system

Abstract

We provide the system. [Solution] Means of obtaining employee information, A means of analyzing the characteristics based on the acquired information about employees, A means for generating training policies suitable for employees based on analyzed characteristics, A means of notifying the generated training plan, A means of providing a plan to support individual learning and skill development in the home environment, A system that includes this.

Description

Technical Field

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

Background Art

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

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] An object is to solve the problem that inappropriate training methods may be provided without understanding the personality and characteristics of employees in detail, resulting in excellent talents leaving the workplace prematurely because they cannot adapt to the workplace environment. As a result, the company can reduce the costs associated with personnel recruitment and training and achieve long-term retention of personnel.

Means for Solving the Problems

[0005] This invention provides a system that acquires information on employees, analyzes their characteristics based on that information, generates and notifies them of training plans tailored to their individual needs. This system solves the problem by updating training plans using employee characteristic data and further acquiring and re-evaluating employee progress information, thereby providing appropriate and effective training support.

[0006] "Employee" refers to an individual who is employed by a certain organization or company, and in this invention, it refers to the person who is the subject of characteristic analysis.

[0007] "Information acquisition means" refers to methods or devices for collecting data about employees, including, for example, functions for gathering information from questionnaires or existing personnel records.

[0008] "Characteristic analysis means" refers to a method or system for analyzing individual personality and ability characteristics based on acquired employee data.

[0009] "Training policy generation method" refers to a method or process for formulating an optimal training policy for an employee based on analyzed employee characteristic data.

[0010] "Notification means" refers to the means of communicating the generated training policy to relevant parties, and includes, for example, functions for notifying information via email or internal management systems.

[0011] "Progress information" refers to data that shows how employees are growing or changing within the workplace, and is collected in the form of performance evaluations and feedback. [Brief explanation of the drawing]

[0012] [Figure 1] This is a conceptual diagram showing an example of the configuration of a data processing system according to the first embodiment. [Figure 2] This is a conceptual diagram showing an example of the essential functions of a data processing device and a smart device according to the first embodiment. [Figure 3] It is a conceptual diagram showing an example of the configuration of a data processing system according to the second embodiment. [Figure 4] It is a conceptual diagram showing an example of the main functions of a data processing device and smart glasses according to the second embodiment. [Figure 5] It is a conceptual diagram showing an example of the configuration of a data processing system according to the third embodiment. [Figure 6] It is a conceptual diagram showing an example of the main functions of a data processing device and a headset-type terminal according to the third embodiment. [Figure 7] It is a conceptual diagram showing an example of the configuration of a data processing system according to the fourth embodiment. [Figure 8] It is a conceptual diagram showing an example of the main functions of a data processing device and a robot according to the fourth embodiment. [Figure 9] It shows an emotion map to which a plurality of emotions are mapped. [Figure 10] It shows an emotion map to which a plurality of emotions are mapped. [Figure 11] It is a sequence diagram showing the processing flow of the data processing system in Example 1. [Figure 12] It is a sequence diagram showing the processing flow of the data processing system in Application Example 1. [Figure 13] It is a sequence diagram showing the processing flow of the data processing system in Example 2 when an emotion engine is combined. [Figure 14] It is a sequence diagram showing the processing flow of the data processing system in Application Example 2 when an emotion engine is combined.

Mode for Carrying Out the Invention

[0013] Hereinafter, an example of an embodiment of a system according to the technology of the present disclosure will be described with reference to the accompanying drawings.

[0014] First, the language used in the following description will be explained.

[0015] In the following embodiments, the numbered processor (hereinafter simply referred to as "processor") may be a single arithmetic unit or a combination of multiple arithmetic units. Also, the processor may be a single type of arithmetic unit or a combination of multiple types of arithmetic units. Examples of arithmetic units include a CPU (Central Processing Unit), a GPU (Graphics Processing Unit), a GPGPU (General-Purpose computing on Graphics Processing Units), an APU (Accelerated Processing Unit), and the like.

[0016] In the following embodiments, the numbered RAM (Random Access Memory) is a memory in which information is temporarily stored and is used as a work memory by the processor.

[0017] In the following embodiments, the numbered storage is one or more non-volatile storage devices that store various programs, various parameters, and the like. Examples of non-volatile storage devices include flash memory (SSD (Solid State Drive)), magnetic disks (e.g., hard disks), or magnetic tapes, and the like.

[0018] In the following embodiments, the numbered communication I / F (Interface) is an interface that includes a communication processor, an antenna, and the like. The communication I / F controls communication between multiple computers. Examples of communication standards applied to the communication I / F include wireless communication standards including 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), or Bluetooth (registered trademark).

[0019] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B." That is, "A and / or B" means that it may be A alone, or B alone, or a combination of A and B. Furthermore, in this specification, the same concept as "A and / or B" applies when expressing three or more things linked by "and / or."

[0020] [First Embodiment]

[0021] Figure 1 shows an example of the configuration of the data processing system 10 according to the first embodiment.

[0022] As shown in Figure 1, the data processing system 10 includes a data processing device 12 and a smart device 14. An example of the data processing device 12 is a server.

[0023] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0024] The smart device 14 comprises a computer 36, a reception device 38, an output device 40, a camera 42, and a communication interface 44. The computer 36 comprises a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The reception device 38, output device 40, and camera 42 are also connected to the bus 52.

[0025] The reception device 38 is equipped with a touch panel 38A and a microphone 38B, etc., and receives user input. The touch panel 38A receives user input by detecting contact with an object (e.g., a pen or finger). The microphone 38B receives user input by detecting the user's voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the data indicating the user input.

[0026] The output device 40 includes a display 40A and a speaker 40B, and presents data to the user 20 by outputting the data in a form perceptible to the user 20 (e.g., audio and / or text). The display 40A displays visible information such as text and images according to instructions from the processor 46. The speaker 40B outputs audio according to instructions from the processor 46. The camera 42 is a small digital camera equipped with an optical system such as a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0027] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various types of information between processor 46 and processor 28 via network 54.

[0028] Figure 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0029] As shown in Figure 2, in the data processing device 12, a specific processing is performed by the processor 28. A specific processing program 56 is stored in the storage 32. The specific processing program 56 is an example of a "program" related to the technology of this disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0030] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0031] In the smart device 14, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The reception output program 60 is used in conjunction with a specific processing program 56 by the data processing system 10. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0032] Next, the specific processing performed by the specific processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the smart device 14 as the "terminal".

[0033] This invention is a system designed to support employees' adaptation to the workplace. This system has the function of analyzing the characteristics of employees and providing individually optimized training plans.

[0034] Specifically, the user first enters basic information about the employee. This information includes the employee's work history, personality assessment results, and existing work experience. The terminal that receives the information then sends it to the server.

[0035] The server utilizes AI models to perform analysis based on the information it receives. This analysis includes using machine learning algorithms to evaluate the personality and characteristics of employees and identify appropriate training methods. Based on the results of this analysis, a customized training plan is generated for each employee.

[0036] For example, if a server assesses an employee as having low stress tolerance, it may be necessary to schedule mental health support meetings and emphasize feedback.

[0037] The generated training plan is sent from the server to the terminal and notified to the user, allowing it to be immediately reflected in actual training activities in the workplace.

[0038] The feedback loop in this process is further enhanced by continuously collecting progress information and allowing the entire system to learn and optimize. Because users can set more detailed training plans based on the collected progress data, organizations can continue to provide an environment that prevents employee turnover and maximizes their potential.

[0039] The following describes the processing flow.

[0040] Step 1:

[0041] The user enters basic information about the employee into the terminal. This information includes name, age, work history, and personality assessment results. The terminal formats this data and prepares it according to the necessary security protocols before sending it to the server.

[0042] Step 2:

[0043] The terminal sends the formatted employee dataset to the server. The data is encrypted and securely transferred over the network.

[0044] Step 3:

[0045] The server interprets the received data and converts it into a format suitable as input for the AI ​​model. Data preprocessing may include imputing missing values ​​and filtering outliers.

[0046] Step 4:

[0047] The server performs characteristic analysis using an AI model. Through machine learning algorithms, it scores the employee's personality, stress tolerance, interpersonal skills, and other characteristics, and generates a profile.

[0048] Step 5:

[0049] Based on the analysis results, the server generates training plans tailored to each employee's characteristics. For example, an employee with low stress tolerance might be offered a training style that emphasizes mental support.

[0050] Step 6:

[0051] The server generates a training plan and sends it to the terminal. The terminal displays this information to the user, and the training manager can review its contents.

[0052] Step 7:

[0053] Based on the training plan received by the user, the actual training activities are planned and implemented. Progress and feedback are collected regularly and entered into the system as feedback data as needed.

[0054] Step 8:

[0055] The device then sends progress information and feedback back to the server. Based on this information, the server re-evaluates and updates the training plan, preparing for the next improvement cycle.

[0056] (Example 1)

[0057] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server," and the smart device 14 will be referred to as the "terminal."

[0058] Companies are required to improve employee adaptability and reduce employee turnover while simultaneously providing tailored training programs to enhance overall organizational productivity. Traditional methods have been time-consuming and inefficient in developing individualized training programs based on employee characteristics, and have lacked effective feedback mechanisms for monitoring adaptation progress. To address these challenges, the development of a system that efficiently analyzes employee information and provides appropriate training programs is necessary.

[0059] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0060] In this invention, the server includes means for using a generating AI model to analyze the characteristics of an employee based on acquired employee information, means for generating a training plan suitable for the employee using prompt statements based on the analyzed characteristics, and means for notifying the generated training plan. This makes it possible to quickly provide individualized training plans that correspond to the characteristics of the employee, and to continuously optimize the training plan based on progress information.

[0061] "Employee information" refers to data necessary to analyze the characteristics of an employee, such as their work history, personality assessment results, and existing work experience.

[0062] "Generative AI models" refer to artificial intelligence technology that uses machine learning algorithms to analyze the characteristics of employees and generate appropriate training plans.

[0063] A "prompt message" refers to a sentence that, when input into a generative AI model, serves as an instruction to generate a training plan based on the characteristics of the employee.

[0064] A "development policy" refers to a plan or program that promotes individually optimized growth and adaptation for each employee.

[0065] "Progress information" refers to data showing the results and status of activities based on employee training policies, and is used to measure the effectiveness of training policies.

[0066] "Means of notification" refers to methods and techniques for communicating the generated training policies to employees and other relevant parties.

[0067] "Feedback" refers to the information used to review and optimize training policies by incorporating the progress and results after their implementation into the system.

[0068] This invention is a system that supports employees' adaptation to the workplace and provides training plans tailored to the employee's characteristics. Specifically, the user first inputs the employee's information into a terminal. This information includes the employee's work history, personality assessment results, and existing work experience. The terminal then transmits this information to a server.

[0069] The server uses a generative AI model to analyze the characteristics of employees. During this process, the AI ​​model generates an appropriate training plan based on prompts. The AI ​​model is designed to find the optimal training program for each employee by comparing it against historical datasets.

[0070] Based on the analysis results, the server generates a training plan tailored to the employee and sends it to the terminal. The terminal notifies the user of this training plan and incorporates it into workplace training activities. The notification is displayed on the terminal screen and communicated in the form of an alert or message.

[0071] As a concrete example, when a new employee joins the company, the user inputs information about that employee. The server inputs a prompt message into the AI ​​model, such as, "Based on the new employee's personality assessment results, please suggest the optimal training plan." If the result determines that the employee is highly sociable but has issues with time management, the server provides a training plan such as, "Have the employee participate in a team-building workshop and take a time management course."

[0072] Furthermore, the server continuously collects employee progress information, forming a feedback loop. This process allows for regular review and optimization of training policies. As a result, the organization has a system that effectively promotes employee adaptation and skill development.

[0073] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0074] Step 1:

[0075] The user enters the employee's information into the terminal. This information includes the employee's work history, personality assessment results, and existing work experience. Specifically, the user enters the required information into a dedicated form and clicks the "Submit" button. This action saves the entered information to the terminal.

[0076] Step 2:

[0077] The terminal packets the employee information sent by the user and sends it to the server using a secure communication protocol (e.g., SSL / TLS). The input is the employee information obtained in step 1, and the output is encrypted information packets. This process is important to ensure the protection of information and reliable communication.

[0078] Step 3:

[0079] The server decodes the received employee information packets and invokes a generating AI model. The input is the decoded employee information, and the output is the result of the AI ​​model's analysis of the characteristics. In this step, the server prompts the AI ​​model with sentences to analyze the information and executes machine learning algorithms. This operation clearly evaluates the characteristics of the employees.

[0080] Step 4:

[0081] Based on the analysis results, the server generates a training plan suitable for the employee using prompt messages. The input is the analysis results of the characteristics obtained in step 3, and the output is a customized training plan for the employee. The server then formulates a concrete training program based on the data output by the generated AI model.

[0082] Step 5:

[0083] The server sends the generated training plan to the terminal. The input is the training plan generated in step 4, and the output is a notification message to the terminal. Specifically, the server packages the training plan in JSON format and sends the data to the terminal.

[0084] Step 6:

[0085] The terminal analyzes the received training plan and notifies the user. The input is the training plan data received from the server, and the output is a notification on the user interface. In actual operation, the terminal displays a pop-up notification or screen message to inform the user of new training activities.

[0086] Step 7:

[0087] Users carry out activities based on training policies and input progress information into the device. The input consists of the results of the activities performed and observed progress information, and the output indicates that this information is being stored on the device. Through this process, users continuously record the effectiveness of their training activities.

[0088] Step 8:

[0089] The terminal sends user-entered progress information to the server, integrating it into the system's overall feedback loop. The input is progress information, and the output is the feedback data sent to the server. This step contributes to the continuous optimization of training strategies.

[0090] (Application Example 1)

[0091] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server," and the smart device 14 will be referred to as the "terminal."

[0092] It is difficult to provide optimal developmental support tailored to individual characteristics not only in the workplace but also in the home environment, and there is a particular problem of insufficient support for learning and skill improvement in daily life.

[0093] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0094] In this invention, the server includes means for acquiring information about employees, means for analyzing the characteristics of employees based on the acquired information, means for generating a training plan suitable for the employee based on the analyzed characteristics, and means for providing a plan to support the individual's learning and ability improvement in the home environment. This enables individualized and effective training support in the home.

[0095] "Employee information" refers to basic attribute data about the employee, such as work history, personality assessment results, and existing work experience.

[0096] "Means for analyzing characteristics" refers to a function that uses machine learning algorithms to perform a process of evaluating the personality and characteristics of employees.

[0097] "Means for generating training policies" refers to a function that executes the process of formulating training plans suitable for employees based on analysis results.

[0098] "Means for notifying training policies" refers to a function that sends the generated training policies to employees or managers, providing information to help them incorporate them into actual training activities.

[0099] "Means of providing plans to support individual learning and skill development in the home environment" refers to a function that provides individuals with optimal plans based on their characteristics in order to efficiently learn and improve their skills at home.

[0100] This invention is a system designed to support individual learning and skill development even in a home environment. To realize this application, a program is needed to acquire employee information and analyze their characteristics based on that information. The program includes a client application written in Python, and data is sent and received via a server-side API using Flask. Furthermore, the AI ​​model uses either TENSORFLOW® or PyTorch to perform the characteristic analysis.

[0101] Information about employees obtained from their devices is transmitted to a server via the internet, where an AI model analyzes the data. This generates a training plan best suited to that individual. The generated training plan is then transmitted back to the device and presented to the user as an individualized training plan. This plan provides concrete support for daily learning activities and, if necessary, offers methods such as relaxation exercises and meditation.

[0102] For example, if an individual wants to improve their stress management, the server can suggest training methods such as "deep breathing exercises" or "meditation sessions." These suggestions are based on the user's characteristics and support more effective learning and growth. By having the user input a prompt such as, "I am a 28-year-old introverted software engineer. Please provide a personalized plan that will help me grow my career," the AI ​​model can generate an optimal plan tailored to that individual's needs.

[0103] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0104] Step 1:

[0105] Users input their information using a terminal. This information includes their personal work history, personality assessment results, and information about specific skills. The entered data is converted into JSON format.

[0106] Step 2:

[0107] The terminal sends the information entered by the user to the server. The server receives this data via an HTTP request and prepares it for analysis. At this point, the input is user information in JSON format, and the output is a dataset in the format required for analysis.

[0108] Step 3:

[0109] The server inputs the received data into a generating AI model. This model uses either TensorFlow or PyTorch to analyze user characteristics. Based on the input data, the AI ​​model performs multidimensional data calculations to evaluate user characteristics. The output of this step is a user-optimized training plan.

[0110] Step 4:

[0111] The server generates individual training plans based on the training policies obtained from the AI ​​model. These plans may include relaxation exercises aimed at stress management. Specifically, the process involves setting up suggested activities corresponding to the analysis results.

[0112] Step 5:

[0113] The server sends the generated training plan to the terminal. The terminal displays this plan to the user and provides information in an actionable format. Based on the on-screen instructions, the user can incorporate the suggested exercises and learning activities into their daily life.

[0114] Step 6:

[0115] Users perform activities according to a given training plan and input their progress information into a terminal. The terminal sends this progress data to a server, which is used to update and re-evaluate future training policies. The input is the user's progress information, and the output is a dataset prepared for the next analysis.

[0116] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0117] This invention is a system for supporting appropriate employee training. By analyzing the characteristics and emotions of employees and appropriately adjusting training policies, it enables better adaptation in the workplace. This system comprehensively grasps the personality traits and emotional state of employees and proposes training tailored to their individual needs.

[0118] Specifically, the user first enters basic information about the employee into the terminal. This information includes the employee's basic profile, work history, and past performance evaluations. The terminal then sends this information to the server.

[0119] The server uses an AI model based on the received information to analyze the employee's personality traits. During this process, it can also acquire the employee's emotional data in real time using its built-in emotion engine.

[0120] For example, through an interface accessed from a terminal, the system monitors video interviews, facial expressions during daily work, voice tone, etc., and an emotional engine automatically analyzes the employee's emotional state. The resulting emotional data is quantified as happiness, stress levels, and motivation.

[0121] Considering this emotional and personality trait data, the server generates a training plan tailored to the employee. This training plan may include, for example, manageable task management for employees with low motivation, or regular mental resource support for employees with low stress tolerance.

[0122] The server sends the generated training plan to the terminal, and the user reviews and implements the plan. Progress data and further emotional information are continuously collected, leading to improvements across the entire system.

[0123] This system provides training policies that take into account both the mental state and job performance capabilities of employees, enabling efficient and sustainable human resource management in the workplace.

[0124] The following describes the processing flow.

[0125] Step 1:

[0126] The user enters the employee's basic information into the terminal. This information includes name, work experience, and personality assessment results. The terminal then prepares to send this data to the server in a secure format (e.g., encrypted JSON).

[0127] Step 2:

[0128] The terminal sends the entered employee data to the server. The server receives the data and stores it in the database in the appropriate format.

[0129] Step 3:

[0130] The server uses an AI model to analyze incoming data. It evaluates the employee's attributes (e.g., communication skills, problem-solving ability) and creates a profile. Furthermore, an emotion engine analyzes the employee's emotional state in real time from their facial expressions and voice input data.

[0131] Step 4:

[0132] The server comprehensively evaluates the characteristics and emotional data of the employees obtained and generates individually tailored training plans. For example, if the emotional data indicates stress, it will include a plan to strengthen mental health support.

[0133] Step 5:

[0134] The server generates a training plan and sends it to the terminal, notifying the user. The user can then review this information and incorporate it into their actual training plan.

[0135] Step 6:

[0136] Users take actions based on the training plan, and data is collected to evaluate their progress and effectiveness. The emotion engine continues to monitor the emotion data and records new feedback as needed.

[0137] Step 7:

[0138] The terminal sends progress data and emotional feedback back to the server. The server uses this information to dynamically adjust training policies and prepare for the next cycle to optimize the employee's growth and workplace adaptation.

[0139] (Example 2)

[0140] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server," and the smart device 14 will be referred to as the "terminal."

[0141] In today's workplace environment, accurately understanding the characteristics and emotional states of employees and providing training plans tailored to their individual needs is a challenging task. In particular, traditional methods often fail to adequately address the needs of individual employees, making it difficult to improve their adaptation and work efficiency.

[0142] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0143] In this invention, the server includes means for analyzing and acquiring characteristic data from acquired employee information, means for acquiring the employee's emotional state using video interview and audio data, and means for adjusting the training policy in real time based on the acquired emotional state data and characteristic data. This makes it possible to generate and implement individualized and dynamic training policies based on the employee's characteristics and emotions.

[0144] An "employee" refers to an individual who is employed by a specific company or organization and performs duties for that company or organization.

[0145] An "information processing device" refers to a computer system used to analyze collected data and process the necessary information.

[0146] A "generative artificial intelligence model" refers to an algorithm that uses machine learning and other AI technologies to generate output that is suited to a specific purpose.

[0147] "Characteristic data" refers to data related to an employee's personality, work performance abilities, etc., and serves as the basis for individual analyses.

[0148] A "training policy" refers to a plan that outlines the optimal education and training guidelines tailored to the characteristics and circumstances of each employee.

[0149] A "display device" refers to a physical interface used to visually display output from a computer or other information processing device.

[0150] "Emotional state" refers to data that indicates an employee's momentary emotions, and includes indicators such as happiness, stress levels, and motivation.

[0151] "Progress data" refers to information about the results and processes achieved by employees during the course of performing their work.

[0152] This invention relates to a system for analyzing the characteristics and emotional state of employees and providing appropriate training plans. This system supports better workplace adaptation by addressing the individual needs of employees.

[0153] The user first enters basic information about the employee into the terminal. This information includes name, age, work history, and past performance evaluations. This allows the user to smoothly input data via a graphical user interface (GUI).

[0154] The terminal sends this information to the information processing device, which then forwards it to the server. The protocol used here is, for example, HTTPS, to maintain data integrity and security.

[0155] The server activates an AI model based on the received information to analyze the employee's personality traits. Natural language processing technology is used for the analysis, processing work history and performance data. The server also implements an emotion engine to acquire emotional states in real time through video interview footage and audio data. This data is quantified as happiness, stress levels, and motivation.

[0156] Based on this trait and emotional data, the server uses a generative AI model to generate appropriate training plans. These generated plans are tailored to individual employees and aim to improve work efficiency. The plans may include manageable task management and regular mental support.

[0157] For example, if an employee exhibits a high stress level, emotional data can detect this and generate a training plan that suggests special task management to reduce the burden. This helps improve their ability to adapt to the workplace.

[0158] Examples of prompts for the generating AI model include, "Based on the employee's recent performance evaluation, analyze their stress level and motivation, and propose a suitable training plan," and "Analyze the video interview recording and report on changes in the employee's emotional state." This ensures that the generated training plan is more relevant to the employee's current situation.

[0159] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0160] Step 1:

[0161] The user enters the employee's basic information into the terminal. This information includes name, age, work history, and past performance evaluations. The data is entered via a GUI on the terminal and transmitted to the server while maintaining data integrity. This input serves as the basis for subsequent analysis processes.

[0162] Step 2:

[0163] The terminal transmits the entered employee information to the information processing device. HTTPS, a secure communication protocol, is used for this transmission. The input data is structured and stored in a database, making it analyzable. The information is then received by the server as output.

[0164] Step 3:

[0165] The server analyzes employee characteristic data from the received data. Using an AI model and natural language processing technology, it analyzes work history and evaluation information to extract personality traits. Employee information is used as input, and characteristic data is output.

[0166] Step 4:

[0167] The server runs an emotion engine to analyze video interviews and audio data to obtain emotional states. Using video analysis technology and audio analysis algorithms, it quantifies happiness, stress levels, and motivation. The input is the employee's video and audio, and the output is quantified emotional data.

[0168] Step 5:

[0169] The server uses a generative AI model that combines trait data and emotion data to generate individual training strategies. The AI ​​analyzes prompt text to derive the optimal training strategy. Trait data and emotion data are used as input, and the output is a specific training strategy.

[0170] Step 6:

[0171] The server sends the generated training plan to the terminal. The history of generated plans is recorded in the database. The input is the training plan, and the output is the training plan displayed on the terminal.

[0172] Step 7:

[0173] The user checks the training policy on their terminal and then puts it into action. Here, the user refers to the training policy to create a specific action plan, and plans work adjustments and training programs. The input is the training policy, and the output is the implementation based on the user's judgment.

[0174] Step 8:

[0175] The terminal sends employee progress data and new emotional information to the server. This creates a continuous feedback loop in the system, which helps optimize the next training plan. The input is progress data, and the output is further data analysis on the server.

[0176] (Application Example 2)

[0177] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as the "server," and the smart device 14 will be referred to as the "terminal."

[0178] In today's work environment, there is a need to fully understand the personality and emotional state of employees and provide individually optimized training plans. However, traditional systems are insufficient in generating flexible training plans based on the characteristics and emotions of employees, making it difficult to promote appropriate job adaptation. Furthermore, there are problems in providing optimal support tailored to an individual's emotional state within the home environment.

[0179] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0180] In this invention, the server includes a device for acquiring employee information, a device for analyzing employee characteristics based on the acquired employee information, a device for generating a training plan suitable for the employee based on the analyzed characteristics, a device for notifying the generated training plan, and a device for a machine used in the home environment to analyze an individual's emotional state and optimize their response. This enables personalized support in the workplace and home environment, facilitating appropriate training and job adaptation.

[0181] A "device for acquiring employee information" is a device equipped with the function of collecting basic personal information, work history, and performance data about employees.

[0182] A "device for analyzing characteristics" is a device that uses an AI model to analyze characteristics such as personality and emotions based on collected information about employees.

[0183] A "device for generating training policies" is a device that automatically formulates training and guidance policies tailored to each individual, taking into account the characteristics of the analyzed employees.

[0184] A "notification device" is a device that has the function of informing the user of the generated training plan.

[0185] "Machines used in the home environment" refers to devices and robots installed to support household activities that can analyze and respond to an individual's emotional state.

[0186] The system for implementing this invention mainly consists of a data acquisition device, an analysis device, a training policy generation device, a notification device, and a machine used in the home environment. First, the user acquires basic information about the employee from a dedicated terminal. This terminal collects the employee's basic profile, work history, and evaluation data and sends it to a server. Based on this information, the server uses an AI model to analyze the employee's personality and emotional characteristics.

[0187] During the analysis, the server utilizes an emotion engine to perform real-time emotional state analysis. Specifically, it uses software that captures facial expressions and voice tone through cameras and microphones, and quantifies happiness levels and stress levels. This process requires highly accurate image recognition and voice analysis technologies, and for example, NEC's facial recognition technology and Google Cloud's natural language processing tools may be used.

[0188] Based on employee characteristic and emotional data, the server generates an individually tailored training plan. This plan is then communicated to the user via their terminal. The plan is designed to help employees adapt to their jobs, maintaining motivation and reducing stress.

[0189] Furthermore, for machines used in a home environment, it is possible to optimize responses according to the individual's emotional state. For example, even after an employee returns home, their emotional state can be assessed, and the machine can be instructed to provide support such as playing relaxing music or adjusting the lighting.

[0190] For example, if an employee may be experiencing stress, the following prompt can be used to take appropriate action: "A family member may be experiencing stress. What activities can be done to help them relax?" In this way, the system achieves efficient and sustainable talent management through individually optimized training and support.

[0191] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0192] Step 1:

[0193] The user enters the employee's basic information using a terminal. Specifically, a form for entering name, age, work history, performance data, etc., is displayed on the terminal. After input, this information is sent from the terminal to the server. The input in this step is the employee's basic information, and the output is a dataset sent to the server.

[0194] Step 2:

[0195] The server analyzes the received employee information and uses an AI model to identify personality traits. Specifically, it performs analysis based on natural language processing techniques and a database to extract the employee's personality pattern. The input for this step is the dataset sent in step 1, and the output is personality trait data.

[0196] Step 3:

[0197] The server utilizes an emotion engine to analyze the employee's emotional state in real time. Specifically, it analyzes video and audio data from cameras and microphones connected to the terminal to quantify happiness and stress levels. This process uses deep learning technology to achieve facial expression recognition and speech recognition. The input for this step is video and audio data, and the output is numerical data of emotional state.

[0198] Step 4:

[0199] The server generates a suitable training plan based on the employee's personality traits and emotional data. Specifically, it uses a generating AI model to simulate motivation improvement measures and stress reduction measures, and constructs the optimal approach. The input for this step is the output data from steps 2 and 3, and the output is a training plan proposal.

[0200] Step 5:

[0201] The server notifies the terminal of the training policy, and the user confirms it. Specifically, the terminal's notification function is used to display the proposed policy to the user, prompting them to approve or revise it. The input for this step is the proposed training policy document, and the output is a notification message to the user.

[0202] Step 6:

[0203] Machines used in the home environment execute responses based on emotional states, using policies generated on a server. For example, they might play music or adjust lighting to help an individual relax. The input for this step is the nurturing policy from step 4, and the output is the specific household support actions performed.

[0204] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0205] Data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of data generation model 58 is ChatGPT (registered trademark) (Internet search).<URL: https: / / openai.com / blog / chatgpt> ), Gemini (registered trademark) (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0206] In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and the specific processing may also be performed by the smart device 14.

[0207] [Second Embodiment]

[0208] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0209] As shown in Figure 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0210] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0211] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication interface 44. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, and camera 42 are also connected to the bus 52.

[0212] The microphone 238 receives voice signals from the user 20 and receives instructions from the user 20. The microphone 238 captures the voice signals from the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0213] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the area around the user 20 (for example, an imaging range defined by a field of view equivalent to the width of a typical healthy person's field of vision).

[0214] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0215] Figure 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Figure 4, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0216] The specific processing program 56 is an example of a "program" relating to the technology of this disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0217] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0218] In the smart glasses 214, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0219] Next, the identification processing performed by the identification processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the smart glasses 214 will be referred to as the "terminal".

[0220] This invention is a system designed to support employees' adaptation to the workplace. This system has the function of analyzing the characteristics of employees and providing individually optimized training plans.

[0221] Specifically, the user first enters basic information about the employee. This information includes the employee's work history, personality assessment results, and existing work experience. The terminal that receives the information then sends it to the server.

[0222] The server utilizes AI models to perform analysis based on the information it receives. This analysis includes using machine learning algorithms to evaluate the personality and characteristics of employees and identify appropriate training methods. Based on the results of this analysis, a customized training plan is generated for each employee.

[0223] For example, if a server assesses an employee as having low stress tolerance, it may be necessary to schedule mental health support meetings and emphasize feedback.

[0224] The generated training plan is sent from the server to the terminal and notified to the user, allowing it to be immediately reflected in actual training activities in the workplace.

[0225] The feedback loop in this process is further enhanced by continuously collecting progress information and allowing the entire system to learn and optimize. Because users can set more detailed training plans based on the collected progress data, organizations can continue to provide an environment that prevents employee turnover and maximizes their potential.

[0226] The following describes the processing flow.

[0227] Step 1:

[0228] The user enters basic information about the employee into the terminal. This information includes name, age, work history, and personality assessment results. The terminal formats this data and prepares it according to the necessary security protocols before sending it to the server.

[0229] Step 2:

[0230] The terminal sends the formatted employee dataset to the server. The data is encrypted and securely transferred over the network.

[0231] Step 3:

[0232] The server interprets the received data and converts it into a format suitable as input for the AI ​​model. Data preprocessing may include imputing missing values ​​and filtering outliers.

[0233] Step 4:

[0234] The server performs characteristic analysis using an AI model. Through machine learning algorithms, it scores the employee's personality, stress tolerance, interpersonal skills, and other characteristics, and generates a profile.

[0235] Step 5:

[0236] Based on the analysis results, the server generates training plans tailored to each employee's characteristics. For example, an employee with low stress tolerance might be offered a training style that emphasizes mental support.

[0237] Step 6:

[0238] The server generates a training plan and sends it to the terminal. The terminal displays this information to the user, and the training manager can review its contents.

[0239] Step 7:

[0240] Based on the training plan received by the user, the actual training activities are planned and implemented. Progress and feedback are collected regularly and entered into the system as feedback data as needed.

[0241] Step 8:

[0242] The device then sends progress information and feedback back to the server. Based on this information, the server re-evaluates and updates the training plan, preparing for the next improvement cycle.

[0243] (Example 1)

[0244] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server" and the smart glasses 214 will be referred to as the "terminal".

[0245] Companies are required to improve employee adaptability and reduce employee turnover while simultaneously providing tailored training programs to enhance overall organizational productivity. Traditional methods have been time-consuming and inefficient in developing individualized training programs based on employee characteristics, and have lacked effective feedback mechanisms for monitoring adaptation progress. To address these challenges, the development of a system that efficiently analyzes employee information and provides appropriate training programs is necessary.

[0246] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0247] In this invention, the server includes means for using a generating AI model to analyze the characteristics of an employee based on acquired employee information, means for generating a training plan suitable for the employee using prompt statements based on the analyzed characteristics, and means for notifying the generated training plan. This makes it possible to quickly provide individualized training plans that correspond to the characteristics of the employee, and to continuously optimize the training plan based on progress information.

[0248] "Employee information" refers to data necessary to analyze the characteristics of an employee, such as their work history, personality assessment results, and existing work experience.

[0249] "Generative AI models" refer to artificial intelligence technology that uses machine learning algorithms to analyze the characteristics of employees and generate appropriate training plans.

[0250] A "prompt message" refers to a sentence that, when input into a generative AI model, serves as an instruction to generate a training plan based on the characteristics of the employee.

[0251] A "development policy" refers to a plan or program that promotes individually optimized growth and adaptation for each employee.

[0252] "Progress information" refers to data showing the results and status of activities based on employee training policies, and is used to measure the effectiveness of training policies.

[0253] "Means of notification" refers to methods and techniques for communicating the generated training policies to employees and other relevant parties.

[0254] "Feedback" refers to the information used to review and optimize training policies by incorporating the progress and results after their implementation into the system.

[0255] This invention is a system that supports employees' adaptation to the workplace and provides training plans tailored to the employee's characteristics. Specifically, the user first inputs the employee's information into a terminal. This information includes the employee's work history, personality assessment results, and existing work experience. The terminal then transmits this information to a server.

[0256] The server uses a generative AI model to analyze the characteristics of employees. During this process, the AI ​​model generates an appropriate training plan based on prompts. The AI ​​model is designed to find the optimal training program for each employee by comparing it against historical datasets.

[0257] Based on the analysis results, the server generates a training plan tailored to the employee and sends it to the terminal. The terminal notifies the user of this training plan and incorporates it into workplace training activities. The notification is displayed on the terminal screen and communicated in the form of an alert or message.

[0258] As a concrete example, when a new employee joins the company, the user inputs information about that employee. The server inputs a prompt message into the AI ​​model, such as, "Based on the new employee's personality assessment results, please suggest the optimal training plan." If the result determines that the employee is highly sociable but has issues with time management, the server provides a training plan such as, "Have the employee participate in a team-building workshop and take a time management course."

[0259] Furthermore, the server continuously collects employee progress information, forming a feedback loop. This process allows for regular review and optimization of training policies. As a result, the organization has a system that effectively promotes employee adaptation and skill development.

[0260] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0261] Step 1:

[0262] The user enters the employee's information into the terminal. This information includes the employee's work history, personality assessment results, and existing work experience. Specifically, the user enters the required information into a dedicated form and clicks the "Submit" button. This action saves the entered information to the terminal.

[0263] Step 2:

[0264] The terminal packets the employee information sent by the user and sends it to the server using a secure communication protocol (e.g., SSL / TLS). The input is the employee information obtained in step 1, and the output is encrypted information packets. This process is important to ensure the protection of information and reliable communication.

[0265] Step 3:

[0266] The server decodes the received employee information packets and invokes a generating AI model. The input is the decoded employee information, and the output is the result of the AI ​​model's analysis of the characteristics. In this step, the server prompts the AI ​​model with sentences to analyze the information and executes machine learning algorithms. This operation clearly evaluates the characteristics of the employees.

[0267] Step 4:

[0268] Based on the analysis results, the server generates a training plan suitable for the employee using prompt messages. The input is the analysis results of the characteristics obtained in step 3, and the output is a customized training plan for the employee. The server then formulates a concrete training program based on the data output by the generated AI model.

[0269] Step 5:

[0270] The server sends the generated training plan to the terminal. The input is the training plan generated in step 4, and the output is a notification message to the terminal. Specifically, the server packages the training plan in JSON format and sends the data to the terminal.

[0271] Step 6:

[0272] The terminal analyzes the received training plan and notifies the user. The input is the training plan data received from the server, and the output is a notification on the user interface. In actual operation, the terminal displays a pop-up notification or screen message to inform the user of new training activities.

[0273] Step 7:

[0274] Users carry out activities based on training policies and input progress information into the device. The input consists of the results of the activities performed and observed progress information, and the output indicates that this information is being stored on the device. Through this process, users continuously record the effectiveness of their training activities.

[0275] Step 8:

[0276] The terminal sends user-entered progress information to the server, integrating it into the system's overall feedback loop. The input is progress information, and the output is the feedback data sent to the server. This step contributes to the continuous optimization of training strategies.

[0277] (Application Example 1)

[0278] Next, Application Example 1 will be described. In the following description, the data processing device 12 is referred to as a "server", and the smart glasses 214 are referred to as a "terminal".

[0279] It is difficult to achieve optimal growth support according to an individual's characteristics not only in the workplace environment but also in the home environment. In particular, there is a problem that support for learning and ability improvement in daily life is insufficient.

[0280] 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.

[0281] In this invention, the server includes means for acquiring information of an employee, means for analyzing characteristics based on the acquired employee information, means for generating a growth policy suitable for the employee based on the analyzed characteristics, and means for providing a plan for supporting an individual's learning and ability improvement in the home environment. As a result, individual and effective growth support in the home becomes possible.

[0282] The "information of an employee" is basic attribute data regarding the employee, such as work history, personality diagnosis results, and existing work experience.

[0283] The "means for analyzing characteristics" is a function for executing a process of evaluating the personality and characteristics of an employee using a machine learning algorithm.

[0284] The "means for generating a growth policy" is a function for executing a process of formulating a growth plan suitable for the employee based on the analysis result.

[0285] The "means for notifying the growth policy" is a function for transmitting the generated growth policy to the employee or the administrator and providing information for incorporating it into actual growth activities.

[0286] The means of "providing a plan for supporting an individual's learning and skill improvement in a home environment" is a function that provides an optimal plan based on characteristics for an individual to efficiently learn and improve skills at home.

[0287] The present invention is a system for supporting an individual's learning and skill improvement even in a home environment. To realize this application example, a program for acquiring information of an employee and analyzing characteristics based on it is required. The program includes a client application using Python and performs data transmission and reception via a server-side API using Flask. Also, TensorFlow or PyTorch is used for the AI model to perform analysis of characteristics.

[0288] The information of the employee acquired from the terminal is transmitted to the server via the Internet, and the AI model on the server analyzes the data. Thereby, a cultivation policy most suitable for that individual is generated. The generated cultivation policy is transmitted to the terminal again and presented to the user as an individual cultivation plan. This plan specifically supports daily learning activities and provides methods such as relaxation exercises and meditation if necessary.

[0289] As a specific example, when an individual wishes to improve stress management, the server can propose cultivation policies such as "deep breathing exercises" and "meditation sessions". This proposal is based on the characteristics of the user and supports more effective learning and growth. By the user inputting a prompt sentence such as "I am a 28-year-old introverted software engineer. Please provide an individual plan that helps with career growth.", the AI model can generate an optimal plan according to the individual needs.

[0290] The flow of specific processing in Application Example 1 will be described using FIG. 12.

[0291] Step 1:

[0292] Users input their information using a terminal. This information includes their personal work history, personality assessment results, and information about specific skills. The entered data is converted into JSON format.

[0293] Step 2:

[0294] The terminal sends the information entered by the user to the server. The server receives this data via an HTTP request and prepares it for analysis. At this point, the input is user information in JSON format, and the output is a dataset in the format required for analysis.

[0295] Step 3:

[0296] The server inputs the received data into a generating AI model. This model uses either TensorFlow or PyTorch to analyze user characteristics. Based on the input data, the AI ​​model performs multidimensional data calculations to evaluate user characteristics. The output of this step is a user-optimized training plan.

[0297] Step 4:

[0298] The server generates individual training plans based on the training policies obtained from the AI ​​model. These plans may include relaxation exercises aimed at stress management. Specifically, the process involves setting up suggested activities corresponding to the analysis results.

[0299] Step 5:

[0300] The server sends the generated training plan to the terminal. The terminal displays this plan to the user and provides information in an actionable format. Based on the on-screen instructions, the user can incorporate the suggested exercises and learning activities into their daily life.

[0301] Step 6:

[0302] The user performs actual activities according to the given training plan and inputs the progress information into the terminal. The terminal transmits this progress data to the server to be used for updating and re-evaluating the future training guidelines. The input is the user's progress information, and the output is a dataset prepared for the next analysis.

[0303] Furthermore, an emotion engine for estimating the user's emotions may be combined. That is, the specific processing unit 290 may estimate the user's emotions using the emotion recognition model 59 and perform specific processing using the user's emotions.

[0304] The present invention is a system for assisting in the appropriate training of employees, which analyzes the characteristics and emotions of employees and enables better adaptation in the workplace by appropriately adjusting the training guidelines. This system comprehensively grasps the personality characteristics and emotional states of employees and proposes training according to individual needs.

[0305] Specifically, first, the user inputs basic information about the employee into the terminal. The information includes the employee's basic profile, work history, evaluation information obtained in the past, and the like. The terminal transmits this information to the server.

[0306] Based on the received information, the server uses an AI model to analyze the personality characteristics of the employee. At this time, the built-in emotion engine can be used to obtain the employee's emotion data in real time.

[0307] For example, through an interface accessed from the terminal, video interviews, expressions during daily work, voice tones, etc. are monitored, and the emotion engine automatically analyzes the emotional state of the employee. The obtained emotion data is quantified as happiness, stress level, and motivation.

[0308] Considering this emotional and personality trait data, the server generates a training plan tailored to the employee. This training plan may include, for example, manageable task management for employees with low motivation, or regular mental resource support for employees with low stress tolerance.

[0309] The server sends the generated training plan to the terminal, and the user reviews and implements the plan. Progress data and further emotional information are continuously collected, leading to improvements across the entire system.

[0310] This system provides training policies that take into account both the mental state and job performance capabilities of employees, enabling efficient and sustainable human resource management in the workplace.

[0311] The following describes the processing flow.

[0312] Step 1:

[0313] The user enters the employee's basic information into the terminal. This information includes name, work experience, and personality assessment results. The terminal then prepares to send this data to the server in a secure format (e.g., encrypted JSON).

[0314] Step 2:

[0315] The terminal sends the entered employee data to the server. The server receives the data and stores it in the database in the appropriate format.

[0316] Step 3:

[0317] The server uses an AI model to analyze incoming data. It evaluates the employee's attributes (e.g., communication skills, problem-solving ability) and creates a profile. Furthermore, an emotion engine analyzes the employee's emotional state in real time from their facial expressions and voice input data.

[0318] Step 4:

[0319] The server comprehensively evaluates the characteristics and emotional data of the employees obtained and generates individually tailored training plans. For example, if the emotional data indicates stress, it will include a plan to strengthen mental health support.

[0320] Step 5:

[0321] The server generates a training plan and sends it to the terminal, notifying the user. The user can then review this information and incorporate it into their actual training plan.

[0322] Step 6:

[0323] Users take actions based on the training plan, and data is collected to evaluate their progress and effectiveness. The emotion engine continues to monitor the emotion data and records new feedback as needed.

[0324] Step 7:

[0325] The terminal sends progress data and emotional feedback back to the server. The server uses this information to dynamically adjust training policies and prepare for the next cycle to optimize the employee's growth and workplace adaptation.

[0326] (Example 2)

[0327] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server" and the smart glasses 214 will be referred to as the "terminal".

[0328] In today's workplace environment, accurately understanding the characteristics and emotional states of employees and providing training plans tailored to their individual needs is a challenging task. In particular, traditional methods often fail to adequately address the needs of individual employees, making it difficult to improve their adaptation and work efficiency.

[0329] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0330] In this invention, the server includes means for analyzing and acquiring characteristic data from acquired employee information, means for acquiring the employee's emotional state using video interview and audio data, and means for adjusting the training policy in real time based on the acquired emotional state data and characteristic data. This makes it possible to generate and implement individualized and dynamic training policies based on the employee's characteristics and emotions.

[0331] An "employee" refers to an individual who is employed by a specific company or organization and performs duties for that company or organization.

[0332] An "information processing device" refers to a computer system used to analyze collected data and process the necessary information.

[0333] A "generative artificial intelligence model" refers to an algorithm that uses machine learning and other AI technologies to generate output that is suited to a specific purpose.

[0334] "Characteristic data" refers to data related to an employee's personality, work performance abilities, etc., and serves as the basis for individual analyses.

[0335] A "training policy" refers to a plan that outlines the optimal education and training guidelines tailored to the characteristics and circumstances of each employee.

[0336] A "display device" refers to a physical interface used to visually display output from a computer or other information processing device.

[0337] "Emotional state" refers to data that indicates an employee's momentary emotions, and includes indicators such as happiness, stress levels, and motivation.

[0338] "Progress data" refers to information about the results and processes achieved by employees during the course of performing their work.

[0339] This invention relates to a system for analyzing the characteristics and emotional state of employees and providing appropriate training plans. This system supports better workplace adaptation by addressing the individual needs of employees.

[0340] The user first enters basic information about the employee into the terminal. This information includes name, age, work history, and past performance evaluations. This allows the user to smoothly input data via a graphical user interface (GUI).

[0341] The terminal sends this information to the information processing device, which then forwards it to the server. The protocol used here is, for example, HTTPS, to maintain data integrity and security.

[0342] The server activates an AI model based on the received information to analyze the employee's personality traits. Natural language processing technology is used for the analysis, processing work history and performance data. The server also implements an emotion engine to acquire emotional states in real time through video interview footage and audio data. This data is quantified as happiness, stress levels, and motivation.

[0343] Based on this trait and emotional data, the server uses a generative AI model to generate appropriate training plans. These generated plans are tailored to individual employees and aim to improve work efficiency. The plans may include manageable task management and regular mental support.

[0344] For example, if an employee exhibits a high stress level, emotional data can detect this and generate a training plan that suggests special task management to reduce the burden. This helps improve their ability to adapt to the workplace.

[0345] Examples of prompts for the generating AI model include, "Based on the employee's recent performance evaluation, analyze their stress level and motivation, and propose a suitable training plan," and "Analyze the video interview recording and report on changes in the employee's emotional state." This ensures that the generated training plan is more relevant to the employee's current situation.

[0346] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0347] Step 1:

[0348] The user enters the employee's basic information into the terminal. This information includes name, age, work history, and past performance evaluations. The data is entered via a GUI on the terminal and transmitted to the server while maintaining data integrity. This input serves as the basis for subsequent analysis processes.

[0349] Step 2:

[0350] The terminal transmits the entered employee information to the information processing device. HTTPS, a secure communication protocol, is used for this transmission. The input data is structured and stored in a database, making it analyzable. The information is then received by the server as output.

[0351] Step 3:

[0352] The server analyzes employee characteristic data from the received data. Using an AI model and natural language processing technology, it analyzes work history and evaluation information to extract personality traits. Employee information is used as input, and characteristic data is output.

[0353] Step 4:

[0354] The server runs an emotion engine to analyze video interviews and audio data to obtain emotional states. Using video analysis technology and audio analysis algorithms, it quantifies happiness, stress levels, and motivation. The input is the employee's video and audio, and the output is quantified emotional data.

[0355] Step 5:

[0356] The server uses a generative AI model that combines trait data and emotion data to generate individual training strategies. The AI ​​analyzes prompt text to derive the optimal training strategy. Trait data and emotion data are used as input, and the output is a specific training strategy.

[0357] Step 6:

[0358] The server sends the generated training plan to the terminal. The history of generated plans is recorded in the database. The input is the training plan, and the output is the training plan displayed on the terminal.

[0359] Step 7:

[0360] The user checks the training policy on their terminal and then puts it into action. Here, the user refers to the training policy to create a specific action plan, and plans work adjustments and training programs. The input is the training policy, and the output is the implementation based on the user's judgment.

[0361] Step 8:

[0362] The terminal sends employee progress data and new emotional information to the server. This creates a continuous feedback loop in the system, which helps optimize the next training plan. The input is progress data, and the output is further data analysis on the server.

[0363] (Application Example 2)

[0364] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as the "server," and the smart glasses 214 will be referred to as the "terminal."

[0365] In today's work environment, there is a need to fully understand the personality and emotional state of employees and provide individually optimized training plans. However, traditional systems are insufficient in generating flexible training plans based on the characteristics and emotions of employees, making it difficult to promote appropriate job adaptation. Furthermore, there are problems in providing optimal support tailored to an individual's emotional state within the home environment.

[0366] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0367] In this invention, the server includes a device for acquiring employee information, a device for analyzing employee characteristics based on the acquired employee information, a device for generating a training plan suitable for the employee based on the analyzed characteristics, a device for notifying the generated training plan, and a device for a machine used in the home environment to analyze an individual's emotional state and optimize their response. This enables personalized support in the workplace and home environment, facilitating appropriate training and job adaptation.

[0368] A "device for acquiring employee information" is a device equipped with the function of collecting basic personal information, work history, and performance data about employees.

[0369] A "device for analyzing characteristics" is a device that uses an AI model to analyze characteristics such as personality and emotions based on collected information about employees.

[0370] A "device for generating training policies" is a device that automatically formulates training and guidance policies tailored to each individual, taking into account the characteristics of the analyzed employees.

[0371] A "notification device" is a device that has the function of informing the user of the generated training plan.

[0372] "Machines used in the home environment" refers to devices and robots installed to support household activities that can analyze and respond to an individual's emotional state.

[0373] The system for implementing this invention mainly consists of a data acquisition device, an analysis device, a training policy generation device, a notification device, and a machine used in the home environment. First, the user acquires basic information about the employee from a dedicated terminal. This terminal collects the employee's basic profile, work history, and evaluation data and sends it to a server. Based on this information, the server uses an AI model to analyze the employee's personality and emotional characteristics.

[0374] During the analysis, the server utilizes an emotion engine to perform real-time emotional state analysis. Specifically, it uses software that captures facial expressions and voice tone through cameras and microphones, and quantifies happiness levels and stress levels. This process requires highly accurate image recognition and voice analysis technologies, and for example, NEC's facial recognition technology and Google Cloud's natural language processing tools may be used.

[0375] Based on employee characteristic and emotional data, the server generates an individually tailored training plan. This plan is then communicated to the user via their terminal. The plan is designed to help employees adapt to their jobs, maintaining motivation and reducing stress.

[0376] Furthermore, for machines used in a home environment, it is possible to optimize responses according to the individual's emotional state. For example, even after an employee returns home, their emotional state can be assessed, and the machine can be instructed to provide support such as playing relaxing music or adjusting the lighting.

[0377] For example, if an employee may be experiencing stress, the following prompt can be used to take appropriate action: "A family member may be experiencing stress. What activities can be done to help them relax?" In this way, the system achieves efficient and sustainable talent management through individually optimized training and support.

[0378] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0379] Step 1:

[0380] The user enters the employee's basic information using a terminal. Specifically, a form for entering name, age, work history, performance data, etc., is displayed on the terminal. After input, this information is sent from the terminal to the server. The input in this step is the employee's basic information, and the output is a dataset sent to the server.

[0381] Step 2:

[0382] The server analyzes the received employee information and uses an AI model to identify personality traits. Specifically, it performs analysis based on natural language processing techniques and a database to extract the employee's personality pattern. The input for this step is the dataset sent in step 1, and the output is personality trait data.

[0383] Step 3:

[0384] The server utilizes an emotion engine to analyze the employee's emotional state in real time. Specifically, it analyzes video and audio data from cameras and microphones connected to the terminal to quantify happiness and stress levels. This process uses deep learning technology to achieve facial expression recognition and speech recognition. The input for this step is video and audio data, and the output is numerical data of emotional state.

[0385] Step 4:

[0386] The server generates a suitable training plan based on the employee's personality traits and emotional data. Specifically, it uses a generating AI model to simulate motivation improvement measures and stress reduction measures, and constructs the optimal approach. The input for this step is the output data from steps 2 and 3, and the output is a training plan proposal.

[0387] Step 5:

[0388] The server notifies the terminal of the training policy, and the user confirms it. Specifically, the terminal's notification function is used to display the proposed policy to the user, prompting them to approve or revise it. The input for this step is the proposed training policy document, and the output is a notification message to the user.

[0389] Step 6:

[0390] Machines used in the home environment execute responses based on emotional states, using policies generated on a server. For example, they might play music or adjust lighting to help an individual relax. The input for this step is the nurturing policy from step 4, and the output is the specific household support actions performed.

[0391] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0392] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). One example of data generation model 58 is ChatGPT (Internet search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0393] In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and the specific processing may also be performed by the smart glasses 214.

[0394] [Third Embodiment]

[0395] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0396] As shown in Figure 5, the data processing system 310 includes a data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0397] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0398] The headset terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a display 343. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and display 343 are also connected to the bus 52.

[0399] The microphone 238 receives voice signals from the user 20 and receives instructions from the user 20. The microphone 238 captures the voice signals from the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0400] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the area around the user 20 (for example, an imaging range defined by a field of view equivalent to the width of a typical healthy person's field of vision).

[0401] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0402] Figure 6 shows an example of the main functions of the data processing device 12 and the headset terminal 314. As shown in Figure 6, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0403] The specific processing program 56 is an example of a "program" relating to the technology of this disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0404] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0405] In the headset terminal 314, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0406] Next, the specific processing performed by the specific processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the headset terminal 314 will be referred to as the "terminal".

[0407] This invention is a system designed to support employees' adaptation to the workplace. This system has the function of analyzing the characteristics of employees and providing individually optimized training plans.

[0408] Specifically, the user first enters basic information about the employee. This information includes the employee's work history, personality assessment results, and existing work experience. The terminal that receives the information then sends it to the server.

[0409] The server utilizes AI models to perform analysis based on the information it receives. This analysis includes using machine learning algorithms to evaluate the personality and characteristics of employees and identify appropriate training methods. Based on the results of this analysis, a customized training plan is generated for each employee.

[0410] For example, if a server assesses an employee as having low stress tolerance, it may be necessary to schedule mental health support meetings and emphasize feedback.

[0411] The generated training plan is sent from the server to the terminal and notified to the user, allowing it to be immediately reflected in actual training activities in the workplace.

[0412] The feedback loop in this process is further enhanced by continuously collecting progress information and allowing the entire system to learn and optimize. Because users can set more detailed training plans based on the collected progress data, organizations can continue to provide an environment that prevents employee turnover and maximizes their potential.

[0413] The following describes the processing flow.

[0414] Step 1:

[0415] The user enters basic information about the employee into the terminal. This information includes name, age, work history, and personality assessment results. The terminal formats this data and prepares it according to the necessary security protocols before sending it to the server.

[0416] Step 2:

[0417] The terminal sends the formatted employee dataset to the server. The data is encrypted and securely transferred over the network.

[0418] Step 3:

[0419] The server interprets the received data and converts it into a format suitable as input for the AI ​​model. Data preprocessing may include imputing missing values ​​and filtering outliers.

[0420] Step 4:

[0421] The server performs characteristic analysis using an AI model. Through machine learning algorithms, it scores the employee's personality, stress tolerance, interpersonal skills, and other characteristics, and generates a profile.

[0422] Step 5:

[0423] Based on the analysis results, the server generates training plans tailored to each employee's characteristics. For example, an employee with low stress tolerance might be offered a training style that emphasizes mental support.

[0424] Step 6:

[0425] The server generates a training plan and sends it to the terminal. The terminal displays this information to the user, and the training manager can review its contents.

[0426] Step 7:

[0427] Based on the training plan received by the user, the actual training activities are planned and implemented. Progress and feedback are collected regularly and entered into the system as feedback data as needed.

[0428] Step 8:

[0429] The device then sends progress information and feedback back to the server. Based on this information, the server re-evaluates and updates the training plan, preparing for the next improvement cycle.

[0430] (Example 1)

[0431] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server," and the headset-type terminal 314 will be referred to as the "terminal."

[0432] Companies are required to improve employee adaptability and reduce employee turnover while simultaneously providing tailored training programs to enhance overall organizational productivity. Traditional methods have been time-consuming and inefficient in developing individualized training programs based on employee characteristics, and have lacked effective feedback mechanisms for monitoring adaptation progress. To address these challenges, the development of a system that efficiently analyzes employee information and provides appropriate training programs is necessary.

[0433] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0434] In this invention, the server includes means for using a generating AI model to analyze the characteristics of an employee based on acquired employee information, means for generating a training plan suitable for the employee using prompt statements based on the analyzed characteristics, and means for notifying the generated training plan. This makes it possible to quickly provide individualized training plans that correspond to the characteristics of the employee, and to continuously optimize the training plan based on progress information.

[0435] "Employee information" refers to data necessary to analyze the characteristics of an employee, such as their work history, personality assessment results, and existing work experience.

[0436] "Generative AI models" refer to artificial intelligence technology that uses machine learning algorithms to analyze the characteristics of employees and generate appropriate training plans.

[0437] A "prompt message" refers to a sentence that, when input into a generative AI model, serves as an instruction to generate a training plan based on the characteristics of the employee.

[0438] A "development policy" refers to a plan or program that promotes individually optimized growth and adaptation for each employee.

[0439] "Progress information" refers to data showing the results and status of activities based on employee training policies, and is used to measure the effectiveness of training policies.

[0440] "Means of notification" refers to methods and techniques for communicating the generated training policies to employees and other relevant parties.

[0441] "Feedback" refers to the information used to review and optimize training policies by incorporating the progress and results after their implementation into the system.

[0442] This invention is a system that supports employees' adaptation to the workplace and provides training plans tailored to the employee's characteristics. Specifically, the user first inputs the employee's information into a terminal. This information includes the employee's work history, personality assessment results, and existing work experience. The terminal then transmits this information to a server.

[0443] The server uses a generative AI model to analyze the characteristics of employees. During this process, the AI ​​model generates an appropriate training plan based on prompts. The AI ​​model is designed to find the optimal training program for each employee by comparing it against historical datasets.

[0444] Based on the analysis results, the server generates a training plan tailored to the employee and sends it to the terminal. The terminal notifies the user of this training plan and incorporates it into workplace training activities. The notification is displayed on the terminal screen and communicated in the form of an alert or message.

[0445] As a concrete example, when a new employee joins the company, the user inputs information about that employee. The server inputs a prompt message into the AI ​​model, such as, "Based on the new employee's personality assessment results, please suggest the optimal training plan." If the result determines that the employee is highly sociable but has issues with time management, the server provides a training plan such as, "Have the employee participate in a team-building workshop and take a time management course."

[0446] Furthermore, the server continuously collects employee progress information, forming a feedback loop. This process allows for regular review and optimization of training policies. As a result, the organization has a system that effectively promotes employee adaptation and skill development.

[0447] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0448] Step 1:

[0449] The user enters the employee's information into the terminal. This information includes the employee's work history, personality assessment results, and existing work experience. Specifically, the user enters the required information into a dedicated form and clicks the "Submit" button. This action saves the entered information to the terminal.

[0450] Step 2:

[0451] The terminal packets the employee information sent by the user and sends it to the server using a secure communication protocol (e.g., SSL / TLS). The input is the employee information obtained in step 1, and the output is encrypted information packets. This process is important to ensure the protection of information and reliable communication.

[0452] Step 3:

[0453] The server decodes the received employee information packets and invokes a generating AI model. The input is the decoded employee information, and the output is the result of the AI ​​model's analysis of the characteristics. In this step, the server prompts the AI ​​model with sentences to analyze the information and executes machine learning algorithms. This operation clearly evaluates the characteristics of the employees.

[0454] Step 4:

[0455] Based on the analysis results, the server generates a training plan suitable for the employee using prompt messages. The input is the analysis results of the characteristics obtained in step 3, and the output is a customized training plan for the employee. The server then formulates a concrete training program based on the data output by the generated AI model.

[0456] Step 5:

[0457] The server sends the generated training plan to the terminal. The input is the training plan generated in step 4, and the output is a notification message to the terminal. Specifically, the server packages the training plan in JSON format and sends the data to the terminal.

[0458] Step 6:

[0459] The terminal analyzes the received training plan and notifies the user. The input is the training plan data received from the server, and the output is a notification on the user interface. In actual operation, the terminal displays a pop-up notification or screen message to inform the user of new training activities.

[0460] Step 7:

[0461] Users carry out activities based on training policies and input progress information into the device. The input consists of the results of the activities performed and observed progress information, and the output indicates that this information is being stored on the device. Through this process, users continuously record the effectiveness of their training activities.

[0462] Step 8:

[0463] The terminal sends user-entered progress information to the server, integrating it into the system's overall feedback loop. The input is progress information, and the output is the feedback data sent to the server. This step contributes to the continuous optimization of training strategies.

[0464] (Application Example 1)

[0465] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server," and the headset-type terminal 314 will be referred to as the "terminal."

[0466] It is difficult to provide optimal developmental support tailored to individual characteristics not only in the workplace but also in the home environment, and there is a particular problem of insufficient support for learning and skill improvement in daily life.

[0467] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0468] In this invention, the server includes means for acquiring information about employees, means for analyzing the characteristics of employees based on the acquired information, means for generating a training plan suitable for the employee based on the analyzed characteristics, and means for providing a plan to support the individual's learning and ability improvement in the home environment. This enables individualized and effective training support in the home.

[0469] "Employee information" refers to basic attribute data about the employee, such as work history, personality assessment results, and existing work experience.

[0470] "Means for analyzing characteristics" refers to a function that uses machine learning algorithms to perform a process of evaluating the personality and characteristics of employees.

[0471] "Means for generating training policies" refers to a function that executes the process of formulating training plans suitable for employees based on analysis results.

[0472] "Means for notifying training policies" refers to a function that sends the generated training policies to employees or managers, providing information to help them incorporate them into actual training activities.

[0473] "Means of providing plans to support individual learning and skill development in the home environment" refers to a function that provides individuals with optimal plans based on their characteristics in order to efficiently learn and improve their skills at home.

[0474] This invention is a system designed to support individual learning and skill development even in a home environment. To realize this application, a program is needed to acquire employee information and analyze their characteristics based on that information. The program includes a client application written in Python, and data is sent and received via a server-side API using Flask. Furthermore, TensorFlow or PyTorch is used for the AI ​​model to perform the characteristic analysis.

[0475] Information about employees obtained from their devices is transmitted to a server via the internet, where an AI model analyzes the data. This generates a training plan best suited to that individual. The generated training plan is then transmitted back to the device and presented to the user as an individualized training plan. This plan provides concrete support for daily learning activities and, if necessary, offers methods such as relaxation exercises and meditation.

[0476] For example, if an individual wants to improve their stress management, the server can suggest training methods such as "deep breathing exercises" or "meditation sessions." These suggestions are based on the user's characteristics and support more effective learning and growth. By having the user input a prompt such as, "I am a 28-year-old introverted software engineer. Please provide a personalized plan that will help me grow my career," the AI ​​model can generate an optimal plan tailored to that individual's needs.

[0477] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0478] Step 1:

[0479] Users input their information using a terminal. This information includes their personal work history, personality assessment results, and information about specific skills. The entered data is converted into JSON format.

[0480] Step 2:

[0481] The terminal sends the information entered by the user to the server. The server receives this data via an HTTP request and prepares it for analysis. At this point, the input is user information in JSON format, and the output is a dataset in the format required for analysis.

[0482] Step 3:

[0483] The server inputs the received data into a generating AI model. This model uses either TensorFlow or PyTorch to analyze user characteristics. Based on the input data, the AI ​​model performs multidimensional data calculations to evaluate user characteristics. The output of this step is a user-optimized training plan.

[0484] Step 4:

[0485] The server generates individual training plans based on the training policies obtained from the AI ​​model. These plans may include relaxation exercises aimed at stress management. Specifically, the process involves setting up suggested activities corresponding to the analysis results.

[0486] Step 5:

[0487] The server sends the generated training plan to the terminal. The terminal displays this plan to the user and provides information in an actionable format. Based on the on-screen instructions, the user can incorporate the suggested exercises and learning activities into their daily life.

[0488] Step 6:

[0489] Users perform activities according to a given training plan and input their progress information into a terminal. The terminal sends this progress data to a server, which is used to update and re-evaluate future training policies. The input is the user's progress information, and the output is a dataset prepared for the next analysis.

[0490] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0491] This invention is a system for supporting appropriate employee training. By analyzing the characteristics and emotions of employees and appropriately adjusting training policies, it enables better adaptation in the workplace. This system comprehensively grasps the personality traits and emotional state of employees and proposes training tailored to their individual needs.

[0492] Specifically, the user first enters basic information about the employee into the terminal. This information includes the employee's basic profile, work history, and past performance evaluations. The terminal then sends this information to the server.

[0493] The server uses an AI model based on the received information to analyze the employee's personality traits. During this process, it can also acquire the employee's emotional data in real time using its built-in emotion engine.

[0494] For example, through an interface accessed from a terminal, the system monitors video interviews, facial expressions during daily work, voice tone, etc., and an emotional engine automatically analyzes the employee's emotional state. The resulting emotional data is quantified as happiness, stress levels, and motivation.

[0495] Considering this emotional and personality trait data, the server generates a training plan tailored to the employee. This training plan may include, for example, manageable task management for employees with low motivation, or regular mental resource support for employees with low stress tolerance.

[0496] The server sends the generated training plan to the terminal, and the user reviews and implements the plan. Progress data and further emotional information are continuously collected, leading to improvements across the entire system.

[0497] This system provides training policies that take into account both the mental state and job performance capabilities of employees, enabling efficient and sustainable human resource management in the workplace.

[0498] The following describes the processing flow.

[0499] Step 1:

[0500] The user enters the employee's basic information into the terminal. This information includes name, work experience, and personality assessment results. The terminal then prepares to send this data to the server in a secure format (e.g., encrypted JSON).

[0501] Step 2:

[0502] The terminal sends the entered employee data to the server. The server receives the data and stores it in the database in the appropriate format.

[0503] Step 3:

[0504] The server uses an AI model to analyze incoming data. It evaluates the employee's attributes (e.g., communication skills, problem-solving ability) and creates a profile. Furthermore, an emotion engine analyzes the employee's emotional state in real time from their facial expressions and voice input data.

[0505] Step 4:

[0506] The server comprehensively evaluates the characteristics and emotional data of the employees obtained and generates individually tailored training plans. For example, if the emotional data indicates stress, it will include a plan to strengthen mental health support.

[0507] Step 5:

[0508] The server generates a training plan and sends it to the terminal, notifying the user. The user can then review this information and incorporate it into their actual training plan.

[0509] Step 6:

[0510] Users take actions based on the training plan, and data is collected to evaluate their progress and effectiveness. The emotion engine continues to monitor the emotion data and records new feedback as needed.

[0511] Step 7:

[0512] The terminal sends progress data and emotional feedback back to the server. The server uses this information to dynamically adjust training policies and prepare for the next cycle to optimize the employee's growth and workplace adaptation.

[0513] (Example 2)

[0514] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server," and the headset-type terminal 314 will be referred to as the "terminal."

[0515] In today's workplace environment, accurately understanding the characteristics and emotional states of employees and providing training plans tailored to their individual needs is a challenging task. In particular, traditional methods often fail to adequately address the needs of individual employees, making it difficult to improve their adaptation and work efficiency.

[0516] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0517] In this invention, the server includes means for analyzing and acquiring characteristic data from acquired employee information, means for acquiring the employee's emotional state using video interview and audio data, and means for adjusting the training policy in real time based on the acquired emotional state data and characteristic data. This makes it possible to generate and implement individualized and dynamic training policies based on the employee's characteristics and emotions.

[0518] An "employee" refers to an individual who is employed by a specific company or organization and performs duties for that company or organization.

[0519] An "information processing device" refers to a computer system used to analyze collected data and process the necessary information.

[0520] A "generative artificial intelligence model" refers to an algorithm that uses machine learning and other AI technologies to generate output that is suited to a specific purpose.

[0521] "Characteristic data" refers to data related to an employee's personality, work performance abilities, etc., and serves as the basis for individual analyses.

[0522] A "training policy" refers to a plan that outlines the optimal education and training guidelines tailored to the characteristics and circumstances of each employee.

[0523] A "display device" refers to a physical interface used to visually display output from a computer or other information processing device.

[0524] "Emotional state" refers to data that indicates an employee's momentary emotions, and includes indicators such as happiness, stress levels, and motivation.

[0525] "Progress data" refers to information about the results and processes achieved by employees during the course of performing their work.

[0526] This invention relates to a system for analyzing the characteristics and emotional state of employees and providing appropriate training plans. This system supports better workplace adaptation by addressing the individual needs of employees.

[0527] The user first enters basic information about the employee into the terminal. This information includes name, age, work history, and past performance evaluations. This allows the user to smoothly input data via a graphical user interface (GUI).

[0528] The terminal sends this information to the information processing device, which then forwards it to the server. The protocol used here is, for example, HTTPS, to maintain data integrity and security.

[0529] The server activates an AI model based on the received information to analyze the employee's personality traits. Natural language processing technology is used for the analysis, processing work history and performance data. The server also implements an emotion engine to acquire emotional states in real time through video interview footage and audio data. This data is quantified as happiness, stress levels, and motivation.

[0530] Based on this trait and emotional data, the server uses a generative AI model to generate appropriate training plans. These generated plans are tailored to individual employees and aim to improve work efficiency. The plans may include manageable task management and regular mental support.

[0531] For example, if an employee exhibits a high stress level, emotional data can detect this and generate a training plan that suggests special task management to reduce the burden. This helps improve their ability to adapt to the workplace.

[0532] Examples of prompts for the generating AI model include, "Based on the employee's recent performance evaluation, analyze their stress level and motivation, and propose a suitable training plan," and "Analyze the video interview recording and report on changes in the employee's emotional state." This ensures that the generated training plan is more relevant to the employee's current situation.

[0533] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0534] Step 1:

[0535] The user enters the employee's basic information into the terminal. This information includes name, age, work history, and past performance evaluations. The data is entered via a GUI on the terminal and transmitted to the server while maintaining data integrity. This input serves as the basis for subsequent analysis processes.

[0536] Step 2:

[0537] The terminal transmits the entered employee information to the information processing device. HTTPS, a secure communication protocol, is used for this transmission. The input data is structured and stored in a database, making it analyzable. The information is then received by the server as output.

[0538] Step 3:

[0539] The server analyzes employee characteristic data from the received data. Using an AI model and natural language processing technology, it analyzes work history and evaluation information to extract personality traits. Employee information is used as input, and characteristic data is output.

[0540] Step 4:

[0541] The server runs an emotion engine to analyze video interviews and audio data to obtain emotional states. Using video analysis technology and audio analysis algorithms, it quantifies happiness, stress levels, and motivation. The input is the employee's video and audio, and the output is quantified emotional data.

[0542] Step 5:

[0543] The server uses a generative AI model that combines trait data and emotion data to generate individual training strategies. The AI ​​analyzes prompt text to derive the optimal training strategy. Trait data and emotion data are used as input, and the output is a specific training strategy.

[0544] Step 6:

[0545] The server sends the generated training plan to the terminal. The history of generated plans is recorded in the database. The input is the training plan, and the output is the training plan displayed on the terminal.

[0546] Step 7:

[0547] The user checks the training policy on their terminal and then puts it into action. Here, the user refers to the training policy to create a specific action plan, and plans work adjustments and training programs. The input is the training policy, and the output is the implementation based on the user's judgment.

[0548] Step 8:

[0549] The terminal sends employee progress data and new emotional information to the server. This creates a continuous feedback loop in the system, which helps optimize the next training plan. The input is progress data, and the output is further data analysis on the server.

[0550] (Application Example 2)

[0551] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as the "server," and the headset-type terminal 314 will be referred to as the "terminal."

[0552] In today's work environment, there is a need to fully understand the personality and emotional state of employees and provide individually optimized training plans. However, traditional systems are insufficient in generating flexible training plans based on the characteristics and emotions of employees, making it difficult to promote appropriate job adaptation. Furthermore, there are problems in providing optimal support tailored to an individual's emotional state within the home environment.

[0553] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0554] In this invention, the server includes a device for acquiring employee information, a device for analyzing employee characteristics based on the acquired employee information, a device for generating a training plan suitable for the employee based on the analyzed characteristics, a device for notifying the generated training plan, and a device for a machine used in the home environment to analyze an individual's emotional state and optimize their response. This enables personalized support in the workplace and home environment, facilitating appropriate training and job adaptation.

[0555] A "device for acquiring employee information" is a device equipped with the function of collecting basic personal information, work history, and performance data about employees.

[0556] A "device for analyzing characteristics" is a device that uses an AI model to analyze characteristics such as personality and emotions based on collected information about employees.

[0557] A "device for generating training policies" is a device that automatically formulates training and guidance policies tailored to each individual, taking into account the characteristics of the analyzed employees.

[0558] A "notification device" is a device that has the function of informing the user of the generated training plan.

[0559] "Machines used in the home environment" refers to devices and robots installed to support household activities that can analyze and respond to an individual's emotional state.

[0560] The system for implementing this invention mainly consists of a data acquisition device, an analysis device, a training policy generation device, a notification device, and a machine used in the home environment. First, the user acquires basic information about the employee from a dedicated terminal. This terminal collects the employee's basic profile, work history, and evaluation data and sends it to a server. Based on this information, the server uses an AI model to analyze the employee's personality and emotional characteristics.

[0561] During the analysis, the server utilizes an emotion engine to perform real-time emotional state analysis. Specifically, it uses software that captures facial expressions and voice tone through cameras and microphones, and quantifies happiness levels and stress levels. This process requires highly accurate image recognition and voice analysis technologies, and for example, NEC's facial recognition technology and Google Cloud's natural language processing tools may be used.

[0562] Based on employee characteristic and emotional data, the server generates an individually tailored training plan. This plan is then communicated to the user via their terminal. The plan is designed to help employees adapt to their jobs, maintaining motivation and reducing stress.

[0563] Furthermore, for machines used in a home environment, it is possible to optimize responses according to the individual's emotional state. For example, even after an employee returns home, their emotional state can be assessed, and the machine can be instructed to provide support such as playing relaxing music or adjusting the lighting.

[0564] For example, if an employee may be experiencing stress, the following prompt can be used to take appropriate action: "A family member may be experiencing stress. What activities can be done to help them relax?" In this way, the system achieves efficient and sustainable talent management through individually optimized training and support.

[0565] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0566] Step 1:

[0567] The user enters the employee's basic information using a terminal. Specifically, a form for entering name, age, work history, performance data, etc., is displayed on the terminal. After input, this information is sent from the terminal to the server. The input in this step is the employee's basic information, and the output is a dataset sent to the server.

[0568] Step 2:

[0569] The server analyzes the received employee information and uses an AI model to identify personality traits. Specifically, it performs analysis based on natural language processing techniques and a database to extract the employee's personality pattern. The input for this step is the dataset sent in step 1, and the output is personality trait data.

[0570] Step 3:

[0571] The server utilizes an emotion engine to analyze the employee's emotional state in real time. Specifically, it analyzes video and audio data from cameras and microphones connected to the terminal to quantify happiness and stress levels. This process uses deep learning technology to achieve facial expression recognition and speech recognition. The input for this step is video and audio data, and the output is numerical data of emotional state.

[0572] Step 4:

[0573] The server generates a suitable training plan based on the employee's personality traits and emotional data. Specifically, it uses a generating AI model to simulate motivation improvement measures and stress reduction measures, and constructs the optimal approach. The input for this step is the output data from steps 2 and 3, and the output is a training plan proposal.

[0574] Step 5:

[0575] The server notifies the terminal of the training policy, and the user confirms it. Specifically, the terminal's notification function is used to display the proposed policy to the user, prompting them to approve or revise it. The input for this step is the proposed training policy document, and the output is a notification message to the user.

[0576] Step 6:

[0577] Machines used in the home environment execute responses based on emotional states, using policies generated on a server. For example, they might play music or adjust lighting to help an individual relax. The input for this step is the nurturing policy from step 4, and the output is the specific household support actions performed.

[0578] The specific processing unit 290 transmits the result of the specific processing to the headset terminal 314. In the headset terminal 314, the control unit 46A causes the speaker 240 and display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0579] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). One example of data generation model 58 is ChatGPT (Internet search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0580] In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and specific processing may also be performed by the headset terminal 314.

[0581] [Fourth Embodiment]

[0582] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0583] As shown in Figure 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0584] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0585] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a controlled object 443. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and controlled object 443 are also connected to the bus 52.

[0586] The microphone 238 receives voice signals from the user 20 and receives instructions from the user 20. The microphone 238 captures the voice signals from the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0587] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the area around the user 20 (for example, an imaging range defined by a field of view equivalent to the width of a typical healthy person's field of vision).

[0588] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0589] The controlled object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the robot 414's emotions can be expressed by controlling these motors. Furthermore, the robot 414's facial expressions can also be expressed by controlling the illumination state of the LEDs in its eyes.

[0590] Figure 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Figure 8, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0591] The specific processing program 56 is an example of a "program" relating to the technology of this disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0592] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0593] In robot 414, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0594] Next, the specific processing performed by the specific processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0595] This invention is a system designed to support employees' adaptation to the workplace. This system has the function of analyzing the characteristics of employees and providing individually optimized training plans.

[0596] Specifically, the user first enters basic information about the employee. This information includes the employee's work history, personality assessment results, and existing work experience. The terminal that receives the information then sends it to the server.

[0597] The server utilizes AI models to perform analysis based on the information it receives. This analysis includes using machine learning algorithms to evaluate the personality and characteristics of employees and identify appropriate training methods. Based on the results of this analysis, a customized training plan is generated for each employee.

[0598] For example, if a server assesses an employee as having low stress tolerance, it may be necessary to schedule mental health support meetings and emphasize feedback.

[0599] The generated training plan is sent from the server to the terminal and notified to the user, allowing it to be immediately reflected in actual training activities in the workplace.

[0600] The feedback loop in this process is further enhanced by continuously collecting progress information and allowing the entire system to learn and optimize. Because users can set more detailed training plans based on the collected progress data, organizations can continue to provide an environment that prevents employee turnover and maximizes their potential.

[0601] The following describes the processing flow.

[0602] Step 1:

[0603] The user enters basic information about the employee into the terminal. This information includes name, age, work history, and personality assessment results. The terminal formats this data and prepares it according to the necessary security protocols before sending it to the server.

[0604] Step 2:

[0605] The terminal sends the formatted employee dataset to the server. The data is encrypted and securely transferred over the network.

[0606] Step 3:

[0607] The server interprets the received data and converts it into a format suitable as input for the AI ​​model. Data preprocessing may include imputing missing values ​​and filtering outliers.

[0608] Step 4:

[0609] The server performs characteristic analysis using an AI model. Through machine learning algorithms, it scores the employee's personality, stress tolerance, interpersonal skills, and other characteristics, and generates a profile.

[0610] Step 5:

[0611] Based on the analysis results, the server generates training plans tailored to each employee's characteristics. For example, an employee with low stress tolerance might be offered a training style that emphasizes mental support.

[0612] Step 6:

[0613] The server generates a training plan and sends it to the terminal. The terminal displays this information to the user, and the training manager can review its contents.

[0614] Step 7:

[0615] Based on the training plan received by the user, the actual training activities are planned and implemented. Progress and feedback are collected regularly and entered into the system as feedback data as needed.

[0616] Step 8:

[0617] The device then sends progress information and feedback back to the server. Based on this information, the server re-evaluates and updates the training plan, preparing for the next improvement cycle.

[0618] (Example 1)

[0619] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0620] Companies are required to improve employee adaptability and reduce employee turnover while simultaneously providing tailored training programs to enhance overall organizational productivity. Traditional methods have been time-consuming and inefficient in developing individualized training programs based on employee characteristics, and have lacked effective feedback mechanisms for monitoring adaptation progress. To address these challenges, the development of a system that efficiently analyzes employee information and provides appropriate training programs is necessary.

[0621] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0622] In this invention, the server includes means for using a generating AI model to analyze the characteristics of an employee based on acquired employee information, means for generating a training plan suitable for the employee using prompt statements based on the analyzed characteristics, and means for notifying the generated training plan. This makes it possible to quickly provide individualized training plans that correspond to the characteristics of the employee, and to continuously optimize the training plan based on progress information.

[0623] "Employee information" refers to data necessary to analyze the characteristics of an employee, such as their work history, personality assessment results, and existing work experience.

[0624] "Generative AI models" refer to artificial intelligence technology that uses machine learning algorithms to analyze the characteristics of employees and generate appropriate training plans.

[0625] A "prompt message" refers to a sentence that, when input into a generative AI model, serves as an instruction to generate a training plan based on the characteristics of the employee.

[0626] A "development policy" refers to a plan or program that promotes individually optimized growth and adaptation for each employee.

[0627] "Progress information" refers to data showing the results and status of activities based on employee training policies, and is used to measure the effectiveness of training policies.

[0628] "Means of notification" refers to methods and techniques for communicating the generated training policies to employees and other relevant parties.

[0629] "Feedback" refers to the information used to review and optimize training policies by incorporating the progress and results after their implementation into the system.

[0630] This invention is a system that supports employees' adaptation to the workplace and provides training plans tailored to the employee's characteristics. Specifically, the user first inputs the employee's information into a terminal. This information includes the employee's work history, personality assessment results, and existing work experience. The terminal then transmits this information to a server.

[0631] The server uses a generative AI model to analyze the characteristics of employees. During this process, the AI ​​model generates an appropriate training plan based on prompts. The AI ​​model is designed to find the optimal training program for each employee by comparing it against historical datasets.

[0632] Based on the analysis results, the server generates a training plan tailored to the employee and sends it to the terminal. The terminal notifies the user of this training plan and incorporates it into workplace training activities. The notification is displayed on the terminal screen and communicated in the form of an alert or message.

[0633] As a concrete example, when a new employee joins the company, the user inputs information about that employee. The server inputs a prompt message into the AI ​​model, such as, "Based on the new employee's personality assessment results, please suggest the optimal training plan." If the result determines that the employee is highly sociable but has issues with time management, the server provides a training plan such as, "Have the employee participate in a team-building workshop and take a time management course."

[0634] Furthermore, the server continuously collects employee progress information, forming a feedback loop. This process allows for regular review and optimization of training policies. As a result, the organization has a system that effectively promotes employee adaptation and skill development.

[0635] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0636] Step 1:

[0637] The user enters the employee's information into the terminal. This information includes the employee's work history, personality assessment results, and existing work experience. Specifically, the user enters the required information into a dedicated form and clicks the "Submit" button. This action saves the entered information to the terminal.

[0638] Step 2:

[0639] The terminal packets the employee information sent by the user and sends it to the server using a secure communication protocol (e.g., SSL / TLS). The input is the employee information obtained in step 1, and the output is encrypted information packets. This process is important to ensure the protection of information and reliable communication.

[0640] Step 3:

[0641] The server decodes the received employee information packets and invokes a generating AI model. The input is the decoded employee information, and the output is the result of the AI ​​model's analysis of the characteristics. In this step, the server prompts the AI ​​model with sentences to analyze the information and executes machine learning algorithms. This operation clearly evaluates the characteristics of the employees.

[0642] Step 4:

[0643] Based on the analysis results, the server generates a training plan suitable for the employee using prompt messages. The input is the analysis results of the characteristics obtained in step 3, and the output is a customized training plan for the employee. The server then formulates a concrete training program based on the data output by the generated AI model.

[0644] Step 5:

[0645] The server sends the generated training plan to the terminal. The input is the training plan generated in step 4, and the output is a notification message to the terminal. Specifically, the server packages the training plan in JSON format and sends the data to the terminal.

[0646] Step 6:

[0647] The terminal analyzes the received training plan and notifies the user. The input is the training plan data received from the server, and the output is a notification on the user interface. In actual operation, the terminal displays a pop-up notification or screen message to inform the user of new training activities.

[0648] Step 7:

[0649] Users carry out activities based on training policies and input progress information into the device. The input consists of the results of the activities performed and observed progress information, and the output indicates that this information is being stored on the device. Through this process, users continuously record the effectiveness of their training activities.

[0650] Step 8:

[0651] The terminal sends user-entered progress information to the server, integrating it into the system's overall feedback loop. The input is progress information, and the output is the feedback data sent to the server. This step contributes to the continuous optimization of training strategies.

[0652] (Application Example 1)

[0653] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0654] It is difficult to provide optimal developmental support tailored to individual characteristics not only in the workplace but also in the home environment, and there is a particular problem of insufficient support for learning and skill improvement in daily life.

[0655] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0656] In this invention, the server includes means for acquiring information about employees, means for analyzing the characteristics of employees based on the acquired information, means for generating a training plan suitable for the employee based on the analyzed characteristics, and means for providing a plan to support the individual's learning and ability improvement in the home environment. This enables individualized and effective training support in the home.

[0657] "Employee information" refers to basic attribute data about the employee, such as work history, personality assessment results, and existing work experience.

[0658] "Means for analyzing characteristics" refers to a function that uses machine learning algorithms to perform a process of evaluating the personality and characteristics of employees.

[0659] "Means for generating training policies" refers to a function that executes the process of formulating training plans suitable for employees based on analysis results.

[0660] "Means for notifying training policies" refers to a function that sends the generated training policies to employees or managers, providing information to help them incorporate them into actual training activities.

[0661] "Means of providing plans to support individual learning and skill development in the home environment" refers to a function that provides individuals with optimal plans based on their characteristics in order to efficiently learn and improve their skills at home.

[0662] This invention is a system designed to support individual learning and skill development even in a home environment. To realize this application, a program is needed to acquire employee information and analyze their characteristics based on that information. The program includes a client application written in Python, and data is sent and received via a server-side API using Flask. Furthermore, TensorFlow or PyTorch is used for the AI ​​model to perform the characteristic analysis.

[0663] Information about employees obtained from their devices is transmitted to a server via the internet, where an AI model analyzes the data. This generates a training plan best suited to that individual. The generated training plan is then transmitted back to the device and presented to the user as an individualized training plan. This plan provides concrete support for daily learning activities and, if necessary, offers methods such as relaxation exercises and meditation.

[0664] For example, if an individual wants to improve their stress management, the server can suggest training methods such as "deep breathing exercises" or "meditation sessions." These suggestions are based on the user's characteristics and support more effective learning and growth. By having the user input a prompt such as, "I am a 28-year-old introverted software engineer. Please provide a personalized plan that will help me grow my career," the AI ​​model can generate an optimal plan tailored to that individual's needs.

[0665] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0666] Step 1:

[0667] Users input their information using a terminal. This information includes their personal work history, personality assessment results, and information about specific skills. The entered data is converted into JSON format.

[0668] Step 2:

[0669] The terminal sends the information entered by the user to the server. The server receives this data via an HTTP request and prepares it for analysis. At this point, the input is user information in JSON format, and the output is a dataset in the format required for analysis.

[0670] Step 3:

[0671] The server inputs the received data into a generating AI model. This model uses either TensorFlow or PyTorch to analyze user characteristics. Based on the input data, the AI ​​model performs multidimensional data calculations to evaluate user characteristics. The output of this step is a user-optimized training plan.

[0672] Step 4:

[0673] The server generates individual training plans based on the training policies obtained from the AI ​​model. These plans may include relaxation exercises aimed at stress management. Specifically, the process involves setting up suggested activities corresponding to the analysis results.

[0674] Step 5:

[0675] The server sends the generated training plan to the terminal. The terminal displays this plan to the user and provides information in an actionable format. Based on the on-screen instructions, the user can incorporate the suggested exercises and learning activities into their daily life.

[0676] Step 6:

[0677] Users perform activities according to a given training plan and input their progress information into a terminal. The terminal sends this progress data to a server, which is used to update and re-evaluate future training policies. The input is the user's progress information, and the output is a dataset prepared for the next analysis.

[0678] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0679] This invention is a system for supporting appropriate employee training. By analyzing the characteristics and emotions of employees and appropriately adjusting training policies, it enables better adaptation in the workplace. This system comprehensively grasps the personality traits and emotional state of employees and proposes training tailored to their individual needs.

[0680] Specifically, the user first enters basic information about the employee into the terminal. This information includes the employee's basic profile, work history, and past performance evaluations. The terminal then sends this information to the server.

[0681] The server uses an AI model based on the received information to analyze the employee's personality traits. During this process, it can also acquire the employee's emotional data in real time using its built-in emotion engine.

[0682] For example, through an interface accessed from a terminal, the system monitors video interviews, facial expressions during daily work, voice tone, etc., and an emotional engine automatically analyzes the employee's emotional state. The resulting emotional data is quantified as happiness, stress levels, and motivation.

[0683] Considering this emotional and personality trait data, the server generates a training plan tailored to the employee. This training plan may include, for example, manageable task management for employees with low motivation, or regular mental resource support for employees with low stress tolerance.

[0684] The server sends the generated training plan to the terminal, and the user reviews and implements the plan. Progress data and further emotional information are continuously collected, leading to improvements across the entire system.

[0685] This system provides training policies that take into account both the mental state and job performance capabilities of employees, enabling efficient and sustainable human resource management in the workplace.

[0686] The following describes the processing flow.

[0687] Step 1:

[0688] The user enters the employee's basic information into the terminal. This information includes name, work experience, and personality assessment results. The terminal then prepares to send this data to the server in a secure format (e.g., encrypted JSON).

[0689] Step 2:

[0690] The terminal sends the entered employee data to the server. The server receives the data and stores it in the database in the appropriate format.

[0691] Step 3:

[0692] The server uses an AI model to analyze incoming data. It evaluates the employee's attributes (e.g., communication skills, problem-solving ability) and creates a profile. Furthermore, an emotion engine analyzes the employee's emotional state in real time from their facial expressions and voice input data.

[0693] Step 4:

[0694] The server comprehensively evaluates the characteristics and emotional data of the employees obtained and generates individually tailored training plans. For example, if the emotional data indicates stress, it will include a plan to strengthen mental health support.

[0695] Step 5:

[0696] The server generates a training plan and sends it to the terminal, notifying the user. The user can then review this information and incorporate it into their actual training plan.

[0697] Step 6:

[0698] Users take actions based on the training plan, and data is collected to evaluate their progress and effectiveness. The emotion engine continues to monitor the emotion data and records new feedback as needed.

[0699] Step 7:

[0700] The terminal sends progress data and emotional feedback back to the server. The server uses this information to dynamically adjust training policies and prepare for the next cycle to optimize the employee's growth and workplace adaptation.

[0701] (Example 2)

[0702] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0703] In today's workplace environment, accurately understanding the characteristics and emotional states of employees and providing training plans tailored to their individual needs is a challenging task. In particular, traditional methods often fail to adequately address the needs of individual employees, making it difficult to improve their adaptation and work efficiency.

[0704] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0705] In this invention, the server includes means for analyzing and acquiring characteristic data from acquired employee information, means for acquiring the employee's emotional state using video interview and audio data, and means for adjusting the training policy in real time based on the acquired emotional state data and characteristic data. This makes it possible to generate and implement individualized and dynamic training policies based on the employee's characteristics and emotions.

[0706] An "employee" refers to an individual who is employed by a specific company or organization and performs duties for that company or organization.

[0707] An "information processing device" refers to a computer system used to analyze collected data and process the necessary information.

[0708] A "generative artificial intelligence model" refers to an algorithm that uses machine learning and other AI technologies to generate output that is suited to a specific purpose.

[0709] "Characteristic data" refers to data related to an employee's personality, work performance abilities, etc., and serves as the basis for individual analyses.

[0710] A "training policy" refers to a plan that outlines the optimal education and training guidelines tailored to the characteristics and circumstances of each employee.

[0711] A "display device" refers to a physical interface used to visually display output from a computer or other information processing device.

[0712] "Emotional state" refers to data that indicates an employee's momentary emotions, and includes indicators such as happiness, stress levels, and motivation.

[0713] "Progress data" refers to information about the results and processes achieved by employees during the course of performing their work.

[0714] This invention relates to a system for analyzing the characteristics and emotional state of employees and providing appropriate training plans. This system supports better workplace adaptation by addressing the individual needs of employees.

[0715] The user first enters basic information about the employee into the terminal. This information includes name, age, work history, and past performance evaluations. This allows the user to smoothly input data via a graphical user interface (GUI).

[0716] The terminal sends this information to the information processing device, which then forwards it to the server. The protocol used here is, for example, HTTPS, to maintain data integrity and security.

[0717] The server activates an AI model based on the received information to analyze the employee's personality traits. Natural language processing technology is used for the analysis, processing work history and performance data. The server also implements an emotion engine to acquire emotional states in real time through video interview footage and audio data. This data is quantified as happiness, stress levels, and motivation.

[0718] Based on this trait and emotional data, the server uses a generative AI model to generate appropriate training plans. These generated plans are tailored to individual employees and aim to improve work efficiency. The plans may include manageable task management and regular mental support.

[0719] For example, if an employee exhibits a high stress level, emotional data can detect this and generate a training plan that suggests special task management to reduce the burden. This helps improve their ability to adapt to the workplace.

[0720] Examples of prompts for the generating AI model include, "Based on the employee's recent performance evaluation, analyze their stress level and motivation, and propose a suitable training plan," and "Analyze the video interview recording and report on changes in the employee's emotional state." This ensures that the generated training plan is more relevant to the employee's current situation.

[0721] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0722] Step 1:

[0723] The user enters the employee's basic information into the terminal. This information includes name, age, work history, and past performance evaluations. The data is entered via a GUI on the terminal and transmitted to the server while maintaining data integrity. This input serves as the basis for subsequent analysis processes.

[0724] Step 2:

[0725] The terminal transmits the entered employee information to the information processing device. HTTPS, a secure communication protocol, is used for this transmission. The input data is structured and stored in a database, making it analyzable. The information is then received by the server as output.

[0726] Step 3:

[0727] The server analyzes employee characteristic data from the received data. Using an AI model and natural language processing technology, it analyzes work history and evaluation information to extract personality traits. Employee information is used as input, and characteristic data is output.

[0728] Step 4:

[0729] The server runs an emotion engine to analyze video interviews and audio data to obtain emotional states. Using video analysis technology and audio analysis algorithms, it quantifies happiness, stress levels, and motivation. The input is the employee's video and audio, and the output is quantified emotional data.

[0730] Step 5:

[0731] The server uses a generative AI model that combines trait data and emotion data to generate individual training strategies. The AI ​​analyzes prompt text to derive the optimal training strategy. Trait data and emotion data are used as input, and the output is a specific training strategy.

[0732] Step 6:

[0733] The server sends the generated training plan to the terminal. The history of generated plans is recorded in the database. The input is the training plan, and the output is the training plan displayed on the terminal.

[0734] Step 7:

[0735] The user checks the training policy on their terminal and then puts it into action. Here, the user refers to the training policy to create a specific action plan, and plans work adjustments and training programs. The input is the training policy, and the output is the implementation based on the user's judgment.

[0736] Step 8:

[0737] The terminal sends employee progress data and new emotional information to the server. This creates a continuous feedback loop in the system, which helps optimize the next training plan. The input is progress data, and the output is further data analysis on the server.

[0738] (Application Example 2)

[0739] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0740] In today's work environment, there is a need to fully understand the personality and emotional state of employees and provide individually optimized training plans. However, traditional systems are insufficient in generating flexible training plans based on the characteristics and emotions of employees, making it difficult to promote appropriate job adaptation. Furthermore, there are problems in providing optimal support tailored to an individual's emotional state within the home environment.

[0741] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0742] In this invention, the server includes a device for acquiring employee information, a device for analyzing employee characteristics based on the acquired employee information, a device for generating a training plan suitable for the employee based on the analyzed characteristics, a device for notifying the generated training plan, and a device for a machine used in the home environment to analyze an individual's emotional state and optimize their response. This enables personalized support in the workplace and home environment, facilitating appropriate training and job adaptation.

[0743] A "device for acquiring employee information" is a device equipped with the function of collecting basic personal information, work history, and performance data about employees.

[0744] A "device for analyzing characteristics" is a device that uses an AI model to analyze characteristics such as personality and emotions based on collected information about employees.

[0745] A "device for generating training policies" is a device that automatically formulates training and guidance policies tailored to each individual, taking into account the characteristics of the analyzed employees.

[0746] A "notification device" is a device that has the function of informing the user of the generated training plan.

[0747] "Machines used in the home environment" refers to devices and robots installed to support household activities that can analyze and respond to an individual's emotional state.

[0748] The system for implementing this invention mainly consists of a data acquisition device, an analysis device, a training policy generation device, a notification device, and a machine used in the home environment. First, the user acquires basic information about the employee from a dedicated terminal. This terminal collects the employee's basic profile, work history, and evaluation data and sends it to a server. Based on this information, the server uses an AI model to analyze the employee's personality and emotional characteristics.

[0749] During the analysis, the server utilizes an emotion engine to perform real-time emotional state analysis. Specifically, it uses software that captures facial expressions and voice tone through cameras and microphones, and quantifies happiness levels and stress levels. This process requires highly accurate image recognition and voice analysis technologies, and for example, NEC's facial recognition technology and Google Cloud's natural language processing tools may be used.

[0750] Based on employee characteristic and emotional data, the server generates an individually tailored training plan. This plan is then communicated to the user via their terminal. The plan is designed to help employees adapt to their jobs, maintaining motivation and reducing stress.

[0751] Furthermore, for machines used in a home environment, it is possible to optimize responses according to the individual's emotional state. For example, even after an employee returns home, their emotional state can be assessed, and the machine can be instructed to provide support such as playing relaxing music or adjusting the lighting.

[0752] For example, if an employee may be experiencing stress, the following prompt can be used to take appropriate action: "A family member may be experiencing stress. What activities can be done to help them relax?" In this way, the system achieves efficient and sustainable talent management through individually optimized training and support.

[0753] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0754] Step 1:

[0755] The user enters the employee's basic information using a terminal. Specifically, a form for entering name, age, work history, performance data, etc., is displayed on the terminal. After input, this information is sent from the terminal to the server. The input in this step is the employee's basic information, and the output is a dataset sent to the server.

[0756] Step 2:

[0757] The server analyzes the received employee information and uses an AI model to identify personality traits. Specifically, it performs analysis based on natural language processing techniques and a database to extract the employee's personality pattern. The input for this step is the dataset sent in step 1, and the output is personality trait data.

[0758] Step 3:

[0759] The server utilizes an emotion engine to analyze the employee's emotional state in real time. Specifically, it analyzes video and audio data from cameras and microphones connected to the terminal to quantify happiness and stress levels. This process uses deep learning technology to achieve facial expression recognition and speech recognition. The input for this step is video and audio data, and the output is numerical data of emotional state.

[0760] Step 4:

[0761] The server generates a suitable training plan based on the employee's personality traits and emotional data. Specifically, it uses a generating AI model to simulate motivation improvement measures and stress reduction measures, and constructs the optimal approach. The input for this step is the output data from steps 2 and 3, and the output is a training plan proposal.

[0762] Step 5:

[0763] The server notifies the terminal of the training policy, and the user confirms it. Specifically, the terminal's notification function is used to display the proposed policy to the user, prompting them to approve or revise it. The input for this step is the proposed training policy document, and the output is a notification message to the user.

[0764] Step 6:

[0765] Machines used in the home environment execute responses based on emotional states, using policies generated on a server. For example, they might play music or adjust lighting to help an individual relax. The input for this step is the nurturing policy from step 4, and the output is the specific household support actions performed.

[0766] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the controlled object 443 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0767] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). One example of data generation model 58 is ChatGPT (Internet search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0768] In the above embodiment, an example was given in which the specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and the specific processing may also be performed by the robot 414.

[0769] Furthermore, the emotion identification model 59, acting as an emotion engine, may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to a specific mapping, which is an emotion map (see Figure 9). Similarly, the emotion identification model 59 may also determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0770] Figure 9 shows an emotion map 400 in which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. The closer to the center of the concentric circles, the more primitive the emotions are located. Further out of the concentric circles, emotions representing states and actions arising from mental states are located. Emotion is a concept that includes feelings and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions occurring in the brain are located. On the right side of the concentric circles, emotions that are generally induced by situational judgment are located. Above and below the concentric circles, emotions that are generally generated from reactions occurring in the brain and induced by situational judgment are located. In addition, the emotion of "pleasure" is located on the upper side of the concentric circles, and the emotion of "displeasure" is located on the lower side. Thus, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions arise, and emotions that are likely to occur simultaneously are mapped close together.

[0771] These emotions are distributed at the 3 o'clock position on the Emotion Map 400, and usually fluctuate between feelings of security and anxiety. In the right half of the Emotion Map 400, situational awareness takes precedence over internal feelings, resulting in a calm impression.

[0772] The inside of the Emotion Map 400 represents inner thoughts, while the outside represents actions. Therefore, the further you go from the outside of the Emotion Map 400, the more visible (expressed in actions) your emotions become.

[0773] Here, human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. Similarly, in robots, cars, motorcycles, etc., emotions can be created based on various balances, such as posture and battery level. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. The emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on a system for analyzing brain physiological signals of speech emotion recognition and emotion, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map contains emotions belonging to a region called "response," where sensation is dominant. The right half of the emotion map contains emotions belonging to a region called "situation," where situational awareness is dominant.

[0774] The emotion map defines two emotions that promote learning. One is the emotion around the middle of the negative "repentance" and "reflection" on the situation side. In other words, it is when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is the emotion around the positive "desire" on the reaction side. In other words, it is when the robot has positive feelings such as "I want more" or "I want to know more."

[0775] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​representing each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple training data sets, which are combinations of user input and emotion values ​​representing each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions located close together have similar values, as shown in the emotion map 900 in Figure 10. Figure 10 shows an example where multiple emotions such as "reassured," "calm," and "confident" have similar emotion values.

[0776] The above description primarily focuses on the functions of the data processing device 12 in relation to this disclosure. However, the system related to this disclosure is not necessarily implemented on a server. The system related to this disclosure may be implemented as a general information processing system. This disclosure may be implemented, for example, as a software program that runs on a personal computer or as an application that runs on a smartphone. The method related to this disclosure may be provided to users in SaaS (Software as a Service) format.

[0777] In the above embodiment, an example was given in which a specific process is performed by a single computer 22. However, the technology of this disclosure is not limited thereto, and a distributed processing of the specific process may be performed by multiple computers, including computer 22. For example, a data generation model 58 may be provided in an external device of the data processing device 12, and the external device may generate data according to the input data.

[0778] In the above embodiment, an example was given in which the specific processing program 56 is stored in the storage 32, but the technology of this disclosure is not limited thereto. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-temporary storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-temporary storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes specific processing according to the specific processing program 56.

[0779] Alternatively, the specific processing program 56 may be stored in a storage device such as a server connected to the data processing device 12 via the network 54, and the specific processing program 56 may be downloaded and installed on the computer 22 in response to a request from the data processing device 12.

[0780] Furthermore, it is not necessary to store the entirety of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store the entirety of the specific processing program 56 in the storage 32; it is acceptable to store only a portion of the specific processing program 56.

[0781] The following types of processors can be used as hardware resources to perform specific processing. Examples of processors include a CPU, a general-purpose processor that functions as a hardware resource to perform specific processing by executing software, i.e., a program. Other examples of processors include dedicated electrical circuits, such as FPGAs (Field-Programmable Gate Arrays), PLDs (Programmable Logic Devices), or ASICs (Application Specific Integrated Circuits), which have circuit configurations specifically designed to perform specific processing. All of these processors have built-in or connected memory, and all of them perform specific processing by using memory.

[0782] The hardware resource that performs a specific process may consist of one of these various processors, or it may consist of a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Alternatively, the hardware resource that performs a specific process may consist of a single processor.

[0783] Examples of configurations using a single processor include, firstly, a configuration in which one or more CPUs and software are combined to form a single processor, and this processor functions as a hardware resource that performs a specific process. Secondly, there is a configuration using a processor that realizes the functions of the entire system, including multiple hardware resources that perform a specific process, on a single IC chip, as exemplified by SoCs (System-on-a-chip). In this way, a specific process is realized using one or more of the above types of processors as hardware resources.

[0784] Furthermore, the hardware structure of these various processors can more specifically utilize electrical circuits that combine circuit elements such as semiconductor devices. Also, the specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps can be deleted, new steps added, or the processing order rearranged, as long as it does not deviate from the main purpose.

[0785] The descriptions and illustrations presented above are detailed explanations of the technical aspects of this disclosure and are merely examples of the technical aspects. For example, the above descriptions of the structure, function, operation, and effect are examples of the structure, function, operation, and effect of the technical aspects of this disclosure. Therefore, it goes without saying that you may delete unnecessary parts, add new elements, or replace elements in the descriptions and illustrations presented above, as long as you do not deviate from the essence of the technical aspects of this disclosure. Furthermore, in order to avoid confusion and facilitate understanding of the technical aspects of this disclosure, explanations of common technical knowledge and the like that do not require special explanation to enable the implementation of the technical aspects of this disclosure have been omitted from the descriptions and illustrations presented above.

[0786] All documents, patent applications, and technical standards described herein are incorporated by reference to the same extent as if each individual document, patent application, and technical standard were specifically and individually noted to be incorporated by reference.

[0787] The following is further disclosed regarding the embodiments described above.

[0788] (Claim 1)

[0789] Means of obtaining employee information,

[0790] A means of analyzing the characteristics based on the acquired information about employees,

[0791] A means for generating training policies suitable for employees based on analyzed characteristics,

[0792] A means of notifying the generated training plan,

[0793] A system that includes this.

[0794] (Claim 2)

[0795] The system according to claim 1, comprising means for updating training policies using acquired employee characteristic data.

[0796] (Claim 3)

[0797] The system according to claim 1, comprising means for obtaining employee progress information and re-evaluating training policies based thereon.

[0798] "Example 1"

[0799] (Claim 1)

[0800] Means of obtaining employee information,

[0801] A method of using a generative AI model to analyze the characteristics based on acquired employee information,

[0802] A means for generating a training policy suitable for an employee using prompt statements based on analyzed characteristics,

[0803] A means of notifying the generated training plan,

[0804] A means of providing feedback on progress in order to implement the training policy,

[0805] A system that includes this.

[0806] (Claim 2)

[0807] The system according to claim 1, comprising means for updating training policies using acquired employee characteristic data.

[0808] (Claim 3)

[0809] The system according to claim 1, comprising means for acquiring employee progress information and continuously optimizing training policies based thereon.

[0810] "Application Example 1"

[0811] (Claim 1)

[0812] Means of obtaining employee information,

[0813] A means of analyzing the characteristics based on the acquired information about employees,

[0814] A means for generating training policies suitable for employees based on analyzed characteristics,

[0815] A means of notifying the generated training plan,

[0816] A means of providing a plan to support individual learning and skill development in the home environment,

[0817] A system that includes this.

[0818] (Claim 2)

[0819] The system according to claim 1, comprising means for updating training policies using acquired employee characteristic data.

[0820] (Claim 3)

[0821] The system according to claim 1, comprising means for obtaining employee progress information and re-evaluating training policies based thereon.

[0822] "Example 2 of combining an emotion engine"

[0823] (Claim 1)

[0824] Means including a device for inputting employee information,

[0825] A means for transmitting the received employee information to an information processing device,

[0826] A means of analyzing and obtaining characteristic data from acquired employee information,

[0827] A means for generating a breeding strategy using an artificial intelligence model based on analyzed characteristic data,

[0828] A means to verify and implement the generated training plan using a display device,

[0829] A means of obtaining the emotional state of an employee using video interviews and audio data,

[0830] A means of adjusting the training policy in real time based on acquired emotional state data and characteristic data,

[0831] A system that includes this.

[0832] (Claim 2)

[0833] The system according to claim 1, comprising means for dynamically updating training policies using collected employee sentiment data.

[0834] (Claim 3)

[0835] The system according to claim 1, comprising means for acquiring employee progress data and re-evaluating and optimizing training policies based on that data.

[0836] "Application example 2 when combining with an emotional engine"

[0837] (Claim 1)

[0838] A device for obtaining employee information,

[0839] A device for analyzing the characteristics of employees based on the acquired information,

[0840] A device for generating training plans suitable for employees based on analyzed characteristics,

[0841] A device for notifying the generated breeding plan,

[0842] A device used in the home environment to analyze an individual's emotional state and optimize their response,

[0843] A system that includes this.

[0844] (Claim 2)

[0845] The system according to claim 1, comprising a device for updating training policies using acquired employee characteristic data.

[0846] (Claim 3)

[0847] The system according to claim 1, comprising a device for acquiring employee progress information and re-evaluating training policies based thereon. [Explanation of Symbols]

[0848] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots< / url:> < / url:> < / url:> < / url:>

Claims

1. Means of obtaining employee information, A means of analyzing the characteristics based on the acquired information about employees, A means for generating training policies suitable for employees based on analyzed characteristics, A means of notifying the generated training plan, A means of providing a plan to support individual learning and skill development in the home environment, A system that includes this.

2. The system according to claim 1, comprising means for updating training policies using acquired employee characteristic data.

3. The system according to claim 1, comprising means for obtaining employee progress information and re-evaluating training policies based thereon.

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