System

Abstract

To provide a system.SOLUTION: A system comprises: means for inputting a prompt sentence instructing a user to propose his / her own career plan; means for a server receiving the prompt sentence and transmitting it to a generative AI model; means for the generative AI model generating the user's career plan means for the server analyzing the career plan and generating an action plan for the user; means for the server presenting the action plan to the user; and means for the user, the user's manager, and the generative AI model formulating a final career plan through a dialogue among on the basis of the action plan.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] Traditional career planning and experience design support relies solely on humans, making it difficult to respond to individual needs and provide efficient support. Additionally, it has been difficult to propose specific career plans that utilize data from the job market and internal personnel evaluation data. [Means for solving the problem]

[0005] This invention develops AI that supports managers and recruitment agents by tuning text generation AI using data from the job market and internal personnel evaluation data. Furthermore, by holding a three-way dialogue between manager, employee, and AI, it supports the formulation of career plans and the design of experiences for those plans. This makes it possible to propose career plans that meet individual needs and provide efficient support. [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] In one embodiment of the present invention, a text generation AI collects data from the job market and data related to a company's internal personnel evaluations, and then uses natural language processing technology to tune the system based on this data. Specifically, data from the job market includes job information, the skills and experience of job seekers, and industry trends, while data related to the company's internal personnel evaluations includes employee performance evaluations, skill sets, and career paths. Based on this data, the text generation AI proposes optimal career plans for each individual user.

[0029] "Example 2"

[0030] Additionally, AI supporting managers and recruitment agents with subordinates will propose specific action plans based on the career plans proposed by the text generation AI. Specifically, it will suggest actions such as acquiring necessary skills, gaining experience, and networking. These suggestions are made through a three-way dialogue between the manager, employee, and AI, and the results are used to formulate career plans and design experiences for them.

[0031] "Example 3"

[0032] As a specific example, if a text-generation AI determines from job market data that there is a growing demand for data analysis skills in the IT industry, and determines from a company's internal personnel evaluation data that an employee does not have data analysis skills, it will suggest that the employee acquire data analysis skills. Then, AI supporting managers and recruitment agents will propose specific action plans for acquiring skills. This allows employees to specifically formulate their own career plans and design the experiences needed to achieve them.

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

[0034] "Example 1"

[0035] Step 1: The text generation AI collects data from the job market and data related to internal personnel evaluations. Specifically, it collects data from the job market, such as job information, the skills and experience of job seekers, and industry trends, as well as data related to internal personnel evaluations, such as employee performance evaluations, skill sets, and career paths.

[0036] Step 2: Based on the collected data, the text generation AI is tuned using natural language processing technology. Through this tuning, the text generation AI gains the ability to propose the optimal career plan for each individual user.

[0037] Step 3: Based on the career plan proposed by the text generation AI, AI supporting managers and recruitment agents with subordinates will propose a specific action plan.

[0038] "Example 2"

[0039] Step 1: The text generation AI determines from job market data that there is a growing demand for data analysis skills in the IT industry.

[0040] Step 2: At the same time, the text generation AI determines from the company's internal performance evaluation data that a certain employee does not have data analysis skills.

[0041] Step 3: Based on these findings, the text-generating AI suggests that the employee learn data analysis skills.

[0042] Step 4: The AI ​​then supports managers and recruitment agencies by proposing specific action plans for acquiring skills, enabling employees to formulate their own career plans and design the experiences needed to achieve them.

[0043] Example 1

[0044] 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."

[0045] Conventional career planning systems were unable to effectively utilize data from the job market or internal personnel evaluation data, making it difficult to provide optimal career plans for individual users. Furthermore, career plans were often generated manually based on user input data, resulting in a lack of efficiency. Furthermore, there were insufficient means for providing the generated career plans to users, and there was a need for them to be provided in a format that users could easily access.

[0046] 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.

[0047] In this invention, the server includes means for collecting data on the job market and data related to internal personnel evaluations, means for preprocessing the collected data, means for tuning a text generation AI model using the preprocessed data, means for generating an optimal career plan based on data input by a user, and means for providing the generated career plan to the user. This makes it possible to effectively utilize data on the job market and data related to internal personnel evaluations, automatically generate optimal career plans for individual users, and efficiently provide them to them.

[0048] "Job market data" refers to information related to job hunting, such as job information, the skills and experience of job seekers, and industry trends.

[0049] "Internal personnel evaluation-related data" refers to information related to personnel evaluation within a company, such as employee performance evaluations, skill sets, and career paths.

[0050] "Means of collection" refers to the methods and technologies used to obtain data on the job market and data related to personnel evaluation within a company.

[0051] "Preprocessing means" refers to methods and techniques for preparing collected data in a form that is easy to analyze.

[0052] "Text generation AI model" refers to an artificial intelligence model that generates text using natural language processing technology.

[0053] "Tuning methods" refers to methods and techniques for optimizing the performance of AI models using collected data.

[0054] "User input data" refers to information entered by the user, such as their skills, experience, and desired career path.

[0055] "Career plan" refers to a career plan or path proposed based on the user's skills, experience, and industry trends.

[0056] "Means of providing" refers to the methods and techniques for communicating the generated career plan to users.

[0057] This invention is a system that collects data on the job market and data related to personnel evaluation within a company, and tunes a text generation AI model based on this data. A specific embodiment of this system is described below.

[0058] Server Processing

[0059] The server first collects data from the job market and data related to the company's internal personnel evaluations. The job market data includes job information, the skills and experience of job seekers, and industry trends. The internal personnel evaluation data includes employee performance evaluations, skill sets, and career paths. These data are then stored in a database.

[0060] The server then preprocesses the data using natural language processing techniques, specifically using the Python libraries NLTK and spaCy to clean and tokenize the text data, and uses the preprocessed data to tune a text-generation AI model using Tensorflow® and PyTorch.

[0061] The tuned AI model generates the optimal career plan based on the user's input data, which includes the user's skills, experience, desired career path, etc. The server analyzes this data and generates the optimal career plan for the user.

[0062] Terminal handling

[0063] The terminal accepts input from the user. The user enters information such as their skills, experience, and desired career path. This information is sent from the terminal to the server. Specifically, the data is sent using an HTTP request.

[0064] The terminal receives the carrier plan sent from the server and displays it to the user. Specifically, it receives the HTTP response and displays the carrier plan. The user can check the proposed carrier plan through the terminal and provide feedback if necessary.

[0065] User operations

[0066] First, the user inputs information such as their skills, experience, and desired career path into the terminal. This information is sent to the server. Next, the user checks the career plan sent from the server on the terminal. The career plan is generated taking into consideration the user's skills, experience, and industry trends. The user can use the proposed career plan as a reference to plan their own career.

[0067] Specific examples

[0068] For example, if a user enters "I'm considering a career as a data scientist," the server will analyze the skills, experience, and industry trends required for data scientists based on job market data and the company's own personnel evaluation data. Based on the analysis results, the server will suggest a career plan to the user, such as "It is recommended that you improve your Python and R skills and participate in machine learning projects."

[0069] Prompt Sentence Examples

[0070] "I'm considering a career as a data scientist. What skills and experience do I need? What are the industry trends?"

[0071] In this way, the system proposes the optimal career plan based on the user's input.

[0072] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0073] Step 1:

[0074] The server collects data on the job market and data related to personnel evaluation within the company.

[0075] Specifically, they use web scraping tools to collect data from job information sites and obtain employee performance evaluation data from their own database.

[0076] Input: URL of job information site, query of company database

[0077] Output: Job market data, internal personnel evaluation data

[0078] Step 2:

[0079] The server pre-processes the collected data.

[0080] Specifically, we use the Python libraries NLTK and spaCy to clean and tokenize text data.

[0081] Input: Job market data, internal personnel evaluation data

[0082] Output: Preprocessed data

[0083] Step 3:

[0084] The server uses the preprocessed data to tune the text generation AI model.

[0085] Specifically, the model is trained using TensorFlow and PyTorch.

[0086] Input: Preprocessed data

[0087] Output: A tuned text generation AI model

[0088] Step 4:

[0089] The terminal accepts input from the user.

[0090] Specifically, users enter information such as their skills, experience, and desired career path.

[0091] Input: User's skills, experience, and desired career path

[0092] Output: User input data

[0093] Step 5:

[0094] The terminal transmits the data entered by the user to the server.

[0095] Specifically, data is transmitted using an HTTP request.

[0096] Input: User-entered data

[0097] Output: Send data to the server

[0098] Step 6:

[0099] The server generates an optimal career plan based on the data input by the user.

[0100] Specifically, a tuned AI model is used to analyze the user's skills, experience, and industry trends to create a career plan.

[0101] Input: User input data, tuned text generation AI model

[0102] Output: Career plan

[0103] Step 7:

[0104] The server transmits the generated carrier plan to the terminal.

[0105] Specifically, the carrier plan is sent using an HTTP response.

[0106] Input: Career plan

[0107] Output: Carrier plan sent to device

[0108] Step 8:

[0109] The terminal receives the carrier plan sent from the server and displays it to the user.

[0110] Specifically, the career plan is visually displayed using a GUI.

[0111] Input: Career plan

[0112] Output: Display career plan to user

[0113] Step 9:

[0114] The user checks the carrier plan displayed on the terminal.

[0115] Specifically, we will read the proposed career plan and provide feedback as needed.

[0116] Input: Career plan

[0117] Output: User feedback (if needed)

[0118] (Application example 1)

[0119] 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."

[0120] Creating and updating employee career plans using traditional methods takes time and effort, making it difficult to provide optimal plans for each employee. Furthermore, there is a lack of ways for brick-and-mortar store employees to check and update their own career plans in real time, making it difficult to effectively support their career growth.

[0121] 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.

[0122] In this invention, the server includes: means for tuning the text generation AI using job market data and internal personnel evaluation-related data; means for developing AI to support managers and career change supporters with subordinates; means for supporting the formulation of career plans and the design of experiences for such plans through a three-way dialogue between managers, employees, and the AI; means for utilizing general job market data and general personnel evaluation data as job market data; means for employees at physical stores to check and update their career plans in real time using smart devices; and means for the text generation AI to analyze employees' performance evaluations, skill sets, and career paths and propose optimal career plans. This enables employees to check and update their own career plans in real time and receive proposals for optimal career plans.

[0123] "Text generation AI" is an artificial intelligence that generates text using natural language processing technology, analyzes data from the job market and data related to personnel evaluation within a company, and proposes the optimal career plan for each individual user.

[0124] "Job market data" refers to general data related to the job market, such as job information, the skills and experience of job seekers, and industry trends.

[0125] "Human resource evaluation-related data" refers to data related to human resource evaluation collected within a company, such as employee performance evaluations, skill sets, and career paths.

[0126] A "manager" is someone who has a subordinate and is responsible for helping employees develop career plans and designing experiences for them.

[0127] A "career change supporter" is a professional who supports individuals who wish to change jobs, provides information about the job market, and assists in the formulation of career plans.

[0128] "Employees" refers to individuals who work in physical stores and who need to develop or update their career plans.

[0129] "Smart devices" refer to electronic devices that can connect to the Internet and have advanced functions, such as smartphones and smart glasses.

[0130] A "career plan" refers to a plan that outlines the career goals that an individual employee wants to achieve in the future and the steps they need to take to achieve them.

[0131] "Natural language processing technology" is a technology for understanding and generating human language, and plays an important role in text generation AI.

[0132] An "online platform" refers to a foundation that provides systems and services accessible via the internet, and provides a forum for dialogue between managers, employees, and AI.

[0133] The system for implementing this invention uses text generation AI to analyze job market data and internal personnel evaluation data, and proposes optimal career plans for individual employees. Specific embodiments of this system are described below.

[0134] System Configuration

[0135] Hardware:

[0136] server

[0137] Smartphone or smart glasses

[0138] software:

[0139] Python

[0140] OpenAI(R) API

[0141] Pandas

[0142] Data collection and preprocessing

[0143] The server collects data from the job market (job information, skills and experience of job seekers, industry trends, etc.) and data related to the company's internal personnel evaluation (employee performance evaluations, skill sets, career paths, etc.) This data is saved in formats such as CSV files and is read using Pandas.

[0144] Parsing data and generating prompts

[0145] The server preprocesses the collected data and extracts the necessary information. Next, based on employee information and job market information, it generates prompts to be input into the text generation AI. Examples of prompts are as follows:

[0146] Example prompt sentence:

[0147] Employee Information:

[0148] Performance: High

[0149] Skills: Data analysis, programming

[0150] Career Path: Data Analyst

[0151] Job market information:

[0152] Job Opening: Data Scientist

[0153] Skills: Data Analysis, Machine Learning

[0154] Industry Trends: Demand for Data Science is on the Rise

[0155] Suggest the best career plan for this employee.

[0156] Creating a career plan

[0157] The server uses the OpenAI API to input prompts into the generative AI model to generate a career plan, which is then displayed in real time on the employee's smartphone or smart glasses.

[0158] Specific examples

[0159] For example, if Employee A has a high performance rating and his or her skill set includes "data analysis," the server will suggest a career path to "data scientist" based on job market data. This suggestion will be displayed in real time on Employee A's smartphone or smart glasses, allowing Employee A to confirm and update his or her career plan.

[0160] In this way, the server allows employees to check and update their career plans in real time and receive suggestions for the most suitable career plans.

[0161] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0162] Step 1:

[0163] The server collects job market data and internal personnel evaluation-related data. This includes job information, job seekers' skills and experience, industry trends, employee performance evaluations, skill sets, and career paths. This data is saved in formats such as CSV files. The input is job market data and personnel evaluation data, and the output is a collection of this data.

[0164] Step 2:

[0165] The server reads the collected data using Pandas and performs preprocessing. Specifically, it extracts necessary information and formats the data. For example, it deletes unnecessary columns and imputes missing values. The input is the data collected in step 1, and the output is the preprocessed data.

[0166] Step 3:

[0167] The server generates prompts based on the preprocessed data, combining employee information and job market information. The prompts include employee performance evaluations, skill sets, career paths, and job market information, required skills, and industry trends. The input is the preprocessed data, and the output is the generated prompt.

[0168] Step 4:

[0169] The server sends the generated prompt to the OpenAI API and generates a career plan using the generative AI model. The input is the prompt, and the output is the generated career plan. Specifically, the server sends an API request and receives a response.

[0170] Step 5:

[0171] The server sends the generated career plan to the employee's smartphone or smart glasses in real time. The input is the generated career plan, and the output is the career plan displayed on the employee's device. Specific operations include sending a notification to the device and displaying the career plan.

[0172] Step 6:

[0173] Users (employees) use smartphones or smart glasses to check and update their career plans in real time. The input is the career plan displayed on the device, and the output is the updated career plan. Specific operations involve operating the device interface to check and update the career plan.

[0174] Example 2

[0175] 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."

[0176] Conventional career planning systems have had difficulty generating specific action plans to propose optimal career plans for individual users, and also have had problems with the inefficient process of formulating career plans through dialogue between managers, members, and AI.

[0177] The identification process by the identification processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means. In this invention, the server includes a means for the user to input a prompt sentence, a means for the server to receive the prompt sentence and send it to the generative AI model, a means for the generative AI model to generate a career plan, a means for the server to analyze the generated career plan and generate a specific action plan, a means for the server to present the action plan to the user, and a means for the user, manager, and AI to formulate a career plan through dialogue. This makes it possible to propose an optimal career plan for each user and generate a specific action plan. Furthermore, a career plan can be formulated efficiently through dialogue between the manager, member, and AI.

[0178] "User" refers to an individual or organization that uses the system to receive career plan proposals.

[0179] A "prompt" is a textual question or request that a user enters into a system.

[0180] "Server" refers to the computer system responsible for receiving prompts from the user and sending them to the generative AI model.

[0181] "Generative AI model" refers to an artificial intelligence model that uses natural language processing technology to analyze prompt sentences and generate career plans.

[0182] A "career plan" refers to a plan that shows a user's career goals and career path.

[0183] An "action plan" is a plan that outlines the specific actions and steps required to realize your career plan.

[0184] "Manager" refers to a person in a managerial position who has subordinates.

[0185] "Member" refers to an individual who works under a manager.

[0186] "Dialogue" refers to the communication between the user, manager, and AI regarding the formulation of a career plan.

[0187] An "online platform" refers to a digital environment in which users, managers, and AI can interact via the internet.

[0188] This invention is a system in which a user inputs a prompt sentence, a server receives the prompt sentence, and sends it to a generative AI model to generate a career plan and propose a specific action plan. Specific embodiments of this system are described below.

[0189] Hardware and software used

[0190] Hardware:

[0191] High-performance server (e.g., server equipped with NVIDIA GPU)

[0192] software:

[0193] Text generation AI (e.g., generative AI models using natural language processing technology)

[0194] Database management system (e.g., MySQL (registered trademark))

[0195] Web server (e.g., Apache (registered trademark))

[0196] Data processing and calculation

[0197] The server receives the prompt sentence from the user and sends it to the generative AI model. The generative AI model analyzes the prompt sentence and generates a career plan according to the user's needs. The generated career plan includes the following elements:

[0198] Acquire the necessary skills

[0199] Accumulation of experience

[0200] How to network

[0201] The server then analyzes the generated career plan and generates a specific action plan. This action plan is created through a three-way dialogue between the user, manager, and AI, and the results are used to formulate the career plan and design the experience required for it.

[0202] Specific examples

[0203] As a concrete example, the following prompt sentence is input to the generative AI model:

[0204] Example prompt sentence:

[0205] "A software engineer in his 30s wants to transition to a project manager position. His current skill set is: Java, Python, and basic project management knowledge. Please suggest the skills and experience he needs and how he can network."

[0206] When the user enters the above prompt, the server sends it to the text generation AI model, which then generates the following career plan:

[0207] Acquisition of necessary skills: Advanced knowledge of project management (e.g., PMP certification), leadership skills, communication skills

[0208] Gaining experience: Experience leading small projects, increased opportunities to receive feedback from team members

[0209] Networking: Attending project management related conferences and seminars to expand your professional network

[0210] The server presents these suggestions to the user, and through a three-way dialogue between the user, manager, and AI, the optimal career plan is formulated.

[0211] In this way, users can formulate and execute career plans based on specific action plans.

[0212] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0213] Step 1:

[0214] The user inputs a prompt through the system interface. For example, the user might input, "A software engineer in his 30s wants to change careers to become a project manager. His current skill set is as follows: Java, Python, and basic knowledge of project management. Please suggest the skills, experience, and networking methods he needs." The input prompt is sent to the system.

[0215] Step 2:

[0216] The server receives the prompt sentence. The server receives the prompt sentence sent by the user and stores it in a database. The received prompt sentence is prepared to be sent to the generative AI model. The input is the prompt sentence, and the output is the prompt sentence ready to be sent to the generative AI model.

[0217] Step 3:

[0218] The server sends a prompt to the generative AI model. The server sends the received prompt to the generative AI model. The generative AI model generates a career plan based on the prompt. The input is the prompt, and the output is the prompt sent to the generative AI model.

[0219] Step 4:

[0220] A generative AI model generates a career plan. The generative AI model analyzes the prompt and generates a career plan that meets the user's needs. For example, it suggests ways to acquire necessary skills, gain experience, and network. The input is the prompt, and the output is the generated career plan.

[0221] Step 5:

[0222] The server receives the generated career plan. The server receives the career plan generated from the generative AI model and stores it in a database. The received career plan is further analyzed. The input is the generated career plan, and the output is a career plan prepared for analysis.

[0223] Step 6:

[0224] The server analyzes the career plan and generates a specific action plan. The server analyzes the received career plan and generates a specific action plan. For example, it suggests obtaining PMP certification, improving leadership skills, and gaining experience as a leader of small projects. The input is a career plan, and the output is a specific action plan.

[0225] Step 7:

[0226] The server presents the action plan to the user. The server renders the generated action plan in HTML format and displays it in the user's browser. The user checks the displayed action plan and makes corrections or additions as necessary. The input is a specific action plan, and the output is the action plan presented to the user.

[0227] Step 8:

[0228] The user, manager, and AI formulate a career plan through dialogue. The three parties, user, manager, and AI, engage in dialogue based on the presented action plan to formulate the optimal career plan. The final design of the career plan is based on the results of the dialogue. The input is the action plan and the results of the dialogue, and the output is the final formulated career plan.

[0229] (Application example 2)

[0230] 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."

[0231] Conventional career planning systems are primarily aimed at human managers and job placement agents, and are not designed to improve the skills or develop career plans for robots working in factories. This makes it difficult to efficiently train robots in the factory and improve their productivity.

[0232] The specific processing by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes: means for tuning the text generation AI using job market data and internal personnel evaluation-related data; means for developing AI to support managers and recruitment agents with subordinates; means for supporting the formulation of career plans and the design of experiences for such plans through a three-way dialogue between managers, members, and the AI; means for utilizing general job market data and general personnel evaluation data as job market data; means for proposing career plans based on the robot's current skill set and the factory's needs to help improve the skills of robots working in factories; means for proposing specific skill acquisition methods, experience accumulation methods, and networking methods; and means for formulating optimal career plans by adjusting the proposals through a three-way dialogue between the robot, manager, and AI. This enables efficient skill acquisition and improved productivity for robots working in factories.

[0233] "Text generation AI" is artificial intelligence that generates text data using natural language processing technology.

[0234] "Job market data" refers to market information and statistical data related to job hunting.

[0235] "Internal personnel evaluation-related data" refers to data related to employee evaluations and performance within a company.

[0236] A "manager with subordinates" refers to a managerial position that has the role of managing and guiding subordinates within an organization.

[0237] A "career agent" is a professional who acts as an intermediary between job seekers and companies and supports job hunting activities.

[0238] A "career plan" refers to an individual's goals and plans for their professional life.

[0239] "Experience design" refers to the process of systematically accumulating the experiences and skills necessary to realize one's career plan.

[0240] "Robots working in factories" refers to automated machinery that performs production work in factories.

[0241] A "skill set" refers to the collection of skills and knowledge required to perform a specific job or task.

[0242] "Factory needs" refers to the requirements and conditions that a factory needs in order to carry out its production activities.

[0243] An "action plan" is a specific plan of action to achieve a specific goal.

[0244] "Networking" refers to the act of contacting people to exchange information and build collaborative relationships.

[0245] "Dialogue" refers to a communication process in which multiple parties exchange opinions and information.

[0246] A system for implementing this invention uses text generation AI to generate career plans and action plans, and supports the improvement of the skills of robots working in factories. A specific embodiment of this system is described below.

[0247] System Configuration

[0248] 1. Hardware:

[0249] Server: A server for running text generation AI.

[0250] Robot: An automated mechanical device that performs tasks in a factory.

[0251] Devices: Computers and tablets used by managers and members.

[0252] 2. Software:

[0253] Python: A programming language used to implement programs.

[0254] OpenAI API: An API for running text generation AI.

[0255] Data processing and calculation

[0256] The server processes and calculates data in the following procedure.

[0257] 1. Data Collection:

[0258] Gather current robot skill sets and factory needs.

[0259] This data is sent to the server in text format.

[0260] 2. Career plan generation:

[0261] The server uses text generation AI to generate a career plan based on the collected data.

[0262] Specifically, the following prompt sentences are used to generate a career plan:

[0263] Robot's current skill set: welding, assembly

[0264] Factory needs: quality control, efficient production

[0265] Please suggest a career plan for this robot.

[0266] 3. Generate an action plan:

[0267] The server generates a specific action plan based on the generated career plan.

[0268] Specifically, generate an action plan using the following prompt:

[0269] Career Plan: This robot should acquire quality control skills and learn new assembly techniques to support efficient production.

[0270] Please propose a concrete action plan.

[0271] 4. Interactive features:

[0272] Through dialogue between the robot, manager, and AI, the proposals are adjusted and the optimal career plan is formulated.

[0273] This dialogue will take place on an online platform.

[0274] Specific examples

[0275] For example, if a robot with welding and assembly skills is required in a factory to ensure quality control and efficient production, the server will perform the following process:

[0276] 1. Data Collection:

[0277] Robot skillset: "Welding, assembly"

[0278] Factory needs: "Quality control, efficient production"

[0279] 2. Career plan generation:

[0280] The server generates a career plan based on the above data.

[0281] Generated career plan: "This robot should acquire quality control skills and learn new assembly techniques to support efficient production."

[0282] 3. Generate an action plan:

[0283] The server generates a specific action plan based on the generated career plan.

[0284] Generated action plan: "Receive training in quality control and new assembly techniques."

[0285] In this way, robots working in factories can acquire skills efficiently and improve productivity.

[0286] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0287] Step 1:

[0288] The server collects the robot's current skill set and the factory's needs. Specifically, managers and employees use terminals to input the robot's skill set (e.g., "welding, assembly") and the factory's needs (e.g., "quality control, efficient production"). This data is sent to the server in text format.

[0289] Step 2:

[0290] The server uses text generation AI to generate a career plan based on the collected data. Specifically, it generates a career plan using the following prompt sentence:

[0291] Robot's current skill set: welding, assembly

[0292] Factory needs: quality control, efficient production

[0293] Please suggest a career plan for this robot.

[0294] The server sends this prompt to the OpenAI API and receives the generated career plan. The output is the career plan (e.g., "This robot should acquire quality control skills and learn new assembly techniques to support efficient production.").

[0295] Step 3:

[0296] The server generates a specific action plan based on the generated career plan. Specifically, the action plan is generated using the following prompt sentence:

[0297] Career Plan: This robot should acquire quality control skills and learn new assembly techniques to support efficient production.

[0298] Please propose a concrete action plan.

[0299] The server sends this prompt to the OpenAI API and receives the generated action plan. The output is the action plan (e.g., "Receive training in quality control and new assembly techniques.").

[0300] Step 4:

[0301] The server presents the generated career plans and action plans to the manager and members, who then review these plans via their terminals and make suggestions for corrections or additions as needed. This dialogue takes place on an online platform.

[0302] Step 5:

[0303] The server determines the final career plan and action plan through a dialogue between the manager, members, and the AI. The finalized plan is used as a concrete guideline for improving the robot's skills.

[0304] Step 6:

[0305] The server sends the determined career plan and action plan to the robot, which then acquires skills and undergoes training based on these plans. Specifically, the robot receives training in quality control and new assembly techniques.

[0306] Example 3

[0307] Next, a description will be given of a third embodiment of the third embodiment. 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."

[0308] In today's job market, the demand for specific skills is rapidly changing, making it difficult for employees to properly formulate their own career plans. While there is a need to effectively utilize personnel evaluation data within companies to suggest appropriate skill acquisition for employees, there is a lack of efficient ways to do this. Furthermore, there is also the challenge of providing managers and career change supporters with concrete action plans to support employees in formulating their career plans.

[0309] 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.

[0310] In this invention, the server includes a means for collecting data on the job market and data related to internal personnel evaluations, a means for analyzing the data on the job market using a generative AI model to identify skills that are in high demand, and a means for analyzing data related to internal personnel evaluations to determine whether employees have specific skills. This enables the server to make suggestions to employees about acquiring appropriate skills, and enables managers and career change supporters with subordinates to provide specific action plans.

[0311] "Job market data" refers to data that includes information about the job market, such as job listings and industry reports.

[0312] "In-house personnel evaluation related data" refers to data that includes information about employee evaluations and skills within a company.

[0313] A "generative AI model" is an artificial intelligence model that uses natural language processing techniques to analyze text data and is trained to perform specific tasks.

[0314] "Skill acquisition suggestions" are messages and action plans that encourage employees to learn specific skills.

[0315] A "manager with subordinates" is an employee who has the role of supervising and guiding subordinates within a company.

[0316] A "career change consultant" is a professional who provides career advice and job information to individuals who wish to change jobs.

[0317] An "action plan" is a specific plan of action required to achieve a specific goal.

[0318] A "terminal" is a device that allows a user to receive information and perform operations.

[0319] A "notification" is a message sent from the system to inform the user of information.

[0320] "Feedback" refers to the reactions and opinions that users provide to a system.

[0321] This invention is a system that collects and analyzes data from the job market and data related to personnel evaluation within a company, and suggests appropriate skill acquisition to employees. A specific embodiment of this system will be described below.

[0322] Hardware and software used

[0323] Hardware: Servers, terminals

[0324] Software: Generative AI models, database management systems (e.g., MySQL, PostgreSQL), natural language processing libraries (e.g., Pandas, Scikit-learn)

[0325] Data collection

[0326] The server collects data on the job market. Specifically, it obtains data from job sites and industry reports via API. For example, it calls the API of a job site to obtain the latest job information. The server also obtains data related to the company's internal personnel evaluations from a database management system. It extracts the necessary data using SQL queries.

[0327] Data analysis

[0328] The server uses a generative AI model to analyze job market data. It preprocesses the data using Python libraries (e.g., Pandas, Scikit-learn) to identify skills that are in high demand. The generative AI model determines that there is a high demand for data analysis skills in the IT industry. For example, it calculates the frequency of occurrence of keywords such as "data analysis," "Python," and "machine learning" from text data.

[0329] Employee Skills Assessment

[0330] The server analyzes the company's internal performance evaluation data to determine whether employees lack specific skills. Specifically, it references the employee's skill set and past evaluation data. The server uses SQL queries to extract employee skill data and input it into the generative AI model.

[0331] Proposal Generation

[0332] The server uses a generative AI model to generate a message suggesting skill acquisition. For example, the message might say, "Demand for data analysis skills is increasing, so we recommend you acquire these skills." The generative AI model generates the message based on the prompt. An example of a prompt: "Analyze the skill demand in the IT industry in the current job market and determine whether the demand for data analysis skills is increasing. Also, based on your company's internal personnel evaluation data, if an employee does not have data analysis skills, generate a message suggesting that the employee acquire these skills."

[0333] Proposing an action plan

[0334] The server uses AI to support managers and career change supporters with subordinates, proposing specific action plans for acquiring skills. For example, taking online courses (e.g., online education platforms), participating in in-house training, or reading specialized books. The server inputs prompt statements into the generative AI model and generates specific action plans. An example of a prompt statement is, "Please propose a specific action plan for acquiring data analysis skills."

[0335] Notifications and Feedback

[0336] The server sends a notification to the device to inform the employee of the proposal. For example, it uses email or an internal messaging system (e.g., a messaging app). The user (employee) chooses whether to accept the proposal and returns feedback to the server. For example, they click an "Accept proposal" button through the device interface.

[0337] In this way, employees can formulate their own career plans in detail and design experiences for them. The flow of the specific processing in the third embodiment will be described with reference to FIG.

[0338] Step 1:

[0339] The server collects data on the job market. Specifically, it obtains data from job sites and industry reports through APIs. As input, it uses the API endpoint of the job site or the URL of the industry report, and obtains the job information or industry report data as output. For example, the server calls the API of a job site to obtain the latest job information.

[0340] Step 2:

[0341] The server retrieves the company's internal personnel evaluation-related data from the database management system. It uses SQL queries as input and obtains employee evaluation data and skill data as output. Specifically, the server executes SQL queries to extract the required data from the database.

[0342] Step 3:

[0343] The server analyzes job market data using a generative AI model. It uses collected job postings and industry report data as input and identifies in-demand skills as output. Specifically, it preprocesses the data using Python libraries (e.g., Pandas, Scikit-learn) and inputs it into the generative AI model. The generative AI model determines that there is a growing demand for data analysis skills in the IT industry.

[0344] Step 4:

[0345] The server analyzes the company's internal performance evaluation data to determine whether employees lack specific skills. It uses employee evaluation data and skill data as input and generates a list of employees with insufficient skills as output. Specifically, the server uses SQL queries to extract employee skill data and input it into a generative AI model.

[0346] Step 5:

[0347] The server uses a generative AI model to generate a message suggesting skill acquisition. It uses the list of employees with skill shortages and the generative AI model as input, and generates a suggested message as output. Specifically, the generative AI model generates a message based on a prompt. An example of a prompt: "Analyze the skill demand in the IT industry in the current job market and determine whether there is an increasing demand for data analysis skills. Also, based on your company's internal personnel evaluation data, if an employee does not have data analysis skills, generate a message suggesting that the employee acquire the skill."

[0348] Step 6:

[0349] The server uses AI to support managers and career change supporters with subordinates, proposing specific action plans for acquiring skills. It uses the proposed message and the generative AI model as input, and generates a specific action plan as output. Specifically, a prompt sentence is input into the generative AI model, and an action plan is generated. An example of a prompt sentence is: "Please propose a specific action plan for acquiring data analysis skills."

[0350] Step 7:

[0351] The server sends a notification to the device to inform the employee of the proposal. It uses the proposal message and the action plan as input and generates a notification message as output. Specifically, it sends the notification via email or an internal messaging system (e.g., a messaging app).

[0352] Step 8:

[0353] The user (employee) chooses whether to accept the proposal and returns feedback to the server. The notification message is used as input and feedback is generated as output. Specifically, the user clicks the "Accept proposal" button through the terminal interface.

[0354] (Application example 3)

[0355] 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."

[0356] Conventional career planning systems are primarily designed for human employees and do not address skill management or training plan formulation for robots working in factories. This makes it difficult for factory managers to grasp the skill status of robots and efficiently train them in the necessary skills.

[0357] 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.

[0358] In this invention, the server includes: means for tuning the text generation AI using job market data and internal personnel evaluation-related data; means for developing AI to support managers and recruitment agents with subordinates; means for supporting the formulation of career plans and the design of experiences for such plans through a three-way dialogue between managers, members, and the AI; means for utilizing general job market data and general personnel evaluation data as job market data; means for managing the skill sets of robots working in factories and suggesting the acquisition of necessary skills; and means for factory managers to check the skill status of robots in real time and create training plans for the necessary skills. This enables efficient skill management and the formulation of training plans for robots working in factories, thereby improving factory productivity.

[0359] "Text generation AI" is an artificial intelligence that uses natural language processing technology to generate text data and provide information tailored to specific purposes.

[0360] "Job market data" refers to data such as job information related to job hunting, job seekers' skill sets, and industry trends.

[0361] "Personnel evaluation-related data" refers to data such as employee performance evaluations, skill evaluations, and promotion histories.

[0362] A "manager" is a person who has subordinates and is responsible for managing and directing work within an organization.

[0363] A "career agent" is a professional who acts as an intermediary between job seekers and companies hiring, and supports job hunting activities.

[0364] "AI" is an abbreviation for artificial intelligence, a technology that mimics human intellectual tasks through machine learning and data analysis.

[0365] A "career plan" is when an individual sets goals for their future occupation or career and develops a specific action plan to achieve them.

[0366] "Experience design" refers to the planning and implementation of specific experiences and training to acquire the skills and knowledge necessary to realize one's career plan.

[0367] A "robot working in a factory" is a mechanical device that automatically performs specific tasks on a factory production line or at a work site.

[0368] A "skill set" is a collection of skills and knowledge required to perform a specific job or function.

[0369] A "training plan" is a specific learning plan or training program for acquiring specific skills or knowledge.

[0370] "Checking in real time" means instantly understanding the current situation and data.

[0371] The system for implementing this invention uses text generation AI to analyze job market data and internal personnel evaluation data, manage the skill sets of robots working in factories, and suggest ways to acquire the necessary skills. A specific embodiment of this system is shown below.

[0372] System Configuration

[0373] Hardware

[0374] Server: A high-performance server for data analysis and running AI models.

[0375] Device: Smartphone or tablet used by factory managers.

[0376] Robot: An automated mechanical device that performs tasks in a factory.

[0377] software

[0378] Text generation AI: An AI model that generates text data using natural language processing techniques.

[0379] Database: A database for storing job market data and personnel evaluation-related data.

[0380] API server: An API server for obtaining data and providing analysis results.

[0381] Data processing and calculation

[0382] Server Processing

[0383] 1. Data acquisition: The server acquires data from the job market and data related to the company's internal personnel evaluations via the API.

[0384] 2. Data analysis: Using text generation AI, we analyze the acquired data and identify current skill demands.

[0385] 3. Checking skill sets: The skill sets of the robots working in the factory are retrieved from the database and compared with the analysis results.

[0386] 4. Propose training plans: If necessary skills are lacking, generate specific training plans and propose them to management.

[0387] Terminal handling

[0388] 1. Real-time confirmation: Factory managers can use smartphones or tablets to check the robot's skill status in real time.

[0389] 2. Receive the training plan: Receive the proposed training plan and put it into action.

[0390] Specific examples

[0391] For example, to obtain the current skill set of a robot with ID "robot_12345" working in a factory, compare it with the latest skill demand data, and propose a training plan for the required skills, use the following prompt sentence:

[0392] Prompt Sentence Examples

[0393] "Get the current skill set of robot ID: robot_12345, compare it with the latest skill demand data, and suggest a training plan for the required skills."

[0394] In this way, it becomes possible to efficiently manage the skills of robots working in factories and develop training plans, thereby improving factory productivity.

[0395] The flow of the specific processing in Application Example 3 will be described with reference to FIG.

[0396] Step 1:

[0397] The server obtains data on the job market and data related to the company's internal personnel evaluations via an API.

[0398] Input: Job market data API, internal personnel evaluation data API

[0399] Output: Acquired data (job market data, personnel evaluation data)

[0400] Specific operation: The server sends a request to the API endpoint to obtain job market data and performance evaluation-related data. The obtained data is stored in a database.

[0401] Step 2:

[0402] The server uses text generation AI to analyze the acquired data and identify current skill demand.

[0403] Input: Acquired data (job market data, personnel evaluation data)

[0404] Output: Skill demand analysis results

[0405] Specific operation: The server inputs the acquired data into a text generation AI model and analyzes skill demand using natural language processing technology. The analysis results are output as data indicating which skills are in high demand.

[0406] Step 3:

[0407] The server retrieves the skill sets of the robots working in the factory from the database and compares them with the analysis results.

[0408] Input: Robot skill set data, skill demand analysis results

[0409] Output: Skills gap identification results

[0410] Specific operation: The server retrieves the skill set of a specific robot from the database and compares it with the skill demand analysis results, thereby identifying skills that the robot does not possess but are in high demand.

[0411] Step 4:

[0412] If the required skills are lacking, the server generates a specific training plan and proposes it to the administrator.

[0413] Input: Skills gap identification results

[0414] Output: Training Plan

[0415] Specific operation: The server generates a training plan for acquiring the necessary skills based on the results of identifying skill gaps. The generated training plan is notified to the administrator.

[0416] Step 5:

[0417] The terminal allows factory managers to check the robot's skill status in real time using a smartphone or tablet.

[0418] Input: Robot skill set data, training plan

[0419] Output: Skill status and training plan displayed on the administrator's screen

[0420] Specific operation: The terminal displays the robot's skill set data and training plan obtained from the server, and the administrator can check it and take necessary actions.

[0421] Step 6:

[0422] The device receives the proposed training plan and puts it into action.

[0423] Input: Training plan

[0424] Output: Training execution status

[0425] Specific operation: The terminal receives the proposed training plan and instructs the robot to start training. The training progress is reported to the administrator in real time.

[0426] 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.

[0427] "Example 1"

[0428] In one embodiment of the present invention, the text generation AI is used in combination with an emotion engine that recognizes the user's emotions. The emotion engine recognizes the user's emotional state from text data, voice data, facial expression data, and other data generated during a conversation with the user. This emotional state is fed back to the text generation AI, which then optimizes career plan suggestions based on this information. Specifically, if the user feels stressed, the text generation AI will suggest a career plan to the user to reduce that stress.

[0429] "Example 2"

[0430] The emotion engine also has the ability to adjust career plan suggestions based on the user's emotional state. For example, if the emotion engine senses that the user is feeling joy, it will feed that information back to the text generation AI, which will then suggest a career plan that will make the user feel joyful. This allows the system to suggest a career plan that is appropriate for the user's emotional state, improving user satisfaction.

[0431] "Example 3"

[0432] Furthermore, the combination of the emotion engine and text generation AI can more accurately understand the user's emotional state and suggest career plans based on that. For example, if the user senses anxiety, the emotion engine will feed that information back to the text generation AI, which will then suggest a career plan to alleviate the user's anxiety. This will result in a career plan that is appropriate for the user's emotional state, improving user satisfaction.

[0433] The processing flow of each embodiment will be described below.

[0434] "Example 1"

[0435] Step 1: A dialogue with the user is initiated. This dialogue can take various forms, such as text, voice, or facial expression data.

[0436] Step 2: The emotion engine recognizes the user's emotional state from the dialogue data, which can include various emotions such as joy, sadness, anger, surprise, etc.

[0437] Step 3: The emotional state recognized by the emotion engine is fed back to the text generation AI.

[0438] Step 4: The text generation AI optimizes career plan suggestions based on this emotional state. For example, if the user feels stressed, the text generation AI will suggest a career plan to reduce stress.

[0439] "Example 2"

[0440] Step 1: A dialogue with the user is initiated. This dialogue can take various forms, such as text, voice, or facial expression data.

[0441] Step 2: The emotion engine recognizes the user's emotional state from the dialogue data, which can include various emotions such as joy, sadness, anger, surprise, etc.

[0442] Step 3: The emotional state recognized by the emotion engine is fed back to the text generation AI.

[0443] Step 4: The text generation AI optimizes career plan suggestions based on this emotional state. For example, if the AI ​​senses that the user is feeling joyful, it will suggest career plans that will make the user feel joyful.

[0444] "Example 3"

[0445] Step 1: A dialogue with the user is initiated. This dialogue can take various forms, such as text, voice, or facial expression data.

[0446] Step 2: The emotion engine recognizes the user's emotional state from the dialogue data, which can include various emotions such as joy, sadness, anger, surprise, etc.

[0447] Step 3: The emotional state recognized by the emotion engine is fed back to the text generation AI.

[0448] Step 4: The text generation AI optimizes career plan suggestions based on this emotional state. For example, if the AI ​​senses that the user is feeling anxious, it will suggest a career plan to alleviate the user's anxiety.

[0449] Example 1

[0450] 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."

[0451] Conventional career planning systems make uniform proposals without considering the user's emotional state, making it difficult to provide the optimal career plan for each individual user. Furthermore, they are unable to effectively utilize job market data or internal personnel evaluation data, making it impossible to generate highly accurate career plans. This leads to problems such as lower user satisfaction and career success rates.

[0452] 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.

[0453] In this invention, the server includes means for collecting data on the job market and data related to personnel evaluation within the company, means for preprocessing the collected data using natural language processing technology, means for tuning the generative AI model based on the preprocessed data, means for recognizing the emotional state of the user using an emotion engine and feeding it back to the generative AI model, and means for proposing an optimal career plan to the user. This makes it possible to propose a highly accurate career plan that takes the emotional state of the user into consideration.

[0454] "Job market data" refers to all information related to the job market, such as job information, the skills and experience of job seekers, and industry trends.

[0455] "Internal personnel evaluation-related data" refers to information related to personnel evaluations collected within the company, such as employee performance evaluations, skill sets, and career paths.

[0456] "Natural language processing technology" is a technology for analyzing text data and understanding its meaning, and includes processes such as text cleaning, tokenization, and stemming.

[0457] A "generative AI model" refers to an artificial intelligence model that learns from collected data and generates the optimal career plan for the user.

[0458] An "emotion engine" refers to technology that recognizes a user's emotional state from text data, voice data, facial expression data, and other data generated during a conversation with the user.

[0459] "Career Planning" refers to specific suggestions and advice for planning a user's career goals and career path.

[0460] This invention is a system that collects data on the job market and internal personnel evaluation data, and uses this data to tune a generative AI model. Furthermore, it uses an emotion engine to recognize the user's emotional state and provides feedback to the generative AI model to suggest the optimal career plan for the user.

[0461] The server first collects data from the job market and data related to the company's internal personnel evaluations. Data from the job market includes job information, the skills and experience of job seekers, and industry trends. Data related to the company's internal personnel evaluations includes employee performance evaluations, skill sets, career paths, and more. This data is then stored in a database.

[0462] The server then preprocesses the collected data using natural language processing techniques, such as text cleaning, tokenization, and stemming, using the Python libraries NLTK and spaCy.

[0463] The server then uses the preprocessed data to tune the generative AI model. Specifically, it combines job market data with the company's own personnel evaluation data to build a model that will propose the optimal career plan to the user. This model is built using machine learning frameworks such as TensorFlow and PyTorch.

[0464] Furthermore, the server integrates an emotion engine with the text generation AI. The emotion engine recognizes the user's emotional state from text data, voice data, facial expression data, and other data generated during user interaction. This emotional state is fed back to the text generation AI in real time.

[0465] Users access the system and enter their information. The device then sends the user's input data to the server. The server then uses a generative AI model and emotion engine to generate the optimal career plan for the user. For example, if the user is feeling stressed, the server will suggest a career plan to reduce stress.

[0466] As an example, User A accesses the system and enters the following information:

[0467] Current job title: Software engineer

[0468] Skillset: Python, Java, Machine Learning

[0469] Career goal: Project manager

[0470] Based on the information User A inputs into the system and User A's emotional state as recognized by the emotion engine (e.g., feeling stressed), the server proposes the following career plan:

[0471] Increase remote work opportunities to reduce stress

[0472] Take an online course to improve your project management skills

[0473] Introduce weekly mental health check-ins

[0474] Examples of prompts include:

[0475] "Please tell us your current job title, skill set, and career goals. Also, tell us how you feel about your work recently (e.g., stressed, satisfied, etc.)."

[0476] In this way, the system can propose the most suitable career plan to the user.

[0477] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0478] Step 1: Data collection

[0479] The server collects data on the job market and data related to personnel evaluation within the company.

[0480] Inputs: Job postings, job seeker skills and experience, industry trends, employee performance reviews, skill sets, career paths

[0481] What it does: The server retrieves data from job boards and industry reports via APIs, and also extracts employee evaluation data from the company's HR system.

[0482] Output: The collected data is stored in a database.

[0483] Step 2: Preprocessing the data

[0484] The server preprocesses the collected data using natural language processing technology.

[0485] Input: Collected text data

[0486] Specific operation: The server removes unnecessary symbols and spaces from the text data, splits the text into words, and converts it to its basic form. This process uses the Python libraries NLTK and spaCy.

[0487] Output: Preprocessed text data

[0488] Step 3: Tune the model

[0489] The server tunes the generative AI model based on the preprocessed data.

[0490] Input: Preprocessed text data

[0491] How it works: The server splits the data into a training set and a test set, trains a generative AI model using the training set, evaluates the accuracy of the model using the test set, and adjusts parameters as needed. This model is built using a machine learning framework such as TensorFlow or PyTorch.

[0492] Output: A tuned generative AI model

[0493] Step 4: Integrating the Emotion Engine

[0494] The server integrates the emotion engine with a text generation AI.

[0495] Input: Text data, voice data, and facial expression data generated during user interaction.

[0496] Specific operation: The server analyzes the user's text data and sends it to the emotion engine. The emotion engine analyzes the voice data and facial expression data to recognize the emotional state. The server receives feedback from the emotion engine and reflects it in the generative AI model.

[0497] Output: Generative AI model that reflects emotional state

[0498] Step 5: Propose a career plan

[0499] Users access the system and enter their information. The device then sends the user's input data to the server, which then uses a generative AI model and emotion engine to generate the optimal career plan for the user.

[0500] Input: User's job title, skill set, career goals, emotional state

[0501] Specific operation: The user inputs their job title, skill set, and career goals through a web form or application. The device sends the input data to the server. The server generates a career plan using the generative AI model and takes into account feedback from the emotion engine. The server sends the generated career plan to the device and displays it to the user.

[0502] Output: The best carrier plan for the user

[0503] (Application example 1)

[0504] 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."

[0505] Conventional career planning systems make uniform suggestions without considering the user's emotional state, making it difficult to reduce the user's stress and anxiety. Furthermore, there is a lack of means for real-time emotion recognition, which leads to a decline in the quality of the dialogue. Furthermore, in career counseling at brick-and-mortar stores, it is difficult to propose an optimal career plan that instantly reflects the user's emotional state.

[0506] The specific processing by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means. In this invention, the server includes: means for tuning the text generation AI using job market data and internal personnel evaluation-related data; means for developing AI to support managers and job change supporters with subordinates; means for supporting the formulation of career plans and the design of experiences for such plans through a three-way dialogue between managers, employees, and the AI; means for utilizing general job market data and general personnel evaluation data as job change market data; means for analyzing the user's emotional state using an emotion recognition engine and optimizing the career plan based on that information; and means for recognizing the user's emotional state in real time using smart glasses and supporting career counseling. This makes it possible to propose an optimal career plan that takes the user's emotional state into consideration.

[0507] "Text generation AI" is an artificial intelligence that uses natural language processing technology to suggest optimal career plans to users based on data from the job market and internal personnel evaluation-related data.

[0508] "Job market data" refers to general data related to the job market, such as job information, the skills and experience of job seekers, and industry trends.

[0509] "Human resource evaluation related data" refers to data related to human resource evaluation within the company, such as employee performance evaluations, skill sets, and career paths.

[0510] A "manager" is a person who has subordinates and is responsible for managing and supervising work within an organization.

[0511] A "career change supporter" is a professional who provides support to people looking to change jobs, such as career counseling and providing job information.

[0512] An "emotion recognition engine" is a technology that analyzes a user's emotional state from text data, voice data, facial expression data, etc.

[0513] "Smart glasses" are wearable devices that have built-in cameras and displays and display information in the user's field of vision.

[0514] A "career plan" is a career plan or path suggested based on the user's skills, experience, and aspirations.

[0515] "Career counseling" is a service that provides consultation and advice to users regarding career choices and career development.

[0516] An "online platform" is a system that forms the basis for services and applications provided via the Internet.

[0517] The system for implementing this invention includes a text generation AI, an emotion recognition engine, smart glasses, and an online platform. The details of each component and their interaction will be described below.

[0518] System Configuration

[0519] 1. Text generation AI:

[0520] The server collects data on the job market and data related to personnel evaluation within the company, and tunes the text generation AI based on this data.

[0521] The text generation AI uses natural language processing technology to suggest the best career plan for users.

[0522] 2. Emotion Recognition Engine:

[0523] The server uses an emotion recognition engine that analyzes the user's text data, voice data, facial expression data, etc. to recognize their emotional state.

[0524] The emotion recognition engine analyzes the user's emotional state in real time and feeds that information back to the text generation AI.

[0525] 3. Smart Glasses:

[0526] Users wear smart glasses and their emotional state is recognized in real time.

[0527] Smart glasses have built-in cameras and displays that display information in the user's field of vision.

[0528] 4. Online Platform:

[0529] The three-way interaction between managers, employees, and AI takes place on an online platform.

[0530] Based on the results of the dialogue, the platform will help develop career plans and design experiences to support them.

[0531] Program processing

[0532] The server first collects data on the job market and internal personnel evaluation data to tune the text generation AI. Next, it uses an emotion recognition engine to analyze the user's emotional state and feeds that information back to the text generation AI. Users wearing the smart glasses have their emotional state recognized in real time and can receive career counseling. A three-way dialogue between the manager, employee, and AI takes place on the online platform, and career plans and experience designs are based on the results.

[0533] Specific examples

[0534] For example, if a user has three years of experience in the IT industry and is skilled in Python and data analysis, the emotion recognition engine can detect the user's stress state and generate the following prompt based on this information:

[0535] Prompt Sentence Examples

[0536] User data: {'skills': ['Python', 'Data Analysis'], 'experience': '3 years', 'industry': 'IT'}

[0537] Emotional state: Stress

[0538] Please suggest the best career plan for you.

[0539] This prompt is then input into a text generation AI, which then proposes the generated career plan to the user, thereby enabling the system to propose the optimal career plan that takes into account the user's emotional state.

[0540] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0541] Step 1:

[0542] The server collects data on the job market and data related to the company's internal personnel evaluations. Specifically, it collects data such as job information, the skills and experience of job seekers, industry trends, employee performance evaluations, skill sets, and career paths. This data is used to tune the text generation AI. The input is job market data and personnel evaluation data, and the output is the tuned text generation AI.

[0543] Step 2:

[0544] The server tunes the text generation AI based on the collected data. It uses natural language processing technology to analyze data from the job market and internal personnel evaluation data, and generates a model to propose the optimal career plan to the user. The input is the collected data, and the output is a generative AI model that proposes the optimal career plan to the user.

[0545] Step 3:

[0546] A user wears smart glasses and receives career counseling. The smart glasses capture the user's facial expression data in real time and send it to an emotion recognition engine. The input is the user's facial expression data, and the output is the data sent to the emotion recognition engine.

[0547] Step 4:

[0548] The server analyzes the user's emotional state using an emotion recognition engine. The emotion recognition engine analyzes the user's text data, voice data, facial expression data, etc. to recognize the user's emotional state. The input is the user's facial expression data, and the output is the analyzed emotional state.

[0549] Step 5:

[0550] The server feeds the analyzed emotional state back to the text generation AI, which then generates a prompt sentence to suggest the user's optimal career plan based on the emotional state. The input is the analyzed emotional state, and the output is the generated prompt sentence.

[0551] Step 6:

[0552] The server uses a text generation AI to generate the optimal career plan for the user based on the generated prompt. The input is the generated prompt, and the output is the career plan proposed to the user.

[0553] Step 7:

[0554] The user confirms the proposed career plan through the smart glasses. The career plan is displayed on the smart glasses display, allowing the user to check it in real time. The input is the proposed career plan, and the output is the user's confirmation.

[0555] Step 8:

[0556] The three parties - manager, employee, and AI - engage in a dialogue on an online platform. Based on the results of the dialogue, a career plan is formulated and the associated experience is designed. The input is the result of the dialogue, and the output is the formulated career plan and experience design.

[0557] Example 2

[0558] 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."

[0559] Conventional career planning systems provide uniform plans without considering the user's emotional state, resulting in low user satisfaction. Another issue is that it takes a great deal of time and effort for managers and career change supporters to individually create career plans. Furthermore, when career plans are created through online dialogue, specific action plans are often lacking.

[0560] 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.

[0561] In this invention, the server includes: means for tuning the text generation AI using job market data and internal personnel evaluation data; means for developing AI to support managers and career change supporters with subordinates; means for supporting the formulation of career plans and the design of experiences for such plans through a three-way dialogue between managers, members, and the AI; means for adjusting the proposed career plan using an emotion engine that analyzes the user's emotional state; means for the generative AI model to generate a career plan based on user input; means for proposing a specific action plan based on the generated career plan; means for the three-way dialogue between managers, members, and the AI ​​on an online platform; and means for utilizing general job market data and general personnel evaluation data as job change market data. This allows for the proposal of a career plan that is appropriate for the user's emotional state, improving user satisfaction. Furthermore, by reducing the burden on managers and career change supporters and providing specific action plans, more effective career plans can be formulated.

[0562] "Text generation AI" is artificial intelligence that uses natural language processing technology to generate text based on user input.

[0563] "Job market data" refers to information and statistical data about the general job market, and is data that shows trends and demand in job hunting.

[0564] "Human resource evaluation related data" refers to information related to employee evaluations within an organization, including data such as performance and skill sets.

[0565] A "manager" is a superior or leader who has subordinates and is responsible for management within an organization.

[0566] A "career change supporter" is an expert or agent who supports job-change activities and provides career advice to job seekers.

[0567] An "emotion engine" is a technology for analyzing a user's emotional state, and is a system that detects emotions from the content of text and dialogue.

[0568] A "generative AI model" is an artificial intelligence model that generates career plans based on user input information.

[0569] "Online platforms" refer to systems for interaction and information sharing that are available via the Internet, including video conferencing and chat tools.

[0570] A "career plan" refers to specific steps or actions taken to plan a user's career goals and career path.

[0571] An "action plan" refers to a specific plan of action for implementing a career plan, and includes actions such as acquiring skills and networking.

[0572] This invention is an AI system for supporting managers and career change supporters, and it proposes optimal career plans to users by combining text generation AI and an emotion engine. Specific embodiments of this system are described below.

[0573] System Overview

[0574] In this system, users input information about their career plans using their devices, and the server generates and adjusts the career plans based on that information using text generation AI and an emotion engine. The generated career plans are optimized through dialogue between the administrator, members, and AI, and specific action plans are proposed.

[0575] Hardware and software used

[0576] Devices: devices such as PCs, smartphones, and tablets

[0577] Server: High-performance cloud or on-premise server

[0578] Text generation AI: Generative AI models using natural language processing techniques (e.g., OpenAI's GPT-4 (registered trademark))

[0579] Emotion engine: Software that analyzes the user's emotional state (e.g., IBM Watson® Tone Analyzer)

[0580] Online platforms: video conferencing systems (e.g., Zoom), chat tools (e.g., Slack)

[0581] Data processing and calculation

[0582] 1. Receiving user input

[0583] A user uses a terminal to access a dedicated web application and inputs information about their career plan, such as their current job description, desired career path, and skill set.

[0584] 2. Generating career plans using text generation AI

[0585] The server receives the user's input information and sends it as a prompt to the generative AI model, which then generates a career plan based on the user's information.

[0586] 3. Emotional state analysis using the emotion engine

[0587] The server sends the user's input text and dialogue content to the emotion engine, which analyzes the user's emotional state and detects emotions such as joy, anxiety, and excitement from the text.

[0588] 4. Adjusting your career plan based on your emotional state

[0589] The server receives feedback from the emotion engine and sends the prompt again to the generative AI model, which then re-proposes a career plan that is appropriate for the user's emotional state.

[0590] 5. Developing career plans through dialogue

[0591] The administrator, members, and AI will interact through an online platform and develop the optimal plan based on the proposed career plan.

[0592] 6. Implement the action plan

[0593] Based on the career plan, users take specific actions, such as enrolling in an online course to learn a new skill or attending an industry event.

[0594] Examples of specific examples and prompts

[0595] Specific examples

[0596] A user types, "I want to be a data scientist."

[0597] The server sends the prompt statement "I want to be a data scientist. My current job is as a marketing analyst and I have basic knowledge of Python" to the generative AI model and receives the generated career plan.

[0598] The emotion engine detects "excitement" and feeds it back to the generative AI model.

[0599] The generative AI model sends a prompt saying, "Please propose a career plan that will excite the user," and receives a re-proposed career plan.

[0600] Administrators will use Zoom to communicate with members, exchange opinions on proposed career plans, and decide on the most suitable plan.

[0601] A user performs an action: "Enroll in an online course to improve their Python skills."

[0602] Prompt Sentence Examples

[0603] "I want to become a data scientist. I'm currently a marketing analyst and have basic knowledge of Python. What should I do next?"

[0604] In this way, a career plan suited to the user's emotional state is proposed, improving user satisfaction. It also reduces the burden on managers and career change supporters, and by providing specific action plans, it enables the formulation of more effective career plans.

[0605] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0606] Step 1:

[0607] A user accesses a dedicated web application using a terminal and inputs information about their career plan, including their current job description, desired career path, skill set, etc. The input data is then sent to the server.

[0608] Step 2:

[0609] The server receives the user's input information and sends it to the generative AI model as a prompt. Specifically, based on the user's input information, it generates a prompt such as, "I want to be a data scientist. My current job is as a marketing analyst, and I have basic knowledge of Python." The generative AI model then generates a career plan based on this prompt and returns it to the server.

[0610] Step 3:

[0611] The server sends the career plan received from the generative AI model to the emotion engine, which analyzes the user's emotional state. The emotion engine detects emotions such as joy, anxiety, and excitement from the text and returns the results to the server. For example, the analysis result may be "The user is excited."

[0612] Step 4:

[0613] The server receives feedback from the emotion engine and sends a prompt to the generative AI model again. Specifically, it sends the prompt, "Please propose a career plan that will excite the user." The generative AI model then proposes a new career plan that is appropriate for the user's emotional state and returns it to the server.

[0614] Step 5:

[0615] The server sends the re-proposed career plan to the online platform for a three-way dialogue between the administrator, members, and AI. The administrator and members communicate using video conferencing systems and chat tools, exchanging opinions about the proposed career plan. The optimal career plan is then formulated.

[0616] Step 6:

[0617] Based on the career plan, users take specific actions, such as enrolling in an online course to learn a new skill or attending an industry event. The server tracks the progress of these actions and provides additional support as needed.

[0618] (Application example 2)

[0619] 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."

[0620] While conventional career plan proposal systems can make suggestions based on a user's skills and experience, they are unable to take into account the user's emotional state, and therefore are unable to fully improve user satisfaction or motivation. Additionally, there is a lack of systems that propose appropriate career paths for factory workers, making it difficult to improve workers' skills and design career paths.

[0621] 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.

[0622] In this invention, the server includes: means for tuning the text generation AI using job market data and internal personnel evaluation-related data; means for developing AI to support managers and recruitment agents with subordinates; means for supporting the formulation of career plans and the design of experiences for such plans through a three-way dialogue between managers, members, and the AI; means for utilizing general job market data and general personnel evaluation data as job market data; means for analyzing the user's emotional state using an emotion engine and adjusting the career plan based on that information; and means for being installed in factory robots to suggest worker skill improvement and career paths. This makes it possible to propose career plans based on the user's emotional state, making it easier to improve worker skills and design career paths.

[0623] "Text generation AI" is an artificial intelligence that generates text using natural language processing technology, and has the function of suggesting career plans based on the user's skills and experience.

[0624] "Job market data" refers to information and statistical data relating to the general job market, which is used to formulate career plans.

[0625] "Personnel evaluation-related data" refers to data related to employee evaluations within a company, and is used to propose career plans.

[0626] The "emotion engine" is a system that has the function of analyzing the user's emotional state and adjusting their career plan based on that information.

[0627] "Manager / member / AI three-way dialogue" refers to the process in which a manager, an employee (member), and an artificial intelligence (AI) formulate a career plan through dialogue.

[0628] "Career Plan" refers to the career plans and goals suggested to you based on your skills and experience.

[0629] "Experience design" refers to the process of systematically accumulating the skills and experiences necessary to realize one's career plan.

[0630] A "factory robot" is an automated machine designed to perform work within a factory, and has the ability to improve the skills of workers and suggest career paths.

[0631] "Online platform" refers to systems and services accessible via the internet that facilitate interaction between managers, members, and AI.

[0632] The system for implementing this invention includes a text generation AI, an emotion engine, a factory robot, and an online platform. Specific embodiments of each element will be described below.

[0633] Text Generation AI

[0634] The server uses text generation AI to generate a career plan based on the user's skills and experience. The text generation AI uses natural language processing technology to analyze job market data and internal personnel evaluation data, and proposes the optimal career plan. For example, if a user has skills in "machine operation" and "quality control" and has "five years of factory work experience," the text generation AI will generate a career plan based on this information.

[0635] Emotion Engine

[0636] The server analyzes the user's emotional state using an emotion engine. The emotion engine analyzes the user's text input and voice data to determine whether the user is feeling joy, stress, excitement, or other emotions. For example, if the user inputs, "I'm excited to learn a new skill," the emotion engine determines that the user is feeling joy.

[0637] Adjusting career plans

[0638] The server adjusts the career plan generated by the text generation AI based on the analysis results of the emotion engine. For example, if the user is feeling stressed, the emotion engine will provide feedback on that information, and the text generation AI will suggest a career plan that will reduce stress.

[0639] Factory Robots

[0640] The factory robots will help workers improve their skills and suggest career paths. An application installed on the factory robot retrieves the worker's skills and experience from a database and uses text generation AI and an emotion engine to suggest the optimal career plan. For example, if a worker who has been working at the factory for five years wants to acquire new skills, the system will suggest the next skills they should acquire and the training program they should participate in based on the worker's current skills and experience.

[0641] Online Platform

[0642] The server supports dialogue between managers, workers, and AI through an online platform. Career plans and related experiences are developed on this platform. For example, a manager and worker can hold an online meeting to discuss career plans proposed by the AI ​​and decide on the most appropriate plan.

[0643] Prompt Sentence Examples

[0644] "Please suggest a career plan based on the user's skills and experience: Machine operation, quality control, 5 years of factory work experience."

[0645] In this way, it becomes possible to propose career plans that correspond to the user's emotional state, making it easier to improve workers' skills and design career paths.

[0646] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0647] Step 1:

[0648] The server retrieves data about the user's skills and experience from the database. The input is the user's ID, and the output is data about the user's skills and experience. This data becomes the basis for the text generation AI to generate a career plan.

[0649] Step 2:

[0650] The server uses text generation AI to generate a career plan based on the acquired user skills and experience. The input is data on the user's skills and experience, and the output is the generated career plan. The text generation AI uses natural language processing technology to propose the optimal career plan to the user.

[0651] Step 3:

[0652] The server uses an emotion engine to analyze the user's emotional state. The input is the user's text input or voice data, and the output is the analyzed emotional state. The emotion engine determines whether the user is feeling joy, stress, excitement, or other emotions.

[0653] Step 4:

[0654] The server adjusts the career plan generated by the text generation AI based on the analysis results of the emotion engine. The input is the analyzed emotional state and the generated career plan, and the output is the adjusted career plan. For example, if the user is feeling stressed, the emotion engine will feed that information back, and the text generation AI will suggest a career plan that will reduce stress.

[0655] Step 5:

[0656] The server sends the adjusted career plan to the factory robot. The input is the adjusted career plan, and the output is the career plan sent to the factory robot. The factory robot then uses this career plan to improve the worker's skills and propose career paths.

[0657] Step 6:

[0658] The terminal supports a three-way dialogue between the manager, the worker, and the AI ​​through an online platform. The input is the adjusted career plan, and the output is the final career plan formulated as a result of the dialogue. For example, the manager and the worker hold an online meeting to discuss the career plans proposed by the AI ​​and decide on the optimal plan.

[0659] Step 7:

[0660] The server saves the final career plan in a database. The input is the final career plan and the output is the saved career plan. This preserves the user's career plan for future reference and updates.

[0661] Example 3

[0662] Next, a description will be given of a third embodiment of the third embodiment. 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."

[0663] In today's labor market, it is important for individual employees to effectively formulate their own career plans and acquire the necessary skills. However, it is not easy for employees to recognize their own skill gaps and formulate appropriate career plans. In addition, there is a lack of career plan proposals that take employees' emotional state into consideration, which can lead to a decline in employee satisfaction and motivation. To solve these issues, a system is needed that utilizes labor market data and internal organizational evaluation data to propose career plans that take employees' emotional state into consideration.

[0664] 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.

[0665] In this invention, the server includes means for tuning the text generation AI using labor market data and organizational evaluation-related data, means for developing AI to support managers and job placement agents, means for supporting the formulation of career plans and the design of experiences for such plans through a three-way dialogue between managers, employees, and the AI, means for analyzing the user's emotional state using an emotion engine and feeding that information back to the text generation AI, and means for proposing an appropriate career plan based on the user's emotional state. This enables employees to recognize their skill gaps and create a career plan suited to their emotional state.

[0666] "Text generation AI" is an artificial intelligence system that uses natural language processing technology to generate and analyze text data.

[0667] "Labor market data" is data that includes information about the labor market, such as job openings and skill demand.

[0668] "Internal evaluation-related data" is data that includes evaluation information regarding employee performance and skills.

[0669] A "manager" is someone who has the role of managing employees within an organization, such as a boss or team leader with subordinates.

[0670] An "employment introducer" is a professional who connects job seekers with companies, such as a recruitment agent or career consultant.

[0671] An "emotion engine" is a technology that analyzes a user's emotional state and feeds that information back to other systems.

[0672] A "career plan" is a specific plan for an employee to achieve their career goals.

[0673] A "skills gap" is the difference between current skills and desired skills.

[0674] An "online platform" is a collection of services and applications provided over the Internet.

[0675] The present invention is implemented using software such as a text generation AI, an emotion engine, and a database management system, and hardware such as a server and a terminal (PC, smartphone). Specific embodiments are described below.

[0676] Hardware and software used

[0677] Hardware: Servers, terminals (PCs, smartphones)

[0678] Software: Text generation AI (e.g., GPT-4), emotion engines, database management systems (e.g., MySQL)

[0679] Processing flow

[0680] 1. Data Collection and Analysis

[0681] The server collects labor market data, specifically from job sites and industry reports, via an API.

[0682] The server inputs the collected data into a text generation AI to analyze skill demand in the IT industry.

[0683] Example: The server sends the prompt "What are the most in-demand skills in the IT industry today?" to a generative AI model and stores the results in a database.

[0684] 2. Evaluating employee data

[0685] The server retrieves reputation-related data within the organization from a database.

[0686] The server inputs the acquired data into a text generation AI to evaluate the skills of employees.

[0687] Example: The server sends the prompt "Does employee A have data analysis skills?" to the generative AI model and stores the results in a database.

[0688] 3. Skill acquisition suggestions

[0689] The server suggests skills acquisition for employees who do not have data analysis skills.

[0690] Example: The server generates a message to employee A saying, "We recommend that you acquire data analysis skills," and sends it to the employee's terminal.

[0691] 4. Propose an action plan

[0692] AI supports managers and job placement agencies through devices (PCs and smartphones) and proposes action plans for acquiring specific skills.

[0693] Example: A message appears on the administrator's device saying, "We are proposing a data analysis skills acquisition plan for employee A. We recommend taking online courses and participating in practical projects."

[0694] 5. Emotional state analysis and feedback

[0695] The emotion engine analyzes the user's emotional state. Specifically, it analyzes the user's input and behavioral data to estimate the emotional state.

[0696] The emotion engine feeds the analysis results back to the text generation AI.

[0697] Example: An emotion engine generates information such as "The user is feeling anxious" and sends it to a text generation AI.

[0698] 6. Emotion-based career planning

[0699] Text generation AI suggests appropriate career plans based on the user's emotional state.

[0700] Example: A text generation AI generates a message that reads, "To reduce your anxiety, we will suggest a plan that will help you set short-term goals and achieve a sense of accomplishment," and sends it to the user's device.

[0701] Example prompt

[0702] "What skills are in high demand in the IT industry today?"

[0703] "Does Employee A have data analysis skills?"

[0704] "Users are feeling anxious. Please suggest a career plan."

[0705] This system allows users to specifically formulate their own career plans and design the experiences necessary to achieve them. Furthermore, the combination of an emotion engine and text generation AI suggests career plans suited to the user's emotional state, improving user satisfaction. The flow of the identification process in Example 3 is explained using Figure 21.

[0706] Step 1:

[0707] Data collection and analysis

[0708] The server collects labor market data, specifically from job sites and industry reports, via an API.

[0709] Input: Data obtained from APIs of job sites and industry reports.

[0710] Data processing: The acquired data is input into a text generation AI and converted into an analyzable format.

[0711] Output: Analysis of skill demand in the labor market.

[0712] Specific operation: The server sends the prompt "What are the most in-demand skills in the IT industry today?" to the generative AI model and stores the results in a database.

[0713] Step 2:

[0714] Employee data assessment

[0715] The server retrieves reputation-related data within the organization from a database.

[0716] Input: Reputation-related data within your organization.

[0717] Data processing: The acquired data is fed into a text generation AI to evaluate employee skills.

[0718] Output: Employee skill assessment results.

[0719] Specific operation: The server sends the prompt sentence "Does employee A have data analysis skills?" to the generative AI model and saves the result in the database.

[0720] Step 3:

[0721] Skill acquisition suggestions

[0722] The server suggests skills acquisition for employees who do not have data analysis skills.

[0723] Input: Employee skill assessment results.

[0724] Data processing: Based on the skill evaluation results, a message suggesting skill acquisition is generated.

[0725] Output: Skill acquisition suggestion message.

[0726] Specific operation: The server generates a message to employee A saying, "We recommend that you acquire data analysis skills," and sends it to the employee's terminal.

[0727] Step 4:

[0728] Proposing an action plan

[0729] AI supports managers and job placement agencies through devices (PCs and smartphones) and proposes action plans for acquiring specific skills.

[0730] Input: Skill acquisition suggestion message.

[0731] Data processing: Generate a specific action plan based on the proposed message.

[0732] Output: Action plan proposal message.

[0733] Specific behavior: A message will appear on the administrator's device saying, "We are proposing a data analysis skills acquisition plan for employee A. We recommend taking online courses and participating in practical projects."

[0734] Step 5:

[0735] Emotional state analysis and feedback

[0736] The emotion engine analyzes the user's emotional state. Specifically, it analyzes the user's input and behavioral data to estimate the emotional state.

[0737] Input: User input and behavioral data.

[0738] Data processing: Analyze emotional states and feed that information back into the text generation AI.

[0739] Output: Information about the user's emotional state.

[0740] Specific behavior: The emotion engine generates information such as "The user is feeling anxious" and sends it to the text generation AI.

[0741] Step 6:

[0742] Emotion-based career planning

[0743] Text generation AI suggests appropriate career plans based on the user's emotional state.

[0744] Input: Information about the user's emotional state.

[0745] Data processing: Generate career plans based on emotional states.

[0746] Output: Career plan suggestion message.

[0747] Specific behavior: The text generation AI generates a message that reads, "To help reduce your anxiety, we will suggest a plan that will help you set short-term goals and achieve a sense of accomplishment," and sends it to the user's device.

[0748] (Application example 3)

[0749] 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."

[0750] Lack of employee skills and unclear career plans are factors that have a negative impact on corporate productivity and employee satisfaction. In addition, proposing a uniform career plan without considering the emotional state of employees may reduce their motivation and performance. Therefore, there is a need for a system that can propose the optimal career plan for each individual employee based on their skill and emotional data.

[0751] 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.

[0752] In this invention, the server includes: means for tuning the text generation AI using job market data and internal personnel evaluation-related data; means for developing AI to support managers and career change supporters with subordinates; means for supporting the formulation of career plans and the design of experiences for such plans through a three-way dialogue between managers, employees, and the AI; means for utilizing general job market data and general personnel evaluation data as job market data; means for acquiring employee skill data and emotional data and proposing career plans based on their emotional state; and means that can be used by employees in their daily work using a smart device, thereby enabling the improvement of employee skills and the optimization of career plans.

[0753] "Text generation AI" is an artificial intelligence that generates text using natural language processing technology, and has the ability to analyze data from the job market and data related to personnel evaluation within a company, and propose optimal career plans.

[0754] "Job Market Data" means data containing information about the job market in general, including job listings, job seeker skill sets, and industry trends.

[0755] "Human resource evaluation data" refers to data used to evaluate employee performance and skills within a company, and includes evaluation results, feedback, skill matrices, etc.

[0756] A "manager" is a person who has subordinates and is responsible for managing and supervising work within an organization.

[0757] A "career change supporter" is a professional who supports job seekers in their job change activities, and includes career consultants and job change agents.

[0758] "Emotional data" refers to data that indicates an employee's emotional state, including stress levels, mood, motivation, etc.

[0759] A "smart device" is an electronic device that can connect to the Internet and has the ability to run applications, including smartphones and smart glasses.

[0760] A "career plan" is a plan for how an employee will progress in their career, including skill acquisition, work experience, goal setting, etc.

[0761] An "online platform" is a collection of services and applications provided over the Internet that provides an environment for users to interact and exchange information.

[0762] A system for implementing this invention is configured by combining a text generation AI, an emotion engine, a smart device, and an online platform. Specific embodiments are described below.

[0763] System Configuration

[0764] 1. Text Generation AI

[0765] The server uses text generation AI to analyze job market data and internal personnel evaluation data, generating optimal suggestions for employee skill sets and career plans. The text generation AI uses OpenAI's API.

[0766] 2. Emotion Engine

[0767] The server uses an emotion engine to obtain employee emotional data in real time. The emotion engine uses the Emotion Recognition API, which monitors employees' stress levels and moods and suggests career plans based on their emotional state.

[0768] 3. Smart Devices

[0769] Employees use smart devices such as smartphones and smart glasses to access the system in their daily work, allowing them to receive career planning suggestions in real time.

[0770] 4. Online Platforms

[0771] The three parties—manager, employee, and AI—interact on an online platform, which is a collection of services and applications provided over the internet, providing an environment for users to interact and exchange information.

[0772] Data processing and calculation

[0773] 1. Obtaining skill data

[0774] The server retrieves employee skill data from the database, including the employee's current skill set and past assessment results.

[0775] 2. Acquiring Emotion Data

[0776] The server uses an emotion engine to obtain real-time employee emotion data, including stress levels and moods.

[0777] 3. Creating a career plan

[0778] The server uses text generation AI to generate a career plan based on the acquired skill data and emotion data. The generated career plan is appropriate for the employee's emotional state.

[0779] Specific examples

[0780] For example, if employee A lacks data analysis skills and has recently been feeling stressed by his work, the server will acquire employee A's skill data and emotion data and input the following prompt sentence into the text generation AI:

[0781] Example prompt sentence:

[0782] Employee Skills Data: {"data_analysis": False, "programming": True}

[0783] Emotion data: {"stress_level": "high", "mood": "anxious"}

[0784] Suggest a suitable career plan for this employee.

[0785] Based on this prompt, the text-generation AI will suggest an online course in data analysis to Mr. A and also provide mental health support to reduce stress, thus providing the employee with a concrete action plan to improve their career.

[0786] The flow of the specific processing in Application Example 3 will be described with reference to FIG.

[0787] Step 1:

[0788] The server retrieves employee skill data from the database. It receives the employee's ID as input and retrieves the employee's skill data, including their skill set and past evaluation results, as output. Specifically, it executes SQL queries to extract the required information from the database.

[0789] Step 2:

[0790] The server uses an emotion engine to obtain employee emotional data in real time. It receives the employee's ID as input and obtains the employee's emotional data, including their stress level and mood, as output. Specifically, it calls the Emotion Recognition API to obtain the emotional data.

[0791] Step 3:

[0792] The server inputs a prompt sentence into the text generation AI based on the acquired skill data and emotion data. It receives skill data and emotion data as input and generates a prompt sentence as output. Specifically, it formats the skill data and emotion data to create a prompt sentence like the one below.

[0793] Employee Skills Data: {"data_analysis": False, "programming": True}

[0794] Emotion data: {"stress_level": "high", "mood": "anxious"}

[0795] Suggest a suitable career plan for this employee.

[0796] Step 4:

[0797] The server inputs the generated prompt sentence into the text generation AI to generate a career plan. It receives the prompt sentence as input and generates a career plan as output. Specifically, it calls the OpenAI API to generate a career plan.

[0798] Step 5:

[0799] The server sends the generated career plan to the smart device. It receives the career plan as input and sends it to the smart device as output. As a specific operation, it displays the career plan using the notification function of the smart device.

[0800] Step 6:

[0801] Users use smart devices to review their career plans and take necessary actions. The input is the career plan, and the output is specific actions, such as registering for online courses or accessing mental health support.

[0802] 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.

[0803] 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.

[0804] Another example of generative AI is Gemini (registered trademark) (Internet search engine). <url: https: gemini.google.com ?hl="ja">) are mentioned.

[0805] 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.

[0806] [Second embodiment]

[0807] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0808] 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.

[0809] 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).

[0810] 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.

[0811] 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.

[0812] 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).

[0813] 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.

[0814] 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.

[0815] 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.

[0816] 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.

[0817] In the smart glasses 214, the reception output process is performed by the processor 46. A reception output program 60 is stored in the storage 50. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[0818] Next, the specific processing by the specific processing unit 290 of the data processing device 12 will be described.

[0819] "Example 1"

[0820] In one embodiment of the present invention, a text generation AI collects data from the job market and data related to a company's internal personnel evaluations, and then uses natural language processing technology to tune the system based on this data. Specifically, data from the job market includes job information, the skills and experience of job seekers, and industry trends, while data related to the company's internal personnel evaluations includes employee performance evaluations, skill sets, and career paths. Based on this data, the text generation AI proposes optimal career plans for each individual user.

[0821] "Example 2"

[0822] Additionally, AI supporting managers and recruitment agents with subordinates will propose specific action plans based on the career plans proposed by the text generation AI. Specifically, it will suggest actions such as acquiring necessary skills, gaining experience, and networking. These suggestions are made through a three-way dialogue between the manager, employee, and AI, and the results are used to formulate career plans and design experiences for them.

[0823] "Example 3"

[0824] As a specific example, if a text-generation AI determines from job market data that there is a growing demand for data analysis skills in the IT industry, and determines from a company's internal personnel evaluation data that an employee does not have data analysis skills, it will suggest that the employee acquire data analysis skills. Then, AI supporting managers and recruitment agents will propose specific action plans for acquiring skills. This allows employees to specifically formulate their own career plans and design the experiences needed to achieve them.

[0825] The processing flow of each embodiment will be described below.

[0826] "Example 1"

[0827] Step 1: The text generation AI collects data from the job market and data related to internal personnel evaluations. Specifically, it collects data from the job market, such as job information, the skills and experience of job seekers, and industry trends, as well as data related to internal personnel evaluations, such as employee performance evaluations, skill sets, and career paths.

[0828] Step 2: Based on the collected data, the text generation AI is tuned using natural language processing technology. Through this tuning, the text generation AI gains the ability to propose the optimal career plan for each individual user.

[0829] Step 3: Based on the career plan proposed by the text generation AI, AI supporting managers and recruitment agents with subordinates will propose a specific action plan.

[0830] "Example 2"

[0831] Step 1: The text generation AI determines from job market data that there is a growing demand for data analysis skills in the IT industry.

[0832] Step 2: At the same time, the text generation AI determines from the company's internal performance evaluation data that a certain employee does not have data analysis skills.

[0833] Step 3: Based on these findings, the text-generating AI suggests that the employee learn data analysis skills.

[0834] Step 4: The AI ​​then supports managers and recruitment agencies by proposing specific action plans for acquiring skills, enabling employees to formulate their own career plans and design the experiences needed to achieve them.

[0835] Example 1

[0836] 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."

[0837] Conventional career planning systems were unable to effectively utilize data from the job market or internal personnel evaluation data, making it difficult to provide optimal career plans for individual users. Furthermore, career plans were often generated manually based on user input data, resulting in a lack of efficiency. Furthermore, there were insufficient means for providing the generated career plans to users, and there was a need for them to be provided in a format that users could easily access.

[0838] 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.

[0839] In this invention, the server includes means for collecting data on the job market and data related to internal personnel evaluations, means for preprocessing the collected data, means for tuning a text generation AI model using the preprocessed data, means for generating an optimal career plan based on data input by a user, and means for providing the generated career plan to the user. This makes it possible to effectively utilize data on the job market and data related to internal personnel evaluations, automatically generate optimal career plans for individual users, and efficiently provide them to them.

[0840] "Job market data" refers to information related to job hunting, such as job information, the skills and experience of job seekers, and industry trends.

[0841] "Internal personnel evaluation-related data" refers to information related to personnel evaluation within a company, such as employee performance evaluations, skill sets, and career paths.

[0842] "Means of collection" refers to the methods and technologies used to obtain data on the job market and data related to personnel evaluation within a company.

[0843] "Preprocessing means" refers to methods and techniques for preparing collected data in a form that is easy to analyze.

[0844] "Text generation AI model" refers to an artificial intelligence model that generates text using natural language processing technology.

[0845] "Tuning methods" refers to methods and techniques for optimizing the performance of AI models using collected data.

[0846] "User input data" refers to information entered by the user, such as their skills, experience, and desired career path.

[0847] "Career plan" refers to a career plan or path proposed based on the user's skills, experience, and industry trends.

[0848] "Means of providing" refers to the methods and techniques for communicating the generated career plan to users.

[0849] This invention is a system that collects data on the job market and data related to personnel evaluation within a company, and tunes a text generation AI model based on this data. A specific embodiment of this system is described below.

[0850] Server Processing

[0851] The server first collects data from the job market and data related to the company's internal personnel evaluations. The job market data includes job information, the skills and experience of job seekers, and industry trends. The internal personnel evaluation data includes employee performance evaluations, skill sets, and career paths. These data are then stored in a database.

[0852] The server then preprocesses this data using natural language processing techniques, specifically using the Python libraries NLTK and spaCy to clean and tokenize the text data, and uses the preprocessed data to tune a text-generation AI model using TensorFlow or PyTorch.

[0853] The tuned AI model generates the optimal career plan based on the user's input data, which includes the user's skills, experience, desired career path, etc. The server analyzes this data and generates the optimal career plan for the user.

[0854] Terminal handling

[0855] The terminal accepts input from the user. The user enters information such as their skills, experience, and desired career path. This information is sent from the terminal to the server. Specifically, the data is sent using an HTTP request.

[0856] The terminal receives the carrier plan sent from the server and displays it to the user. Specifically, it receives the HTTP response and displays the carrier plan. The user can check the proposed carrier plan through the terminal and provide feedback if necessary.

[0857] User operations

[0858] First, the user inputs information such as their skills, experience, and desired career path into the terminal. This information is sent to the server. Next, the user checks the career plan sent from the server on the terminal. The career plan is generated taking into consideration the user's skills, experience, and industry trends. The user can use the proposed career plan as a reference to plan their own career.

[0859] Specific examples

[0860] For example, if a user enters "I'm considering a career as a data scientist," the server will analyze the skills, experience, and industry trends required for data scientists based on job market data and the company's own personnel evaluation data. Based on the analysis results, the server will suggest a career plan to the user, such as "It is recommended that you improve your Python and R skills and participate in machine learning projects."

[0861] Prompt Sentence Examples

[0862] "I'm considering a career as a data scientist. What skills and experience do I need? What are the industry trends?"

[0863] In this way, the system proposes the optimal career plan based on the user's input.

[0864] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0865] Step 1:

[0866] The server collects data on the job market and data related to personnel evaluation within the company.

[0867] Specifically, they use web scraping tools to collect data from job information sites and obtain employee performance evaluation data from their own database.

[0868] Input: URL of job information site, query of company database

[0869] Output: Job market data, internal personnel evaluation data

[0870] Step 2:

[0871] The server pre-processes the collected data.

[0872] Specifically, we use the Python libraries NLTK and spaCy to clean and tokenize text data.

[0873] Input: Job market data, internal personnel evaluation data

[0874] Output: Preprocessed data

[0875] Step 3:

[0876] The server uses the preprocessed data to tune the text generation AI model.

[0877] Specifically, the model is trained using TensorFlow and PyTorch.

[0878] Input: Preprocessed data

[0879] Output: A tuned text generation AI model

[0880] Step 4:

[0881] The terminal accepts input from the user.

[0882] Specifically, users enter information such as their skills, experience, and desired career path.

[0883] Input: User's skills, experience, and desired career path

[0884] Output: User input data

[0885] Step 5:

[0886] The terminal transmits the data entered by the user to the server.

[0887] Specifically, data is transmitted using an HTTP request.

[0888] Input: User-entered data

[0889] Output: Send data to the server

[0890] Step 6:

[0891] The server generates an optimal career plan based on the data input by the user.

[0892] Specifically, a tuned AI model is used to analyze the user's skills, experience, and industry trends to create a career plan.

[0893] Input: User input data, tuned text generation AI model

[0894] Output: Career plan

[0895] Step 7:

[0896] The server transmits the generated carrier plan to the terminal.

[0897] Specifically, the carrier plan is sent using an HTTP response.

[0898] Input: Career plan

[0899] Output: Carrier plan sent to device

[0900] Step 8:

[0901] The terminal receives the carrier plan sent from the server and displays it to the user.

[0902] Specifically, the career plan is visually displayed using a GUI.

[0903] Input: Career plan

[0904] Output: Display career plan to user

[0905] Step 9:

[0906] The user checks the carrier plan displayed on the terminal.

[0907] Specifically, we will read the proposed career plan and provide feedback as needed.

[0908] Input: Career plan

[0909] Output: User feedback (if needed)

[0910] (Application example 1)

[0911] 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."

[0912] Formulating and updating employee career plans using traditional methods takes time and effort, making it difficult to provide optimal plans for each employee. Furthermore, there is a lack of ways for brick-and-mortar store employees to check and update their own career plans in real time, making it difficult to effectively support their career growth.

[0913] 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.

[0914] In this invention, the server includes: means for tuning the text generation AI using job market data and internal personnel evaluation-related data; means for developing AI to support managers and career change supporters with subordinates; means for supporting the formulation of career plans and the design of experiences for such plans through a three-way dialogue between managers, employees, and the AI; means for utilizing general job market data and general personnel evaluation data as job market data; means for employees at physical stores to check and update their career plans in real time using smart devices; and means for the text generation AI to analyze employees' performance evaluations, skill sets, and career paths and propose optimal career plans. This enables employees to check and update their own career plans in real time and receive proposals for optimal career plans.

[0915] "Text generation AI" is an artificial intelligence that generates text using natural language processing technology, analyzes data from the job market and data related to personnel evaluation within a company, and proposes the optimal career plan for each individual user.

[0916] "Job market data" refers to general data related to the job market, such as job information, the skills and experience of job seekers, and industry trends.

[0917] "Human resource evaluation-related data" refers to data related to human resource evaluation collected within a company, such as employee performance evaluations, skill sets, and career paths.

[0918] A "manager" is someone who has a subordinate and is responsible for helping employees develop career plans and designing experiences for them.

[0919] A "career change supporter" is a professional who supports individuals who wish to change jobs, provides information about the job market, and assists in the formulation of career plans.

[0920] "Employees" refers to individuals who work in physical stores and who need to develop or update their career plans.

[0921] "Smart devices" refer to electronic devices that can connect to the Internet and have advanced functions, such as smartphones and smart glasses.

[0922] A "career plan" refers to a plan that outlines the career goals that an individual employee wants to achieve in the future and the steps they need to take to achieve them.

[0923] "Natural language processing technology" is a technology for understanding and generating human language, and plays an important role in text generation AI.

[0924] An "online platform" refers to a foundation that provides systems and services accessible via the internet, and provides a forum for dialogue between managers, employees, and AI.

[0925] The system for implementing this invention uses text generation AI to analyze job market data and internal personnel evaluation data, and proposes optimal career plans for individual employees. Specific embodiments of this system are described below.

[0926] System Configuration

[0927] Hardware:

[0928] server

[0929] Smartphone or smart glasses

[0930] software:

[0931] Python

[0932] OpenAI API

[0933] Pandas

[0934] Data collection and preprocessing

[0935] The server collects data from the job market (job information, skills and experience of job seekers, industry trends, etc.) and data related to the company's internal personnel evaluation (employee performance evaluations, skill sets, career paths, etc.) This data is saved in formats such as CSV files and is read using Pandas.

[0936] Parsing data and generating prompts

[0937] The server preprocesses the collected data and extracts the necessary information. Next, based on employee information and job market information, it generates prompts to be input into the text generation AI. Examples of prompts are as follows:

[0938] Example prompt sentence:

[0939] Employee Information:

[0940] Performance: High

[0941] Skills: Data analysis, programming

[0942] Career Path: Data Analyst

[0943] Job market information:

[0944] Job Opening: Data Scientist

[0945] Skills: Data Analysis, Machine Learning

[0946] Industry Trends: Demand for Data Science is on the Rise

[0947] Suggest the best career plan for this employee.

[0948] Creating a career plan

[0949] The server uses the OpenAI API to input prompts into the generative AI model to generate a career plan, which is then displayed in real time on the employee's smartphone or smart glasses.

[0950] Specific examples

[0951] For example, if Employee A has a high performance rating and his or her skill set includes "data analysis," the server will suggest a career path to "data scientist" based on job market data. This suggestion will be displayed in real time on Employee A's smartphone or smart glasses, allowing Employee A to confirm and update his or her career plan.

[0952] In this way, the server allows employees to check and update their career plans in real time and receive suggestions for the most suitable career plans.

[0953] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0954] Step 1:

[0955] The server collects job market data and internal personnel evaluation-related data. This includes job information, job seekers' skills and experience, industry trends, employee performance evaluations, skill sets, and career paths. This data is saved in formats such as CSV files. The input is job market data and personnel evaluation data, and the output is a collection of this data.

[0956] Step 2:

[0957] The server reads the collected data using Pandas and performs preprocessing. Specifically, it extracts necessary information and formats the data. For example, it deletes unnecessary columns and imputes missing values. The input is the data collected in step 1, and the output is the preprocessed data.

[0958] Step 3:

[0959] The server generates prompts based on the preprocessed data, combining employee information and job market information. The prompts include employee performance evaluations, skill sets, career paths, and job market information, required skills, and industry trends. The input is the preprocessed data, and the output is the generated prompt.

[0960] Step 4:

[0961] The server sends the generated prompt to the OpenAI API and generates a career plan using the generative AI model. The input is the prompt, and the output is the generated career plan. Specifically, the server sends an API request and receives a response.

[0962] Step 5:

[0963] The server sends the generated career plan to the employee's smartphone or smart glasses in real time. The input is the generated career plan, and the output is the career plan displayed on the employee's device. Specific operations include sending a notification to the device and displaying the career plan.

[0964] Step 6:

[0965] Users (employees) use smartphones or smart glasses to check and update their career plans in real time. The input is the career plan displayed on the device, and the output is the updated career plan. Specific operations involve operating the device interface to check and update the career plan.

[0966] Example 2

[0967] 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."

[0968] Conventional career planning systems have had difficulty generating specific action plans to propose optimal career plans for individual users, and also have had problems with the inefficient process of formulating career plans through dialogue between managers, members, and AI.

[0969] The identification process by the identification processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means. In this invention, the server includes a means for the user to input a prompt sentence, a means for the server to receive the prompt sentence and send it to the generative AI model, a means for the generative AI model to generate a career plan, a means for the server to analyze the generated career plan and generate a specific action plan, a means for the server to present the action plan to the user, and a means for the user, manager, and AI to formulate a career plan through dialogue. This makes it possible to propose an optimal career plan for each user and generate a specific action plan. Furthermore, a career plan can be formulated efficiently through dialogue between the manager, member, and AI.

[0970] "User" refers to an individual or organization that uses the system to receive career plan proposals.

[0971] A "prompt" is a textual question or request that a user enters into a system.

[0972] "Server" refers to the computer system responsible for receiving prompts from the user and sending them to the generative AI model.

[0973] "Generative AI model" refers to an artificial intelligence model that uses natural language processing technology to analyze prompt sentences and generate career plans.

[0974] A "career plan" refers to a plan that shows a user's career goals and career path.

[0975] An "action plan" is a plan that outlines the specific actions and steps required to realize your career plan.

[0976] "Manager" refers to a person in a managerial position who has subordinates.

[0977] "Member" refers to an individual who works under a manager.

[0978] "Dialogue" refers to the communication between the user, manager, and AI regarding the formulation of a career plan.

[0979] An "online platform" refers to a digital environment in which users, managers, and AI can interact via the internet.

[0980] This invention is a system in which a user inputs a prompt sentence, a server receives the prompt sentence, and sends it to a generative AI model to generate a career plan and propose a specific action plan. Specific embodiments of this system are described below.

[0981] Hardware and software used

[0982] Hardware:

[0983] High-performance server (e.g., server equipped with NVIDIA GPU)

[0984] software:

[0985] Text generation AI (e.g., generative AI models using natural language processing technology)

[0986] Database management system (e.g. MySQL)

[0987] Web server (e.g. Apache)

[0988] Data processing and calculation

[0989] The server receives the prompt sentence from the user and sends it to the generative AI model. The generative AI model analyzes the prompt sentence and generates a career plan according to the user's needs. The generated career plan includes the following elements:

[0990] Acquire the necessary skills

[0991] Accumulation of experience

[0992] How to network

[0993] The server then analyzes the generated career plan and generates a specific action plan. This action plan is created through a three-way dialogue between the user, manager, and AI, and the results are used to formulate the career plan and design the experience required for it.

[0994] Specific examples

[0995] As a concrete example, the following prompt sentence is input to the generative AI model:

[0996] Example prompt sentence:

[0997] "A software engineer in his 30s wants to transition to a project manager position. His current skill set is: Java, Python, and basic project management knowledge. Please suggest the skills and experience he needs and how he can network."

[0998] When the user enters the above prompt, the server sends it to the text generation AI model, which then generates the following career plan:

[0999] Acquisition of necessary skills: Advanced knowledge of project management (e.g., PMP certification), leadership skills, communication skills

[1000] Gaining experience: Experience leading small projects, increased opportunities to receive feedback from team members

[1001] Networking: Attending project management related conferences and seminars to expand your professional network

[1002] The server presents these suggestions to the user, and through a three-way dialogue between the user, manager, and AI, the optimal career plan is formulated.

[1003] In this way, users can formulate and execute career plans based on specific action plans.

[1004] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1005] Step 1:

[1006] The user inputs a prompt through the system interface. For example, the user might input, "A software engineer in his 30s wants to change careers to become a project manager. His current skill set is as follows: Java, Python, and basic knowledge of project management. Please suggest the skills, experience, and networking methods he needs." The input prompt is sent to the system.

[1007] Step 2:

[1008] The server receives the prompt sentence. The server receives the prompt sentence sent by the user and stores it in a database. The received prompt sentence is prepared to be sent to the generative AI model. The input is the prompt sentence, and the output is the prompt sentence ready to be sent to the generative AI model.

[1009] Step 3:

[1010] The server sends a prompt to the generative AI model. The server sends the received prompt to the generative AI model. The generative AI model generates a career plan based on the prompt. The input is the prompt, and the output is the prompt sent to the generative AI model.

[1011] Step 4:

[1012] A generative AI model generates a career plan. The generative AI model analyzes the prompt and generates a career plan that meets the user's needs. For example, it suggests ways to acquire necessary skills, gain experience, and network. The input is the prompt, and the output is the generated career plan.

[1013] Step 5:

[1014] The server receives the generated career plan. The server receives the career plan generated from the generative AI model and stores it in a database. The received career plan is further analyzed. The input is the generated career plan, and the output is a career plan prepared for analysis.

[1015] Step 6:

[1016] The server analyzes the career plan and generates a specific action plan. The server analyzes the received career plan and generates a specific action plan. For example, it suggests obtaining PMP certification, improving leadership skills, and gaining experience as a leader of small projects. The input is a career plan, and the output is a specific action plan.

[1017] Step 7:

[1018] The server presents the action plan to the user. The server renders the generated action plan in HTML format and displays it in the user's browser. The user checks the displayed action plan and makes corrections or additions as necessary. The input is a specific action plan, and the output is the action plan presented to the user.

[1019] Step 8:

[1020] The user, manager, and AI formulate a career plan through dialogue. The three parties, user, manager, and AI, engage in dialogue based on the presented action plan to formulate the optimal career plan. The final design of the career plan is based on the results of the dialogue. The input is the action plan and the results of the dialogue, and the output is the final formulated career plan.

[1021] (Application example 2)

[1022] 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."

[1023] Conventional career planning systems are primarily aimed at human managers and job placement agents, and are not designed to improve the skills or develop career plans for robots working in factories. This makes it difficult to efficiently train robots in the factory and improve their productivity.

[1024] The specific processing by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes: means for tuning the text generation AI using job market data and internal personnel evaluation-related data; means for developing AI to support managers and recruitment agents with subordinates; means for supporting the formulation of career plans and the design of experiences for such plans through a three-way dialogue between managers, members, and the AI; means for utilizing general job market data and general personnel evaluation data as job market data; means for proposing career plans based on the robot's current skill set and the factory's needs to help improve the skills of robots working in factories; means for proposing specific skill acquisition methods, experience accumulation methods, and networking methods; and means for formulating optimal career plans by adjusting the proposals through a three-way dialogue between the robot, manager, and AI. This enables efficient skill acquisition and improved productivity for robots working in factories.

[1025] "Text generation AI" is artificial intelligence that generates text data using natural language processing technology.

[1026] "Job market data" refers to market information and statistical data related to job hunting.

[1027] "Internal personnel evaluation-related data" refers to data related to employee evaluations and performance within a company.

[1028] A "manager with subordinates" refers to a managerial position that has the role of managing and guiding subordinates within an organization.

[1029] A "career agent" is a professional who acts as an intermediary between job seekers and companies and supports job hunting activities.

[1030] A "career plan" refers to an individual's goals and plans for their professional life.

[1031] "Experience design" refers to the process of systematically accumulating the experiences and skills necessary to realize one's career plan.

[1032] "Robots working in factories" refers to automated machinery that performs production work in factories.

[1033] A "skill set" refers to the collection of skills and knowledge required to perform a specific job or task.

[1034] "Factory needs" refers to the requirements and conditions that a factory needs in order to carry out its production activities.

[1035] An "action plan" is a specific plan of action to achieve a specific goal.

[1036] "Networking" refers to the act of contacting people to exchange information and build collaborative relationships.

[1037] "Dialogue" refers to a communication process in which multiple parties exchange opinions and information.

[1038] A system for implementing this invention uses text generation AI to generate career plans and action plans, and supports the improvement of the skills of robots working in factories. A specific embodiment of this system is described below.

[1039] System Configuration

[1040] 1. Hardware:

[1041] Server: A server for running text generation AI.

[1042] Robot: An automated mechanical device that performs tasks in a factory.

[1043] Devices: Computers and tablets used by managers and members.

[1044] 2. Software:

[1045] Python: A programming language used to implement programs.

[1046] OpenAI API: An API for running text generation AI.

[1047] Data processing and calculation

[1048] The server processes and calculates data in the following procedure.

[1049] 1. Data Collection:

[1050] Gather current robot skill sets and factory needs.

[1051] This data is sent to the server in text format.

[1052] 2. Career plan generation:

[1053] The server uses text generation AI to generate a career plan based on the collected data.

[1054] Specifically, the following prompt sentences are used to generate a career plan:

[1055] Robot's current skill set: welding, assembly

[1056] Factory needs: quality control, efficient production

[1057] Please suggest a career plan for this robot.

[1058] 3. Generate an action plan:

[1059] The server generates a specific action plan based on the generated career plan.

[1060] Specifically, generate an action plan using the following prompt:

[1061] Career Plan: This robot should acquire quality control skills and learn new assembly techniques to support efficient production.

[1062] Please propose a concrete action plan.

[1063] 4. Interactive features:

[1064] Through dialogue between the robot, manager, and AI, the proposals are adjusted and the optimal career plan is formulated.

[1065] This dialogue will take place on an online platform.

[1066] Specific examples

[1067] For example, if a robot with welding and assembly skills is required in a factory to ensure quality control and efficient production, the server will perform the following process:

[1068] 1. Data Collection:

[1069] Robot skillset: "Welding, assembly"

[1070] Factory needs: "Quality control, efficient production"

[1071] 2. Career plan generation:

[1072] The server generates a career plan based on the above data.

[1073] Generated career plan: "This robot should acquire quality control skills and learn new assembly techniques to support efficient production."

[1074] 3. Generate an action plan:

[1075] The server generates a specific action plan based on the generated career plan.

[1076] Generated action plan: "Receive training in quality control and new assembly techniques."

[1077] In this way, robots working in factories can acquire skills efficiently and improve productivity.

[1078] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1079] Step 1:

[1080] The server collects the robot's current skill set and the factory's needs. Specifically, managers and employees use terminals to input the robot's skill set (e.g., "welding, assembly") and the factory's needs (e.g., "quality control, efficient production"). This data is sent to the server in text format.

[1081] Step 2:

[1082] The server uses text generation AI to generate a career plan based on the collected data. Specifically, it generates a career plan using the following prompt sentence:

[1083] Robot's current skill set: welding, assembly

[1084] Factory needs: quality control, efficient production

[1085] Please suggest a career plan for this robot.

[1086] The server sends this prompt to the OpenAI API and receives the generated career plan. The output is the career plan (e.g., "This robot should acquire quality control skills and learn new assembly techniques to support efficient production.").

[1087] Step 3:

[1088] The server generates a specific action plan based on the generated career plan. Specifically, the action plan is generated using the following prompt sentence:

[1089] Career Plan: This robot should acquire quality control skills and learn new assembly techniques to support efficient production.

[1090] Please propose a concrete action plan.

[1091] The server sends this prompt to the OpenAI API and receives the generated action plan. The output is the action plan (e.g., "Receive training in quality control and new assembly techniques.").

[1092] Step 4:

[1093] The server presents the generated career plans and action plans to the manager and members, who then review these plans via their terminals and make suggestions for corrections or additions as needed. This dialogue takes place on an online platform.

[1094] Step 5:

[1095] The server determines the final career plan and action plan through a dialogue between the manager, members, and the AI. The finalized plan is used as a concrete guideline for improving the robot's skills.

[1096] Step 6:

[1097] The server sends the determined career plan and action plan to the robot, which then acquires skills and undergoes training based on these plans. Specifically, the robot receives training in quality control and new assembly techniques.

[1098] Example 3

[1099] 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."

[1100] In today's job market, the demand for specific skills is rapidly changing, making it difficult for employees to properly formulate their own career plans. While there is a need to effectively utilize personnel evaluation data within companies to suggest appropriate skill acquisition for employees, there is a lack of efficient ways to do this. Furthermore, there is also the challenge of providing managers and career change supporters with concrete action plans to support employees in formulating their career plans.

[1101] 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.

[1102] In this invention, the server includes a means for collecting data on the job market and data related to internal personnel evaluations, a means for analyzing the data on the job market using a generative AI model to identify skills that are in high demand, and a means for analyzing data related to internal personnel evaluations to determine whether employees have specific skills. This enables the server to make suggestions to employees about acquiring appropriate skills, and enables managers and career change supporters with subordinates to provide specific action plans.

[1103] "Job market data" refers to data that includes information about the job market, such as job listings and industry reports.

[1104] "In-house personnel evaluation related data" refers to data that includes information about employee evaluations and skills within a company.

[1105] A "generative AI model" is an artificial intelligence model that uses natural language processing techniques to analyze text data and is trained to perform specific tasks.

[1106] "Skill acquisition suggestions" are messages and action plans that encourage employees to learn specific skills.

[1107] A "manager with subordinates" is an employee who has the role of supervising and guiding subordinates within a company.

[1108] A "career change consultant" is a professional who provides career advice and job information to individuals who wish to change jobs.

[1109] An "action plan" is a specific plan of action required to achieve a specific goal.

[1110] A "terminal" is a device that allows a user to receive information and perform operations.

[1111] A "notification" is a message sent from the system to inform the user of information.

[1112] "Feedback" refers to the reactions and opinions that users provide to a system.

[1113] This invention is a system that collects and analyzes data from the job market and data related to personnel evaluation within a company, and suggests appropriate skill acquisition to employees. A specific embodiment of this system will be described below.

[1114] Hardware and software used

[1115] Hardware: Servers, terminals

[1116] Software: Generative AI models, database management systems (e.g., MySQL, PostgreSQL), natural language processing libraries (e.g., Pandas, Scikit-learn)

[1117] Data collection

[1118] The server collects data on the job market. Specifically, it obtains data from job sites and industry reports via API. For example, it calls the API of a job site to obtain the latest job information. The server also obtains data related to the company's internal personnel evaluations from a database management system. It extracts the necessary data using SQL queries.

[1119] Data analysis

[1120] The server uses a generative AI model to analyze job market data. It preprocesses the data using Python libraries (e.g., Pandas, Scikit-learn) to identify skills that are in high demand. The generative AI model determines that there is a high demand for data analysis skills in the IT industry. For example, it calculates the frequency of occurrence of keywords such as "data analysis," "Python," and "machine learning" from text data.

[1121] Employee Skills Assessment

[1122] The server analyzes the company's internal performance evaluation data to determine whether employees lack specific skills. Specifically, it references the employee's skill set and past evaluation data. The server uses SQL queries to extract employee skill data and input it into the generative AI model.

[1123] Proposal Generation

[1124] The server uses a generative AI model to generate a message suggesting skill acquisition. For example, the message might say, "Demand for data analysis skills is increasing, so we recommend you acquire these skills." The generative AI model generates the message based on the prompt. An example of a prompt: "Analyze the skill demand in the IT industry in the current job market and determine whether the demand for data analysis skills is increasing. Also, based on your company's internal personnel evaluation data, if an employee does not have data analysis skills, generate a message suggesting that the employee acquire these skills."

[1125] Proposing an action plan

[1126] The server uses AI to support managers and career change supporters with subordinates, proposing specific action plans for acquiring skills. For example, taking online courses (e.g., online education platforms), participating in in-house training, or reading specialized books. The server inputs prompt statements into the generative AI model and generates specific action plans. An example of a prompt statement is, "Please propose a specific action plan for acquiring data analysis skills."

[1127] Notifications and Feedback

[1128] The server sends a notification to the device to inform the employee of the proposal. For example, it uses email or an internal messaging system (e.g., a messaging app). The user (employee) chooses whether to accept the proposal and returns feedback to the server. For example, they click an "Accept proposal" button through the device interface.

[1129] In this way, employees can formulate their own career plans in detail and design experiences for them. The flow of the specific processing in the third embodiment will be described with reference to FIG.

[1130] Step 1:

[1131] The server collects data on the job market. Specifically, it obtains data from job sites and industry reports through APIs. As input, it uses the API endpoint of the job site or the URL of the industry report, and obtains the job information or industry report data as output. For example, the server calls the API of a job site to obtain the latest job information.

[1132] Step 2:

[1133] The server retrieves the company's internal personnel evaluation-related data from the database management system. It uses SQL queries as input and obtains employee evaluation data and skill data as output. Specifically, the server executes SQL queries to extract the required data from the database.

[1134] Step 3:

[1135] The server analyzes job market data using a generative AI model. It uses collected job postings and industry report data as input and identifies in-demand skills as output. Specifically, it preprocesses the data using Python libraries (e.g., Pandas, Scikit-learn) and inputs it into the generative AI model. The generative AI model determines that there is a growing demand for data analysis skills in the IT industry.

[1136] Step 4:

[1137] The server analyzes the company's internal performance evaluation data to determine whether employees lack specific skills. It uses employee evaluation data and skill data as input and generates a list of employees with insufficient skills as output. Specifically, the server uses SQL queries to extract employee skill data and input it into a generative AI model.

[1138] Step 5:

[1139] The server uses a generative AI model to generate a message suggesting skill acquisition. It uses the list of employees with skill shortages and the generative AI model as input, and generates a suggested message as output. Specifically, the generative AI model generates a message based on a prompt. An example of a prompt: "Analyze the skill demand in the IT industry in the current job market and determine whether there is an increasing demand for data analysis skills. Also, based on your company's internal personnel evaluation data, if an employee does not have data analysis skills, generate a message suggesting that the employee acquire the skill."

[1140] Step 6:

[1141] The server uses AI to support managers and career change supporters with subordinates, proposing specific action plans for acquiring skills. It uses the proposed message and the generative AI model as input, and generates a specific action plan as output. Specifically, a prompt sentence is input into the generative AI model, and an action plan is generated. An example of a prompt sentence is: "Please propose a specific action plan for acquiring data analysis skills."

[1142] Step 7:

[1143] The server sends a notification to the device to inform the employee of the proposal. It uses the proposal message and the action plan as input and generates a notification message as output. Specifically, it sends the notification via email or an internal messaging system (e.g., a messaging app).

[1144] Step 8:

[1145] The user (employee) chooses whether to accept the proposal and returns feedback to the server. The notification message is used as input and feedback is generated as output. Specifically, the user clicks the "Accept proposal" button through the terminal interface.

[1146] (Application example 3)

[1147] 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."

[1148] Conventional career planning systems are primarily designed for human employees and do not address skill management or training plan formulation for robots working in factories. This makes it difficult for factory managers to grasp the skill status of robots and efficiently train them in the necessary skills.

[1149] 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.

[1150] In this invention, the server includes: means for tuning the text generation AI using job market data and internal personnel evaluation-related data; means for developing AI to support managers and recruitment agents with subordinates; means for supporting the formulation of career plans and the design of experiences for such plans through a three-way dialogue between managers, members, and the AI; means for utilizing general job market data and general personnel evaluation data as job market data; means for managing the skill sets of robots working in factories and suggesting the acquisition of necessary skills; and means for factory managers to check the skill status of robots in real time and create training plans for the necessary skills. This enables efficient skill management and the formulation of training plans for robots working in factories, thereby improving factory productivity.

[1151] "Text generation AI" is an artificial intelligence that uses natural language processing technology to generate text data and provide information tailored to specific purposes.

[1152] "Job market data" refers to data such as job information related to job hunting, job seekers' skill sets, and industry trends.

[1153] "Personnel evaluation-related data" refers to data such as employee performance evaluations, skill evaluations, and promotion histories.

[1154] A "manager" is a person who has subordinates and is responsible for managing and directing work within an organization.

[1155] A "career agent" is a professional who acts as an intermediary between job seekers and companies hiring, and supports job hunting activities.

[1156] "AI" is an abbreviation for artificial intelligence, a technology that mimics human intellectual tasks through machine learning and data analysis.

[1157] A "career plan" is when an individual sets goals for their future occupation or career and develops a specific action plan to achieve them.

[1158] "Experience design" refers to the planning and implementation of specific experiences and training to acquire the skills and knowledge necessary to realize one's career plan.

[1159] A "robot working in a factory" is a mechanical device that automatically performs specific tasks on a factory production line or at a work site.

[1160] A "skill set" is a collection of skills and knowledge required to perform a specific job or function.

[1161] A "training plan" is a specific learning plan or training program for acquiring specific skills or knowledge.

[1162] "Checking in real time" means instantly understanding the current situation and data.

[1163] The system for implementing this invention uses text generation AI to analyze job market data and internal personnel evaluation data, manage the skill sets of robots working in factories, and suggest ways to acquire the necessary skills. A specific embodiment of this system is shown below.

[1164] System Configuration

[1165] Hardware

[1166] Server: A high-performance server for data analysis and running AI models.

[1167] Device: Smartphone or tablet used by factory managers.

[1168] Robot: An automated mechanical device that performs tasks in a factory.

[1169] software

[1170] Text generation AI: An AI model that generates text data using natural language processing techniques.

[1171] Database: A database for storing job market data and personnel evaluation-related data.

[1172] API server: An API server for obtaining data and providing analysis results.

[1173] Data processing and calculation

[1174] Server Processing

[1175] 1. Data acquisition: The server acquires data from the job market and data related to the company's internal personnel evaluations via the API.

[1176] 2. Data analysis: Using text generation AI, we analyze the acquired data and identify current skill demands.

[1177] 3. Checking skill sets: The skill sets of the robots working in the factory are retrieved from the database and compared with the analysis results.

[1178] 4. Propose training plans: If necessary skills are lacking, generate specific training plans and propose them to management.

[1179] Terminal handling

[1180] 1. Real-time confirmation: Factory managers can use smartphones or tablets to check the robot's skill status in real time.

[1181] 2. Receive the training plan: Receive the proposed training plan and put it into action.

[1182] Specific examples

[1183] For example, to obtain the current skill set of a robot with ID "robot_12345" working in a factory, compare it with the latest skill demand data, and propose a training plan for the required skills, use the following prompt sentence:

[1184] Prompt Sentence Examples

[1185] "Get the current skill set of robot ID: robot_12345, compare it with the latest skill demand data, and suggest a training plan for the required skills."

[1186] In this way, it becomes possible to efficiently manage the skills of robots working in factories and develop training plans, thereby improving factory productivity.

[1187] The flow of the specific processing in Application Example 3 will be described with reference to FIG.

[1188] Step 1:

[1189] The server obtains data on the job market and data related to the company's internal personnel evaluations via an API.

[1190] Input: Job market data API, internal personnel evaluation data API

[1191] Output: Acquired data (job market data, personnel evaluation data)

[1192] Specific operation: The server sends a request to the API endpoint to obtain job market data and performance evaluation-related data. The obtained data is stored in a database.

[1193] Step 2:

[1194] The server uses text generation AI to analyze the acquired data and identify current skill demand.

[1195] Input: Acquired data (job market data, personnel evaluation data)

[1196] Output: Skill demand analysis results

[1197] Specific operation: The server inputs the acquired data into a text generation AI model and analyzes skill demand using natural language processing technology. The analysis results are output as data indicating which skills are in high demand.

[1198] Step 3:

[1199] The server retrieves the skill sets of the robots working in the factory from the database and compares them with the analysis results.

[1200] Input: Robot skill set data, skill demand analysis results

[1201] Output: Skills gap identification results

[1202] Specific operation: The server retrieves the skill set of a specific robot from the database and compares it with the skill demand analysis results, thereby identifying skills that the robot does not possess but are in high demand.

[1203] Step 4:

[1204] If the required skills are lacking, the server generates a specific training plan and proposes it to the administrator.

[1205] Input: Skills gap identification results

[1206] Output: Training Plan

[1207] Specific operation: The server generates a training plan for acquiring the necessary skills based on the results of identifying skill gaps. The generated training plan is notified to the administrator.

[1208] Step 5:

[1209] The terminal allows factory managers to check the robot's skill status in real time using a smartphone or tablet.

[1210] Input: Robot skill set data, training plan

[1211] Output: Skill status and training plan displayed on the administrator's screen

[1212] Specific operation: The terminal displays the robot's skill set data and training plan obtained from the server, and the administrator can check it and take necessary actions.

[1213] Step 6:

[1214] The device receives the proposed training plan and puts it into action.

[1215] Input: Training plan

[1216] Output: Training execution status

[1217] Specific operation: The terminal receives the proposed training plan and instructs the robot to start training. The training progress is reported to the administrator in real time.

[1218] 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.

[1219] "Example 1"

[1220] In one embodiment of the present invention, the text generation AI is used in combination with an emotion engine that recognizes the user's emotions. The emotion engine recognizes the user's emotional state from text data, voice data, facial expression data, and other data generated during a conversation with the user. This emotional state is fed back to the text generation AI, which then optimizes career plan suggestions based on this information. Specifically, if the user feels stressed, the text generation AI will suggest a career plan to the user to reduce that stress.

[1221] "Example 2"

[1222] The emotion engine also has the ability to adjust career plan suggestions based on the user's emotional state. For example, if the emotion engine senses that the user is feeling joy, it will feed that information back to the text generation AI, which will then suggest a career plan that will make the user feel joyful. This allows the system to suggest a career plan that is appropriate for the user's emotional state, improving user satisfaction.

[1223] "Example 3"

[1224] Furthermore, the combination of the emotion engine and text generation AI can more accurately understand the user's emotional state and suggest career plans based on that. For example, if the user senses anxiety, the emotion engine will feed that information back to the text generation AI, which will then suggest a career plan to alleviate the user's anxiety. This will result in a career plan that is appropriate for the user's emotional state, improving user satisfaction.

[1225] The processing flow of each embodiment will be described below.

[1226] "Example 1"

[1227] Step 1: A dialogue with the user is initiated. This dialogue can take various forms, such as text, voice, or facial expression data.

[1228] Step 2: The emotion engine recognizes the user's emotional state from the dialogue data, which can include various emotions such as joy, sadness, anger, surprise, etc.

[1229] Step 3: The emotional state recognized by the emotion engine is fed back to the text generation AI.

[1230] Step 4: The text generation AI optimizes career plan suggestions based on this emotional state. For example, if the user feels stressed, the text generation AI will suggest a career plan to reduce stress.

[1231] "Example 2"

[1232] Step 1: A dialogue with the user is initiated. This dialogue can take various forms, such as text, voice, or facial expression data.

[1233] Step 2: The emotion engine recognizes the user's emotional state from the dialogue data, which can include various emotions such as joy, sadness, anger, surprise, etc.

[1234] Step 3: The emotional state recognized by the emotion engine is fed back to the text generation AI.

[1235] Step 4: The text generation AI optimizes career plan suggestions based on this emotional state. For example, if the AI ​​senses that the user is feeling joyful, it will suggest career plans that will make the user feel joyful.

[1236] "Example 3"

[1237] Step 1: A dialogue with the user is initiated. This dialogue can take various forms, such as text, voice, or facial expression data.

[1238] Step 2: The emotion engine recognizes the user's emotional state from the dialogue data, which can include various emotions such as joy, sadness, anger, surprise, etc.

[1239] Step 3: The emotional state recognized by the emotion engine is fed back to the text generation AI.

[1240] Step 4: The text generation AI optimizes career plan suggestions based on this emotional state. For example, if the AI ​​senses that the user is feeling anxious, it will suggest a career plan to alleviate the user's anxiety.

[1241] Example 1

[1242] 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."

[1243] Conventional career planning systems make uniform proposals without considering the user's emotional state, making it difficult to provide the optimal career plan for each individual user. Furthermore, they are unable to effectively utilize job market data or internal personnel evaluation data, making it impossible to generate highly accurate career plans. This leads to problems such as lower user satisfaction and career success rates.

[1244] 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.

[1245] In this invention, the server includes means for collecting data on the job market and data related to personnel evaluation within the company, means for preprocessing the collected data using natural language processing technology, means for tuning the generative AI model based on the preprocessed data, means for recognizing the emotional state of the user using an emotion engine and feeding it back to the generative AI model, and means for proposing an optimal career plan to the user. This makes it possible to propose a highly accurate career plan that takes the emotional state of the user into consideration.

[1246] "Job market data" refers to all information related to the job market, such as job information, the skills and experience of job seekers, and industry trends.

[1247] "Internal personnel evaluation-related data" refers to information related to personnel evaluations collected within the company, such as employee performance evaluations, skill sets, and career paths.

[1248] "Natural language processing technology" is a technology for analyzing text data and understanding its meaning, and includes processes such as text cleaning, tokenization, and stemming.

[1249] A "generative AI model" refers to an artificial intelligence model that learns from collected data and generates the optimal career plan for the user.

[1250] An "emotion engine" refers to technology that recognizes a user's emotional state from text data, voice data, facial expression data, and other data generated during a conversation with the user.

[1251] "Career Planning" refers to specific suggestions and advice for planning a user's career goals and career path.

[1252] This invention is a system that collects data on the job market and internal personnel evaluation data, and uses this data to tune a generative AI model. Furthermore, it uses an emotion engine to recognize the user's emotional state and provides feedback to the generative AI model to suggest the optimal career plan for the user.

[1253] The server first collects data from the job market and data related to the company's internal personnel evaluations. Data from the job market includes job information, the skills and experience of job seekers, and industry trends. Data related to the company's internal personnel evaluations includes employee performance evaluations, skill sets, career paths, and more. This data is then stored in a database.

[1254] The server then preprocesses the collected data using natural language processing techniques, such as text cleaning, tokenization, and stemming, using the Python libraries NLTK and spaCy.

[1255] The server then uses the preprocessed data to tune the generative AI model. Specifically, it combines job market data with the company's own personnel evaluation data to build a model that will propose the optimal career plan to the user. This model is built using machine learning frameworks such as TensorFlow and PyTorch.

[1256] Furthermore, the server integrates an emotion engine with the text generation AI. The emotion engine recognizes the user's emotional state from text data, voice data, facial expression data, and other data generated during user interaction. This emotional state is fed back to the text generation AI in real time.

[1257] Users access the system and enter their information. The device then sends the user's input data to the server. The server then uses a generative AI model and emotion engine to generate the optimal career plan for the user. For example, if the user is feeling stressed, the server will suggest a career plan to reduce stress.

[1258] As an example, User A accesses the system and enters the following information:

[1259] Current job title: Software engineer

[1260] Skillset: Python, Java, Machine Learning

[1261] Career goal: Project manager

[1262] Based on the information User A inputs into the system and User A's emotional state as recognized by the emotion engine (e.g., feeling stressed), the server proposes the following career plan:

[1263] Increase remote work opportunities to reduce stress

[1264] Take an online course to improve your project management skills

[1265] Introduce weekly mental health check-ins

[1266] Examples of prompts include:

[1267] "Please tell us your current job title, skill set, and career goals. Also, please tell us about your recent emotional state towards work (e.g., stressed, satisfied, etc.)."

[1268] In this way, the system can propose the most suitable career plan to the user.

[1269] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1270] Step 1: Data collection

[1271] The server collects data on the job market and data related to personnel evaluation within the company.

[1272] Inputs: Job postings, job seeker skills and experience, industry trends, employee performance reviews, skill sets, career paths

[1273] What it does: The server retrieves data from job boards and industry reports via APIs, and also extracts employee evaluation data from the company's HR system.

[1274] Output: The collected data is stored in a database.

[1275] Step 2: Preprocessing the data

[1276] The server preprocesses the collected data using natural language processing technology.

[1277] Input: Collected text data

[1278] Specific operation: The server removes unnecessary symbols and spaces from the text data, splits the text into words, and converts it to its basic form. This process uses the Python libraries NLTK and spaCy.

[1279] Output: Preprocessed text data

[1280] Step 3: Tune the model

[1281] The server tunes the generative AI model based on the preprocessed data.

[1282] Input: Preprocessed text data

[1283] How it works: The server splits the data into a training set and a test set, trains a generative AI model using the training set, evaluates the accuracy of the model using the test set, and adjusts parameters as needed. This model is built using a machine learning framework such as TensorFlow or PyTorch.

[1284] Output: A tuned generative AI model

[1285] Step 4: Integrating the Emotion Engine

[1286] The server integrates the emotion engine with a text generation AI.

[1287] Input: Text data, voice data, and facial expression data generated during user interaction.

[1288] Specific operation: The server analyzes the user's text data and sends it to the emotion engine. The emotion engine analyzes the voice data and facial expression data to recognize the emotional state. The server receives feedback from the emotion engine and reflects it in the generative AI model.

[1289] Output: Generative AI model that reflects emotional state

[1290] Step 5: Propose a career plan

[1291] Users access the system and enter their information. The device then sends the user's input data to the server, which then uses a generative AI model and emotion engine to generate the optimal career plan for the user.

[1292] Input: User's job title, skill set, career goals, emotional state

[1293] Specific operation: The user inputs their job title, skill set, and career goals through a web form or application. The device sends the input data to the server. The server generates a career plan using the generative AI model and takes into account feedback from the emotion engine. The server sends the generated career plan to the device and displays it to the user.

[1294] Output: The best carrier plan for the user

[1295] (Application example 1)

[1296] 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."

[1297] Conventional career planning systems make uniform suggestions without considering the user's emotional state, making it difficult to reduce the user's stress and anxiety. Furthermore, there is a lack of means for real-time emotion recognition, which leads to a decline in the quality of the dialogue. Furthermore, in career counseling at brick-and-mortar stores, it is difficult to propose an optimal career plan that instantly reflects the user's emotional state.

[1298] The specific processing by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means. In this invention, the server includes: means for tuning the text generation AI using job market data and internal personnel evaluation-related data; means for developing AI to support managers and job change supporters with subordinates; means for supporting the formulation of career plans and the design of experiences for such plans through a three-way dialogue between managers, employees, and the AI; means for utilizing general job market data and general personnel evaluation data as job change market data; means for analyzing the user's emotional state using an emotion recognition engine and optimizing the career plan based on that information; and means for recognizing the user's emotional state in real time using smart glasses and supporting career counseling. This makes it possible to propose an optimal career plan that takes the user's emotional state into consideration.

[1299] "Text generation AI" is an artificial intelligence that uses natural language processing technology to suggest optimal career plans to users based on data from the job market and internal personnel evaluation-related data.

[1300] "Job market data" refers to general data related to the job market, such as job information, the skills and experience of job seekers, and industry trends.

[1301] "Human resource evaluation related data" refers to data related to human resource evaluation within the company, such as employee performance evaluations, skill sets, and career paths.

[1302] A "manager" is a person who has subordinates and is responsible for managing and supervising work within an organization.

[1303] A "career change supporter" is a professional who provides support to people looking to change jobs, such as career counseling and providing job information.

[1304] An "emotion recognition engine" is a technology that analyzes a user's emotional state from text data, voice data, facial expression data, etc.

[1305] "Smart glasses" are wearable devices that have built-in cameras and displays and display information in the user's field of vision.

[1306] A "career plan" is a career plan or path suggested based on the user's skills, experience, and aspirations.

[1307] "Career counseling" is a service that provides consultation and advice to users regarding career choices and career development.

[1308] An "online platform" is a system that forms the basis for services and applications provided via the Internet.

[1309] The system for implementing this invention includes a text generation AI, an emotion recognition engine, smart glasses, and an online platform. The details of each component and their interaction will be described below.

[1310] System Configuration

[1311] 1. Text generation AI:

[1312] The server collects data on the job market and data related to personnel evaluation within the company, and tunes the text generation AI based on this data.

[1313] The text generation AI uses natural language processing technology to suggest the best career plan for users.

[1314] 2. Emotion Recognition Engine:

[1315] The server uses an emotion recognition engine that analyzes the user's text data, voice data, facial expression data, etc. to recognize their emotional state.

[1316] The emotion recognition engine analyzes the user's emotional state in real time and feeds that information back to the text generation AI.

[1317] 3. Smart Glasses:

[1318] Users wear smart glasses and their emotional state is recognized in real time.

[1319] Smart glasses have built-in cameras and displays that display information in the user's field of vision.

[1320] 4. Online Platform:

[1321] The three-way interaction between managers, employees, and AI takes place on an online platform.

[1322] Based on the results of the dialogue, the platform will help develop career plans and design experiences to support them.

[1323] Program processing

[1324] The server first collects data on the job market and internal personnel evaluation data to tune the text generation AI. Next, it uses an emotion recognition engine to analyze the user's emotional state and feeds that information back to the text generation AI. Users wearing the smart glasses have their emotional state recognized in real time and can receive career counseling. A three-way dialogue between the manager, employee, and AI takes place on the online platform, and career plans and experience designs are based on the results.

[1325] Specific examples

[1326] For example, if a user has three years of experience in the IT industry and is skilled in Python and data analysis, the emotion recognition engine can detect the user's stress state and generate the following prompt based on this information:

[1327] Prompt Sentence Examples

[1328] User data: {'skills': ['Python', 'Data Analysis'], 'experience': '3 years', 'industry': 'IT'}

[1329] Emotional state: Stress

[1330] Please suggest the best career plan for you.

[1331] This prompt is then input into a text generation AI, which then proposes the generated career plan to the user, thereby enabling the system to propose the optimal career plan that takes into account the user's emotional state.

[1332] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1333] Step 1:

[1334] The server collects data on the job market and data related to the company's internal personnel evaluations. Specifically, it collects data such as job information, the skills and experience of job seekers, industry trends, employee performance evaluations, skill sets, and career paths. This data is used to tune the text generation AI. The input is job market data and personnel evaluation data, and the output is the tuned text generation AI.

[1335] Step 2:

[1336] The server tunes the text generation AI based on the collected data. It uses natural language processing technology to analyze data from the job market and internal personnel evaluation data, and generates a model to propose the optimal career plan to the user. The input is the collected data, and the output is a generative AI model that proposes the optimal career plan to the user.

[1337] Step 3:

[1338] A user wears smart glasses and receives career counseling. The smart glasses capture the user's facial expression data in real time and send it to an emotion recognition engine. The input is the user's facial expression data, and the output is the data sent to the emotion recognition engine.

[1339] Step 4:

[1340] The server analyzes the user's emotional state using an emotion recognition engine. The emotion recognition engine analyzes the user's text data, voice data, facial expression data, etc. to recognize the user's emotional state. The input is the user's facial expression data, and the output is the analyzed emotional state.

[1341] Step 5:

[1342] The server feeds the analyzed emotional state back to the text generation AI, which then generates a prompt sentence to suggest the user's optimal career plan based on the emotional state. The input is the analyzed emotional state, and the output is the generated prompt sentence.

[1343] Step 6:

[1344] The server uses a text generation AI to generate the optimal career plan for the user based on the generated prompt. The input is the generated prompt, and the output is the career plan proposed to the user.

[1345] Step 7:

[1346] The user confirms the proposed career plan through the smart glasses. The career plan is displayed on the smart glasses display, allowing the user to check it in real time. The input is the proposed career plan, and the output is the user's confirmation.

[1347] Step 8:

[1348] The three parties - manager, employee, and AI - engage in a dialogue on an online platform. Based on the results of the dialogue, a career plan is formulated and the associated experience is designed. The input is the result of the dialogue, and the output is the formulated career plan and experience design.

[1349] Example 2

[1350] 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."

[1351] Conventional career planning systems provide uniform plans without considering the user's emotional state, resulting in low user satisfaction. Another issue is that it takes a great deal of time and effort for managers and career change supporters to individually create career plans. Furthermore, when career plans are created through online dialogue, specific action plans are often lacking.

[1352] 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.

[1353] In this invention, the server includes: means for tuning the text generation AI using job market data and internal personnel evaluation data; means for developing AI to support managers and career change supporters with subordinates; means for supporting the formulation of career plans and the design of experiences for such plans through a three-way dialogue between managers, members, and the AI; means for adjusting the proposed career plan using an emotion engine that analyzes the user's emotional state; means for the generative AI model to generate a career plan based on user input; means for proposing a specific action plan based on the generated career plan; means for the three-way dialogue between managers, members, and the AI ​​on an online platform; and means for utilizing general job market data and general personnel evaluation data as job change market data. This allows for the proposal of a career plan that is appropriate for the user's emotional state, improving user satisfaction. Furthermore, by reducing the burden on managers and career change supporters and providing specific action plans, more effective career plans can be formulated.

[1354] "Text generation AI" is artificial intelligence that uses natural language processing technology to generate text based on user input.

[1355] "Job market data" refers to information and statistical data about the general job market, and is data that shows trends and demand in job hunting.

[1356] "Human resource evaluation related data" refers to information related to employee evaluations within an organization, including data such as performance and skill sets.

[1357] A "manager" is a superior or leader who has subordinates and is responsible for management within an organization.

[1358] A "career change supporter" is an expert or agent who supports job-change activities and provides career advice to job seekers.

[1359] An "emotion engine" is a technology for analyzing a user's emotional state, and is a system that detects emotions from the content of text and dialogue.

[1360] A "generative AI model" is an artificial intelligence model that generates career plans based on user input information.

[1361] "Online platforms" refer to systems for interaction and information sharing that are available via the Internet, including video conferencing and chat tools.

[1362] A "career plan" refers to specific steps or actions taken to plan a user's career goals and career path.

[1363] An "action plan" refers to a specific plan of action for implementing a career plan, and includes actions such as acquiring skills and networking.

[1364] This invention is an AI system for supporting managers and career change supporters, and proposes optimal career plans to users by combining text generation AI and an emotion engine. Specific embodiments of this system are described below.

[1365] System Overview

[1366] In this system, users input information about their career plans using their devices, and the server generates and adjusts the career plans based on that information using text generation AI and an emotion engine. The generated career plans are optimized through dialogue between the administrator, members, and AI, and specific action plans are proposed.

[1367] Hardware and software used

[1368] Devices: devices such as PCs, smartphones, and tablets

[1369] Server: High-performance cloud or on-premise server

[1370] Text generation AI: Generative AI models using natural language processing techniques (e.g., OpenAI's GPT-4)

[1371] Emotion engine: Software that analyzes the user's emotional state (e.g., IBM Watson Tone Analyzer)

[1372] Online platforms: video conferencing systems (e.g., Zoom), chat tools (e.g., Slack)

[1373] Data processing and calculation

[1374] 1. Receiving user input

[1375] A user uses a terminal to access a dedicated web application and inputs information about their career plan, such as their current job description, desired career path, and skill set.

[1376] 2. Generating career plans using text generation AI

[1377] The server receives the user's input information and sends it as a prompt to the generative AI model, which then generates a career plan based on the user's information.

[1378] 3. Emotional state analysis using the emotion engine

[1379] The server sends the user's input text and dialogue content to the emotion engine, which analyzes the user's emotional state and detects emotions such as joy, anxiety, and excitement from the text.

[1380] 4. Adjusting your career plan based on your emotional state

[1381] The server receives feedback from the emotion engine and sends the prompt again to the generative AI model, which then re-proposes a career plan that is appropriate for the user's emotional state.

[1382] 5. Developing career plans through dialogue

[1383] The administrator, members, and AI will interact through an online platform and develop the optimal plan based on the proposed career plan.

[1384] 6. Implement the action plan

[1385] Based on the career plan, users take specific actions, such as enrolling in an online course to learn a new skill or attending an industry event.

[1386] Examples of specific examples and prompts

[1387] Specific examples

[1388] A user types, "I want to be a data scientist."

[1389] The server sends the prompt statement "I want to be a data scientist. My current job is as a marketing analyst and I have basic knowledge of Python" to the generative AI model and receives the generated career plan.

[1390] The emotion engine detects "excitement" and feeds it back to the generative AI model.

[1391] The generative AI model sends a prompt saying, "Please propose a career plan that will excite the user," and receives a re-proposed career plan.

[1392] Administrators will use Zoom to communicate with members, exchange opinions on proposed career plans, and decide on the most suitable plan.

[1393] A user performs an action: "Enroll in an online course to improve their Python skills."

[1394] Prompt Sentence Examples

[1395] "I want to become a data scientist. I'm currently a marketing analyst and have basic knowledge of Python. What should I do next?"

[1396] In this way, a career plan suited to the user's emotional state is proposed, improving user satisfaction. It also reduces the burden on managers and career change supporters, and by providing specific action plans, it enables the formulation of more effective career plans.

[1397] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1398] Step 1:

[1399] A user accesses a dedicated web application using a terminal and inputs information about their career plan, including their current job description, desired career path, skill set, etc. The input data is then sent to the server.

[1400] Step 2:

[1401] The server receives the user's input information and sends it to the generative AI model as a prompt. Specifically, based on the user's input information, it generates a prompt such as, "I want to be a data scientist. My current job is as a marketing analyst, and I have basic knowledge of Python." The generative AI model then generates a career plan based on this prompt and returns it to the server.

[1402] Step 3:

[1403] The server sends the career plan received from the generative AI model to the emotion engine, which analyzes the user's emotional state. The emotion engine detects emotions such as joy, anxiety, and excitement from the text and returns the results to the server. For example, the analysis result may be "The user is excited."

[1404] Step 4:

[1405] The server receives feedback from the emotion engine and sends a prompt to the generative AI model again. Specifically, it sends the prompt, "Please propose a career plan that will excite the user." The generative AI model then proposes a new career plan that is appropriate for the user's emotional state and returns it to the server.

[1406] Step 5:

[1407] The server sends the re-proposed career plan to the online platform for a three-way dialogue between the administrator, members, and AI. The administrator and members communicate using video conferencing systems and chat tools, exchanging opinions about the proposed career plan. The optimal career plan is then formulated.

[1408] Step 6:

[1409] Based on the career plan, users take specific actions, such as enrolling in an online course to learn a new skill or attending an industry event. The server tracks the progress of these actions and provides additional support as needed.

[1410] (Application example 2)

[1411] 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."

[1412] While conventional career plan proposal systems can make suggestions based on a user's skills and experience, they are unable to take into account the user's emotional state, and therefore are unable to fully improve user satisfaction or motivation. Additionally, there is a lack of systems that propose appropriate career paths for factory workers, making it difficult to improve workers' skills and design career paths.

[1413] 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.

[1414] In this invention, the server includes: means for tuning the text generation AI using job market data and internal personnel evaluation-related data; means for developing AI to support managers and recruitment agents with subordinates; means for supporting the formulation of career plans and the design of experiences for such plans through a three-way dialogue between managers, members, and the AI; means for utilizing general job market data and general personnel evaluation data as job market data; means for analyzing the user's emotional state using an emotion engine and adjusting the career plan based on that information; and means for being installed in factory robots to suggest worker skill improvement and career paths. This makes it possible to propose career plans based on the user's emotional state, making it easier to improve worker skills and design career paths.

[1415] "Text generation AI" is an artificial intelligence that generates text using natural language processing technology, and has the function of suggesting career plans based on the user's skills and experience.

[1416] "Job market data" refers to information and statistical data relating to the general job market, which is used to formulate career plans.

[1417] "Personnel evaluation-related data" refers to data related to employee evaluations within a company, and is used to propose career plans.

[1418] The "emotion engine" is a system that has the function of analyzing the user's emotional state and adjusting their career plan based on that information.

[1419] "Manager / member / AI three-way dialogue" refers to the process in which a manager, an employee (member), and an artificial intelligence (AI) formulate a career plan through dialogue.

[1420] "Career Plan" refers to the career plans and goals suggested to you based on your skills and experience.

[1421] "Experience design" refers to the process of systematically accumulating the skills and experiences necessary to realize one's career plan.

[1422] A "factory robot" is an automated machine designed to perform work within a factory, and has the ability to improve the skills of workers and suggest career paths.

[1423] "Online platform" refers to systems and services accessible via the internet that facilitate interaction between managers, members, and AI.

[1424] The system for implementing this invention includes a text generation AI, an emotion engine, a factory robot, and an online platform. Specific embodiments of each element will be described below.

[1425] Text Generation AI

[1426] The server uses text generation AI to generate a career plan based on the user's skills and experience. The text generation AI uses natural language processing technology to analyze job market data and internal personnel evaluation data, and proposes the optimal career plan. For example, if a user has skills in "machine operation" and "quality control" and has "five years of factory work experience," the text generation AI will generate a career plan based on this information.

[1427] Emotion Engine

[1428] The server analyzes the user's emotional state using an emotion engine. The emotion engine analyzes the user's text input and voice data to determine whether the user is feeling joy, stress, excitement, or other emotions. For example, if the user inputs, "I'm excited to learn a new skill," the emotion engine determines that the user is feeling joy.

[1429] Adjusting career plans

[1430] The server adjusts the career plan generated by the text generation AI based on the analysis results of the emotion engine. For example, if the user is feeling stressed, the emotion engine will provide feedback on that information, and the text generation AI will suggest a career plan that will reduce stress.

[1431] Factory Robots

[1432] The factory robots will help workers improve their skills and suggest career paths. An application installed on the factory robot retrieves the worker's skills and experience from a database and uses text generation AI and an emotion engine to suggest the optimal career plan. For example, if a worker who has been working at the factory for five years wants to acquire new skills, the system will suggest the next skills they should acquire and the training program they should participate in based on the worker's current skills and experience.

[1433] Online Platform

[1434] The server supports dialogue between managers, workers, and AI through an online platform. Career plans and related experiences are developed on this platform. For example, a manager and worker can hold an online meeting to discuss career plans proposed by the AI ​​and decide on the most appropriate plan.

[1435] Prompt Sentence Examples

[1436] "Please suggest a career plan based on the user's skills and experience: Machine operation, quality control, 5 years of factory work experience."

[1437] In this way, it becomes possible to propose career plans that correspond to the user's emotional state, making it easier to improve workers' skills and design career paths.

[1438] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1439] Step 1:

[1440] The server retrieves data about the user's skills and experience from the database. The input is the user's ID, and the output is data about the user's skills and experience. This data becomes the basis for the text generation AI to generate a career plan.

[1441] Step 2:

[1442] The server uses text generation AI to generate a career plan based on the acquired user skills and experience. The input is data on the user's skills and experience, and the output is the generated career plan. The text generation AI uses natural language processing technology to propose the optimal career plan to the user.

[1443] Step 3:

[1444] The server uses an emotion engine to analyze the user's emotional state. The input is the user's text input or voice data, and the output is the analyzed emotional state. The emotion engine determines whether the user is feeling joy, stress, excitement, or other emotions.

[1445] Step 4:

[1446] The server adjusts the career plan generated by the text generation AI based on the analysis results of the emotion engine. The input is the analyzed emotional state and the generated career plan, and the output is the adjusted career plan. For example, if the user is feeling stressed, the emotion engine will feed that information back, and the text generation AI will suggest a career plan that will reduce stress.

[1447] Step 5:

[1448] The server sends the adjusted career plan to the factory robot. The input is the adjusted career plan, and the output is the career plan sent to the factory robot. The factory robot then uses this career plan to improve the worker's skills and propose career paths.

[1449] Step 6:

[1450] The terminal supports a three-way dialogue between the manager, the worker, and the AI ​​through an online platform. The input is the adjusted career plan, and the output is the final career plan formulated as a result of the dialogue. For example, the manager and the worker hold an online meeting to discuss the career plans proposed by the AI ​​and decide on the optimal plan.

[1451] Step 7:

[1452] The server saves the final career plan in a database. The input is the final career plan and the output is the saved career plan. This preserves the user's career plan for future reference and updates.

[1453] Example 3

[1454] 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."

[1455] In today's labor market, it is important for individual employees to effectively formulate their own career plans and acquire the necessary skills. However, it is not easy for employees to recognize their own skill gaps and formulate appropriate career plans. In addition, there is a lack of career plan proposals that take employees' emotional state into consideration, which can lead to a decline in employee satisfaction and motivation. To solve these issues, a system is needed that utilizes labor market data and internal organizational evaluation data to propose career plans that take employees' emotional state into consideration.

[1456] 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.

[1457] In this invention, the server includes means for tuning the text generation AI using labor market data and organizational evaluation-related data, means for developing AI to support managers and job placement agents, means for supporting the formulation of career plans and the design of experiences for such plans through a three-way dialogue between managers, employees, and the AI, means for analyzing the user's emotional state using an emotion engine and feeding that information back to the text generation AI, and means for proposing an appropriate career plan based on the user's emotional state. This enables employees to recognize their skill gaps and create a career plan suited to their emotional state.

[1458] "Text generation AI" is an artificial intelligence system that uses natural language processing technology to generate and analyze text data.

[1459] "Labor market data" is data that includes information about the labor market, such as job openings and skill demand.

[1460] "Internal evaluation-related data" is data that includes evaluation information regarding employee performance and skills.

[1461] A "manager" is someone who has the role of managing employees within an organization, such as a boss or team leader with subordinates.

[1462] An "employment introducer" is a professional who connects job seekers with companies, such as a recruitment agent or career consultant.

[1463] An "emotion engine" is a technology that analyzes a user's emotional state and feeds that information back to other systems.

[1464] A "career plan" is a specific plan for an employee to achieve their career goals.

[1465] A "skills gap" is the difference between current skills and desired skills.

[1466] An "online platform" is a collection of services and applications provided over the Internet.

[1467] The present invention is implemented using software such as a text generation AI, an emotion engine, and a database management system, and hardware such as a server and a terminal (PC, smartphone). Specific embodiments are described below.

[1468] Hardware and software used

[1469] Hardware: Servers, terminals (PCs, smartphones)

[1470] Software: Text generation AI (e.g., GPT-4), emotion engines, database management systems (e.g., MySQL)

[1471] Processing flow

[1472] 1. Data Collection and Analysis

[1473] The server collects labor market data, specifically from job sites and industry reports, via an API.

[1474] The server inputs the collected data into a text generation AI to analyze skill demand in the IT industry.

[1475] Example: The server sends the prompt "What are the most in-demand skills in the IT industry today?" to a generative AI model and stores the results in a database.

[1476] 2. Evaluating employee data

[1477] The server retrieves reputation-related data within the organization from a database.

[1478] The server inputs the acquired data into a text generation AI to evaluate the skills of employees.

[1479] Example: The server sends the prompt "Does employee A have data analysis skills?" to the generative AI model and stores the results in a database.

[1480] 3. Skill acquisition suggestions

[1481] The server suggests skills acquisition for employees who do not have data analysis skills.

[1482] Example: The server generates a message to employee A saying, "We recommend that you acquire data analysis skills," and sends it to the employee's terminal.

[1483] 4. Propose an action plan

[1484] AI supports managers and job placement agencies through devices (PCs and smartphones) and proposes action plans for acquiring specific skills.

[1485] Example: A message appears on the administrator's device saying, "We are proposing a data analysis skills acquisition plan for employee A. We recommend taking online courses and participating in practical projects."

[1486] 5. Emotional state analysis and feedback

[1487] The emotion engine analyzes the user's emotional state. Specifically, it analyzes the user's input and behavioral data to estimate the emotional state.

[1488] The emotion engine feeds the analysis results back to the text generation AI.

[1489] Example: An emotion engine generates information such as "The user is feeling anxious" and sends it to a text generation AI.

[1490] 6. Emotion-based career planning

[1491] Text generation AI suggests appropriate career plans based on the user's emotional state.

[1492] Example: A text generation AI generates a message that reads, "To reduce your anxiety, we will suggest a plan that will help you set short-term goals and achieve a sense of accomplishment," and sends it to the user's device.

[1493] Example prompt

[1494] "What skills are in high demand in the IT industry today?"

[1495] "Does Employee A have data analysis skills?"

[1496] "Users are feeling anxious. Please suggest a career plan."

[1497] This system allows users to specifically formulate their own career plans and design the experiences necessary to achieve them. Furthermore, the combination of an emotion engine and text generation AI suggests career plans suited to the user's emotional state, improving user satisfaction. The flow of the identification process in Example 3 is explained using Figure 21.

[1498] Step 1:

[1499] Data collection and analysis

[1500] The server collects labor market data, specifically from job sites and industry reports, via an API.

[1501] Input: Data obtained from APIs of job sites and industry reports.

[1502] Data processing: The acquired data is input into a text generation AI and converted into an analyzable format.

[1503] Output: Analysis of skill demand in the labor market.

[1504] Specific operation: The server sends the prompt "What are the most in-demand skills in the IT industry today?" to the generative AI model and stores the results in a database.

[1505] Step 2:

[1506] Employee data assessment

[1507] The server retrieves reputation-related data within the organization from a database.

[1508] Input: Reputation-related data within your organization.

[1509] Data processing: The acquired data is fed into a text generation AI to evaluate employee skills.

[1510] Output: Employee skill assessment results.

[1511] Specific operation: The server sends the prompt sentence "Does employee A have data analysis skills?" to the generative AI model and saves the result in the database.

[1512] Step 3:

[1513] Skill acquisition suggestions

[1514] The server suggests skills acquisition for employees who do not have data analysis skills.

[1515] Input: Employee skill assessment results.

[1516] Data processing: Based on the skill evaluation results, a message suggesting skill acquisition is generated.

[1517] Output: Skill acquisition suggestion message.

[1518] Specific operation: The server generates a message to employee A saying, "We recommend that you acquire data analysis skills," and sends it to the employee's terminal.

[1519] Step 4:

[1520] Proposing an action plan

[1521] AI supports managers and job placement agencies through devices (PCs and smartphones) and proposes action plans for acquiring specific skills.

[1522] Input: Skill acquisition suggestion message.

[1523] Data processing: Generate a specific action plan based on the proposed message.

[1524] Output: Action plan proposal message.

[1525] Specific behavior: A message will appear on the administrator's device saying, "We are proposing a data analysis skills acquisition plan for employee A. We recommend taking online courses and participating in practical projects."

[1526] Step 5:

[1527] Emotional state analysis and feedback

[1528] The emotion engine analyzes the user's emotional state. Specifically, it analyzes the user's input and behavioral data to estimate the emotional state.

[1529] Input: User input and behavioral data.

[1530] Data processing: Analyze emotional states and feed that information back into the text generation AI.

[1531] Output: Information about the user's emotional state.

[1532] Specific behavior: The emotion engine generates information such as "The user is feeling anxious" and sends it to the text generation AI.

[1533] Step 6:

[1534] Emotion-based career planning

[1535] Text generation AI suggests appropriate career plans based on the user's emotional state.

[1536] Input: Information about the user's emotional state.

[1537] Data processing: Generate career plans based on emotional states.

[1538] Output: Career plan suggestion message.

[1539] What it does: The text generation AI generates a message that reads, "To help reduce your anxiety, we'll suggest a plan that will help you set short-term goals and achieve a sense of accomplishment," and sends it to the user's device.

[1540] (Application example 3)

[1541] 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."

[1542] Lack of employee skills and unclear career plans are factors that have a negative impact on corporate productivity and employee satisfaction. In addition, proposing a uniform career plan without considering the emotional state of employees may reduce their motivation and performance. Therefore, there is a need for a system that can propose the optimal career plan for each individual employee based on their skill and emotional data.

[1543] 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.

[1544] In this invention, the server includes: means for tuning the text generation AI using job market data and internal personnel evaluation-related data; means for developing AI to support managers and career change supporters with subordinates; means for supporting the formulation of career plans and the design of experiences for such plans through a three-way dialogue between managers, employees, and the AI; means for utilizing general job market data and general personnel evaluation data as job market data; means for acquiring employee skill data and emotional data and proposing career plans based on their emotional state; and means that can be used by employees in their daily work using a smart device, thereby enabling the improvement of employee skills and the optimization of career plans.

[1545] "Text generation AI" is an artificial intelligence that generates text using natural language processing technology, and has the ability to analyze data from the job market and data related to personnel evaluation within a company, and propose optimal career plans.

[1546] "Job Market Data" means data containing information about the job market in general, including job listings, job seeker skill sets, and industry trends.

[1547] "Human resource evaluation data" refers to data used to evaluate employee performance and skills within a company, and includes evaluation results, feedback, skill matrices, etc.

[1548] A "manager" is a person who has subordinates and is responsible for managing and supervising work within an organization.

[1549] A "career change supporter" is a professional who supports job seekers in their job change activities, and includes career consultants and job change agents.

[1550] "Emotional data" refers to data that indicates an employee's emotional state, including stress levels, mood, motivation, etc.

[1551] A "smart device" is an electronic device that can connect to the Internet and has the ability to run applications, including smartphones and smart glasses.

[1552] A "career plan" is a plan for how an employee will progress in their career, including skill acquisition, work experience, goal setting, etc.

[1553] An "online platform" is a collection of services and applications provided over the Internet that provides an environment for users to interact and exchange information.

[1554] A system for implementing this invention is configured by combining a text generation AI, an emotion engine, a smart device, and an online platform. Specific embodiments are described below.

[1555] System Configuration

[1556] 1. Text Generation AI

[1557] The server uses text generation AI to analyze job market data and internal personnel evaluation data, generating optimal suggestions for employee skill sets and career plans. The text generation AI uses OpenAI's API.

[1558] 2. Emotion Engine

[1559] The server uses an emotion engine to obtain employee emotional data in real time. The emotion engine uses the Emotion Recognition API, which monitors employees' stress levels and moods and suggests career plans based on their emotional state.

[1560] 3. Smart Devices

[1561] Employees use smart devices such as smartphones and smart glasses to access the system in their daily work, allowing them to receive career planning suggestions in real time.

[1562] 4. Online Platforms

[1563] The three parties—manager, employee, and AI—interact on an online platform, which is a collection of services and applications provided over the internet, providing an environment for users to interact and exchange information.

[1564] Data processing and calculation

[1565] 1. Obtaining skill data

[1566] The server retrieves employee skill data from the database, including the employee's current skill set and past evaluation results.

[1567] 2. Acquiring Emotion Data

[1568] The server uses an emotion engine to obtain real-time employee emotion data, including stress levels and moods.

[1569] 3. Creating a career plan

[1570] The server uses text generation AI to generate a career plan based on the acquired skill data and emotion data. The generated career plan is appropriate for the employee's emotional state.

[1571] Specific examples

[1572] For example, if employee A lacks data analysis skills and has recently been feeling stressed by his work, the server will acquire employee A's skill data and emotion data and input the following prompt sentence into the text generation AI:

[1573] Example prompt sentence:

[1574] Employee Skills Data: {"data_analysis": False, "programming": True}

[1575] Emotion data: {"stress_level": "high", "mood": "anxious"}

[1576] Suggest a suitable career plan for this employee.

[1577] Based on this prompt, the text-generation AI will suggest an online course in data analysis to Mr. A and also provide mental health support to reduce stress, thus providing the employee with a concrete action plan to improve their career.

[1578] The flow of the specific processing in Application Example 3 will be described with reference to FIG.

[1579] Step 1:

[1580] The server retrieves employee skill data from the database. It receives the employee's ID as input and retrieves the employee's skill data, including their skill set and past evaluation results, as output. Specifically, it executes SQL queries to extract the required information from the database.

[1581] Step 2:

[1582] The server uses an emotion engine to obtain employee emotional data in real time. It receives the employee's ID as input and obtains the employee's emotional data, including their stress level and mood, as output. Specifically, it calls the Emotion Recognition API to obtain the emotional data.

[1583] Step 3:

[1584] The server inputs a prompt sentence into the text generation AI based on the acquired skill data and emotion data. It receives skill data and emotion data as input and generates a prompt sentence as output. Specifically, it formats the skill data and emotion data to create a prompt sentence like the one below.

[1585] Employee Skills Data: {"data_analysis": False, "programming": True}

[1586] Emotion data: {"stress_level": "high", "mood": "anxious"}

[1587] Suggest a suitable career plan for this employee.

[1588] Step 4:

[1589] The server inputs the generated prompt sentence into the text generation AI to generate a career plan. It receives the prompt sentence as input and generates a career plan as output. Specifically, it calls the OpenAI API to generate a career plan.

[1590] Step 5:

[1591] The server sends the generated career plan to the smart device. It receives the career plan as input and sends it to the smart device as output. As a specific operation, it displays the career plan using the notification function of the smart device.

[1592] Step 6:

[1593] Users use smart devices to review their career plans and take necessary actions. The input is the career plan, and the output is specific actions, such as registering for online courses or accessing mental health support.

[1594] 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.

[1595] 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.

[1596] Another example of generative AI is Gemini (internet search engine). <url: https: gemini.google.com ?hl="ja">) are mentioned.

[1597] 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.

[1598] [Third embodiment]

[1599] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[1600] 5, the data processing system 310 includes the data processing device 12 and a headset type terminal 314. An example of the data processing device 12 is a server.

[1601] 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).

[1602] 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.

[1603] 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.

[1604] 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).

[1605] 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.

[1606] 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.

[1607] 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.

[1608] 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.

[1609] 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.

[1610] Next, the specific processing by the specific processing unit 290 of the data processing device 12 will be described.

[1611] "Example 1"

[1612] In one embodiment of the present invention, a text generation AI collects data from the job market and data related to a company's internal personnel evaluations, and then uses natural language processing technology to tune the system based on this data. Specifically, data from the job market includes job information, the skills and experience of job seekers, and industry trends, while data related to the company's internal personnel evaluations includes employee performance evaluations, skill sets, and career paths. Based on this data, the text generation AI proposes optimal career plans for each individual user.

[1613] "Example 2"

[1614] Additionally, AI supporting managers and recruitment agents with subordinates will propose specific action plans based on the career plans proposed by the text generation AI. Specifically, it will suggest actions such as acquiring necessary skills, gaining experience, and networking. These suggestions are made through a three-way dialogue between the manager, employee, and AI, and the results are used to formulate career plans and design experiences for them.

[1615] "Example 3"

[1616] As a specific example, if a text-generation AI determines from job market data that there is a growing demand for data analysis skills in the IT industry, and determines from a company's internal personnel evaluation data that an employee does not have data analysis skills, it will suggest that the employee acquire data analysis skills. Then, AI supporting managers and recruitment agents will propose specific action plans for acquiring skills. This allows employees to specifically formulate their own career plans and design the experiences needed to achieve them.

[1617] The processing flow of each embodiment will be described below.

[1618] "Example 1"

[1619] Step 1: The text generation AI collects data from the job market and data related to internal personnel evaluations. Specifically, it collects data from the job market, such as job information, the skills and experience of job seekers, and industry trends, as well as data related to internal personnel evaluations, such as employee performance evaluations, skill sets, and career paths.

[1620] Step 2: Based on the collected data, the text generation AI is tuned using natural language processing technology. Through this tuning, the text generation AI gains the ability to propose the optimal career plan for each individual user.

[1621] Step 3: Based on the career plan proposed by the text generation AI, AI supporting managers and recruitment agents with subordinates will propose a specific action plan.

[1622] "Example 2"

[1623] Step 1: The text generation AI determines from job market data that there is a growing demand for data analysis skills in the IT industry.

[1624] Step 2: At the same time, the text generation AI determines from the company's internal performance evaluation data that a certain employee does not have data analysis skills.

[1625] Step 3: Based on these findings, the text-generating AI suggests that the employee learn data analysis skills.

[1626] Step 4: The AI ​​then supports managers and recruitment agencies by proposing specific action plans for acquiring skills, enabling employees to formulate their own career plans and design the experiences needed to achieve them.

[1627] Example 1

[1628] 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."

[1629] Conventional career planning systems were unable to effectively utilize data from the job market or internal personnel evaluation data, making it difficult to provide optimal career plans for individual users. Furthermore, career plans were often generated manually based on user input data, resulting in a lack of efficiency. Furthermore, there were insufficient means for providing the generated career plans to users, and there was a need for them to be provided in a format that users could easily access.

[1630] 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.

[1631] In this invention, the server includes means for collecting data on the job market and data related to internal personnel evaluations, means for preprocessing the collected data, means for tuning a text generation AI model using the preprocessed data, means for generating an optimal career plan based on data input by a user, and means for providing the generated career plan to the user. This makes it possible to effectively utilize data on the job market and data related to internal personnel evaluations, automatically generate optimal career plans for individual users, and efficiently provide them to them.

[1632] "Job market data" refers to information related to job hunting, such as job information, the skills and experience of job seekers, and industry trends.

[1633] "Internal personnel evaluation-related data" refers to information related to personnel evaluation within a company, such as employee performance evaluations, skill sets, and career paths.

[1634] "Means of collection" refers to the methods and technologies used to obtain data on the job market and data related to personnel evaluation within a company.

[1635] "Preprocessing means" refers to methods and techniques for preparing collected data in a form that is easy to analyze.

[1636] "Text generation AI model" refers to an artificial intelligence model that generates text using natural language processing technology.

[1637] "Tuning methods" refers to methods and techniques for optimizing the performance of AI models using collected data.

[1638] "User input data" refers to information entered by the user, such as their skills, experience, and desired career path.

[1639] "Career plan" refers to a career plan or path proposed based on the user's skills, experience, and industry trends.

[1640] "Means of providing" refers to the methods and techniques for communicating the generated career plan to users.

[1641] This invention is a system that collects data on the job market and data related to personnel evaluation within a company, and tunes a text generation AI model based on this data. A specific embodiment of this system is described below.

[1642] Server Processing

[1643] The server first collects data from the job market and data related to the company's internal personnel evaluations. The job market data includes job information, the skills and experience of job seekers, and industry trends. The internal personnel evaluation data includes employee performance evaluations, skill sets, and career paths. These data are then stored in a database.

[1644] The server then preprocesses this data using natural language processing techniques, specifically using the Python libraries NLTK and spaCy to clean and tokenize the text data, and uses the preprocessed data to tune a text-generation AI model using TensorFlow or PyTorch.

[1645] The tuned AI model generates the optimal career plan based on the user's input data, which includes the user's skills, experience, desired career path, etc. The server analyzes this data and generates the optimal career plan for the user.

[1646] Terminal handling

[1647] The terminal accepts input from the user. The user enters information such as their skills, experience, and desired career path. This information is sent from the terminal to the server. Specifically, the data is sent using an HTTP request.

[1648] The terminal receives the carrier plan sent from the server and displays it to the user. Specifically, it receives the HTTP response and displays the carrier plan. The user can check the proposed carrier plan through the terminal and provide feedback if necessary.

[1649] User operations

[1650] First, the user inputs information such as their skills, experience, and desired career path into the terminal. This information is sent to the server. Next, the user checks the career plan sent from the server on the terminal. The career plan is generated taking into consideration the user's skills, experience, and industry trends. The user can use the proposed career plan as a reference to plan their own career.

[1651] Specific examples

[1652] For example, if a user enters "I'm considering a career as a data scientist," the server will analyze the skills, experience, and industry trends required for data scientists based on job market data and the company's own personnel evaluation data. Based on the analysis results, the server will suggest a career plan to the user, such as "It is recommended that you improve your Python and R skills and participate in machine learning projects."

[1653] Prompt Sentence Examples

[1654] "I'm considering a career as a data scientist. What skills and experience do I need? What are the industry trends?"

[1655] In this way, the system proposes the optimal career plan based on the user's input.

[1656] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1657] Step 1:

[1658] The server collects data on the job market and data related to personnel evaluation within the company.

[1659] Specifically, they use web scraping tools to collect data from job information sites and obtain employee performance evaluation data from their own database.

[1660] Input: URL of job information site, query of company database

[1661] Output: Job market data, internal personnel evaluation data

[1662] Step 2:

[1663] The server pre-processes the collected data.

[1664] Specifically, we use the Python libraries NLTK and spaCy to clean and tokenize text data.

[1665] Input: Job market data, internal personnel evaluation data

[1666] Output: Preprocessed data

[1667] Step 3:

[1668] The server uses the preprocessed data to tune the text generation AI model.

[1669] Specifically, the model is trained using TensorFlow and PyTorch.

[1670] Input: Preprocessed data

[1671] Output: A tuned text generation AI model

[1672] Step 4:

[1673] The terminal accepts input from the user.

[1674] Specifically, users enter information such as their skills, experience, and desired career path.

[1675] Input: User's skills, experience, and desired career path

[1676] Output: User input data

[1677] Step 5:

[1678] The terminal transmits the data entered by the user to the server.

[1679] Specifically, data is transmitted using an HTTP request.

[1680] Input: User-entered data

[1681] Output: Send data to the server

[1682] Step 6:

[1683] The server generates an optimal career plan based on the data input by the user.

[1684] Specifically, a tuned AI model is used to analyze the user's skills, experience, and industry trends to create a career plan.

[1685] Input: User input data, tuned text generation AI model

[1686] Output: Career plan

[1687] Step 7:

[1688] The server transmits the generated carrier plan to the terminal.

[1689] Specifically, the carrier plan is sent using an HTTP response.

[1690] Input: Career plan

[1691] Output: Carrier plan sent to device

[1692] Step 8:

[1693] The terminal receives the carrier plan sent from the server and displays it to the user.

[1694] Specifically, the career plan is visually displayed using a GUI.

[1695] Input: Career plan

[1696] Output: Display career plan to user

[1697] Step 9:

[1698] The user checks the carrier plan displayed on the terminal.

[1699] Specifically, we will read the proposed career plan and provide feedback as needed.

[1700] Input: Career plan

[1701] Output: User feedback (if required)

[1702] (Application example 1)

[1703] 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."

[1704] Formulating and updating employee career plans using traditional methods takes time and effort, making it difficult to provide optimal plans for each employee. Furthermore, there is a lack of ways for brick-and-mortar store employees to check and update their own career plans in real time, making it difficult to effectively support their career growth.

[1705] 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.

[1706] In this invention, the server includes: means for tuning the text generation AI using job market data and internal personnel evaluation-related data; means for developing AI to support managers and career change supporters with subordinates; means for supporting the formulation of career plans and the design of experiences for such plans through a three-way dialogue between managers, employees, and the AI; means for utilizing general job market data and general personnel evaluation data as job market data; means for employees at physical stores to check and update their career plans in real time using smart devices; and means for the text generation AI to analyze employees' performance evaluations, skill sets, and career paths and propose optimal career plans. This enables employees to check and update their own career plans in real time and receive proposals for optimal career plans.

[1707] "Text generation AI" is an artificial intelligence that generates text using natural language processing technology, analyzes data from the job market and data related to personnel evaluation within a company, and proposes the optimal career plan for each individual user.

[1708] "Job market data" refers to general data related to the job market, such as job information, the skills and experience of job seekers, and industry trends.

[1709] "Human resource evaluation-related data" refers to data related to human resource evaluation collected within a company, such as employee performance evaluations, skill sets, and career paths.

[1710] A "manager" is someone who has a subordinate and is responsible for helping employees develop career plans and designing experiences for them.

[1711] A "career change supporter" is a professional who supports individuals who wish to change jobs, provides information about the job market, and assists in the formulation of career plans.

[1712] "Employees" refers to individuals who work in physical stores and who need to develop or update their career plans.

[1713] "Smart devices" refer to electronic devices that can connect to the Internet and have advanced functions, such as smartphones and smart glasses.

[1714] A "career plan" refers to a plan that outlines the career goals that an individual employee wants to achieve in the future and the steps they need to take to achieve them.

[1715] "Natural language processing technology" is a technology for understanding and generating human language, and plays an important role in text generation AI.

[1716] An "online platform" refers to a foundation that provides systems and services accessible via the internet, and provides a forum for dialogue between managers, employees, and AI.

[1717] The system for implementing this invention uses text generation AI to analyze job market data and internal personnel evaluation data, and proposes optimal career plans for individual employees. Specific embodiments of this system are described below.

[1718] System Configuration

[1719] Hardware:

[1720] server

[1721] Smartphone or smart glasses

[1722] software:

[1723] Python

[1724] OpenAI API

[1725] Pandas

[1726] Data collection and preprocessing

[1727] The server collects data from the job market (job information, skills and experience of job seekers, industry trends, etc.) and data related to the company's internal personnel evaluation (employee performance evaluations, skill sets, career paths, etc.) This data is saved in formats such as CSV files and is read using Pandas.

[1728] Parsing data and generating prompts

[1729] The server preprocesses the collected data and extracts the necessary information. Next, based on employee information and job market information, it generates prompts to be input into the text generation AI. Examples of prompts are as follows:

[1730] Example prompt sentence:

[1731] Employee Information:

[1732] Performance: High

[1733] Skills: Data analysis, programming

[1734] Career Path: Data Analyst

[1735] Job market information:

[1736] Job Opening: Data Scientist

[1737] Skills: Data Analysis, Machine Learning

[1738] Industry Trends: Demand for Data Science is on the Rise

[1739] Suggest the best career plan for this employee.

[1740] Creating a career plan

[1741] The server uses the OpenAI API to input prompts into the generative AI model to generate a career plan, which is then displayed in real time on the employee's smartphone or smart glasses.

[1742] Specific examples

[1743] For example, if Employee A has a high performance rating and his or her skill set includes "data analysis," the server will suggest a career path to "data scientist" based on job market data. This suggestion will be displayed in real time on Employee A's smartphone or smart glasses, allowing Employee A to confirm and update his or her career plan.

[1744] In this way, the server allows employees to check and update their career plans in real time and receive suggestions for the most suitable career plans.

[1745] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1746] Step 1:

[1747] The server collects job market data and internal personnel evaluation-related data. This includes job information, job seekers' skills and experience, industry trends, employee performance evaluations, skill sets, and career paths. This data is saved in formats such as CSV files. The input is job market data and personnel evaluation data, and the output is a collection of this data.

[1748] Step 2:

[1749] The server reads the collected data using Pandas and performs preprocessing. Specifically, it extracts necessary information and formats the data. For example, it deletes unnecessary columns and imputes missing values. The input is the data collected in step 1, and the output is the preprocessed data.

[1750] Step 3:

[1751] The server generates prompts based on the preprocessed data, combining employee information and job market information. The prompts include employee performance evaluations, skill sets, career paths, and job market information, required skills, and industry trends. The input is the preprocessed data, and the output is the generated prompt.

[1752] Step 4:

[1753] The server sends the generated prompt to the OpenAI API and generates a career plan using the generative AI model. The input is the prompt, and the output is the generated career plan. Specifically, the server sends an API request and receives a response.

[1754] Step 5:

[1755] The server sends the generated career plan to the employee's smartphone or smart glasses in real time. The input is the generated career plan, and the output is the career plan displayed on the employee's device. Specific operations include sending a notification to the device and displaying the career plan.

[1756] Step 6:

[1757] Users (employees) use smartphones or smart glasses to check and update their career plans in real time. The input is the career plan displayed on the device, and the output is the updated career plan. Specific operations involve operating the device interface to check and update the career plan.

[1758] Example 2

[1759] 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."

[1760] Conventional career planning systems have had difficulty generating specific action plans to propose optimal career plans for individual users, and also have had problems with the inefficient process of formulating career plans through dialogue between managers, members, and AI.

[1761] The identification process by the identification processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means. In this invention, the server includes a means for the user to input a prompt sentence, a means for the server to receive the prompt sentence and send it to the generative AI model, a means for the generative AI model to generate a career plan, a means for the server to analyze the generated career plan and generate a specific action plan, a means for the server to present the action plan to the user, and a means for the user, manager, and AI to formulate a career plan through dialogue. This makes it possible to propose an optimal career plan for each user and generate a specific action plan. Furthermore, a career plan can be formulated efficiently through dialogue between the manager, member, and AI.

[1762] "User" refers to an individual or organization that uses the system to receive career plan proposals.

[1763] A "prompt" is a textual question or request that a user enters into a system.

[1764] "Server" refers to the computer system responsible for receiving prompts from the user and sending them to the generative AI model.

[1765] "Generative AI model" refers to an artificial intelligence model that uses natural language processing technology to analyze prompt sentences and generate career plans.

[1766] A "career plan" refers to a plan that shows a user's career goals and career path.

[1767] An "action plan" is a plan that outlines the specific actions and steps required to realize your career plan.

[1768] "Manager" refers to a person in a managerial position who has subordinates.

[1769] "Member" refers to an individual who works under a manager.

[1770] "Dialogue" refers to the communication between the user, manager, and AI regarding the formulation of a career plan.

[1771] An "online platform" refers to a digital environment in which users, managers, and AI can interact via the internet.

[1772] This invention is a system in which a user inputs a prompt sentence, a server receives the prompt sentence, and sends it to a generative AI model to generate a career plan and propose a specific action plan. Specific embodiments of this system are described below.

[1773] Hardware and software used

[1774] Hardware:

[1775] High-performance server (e.g., server equipped with NVIDIA GPU)

[1776] software:

[1777] Text generation AI (e.g., generative AI models using natural language processing technology)

[1778] Database management system (e.g. MySQL)

[1779] Web server (e.g. Apache)

[1780] Data processing and calculation

[1781] The server receives the prompt sentence from the user and sends it to the generative AI model. The generative AI model analyzes the prompt sentence and generates a career plan according to the user's needs. The generated career plan includes the following elements:

[1782] Acquire the necessary skills

[1783] Accumulation of experience

[1784] How to network

[1785] The server then analyzes the generated career plan and generates a specific action plan. This action plan is created through a three-way dialogue between the user, manager, and AI, and the results are used to formulate the career plan and design the experience required for it.

[1786] Specific examples

[1787] As a concrete example, the following prompt sentence is input to the generative AI model:

[1788] Example prompt sentence:

[1789] "A software engineer in his 30s wants to transition to a project manager position. His current skill set is: Java, Python, and basic project management knowledge. Please suggest the skills and experience he needs and how he can network."

[1790] When the user enters the above prompt, the server sends it to the text generation AI model, which then generates the following career plan:

[1791] Acquisition of necessary skills: Advanced knowledge of project management (e.g., PMP certification), leadership skills, communication skills

[1792] Gaining experience: Experience leading small projects, increased opportunities to receive feedback from team members

[1793] Networking: Attending project management related conferences and seminars to expand your professional network

[1794] The server presents these suggestions to the user, and through a three-way dialogue between the user, manager, and AI, the optimal career plan is formulated.

[1795] In this way, users can formulate and execute career plans based on specific action plans.

[1796] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1797] Step 1:

[1798] The user inputs a prompt through the system interface. For example, the user might input, "A software engineer in his 30s wants to change careers to become a project manager. His current skill set is as follows: Java, Python, and basic knowledge of project management. Please suggest the skills, experience, and networking methods he needs." The input prompt is sent to the system.

[1799] Step 2:

[1800] The server receives the prompt sentence. The server receives the prompt sentence sent by the user and stores it in a database. The received prompt sentence is prepared to be sent to the generative AI model. The input is the prompt sentence, and the output is the prompt sentence ready to be sent to the generative AI model.

[1801] Step 3:

[1802] The server sends a prompt to the generative AI model. The server sends the received prompt to the generative AI model. The generative AI model generates a career plan based on the prompt. The input is the prompt, and the output is the prompt sent to the generative AI model.

[1803] Step 4:

[1804] A generative AI model generates a career plan. The generative AI model analyzes the prompt and generates a career plan that meets the user's needs. For example, it suggests ways to acquire necessary skills, gain experience, and network. The input is the prompt, and the output is the generated career plan.

[1805] Step 5:

[1806] The server receives the generated career plan. The server receives the career plan generated from the generative AI model and stores it in a database. The received career plan is further analyzed. The input is the generated career plan, and the output is a career plan prepared for analysis.

[1807] Step 6:

[1808] The server analyzes the career plan and generates a specific action plan. The server analyzes the received career plan and generates a specific action plan. For example, it suggests obtaining PMP certification, improving leadership skills, and gaining experience as a leader of small projects. The input is a career plan, and the output is a specific action plan.

[1809] Step 7:

[1810] The server presents the action plan to the user. The server renders the generated action plan in HTML format and displays it in the user's browser. The user checks the displayed action plan and makes corrections or additions as necessary. The input is a specific action plan, and the output is the action plan presented to the user.

[1811] Step 8:

[1812] The user, manager, and AI formulate a career plan through dialogue. The three parties, user, manager, and AI, engage in dialogue based on the presented action plan to formulate the optimal career plan. The final design of the career plan is based on the results of the dialogue. The input is the action plan and the results of the dialogue, and the output is the final formulated career plan.

[1813] (Application example 2)

[1814] 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."

[1815] Conventional career planning systems are primarily aimed at human managers and job placement agents, and are not designed to improve the skills or develop career plans for robots working in factories. This makes it difficult to efficiently train robots in skills and improve their productivity in factories.

[1816] The specific processing by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes: means for tuning the text generation AI using job market data and internal personnel evaluation-related data; means for developing AI to support managers and recruitment agents with subordinates; means for supporting the formulation of career plans and the design of experiences for such plans through a three-way dialogue between managers, members, and the AI; means for utilizing general job market data and general personnel evaluation data as job market data; means for proposing career plans based on the robot's current skill set and the factory's needs to help improve the skills of robots working in factories; means for proposing specific skill acquisition methods, experience accumulation methods, and networking methods; and means for formulating optimal career plans by adjusting the proposals through a three-way dialogue between the robot, manager, and AI. This enables efficient skill acquisition and improved productivity for robots working in factories.

[1817] "Text generation AI" is artificial intelligence that generates text data using natural language processing technology.

[1818] "Job market data" refers to market information and statistical data related to job hunting.

[1819] "Internal personnel evaluation-related data" refers to data related to employee evaluations and performance within a company.

[1820] A "manager with subordinates" refers to a managerial position that has the role of managing and guiding subordinates within an organization.

[1821] A "career agent" is a professional who acts as an intermediary between job seekers and companies and supports job hunting activities.

[1822] A "career plan" refers to an individual's goals and plans for their professional life.

[1823] "Experience design" refers to the process of systematically accumulating the experiences and skills necessary to realize one's career plan.

[1824] "Robots working in factories" refers to automated machinery that performs production work in factories.

[1825] A "skill set" refers to the collection of skills and knowledge required to perform a specific job or task.

[1826] "Factory needs" refers to the requirements and conditions that a factory needs in order to carry out its production activities.

[1827] An "action plan" is a specific plan of action to achieve a specific goal.

[1828] "Networking" refers to the act of contacting people to exchange information and build collaborative relationships.

[1829] "Dialogue" refers to a communication process in which multiple parties exchange opinions and information.

[1830] A system for implementing this invention uses text generation AI to generate career plans and action plans, and supports the improvement of the skills of robots working in factories. A specific embodiment of this system is described below.

[1831] System Configuration

[1832] 1. Hardware:

[1833] Server: A server for running text generation AI.

[1834] Robot: An automated mechanical device that performs tasks in a factory.

[1835] Devices: Computers and tablets used by managers and members.

[1836] 2. Software:

[1837] Python: A programming language used to implement programs.

[1838] OpenAI API: An API for running text generation AI.

[1839] Data processing and calculation

[1840] The server processes and calculates data in the following procedure.

[1841] 1. Data Collection:

[1842] Gather current robot skill sets and factory needs.

[1843] This data is sent to the server in text format.

[1844] 2. Career plan generation:

[1845] The server uses text generation AI to generate a career plan based on the collected data.

[1846] Specifically, the following prompt sentences are used to generate a career plan:

[1847] Robot's current skill set: welding, assembly

[1848] Factory needs: quality control, efficient production

[1849] Please suggest a career plan for this robot.

[1850] 3. Generate an action plan:

[1851] The server generates a specific action plan based on the generated career plan.

[1852] Specifically, generate an action plan using the following prompt:

[1853] Career Plan: This robot should acquire quality control skills and learn new assembly techniques to support efficient production.

[1854] Please propose a concrete action plan.

[1855] 4. Interactive features:

[1856] Through dialogue between the robot, manager, and AI, the proposals are adjusted and the optimal career plan is formulated.

[1857] This dialogue will take place on an online platform.

[1858] Specific examples

[1859] For example, if a robot with welding and assembly skills is required in a factory to ensure quality control and efficient production, the server will perform the following process:

[1860] 1. Data Collection:

[1861] Robot skillset: "Welding, assembly"

[1862] Factory needs: "Quality control, efficient production"

[1863] 2. Career plan generation:

[1864] The server generates a career plan based on the above data.

[1865] Generated career plan: "This robot should acquire quality control skills and learn new assembly techniques to support efficient production."

[1866] 3. Generate an action plan:

[1867] The server generates a specific action plan based on the generated career plan.

[1868] Generated action plan: "Receive training in quality control and new assembly techniques."

[1869] In this way, robots working in factories can acquire skills efficiently and improve productivity.

[1870] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1871] Step 1:

[1872] The server collects the robot's current skill set and the factory's needs. Specifically, managers and employees use terminals to input the robot's skill set (e.g., "welding, assembly") and the factory's needs (e.g., "quality control, efficient production"). This data is sent to the server in text format.

[1873] Step 2:

[1874] The server uses text generation AI to generate a career plan based on the collected data. Specifically, it generates a career plan using the following prompt sentence:

[1875] Robot's current skill set: welding, assembly

[1876] Factory needs: quality control, efficient production

[1877] Please suggest a career plan for this robot.

[1878] The server sends this prompt to the OpenAI API and receives the generated career plan. The output is the career plan (e.g., "This robot should acquire quality control skills and learn new assembly techniques to support efficient production.").

[1879] Step 3:

[1880] The server generates a specific action plan based on the generated career plan. Specifically, the action plan is generated using the following prompt sentence:

[1881] Career Plan: This robot should acquire quality control skills and learn new assembly techniques to support efficient production.

[1882] Please propose a concrete action plan.

[1883] The server sends this prompt to the OpenAI API and receives the generated action plan. The output is the action plan (e.g., "Receive training in quality control and new assembly techniques.").

[1884] Step 4:

[1885] The server presents the generated career plans and action plans to the manager and members, who then review these plans via their terminals and make suggestions for corrections or additions as needed. This dialogue takes place on an online platform.

[1886] Step 5:

[1887] The server determines the final career plan and action plan through a dialogue between the manager, members, and the AI. The finalized plan is used as a concrete guideline for improving the robot's skills.

[1888] Step 6:

[1889] The server sends the determined career plan and action plan to the robot, which then acquires skills and undergoes training based on these plans. Specifically, the robot receives training in quality control and new assembly techniques.

[1890] Example 3

[1891] 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 headset type terminal 314 will be referred to as a "terminal."

[1892] In today's job market, the demand for specific skills is rapidly changing, making it difficult for employees to properly formulate their own career plans. While there is a need to effectively utilize personnel evaluation data within companies to suggest appropriate skill acquisition for employees, there is a lack of efficient ways to do this. Furthermore, there is also the challenge of providing managers and career change supporters with concrete action plans to support employees in formulating their career plans.

[1893] 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.

[1894] In this invention, the server includes a means for collecting data on the job market and data related to internal personnel evaluations, a means for analyzing the data on the job market using a generative AI model to identify skills that are in high demand, and a means for analyzing data related to internal personnel evaluations to determine whether employees have specific skills. This enables the server to make suggestions to employees about acquiring appropriate skills, and enables managers and career change supporters with subordinates to provide specific action plans.

[1895] "Job market data" refers to data that includes information about the job market, such as job listings and industry reports.

[1896] "In-house personnel evaluation related data" refers to data that includes information about employee evaluations and skills within a company.

[1897] A "generative AI model" is an artificial intelligence model that uses natural language processing techniques to analyze text data and is trained to perform specific tasks.

[1898] "Skill acquisition suggestions" are messages and action plans that encourage employees to learn specific skills.

[1899] A "manager with subordinates" is an employee who has the role of supervising and guiding subordinates within a company.

[1900] A "career change consultant" is a professional who provides career advice and job information to individuals who wish to change jobs.

[1901] An "action plan" is a specific plan of action required to achieve a specific goal.

[1902] A "terminal" is a device that allows a user to receive information and perform operations.

[1903] A "notification" is a message sent from the system to inform the user of information.

[1904] "Feedback" refers to the reactions and opinions that users provide to a system.

[1905] This invention is a system that collects and analyzes data from the job market and data related to personnel evaluation within a company, and suggests appropriate skill acquisition to employees. A specific embodiment of this system will be described below.

[1906] Hardware and software used

[1907] Hardware: Servers, terminals

[1908] Software: Generative AI models, database management systems (e.g., MySQL, PostgreSQL), natural language processing libraries (e.g., Pandas, Scikit-learn)

[1909] Data collection

[1910] The server collects data on the job market. Specifically, it obtains data from job sites and industry reports via API. For example, it calls the API of a job site to obtain the latest job information. The server also obtains data related to the company's internal personnel evaluations from a database management system. It extracts the necessary data using SQL queries.

[1911] Data analysis

[1912] The server uses a generative AI model to analyze job market data. It preprocesses the data using Python libraries (e.g., Pandas, Scikit-learn) to identify skills that are in high demand. The generative AI model determines that there is a high demand for data analysis skills in the IT industry. For example, it calculates the frequency of occurrence of keywords such as "data analysis," "Python," and "machine learning" from text data.

[1913] Employee Skills Assessment

[1914] The server analyzes the company's internal performance evaluation data to determine whether employees lack specific skills. Specifically, it references the employee's skill set and past evaluation data. The server uses SQL queries to extract employee skill data and input it into the generative AI model.

[1915] Proposal Generation

[1916] The server uses a generative AI model to generate a message suggesting skill acquisition. For example, the message might say, "Demand for data analysis skills is increasing, so we recommend you acquire these skills." The generative AI model generates the message based on the prompt. An example of a prompt: "Analyze the skill demand in the IT industry in the current job market and determine whether the demand for data analysis skills is increasing. Also, based on your company's internal personnel evaluation data, if an employee does not have data analysis skills, generate a message suggesting that the employee acquire these skills."

[1917] Proposing an action plan

[1918] The server uses AI to support managers and career change supporters with subordinates, proposing specific action plans for acquiring skills. For example, taking online courses (e.g., online education platforms), participating in in-house training, or reading specialized books. The server inputs prompt statements into the generative AI model and generates specific action plans. An example of a prompt statement is, "Please propose a specific action plan for acquiring data analysis skills."

[1919] Notifications and Feedback

[1920] The server sends a notification to the device to inform the employee of the proposal. For example, it uses email or an internal messaging system (e.g., a messaging app). The user (employee) chooses whether to accept the proposal and returns feedback to the server. For example, they click an "Accept proposal" button through the device interface.

[1921] In this way, employees can formulate their own career plans in detail and design experiences for them. The flow of the specific processing in the third embodiment will be described with reference to FIG.

[1922] Step 1:

[1923] The server collects data on the job market. Specifically, it obtains data from job sites and industry reports through APIs. As input, it uses the API endpoint of the job site or the URL of the industry report, and obtains the job information or industry report data as output. For example, the server calls the API of a job site to obtain the latest job information.

[1924] Step 2:

[1925] The server retrieves the company's internal personnel evaluation-related data from the database management system. It uses SQL queries as input and obtains employee evaluation data and skill data as output. Specifically, the server executes SQL queries to extract the required data from the database.

[1926] Step 3:

[1927] The server analyzes job market data using a generative AI model. It uses collected job postings and industry report data as input and identifies in-demand skills as output. Specifically, it preprocesses the data using Python libraries (e.g., Pandas, Scikit-learn) and inputs it into the generative AI model. The generative A...

Claims

1. A means for inputting a prompt sentence for instructing the user to propose his / her career plan; A server receives the prompt sentence and transmits it to a generative AI model; A means for generating a career plan for the user by the generative AI model; A means for the server to analyze the career plan and generate an action plan for the user; means for the server to present the action plan to the user; A means for formulating a final career plan through dialogue between the user, the user's manager, and the generative AI model based on the action plan; A system including:

2. The system of claim 1 , wherein the generative AI model has a function of analyzing the prompt sentence using natural language processing technology and proposing an optimal career plan for each individual user.

3. The system of claim 1 , wherein the interaction between the user, the user's manager, and the generative AI model occurs on an online platform, and the final career plan and experiences for the final career plan are developed based on the results of the interaction.

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