

[0865] Furthermore, the generative AI periodically monitors employees' learning progress. For example, if an employee completes an online project management course, the generative AI receives that information and updates their study plan. In this way, the system helps employees improve their skills efficiently.

[0866] The processing flow of each embodiment will be described below.

[0867] "Example 1"

[0868] Step 1: Employees enter information about the job they are aiming for through the system interface.

[0869] Step 2: The generative AI calculates the difference in required skills and qualifications based on the employee's current skills and qualifications and the input job information.

[0870] Step 3: The generative AI generates an individual study plan based on the calculated differences and provides it to the employee.

[0871] Step 4: Generative AI periodically checks employees' learning progress and updates their study plans as needed.

[0872] "Example 2"

[0873] Step 1: An employee enters "Project Manager" as the job title they are aiming for in the system.

[0874] Step 2: The generative AI calculates the difference between an employee's current skills (e.g., programming skills and communication skills) and the skills required for a project manager (e.g., project management skills and leadership skills).

[0875] Step 3: Generative AI generates a study plan based on these differences (e.g., taking an online course in project management, reading a book on leadership, etc.) and provides it to the employee.

[0876] Step 4: The generative AI periodically checks the employee's learning progress. For example, if an employee completes an online course on project management, it receives that information and updates the employee's study plan.

[0877] Step 1: Generative AI periodically checks employees' learning progress.

[0878] Step 2: The generative AI receives the employee's learning progress information and updates the study plan as needed.

[0879] Step 3: In this way, the system helps employees improve their skills efficiently.

[0880] Example 1

[0881] Next, a description will be given of Example 1 of Form 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."

[0882] With conventional reskilling systems, it was difficult to accurately grasp the gap in skills and qualifications required for the job an employee was aiming for and to create an individual study plan. In addition, efficient reskilling could not be achieved because learning progress management and study plan updates were done manually. This led to problems such as delays in employee skill development and a decline in the company's overall competitiveness.

[0883] 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.

[0884] In this invention, the server includes a means for inputting information about the job type the employee is aiming for, a means for the generative AI model to calculate the gap in required skills and qualifications based on the employee's current skills and qualification information, a means for generating an individual study plan based on the calculated gap information, a means for providing the generated study plan to the employee, and a means for periodically checking the employee's learning progress and updating the study plan as necessary. This allows the employee to accurately understand the gap in skills and qualifications required for the job they are aiming for, enabling efficient reskilling.

[0885] The "means for inputting information about the job type that an employee aims for" is a function that provides an interface for inputting information about the job type that an employee has set as his or her career goal.

[0886] A "generative AI model" is an artificial intelligence model that calculates the difference in skills and qualifications required for a desired job based on an employee's current skills and qualifications.

[0887] "Means for calculating the difference in required skills and qualifications" refers to a function that uses a generative AI model to calculate the difference between an employee's current skills and qualifications and the skills and qualifications required for the desired job.

[0888] The "means for generating individual study plans" is a function that automatically creates a customized study plan for each employee based on the calculated difference information.

[0889] The "means of providing study plans to employees" is a function that presents the generated study plans to employees and provides guidelines for advancing their studies.

[0890] "Means for regularly checking learning progress" refers to a function that periodically checks the progress of employees as they proceed with their studies according to their study plan.

[0891] "Means for updating study plans" refers to the function of amending and updating existing study plans as needed according to the employee's learning progress.

[0892] This invention relates to an in-house reskilling promotion system that utilizes a generative AI model. This system involves inputting information about the job type an employee is aiming for, and the generative AI model calculates the gap in skills and qualifications required based on the employee's current skills and qualifications, and then generates and provides an individual study plan. It also includes a function to regularly check the employee's learning progress and update the study plan as necessary.

[0893] Hardware and software used

[0894] The system uses the following hardware and software:

[0895] Server: Built using Python's Flask framework. The database uses MySQL.

[0896] Generative AI models: For example, use OpenAI's GPT-4.

[0897] Device: Web browser used by employees. The interface is built using HTML, CSS, and JavaScript.

[0898] Explanation of program processing

[0899] Server Processing

[0900] The server provides an interface for employees to input their desired job information. After employees input their job information, the server receives the information and stores it in a database. The server then retrieves the employee's current skills and qualifications from the database and sends prompts to the generative AI model.

[0901] Processing generative AI models

[0902] Based on the prompt received, the generative AI model calculates the difference between the employee's current skills and the skills required for the desired job. This difference information is returned to the server in JSON format.

[0903] Server processing (study plan generation)

[0904] The server generates an individual study plan based on the differential information returned by the generative AI model. This study plan is automatically generated using a Python script and provided to the employee.

[0905] Terminal handling

[0906] The employee's device displays the study plan provided by the server, and the employee proceeds with their studies according to this plan.

[0907] Server processing (checking learning progress)

[0908] The server provides an interface for employees to periodically check their learning progress. When employees enter their progress information, the server stores it in a database and updates their study plans as needed.

[0909] Specific examples

[0910] For example, suppose Employee A wants to become a "data scientist." Employee A enters the job title "data scientist" through the interface. The server retrieves Employee A's current skill information (e.g., basic knowledge of Python, basic statistics, etc.) from the database and sends the following prompt to the generative AI model:

[0911] Current employee skills: Basic knowledge of Python, basic statistics

[0912] Job title: Data scientist

[0913] Calculate the skill gap you need and generate a study plan.

[0914] The generative AI model calculates the difference in required skills and returns it to the server. Based on this difference information, the server generates a study plan for employee A, such as "Take an online course to learn the basics of machine learning," and provides it to employee A. Employee A studies according to this study plan and enters their progress. The server checks employee A's progress and updates the study plan as necessary.

[0915] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0916] Step 1:

[0917] The user enters job information

[0918] Users access the interface provided through a web browser. This interface is built using HTML, CSS, and JavaScript. Users enter information about the job they are aiming for into the input form and click the "Submit" button. The input data is the job information they are aiming for.

[0919] Step 2:

[0920] The server receives the input information and stores it in a database

[0921] The server receives the job type information submitted by the user. The received information is processed using the Python Flask framework. The server saves this information in a MySQL database. The input data is the job type information submitted by the user, and the output data is the job type information saved in the database.

[0922] Step 3:

[0923] The server retrieves the user's current skills and credentials from a database

[0924] The server retrieves the user's current skills and credentials from the database using a query, which is executed using a SQL statement, where the input data is the user's ID and the output data is the user's current skills and credentials.

[0925] Step 4:

[0926] The server sends a prompt to the generative AI model

[0927] The server generates a prompt based on the user's current skill information and desired job type. The generated prompt is sent to a generative AI model (e.g., OpenAI's GPT-4) using an HTTP request. The input data is the user's current skill information and desired job type, and the output data is the generated prompt.

[0928] Step 5:

[0929] Generative AI model calculates gaps in required skills and qualifications

[0930] Based on the received prompt, the generative AI model calculates the difference between the user's current skills and the skills required for the desired job. This difference information is returned to the server in JSON format. The input data is the prompt, and the output data is the difference information between skills and qualifications.

[0931] Step 6:

[0932] The server generates a study plan based on the difference information.

[0933] The server generates an individualized study plan based on the difference information returned from the generative AI model. This study plan is automatically generated using a Python script. The input data is the difference information for skills and qualifications, and the output data is the generated study plan.

[0934] Step 7:

[0935] The server provides the user with a study plan

[0936] The server provides the generated study plan to the user, who can then view it through a web browser. The input data is the generated study plan, and the output data is the study plan provided to the user.

[0937] Step 8:

[0938] User enters learning progress

[0939] The user accesses an interface for inputting learning progress. The user enters progress information into the input form and clicks the "Submit" button. The input data is the learning progress information, and the output data is the progress information sent to the server.

[0940] Step 9:

[0941] The server checks your learning progress and updates your study plan as needed

[0942] The server receives the learning progress information sent by the user and stores it in a database. The server periodically checks this progress information and updates the study plan as necessary. For example, if a user completes "Fundamentals of Machine Learning," it adds "Applications of Machine Learning" to the study plan as the next step. The input data is the learning progress information, and the output data is the updated study plan.

[0943] (Application example 1)

[0944] Next, a description will be given of Application Example 1 of Form 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."

[0945] With conventional reskilling systems, it was difficult to accurately grasp the gap in skills and qualifications required for the job an employee was aiming for and provide an individual study plan. Furthermore, the system lacked the functionality to regularly check learning progress and update study plans as needed, making effective reskilling impossible. In particular, factory robot operators require specialized skills and qualifications, making it difficult for conventional systems to handle this.

[0946] 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.

[0947] In this invention, the server includes: a means for inputting information about other jobs that employees aspire to using generative AI; a means for the generative AI to check employee information, identify differences between the employee's current skills and qualifications, and suggest necessary skills and qualifications; a means for providing study plans and internal company information and managing progress; a means for inputting information about the job that a factory robot operator aspires to; a means for the generative AI to calculate the difference in required skills and qualifications based on the operator's current skills and qualifications; a means for generating an individual study plan based on the difference and providing it to the operator; and a means for periodically checking learning progress and updating the study plan as needed. This makes it possible to accurately grasp the skills and qualifications required for the jobs that employees and factory robot operators aspire to and provide individual study plans. Furthermore, by periodically checking learning progress and updating the study plan as needed, effective reskilling can be achieved.

[0948] "Generative AI" is an artificial intelligence technology that generates new information and content based on input data.

[0949] An "employee" is an individual employed by a business or organization.

[0950] "Occupation information" is detailed information about a specific occupation or position.

[0951] "Skills" are the abilities and techniques required to carry out specific tasks or work.

[0952] A "qualification" is a certification or certification required to perform a particular occupation or job.

[0953] A "difference" is a difference or deficiency that exists between the current state and the desired state.

[0954] A "study plan" is a plan that outlines the study content and schedule necessary to achieve a specific goal.

[0955] "Progress management" is the process of regularly checking the progress of a plan and making adjustments as necessary.

[0956] A "factory robot" is a mechanical device used to perform automated tasks in a factory.

[0957] An "operator" is an individual whose role is to operate and manage a machine or system.

[0958] The system for implementing this invention utilizes generative AI to input information about the job type that an employee or factory robot operator is aiming for, calculates the gap in required skills and qualifications, and provides an individual study plan. A specific embodiment of this system is described below.

[0959] 1. System Program

[0960] The server uses generative AI to provide a means for employees and operators to input information about the job they are aiming for. When a user inputs the job information using a smartphone or computer, the server receives the information and sends it to the generative AI.

[0961] The generative AI calculates the difference in skills and qualifications required for the desired job based on the user's current skills and qualifications. This difference information is sent to the server and displayed to the user.

[0962] The server then uses generative AI to generate a personalized study plan based on the differences, which is then displayed on the user's smartphone or computer.

[0963] Furthermore, the server periodically checks the user's learning progress and updates the study plan as necessary, allowing the user to always proceed with their studies based on the most up-to-date study plan.

[0964] 2. Hardware and Software Used

[0965] Hardware: smartphones, PCs, servers

[0966] Software: Python, OpenAI API

[0967] 3. Data processing and calculation

[0968] The server receives the job information and current skills and qualifications entered by the user and sends them to the generative AI. The generative AI calculates the difference in skills and qualifications based on this information and generates an individual study plan. The generated study plan is provided to the user via the server.

[0969] 4. Specific Examples

[0970] For example, if a factory robot operator aims to become a "robot control engineer," they would input "basic programming" and "machine operation" as their current skills. The generative AI would then calculate "advanced programming," "robot control," and "data analysis" as the skills required for the desired job, and calculate the difference.

[0971] Prompt Sentence Examples

[0972] Prompts to create a study plan to close skills gaps:

[0973] Create a study plan to fill the following skill gaps: Advanced Programming, Robotics, Data Analysis

[0974] Prompts to update your study plan based on your progress:

[0975] Your current learning progress is as follows: Completed basic programming. Please update your study plan accordingly.

[0976] In this way, users can achieve effective reskilling through systems that utilize generative AI.

[0977] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0978] Step 1:

[0979] The user inputs the desired job information using a smartphone or computer. The input job information is sent from the device to the server. The server receives this information and sends it to the generative AI.

[0980] Step 2:

[0981] The server provides an interface for users to input their current skills and credentials. Users input their skills and credentials and send them to the server from their device. The server receives this information and sends it to the generative AI.

[0982] Step 3:

[0983] The generative AI calculates the difference in the required skills and qualifications based on the desired job information received from the server and the current skill and qualification information. Specifically, it compares the list of skills and qualifications required for the desired job with the user's current list of skills and qualifications and calculates the difference. This difference information is sent from the generative AI to the server.

[0984] Step 4:

[0985] The server generates an individual study plan based on the difference information received from the generative AI. It sends a prompt to the generative AI to create a study plan to fill in the gaps, and receives the generated study plan. The server then sends this study plan to the user's device.

[0986] Step 5:

[0987] The user studies based on the study plan received from the server. The user periodically reports their learning progress from their device to the server. The server receives this progress information and sends it to the generative AI.

[0988] Step 6:

[0989] The generative AI updates the study plan as needed based on the learning progress information received from the server. Specifically, it adjusts the content and schedule of the study plan taking into account the current progress. The updated study plan is then sent from the generative AI to the server.

[0990] Step 7:

[0991] The server sends the updated study plan received from the generative AI to the user's device, and the user continues studying based on the updated study plan.

[0992] In this way, users can achieve effective reskilling through systems that utilize generative AI.

[0993] Example 2

[0994] Next, a description will be given of Example 2 of Form 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."

[0995] With conventional skill improvement systems, it was difficult to efficiently identify the skills and qualifications required for the job an employee was aiming for and to create a specific study plan based on that. Progress management and updating of study plans also had to be done manually, which was time-consuming and labor-intensive. This could delay employee skill development and affect productivity across the company.

[0996] 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.

[0997] In this invention, the server includes a means for inputting information about the job type the employee is aiming for, a means for the generative AI model to acquire the employee's current skill data, a means for the generative AI model to acquire the skill set required for the desired job, a means for the generative AI model to calculate the difference between the employee's current skills and the required skills, a means for the generative AI model to generate a study plan based on the difference, and a means for the generative AI model to provide the generated study plan to the employee. This allows employees to efficiently grasp the skills and qualifications required for the desired job and automatically create a specific study plan based on that. Progress management and study plan updates can also be performed automatically, significantly reducing time and effort.

[0998] An "employee" is an individual who belongs to a company or organization and performs a specific job or role.

[0999] "Targeted occupation information" is information about the occupation that the employee wants to have in the future.

[1000] A "generative AI model" is a model that uses artificial intelligence techniques to analyze data and automatically perform specific tasks.

[1001] "Current skills data" refers to information about the skills and qualifications that employees currently possess.

[1002] A "required skill set" is the set of skills and qualifications required to work in a particular occupation.

[1003] A "difference" is a gap or shortfall between current skill data and the required skill set.

[1004] A "study plan" is a specific learning plan for an employee to acquire the skills and qualifications necessary for the job they are aiming for.

[1005] Progress management is the process of monitoring and, if necessary, adjusting the progress of employees as they acquire skills and qualifications based on their study plans.

[1006] This invention is a system that efficiently identifies the skills and qualifications required for the job an employee is aiming for and automatically creates a specific study plan based on that. A specific embodiment of this system will be described below.

[1007] Hardware and software used

[1008] This system uses the following hardware and software:

[1009] Server: Provides the database management system (DBMS) and computational resources to run generative AI models.

[1010] Terminal: The device (PC, smartphone, tablet, etc.) through which a user accesses the system.

[1011] Generative AI model: An artificial intelligence model that analyzes employee skill data and generates study plans.

[1012] System processing flow

[1013] 1. Enter the job information the user is aiming for

[1014] A user accesses the system interface using a terminal and inputs the job type information they are aiming for, for example, "project manager."

[1015] 2. The server retrieves the employee's current skill data.

[1016] The server retrieves current skill data from the user's profile information. This data includes programming skills, communication skills, etc. For example, the server retrieves information such as "Programming skills: High" and "Communication skills: Medium" from the database.

[1017] 3. Acquire the skill set necessary for the job the server is aiming for

[1018] The server retrieves the skill set required for the desired job (project manager) from the database. For example, it retrieves information such as "Project management skills: High" and "Leadership skills: High."

[1019] 4. The server calculates the difference between the current skill and the required skill.

[1020] The server compares the current skills with the required skills and calculates the difference. For example, "Project management skills: Low" and "Leadership skills: Low" are calculated as the difference.

[1021] 5. The generative AI model generates a study plan based on the differences

[1022] The generative AI model generates a study plan based on the calculated differences, suggesting specific actions such as "take an online course in project management" or "read a book on leadership."

[1023] 6. The server provides the generated study plan to the user.

[1024] The server displays the generated study plan on the user's interface, allowing the user to improve their skills according to the plan provided.

[1025] Examples and prompts

[1026] Examples:

[1027] If an employee wants to become a project manager, they can input the following prompt into the generative AI model:

[1028] Example prompt sentence:

[1029] An employee is aiming to become a project manager. Their current skill set is as follows:

[1030] Programming skills: High

[1031] Communication Skills: Medium

[1032] Project Management Skills: Low

[1033] Leadership Skills: Low

[1034] The skill set required for a project manager is:

[1035] Programming skills: Medium

[1036] Communication Skills: High

[1037] Project Management Skills: High

[1038] Leadership Skills: High

[1039] Generate a study plan based on the difference between your current skills and the skills you need.

[1040] By inputting this prompt into a generative AI model, a specific study plan is generated, suggesting actions such as "take an online course in project management" or "read a book on leadership."

[1041] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1042] Step 1:

[1043] Enter the job information the user is aiming for

[1044] A user uses a terminal to access the system interface and inputs information about the job type they are aiming for. For example, they might input "project manager." This input data is sent to the server. The input data is the "job type information" and the output is the job type information sent to the server.

[1045] Step 2:

[1046] The server retrieves the employee's current skill data.

[1047] The server obtains current skill data from the user's profile information. This data includes programming skills, communication skills, etc. For example, it obtains information such as "Programming skills: High" and "Communication skills: Medium" from the database. The input data is the user's ID, and the output is the current skill data.

[1048] Step 3:

[1049] Acquire the skill set required for the job the server is aiming for

[1050] The server retrieves the skill set required for the desired job (project manager) from the database. For example, it retrieves information such as "Project management skills: high" and "Leadership skills: high." The input data is the desired job information, and the output is the required skill set.

[1051] Step 4:

[1052] The server calculates the difference between the current skill and the required skill.

[1053] The server compares the current skills with the required skills and calculates the difference. For example, "Project management skills: Low" and "Leadership skills: Low" are calculated as the difference. The input data is the current skill data and the required skill set, and the output is the skill difference.

[1054] Step 5:

[1055] A generative AI model generates a study plan based on the differences

[1056] The generative AI model generates a study plan based on the calculated differences. For example, it suggests specific actions such as "take an online course in project management" or "read a book on leadership." The input data is the skill differences, and the output is a study plan.

[1057] Step 6:

[1058] The server provides the generated study plan to the user.

[1059] The server displays the generated study plan on the user's interface, and the user can improve their skills according to the provided plan. The input data is the study plan, and the output is the study plan provided to the user.

[1060] (Application example 2)

[1061] Next, a description will be given of Application Example 2 of Form 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."

[1062] With conventional employee training systems, it was difficult to efficiently calculate the gap between the skills required for an employee's desired job and their current skills, and to provide an appropriate study plan. Furthermore, there was a lack of systems specifically designed to improve the skills of robot operators, making it difficult to provide specific study plans for operators to reach their desired skill set. This delayed the improvement of the skills of employees and robot operators, hindering productivity improvements.

[1063] 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.

[1064] In this invention, the server includes: a means for utilizing generative AI to input information about other jobs that employees aspire to; a means for the generative AI to check employee information, identify differences between the current skill set and the employee's own, and suggest necessary skills and qualifications; a means for providing study plans and internal company information and managing progress; a means for inputting the skill set that the robot operator is aiming for; a means for inputting the current skill set or conducting a skill test; a means for the generative AI to calculate differences between the current skill set and the required skills and generate a study plan; and a means for providing the generated study plan to the operator. This makes it possible to efficiently provide specific study plans for employees and robot operators to achieve their desired skill sets.

[1065] "Generative AI" is an artificial intelligence technology that generates new information and content based on input data.

[1066] An "employee" is an individual who belongs to a company or organization and performs work.

[1067] "Occupation information" is detailed information about a specific occupation or position.

[1068] "Skills" are the knowledge and abilities required to carry out a specific job or task.

[1069] A "qualification" is a certification or certificate required to perform a particular occupation or job.

[1070] A "study plan" is a specific study schedule and method for acquiring the desired skills or qualifications.

[1071] "Progress management" is the process of monitoring progress against a plan and making adjustments as necessary.

[1072] A "robot operator" is an engineer who operates and manages robots in factories and on production lines.

[1073] A "skill set" is a set of skills and abilities required for a particular occupation or position.

[1074] A "skills test" is an exam or test used to assess an individual's skills or abilities.

[1075] A "difference" is the difference or gap between the current state and the target state.

[1076] A "means for generating a study plan" is a method or system for creating a specific study plan for acquiring the required skills or qualifications.

[1077] "Means for providing to operator" refers to a method or system for transmitting the generated study plan to the robot operator.

[1078] The system for implementing this invention utilizes generative AI to help employees and robot operators improve their skills. Specific embodiments are described below.

[1079] System Configuration

[1080] The system consists of the following main components:

[1081] 1. Server: Executes generative AI models and processes data.

[1082] 2. Terminals: Devices used by employees and robot operators (PCs, smartphones, tablets, etc.).

[1083] 3. Database: Stores employee and robot operator skill information, target skill sets, study plans, etc.

[1084] Hardware / Software used

[1085] Hardware: PCs, servers, smartphones, tablets

[1086] Software: Python, OpenAI API, database management system (e.g., MySQL, PostgreSQL)

[1087] Data processing / data calculation

[1088] 1. Data entry: Employees and robot operators enter their current and desired skill sets via a terminal.

[1089] 2. Prompt generation: The server sends a prompt to the generative AI model (OpenAI's GPT-3) based on the input skill information.

[1090] 3. Calculating the difference and generating a study plan: The generative AI model calculates the skill difference and generates a study plan.

[1091] 4. Result output: The generated study plan is displayed on the terminal and provided to employees or robot operators.

[1092] Specific examples

[1093] For example, a robot operator inputs "basic programming skills, simple machine maintenance" as his current skills and "advanced programming skills, complex machine maintenance, troubleshooting" as his desired skills. Based on this information, the server sends the following prompt to the generative AI model:

[1094] Prompt Sentence Examples

[1095] Current skills: Basic programming skills, simple machine maintenance

[1096] Skills to aim for: Advanced programming skills, complex machine maintenance, troubleshooting

[1097] Calculate the skill gap you need and generate a study plan.

[1098] The generative AI model calculates skill gaps based on this prompt and generates a specific study plan, such as "take an online course to acquire advanced programming skills," "receive hands-on training in complex machine maintenance," or "read reference books on troubleshooting."

[1099] In this way, it is possible to efficiently provide employees and robot operators with specific study plans to reach their desired skill sets.

[1100] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1101] Step 1:

[1102] The user uses a terminal to input the current skill set and the desired skill set.

[1103] Input: Current skill set, desired skill set

[1104] Output: Input skill information

[1105] Specific operation: The user enters their current skill set, such as "basic programming skills, simple machine maintenance," and their desired skill set, such as "advanced programming skills, complex machine maintenance, troubleshooting," into an input form on the terminal.

[1106] Step 2:

[1107] The terminal transmits the input skill information to the server.

[1108] Input: Skill information entered by the user

[1109] Output: Skill information sent to the server

[1110] Specific operation: The terminal transmits the skill information input by the user to the server as a data packet.

[1111] Step 3:

[1112] The server generates a prompt sentence based on the received skill information.

[1113] Input: Received skill information

[1114] Output: Generated prompt statement

[1115] Specific operation: The server generates a prompt in the form of "Current skills: Basic programming skills, simple machine maintenance. Target skills: Advanced programming skills, complex machine maintenance, troubleshooting. Calculate the required skill difference and generate a study plan."

[1116] Step 4:

[1117] The server sends the generated prompt to the generative AI model.

[1118] Input: Generated prompt text

[1119] Output: The prompt sent to the generative AI model

[1120] Specific operation: The server sends the generated prompt text to the generative AI model via the OpenAI API.

[1121] Step 5:

[1122] The generative AI model calculates skill differences based on prompt statements and generates a study plan.

[1123] Input: prompt statement

[1124] Output: Skill Diff and Study Plan

[1125] Specific operation: The generative AI model analyzes the prompt, calculates the difference between the current skill and the desired skill, and generates a specific study plan to fill that gap.

[1126] Step 6:

[1127] The server receives the generated study plan and transmits it to the terminal.

[1128] Input: Generated study plan

[1129] Output: Study plan sent to device

[1130] Specific operation: The server sends the study plan received from the generative AI model to the terminal as a data packet.

[1131] Step 7:

[1132] The terminal displays the received study plan to the user.

[1133] Input: Received study plan

[1134] Output: The study plan displayed to the user

[1135] Specific operation: The device displays the received study plan on the screen so that the user can check it.

[1136] Example 3

[1137] Next, a description will be given of Example 3 of Form Example 3. 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."

[1138] Previous employee skill improvement support systems made it difficult to efficiently manage employees' learning progress and provide appropriate feedback. Furthermore, they lacked the functionality to automatically calculate the gap in skills and qualifications required for the job an employee was aiming for and update their study plan, which could delay employee skill improvement. This led to problems such as a decline in productivity and competitiveness across the company.

[1139] The specific processing by the specific processing unit 290 of the data processing device 12 in the third embodiment is realized by the following means.

[1140] In this invention, the server includes a means for collecting employee learning progress data, a means for inputting the collected data into a generative AI model, a means for the generative AI model to analyze the learning progress data and generate feedback, a means for updating a study plan based on the generated feedback, and a means for displaying the updated study plan to the employee. This makes it possible to efficiently manage an employee's learning progress and provide appropriate feedback. Furthermore, by automatically calculating the gap in skills and qualifications required for the employee's desired job and updating the study plan, it is possible to quickly support the employee's skill development.

[1141] "Generative AI" is an artificial intelligence technology that generates new information and suggestions based on data.

[1142] "Employee information" refers to data about individuals, such as an employee's skills, qualifications, and learning history.

[1143] The "difference" is the gap between an employee's current skills and qualifications and those required for the job they are aiming for.

[1144] A "study plan" is a plan that outlines the content and schedule for employees to study in order to improve their skills.

[1145] "Internal information" refers to information and resources shared within a company, such as training programs and educational materials.

[1146] Progress management is the process of tracking employees' progress in learning and skill development and making adjustments and providing support as needed.

[1147] "Learning progress data" is data that indicates how far an employee has progressed in their learning.

[1148] A "generative AI model" is an artificial intelligence model that analyzes data and generates new information and suggestions.

[1149] "Feedback" refers to advice or suggestions provided by a generative AI model based on its analysis results.

[1150] An "updated study plan" is a study plan that has been updated to its latest state based on feedback from the generative AI model.

[1151] A "display means" is a method or device for visually presenting the updated study plan and feedback to the employee.

[1152] This invention is a system for efficiently supporting employee skill development. This system utilizes a generative AI model to manage employees' learning progress and provide appropriate feedback to promote employee skill development.

[1153] Hardware and software used

[1154] This system uses the following hardware and software:

[1155] Server: Collects learning progress data, inputs data into the generative AI model, generates feedback, and updates study plans.

[1156] Device: Display the updated study plan to the employee.

[1157] Generative AI models: For example, use generative AI models such as OpenAI's GPT-4.

[1158] Data processing and calculation

[1159] 1. The server collects employee learning progress data. For example, if an employee completes an online course on project management, the completion data is sent to the server.

[1160] 2. The server inputs the collected data into the generative AI model. The data is converted into an appropriate format and sent to the generative AI model.

[1161] 3. The generative AI model analyzes the input learning progress data and determines the employee's current skill level and what they should learn next.

[1162] 4. The generative AI model generates feedback based on the analysis results, providing specific advice such as, "The next thing you should learn is risk management and team leadership."

[1163] 5. The server receives feedback from the generative AI model and updates the employee's study plan, for example adding "Risk Management 101" and "Team Leadership 101" to the employee's study schedule.

[1164] 6. The device displays the updated study plan to the employee, who can then use it to take the next step in their studies.

[1165] Specific examples

[1166] For example, if employee A completes an online course on project management, the following prompt sentences can be input into the generative AI model:

[1167] Example prompt sentence:

[1168] Employee A has completed an online course on project management. What would you suggest they learn next?

[1169] The specific operation flow:

[1170] 1. The server receives data that Employee A has completed an online course on project management.

[1171] 2. The server inputs this data into a generative AI model (e.g., GPT-4).

[1172] 3. The generative AI model analyzes Employee A's learning progress and generates feedback such as, "The next things you should learn are risk management and team leadership."

[1173] 4. The server receives this feedback and updates Employee A's study plan by adding "Risk Management 101" and "Team Leadership 101."

[1174] 5. The device displays the updated study plan to Employee A, who can then proceed to the next step in his / her studies.

[1175] In this way, the system helps employees improve their skills efficiently. The flow of the specific process in the third embodiment will be described with reference to FIG.

[1176] Step 1:

[1177] The server collects employee learning progress data. For example, when an employee completes an online project management course, the completion data is sent to the server. The input is learning progress data from the LMS (Learning Management System), and the output is the collected learning progress data. Specifically, the server obtains data from the LMS through an API.

[1178] Step 2:

[1179] The server inputs the collected data into the generative AI model. The input is the collected learning progress data, and the output is the data sent to the generative AI model. Specifically, the server converts the data into JSON format and sends it to the generative AI model. For example, it generates JSON data like the one below.

[1180] json

[1181] {

[1182] "employee_id": "12345",

[1183] "course_completed": "Project Management 101",

[1184] "completion_date": "2023-10-01"

[1185] }

[1186] Step 3:

[1187] The generative AI model analyzes the input learning progress data. The input is JSON data sent from the server, and the output is the analysis results. Specifically, the generative AI model evaluates the employee's learning history and skill level, and generates feedback such as, "The next things you should learn are risk management and team leadership."

[1188] Step 4:

[1189] The generative AI model generates feedback based on the analysis results. The input is the analysis results, and the output is the feedback. Specifically, the generative AI model uses natural language generation technology to provide specific advice. For example, it generates feedback such as, "The next thing you should learn is risk management and team leadership."

[1190] Step 5:

[1191] The server receives feedback from the generative AI model and updates the employee's study plan. The input is the feedback from the generative AI model, and the output is the updated study plan. Specifically, the server adds "Risk Management 101" and "Team Leadership 101" to the employee's study schedule.

[1192] Step 6:

[1193] The terminal displays the updated study plan to the employee. The input is the updated study plan sent from the server, and the output is the study plan displayed to the employee. Specifically, the terminal sends a push notification to the employee's smartphone, displaying the message "The next course to study has been updated."

[1194] In this way, the system helps employees improve their skills efficiently.

[1195] (Application example 3)

[1196] Next, a description will be given of Application Example 3 of Form Example 3. 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."

[1197] With conventional employee skill improvement support systems, it was difficult to efficiently manage employee learning progress and provide appropriate study plans. Furthermore, there was a lack of means to manage the learning progress of factory robots and update study plans as needed, making it difficult to efficiently improve the robots' skills. This resulted in delays in improving the skills of employees and robots, leading to problems such as reduced productivity.

[1198] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 3 is realized by the following means.

[1199] In this invention, the server includes means for utilizing generative AI to input information on other jobs that employees aspire to, means for the generative AI to check employee information, identify differences with the employee's own information, and suggest necessary skills and qualifications, means for providing study plans and internal company information and managing progress, means for managing the robot's learning progress and updating the study plan as needed, means for saving and reading the robot's learning progress data, and means for generating a study plan for the robot. This makes it possible to efficiently manage the learning progress of employees and robots and provide appropriate study plans.

[1200] "Generative AI" is an artificial intelligence technology that generates new information and content based on data.

[1201] "Employee information" refers to data such as an employee's skills, qualifications, work history, and learning progress.

[1202] "Difference" refers to the gap or difference that exists between the current state and the target state.

[1203] A "study plan" is a plan that outlines the study content and schedule required to acquire specific skills or qualifications.

[1204] "Internal information" refers to information and data shared within a company, including materials and resources that can help employees improve their skills.

[1205] "Progress management" is the process of monitoring the progress of learning or work and making adjustments or providing support as needed.

[1206] A "robot" refers to an automated mechanical device that performs tasks in factories and on production lines.

[1207] "Learning progress" is data that indicates the current level of achievement or progress toward a specific learning goal.

[1208] A "learning plan" is a plan that indicates the learning content and schedule required for a robot to acquire new work procedures and skills.

[1209] "Storage" is the process of recording data so that it can be accessed at a later time.

[1210] "Loading" is the process of retrieving stored data and making it available for use again.

[1211] The system for implementing this invention utilizes generative AI to manage the learning progress of employees and robots and update study plans as needed. A specific embodiment of this system is described below.

[1212] System Configuration

[1213] The system consists of the following major components:

[1214] 1. Server: Runs the generative AI and stores and processes data.

[1215] 2. Devices: Computers and smartphones used by employees and managers.

[1216] 3. Robot: An automated mechanical device that performs tasks in a factory or on a production line.

[1217] Hardware and software used

[1218] Hardware: Servers, terminals (PCs, smartphones), factory robots

[1219] Software: Python, JSON, generative AI models

[1220] Data processing and calculation

[1221] The server processes and calculates data using the following means:

[1222] 1. Enter and confirm employee information: Enter information about other jobs that employees aspire to, and the generative AI will confirm the employee information.

[1223] 2. Calculating the difference: Generative AI automatically calculates the difference in skills and qualifications required for the desired job based on the employee's current skills and qualifications.

[1224] 3. Providing study plans and progress management: Generative AI provides study plans and internal company information and manages progress.

[1225] 4. Managing the robot's learning progress: Generative AI manages the robot's learning progress and updates the study plan as needed.

[1226] 5. Save and load data: Save and load the robot's learning progress data.

[1227] Specific examples

[1228] For example, if an employee completes an online course in "Project Management," the generative AI will receive that information and update their study plan. Or, if a factory robot completes 50% of its "Assembly Line Training," the study plan will be updated to read "In Progress: 50%."

[1229] Prompt Sentence Examples

[1230] Below is an example of a prompt sentence to input to the generative AI model.

[1231] Robot ID: robot_001

[1232] Course Name: Assembly Line Training

[1233] Progress: 50%

[1234] This system makes it possible to efficiently manage the learning progress of employees and robots and provide appropriate study plans.

[1235] The flow of the specific processing in Application Example 3 will be described with reference to FIG.

[1236] Step 1:

[1237] The user uses the terminal to input employee information and information about the desired occupation.

[1238] Input: Employee skills, qualifications, work history, learning progress, desired job information

[1239] Output: Employee information and desired job information sent to the server

[1240] Specific operation: The user enters the necessary information into the input form on the terminal and presses the send button. The terminal then sends this information to the server.

[1241] Step 2:

[1242] The server uses generative AI to check employee information and calculate the difference in skills and qualifications required for the desired job.

[1243] Input: Employee information, desired job information

[1244] Output: Difference in required skills and qualifications

[1245] Specific operation: The server inputs the received employee information and desired job information into the generative AI and calculates the difference. The generative AI compares the employee's current skills with the skills required for the desired job and calculates the difference.

[1246] Step 3:

[1247] The server uses generative AI to generate a study plan suitable for the employee and sends it to the device.

[1248] Input: Difference in required skills and qualifications

[1249] Output:Study plan

[1250] Specific operation: The server uses generative AI to generate a study plan based on the differential data. The generated study plan is sent to the device and displayed to the user.

[1251] Step 4:

[1252] The user uses the device to study based on the study plan.

[1253] Input: Study plan

[1254] Output: Learning progress data

[1255] Specific operation: The user studies according to the study plan displayed on the device and enters their progress into the device. The device then sends the learning progress data to the server.

[1256] Step 5:

[1257] The server uses generative AI to check learning progress data and update study plans as needed.

[1258] Input: Learning progress data

[1259] Output: Updated study plan

[1260] Specific operation: The server inputs the received learning progress data into the generative AI, checks the progress, updates the study plan as needed, and sends the updated study plan to the device.

[1261] Step 6:

[1262] The robot records its learning progress and sends it to the server.

[1263] Input: Robot learning progress data

[1264] Output: Learning progress data sent to the server

[1265] Specific operation: The robot records its own learning progress and periodically sends it to the server.

[1266] Step 7:

[1267] The server stores the robot's learning progress data and updates the study plan as needed.

[1268] Input: Robot learning progress data

[1269] Output: Updated robot study plan

[1270] Specific operation: The server stores the received learning progress data of the robot, checks the progress using generative AI, updates the study plan as needed, and sends it to the robot.

[1271] 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.

[1272] "Example 1"

[1273] In one embodiment of the present invention, the generative AI includes an emotion engine. The emotion engine recognizes an employee's emotional state and makes skill-up suggestions that take that emotional state into account. Specifically, if an employee is feeling stressed, the emotion engine communicates that information to the generative AI. Based on that information, the generative AI makes skill-up suggestions to the employee to reduce stress, such as suggesting online courses for stress management training or relaxation techniques.

[1274] "Example 2"

[1275] The emotion engine also automatically calculates the difference in required skills and qualifications based on the employee's emotional state. For example, if an employee is feeling happy, the emotion engine conveys that information to the generative AI. Based on that information, the generative AI makes suggestions for improving skills that will bring joy to the employee. For example, if an employee is interested in programming, the generative AI will make suggestions for improving programming skills.

[1276] "Example 3"

[1277] Furthermore, the emotion engine takes into account the employee's emotional state to manage their progress and update their study plan as needed. For example, if an employee is feeling frustrated, the emotion engine relays that information to the generative AI, which then uses that information to update the employee's study plan and provide new learning resources to reduce frustration.

[1278] The processing flow of each embodiment will be described below.

[1279] "Example 1"

[1280] Step 1: The emotion engine recognizes the employee's emotional state.

[1281] Step 2: The emotion engine communicates its emotional state to the generative AI.

[1282] Step 3: Based on this information, the generative AI makes suggestions to employees about improving their skills to reduce stress.

[1283] Step 4: The generative AI suggests online courses, for example, for stress management training or relaxation techniques.

[1284] "Example 2"

[1285] Step 1: The emotion engine recognizes the employee's emotional state.

[1286] Step 2: The emotion engine communicates this information to the generative AI.

[1287] Step 3: Based on this information, the generative AI makes suggestions for skill development that will delight employees.

[1288] Step 4: For example, if an employee is interested in programming, the generative AI will suggest ways to improve their programming skills.

[1289] "Example 3"

[1290] Step 1: The emotion engine recognizes the employee's emotional state.

[1291] Step 2: The emotion engine communicates this information to the generative AI.

[1292] Step 3: Generative AI uses this information to update employees' study plans and provide new learning resources to reduce frustration.

[1293] Example 1

[1294] Next, a description will be given of Example 1 of Form 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."

[1295] With conventional in-house reskilling systems, it was difficult to accurately grasp the gap in skills and qualifications required for the job an employee was aiming for and provide an individual study plan. Furthermore, since suggestions for improving skills were not made taking into account the emotional state of the employee, there was a problem of decreased motivation to study. Furthermore, management of learning progress was often done manually, making it impossible to achieve efficient reskilling.

[1296] 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.

[1297] In this invention, the server includes: a means for inputting information about the job type an employee is aiming for; a means for a generative AI to calculate the gap in skills and qualifications required for the desired job based on the employee's current skill and qualification information; a means for the generative AI to generate an individual study plan based on the calculated gap and provide it to the employee; a means for periodically checking the employee's learning progress and updating the study plan as necessary; a means for an emotion engine to recognize the employee's emotional state and communicate that information to the generative AI; and a means for the generative AI to make skill improvement suggestions taking the employee's emotional state into account. This makes it possible to accurately grasp the gap in skills and qualifications required for the employee's desired job and provide an individual study plan. Furthermore, because skill improvement suggestions are made taking the employee's emotional state into account, learning motivation is improved and efficient reskilling is achieved.

[1298] The "means for inputting information about the job type that an employee is aiming for" refers to a device or software that provides an interface for employees to input information about the job type that they are aiming for as their career goal.

[1299] "Generative AI" refers to artificial intelligence technology that generates necessary information based on input data. Specifically, it has the ability to calculate differences in employees' skills and qualifications and generate study plans.

[1300] "Employee current skills and qualifications" refers to data about the skills and qualifications that employees currently possess, including past work experience and qualifications they have obtained.

[1301] A "means for calculating the difference in skills and qualifications" is a device or software that compares the skills and qualifications required for the job an employee is aiming for with the employee's current skills and qualifications and calculates the difference.

[1302] "Means for generating individual study plans and providing them to employees" refers to a device or software that creates an optimal study plan for each employee based on the calculated differences in skills and qualifications, and provides it to the employee.

[1303] "Means for regularly checking employees' learning progress and updating study plans as necessary" refers to devices or software that regularly monitor employees' learning status and update their study plans as appropriate based on their progress.

[1304] An "emotion engine" is a device or software that recognizes the emotional state of employees and conveys that information to generative AI.

[1305] The "means for making suggestions for skill improvement taking into consideration emotional state" is a device or software for suggesting appropriate methods for skill improvement to employees based on the emotional state of the employees obtained from the emotion engine.

[1306] This invention relates to an in-house reskilling promotion system that utilizes generative AI. This system provides an interface where employees can input information about the job they are aiming for, and the generative AI calculates the difference in the required skills and qualifications based on that information and the employee's current skills and qualifications. It then generates an individual study plan based on this difference and provides it to the employee. It also regularly checks the employee's learning progress and updates the study plan as necessary. The generative AI, which includes an emotion engine, recognizes the employee's emotional state and makes suggestions for skill improvement that take that emotional state into account.

[1307] Hardware and software used

[1308] Hardware: Servers, devices (PCs, tablets, smartphones)

[1309] Software: Generative AI models (e.g., OpenAI GPT-4), emotion engines, database management systems (e.g., MySQL), web interfaces (e.g., React)

[1310] Specific operation of the system

[1311] 1. The user (employee) uses a terminal to access the web interface, enters the desired job information into the input form, and clicks the "Submit" button.

[1312] 2. The server receives the job information sent by the user and stores it in a database, typically using a database management system such as MySQL.

[1313] 3. The server accesses the database to retrieve the user's current skills and credentials, which are then passed to the generative AI.

[1314] 4. Generative AI (e.g., OpenAI GPT-4) calculates the difference in required skills and qualifications based on the user's desired job type and current skills and qualifications. For example, if the user is aiming to become a "project manager," the generative AI will determine that "the user lacks knowledge of project management."

[1315] 5. The generative AI generates an individual study plan based on the calculated differences. For example, it generates a study plan such as "take an online course in project management." The generated study plan is sent to the server.

[1316] 6. The server provides the generated study plan to the user, who can then view the study plan on a web interface via their device.

[1317] 7. The user follows the provided study plan, for example, enrolls in an online course and begins studying.

[1318] 8. The server periodically checks the user's learning progress and updates the study plan accordingly.

[1319] 9. The emotion engine recognizes the user's emotional state and conveys that information to the generative AI. For example, if the user is feeling stressed, it conveys that information to the generative AI.

[1320] 10. Generative AI will take into account emotional state and provide users with skill-building suggestions to reduce stress, such as suggesting online courses for stress management training or relaxation techniques.

[1321] Specific examples

[1322] For example, let's say a user wants to become a "project manager." The user types "project manager" into their device. The server receives this information and stores it in a database. The server then retrieves the user's current skills and qualifications from the database. Based on this information, the generative AI calculates the difference in skills and qualifications required to become a project manager. For example, it may determine that "knowledge of project management is lacking." Based on this difference, the generative AI generates a study plan such as "take an online course in project management" and sends it to the server. The server provides this study plan to the user.

[1323] Prompt Sentence Examples

[1324] "What is the difference between the skills and qualifications required to become a project manager? My current skills are: programming, data analysis, and basic project management."

[1325] In this way, an internal reskilling promotion system that utilizes generative AI efficiently supports employees in improving their skills.

[1326] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1327] Step 1:

[1328] The user enters job type information.

[1329] Input: Job type information the user is aiming for (e.g., project manager)

[1330] How it works: A user accesses the web interface using a device (PC, tablet, smartphone), enters information about the job they are seeking into the input form, and clicks the "Submit" button.

[1331] Output: The desired job information is sent to the server.

[1332] Step 2:

[1333] The server receives the input information and stores it in a database.

[1334] Input: Job information submitted by the user

[1335] How it works: The server receives the job information submitted by the user and stores it in a database, typically using a database management system such as MySQL.

[1336] Output: Job information is saved in the database.

[1337] Step 3:

[1338] The server retrieves the user's current skills and credentials from a database.

[1339] Input: User job title information stored in the database

[1340] How it works: The server accesses a database to retrieve the user's current skills and credentials.

[1341] Output: The acquired skills and credentials are passed to the generative AI.

[1342] Step 4:

[1343] Generative AI calculates the differences in skills and qualifications.

[1344] Input: User's desired job type and current skills and qualifications

[1345] How it works: Generative AI (e.g., OpenAI GPT-4) calculates the difference in required skills and qualifications based on the user's desired job type and current skills and qualifications. For example, if the user is aiming to become a "project manager," the generative AI will determine that "the user lacks knowledge of project management."

[1346] Output: Calculated skill or qualification difference

[1347] Step 5:

[1348] The generative AI generates an individual study plan and sends it to the server.

[1349] Input: Calculated skill or qualification difference

[1350] How it works: The generative AI generates an individual study plan based on the calculated differences. For example, it generates a study plan such as "take an online course in project management." The generated study plan is sent to the server.

[1351] Output: Generated study plan

[1352] Step 6:

[1353] The server provides the study plan to the user.

[1354] Input: Generated study plan

[1355] Operation: The server provides the generated study plan to the user, who can then view it on a web interface via their device.

[1356] Output: User can check the study plan

[1357] Step 7:

[1358] The user progresses through their studies according to the study plan.

[1359] Input: Provided study plan

[1360] Action: The user follows the provided study plan, for example, enrolls in an online course and begins studying.

[1361] Output: Learning progress

[1362] Step 8:

[1363] The server periodically checks the learning progress and updates the study plan as needed.

[1364] Input: User's learning progress

[1365] How it works: The server periodically checks the user's learning progress and updates the study plan accordingly.

[1366] Output: Updated study plan

[1367] Step 9:

[1368] The emotion engine recognizes the user's emotional state and conveys it to the generative AI.

[1369] Input: User's emotional state

[1370] How it works: The emotion engine recognizes the user's emotional state and, for example, if the user is feeling stressed, it conveys that information to the generative AI.

[1371] Output: Emotional state information

[1372] Step 10:

[1373] Generative AI takes into account your emotional state and makes suggestions for improving your skills.

[1374] Input: Emotional state information

[1375] How it works: Generative AI takes into account emotional state and makes suggestions to the user to improve their skills to reduce stress, such as suggesting online courses to learn stress management training or relaxation techniques.

[1376] Output: Suggestions for skill development

[1377] (Application example 1)

[1378] Next, a description will be given of Application Example 1 of Form 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."

[1379] Conventional reskilling systems are primarily targeted at human employees and do not address the skill development and stress management of factory robots. This poses a challenge, making it difficult to efficiently develop the skills and optimize the performance of factory robots. Furthermore, there are no systems available that evaluate robot performance and make suggestions to reduce stress. This could potentially lead to a decline in the productivity of robots in factories.

[1380] 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.

[1381] In this invention, the server includes: a means for utilizing generative AI to input information on other jobs that employees aspire to; a means for the generative AI to check employee information, compare it with its own and suggest necessary skills and qualifications; a means for providing study plans and internal company information and managing progress; a means for inputting information on the work and tasks that the robot aims to do; a means for comparing the robot's current skills and abilities with the required skills and abilities to calculate the difference; a means for generating an individual training plan and providing it to the robot; a means for periodically checking the robot's operation data and updating the training plan as necessary; and a means for evaluating the robot's operation performance and making suggestions to reduce stress. This enables efficient skill development and performance optimization of factory robots.

[1382] "Generative AI" is a type of artificial intelligence that generates new information and suggestions based on input data.

[1383] An "employee" is a person who belongs to a company or organization and performs work for that company or organization.

[1384] "Job information" refers to detailed information about a specific job or role, including required skills, qualifications, and job duties.

[1385] "Skills" refer to the abilities and techniques required to carry out a specific job or task.

[1386] "Qualification" refers to the certification or certificate required to perform a particular job or function.

[1387] A "study plan" refers to a plan that specifically outlines the study content and study schedule necessary to achieve a goal.

[1388] "Internal information" refers to information shared within a company or organization, including data and materials related to business operations.

[1389] "Progress management" refers to the process of monitoring the progress of planned work or learning and making adjustments or improvements as needed.

[1390] A "robot" refers to a mechanical device that performs tasks automatically according to programmed instructions.

[1391] "Task information" refers to detailed information about a specific task or job, including necessary procedures and conditions.

[1392] "Ability" refers to the knowledge, skills, physical strength, etc. required to carry out a specific task or work.

[1393] A "training plan" refers to a plan that specifies the training content and schedule for improving specific skills and abilities.

[1394] "Operation data" refers to data related to the operation of a robot or machine, including the speed and accuracy of the operation, error information, etc.

[1395] "Motion performance" refers to an indicator that evaluates how efficiently and accurately a robot or machine can perform a specific task.

[1396] "Stress reduction" refers to methods or means for reducing mental or physical strain.

[1397] The system for carrying out this invention utilizes generative AI to promote skill development for employees and robots. A specific embodiment of this system will be described below.

[1398] System configuration

[1399] The server includes the following means:

[1400] 1. A means for inputting job information: A means for inputting information about other jobs that employees aspire to, as well as information about the work and tasks that the robot aims to perform.

[1401] 2. Skill difference calculation method: A method for generative AI to calculate the difference between the current skills and abilities of employees or robots and the skills and abilities required for the desired job type or task.

[1402] 3. Training plan generation means: A means for generating individual study and training plans and providing them to employees and robots.

[1403] 4. Progress management method: A method for regularly checking the learning progress and operation data of employees and robots, and updating the plan as necessary.

[1404] 5. Performance evaluation tools: A tool to evaluate the robot's operational performance and provide suggestions to reduce stress.

[1405] Hardware and software used

[1406] Hardware: Servers, employee devices (PCs, smartphones), robots

[1407] Software: Generative AI model, emotion engine, database management system

[1408] Data processing and calculation

[1409] The server receives job and task information input from employees and robots, and uses a generative AI model to calculate the required skills and qualification gaps. Based on this, it generates individual study and training plans and provides them to employees and robots. It also uses progress management tools to regularly check learning progress and behavior data and update plans as needed. It also uses an emotion engine to evaluate the robot's behavioral performance and make suggestions to reduce stress.

[1410] Specific examples

[1411] For example, in a scenario where a robot aiming to perform welding tasks needs to generate a training plan to close the gap between its current and desired skills and adjust the plan based on performance data, the server might input the following prompt into the generative AI model:

[1412] If a robot is aiming to perform welding tasks, calculate the gap between current and desired skills, generate a personalized training plan, adjust the training plan based on the robot's performance data, and provide suggestions for stress reduction if stress levels are high.

[1413] Based on this prompt, the generative AI model can provide specific training plans and stress reduction suggestions to help the robot improve its skills.

[1414] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1415] Step 1:

[1416] The user uses the terminal to input information about other jobs that employees aspire to, as well as information about the work and tasks that the robot aims to perform.

[1417] Input: Job type information, task information

[1418] Output: Entered job type information, task information

[1419] Step 2:

[1420] The server uses a generative AI model to calculate the difference between the current skills and abilities of employees and robots and the skills and abilities required for the desired job or task, based on the input job and task information.

[1421] Input: Job information, task information, current skills and abilities

[1422] Output: Difference in skills and abilities

[1423] Specific operation: The server retrieves the current skills and abilities of employees and robots from the database and inputs them into the generative AI model. The generative AI model calculates the difference in the required skills and abilities and returns it to the server.

[1424] Step 3:

[1425] The server generates individual study and training plans based on differences in skills and abilities and provides them to employees and robots.

[1426] Input: Difference in skills or abilities

[1427] Output: Study plan, training plan

[1428] Specific operation: The server uses a generative AI model to generate specific study and training plans to fill gaps in skills and abilities, and sends them to the device.

[1429] Step 4:

[1430] The server uses a progress management means to periodically check the learning progress and operation data of employees and robots, and updates the plan as necessary.

[1431] Input: Learning progress, behavior data

[1432] Output: Updated study plan, training plan

[1433] Specific behavior: The server periodically collects learning progress and behavior data from the device and robot, reevaluates the plan using the generative AI model, and updates it as necessary.

[1434] Step 5:

[1435] The server uses an emotion engine to evaluate the robot's performance and make suggestions to reduce stress.

[1436] Input: Operational performance data

[1437] Output: Stress reduction suggestions

[1438] Specific Actions: The server collects the robot's performance data and uses an emotion engine to assess its stress level. If the stress level is high, the server uses a generative AI model to generate specific suggestions for stress reduction and provides them to the robot.

[1439] Example 2

[1440] Next, a description will be given of Example 2 of Form 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."

[1441] Conventional career support systems have difficulty calculating the difference between a user's current skills and the skills required for the desired job, and providing an appropriate learning plan. Furthermore, because the system does not consider the user's emotional state when proposing ways to improve skills, it is difficult to maintain the user's motivation. This prevents users from effectively improving their skills, and the system provides insufficient support for achieving career goals.

[1442] 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.

[1443] In this invention, the server utilizes generative artificial intelligence and includes: means for inputting information about the job type the user is aiming for; means for the generative artificial intelligence to check the user information, identify differences between the user's own information and the information and suggest necessary skills and qualifications; means for providing study plans and organizational information and managing progress; means for analyzing the user's emotional state using an emotion analysis engine; and means for the generative artificial intelligence to suggest skill development based on the emotional state. This makes it possible to accurately calculate the difference between the user's current skills and the skills required for the desired job and provide an appropriate study plan. Furthermore, by suggesting skill development that takes the user's emotional state into consideration, it is possible to maintain the user's motivation and support effective skill development.

[1444] "Generative AI" is an AI technology that calculates the difference in required skills and qualifications based on information input by the user, and generates appropriate study plans and suggestions.

[1445] "Users" are individuals who use the system to advance their careers.

[1446] "Occupation information" is information about the occupation or position that the user is aiming for.

[1447] "Skills" are the knowledge and techniques required to perform a specific job or role.

[1448] A "qualification" is a certification or certificate required to perform a particular job or role.

[1449] A "study plan" is a plan that indicates the specific study content and methods for acquiring the skills and qualifications necessary for the job the user is aiming for.

[1450] "In-organization information" refers to learning resources and support information provided within the organization to which the user belongs.

[1451] "Progress management" is the process of monitoring the progress of users' learning and skill development, and making adjustments and providing support as needed.

[1452] An "emotion analysis engine" is a technology that analyzes a user's emotional state and makes appropriate suggestions based on that information.

[1453] "Emotional state" refers to the emotional state, such as happiness, sadness, or excitement, that a user feels while using the system.

[1454] This invention is a system that utilizes generative artificial intelligence and a sentiment analysis engine to propose skills and qualifications required for the job a user is aiming for and provide a study plan. Specific embodiments of this system are described below.

[1455] The server utilizes generative artificial intelligence to provide a means for users to input information about the job they are aiming for. Users access the system's web interface and input the job they are aiming for (e.g., project manager). For example, a user might input "project manager."

[1456] Next, the server obtains the user's current skill data. This data is collected from the user's past work history and self-evaluation sheets. For example, the user's skill data might be "Programming skills: intermediate, communication skills: advanced."

[1457] The server uses generative artificial intelligence (e.g., OpenAI's GPT-4) to calculate the difference between the user's current skills and the skills required for the desired job. For example, if the skills required for a project manager are "Project management skills: advanced, leadership skills: intermediate," the server calculates the difference between these skills and the user's current skills.

[1458] The server then generates a learning plan based on the skill gap, making specific suggestions such as "take an online course on project management" or "read a book on leadership." The generated learning plan is then displayed on the user's device.

[1459] Furthermore, the server uses an emotion analysis engine (e.g., emotion recognition software) to analyze the user's emotional state in real time. For example, if the user feels joy while using the system, that information will be detected by the emotion analysis engine.

[1460] The server transmits the emotional state information obtained from the emotion analysis engine to the generative AI. Based on this information, the generative AI makes suggestions for skill improvement that will bring joy to the user. For example, if a user is interested in programming, it will make suggestions for improving programming skills.

[1461] As a concrete example, consider the case where a user is aiming to become a project manager. The user enters "project manager" as the desired occupation into the system. The server obtains the user's current skill data and calculates the skill difference using a generative artificial intelligence model. For example, if the user's current skills are "programming skills: intermediate, communication skills: advanced" and the skills required for a project manager are "project management skills: advanced, leadership skills: intermediate," the server will generate a study plan such as "take an online course in project management" and "read books on leadership."

[1462] Example prompt sentence:

[1463] Calculate the skill gap required for the user to become a project manager and propose a study plan. The user's current skills are "Programming Skills: Intermediate, Communication Skills: Advanced." The skills required for a project manager are "Project Management Skills: Advanced, Leadership Skills: Intermediate."

[1464] In this way, a system is realized in which the server, terminal, and user work together to support the user's career advancement.

[1465] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1466] Step 1:

[1467] The user inputs the job type he or she is aiming for.

[1468] The user accesses the system's web interface and enters the desired job title (e.g., project manager). The entered job title information is sent to the server. Specifically, the user enters "project manager" in the search bar and clicks the submit button. Input: desired job title information, Output: job title information sent to the server.

[1469] Step 2:

[1470] The server retrieves the user's current skill data.

[1471] The server queries and retrieves the user's skill data from the database. This data is collected from the user's past work history and self-assessment sheets. Specifically, the server retrieves the data using an SQL query such as "SELECT FROM skills WHERE user_id = '12345'". Input: User ID, Output: User's current skill data.

[1472] Step 3:

[1473] The server calculates the skill difference using a generative AI model.

[1474] The server sends a prompt to a generative AI model (e.g., OpenAI's GPT-4) and calculates the difference between the user's current skills and the skills required for the desired job. Specifically, the server sends the prompt, "The user's current skills are 'Programming skills: intermediate, communication skills: advanced'. The skills required for a project manager are 'Project management skills: advanced, leadership skills: intermediate'. Please calculate the skill difference." to the generative AI model. Input: User's current skill data, skill information required for the desired job, Output: Skill difference.

[1475] Step 4:

[1476] The server generates a study plan and provides it to the user.

[1477] The server generates a learning plan based on the skill difference. Specifically, the server generates specific suggestions such as "take an online course on project management" or "read a book on leadership" and displays them on the user's dashboard. Input: skill difference, output: learning plan.

[1478] Step 5:

[1479] The server uses an emotion analysis engine to analyze the user's emotional state.

[1480] The server uses an emotion analysis engine (e.g., emotion recognition software) to analyze the user's emotional state in real time. Specifically, the server sends the user's facial expression and voice data to the emotion analysis engine, which then analyzes the user's emotional state. Input: User's facial expression and voice data, Output: Information on the user's emotional state.

[1481] Step 6:

[1482] The server makes suggestions for skill improvement based on the emotional state.

[1483] The server transmits the emotional state information obtained from the emotion analysis engine to the generative AI. Based on this information, the generative AI makes suggestions for skill improvement that will bring joy to the user. Specifically, the server generates suggestions such as "Take an online course on the latest programming language to improve your programming skills" and provides them to the user. Input: Emotional state information, Output: Skill improvement suggestions based on emotional state.

[1484] (Application example 2)

[1485] Next, a description will be given of Application Example 2 of Form 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."

[1486] Conventional skill improvement support systems can calculate the difference between an employee's current skills and the skills required for the desired job and provide a study plan, but they lack suggestions for improving motivation that take into account the employee's emotional state.In addition, there is no system specialized in supporting the skill improvement of factory workers, making it difficult to efficiently improve workers' skills.

[1487] 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.

[1488] In this invention, the server includes means for utilizing generative AI to input information on other jobs that employees aspire to, means for the generative AI to check employee information, identify differences with the employee's own information, and suggest necessary skills and qualifications, means for providing study plans and internal company information and managing progress, means for analyzing the emotional state of employees using an emotion engine and making suggestions to increase motivation, and means for supporting the skill development of factory workers. This makes it possible to make suggestions to increase motivation that take into account the emotional state of employees, thereby enabling efficient skill development of factory workers.

[1489] "Generative AI" refers to artificial intelligence that generates new information and suggestions based on input data.

[1490] "Employee information" refers to information about individuals, such as an employee's skills, qualifications, work history, and desired job type.

[1491] The "difference" refers to the gap between an employee's current skills and qualifications and those required for the job they are aiming for.

[1492] A "study plan" is a specific learning plan for an employee to acquire the skills and qualifications necessary for the job they are aiming for.

[1493] "Internal information" refers to internal company resources and information available to employees.

[1494] "Progress management" is the process of monitoring employees' progress in learning and skill development and making adjustments and providing support as needed.

[1495] The "emotion engine" is a system that analyzes employees' emotional state and makes appropriate suggestions and feedback based on that information.

[1496] "Motivational suggestions" are specific suggestions that take into account employees' emotional state to improve their motivation to learn and work.

[1497] "Factory workers" means employees working in a factory.

[1498] "Upskilling support measures" are methods and tools that help workers acquire new skills and improve existing skills.

[1499] A system for implementing the present invention includes a generative AI, an emotion engine, and various means for supporting the skill development of factory workers. Specific embodiments are described below.

[1500] System Configuration

[1501] The system consists of the following main components:

[1502] 1. Generative AI: Using the OpenAI API, calculate the difference between an employee's current skills and the skills required for the desired job and generate a study plan.

[1503] 2. Emotion Engine: Uses an emotion recognition library to analyze employees' emotional state and provide motivational suggestions.

[1504] 3. User Interface: An interface for factory workers to input information and review the generated study plans and suggestions.

[1505] Hardware and Software

[1506] Hardware: Factory robots

[1507] Software: Python, OpenAI API, emotion recognition library

[1508] Data processing and calculation

[1509] 1. Enter employee information: The user enters the job type they are aiming for and their current skills.

[1510] 2. Calculating skill gaps: Generative AI calculates the gap between an employee's current skills and the skills required for the desired job.

[1511] 3. Study plan generation: The generative AI generates a specific study plan based on the calculated differences.

[1512] 4. Emotional state analysis: The emotion engine analyzes the emotional state of employees and provides that information to the generative AI.

[1513] 5. Motivation suggestions: Based on information from the emotion engine, the generative AI makes suggestions to increase employee motivation.

[1514] Specific examples

[1515] For example, if Worker A is aiming to become a "project manager," the system calculates the difference between his current skills (programming: 3, communication: 4) and his desired skills (project management: 5, leadership: 4) and generates a study plan. If the emotion engine analyzes Worker A's emotional state as "joy," the generative AI will suggest improving his programming skills.

[1516] Prompt Sentence Examples

[1517] Generate a study plan based on the following skill gaps: {'Project Management': 5, 'Leadership': 4}

[1518] In this way, it becomes possible to propose ways to improve motivation that take into account the emotional state of employees, thereby enabling efficient skill development for factory workers.

[1519] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1520] Step 1:

[1521] The user inputs the desired job type and current skill information. The user uses a terminal to input the desired job type (e.g., project manager) and current skills (e.g., programming: 3, communication: 4). The input data is sent to the server.

[1522] Step 2:

[1523] The server uses generative AI to calculate the difference between the user's current skills and the skills required for the desired job. The server compares the input current skill information with the skill information required for the desired job and calculates the difference. For example, if project management skill level 5 is required and the user's current skill level is 3, the difference will be 2.

[1524] Step 3:

[1525] The server uses a generative AI to generate a study plan based on the calculated difference. The server sends the difference information to the generative AI as a prompt to generate a study plan. For example, the server sends the prompt "Please generate a study plan based on the following skill gaps: {'Project management': 2}" to the generative AI. The generative AI generates an appropriate study plan (e.g., take an online course in project management) and returns it to the server.

[1526] Step 4:

[1527] The server uses the emotion engine to analyze the user's emotional state. The server inputs the user's emotional data (e.g., facial expressions, tone of voice, etc.) into the emotion engine and analyzes the emotional state (e.g., joy, sadness). The emotion engine returns the analysis results to the server.

[1528] Step 5:

[1529] The server uses a generative AI to make suggestions to increase motivation based on the user's emotional state. The server inputs the analysis results from the emotion engine into the generative AI, which then generates suggestions to increase motivation. For example, if the user is feeling "joy," the generative AI will generate a suggestion such as, "Let's aim to improve your programming skills!"

[1530] Step 6:

[1531] The server provides the generated study plan and motivation suggestions to the user. The server sends the generated study plan and motivation suggestions to the terminal so that the user can check them. The user can check the study plan and motivation suggestions through the terminal and put them into practice.

[1532] In this way, it becomes possible to propose ways to improve motivation that take into account the emotional state of the user, thereby enabling efficient skill improvement for factory workers.

[1533] Example 3

[1534] Next, a description will be given of Example 3 of Form Example 3. 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."

[1535] With conventional employee skill improvement support systems, it was difficult to grasp employees' learning progress and emotional state in real time and flexibly update their study plans based on that information. As a result, employees were unable to improve their skills efficiently, leading to problems such as a decline in motivation to learn and increased frustration.

[1536] The specific processing by the specific processing unit 290 of the data processing device 12 in the third embodiment is realized by the following means.

[1537] In this invention, the server includes: means for utilizing generative AI to input information on other jobs that the employee aspires to; means for the generative AI to check the employee information, determine differences with the employee's own information, and suggest necessary skills and qualifications; means for providing study plans and internal company information and managing progress; means for receiving the employee's learning progress information and inputting it into the generative AI model; means for the generative AI model to update the study plan based on the learning progress; means for the terminal to analyze the employee's emotional state and send the emotional information to the server; and means for the generative AI model to re-update the study plan based on the emotional information. This makes it possible to grasp the employee's learning progress and emotional state in real time and flexibly update the study plan based on that information.

[1538] "Generative AI" is an artificial intelligence technology that generates new information and suggestions based on input data.

[1539] "Employee information" refers to information that includes data such as an employee's skills, qualifications, learning progress, and emotional state.

[1540] A "study plan" is a plan that shows the learning content and schedule required for employees to improve their skills.

[1541] "Progress management" is the process of monitoring employees' learning progress and updating their study plans as needed.

[1542] "Emotional state" is information that indicates an employee's current emotional or psychological state.

[1543] The "emotion engine" is a technology that analyzes employees' facial expressions, tone of voice, etc. to determine their emotional state.

[1544] A "server" is a computer system that receives, stores, and processes data.

[1545] A "terminal" is a device used by a user, such as a PC, tablet, or smartphone.

[1546] A "prompt" is an instruction given to a generative AI to generate specific information or suggestions.

[1547] This invention is a system that grasps an employee's learning progress and emotional state in real time and flexibly updates the study plan based on that. A specific embodiment of this system will be described below.

[1548] Hardware and software used

[1549] Hardware: Servers, devices (PCs, tablets, smartphones)

[1550] Software: Generative AI models (e.g., GPT-4), emotion engines

[1551] System configuration

[1552] 1. Checking user learning progress

[1553] A user takes an online course in project management.

[1554] When you complete a course, the device records the completion information.

[1555] As a specific operation, the user clicks the "Done" button.

[1556] 2. Receipt of learning progress information by the server

[1557] The terminal transmits the user's learning progress information to the server.

[1558] The server receives this information and stores it in a database.

[1559] Specifically, the terminal sends an HTTP request to the server, and the server processes the request.

[1560] 3. Updating study plans with generative AI models

[1561] The server inputs the received learning progress information into the generative AI model.

[1562] The generative AI model generates a new study plan based on the user's progress.

[1563] Specifically, the server sends an API request to the generating AI model and receives the generated study plan.

[1564] 4. Checking emotional state via device

[1565] While the user is learning, the device uses the camera and microphone to analyze their emotional state.

[1566] The emotion engine analyzes the user's facial expressions and tone of voice to determine their emotional state.

[1567] Specifically, the device calls the emotion engine's API and obtains the analysis results.

[1568] 5. Sending emotional information to the server

[1569] The device transmits the analyzed emotion information to the server.

[1570] The server receives this information and stores it in a database.

[1571] Specifically, the terminal sends an HTTP request to the server, and the server processes the request.

[1572] 6. Re-updating study plans using generative AI models

[1573] The server inputs the emotional information into the generative AI model.

[1574] The generative AI model then updates the user's study plan based on the emotional information.

[1575] Specifically, the server sends an API request to the generative AI model and receives the generated new study plan.

[1576] Specific examples

[1577] Prompt Sentence Examples

[1578] "User completed an online course on project management. What courses should they take next?"

[1579] "User is frustrated. Can you suggest learning resources to help alleviate their frustration?"

[1580] This system monitors the user's learning progress and emotional state in real time and can flexibly update the study plan based on the results. This allows employees to efficiently improve their skills. The flow of the specific processing in Example 3 will be explained using Figure 21.

[1581] Step 1:

[1582] A user takes and completes an online course on project management.

[1583] Input: A user takes an online course and clicks the Finish button.

[1584] Specific operation: When the user clicks the "Done" button, the device records the completion information.

[1585] Output: Learning progress information is logged to the terminal.

[1586] Step 2:

[1587] The terminal transmits the user's learning progress information to the server.

[1588] Input: Learning progress information recorded on the device.

[1589] Specific operation: The device sends an HTTP request to the server and transmits learning progress information.

[1590] Output: The server receives the learning progress information and stores it in a database.

[1591] Step 3:

[1592] The learning progress information received by the server is input into the generative AI model.

[1593] Input: Learning progress information stored on the server.

[1594] Specific operation: The server sends an API request to the generated AI model and inputs learning progress information.

[1595] Output: The generative AI model generates a new study plan.

[1596] Step 4:

[1597] A generative AI model generates a new study plan based on the user's progress.

[1598] Input: Learning progress information.

[1599] How it works: A generative AI model analyzes learning progress information and suggests next courses and resources to learn.

[1600] Output: New study plan.

[1601] Step 5:

[1602] The device analyzes the user's emotional state.

[1603] Input: The user's facial expression and tone of voice.

[1604] Specific operation: The device uses the camera and microphone to call the emotion engine's API and analyze the emotional state.

[1605] Output: Emotional state analysis result.

[1606] Step 6:

[1607] The device transmits the analyzed emotion information to the server.

[1608] Input: Emotional state analysis results.

[1609] Specific operation: The device sends an HTTP request to the server and transmits emotion information.

[1610] Output: The server receives the emotion information and stores it in a database.

[1611] Step 7:

[1612] The server inputs the emotional information into the generative AI model.

[1613] Input: Emotion information stored on the server.

[1614] Specific operation: The server sends an API request to the generated AI model and inputs emotion information.

[1615] Output: The generative AI model updates the study plan again based on the emotional information.

[1616] Step 8:

[1617] The generative AI model then updates the user's study plan based on the emotional information.

[1618] Input: Emotion information.

[1619] Specific behavior: The generative AI model analyzes emotional information and suggests new learning resources to reduce frustration.

[1620] Output: Updated study plan.

[1621] (Application example 3)

[1622] Next, a description will be given of Application Example 3 of Form Example 3. 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."

[1623] Conventional employee skill improvement systems manage progress without taking into account the emotional state of employees, which often leads to frustration and reduced learning efficiency. Furthermore, when factory robot operators learn new operating procedures and safety protocols, they often lack adequate support, which can slow their learning progress. To solve these problems, a system that takes into account the emotional state of employees and operators and provides appropriate learning resources is needed.

[1624] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 3 is realized by the following means.

[1625] In this invention, the server includes means for utilizing generative AI to input information on other jobs that employees aspire to, means for the generative AI to check employee information, identify differences with the employee's own, and suggest necessary skills and qualifications, means for providing study plans and internal company information and managing progress, means for monitoring the employee's emotional state using an emotion engine, means for the generative AI to update the study plan based on information from the emotion engine and provide appropriate learning resources, means for periodically checking the operator's learning progress and emotional state, and means for updating the study plan when the operator feels frustrated. This makes it possible to efficiently improve the skills of employees and operators while taking into account their emotional state.

[1626] "Generative AI" is artificial intelligence that generates new data and plans based on input information.

[1627] "Employee information" refers to information that includes data such as an employee's skills, qualifications, work history, and learning progress.

[1628] A "diff" refers to the difference that exists between the current state and the desired state.

[1629] A "study plan" is a plan that includes the study schedule and resources for employees to acquire the skills and qualifications they are aiming for.

[1630] "Internal information" refers to information such as learning resources and training programs provided within a company.

[1631] Progress management is the process of tracking employees' learning and skill development progress and making adjustments as needed.

[1632] The "Emotion Engine" is a system that analyzes employees' emotional states and provides that information.

[1633] "Monitoring" refers to the continuous observation and recording of a particular condition or progress.

[1634] "Learning resources" refers to the learning materials and tools employees use to acquire skills and qualifications.

[1635] "Operator" refers to the person who operates factory robots and machines.

[1636] "Frustration" refers to the stress and dissatisfaction felt when faced with a situation that makes it difficult to achieve a goal.

[1637] A system for implementing this invention includes a generative AI, an emotion engine, a learning progress tracker, and emotion monitoring hardware. The specific configuration and operation of the system are described below.

[1638] The server utilizes generative AI to provide a means for employees to input information about other jobs they aspire to. When an employee inputs the job information they are aiming for, the generative AI checks the employee information and automatically calculates the difference between their current skills and qualifications and the skills and qualifications required for the desired job. This allows employees to clearly understand the skills and qualifications they need.

[1639] The server then provides study plans and internal company information, and provides a means for progress management. The generative AI periodically checks employees' learning progress and updates study plans as necessary, allowing employees to improve their skills efficiently.

[1640] Furthermore, the server provides a means to monitor employees' emotional states using an emotion engine. The emotion engine analyzes employees' emotional states using cameras and sensors and transmits that information to the generative AI. The generative AI updates study plans based on the information from the emotion engine and provides appropriate learning resources. For example, if an employee is feeling frustrated, the generative AI uses that information to provide new learning resources to alleviate the frustration.

[1641] As a concrete example, consider a factory robot operator learning how to operate a new machine. If the operator is frustrated, the emotion engine detects this information and the generative AI updates the operator's learning plan, for example by providing a simpler instructional video or interactive simulation.

[1642] An example of a prompt is as follows:

[1643] "Operators are experiencing frustration while learning new machine operating procedures. Please update operator learning plans and provide new learning resources to reduce frustration."

[1644] In this way, the server can efficiently improve the skills of employees and operators while taking into account their emotional state.

[1645] The flow of the specific processing in Application Example 3 will be described with reference to FIG.

[1646] Step 1:

[1647] The user uses the terminal to input information about the job type he or she is aiming for.

[1648] Input: Information about the job you are aiming for

[1649] Output: The desired job information is sent to the server

[1650] Specific operation: The user enters the desired job information into the input form on the terminal and presses the send button. The server receives this information.

[1651] Step 2:

[1652] The server uses generative AI to check employee information and calculate the difference between current skills and qualifications and those required for the desired job.

[1653] Input: Employee information, desired job type information

[1654] Output: Difference in skills and qualifications

[1655] Specific operation: The server retrieves employee skills and qualifications from the employee information database, compares them with the skills and qualifications required for the job the generative AI is aiming for, and calculates the difference.

[1656] Step 3:

[1657] The server uses generative AI to create a study plan and provides it to the device.

[1658] Input: Difference in skills and qualifications

[1659] Output:Study plan

[1660] Specific operation: Based on the differences in skills and qualifications, the generative AI creates a study plan for employees to acquire the necessary skills and qualifications and sends it to the device.

[1661] Step 4:

[1662] The server uses an emotion engine to monitor the emotional state of employees.

[1663] Input: Employee emotion data (obtained from cameras and sensors)

[1664] Output: Emotional state information

[1665] Specific operation: The emotion engine analyzes data obtained from cameras and sensors to determine the employee's emotional state, which is then sent to the server.

[1666] Step 5:

[1667] Based on the information from the emotion engine, the server uses generative AI to update the study plan and provide appropriate learning resources.

[1668] Input: Emotional state information, current study plan

[1669] Output: Updated study plan, learning resources

[1670] How it works: Based on emotional state information, generative AI updates study plans and provides appropriate learning resources (e.g., brief instructional videos or interactive simulations) if employees are feeling frustrated.

[1671] Step 6:

[1672] The server periodically checks the operator's learning progress and emotional state and updates the learning plan as needed.

[1673] Input: learning progress data, emotional state information

[1674] Output: Updated lesson plan

[1675] Specific operation: The server periodically checks the learning progress data and emotional state information, and the generative AI updates the learning plan as needed. If the operator is feeling frustrated, it provides appropriate learning resources.

[1676] 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.

[1677] 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> ) 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.

[1678] Another example of generative AI is Gemini (internet search engine). <url: https: gemini.google.com ?hl="ja">) are mentioned.

[1679] 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.

[1680] [Third embodiment]

[1681] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[1682] 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.

[1683] 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).

[1684] 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.

[1685] 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.

[1686] 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).

[1687] 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.

[1688] 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.

[1689] 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.

[1690] 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.

[1691] 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.

[1692] Next, the specific processing by the specific processing unit 290 of the data processing device 12 will be described.

[1693] "Example 1"

[1694] One embodiment of the present invention is an in-house reskilling promotion system that utilizes generative AI. This system provides an interface where employees can input information about the job they are aiming for. Based on this input information and the employee's current skills and qualifications, the generative AI calculates the difference in the required skills and qualifications. Based on this difference, the system then generates an individual study plan and provides it to the employee. The system also periodically checks the employee's learning progress and updates the study plan as necessary.

[1695] "Example 2"

[1696] As a specific example, if an employee wants to become a project manager, they enter "project manager" as the desired job title into the system. The generative AI calculates the difference between the employee's current skills (e.g., programming skills and communication skills) and the skills required for a project manager (e.g., project management skills and leadership skills). Based on this difference, the system generates a study plan (e.g., taking an online course on project management, reading books on leadership, etc.) and provides it to the employee.

[1697] "Example 3"

[1698] Furthermore, the generative AI periodically monitors employees' learning progress. For example, if an employee completes an online project management course, the generative AI receives that information and updates their study plan. In this way, the system helps employees improve their skills efficiently.

[1699] The processing flow of each embodiment will be described below.

[1700] "Example 1"

[1701] Step 1: Employees enter information about the job they are aiming for through the system interface.

[1702] Step 2: The generative AI calculates the difference in required skills and qualifications based on the employee's current skills and qualifications and the input job information.

[1703] Step 3: The generative AI generates an individual study plan based on the calculated differences and provides it to the employee.

[1704] Step 4: Generative AI periodically checks employees' learning progress and updates their study plans as needed.

[1705] "Example 2"

[1706] Step 1: An employee enters "Project Manager" as the job title they are aiming for in the system.

[1707] Step 2: The generative AI calculates the difference between an employee's current skills (e.g., programming skills and communication skills) and the skills required for a project manager (e.g., project management skills and leadership skills).

[1708] Step 3: Generative AI generates a study plan based on these differences (e.g., taking an online course in project management, reading a book on leadership, etc.) and provides it to the employee.

[1709] Step 4: The generative AI periodically checks the employee's learning progress. For example, if an employee completes an online course on project management, it receives that information and updates the employee's study plan.

[1710] Step 1: Generative AI periodically checks employees' learning progress.

[1711] Step 2: The generative AI receives the employee's learning progress information and updates the study plan as needed.

[1712] Step 3: In this way, the system helps employees improve their skills efficiently.

[1713] Example 1

[1714] Next, a description will be given of Example 1 of Form 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."

[1715] With conventional reskilling systems, it was difficult to accurately grasp the gap in skills and qualifications required for the job an employee was aiming for and to create an individual study plan. In addition, efficient reskilling could not be achieved because learning progress management and study plan updates were done manually. This led to problems such as delays in employee skill development and a decline in the company's overall competitiveness.

[1716] 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.

[1717] In this invention, the server includes a means for inputting information about the job type the employee is aiming for, a means for the generative AI model to calculate the gap in required skills and qualifications based on the employee's current skills and qualification information, a means for generating an individual study plan based on the calculated gap information, a means for providing the generated study plan to the employee, and a means for periodically checking the employee's learning progress and updating the study plan as necessary. This allows the employee to accurately understand the gap in skills and qualifications required for the job they are aiming for, enabling efficient reskilling.

[1718] The "means for inputting information about the job type that an employee aims for" is a function that provides an interface for inputting information about the job type that an employee has set as his or her career goal.

[1719] A "generative AI model" is an artificial intelligence model that calculates the difference in skills and qualifications required for a desired job based on an employee's current skills and qualifications.

[1720] "Means for calculating the difference in required skills and qualifications" refers to a function that uses a generative AI model to calculate the difference between an employee's current skills and qualifications and the skills and qualifications required for the desired job.

[1721] The "means for generating individual study plans" is a function that automatically creates a customized study plan for each employee based on the calculated difference information.

[1722] The "means of providing study plans to employees" is a function that presents the generated study plans to employees and provides guidelines for advancing their studies.

[1723] "Means for regularly checking learning progress" refers to a function that periodically checks the progress of employees as they proceed with their studies according to their study plan.

[1724] "Means for updating study plans" refers to the function of amending and updating existing study plans as needed according to the employee's learning progress.

[1725] This invention relates to an in-house reskilling promotion system that utilizes a generative AI model. This system involves inputting information about the job type an employee is aiming for, and the generative AI model calculates the gap in skills and qualifications required based on the employee's current skills and qualifications, and then generates and provides an individual study plan. It also includes a function to regularly check the employee's learning progress and update the study plan as necessary.

[1726] Hardware and software used

[1727] The system uses the following hardware and software:

[1728] Server: Built using Python's Flask framework. The database uses MySQL.

[1729] Generative AI models: For example, use OpenAI's GPT-4.

[1730] Device: Web browser used by employees. The interface is built using HTML, CSS, and JavaScript.

[1731] Explanation of program processing

[1732] Server Processing

[1733] The server provides an interface for employees to input their desired job information. After employees input their job information, the server receives the information and stores it in a database. The server then retrieves the employee's current skills and qualifications from the database and sends prompts to the generative AI model.

[1734] Processing generative AI models

[1735] Based on the prompt received, the generative AI model calculates the difference between the employee's current skills and the skills required for the desired job. This difference information is returned to the server in JSON format.

[1736] Server processing (study plan generation)

[1737] The server generates an individual study plan based on the differential information returned by the generative AI model. This study plan is automatically generated using a Python script and provided to the employee.

[1738] Terminal handling

[1739] The employee's device displays the study plan provided by the server, and the employee proceeds with their studies according to this plan.

[1740] Server processing (checking learning progress)

[1741] The server provides an interface for employees to periodically check their learning progress. When employees enter their progress information, the server stores it in a database and updates their study plans as needed.

[1742] Specific examples

[1743] For example, suppose Employee A wants to become a "data scientist." Employee A enters the job title "data scientist" through the interface. The server retrieves Employee A's current skill information (e.g., basic knowledge of Python, basic statistics, etc.) from the database and sends the following prompt to the generative AI model:

[1744] Current employee skills: Basic knowledge of Python, basic statistics

[1745] Job title: Data scientist

[1746] Calculate the skill gap you need and generate a study plan.

[1747] The generative AI model calculates the difference in required skills and returns it to the server. Based on this difference information, the server generates a study plan for employee A, such as "Take an online course to learn the basics of machine learning," and provides it to employee A. Employee A studies according to this study plan and enters their progress. The server checks employee A's progress and updates the study plan as necessary.

[1748] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1749] Step 1:

[1750] The user enters job information

[1751] Users access the interface provided through a web browser. This interface is built using HTML, CSS, and JavaScript. Users enter information about the job they are aiming for into the input form and click the "Submit" button. The input data is the job information they are aiming for.

[1752] Step 2:

[1753] The server receives the input information and stores it in a database

[1754] The server receives the job type information submitted by the user. The received information is processed using the Python Flask framework. The server saves this information in a MySQL database. The input data is the job type information submitted by the user, and the output data is the job type information saved in the database.

[1755] Step 3:

[1756] The server retrieves the user's current skills and credentials from a database

[1757] The server retrieves the user's current skills and credentials from the database using a query, which is executed using a SQL statement, where the input data is the user's ID and the output data is the user's current skills and credentials.

[1758] Step 4:

[1759] The server sends a prompt to the generative AI model

[1760] The server generates a prompt based on the user's current skill information and desired job type. The generated prompt is sent to a generative AI model (e.g., OpenAI's GPT-4) using an HTTP request. The input data is the user's current skill information and desired job type, and the output data is the generated prompt.

[1761] Step 5:

[1762] Generative AI model calculates gaps in required skills and qualifications

[1763] Based on the received prompt, the generative AI model calculates the difference between the user's current skills and the skills required for the desired job. This difference information is returned to the server in JSON format. The input data is the prompt, and the output data is the difference information between skills and qualifications.

[1764] Step 6:

[1765] The server generates a study plan based on the difference information.

[1766] The server generates an individualized study plan based on the difference information returned from the generative AI model. This study plan is automatically generated using a Python script. The input data is the difference information for skills and qualifications, and the output data is the generated study plan.

[1767] Step 7:

[1768] The server provides the user with a study plan

[1769] The server provides the generated study plan to the user, who can then view it through a web browser. The input data is the generated study plan, and the output data is the study plan provided to the user.

[1770] Step 8:

[1771] User enters learning progress

[1772] The user accesses an interface for inputting learning progress. The user enters progress information into the input form and clicks the "Submit" button. The input data is the learning progress information, and the output data is the progress information sent to the server.

[1773] Step 9:

[1774] The server checks your learning progress and updates your study plan as needed

[1775] The server receives the learning progress information sent by the user and stores it in a database. The server periodically checks this progress information and updates the study plan as necessary. For example, if a user completes "Fundamentals of Machine Learning," it adds "Applications of Machine Learning" to the study plan as the next step. The input data is the learning progress information, and the output data is the updated study plan.

[1776] (Application example 1)

[1777] Next, a description will be given of Application Example 1 of Form 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."

[1778] With conventional reskilling systems, it was difficult to accurately grasp the gap in skills and qualifications required for the job an employee was aiming for and provide an individual study plan. Furthermore, the system lacked the functionality to regularly check learning progress and update study plans as needed, making effective reskilling impossible. In particular, factory robot operators require specialized skills and qualifications, making it difficult for conventional systems to handle this.

[1779] 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.

[1780] In this invention, the server includes: a means for inputting information about other jobs that employees aspire to using generative AI; a means for the generative AI to check employee information, identify differences between the employee's current skills and qualifications, and suggest necessary skills and qualifications; a means for providing study plans and internal company information and managing progress; a means for inputting information about the job that a factory robot operator aspires to; a means for the generative AI to calculate the difference in required skills and qualifications based on the operator's current skills and qualifications; a means for generating an individual study plan based on the difference and providing it to the operator; and a means for periodically checking learning progress and updating the study plan as needed. This makes it possible to accurately grasp the skills and qualifications required for the jobs that employees and factory robot operators aspire to and provide individual study plans. Furthermore, by periodically checking learning progress and updating the study plan as needed, effective reskilling can be achieved.

[1781] "Generative AI" is an artificial intelligence technology that generates new information and content based on input data.

[1782] An "employee" is an individual employed by a business or organization.

[1783] "Occupation information" is detailed information about a specific occupation or position.

[1784] "Skills" are the abilities and techniques required to carry out specific tasks or work.

[1785] A "qualification" is a certification or certification required to perform a particular occupation or job.

[1786] A "difference" is a difference or deficiency that exists between the current state and the desired state.

[1787] A "study plan" is a plan that outlines the study content and schedule necessary to achieve a specific goal.

[1788] "Progress management" is the process of regularly checking the progress of a plan and making adjustments as necessary.

[1789] A "factory robot" is a mechanical device used to perform automated tasks in a factory.

[1790] An "operator" is an individual whose role is to operate and manage a machine or system.

[1791] The system for implementing this invention utilizes generative AI to input information about the job type that an employee or factory robot operator is aiming for, calculates the gap in required skills and qualifications, and provides an individual study plan. A specific embodiment of this system is described below.

[1792] 1. System Program

[1793] The server uses generative AI to provide a means for employees and operators to input information about the job they are aiming for. When a user inputs the job information using a smartphone or computer, the server receives the information and sends it to the generative AI.

[1794] The generative AI calculates the difference in skills and qualifications required for the desired job based on the user's current skills and qualifications. This difference information is sent to the server and displayed to the user.

[1795] The server then uses generative AI to generate a personalized study plan based on the differences, which is then displayed on the user's smartphone or computer.

[1796] Furthermore, the server periodically checks the user's learning progress and updates the study plan as necessary, allowing the user to always proceed with their studies based on the most up-to-date study plan.

[1797] 2. Hardware and Software Used

[1798] Hardware: smartphones, PCs, servers

[1799] Software: Python, OpenAI API

[1800] 3. Data processing and calculation

[1801] The server receives the job information and current skills and qualifications entered by the user and sends them to the generative AI. The generative AI calculates the difference in skills and qualifications based on this information and generates an individual study plan. The generated study plan is provided to the user via the server.

[1802] 4. Specific Examples

[1803] For example, if a factory robot operator aims to become a "robot control engineer," they would input "basic programming" and "machine operation" as their current skills. The generative AI would then calculate "advanced programming," "robot control," and "data analysis" as the skills required for the desired job, and calculate the difference.

[1804] Prompt Sentence Examples

[1805] Prompts to create a study plan to close skills gaps:

[1806] Create a study plan to fill the following skill gaps: Advanced Programming, Robotics, Data Analysis

[1807] Prompts to update your study plan based on your progress:

[1808] Your current learning progress is as follows: Completed basic programming. Please update your study plan accordingly.

[1809] In this way, users can achieve effective reskilling through systems that utilize generative AI.

[1810] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1811] Step 1:

[1812] The user inputs the desired job information using a smartphone or computer. The input job information is sent from the device to the server. The server receives this information and sends it to the generative AI.

[1813] Step 2:

[1814] The server provides an interface for users to input their current skills and credentials. Users input their skills and credentials and send them to the server from their device. The server receives this information and sends it to the generative AI.

[1815] Step 3:

[1816] The generative AI calculates the difference in the required skills and qualifications based on the desired job information received from the server and the current skill and qualification information. Specifically, it compares the list of skills and qualifications required for the desired job with the user's current list of skills and qualifications and calculates the difference. This difference information is sent from the generative AI to the server.

[1817] Step 4:

[1818] The server generates an individual study plan based on the difference information received from the generative AI. It sends a prompt to the generative AI to create a study plan to fill in the gaps, and receives the generated study plan. The server then sends this study plan to the user's device.

[1819] Step 5:

[1820] The user studies based on the study plan received from the server. The user periodically reports their learning progress from their device to the server. The server receives this progress information and sends it to the generative AI.

[1821] Step 6:

[1822] The generative AI updates the study plan as needed based on the learning progress information received from the server. Specifically, it adjusts the content and schedule of the study plan taking into account the current progress. The updated study plan is then sent from the generative AI to the server.

[1823] Step 7:

[1824] The server sends the updated study plan received from the generative AI to the user's device, and the user continues studying based on the updated study plan.

[1825] In this way, users can achieve effective reskilling through systems that utilize generative AI.

[1826] Example 2

[1827] Next, a description will be given of Example 2 of Form 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."

[1828] With conventional skill improvement systems, it was difficult to efficiently identify the skills and qualifications required for the job an employee was aiming for and to create a specific study plan based on that. Progress management and updating of study plans also had to be done manually, which was time-consuming and labor-intensive. This could delay employee skill development and affect productivity across the company.

[1829] 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.

[1830] In this invention, the server includes a means for inputting information about the job type the employee is aiming for, a means for the generative AI model to acquire the employee's current skill data, a means for the generative AI model to acquire the skill set required for the desired job, a means for the generative AI model to calculate the difference between the employee's current skills and the required skills, a means for the generative AI model to generate a study plan based on the difference, and a means for the generative AI model to provide the generated study plan to the employee. This allows employees to efficiently grasp the skills and qualifications required for the desired job and automatically create a specific study plan based on that. Progress management and study plan updates can also be performed automatically, significantly reducing time and effort.

[1831] An "employee" is an individual who belongs to a company or organization and performs a specific job or role.

[1832] "Targeted occupation information" is information about the occupation that the employee wants to have in the future.

[1833] A "generative AI model" is a model that uses artificial intelligence techniques to analyze data and automatically perform specific tasks.

[1834] "Current skills data" refers to information about the skills and qualifications that employees currently possess.

[1835] A "required skill set" is the set of skills and qualifications required to work in a particular occupation.

[1836] A "difference" is a gap or shortfall between current skill data and the required skill set.

[1837] A "study plan" is a specific learning plan for an employee to acquire the skills and qualifications necessary for the job they are aiming for.

[1838] Progress management is the process of monitoring and, if necessary, adjusting the progress of employees as they acquire skills and qualifications based on their study plans.

[1839] This invention is a system that efficiently identifies the skills and qualifications required for the job an employee is aiming for and automatically creates a specific study plan based on that. A specific embodiment of this system will be described below.

[1840] Hardware and software used

[1841] This system uses the following hardware and software:

[1842] Server: Provides the database management system (DBMS) and computational resources to run generative AI models.

[1843] Terminal: The device (PC, smartphone, tablet, etc.) through which a user accesses the system.

[1844] Generative AI model: An artificial intelligence model that analyzes employee skill data and generates study plans.

[1845] System processing flow

[1846] 1. Enter the job information the user is aiming for

[1847] A user accesses the system interface using a terminal and inputs the job type information they are aiming for, for example, "project manager."

[1848] 2. The server retrieves the employee's current skill data.

[1849] The server retrieves current skill data from the user's profile information. This data includes programming skills, communication skills, etc. For example, the server retrieves information such as "Programming skills: High" and "Communication skills: Medium" from the database.

[1850] 3. Acquire the skill set necessary for the job the server is aiming for

[1851] The server retrieves the skill set required for the desired job (project manager) from the database. For example, it retrieves information such as "Project management skills: High" and "Leadership skills: High."

[1852] 4. The server calculates the difference between the current skill and the required skill.

[1853] The server compares the current skills with the required skills and calculates the difference. For example, "Project management skills: Low" and "Leadership skills: Low" are calculated as the difference.

[1854] 5. The generative AI model generates a study plan based on the differences

[1855] The generative AI model generates a study plan based on the calculated differences, suggesting specific actions such as "take an online course in project management" or "read a book on leadership."

[1856] 6. The server provides the generated study plan to the user.

[1857] The server displays the generated study plan on the user's interface, allowing the user to improve their skills according to the plan provided.

[1858] Examples and prompts

[1859] Examples:

[1860] If an employee wants to become a project manager, they can input the following prompt into the generative AI model:

[1861] Example prompt sentence:

[1862] An employee is aiming to become a project manager. Their current skill set is as follows:

[1863] Programming skills: High

[1864] Communication Skills: Medium

[1865] Project Management Skills: Low

[1866] Leadership Skills: Low

[1867] The skill set required for a project manager is:

[1868] Programming skills: Medium

[1869] Communication Skills: High

[1870] Project Management Skills: High

[1871] Leadership Skills: High

[1872] Generate a study plan based on the difference between your current skills and the skills you need.

[1873] By inputting this prompt into a generative AI model, a specific study plan is generated, suggesting actions such as "take an online course in project management" or "read a book on leadership."

[1874] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1875] Step 1:

[1876] Enter the job information the user is aiming for

[1877] A user uses a terminal to access the system interface and inputs information about the job type they are aiming for. For example, they might input "project manager." This input data is sent to the server. The input data is the "job type information" and the output is the job type information sent to the server.

[1878] Step 2:

[1879] The server retrieves the employee's current skill data.

[1880] The server obtains current skill data from the user's profile information. This data includes programming skills, communication skills, etc. For example, it obtains information such as "Programming skills: High" and "Communication skills: Medium" from the database. The input data is the user's ID, and the output is the current skill data.

[1881] Step 3:

[1882] Acquire the skill set required for the job the server is aiming for

[1883] The server retrieves the skill set required for the desired job (project manager) from the database. For example, it retrieves information such as "Project management skills: high" and "Leadership skills: high." The input data is the desired job information, and the output is the required skill set.

[1884] Step 4:

[1885] The server calculates the difference between the current skill and the required skill.

[1886] The server compares the current skills with the required skills and calculates the difference. For example, "Project management skills: Low" and "Leadership skills: Low" are calculated as the difference. The input data is the current skill data and the required skill set, and the output is the skill difference.

[1887] Step 5:

[1888] A generative AI model generates a study plan based on the differences

[1889] The generative AI model generates a study plan based on the calculated differences. For example, it suggests specific actions such as "take an online course in project management" or "read a book on leadership." The input data is the skill differences, and the output is a study plan.

[1890] Step 6:

[1891] The server provides the generated study plan to the user.

[1892] The server displays the generated study plan on the user's interface, and the user can improve their skills according to the provided plan. The input data is the study plan, and the output is the study plan provided to the user.

[1893] (Application example 2)

[1894] Next, a description will be given of Application Example 2 of Form 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."

[1895] With conventional employee training systems, it was difficult to efficiently calculate the gap between the skills required for an employee's desired job and their current skills, and to provide an appropriate study plan. Furthermore, there was a lack of systems specifically designed to improve the skills of robot operators, making it difficult to provide specific study plans for operators to reach their desired skill set. This delayed the improvement of the skills of employees and robot operators, hindering productivity improvements.

[1896] 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.

[1897] In this invention, the server includes: a means for utilizing generative AI to input information about other jobs that employees aspire to; a means for the generative AI to check employee information, identify differences between the current skill set and the employee's own, and suggest necessary skills and qualifications; a means for providing study plans and internal company information and managing progress; a means for inputting the skill set that the robot operator is aiming for; a means for inputting the current skill set or conducting a skill test; a means for the generative AI to calculate differences between the current skill set and the required skills and generate a study plan; and a means for providing the generated study plan to the operator. This makes it possible to efficiently provide specific study plans for employees and robot operators to achieve their desired skill sets.

[1898] "Generative AI" is an artificial intelligence technology that generates new information and content based on input data.

[1899] An "employee" is an individual who belongs to a company or organization and performs work.

[1900] "Occupation information" is detailed information about a specific occupation or position.

[1901] "Skills" are the knowledge and abilities required to carry out a specific job or task.

[1902] A "qualification" is a certification or certificate required to perform a particular occupation or job.

[1903] A "study plan" is a specific study schedule and method for acquiring the desired skills or qualifications.

[1904] "Progress management" is the process of monitoring progress against a plan and making adjustments as necessary.

[1905] A "robot operator" is an engineer who operates and manages robots in factories and on production lines.

[1906] A "skill set" is a set of skills and abilities required for a particular occupation or position.

[1907] A "skills test" is an exam or test used to assess an individual's skills or abilities.

[1908] A "difference" is the difference or gap between the current state and the target state.

[1909] A "means for generating a study plan" is a method or system for creating a specific study plan for acquiring the required skills or qualifications.

[1910] "Means for providing to operator" refers to a method or system for transmitting the generated study plan to the robot operator.

[1911] The system for implementing this invention utilizes generative AI to help employees and robot operators improve their skills. Specific embodiments are described below.

[1912] System Configuration

[1913] The system consists of the following main components:

[1914] 1. Server: Executes generative AI models and processes data.

[1915] 2. Terminals: Devices used by employees and robot operators (PCs, smartphones, tablets, etc.).

[1916] 3. Database: Stores employee and robot operator skill information, target skill sets, study plans, etc.

[1917] Hardware / Software used

[1918] Hardware: PCs, servers, smartphones, tablets

[1919] Software: Python, OpenAI API, database management system (e.g., MySQL, PostgreSQL)

[1920] Data processing / data calculation

[1921] 1. Data entry: Employees and robot operators enter their current and desired skill sets via a terminal.

[1922] 2. Prompt generation: The server sends a prompt to the generative AI model (OpenAI's GPT-3) based on the input skill information.

[1923] 3. Calculating the difference and generating a study plan: The generative AI model calculates the skill difference and generates a study plan.

[1924] 4. Result output: The generated study plan is displayed on the terminal and provided to employees or robot operators.

[1925] Specific examples

[1926] For example, a robot operator inputs "basic programming skills, simple machine maintenance" as his current skills and "advanced programming skills, complex machine maintenance, troubleshooting" as his desired skills. Based on this information, the server sends the following prompt to the generative AI model:

[1927] Prompt Sentence Examples

[1928] Current skills: Basic programming skills, simple machine maintenance

[1929] Skills to aim for: Advanced programming skills, complex machine maintenance, troubleshooting

[1930] Calculate the skill gap you need and generate a study plan.

[1931] The generative AI model calculates skill gaps based on this prompt and generates a specific study plan, such as "take an online course to acquire advanced programming skills," "receive hands-on training in complex machine maintenance," or "read reference books on troubleshooting."

[1932] In this way, it is possible to efficiently provide employees and robot operators with specific study plans to reach their desired skill sets.

[1933] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1934] Step 1:

[1935] The user uses a terminal to input the current skill set and the desired skill set.

[1936] Input: Current skill set, desired skill set

[1937] Output: Input skill information

[1938] Specific operation: The user enters their cur...

Claims

1. A means for a user to input job type information; A means for comparing the user's skill information with the input job type information and using a generative AI to suggest skills that the user lacks; A means for generating a learning plan for the user based on the skills shortage using the generative AI; means for receiving progress information of the user's learning plan; A means for updating the learning plan based on the progress information using the generative AI; A system including:

2. A means for acquiring emotion information of the user; The updating means updates the study plan based on the emotion information of the user. The system of claim 1 .

3. The updating means updates the learning plan using the generative AI based on a prompt sentence corresponding to the user's emotional information. The system of claim 2.

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