system

Abstract

We provide the system. [Solution] A means of collecting personnel competency information and performance evaluation information, A means for training a generative AI model using the collected information, A means for proposing the optimal allocation of personnel using the aforementioned generation AI model, A means of presenting a method for acquiring the skills that are lacking based on the above proposal, A means of presenting layout proposals to optimize factory operational efficiency, 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, the method 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] Conventionally, career counseling for employees has depended on the knowledge and experience of personnel staff, and has often lacked accuracy and reliability. In addition, employees with low performance in their current positions tend to miss the possibility of being active in suitable positions, so cases where employees choose to resign on their own have occurred. Furthermore, in a situation where employees are being promoted to career self-discipline, there is a problem that they do not know what to start with.

Means for Solving the Problems

[0005] This invention solves the above problems by providing a system that collects employee experience data and performance evaluation data and uses it to train a generative AI model. The generative AI model proposes appropriate employee placement and further presents methods for acquiring necessary skills. This promotes the placement of the right people in the right positions and supports employees in building better career plans. Furthermore, by receiving feedback from employees and adjusting career plans as needed, it realizes support for career autonomy tailored to the needs of each employee.

[0006] "Employee experience data" refers to information related to an employee's past work experience, skills, and other achievements.

[0007] "Performance evaluation data" refers to data that includes evaluation information regarding employees' job performance and achievements.

[0008] A "generative AI model" refers to an artificial intelligence model that has been trained to analyze input data and make predictions and suggestions based on a specific purpose.

[0009] "Appropriate placement" refers to assigning employees to departments or jobs that are best suited to their abilities and aptitudes.

[0010] "Skill acquisition methods" refer to specific procedures and resources for employees to effectively learn and acquire the skills they lack.

[0011] "Feedback" refers to the act of responding to information or suggestions received by an employee, as well as the content of such responses or opinions.

[0012] A "career plan" refers to the plan or strategy that an employee sets up to achieve their career goals. [Brief explanation of the drawing]

[0013] [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] It is a conceptual diagram showing an example of the main functions of a data processing device and a smart device according to the first embodiment. [Figure 3] It is a conceptual diagram showing an example of the configuration of a data processing system according to the second embodiment. [Figure 4] It is a conceptual diagram showing an example of the main functions of a data processing device and smart glasses according to the second embodiment. [Figure 5] It is a conceptual diagram showing an example of the configuration of a data processing system according to the third embodiment. [Figure 6] It is a conceptual diagram showing an example of the main functions of a data processing device and a headset-type terminal according to the third embodiment. [Figure 7] It is a conceptual diagram showing an example of the configuration of a data processing system according to the fourth embodiment. [Figure 8] It is a conceptual diagram showing an example of the main functions of a data processing device and a robot according to the fourth embodiment. [Figure 9] Shows an emotion map to which multiple emotions are mapped. [Figure 10] Shows an emotion map to which multiple emotions are mapped. [Figure 11] It is a sequence diagram showing the processing flow of the data processing system in Example 1. [Figure 12] It is a sequence diagram showing the processing flow of the data processing system in Application Example 1. [Figure 13] It is a sequence diagram showing the processing flow of the data processing system in Example 2 when an emotion engine is combined. [Figure 14] It is a sequence diagram showing the processing flow of the data processing system in Application Example 2 when an emotion engine is combined.

Mode for Carrying Out the Invention

[0014] 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.

[0015] First, the terms used in the following description will be explained.

[0016] In the following embodiments, the labeled 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.

[0017] In the following embodiments, the labeled RAM (Random Access Memory) is a memory in which information is temporarily stored and is used as a work memory by the processor.

[0018] In the following embodiments, the labeled storage is one or more non-volatile storage devices that store various programs and various parameters, etc. Examples of non-volatile storage devices include flash memory (SSD (Solid State Drive)), magnetic disks (e.g., hard disks), or magnetic tapes, etc.

[0019] In the following embodiments, the labeled communication I / F (Interface) is an interface including a communication processor and an antenna, etc. The communication I / F controls communication between multiple computers. Examples of communication standards applied to the communication I / F include wireless communication standards including 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), or Bluetooth (registered trademark), etc.

[0020] 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."

[0021] [First Embodiment]

[0022] Figure 1 shows an example of the configuration of the data processing system 10 according to the first embodiment.

[0023] 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.

[0024] 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).

[0025] 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.

[0026] 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.

[0027] 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.

[0028] 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.

[0029] Figure 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0030] 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.

[0031] 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.

[0032] 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.

[0033] 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".

[0034] This invention provides a system to support employees' career development. The system aims to train a generating AI model using employee experience data and performance evaluation data, and to propose appropriate job placements.

[0035] The server first collects data on each employee's past work experience and skills. This includes information such as the projects the employee has been involved in and the skills they possess. The server also collects performance evaluation data, accumulating information to evaluate each employee's individual performance.

[0036] Next, the server uses this data to train a generative AI model. Based on historical data, the generative AI model predicts which department or position an employee can perform best in. This process allows the server to show each employee the optimal career path.

[0037] The terminal displays optimal placement suggestions sent from the server to the employee. This information specifically indicates which department best suits the employee's skill set and which skills are lacking. The terminal also presents the employee with specific learning resources and training programs to address any skill gaps.

[0038] Users can make decisions based on their career plans and suggested learning methods. This allows employees to take clear steps toward their career goals.

[0039] To give a concrete example, if an employee expresses interest in taking on a new project in the sales department, the terminal analyzes their past project experience and skills to present the skill set required for the sales department and highlight any skill gaps they may have. Furthermore, the server suggests specific training courses and online resources to bridge those gaps. In this way, employees can improve their skills and prepare to take on new roles.

[0040] The following describes the processing flow.

[0041] Step 1:

[0042] The terminal provides an interface for users to input information related to their past career development, current skills, and work performance evaluations. Users input the necessary information and send the data to the server.

[0043] Step 2:

[0044] The server stores user data received from terminals in a centralized database. The server organizes the data into a parseable format and verifies its integrity.

[0045] Step 3:

[0046] The server uses stored data to train the generated AI model. This includes a process of learning past employee success trends and optimal talent placement patterns.

[0047] Step 4:

[0048] The server uses a trained AI model to generate suggestions for the most suitable department and position based on the user's skills and experience. Furthermore, it identifies any missing skills or required experience.

[0049] Step 5:

[0050] The server evaluates specific training resources and learning methods to help the user improve their skills, along with suggesting the optimal carrier placement, and sends this information to the terminal.

[0051] Step 6:

[0052] The device presents information received from the server to the user in an easy-to-understand manner. The user views and considers the recommended carrier configuration and learning methods.

[0053] Step 7:

[0054] Users provide feedback and make selections regarding the suggestions through their device. The device sends user feedback to the server and initiates a process to further refine the suggestions as needed.

[0055] (Example 1)

[0056] 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."

[0057] In modern companies, there is a need for efficient personnel allocation in order to utilize the abilities of each employee in the most optimal way and improve performance. However, systematically analyzing the diverse experience and skill sets of employees and making accurate placement proposals based on that analysis is difficult. Furthermore, there is a lack of means to support employees' career development based on those proposals. Therefore, companies are required to efficiently provide concrete learning methods to bridge the skill gaps of their employees.

[0058] 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.

[0059] In this invention, the server includes means for aggregating personnel's work experience information and evaluation information, means for constructing a generative machine learning model using the aggregated information, and means for proposing the optimal placement of personnel using the generative machine learning model. This makes it possible to analyze the skills and work requirements of each employee in detail and propose the optimal placement and growth strategy.

[0060] "Human resources" refers to individual employees or staff members who play an active role within a company or organization.

[0061] "Work experience information" refers to specific information about projects and tasks that employees have worked on in the past.

[0062] "Evaluation information" refers to information regarding performance evaluations given based on an employee's ability to perform their job and their results.

[0063] "Aggregation" refers to the process of gathering and organizing dispersed information in one place.

[0064] A "generative machine learning model" refers to a statistical model that uses historical data to make predictions or classifications for a specific purpose.

[0065] "Optimal placement" refers to assigning employees to jobs and positions that maximize their individual capabilities, taking into account their skills and the organization's needs.

[0066] "Proposal" refers to the act of providing information that indicates the optimal action or option based on the analysis results.

[0067] A "skill gap" refers to the difference between your current skill set and the skills required for your target job or tasks.

[0068] "Learning methods" refer to means such as courses, training, and resources available to acquire lacking skills.

[0069] This invention provides a system to support employees' career development. This system aggregates employees' work experience information and evaluation information, and uses this information to build a generative machine learning model to propose optimal placements.

[0070] The server uses a cloud database to collect employee work experience and evaluation information. Specifically, it imports project history and evaluation data via APIs. This information is stored in the database for later analysis.

[0071] Next, the server uses Python and machine learning libraries such as TENSORFLOW® to build a generative machine learning model based on the collected data. This model is used to predict which tasks or roles employees are best suited for and can maximize their performance in. The model is trained using a dataset that combines historical performance data and business requirements.

[0072] The terminal displays suggestions received from the server to the employee. For example, it uses a GUI to visually represent how well the employee's skill set matches the suggested placement. Furthermore, it uses pop-up messages and notification features to present learning resources and training programs for any missing skills.

[0073] Based on the information presented, users can develop their own career plans and select learning methods based on the suggestions. For example, if a user is interested in a position in the sales department, the system will clearly identify the skills required for the sales department and the user's skill gap, and suggest online courses to bridge that gap. Through this process, employees can hone their skills and prepare to take the next step in their careers.

[0074] Examples of prompts include, "Analyze and propose appropriate placements in the new department based on employees' past project experience and skills," and "Compare the skills required for a specific position with the employees' current skills and suggest learning methods."

[0075] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0076] Step 1:

[0077] The server collects work experience and evaluation information from employees into a database. First, it obtains employee project history and skill sets as input data via an API. Using this input data, it executes a process to accumulate information in a cloud database. As a result, detailed historical data for each employee is saved as output to the database.

[0078] Step 2:

[0079] The server uses the collected data to build a generative AI model. Using Python and machine learning libraries such as TensorFlow, it uses past work experience and evaluation information obtained from the database as input data for the model. This data is processed, and machine learning algorithms are used to analyze patterns and train the predictive model. The output of this process is the generation of an AI model that predicts the optimal placement of employees.

[0080] Step 3:

[0081] The server generates placement suggestions using a trained AI model. The AI ​​model analyzes employee skills and the company's required competency based on collected data. The server inputs the employee's current status into the AI ​​model, obtaining the optimal department and position as output. This output is then prepared as an appropriate placement suggestion for each employee.

[0082] Step 4:

[0083] The terminal presents employees with appropriate placement suggestions sent from the server. The terminal visually represents, through its user interface, how well the employee's skill set matches the suggested placement. Based on this input data, it outputs information about necessary training and any missing skills. Specifically, it displays the data visually using bar graphs and charts, and provides links to learning resources via pop-up notifications.

[0084] Step 5:

[0085] Based on the information provided by the device, the user reconsiders their career plan. They review the proposed placements and learning methods, and select specific training courses to improve their skills. Based on this input information, a new action plan for skill acquisition is formulated as output. Specific actions include online course registration and scheduling.

[0086] (Application Example 1)

[0087] 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."

[0088] In today's production environment, it is crucial to maximize the capabilities of human resources within factories and improve operational efficiency. However, accurately assigning workers to roles that reflect their individual skills and past experience is difficult, resulting in wasted resources and situations where human potential is not fully realized. Solving this problem and achieving efficient human resource management with the right people in the right positions is essential.

[0089] 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.

[0090] In this invention, the server includes means for collecting personnel competence information and work evaluation information, means for training a generative AI model using the collected information, and means for proposing the optimal placement of personnel using the generative AI model. This enables the optimization of personnel roles within the factory, improving operational efficiency and maximizing the potential of personnel.

[0091] "Human resources" refers to individual workers who possess specific skills and experience and are engaged in work within an organization.

[0092] "Competency information" refers to data on the skills and expertise that workers possess, and serves as the basis for evaluating their usefulness in the workplace.

[0093] "Performance evaluation information" refers to data used to evaluate a worker's performance based on the results and efficiency of tasks they have performed in the past.

[0094] A "generative AI model" refers to artificial intelligence that is trained based on collected data and capable of performing specific tasks or making predictions.

[0095] "Optimal placement" refers to assigning individual workers to positions where they can perform most effectively, based on their abilities and the requirements of their work.

[0096] A "factory" refers to a production site equipped with specialized equipment and facilities for manufacturing and assembling products.

[0097] "Business efficiency" refers to the effectiveness of work in order to minimize the time and effort required to perform tasks and maximize results.

[0098] "Resources" is a general term for the personnel, equipment, time, and information available within an organization, and refers to the elements necessary for carrying out business operations.

[0099] A description of the embodiment for carrying out the invention will be provided.

[0100] This invention is a system for achieving optimal personnel allocation within a factory. The server collects personnel ability information and performance evaluation information, and uses this information to train a generative AI model. The generative AI model is built using machine learning libraries such as PyTorch and TensorFlow, which are based on Python. The data collected by the server is centrally stored in a database (e.g., SQLite or Google FI® rebase) and used as material for analysis. This analysis allows for the prediction of the optimal allocation of each personnel.

[0101] The server sends the optimal placement determined by the generated AI model to the terminal, and factory managers can receive this suggestion using their smartphones or tablets. Managers refer to the suggestion and make changes or reassignments to personnel within the factory. Based on the suggested placement, the system also provides specific training courses and online resources for the skills that each employee needs to acquire.

[0102] The administrator, as the user, can optimize operations based on the provided plan. For example, to distribute the workload on a factory production line, highly skilled workers can be assigned to the highest-demand work stations to improve efficiency.

[0103] An example of a prompt message is: "For departmental placement planning, use employee work experience and evaluation data from the past three months to predict the optimal placement. Please propose a plan considering employee ID, job ID, and skill evaluation." This system enables the optimal use of resources in the factory and leads to efficient operation.

[0104] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0105] Step 1:

[0106] The server collects personnel competency information and performance evaluation information into a database. Inputs include workers' skill sets and past project data. The server integrates and stores this data. SQLite or Google® Firebase are used for the database, and the stored data is then used for subsequent processing.

[0107] Step 2:

[0108] The server uses the collected information to train a generative AI model. The input here is the data collected and stored in Step 1. The server uses Python, leveraging the PyTorch and TensorFlow libraries to build and train the AI ​​model. This model forms the basis for predicting the optimal placement of personnel. The output is an inference model for placement prediction.

[0109] Step 3:

[0110] The server uses a trained AI model to predict the optimal placement of personnel. In this step, the AI ​​model obtained in step 2 and real-time work requests are used as input to estimate the appropriate location and role for each personnel. The output is a proposal for the optimal placement for each personnel.

[0111] Step 4:

[0112] The terminal receives optimal placement suggestions sent from the server and displays them to the factory manager. The input is the placement suggestion data generated in step 3. The terminal converts this data into a readable format and presents it in a way that is easy for the manager to understand. Specifically, it displays it graphically on a smartphone or tablet interface.

[0113] Step 5:

[0114] The user, the factory manager, evaluates the presented optimal placement proposal and implements the placement plan as needed. The manager provides feedback to the system based on the prompts to support improvements to future proposals. This feedback is then returned to step 1 and repeatedly used as data to train a newly generated AI model.

[0115] 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.

[0116] This invention is a system that combines emotion recognition functionality with a system designed to promote employee career development. This enables the system to optimize suggestions and interfaces while considering the user's emotional state.

[0117] The server collects employee experience data and performance evaluation data, and uses this to train a generative AI model. Based on the insights gained from this training, it suggests the most suitable department and position for each employee. Furthermore, if an employee lacks the necessary skills for appropriate placement, it provides guidance on how to acquire those skills.

[0118] In addition, the emotion engine analyzes the user's facial expressions and voice data during interactions to infer their emotional state. This allows the system to adjust the career plan it proposes to make it more relatable. For example, if it determines that the user is stressed, it can make suggestions more cautious or include encouraging messages.

[0119] The device not only displays suggestions and learning methods sent from the server, but also recognizes the user's emotions and provides a flexible interface based on them. The system's emotionally responsive approach allows users to make more conscious choices about their career path.

[0120] For example, if an employee is feeling anxious about moving to a new department, the emotion engine will detect this anxiety, and the terminal will display the system's suggestions in a reassuring tone. It can also present specific success stories and messages from senior employees. This makes users more likely to accept the suggestions.

[0121] This structure makes it possible to provide flexible and emotionally responsive career development support to each individual employee.

[0122] The following describes the processing flow.

[0123] Step 1:

[0124] The terminal provides an interface for users to input information about their past work experience, current skills, and performance evaluations. Users input this information and send the data to the server.

[0125] Step 2:

[0126] The server stores user data sent from the terminal in a database. The server then organizes the stored data for training a generating AI model.

[0127] Step 3:

[0128] The server trains a generative AI model to determine suitable career placements for employees. This model improves its accuracy by learning from past success stories and skill patterns.

[0129] Step 4:

[0130] The terminal presents the user with carrier placement suggestions generated by an AI model sent from the server. This suggestion includes the skills required for the suggested placement and how to acquire them.

[0131] Step 5:

[0132] The emotion engine analyzes user facial expressions and voice data in real time to determine the user's emotional state. The server collects this emotion data and uses it to adjust suggestions and the interface.

[0133] Step 6:

[0134] The server adjusts the content presented based on the results of the emotion engine, generating a career plan that takes the user's emotions into consideration. If necessary, it changes the tone and content of the messages displayed on the device.

[0135] Step 7:

[0136] The user reviews the optimized plan and sends feedback to the server via their device. The server readjusts the career plan based on the feedback and makes further suggestions that take their emotional state into consideration.

[0137] (Example 2)

[0138] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server" and the smart device 14 as the "terminal".

[0139] Conventional career development support systems often fail to consider the user's emotional state when making suggestions, resulting in ineffective acceptance of those suggestions. Furthermore, beyond simply presenting methods for acquiring technical skills, a function is needed to adjust the interface based on the user's emotions. The challenge lies in effectively supporting career development while alleviating user anxiety and stress.

[0140] 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.

[0141] In this invention, the server includes means for collecting employee work data, means for training a machine learning model, and means for acquiring and analyzing user facial and voice data to infer emotional state. This makes it possible to suggest appropriate job roles and adjust the interface according to the user's emotional state.

[0142] "Employee work data" refers to digital data that includes each employee's work history and performance evaluation information within a company.

[0143] A "machine learning model" is a model constructed using algorithms that learn patterns based on data, and is used for prediction and classification.

[0144] "Job assignment" is a concept that refers to assigning employees to appropriate jobs within an organization based on their individual abilities and aptitudes.

[0145] "Methods of acquiring skills" refers to information that describes the methods and means of obtaining the abilities and knowledge necessary to perform a specific job.

[0146] "User facial and voice data" refers to digital information including the user's facial movements and changes in voice, and is used to analyze their emotional state.

[0147] "Means for inferring emotional state" refers to technologies and methods for analyzing facial and voice data obtained from users to determine their psychological state and emotions.

[0148] "Interface adjustment" is a technique that optimizes the user's operating environment and screen display by changing them to match the user's situation and emotions.

[0149] This invention is a system designed to support employees' career development, utilizing emotion recognition to provide users with optimal suggestions. The system operates according to the following procedure.

[0150] First, the server collects employee work data from the company's database. This data includes employees' past work experience, performance evaluations, and skill sets, and is retrieved using SQL queries in the database management system (DBMS).

[0151] Next, the server uses the collected business data to train generative AI models using machine learning frameworks such as TensorFlow and PyTorch. These models learn trends related to employees' career development and are used to predict which positions or departments are suitable for them.

[0152] The device also uses a camera and microphone to capture the user's facial expressions and voice in real time. This data is analyzed by an emotion engine to infer the user's emotional state. This process utilizes OpenCV and other voice analysis technologies.

[0153] When a user uses this system, the terminal not only displays job assignment suggestions sent from the server, but also adjusts the interface based on the user's emotions. For example, if the user expresses anxiety, the terminal can present the suggestions in a calmer tone and display encouraging messages and relevant success stories.

[0154] For example, a user might be stressed after receiving a proposal to transfer to a new department. In this case, if the emotion engine detects the user's stress level, the device can present the proposal in a way that makes it easier to accept and display a reassuring message.

[0155] Furthermore, an example of a prompt given to the generating AI model might be something like, "Based on the employee's past work performance and current skills, please suggest the most suitable next career step."

[0156] Therefore, this system can provide career support that takes the emotions of each individual employee into consideration.

[0157] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0158] Step 1:

[0159] The server retrieves employee work data from the company's database. Employee IDs and necessary data items are provided as input. The server extracts data from the database using SQL queries, obtaining employee experience and evaluation information as output. Specifically, it performs data cleansing to remove noisy data.

[0160] Step 2:

[0161] The server trains a machine learning model using the business data collected in Step 1. The input is pre-processed employee business data. The server builds a generative AI model using TensorFlow or PyTorch and optimizes the model's parameters. The output is a trained model related to employee career development. Specifically, the data is split into a training set and a validation set, and the generalization performance of the model is evaluated.

[0162] Step 3:

[0163] The server uses a trained model to generate suggestions for suitable job placements for employees. The inputs are the trained generative AI model and the employees' latest work data. Based on these inputs, the server performs data calculations to suggest the most suitable job and department for each employee. The output is a suggestion for appropriate job placement. Specifically, the generative AI model is given the prompt "Suggest the optimal career path based on the employee's work history."

[0164] Step 4:

[0165] The terminal receives job assignment suggestions sent from the server and displays them to the user. The input is the suggestion information sent from the server. The terminal displays the received information on a user interface such as the screen. The output is a career suggestion presented to the user on the screen. Specifically, the information is arranged in a visually easy-to-understand format using GUI widgets.

[0166] Step 5:

[0167] The device acquires the user's facial expressions and voice, and begins processing to analyze their emotional state. The input is real-time data from the user obtained through the camera and microphone. The device sends this information to an emotion engine, which analyzes emotions using speech recognition and image processing technologies. The output is the user's emotional state, expressed as a numerical value or category. Specifically, the device adjusts the color scheme or message content of the user interface based on the analysis results.

[0168] Step 6:

[0169] The server optimizes the presentation of career suggestions using emotion analysis results. The input consists of emotional state data and suggested job placements. Based on this, the server calculates appropriate interface changes and generates optimized presentation content as output. Specifically, if the user is feeling anxious, the server makes adjustments such as adding encouraging words to the suggestion message.

[0170] (Application Example 2)

[0171] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as the "server," and the smart device 14 will be referred to as the "terminal."

[0172] In today's work environment, there is a need to accurately understand the individual career needs and emotional states of each employee and provide them with the optimal career plan based on that understanding. However, existing systems often fail to address employees' emotions and end up offering uniform proposals. As a result, employees feel anxiety and resistance to the proposals, and problems arise in the smooth progress of their career development. This invention aims to solve these problems and more effectively support employees' career development.

[0173] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0174] In this invention, the server includes means for collecting employee experience information and performance evaluation information, means for training a predictive model using the collected information, means for proposing appropriate employee placement using the predictive model, means for emotion recognition for analyzing facial and voice data and inferring the user's emotional state, and means for adjusting the method of presenting career plans according to the user's emotional state. This makes it possible to provide flexible career plans based on the employee's emotional state.

[0175] "Employee experience information" refers to the history of skills, knowledge, and experience that an employee has acquired through their work.

[0176] "Performance evaluation information" refers to evaluations and feedback regarding employees' work results and performance.

[0177] A "predictive model" is the structure of artificial intelligence trained to predict future outcomes based on collected data.

[0178] "Appropriate placement" refers to assigning employees to jobs and roles that are best suited to their abilities and characteristics.

[0179] "Methods for acquiring missing skills" refers to the means and training methods for employees to efficiently learn the necessary skills.

[0180] "Facial and voice data" refers to digital information that shows the user's facial movements and voice tone.

[0181] "Emotion recognition methods" refer to technologies that analyze facial expressions and voice data and use that information to infer an individual's emotional state.

[0182] "Adjusting the presentation method of career plans" refers to changing the form and content of the proposed career plan to suit the individual's emotional state.

[0183] This invention is a system designed to support the individual career development of employees, and in particular, enables flexible career plan proposals tailored to the user's emotional state.

[0184] The server collects employee experience and performance evaluation information and uses it to train a predictive model. The collected information is stored in a database and analyzed using a specific algorithm (e.g., deep learning). The predictive model, as a generative AI model, is used to evaluate employees' skills and aptitudes and propose optimal placements and required skills. For emotion recognition, a camera (e.g., a high-resolution webcam) is used to acquire user facial data in real time, and a voice input device (e.g., a microphone) is used to analyze voice data. This utilizes facial recognition libraries such as OpenCV and the Google Cloud Speech-to-Text API. This allows the system to infer user emotions and present career plans in an emotionally responsive manner.

[0185] The device displays career plans and learning methods suggested by the server and dynamically adjusts the interface based on the user's emotions. Specifically, if the user shows anxiety, it displays encouraging messages on the screen and provides guidance in a reassuring tone. This functionality is achieved through the display and speaker installed in the device.

[0186] Users can interact naturally with the suggestions provided through this system. For example, if a user is feeling anxious about a new job, the emotion engine will detect this anxiety and provide a message such as, "Your experience will be valuable in your new role. Please refer to past success stories." In this way, emotion-based suggestions allow users to engage more proactively in their career development.

[0187] An example of a prompt would be, "Generate an encouraging message that takes into account the user's feelings of anxiety about their promotion." This allows the generative AI model to generate an appropriate message and provide the user with the most relevant information.

[0188] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0189] Step 1:

[0190] The server collects employee experience and performance evaluation information from a database. This input includes employee work history, past performance evaluations, and acquired skills. Based on this data, the server performs appropriate data cleaning and normalization, and processes it into a format that can be applied to training predictive models.

[0191] Step 2:

[0192] The server uses the collected information to train a generative AI model. Here, the data is used as input, and a deep learning algorithm forms a predictive model. As output, it generates a list of optimal placements and areas where skills are lacking for each employee. This results in personalized predictions for each employee.

[0193] Step 3:

[0194] The server presents the user's device with prediction results along with instructions on how to acquire the necessary skills. At this stage, the server sends the results of the already formed prediction model to the device and suggests the next action the user should take. Specifically, this may include links to online courses or training programs.

[0195] Step 4:

[0196] The device transmits facial and voice data acquired from the user to the server. The input here is biometric data obtained through the camera and microphone. Based on this, the server performs emotion recognition using OpenCV or the Google Cloud Speech-to-Text API.

[0197] Step 5:

[0198] The server uses the emotion recognition results to generate prompt messages through a generative AI model, adjusting the career plan presentation to suit the user's emotional state. The data calculations here include message generation that reflects the emotional state through the prompt messages. The output is an emotionally resonant suggestion message that is easily accepted by the user.

[0199] Step 6:

[0200] Users receive a career plan presented via their device and provide feedback. Based on this input, the device sends feedback data to the server. This feedback data is used to improve the proposed plan and contribute to further enhancing the accuracy of the model.

[0201] 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.

[0202] 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.

[0203] 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.

[0204] [Second Embodiment]

[0205] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0206] 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.

[0207] 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).

[0208] 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.

[0209] 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.

[0210] 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).

[0211] 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.

[0212] 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.

[0213] 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.

[0214] 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.

[0215] 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.

[0216] 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".

[0217] This invention provides a system to support employees' career development. The system aims to train a generating AI model using employee experience data and performance evaluation data, and to propose appropriate job placements.

[0218] The server first collects data on each employee's past work experience and skills. This includes information such as the projects the employee has been involved in and the skills they possess. The server also collects performance evaluation data, accumulating information to evaluate each employee's individual performance.

[0219] Next, the server uses this data to train a generative AI model. Based on historical data, the generative AI model predicts which department or position an employee can perform best in. This process allows the server to show each employee the optimal career path.

[0220] The terminal displays optimal placement suggestions sent from the server to the employee. This information specifically indicates which department best suits the employee's skill set and which skills are lacking. The terminal also presents the employee with specific learning resources and training programs to address any skill gaps.

[0221] Users can make decisions based on their career plans and suggested learning methods. This allows employees to take clear steps toward their career goals.

[0222] To give a concrete example, if an employee expresses interest in taking on a new project in the sales department, the terminal analyzes their past project experience and skills to present the skill set required for the sales department and highlight any skill gaps they may have. Furthermore, the server suggests specific training courses and online resources to bridge those gaps. In this way, employees can improve their skills and prepare to take on new roles.

[0223] The following describes the processing flow.

[0224] Step 1:

[0225] The terminal provides an interface for users to input information related to their past career development, current skills, and work performance evaluations. Users input the necessary information and send the data to the server.

[0226] Step 2:

[0227] The server stores user data received from terminals in a centralized database. The server organizes the data into a parseable format and verifies its integrity.

[0228] Step 3:

[0229] The server uses stored data to train the generated AI model. This includes a process of learning past employee success trends and optimal talent placement patterns.

[0230] Step 4:

[0231] The server uses a trained AI model to generate suggestions for the most suitable department and position based on the user's skills and experience. Furthermore, it identifies any missing skills or required experience.

[0232] Step 5:

[0233] The server evaluates specific training resources and learning methods to help the user improve their skills, along with suggesting the optimal carrier placement, and sends this information to the terminal.

[0234] Step 6:

[0235] The device presents information received from the server to the user in an easy-to-understand manner. The user views and considers the recommended carrier configuration and learning methods.

[0236] Step 7:

[0237] Users provide feedback and make selections regarding the suggestions through their device. The device sends user feedback to the server and initiates a process to further refine the suggestions as needed.

[0238] (Example 1)

[0239] 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."

[0240] In modern companies, there is a need for efficient personnel allocation in order to utilize the abilities of each employee in the most optimal way and improve performance. However, systematically analyzing the diverse experience and skill sets of employees and making accurate placement proposals based on that analysis is difficult. Furthermore, there is a lack of means to support employees' career development based on those proposals. Therefore, companies are required to efficiently provide concrete learning methods to bridge the skill gaps of their employees.

[0241] 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.

[0242] In this invention, the server includes means for aggregating personnel's work experience information and evaluation information, means for constructing a generative machine learning model using the aggregated information, and means for proposing the optimal placement of personnel using the generative machine learning model. This makes it possible to analyze the skills and work requirements of each employee in detail and propose the optimal placement and growth strategy.

[0243] "Human resources" refers to individual employees or staff members who play an active role within a company or organization.

[0244] "Work experience information" refers to specific information about projects and tasks that employees have worked on in the past.

[0245] "Evaluation information" refers to information regarding performance evaluations given based on an employee's ability to perform their job and their results.

[0246] "Aggregation" refers to the process of gathering and organizing dispersed information in one place.

[0247] A "generative machine learning model" refers to a statistical model that uses historical data to make predictions or classifications for a specific purpose.

[0248] "Optimal placement" refers to assigning employees to jobs and positions that maximize their individual capabilities, taking into account their skills and the organization's needs.

[0249] "Proposal" refers to the act of providing information that indicates the optimal action or option based on the analysis results.

[0250] A "skill gap" refers to the difference between your current skill set and the skills required for your target job or tasks.

[0251] "Learning methods" refer to means such as courses, training, and resources available to acquire lacking skills.

[0252] This invention provides a system to support employees' career development. This system aggregates employees' work experience information and evaluation information, and uses this information to build a generative machine learning model to propose optimal placements.

[0253] The server uses a cloud database to collect employee work experience and evaluation information. Specifically, it imports project history and evaluation data via APIs. This information is stored in the database for later analysis.

[0254] Next, the server uses Python and machine learning libraries such as TensorFlow to build a generative machine learning model based on the collected data. This model is used to predict which tasks or roles employees are best suited for and can maximize their performance in. The model is trained using a dataset that combines historical performance data and business requirements.

[0255] The terminal displays suggestions received from the server to the employee. For example, it uses a GUI to visually represent how well the employee's skill set matches the suggested placement. Furthermore, it uses pop-up messages and notification features to present learning resources and training programs for any missing skills.

[0256] Based on the information presented, users can develop their own career plans and select learning methods based on the suggestions. For example, if a user is interested in a position in the sales department, the system will clearly identify the skills required for the sales department and the user's skill gap, and suggest online courses to bridge that gap. Through this process, employees can hone their skills and prepare to take the next step in their careers.

[0257] Examples of prompts include, "Analyze and propose appropriate placements in the new department based on employees' past project experience and skills," and "Compare the skills required for a specific position with the employees' current skills and suggest learning methods."

[0258] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0259] Step 1:

[0260] The server collects work experience and evaluation information from employees into a database. First, it obtains employee project history and skill sets as input data via an API. Using this input data, it executes a process to accumulate information in a cloud database. As a result, detailed historical data for each employee is saved as output to the database.

[0261] Step 2:

[0262] The server uses the collected data to build a generative AI model. Using Python and machine learning libraries such as TensorFlow, it uses past work experience and evaluation information obtained from the database as input data for the model. This data is processed, and machine learning algorithms are used to analyze patterns and train the predictive model. The output of this process is the generation of an AI model that predicts the optimal placement of employees.

[0263] Step 3:

[0264] The server generates placement suggestions using a trained AI model. The AI ​​model analyzes employee skills and the company's required competency based on collected data. The server inputs the employee's current status into the AI ​​model, obtaining the optimal department and position as output. This output is then prepared as an appropriate placement suggestion for each employee.

[0265] Step 4:

[0266] The terminal presents employees with appropriate placement suggestions sent from the server. The terminal visually represents, through its user interface, how well the employee's skill set matches the suggested placement. Based on this input data, it outputs information about necessary training and any missing skills. Specifically, it displays the data visually using bar graphs and charts, and provides links to learning resources via pop-up notifications.

[0267] Step 5:

[0268] Based on the information provided by the device, the user reconsiders their career plan. They review the proposed placements and learning methods, and select specific training courses to improve their skills. Based on this input information, a new action plan for skill acquisition is formulated as output. Specific actions include online course registration and scheduling.

[0269] (Application Example 1)

[0270] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server," and the smart glasses 214 will be referred to as the "terminal."

[0271] In today's production environment, it is crucial to maximize the capabilities of human resources within factories and improve operational efficiency. However, accurately assigning workers to roles that reflect their individual skills and past experience is difficult, resulting in wasted resources and situations where human potential is not fully realized. Solving this problem and achieving efficient human resource management with the right people in the right positions is essential.

[0272] 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.

[0273] In this invention, the server includes means for collecting personnel competence information and work evaluation information, means for training a generative AI model using the collected information, and means for proposing the optimal placement of personnel using the generative AI model. This enables the optimization of personnel roles within the factory, improving operational efficiency and maximizing the potential of personnel.

[0274] "Human resources" refers to individual workers who possess specific skills and experience and are engaged in work within an organization.

[0275] "Competency information" refers to data on the skills and expertise that workers possess, and serves as the basis for evaluating their usefulness in the workplace.

[0276] "Performance evaluation information" refers to data used to evaluate a worker's performance based on the results and efficiency of tasks they have performed in the past.

[0277] A "generative AI model" refers to artificial intelligence that is trained based on collected data and capable of performing specific tasks or making predictions.

[0278] "Optimal allocation" refers to the placement of each worker in a position where they can perform most effectively based on their individual abilities and job requirements.

[0279] "Factory" refers to the production site equipped with specialized facilities and equipment for manufacturing and assembling products.

[0280] "Business efficiency" refers to the effectiveness of work in minimizing the time and effort required to perform a task and maximizing the results.

[0281] "Resources" is a general term for human resources, equipment, time, information, etc. available within an organization, referring to the elements necessary for performing tasks.

[0282] The form for implementing the invention will be described.

[0283] The present invention is a system for realizing the optimal allocation of human resources within a factory. The server is responsible for integrating the ability information and job evaluation information of human resources and training a generated AI model based on this information. The generated AI model is constructed using machine learning libraries such as PyTorch or TensorFlow based on Python. The data collected by the server is uniformly stored in a database (e.g., SQLite or Google Firebase) and serves as materials for analysis. Through this analysis, the optimal allocation of each human resource can be predicted.

[0284] The server sends the optimal allocation derived by the generated AI model to the terminal, and the factory manager can receive this proposal using a smartphone or tablet. The manager refers to the proposal and makes changes or reallocations to the human resources within the factory. Also, based on the presented allocation, specific training courses and online resources are presented for the unacquired abilities that each human resource should acquire.

[0285] As an administrator who is a user, it is possible to optimize operations based on the provided plan. As a specific example, in order to distribute the load on the manufacturing line in the factory, highly skilled workers can be placed at the workstations with the highest demand, aiming to improve efficiency.

[0286] As an example of a prompt sentence, "For the department layout plan, please predict the optimal layout using the business experience and evaluation data of employees in the past three months. Please consider employee ID, job ID, and skill evaluation and propose a plan." can be cited. With this system, it becomes possible to optimize the utilization of resources in the factory and achieve efficient operation of business.

[0287] The flow of the specific process in Application Example 1 will be described using FIG. 12.

[0288] Step 1:

[0289] The server collects the ability information and business evaluation information of human resources in the database. The inputs include the skill set of workers and past project data. The server integrates and stores these data. SQLite or Google Firebase is used in the database to provide the accumulated data for subsequent processing.

[0290] Step 2:

[0291] The server trains a generated AI model using the collected information. The input here is the data collected and saved in Step 1. The server uses Python and utilizes PyTorch or TensorFlow libraries to build and train the AI model. This model serves as a basis for predicting the optimal placement of human resources. As an output, an inference model for placement prediction is obtained.

[0292] Step 3:

[0293] The server uses a trained AI model to predict the optimal placement of personnel. In this step, the AI ​​model obtained in step 2 and real-time work requests are used as input to estimate the appropriate location and role for each personnel. The output is a proposal for the optimal placement for each personnel.

[0294] Step 4:

[0295] The terminal receives optimal placement suggestions sent from the server and displays them to the factory manager. The input is the placement suggestion data generated in step 3. The terminal converts this data into a readable format and presents it in a way that is easy for the manager to understand. Specifically, it displays it graphically on a smartphone or tablet interface.

[0296] Step 5:

[0297] The user, the factory manager, evaluates the presented optimal placement proposal and implements the placement plan as needed. The manager provides feedback to the system based on the prompts to support improvements to future proposals. This feedback is then returned to step 1 and repeatedly used as data to train a newly generated AI model.

[0298] 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.

[0299] This invention is a system that combines emotion recognition functionality with a system designed to promote employee career development. This enables the system to optimize suggestions and interfaces while considering the user's emotional state.

[0300] The server collects employee experience data and performance evaluation data, and uses this to train a generative AI model. Based on the insights gained from this training, it suggests the most suitable department and position for each employee. Furthermore, if an employee lacks the necessary skills for appropriate placement, it provides guidance on how to acquire those skills.

[0301] In addition, the emotion engine analyzes the user's facial expressions and voice data during interactions to infer their emotional state. This allows the system to adjust the career plan it proposes to make it more relatable. For example, if it determines that the user is stressed, it can make suggestions more cautious or include encouraging messages.

[0302] The device not only displays suggestions and learning methods sent from the server, but also recognizes the user's emotions and provides a flexible interface based on them. The system's emotionally responsive approach allows users to make more conscious choices about their career path.

[0303] For example, if an employee is feeling anxious about moving to a new department, the emotion engine will detect this anxiety, and the terminal will display the system's suggestions in a reassuring tone. It can also present specific success stories and messages from senior employees. This makes users more likely to accept the suggestions.

[0304] This structure makes it possible to provide flexible and emotionally responsive career development support to each individual employee.

[0305] The following describes the processing flow.

[0306] Step 1:

[0307] The terminal provides an interface for users to input information about their past work experience, current skills, and performance evaluations. Users input this information and send the data to the server.

[0308] Step 2:

[0309] The server saves the user's data sent from the terminal to the database. The server organizes the saved data for training the generative AI model.

[0310] Step 3:

[0311] The server trains the generative AI model and determines a career placement suitable for the employee. This model improves its accuracy by learning past success cases and skill patterns.

[0312] Step 4:

[0313] The terminal presents the user with a career placement proposal by the AI model sent from the server. This presentation also includes the skills required for the proposed placement and how to acquire them.

[0314] Step 5:

[0315] The emotion engine analyzes the user's facial expression and voice data in real time and determines the user's emotional state. The server collects this emotion data and uses it as information for adjusting proposals and interfaces.

[0316] Step 6:

[0317] The server adjusts the presentation content based on the results of the emotion engine and generates a career plan that takes into account the user's emotions. As needed, it changes the tone and content of the message displayed on the terminal.

[0318] Step 7:

[0319] The user checks the optimized plan and sends feedback to the server through the terminal. The server readjusts the career plan according to the feedback and makes further proposals considering the emotional state.

[0320] (Example 2)

[0321] 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".

[0322] Conventional career development support systems often fail to consider the user's emotional state when making suggestions, resulting in ineffective acceptance of those suggestions. Furthermore, beyond simply presenting methods for acquiring technical skills, a function is needed to adjust the interface based on the user's emotions. The challenge lies in effectively supporting career development while alleviating user anxiety and stress.

[0323] 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.

[0324] In this invention, the server includes means for collecting employee work data, means for training a machine learning model, and means for acquiring and analyzing user facial and voice data to infer emotional state. This makes it possible to suggest appropriate job roles and adjust the interface according to the user's emotional state.

[0325] "Employee work data" refers to digital data that includes each employee's work history and performance evaluation information within a company.

[0326] A "machine learning model" is a model constructed using algorithms that learn patterns based on data, and is used for prediction and classification.

[0327] "Job assignment" is a concept that refers to assigning employees to appropriate jobs within an organization based on their individual abilities and aptitudes.

[0328] "Methods of acquiring skills" refers to information that describes the methods and means of obtaining the abilities and knowledge necessary to perform a specific job.

[0329] "User facial and voice data" refers to digital information including the user's facial movements and changes in voice, and is used to analyze their emotional state.

[0330] "Means for inferring emotional state" refers to technologies and methods for analyzing facial and voice data obtained from users to determine their psychological state and emotions.

[0331] "Interface adjustment" is a technique that optimizes the user's operating environment and screen display by changing them to match the user's situation and emotions.

[0332] This invention is a system designed to support employees' career development, utilizing emotion recognition to provide users with optimal suggestions. The system operates according to the following procedure.

[0333] First, the server collects employee work data from the company's database. This data includes employees' past work experience, performance evaluations, and skill sets, and is retrieved using SQL queries in the database management system (DBMS).

[0334] Next, the server uses the collected business data to train generative AI models using machine learning frameworks such as TensorFlow and PyTorch. These models learn trends related to employees' career development and are used to predict which positions or departments are suitable for them.

[0335] The device also uses a camera and microphone to capture the user's facial expressions and voice in real time. This data is analyzed by an emotion engine to infer the user's emotional state. This process utilizes OpenCV and other voice analysis technologies.

[0336] When a user uses this system, the terminal not only displays job assignment suggestions sent from the server, but also adjusts the interface based on the user's emotions. For example, if the user expresses anxiety, the terminal can present the suggestions in a calmer tone and display encouraging messages and relevant success stories.

[0337] For example, a user might be stressed after receiving a proposal to transfer to a new department. In this case, if the emotion engine detects the user's stress level, the device can present the proposal in a way that makes it easier to accept and display a reassuring message.

[0338] Furthermore, an example of a prompt given to the generating AI model might be something like, "Based on the employee's past work performance and current skills, please suggest the most suitable next career step."

[0339] Therefore, this system can provide career support that takes the emotions of each individual employee into consideration.

[0340] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0341] Step 1:

[0342] The server retrieves employee work data from the company's database. Employee IDs and necessary data items are provided as input. The server extracts data from the database using SQL queries, obtaining employee experience and evaluation information as output. Specifically, it performs data cleansing to remove noisy data.

[0343] Step 2:

[0344] The server trains a machine learning model using the business data collected in Step 1. The input is pre-processed employee business data. The server builds a generative AI model using TensorFlow or PyTorch and optimizes the model's parameters. The output is a trained model related to employee career development. Specifically, the data is split into a training set and a validation set, and the generalization performance of the model is evaluated.

[0345] Step 3:

[0346] The server uses a trained model to generate suggestions for suitable job placements for employees. The inputs are the trained generative AI model and the employees' latest work data. Based on these inputs, the server performs data calculations to suggest the most suitable job and department for each employee. The output is a suggestion for appropriate job placement. Specifically, the generative AI model is given the prompt "Suggest the optimal career path based on the employee's work history."

[0347] Step 4:

[0348] The terminal receives job assignment suggestions sent from the server and displays them to the user. The input is the suggestion information sent from the server. The terminal displays the received information on a user interface such as the screen. The output is a career suggestion presented to the user on the screen. Specifically, the information is arranged in a visually easy-to-understand format using GUI widgets.

[0349] Step 5:

[0350] The device acquires the user's facial expressions and voice, and begins processing to analyze their emotional state. The input is real-time data from the user obtained through the camera and microphone. The device sends this information to an emotion engine, which analyzes emotions using speech recognition and image processing technologies. The output is the user's emotional state, expressed as a numerical value or category. Specifically, the device adjusts the color scheme or message content of the user interface based on the analysis results.

[0351] Step 6:

[0352] The server optimizes the presentation of career suggestions using emotion analysis results. The input consists of emotional state data and suggested job placements. Based on this, the server calculates appropriate interface changes and generates optimized presentation content as output. Specifically, if the user is feeling anxious, the server makes adjustments such as adding encouraging words to the suggestion message.

[0353] (Application Example 2)

[0354] 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."

[0355] In today's work environment, there is a need to accurately understand the individual career needs and emotional states of each employee and provide them with the optimal career plan based on that understanding. However, existing systems often fail to address employees' emotions and end up offering uniform proposals. As a result, employees feel anxiety and resistance to the proposals, and problems arise in the smooth progress of their career development. This invention aims to solve these problems and more effectively support employees' career development.

[0356] 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.

[0357] In this invention, the server includes means for collecting employee experience information and performance evaluation information, means for training a predictive model using the collected information, means for proposing appropriate employee placement using the predictive model, means for emotion recognition for analyzing facial and voice data and inferring the user's emotional state, and means for adjusting the method of presenting career plans according to the user's emotional state. This makes it possible to provide flexible career plans based on the employee's emotional state.

[0358] "Employee experience information" refers to the history of skills, knowledge, and experience that an employee has acquired through their work.

[0359] "Performance evaluation information" refers to evaluations and feedback regarding employees' work results and performance.

[0360] A "predictive model" is the structure of artificial intelligence trained to predict future outcomes based on collected data.

[0361] "Appropriate placement" refers to assigning employees to jobs and roles that are best suited to their abilities and characteristics.

[0362] "Methods for acquiring missing skills" refers to the means and training methods for employees to efficiently learn the necessary skills.

[0363] "Facial and voice data" refers to digital information that shows the user's facial movements and voice tone.

[0364] "Emotion recognition methods" refer to technologies that analyze facial expressions and voice data and use that information to infer an individual's emotional state.

[0365] "Adjusting the presentation method of career plans" refers to changing the form and content of the proposed career plan to suit the individual's emotional state.

[0366] This invention is a system designed to support the individual career development of employees, and in particular, enables flexible career plan proposals tailored to the user's emotional state.

[0367] The server collects employee experience and performance evaluation information and uses it to train a predictive model. The collected information is stored in a database and analyzed using a specific algorithm (e.g., deep learning). The predictive model, as a generative AI model, is used to evaluate employees' skills and aptitudes and propose optimal placements and required skills. For emotion recognition, a camera (e.g., a high-resolution webcam) is used to acquire user facial data in real time, and a voice input device (e.g., a microphone) is used to analyze voice data. This utilizes facial recognition libraries such as OpenCV and the Google Cloud Speech-to-Text API. This allows the system to infer user emotions and present career plans in an emotionally responsive manner.

[0368] The device displays career plans and learning methods suggested by the server and dynamically adjusts the interface based on the user's emotions. Specifically, if the user shows anxiety, it displays encouraging messages on the screen and provides guidance in a reassuring tone. This functionality is achieved through the display and speaker installed in the device.

[0369] Users can interact naturally with the suggestions provided through this system. For example, if a user is feeling anxious about a new job, the emotion engine will detect this anxiety and provide a message such as, "Your experience will be valuable in your new role. Please refer to past success stories." In this way, emotion-based suggestions allow users to engage more proactively in their career development.

[0370] An example of a prompt would be, "Generate an encouraging message that takes into account the user's feelings of anxiety about their promotion." This allows the generative AI model to generate an appropriate message and provide the user with the most relevant information.

[0371] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0372] Step 1:

[0373] The server collects employee experience and performance evaluation information from a database. This input includes employee work history, past performance evaluations, and acquired skills. Based on this data, the server performs appropriate data cleaning and normalization, and processes it into a format that can be applied to training predictive models.

[0374] Step 2:

[0375] The server uses the collected information to train a generative AI model. Here, the data is used as input, and a deep learning algorithm forms a predictive model. As output, it generates a list of optimal placements and areas where skills are lacking for each employee. This results in personalized predictions for each employee.

[0376] Step 3:

[0377] The server presents the user's device with prediction results along with instructions on how to acquire the necessary skills. At this stage, the server sends the results of the already formed prediction model to the device and suggests the next action the user should take. Specifically, this may include links to online courses or training programs.

[0378] Step 4:

[0379] The device transmits facial and voice data acquired from the user to the server. The input here is biometric data obtained through the camera and microphone. Based on this, the server performs emotion recognition using OpenCV or the Google Cloud Speech-to-Text API.

[0380] Step 5:

[0381] The server uses the emotion recognition results to generate prompt messages through a generative AI model, adjusting the career plan presentation to suit the user's emotional state. The data calculations here include message generation that reflects the emotional state through the prompt messages. The output is an emotionally resonant suggestion message that is easily accepted by the user.

[0382] Step 6:

[0383] Users receive a career plan presented via their device and provide feedback. Based on this input, the device sends feedback data to the server. This feedback data is used to improve the proposed plan and contribute to further enhancing the accuracy of the model.

[0384] 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.

[0385] 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.

[0386] 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.

[0387] [Third Embodiment]

[0388] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0389] 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.

[0390] 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).

[0391] 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.

[0392] 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.

[0393] 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).

[0394] 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.

[0395] 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.

[0396] 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.

[0397] 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.

[0398] 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.

[0399] 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".

[0400] This invention provides a system to support employees' career development. The system aims to train a generating AI model using employee experience data and performance evaluation data, and to propose appropriate job placements.

[0401] The server first collects data on each employee's past work experience and skills. This includes information such as the projects the employee has been involved in and the skills they possess. The server also collects performance evaluation data, accumulating information to evaluate each employee's individual performance.

[0402] Next, the server uses this data to train a generative AI model. Based on historical data, the generative AI model predicts which department or position an employee can perform best in. This process allows the server to show each employee the optimal career path.

[0403] The terminal displays optimal placement suggestions sent from the server to the employee. This information specifically indicates which department best suits the employee's skill set and which skills are lacking. The terminal also presents the employee with specific learning resources and training programs to address any skill gaps.

[0404] Users can make decisions based on their career plans and suggested learning methods. This allows employees to take clear steps toward their career goals.

[0405] To give a concrete example, if an employee expresses interest in taking on a new project in the sales department, the terminal analyzes their past project experience and skills to present the skill set required for the sales department and highlight any skill gaps they may have. Furthermore, the server suggests specific training courses and online resources to bridge those gaps. In this way, employees can improve their skills and prepare to take on new roles.

[0406] The following describes the processing flow.

[0407] Step 1:

[0408] The terminal provides an interface for users to input information related to their past career development, current skills, and work performance evaluations. Users input the necessary information and send the data to the server.

[0409] Step 2:

[0410] The server stores user data received from terminals in a centralized database. The server organizes the data into a parseable format and verifies its integrity.

[0411] Step 3:

[0412] The server uses stored data to train the generated AI model. This includes a process of learning past employee success trends and optimal talent placement patterns.

[0413] Step 4:

[0414] The server uses a trained AI model to generate suggestions for the most suitable department and position based on the user's skills and experience. Furthermore, it identifies any missing skills or required experience.

[0415] Step 5:

[0416] The server evaluates specific training resources and learning methods to help the user improve their skills, along with suggesting the optimal carrier placement, and sends this information to the terminal.

[0417] Step 6:

[0418] The device presents information received from the server to the user in an easy-to-understand manner. The user views and considers the recommended carrier configuration and learning methods.

[0419] Step 7:

[0420] Users provide feedback and make selections regarding the suggestions through their device. The device sends user feedback to the server and initiates a process to further refine the suggestions as needed.

[0421] (Example 1)

[0422] 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."

[0423] In modern companies, there is a need for efficient personnel allocation in order to utilize the abilities of each employee in the most optimal way and improve performance. However, systematically analyzing the diverse experience and skill sets of employees and making accurate placement proposals based on that analysis is difficult. Furthermore, there is a lack of means to support employees' career development based on those proposals. Therefore, companies are required to efficiently provide concrete learning methods to bridge the skill gaps of their employees.

[0424] 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.

[0425] In this invention, the server includes means for aggregating personnel's work experience information and evaluation information, means for constructing a generative machine learning model using the aggregated information, and means for proposing the optimal placement of personnel using the generative machine learning model. This makes it possible to analyze the skills and work requirements of each employee in detail and propose the optimal placement and growth strategy.

[0426] "Human resources" refers to individual employees or staff members who play an active role within a company or organization.

[0427] "Work experience information" refers to specific information about projects and tasks that employees have worked on in the past.

[0428] "Evaluation information" refers to information regarding performance evaluations given based on an employee's ability to perform their job and their results.

[0429] "Aggregation" refers to the process of gathering and organizing dispersed information in one place.

[0430] A "generative machine learning model" refers to a statistical model that uses historical data to make predictions or classifications for a specific purpose.

[0431] "Optimal placement" refers to assigning employees to jobs and positions that maximize their individual capabilities, taking into account their skills and the organization's needs.

[0432] "Proposal" refers to the act of providing information that indicates the optimal action or option based on the analysis results.

[0433] A "skill gap" refers to the difference between your current skill set and the skills required for your target job or tasks.

[0434] "Learning methods" refer to means such as courses, training, and resources available to acquire lacking skills.

[0435] This invention provides a system to support employees' career development. This system aggregates employees' work experience information and evaluation information, and uses this information to build a generative machine learning model to propose optimal placements.

[0436] The server uses a cloud database to collect employee work experience and evaluation information. Specifically, it imports project history and evaluation data via APIs. This information is stored in the database for later analysis.

[0437] Next, the server uses Python and machine learning libraries such as TensorFlow to build a generative machine learning model based on the collected data. This model is used to predict which tasks or roles employees are best suited for and can maximize their performance in. The model is trained using a dataset that combines historical performance data and business requirements.

[0438] The terminal displays suggestions received from the server to the employee. For example, it uses a GUI to visually represent how well the employee's skill set matches the suggested placement. Furthermore, it uses pop-up messages and notification features to present learning resources and training programs for any missing skills.

[0439] Based on the information presented, users can develop their own career plans and select learning methods based on the suggestions. For example, if a user is interested in a position in the sales department, the system will clearly identify the skills required for the sales department and the user's skill gap, and suggest online courses to bridge that gap. Through this process, employees can hone their skills and prepare to take the next step in their careers.

[0440] Examples of prompts include, "Analyze and propose appropriate placements in the new department based on employees' past project experience and skills," and "Compare the skills required for a specific position with the employees' current skills and suggest learning methods."

[0441] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0442] Step 1:

[0443] The server collects work experience and evaluation information from employees into a database. First, it obtains employee project history and skill sets as input data via an API. Using this input data, it executes a process to accumulate information in a cloud database. As a result, detailed historical data for each employee is saved as output to the database.

[0444] Step 2:

[0445] The server uses the collected data to build a generative AI model. Using Python and machine learning libraries such as TensorFlow, it uses past work experience and evaluation information obtained from the database as input data for the model. This data is processed, and machine learning algorithms are used to analyze patterns and train the predictive model. The output of this process is the generation of an AI model that predicts the optimal placement of employees.

[0446] Step 3:

[0447] The server generates placement suggestions using a trained AI model. The AI ​​model analyzes employee skills and the company's required competency based on collected data. The server inputs the employee's current status into the AI ​​model, obtaining the optimal department and position as output. This output is then prepared as an appropriate placement suggestion for each employee.

[0448] Step 4:

[0449] The terminal presents employees with appropriate placement suggestions sent from the server. The terminal visually represents, through its user interface, how well the employee's skill set matches the suggested placement. Based on this input data, it outputs information about necessary training and any missing skills. Specifically, it displays the data visually using bar graphs and charts, and provides links to learning resources via pop-up notifications.

[0450] Step 5:

[0451] Based on the information provided by the device, the user reconsiders their career plan. They review the proposed placements and learning methods, and select specific training courses to improve their skills. Based on this input information, a new action plan for skill acquisition is formulated as output. Specific actions include online course registration and scheduling.

[0452] (Application Example 1)

[0453] 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."

[0454] In today's production environment, it is crucial to maximize the capabilities of human resources within factories and improve operational efficiency. However, accurately assigning workers to roles that reflect their individual skills and past experience is difficult, resulting in wasted resources and situations where human potential is not fully realized. Solving this problem and achieving efficient human resource management with the right people in the right positions is essential.

[0455] 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.

[0456] In this invention, the server includes means for collecting personnel competence information and work evaluation information, means for training a generative AI model using the collected information, and means for proposing the optimal placement of personnel using the generative AI model. This enables the optimization of personnel roles within the factory, improving operational efficiency and maximizing the potential of personnel.

[0457] "Human resources" refers to individual workers who possess specific skills and experience and are engaged in work within an organization.

[0458] "Competency information" refers to data on the skills and expertise that workers possess, and serves as the basis for evaluating their usefulness in the workplace.

[0459] "Performance evaluation information" refers to data used to evaluate a worker's performance based on the results and efficiency of tasks they have performed in the past.

[0460] A "generative AI model" refers to artificial intelligence that is trained based on collected data and capable of performing specific tasks or making predictions.

[0461] "Optimal placement" refers to assigning individual workers to positions where they can perform most effectively, based on their abilities and the requirements of their work.

[0462] A "factory" refers to a production site equipped with specialized equipment and facilities for manufacturing and assembling products.

[0463] "Business efficiency" refers to the effectiveness of work in order to minimize the time and effort required to perform tasks and maximize results.

[0464] "Resources" is a general term for the personnel, equipment, time, and information available within an organization, and refers to the elements necessary for carrying out business operations.

[0465] A description of the embodiment for carrying out the invention will be provided.

[0466] This invention is a system for achieving optimal personnel allocation within a factory. The server collects personnel ability information and performance evaluation information, and uses this information to train a generative AI model. The generative AI model is built using machine learning libraries such as PyTorch and TensorFlow, which are based on Python. The data collected by the server is centrally stored in a database (e.g., SQLite or Google Firebase) and used as material for analysis. This analysis allows for the prediction of the optimal allocation of each personnel.

[0467] The server sends the optimal placement determined by the generated AI model to the terminal, and factory managers can receive this suggestion using their smartphones or tablets. Managers refer to the suggestion and make changes or reassignments to personnel within the factory. Based on the suggested placement, the system also provides specific training courses and online resources for the skills that each employee needs to acquire.

[0468] The administrator, as the user, can optimize operations based on the provided plan. For example, to distribute the workload on a factory production line, highly skilled workers can be assigned to the highest-demand work stations to improve efficiency.

[0469] An example of a prompt message is: "For departmental placement planning, use employee work experience and evaluation data from the past three months to predict the optimal placement. Please propose a plan considering employee ID, job ID, and skill evaluation." This system enables the optimal use of resources in the factory and leads to efficient operation.

[0470] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0471] Step 1:

[0472] The server collects personnel competency information and performance evaluation information into a database. Inputs include workers' skill sets and past project data. The server integrates and stores this data. SQLite or Google Firebase are used for the database, and the accumulated data is then used for subsequent processing.

[0473] Step 2:

[0474] The server uses the collected information to train a generative AI model. The input here is the data collected and stored in Step 1. The server uses Python, leveraging the PyTorch and TensorFlow libraries to build and train the AI ​​model. This model forms the basis for predicting the optimal placement of personnel. The output is an inference model for placement prediction.

[0475] Step 3:

[0476] The server uses a trained AI model to predict the optimal placement of personnel. In this step, the AI ​​model obtained in step 2 and real-time work requests are used as input to estimate the appropriate location and role for each personnel. The output is a proposal for the optimal placement for each personnel.

[0477] Step 4:

[0478] The terminal receives optimal placement suggestions sent from the server and displays them to the factory manager. The input is the placement suggestion data generated in step 3. The terminal converts this data into a readable format and presents it in a way that is easy for the manager to understand. Specifically, it displays it graphically on a smartphone or tablet interface.

[0479] Step 5:

[0480] The user, the factory manager, evaluates the presented optimal placement proposal and implements the placement plan as needed. The manager provides feedback to the system based on the prompts to support improvements to future proposals. This feedback is then returned to step 1 and repeatedly used as data to train a newly generated AI model.

[0481] 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.

[0482] This invention is a system that combines emotion recognition functionality with a system designed to promote employee career development. This enables the system to optimize suggestions and interfaces while considering the user's emotional state.

[0483] The server collects employee experience data and performance evaluation data, and uses this to train a generative AI model. Based on the insights gained from this training, it suggests the most suitable department and position for each employee. Furthermore, if an employee lacks the necessary skills for appropriate placement, it provides guidance on how to acquire those skills.

[0484] In addition, the emotion engine analyzes the user's facial expressions and voice data during interactions to infer their emotional state. This allows the system to adjust the career plan it proposes to make it more relatable. For example, if it determines that the user is stressed, it can make suggestions more cautious or include encouraging messages.

[0485] The device not only displays suggestions and learning methods sent from the server, but also recognizes the user's emotions and provides a flexible interface based on them. The system's emotionally responsive approach allows users to make more conscious choices about their career path.

[0486] For example, if an employee is feeling anxious about moving to a new department, the emotion engine will detect this anxiety, and the terminal will display the system's suggestions in a reassuring tone. It can also present specific success stories and messages from senior employees. This makes users more likely to accept the suggestions.

[0487] This structure makes it possible to provide flexible and emotionally responsive career development support to each individual employee.

[0488] The following describes the processing flow.

[0489] Step 1:

[0490] The terminal provides an interface for users to input information about their past work experience, current skills, and performance evaluations. Users input this information and send the data to the server.

[0491] Step 2:

[0492] The server stores user data sent from the terminal in a database. The server then organizes the stored data for training a generating AI model.

[0493] Step 3:

[0494] The server trains a generative AI model to determine suitable career placements for employees. This model improves its accuracy by learning from past success stories and skill patterns.

[0495] Step 4:

[0496] The terminal presents the user with carrier placement suggestions generated by an AI model sent from the server. This suggestion includes the skills required for the suggested placement and how to acquire them.

[0497] Step 5:

[0498] The emotion engine analyzes user facial expressions and voice data in real time to determine the user's emotional state. The server collects this emotion data and uses it to adjust suggestions and the interface.

[0499] Step 6:

[0500] The server adjusts the content presented based on the results of the emotion engine, generating a career plan that takes the user's emotions into consideration. If necessary, it changes the tone and content of the messages displayed on the device.

[0501] Step 7:

[0502] The user reviews the optimized plan and sends feedback to the server via their device. The server readjusts the career plan based on the feedback and makes further suggestions that take their emotional state into consideration.

[0503] (Example 2)

[0504] 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."

[0505] Conventional career development support systems often fail to consider the user's emotional state when making suggestions, resulting in ineffective acceptance of those suggestions. Furthermore, beyond simply presenting methods for acquiring technical skills, a function is needed to adjust the interface based on the user's emotions. The challenge lies in effectively supporting career development while alleviating user anxiety and stress.

[0506] 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.

[0507] In this invention, the server includes means for collecting employee work data, means for training a machine learning model, and means for acquiring and analyzing user facial and voice data to infer emotional state. This makes it possible to suggest appropriate job roles and adjust the interface according to the user's emotional state.

[0508] "Employee work data" refers to digital data that includes each employee's work history and performance evaluation information within a company.

[0509] A "machine learning model" is a model constructed using algorithms that learn patterns based on data, and is used for prediction and classification.

[0510] "Job assignment" is a concept that refers to assigning employees to appropriate jobs within an organization based on their individual abilities and aptitudes.

[0511] "Methods of acquiring skills" refers to information that describes the methods and means of obtaining the abilities and knowledge necessary to perform a specific job.

[0512] "User facial and voice data" refers to digital information including the user's facial movements and changes in voice, and is used to analyze their emotional state.

[0513] "Means for inferring emotional state" refers to technologies and methods for analyzing facial and voice data obtained from users to determine their psychological state and emotions.

[0514] "Interface adjustment" is a technique that optimizes the user's operating environment and screen display by changing them to match the user's situation and emotions.

[0515] This invention is a system designed to support employees' career development, utilizing emotion recognition to provide users with optimal suggestions. The system operates according to the following procedure.

[0516] First, the server collects employee work data from the company's database. This data includes employees' past work experience, performance evaluations, and skill sets, and is retrieved using SQL queries in the database management system (DBMS).

[0517] Next, the server uses the collected business data to train generative AI models using machine learning frameworks such as TensorFlow and PyTorch. These models learn trends related to employees' career development and are used to predict which positions or departments are suitable for them.

[0518] The device also uses a camera and microphone to capture the user's facial expressions and voice in real time. This data is analyzed by an emotion engine to infer the user's emotional state. This process utilizes OpenCV and other voice analysis technologies.

[0519] When a user uses this system, the terminal not only displays job assignment suggestions sent from the server, but also adjusts the interface based on the user's emotions. For example, if the user expresses anxiety, the terminal can present the suggestions in a calmer tone and display encouraging messages and relevant success stories.

[0520] For example, a user might be stressed after receiving a proposal to transfer to a new department. In this case, if the emotion engine detects the user's stress level, the device can present the proposal in a way that makes it easier to accept and display a reassuring message.

[0521] Furthermore, an example of a prompt given to the generating AI model might be something like, "Based on the employee's past work performance and current skills, please suggest the most suitable next career step."

[0522] Therefore, this system can provide career support that takes the emotions of each individual employee into consideration.

[0523] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0524] Step 1:

[0525] The server retrieves employee work data from the company's database. Employee IDs and necessary data items are provided as input. The server extracts data from the database using SQL queries, obtaining employee experience and evaluation information as output. Specifically, it performs data cleansing to remove noisy data.

[0526] Step 2:

[0527] The server trains a machine learning model using the business data collected in Step 1. The input is pre-processed employee business data. The server builds a generative AI model using TensorFlow or PyTorch and optimizes the model's parameters. The output is a trained model related to employee career development. Specifically, the data is split into a training set and a validation set, and the generalization performance of the model is evaluated.

[0528] Step 3:

[0529] The server uses a trained model to generate suggestions for suitable job placements for employees. The inputs are the trained generative AI model and the employees' latest work data. Based on these inputs, the server performs data calculations to suggest the most suitable job and department for each employee. The output is a suggestion for appropriate job placement. Specifically, the generative AI model is given the prompt "Suggest the optimal career path based on the employee's work history."

[0530] Step 4:

[0531] The terminal receives job assignment suggestions sent from the server and displays them to the user. The input is the suggestion information sent from the server. The terminal displays the received information on a user interface such as the screen. The output is a career suggestion presented to the user on the screen. Specifically, the information is arranged in a visually easy-to-understand format using GUI widgets.

[0532] Step 5:

[0533] The device acquires the user's facial expressions and voice, and begins processing to analyze their emotional state. The input is real-time data from the user obtained through the camera and microphone. The device sends this information to an emotion engine, which analyzes emotions using speech recognition and image processing technologies. The output is the user's emotional state, expressed as a numerical value or category. Specifically, the device adjusts the color scheme or message content of the user interface based on the analysis results.

[0534] Step 6:

[0535] The server optimizes the presentation of career suggestions using emotion analysis results. The input consists of emotional state data and suggested job placements. Based on this, the server calculates appropriate interface changes and generates optimized presentation content as output. Specifically, if the user is feeling anxious, the server makes adjustments such as adding encouraging words to the suggestion message.

[0536] (Application Example 2)

[0537] 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."

[0538] In today's work environment, there is a need to accurately understand the individual career needs and emotional states of each employee and provide them with the optimal career plan based on that understanding. However, existing systems often fail to address employees' emotions and end up offering uniform proposals. As a result, employees feel anxiety and resistance to the proposals, and problems arise in the smooth progress of their career development. This invention aims to solve these problems and more effectively support employees' career development.

[0539] 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.

[0540] In this invention, the server includes means for collecting employee experience information and performance evaluation information, means for training a predictive model using the collected information, means for proposing appropriate employee placement using the predictive model, means for emotion recognition for analyzing facial and voice data and inferring the user's emotional state, and means for adjusting the method of presenting career plans according to the user's emotional state. This makes it possible to provide flexible career plans based on the employee's emotional state.

[0541] "Employee experience information" refers to the history of skills, knowledge, and experience that an employee has acquired through their work.

[0542] "Performance evaluation information" refers to evaluations and feedback regarding employees' work results and performance.

[0543] A "predictive model" is the structure of artificial intelligence trained to predict future outcomes based on collected data.

[0544] "Appropriate placement" refers to assigning employees to jobs and roles that are best suited to their abilities and characteristics.

[0545] "Methods for acquiring missing skills" refers to the means and training methods for employees to efficiently learn the necessary skills.

[0546] "Facial and voice data" refers to digital information that shows the user's facial movements and voice tone.

[0547] "Emotion recognition methods" refer to technologies that analyze facial expressions and voice data and use that information to infer an individual's emotional state.

[0548] "Adjusting the presentation method of career plans" refers to changing the form and content of the proposed career plan to suit the individual's emotional state.

[0549] This invention is a system designed to support the individual career development of employees, and in particular, enables flexible career plan proposals tailored to the user's emotional state.

[0550] The server collects employee experience and performance evaluation information and uses it to train a predictive model. The collected information is stored in a database and analyzed using a specific algorithm (e.g., deep learning). The predictive model, as a generative AI model, is used to evaluate employees' skills and aptitudes and propose optimal placements and required skills. For emotion recognition, a camera (e.g., a high-resolution webcam) is used to acquire user facial data in real time, and a voice input device (e.g., a microphone) is used to analyze voice data. This utilizes facial recognition libraries such as OpenCV and the Google Cloud Speech-to-Text API. This allows the system to infer user emotions and present career plans in an emotionally responsive manner.

[0551] The device displays career plans and learning methods suggested by the server and dynamically adjusts the interface based on the user's emotions. Specifically, if the user shows anxiety, it displays encouraging messages on the screen and provides guidance in a reassuring tone. This functionality is achieved through the display and speaker installed in the device.

[0552] Users can interact naturally with the suggestions provided through this system. For example, if a user is feeling anxious about a new job, the emotion engine will detect this anxiety and provide a message such as, "Your experience will be valuable in your new role. Please refer to past success stories." In this way, emotion-based suggestions allow users to engage more proactively in their career development.

[0553] An example of a prompt would be, "Generate an encouraging message that takes into account the user's feelings of anxiety about their promotion." This allows the generative AI model to generate an appropriate message and provide the user with the most relevant information.

[0554] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0555] Step 1:

[0556] The server collects employee experience and performance evaluation information from a database. This input includes employee work history, past performance evaluations, and acquired skills. Based on this data, the server performs appropriate data cleaning and normalization, and processes it into a format that can be applied to training predictive models.

[0557] Step 2:

[0558] The server uses the collected information to train a generative AI model. Here, the data is used as input, and a deep learning algorithm forms a predictive model. As output, it generates a list of optimal placements and areas where skills are lacking for each employee. This results in personalized predictions for each employee.

[0559] Step 3:

[0560] The server presents the user's device with prediction results along with instructions on how to acquire the necessary skills. At this stage, the server sends the results of the already formed prediction model to the device and suggests the next action the user should take. Specifically, this may include links to online courses or training programs.

[0561] Step 4:

[0562] The device transmits facial and voice data acquired from the user to the server. The input here is biometric data obtained through the camera and microphone. Based on this, the server performs emotion recognition using OpenCV or the Google Cloud Speech-to-Text API.

[0563] Step 5:

[0564] The server uses the emotion recognition results to generate prompt messages through a generative AI model, adjusting the career plan presentation to suit the user's emotional state. The data calculations here include message generation that reflects the emotional state through the prompt messages. The output is an emotionally resonant suggestion message that is easily accepted by the user.

[0565] Step 6:

[0566] Users receive a career plan presented via their device and provide feedback. Based on this input, the device sends feedback data to the server. This feedback data is used to improve the proposed plan and contribute to further enhancing the accuracy of the model.

[0567] 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.

[0568] 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.

[0569] 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.

[0570] [Fourth Embodiment]

[0571] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0572] 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.

[0573] 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).

[0574] 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.

[0575] 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.

[0576] 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).

[0577] 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.

[0578] 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.

[0579] 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.

[0580] 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.

[0581] 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.

[0582] 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.

[0583] 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".

[0584] This invention provides a system to support employees' career development. The system aims to train a generating AI model using employee experience data and performance evaluation data, and to propose appropriate job placements.

[0585] The server first collects data on each employee's past work experience and skills. This includes information such as the projects the employee has been involved in and the skills they possess. The server also collects performance evaluation data, accumulating information to evaluate each employee's individual performance.

[0586] Next, the server uses this data to train a generative AI model. Based on historical data, the generative AI model predicts which department or position an employee can perform best in. This process allows the server to show each employee the optimal career path.

[0587] The terminal displays optimal placement suggestions sent from the server to the employee. This information specifically indicates which department best suits the employee's skill set and which skills are lacking. The terminal also presents the employee with specific learning resources and training programs to address any skill gaps.

[0588] Users can make decisions based on their career plans and suggested learning methods. This allows employees to take clear steps toward their career goals.

[0589] To give a concrete example, if an employee expresses interest in taking on a new project in the sales department, the terminal analyzes their past project experience and skills to present the skill set required for the sales department and highlight any skill gaps they may have. Furthermore, the server suggests specific training courses and online resources to bridge those gaps. In this way, employees can improve their skills and prepare to take on new roles.

[0590] The following describes the processing flow.

[0591] Step 1:

[0592] The terminal provides an interface for users to input information related to their past career development, current skills, and work performance evaluations. Users input the necessary information and send the data to the server.

[0593] Step 2:

[0594] The server stores user data received from terminals in a centralized database. The server organizes the data into a parseable format and verifies its integrity.

[0595] Step 3:

[0596] The server uses stored data to train the generated AI model. This includes a process of learning past employee success trends and optimal talent placement patterns.

[0597] Step 4:

[0598] The server uses a trained AI model to generate suggestions for the most suitable department and position based on the user's skills and experience. Furthermore, it identifies any missing skills or required experience.

[0599] Step 5:

[0600] The server evaluates specific training resources and learning methods to help the user improve their skills, along with suggesting the optimal carrier placement, and sends this information to the terminal.

[0601] Step 6:

[0602] The device presents information received from the server to the user in an easy-to-understand manner. The user views and considers the recommended carrier configuration and learning methods.

[0603] Step 7:

[0604] Users provide feedback and make selections regarding the suggestions through their device. The device sends user feedback to the server and initiates a process to further refine the suggestions as needed.

[0605] (Example 1)

[0606] 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".

[0607] In modern companies, there is a need for efficient personnel allocation in order to utilize the abilities of each employee in the most optimal way and improve performance. However, systematically analyzing the diverse experience and skill sets of employees and making accurate placement proposals based on that analysis is difficult. Furthermore, there is a lack of means to support employees' career development based on those proposals. Therefore, companies are required to efficiently provide concrete learning methods to bridge the skill gaps of their employees.

[0608] 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.

[0609] In this invention, the server includes means for aggregating personnel's work experience information and evaluation information, means for constructing a generative machine learning model using the aggregated information, and means for proposing the optimal placement of personnel using the generative machine learning model. This makes it possible to analyze the skills and work requirements of each employee in detail and propose the optimal placement and growth strategy.

[0610] "Human resources" refers to individual employees or staff members who play an active role within a company or organization.

[0611] "Work experience information" refers to specific information about projects and tasks that employees have worked on in the past.

[0612] "Evaluation information" refers to information regarding performance evaluations given based on an employee's ability to perform their job and their results.

[0613] "Aggregation" refers to the process of gathering and organizing dispersed information in one place.

[0614] A "generative machine learning model" refers to a statistical model that uses historical data to make predictions or classifications for a specific purpose.

[0615] "Optimal placement" refers to assigning employees to jobs and positions that maximize their individual capabilities, taking into account their skills and the organization's needs.

[0616] "Proposal" refers to the act of providing information that indicates the optimal action or option based on the analysis results.

[0617] A "skill gap" refers to the difference between your current skill set and the skills required for your target job or tasks.

[0618] "Learning methods" refer to means such as courses, training, and resources available to acquire lacking skills.

[0619] This invention provides a system to support employees' career development. This system aggregates employees' work experience information and evaluation information, and uses this information to build a generative machine learning model to propose optimal placements.

[0620] The server uses a cloud database to collect employee work experience and evaluation information. Specifically, it imports project history and evaluation data via APIs. This information is stored in the database for later analysis.

[0621] Next, the server uses Python and machine learning libraries such as TensorFlow to build a generative machine learning model based on the collected data. This model is used to predict which tasks or roles employees are best suited for and can maximize their performance in. The model is trained using a dataset that combines historical performance data and business requirements.

[0622] The terminal displays suggestions received from the server to the employee. For example, it uses a GUI to visually represent how well the employee's skill set matches the suggested placement. Furthermore, it uses pop-up messages and notification features to present learning resources and training programs for any missing skills.

[0623] Based on the information presented, users can develop their own career plans and select learning methods based on the suggestions. For example, if a user is interested in a position in the sales department, the system will clearly identify the skills required for the sales department and the user's skill gap, and suggest online courses to bridge that gap. Through this process, employees can hone their skills and prepare to take the next step in their careers.

[0624] Examples of prompts include, "Analyze and propose appropriate placements in the new department based on employees' past project experience and skills," and "Compare the skills required for a specific position with the employees' current skills and suggest learning methods."

[0625] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0626] Step 1:

[0627] The server collects work experience and evaluation information from employees into a database. First, it obtains employee project history and skill sets as input data via an API. Using this input data, it executes a process to accumulate information in a cloud database. As a result, detailed historical data for each employee is saved as output to the database.

[0628] Step 2:

[0629] The server uses the collected data to build a generative AI model. Using Python and machine learning libraries such as TensorFlow, it uses past work experience and evaluation information obtained from the database as input data for the model. This data is processed, and machine learning algorithms are used to analyze patterns and train the predictive model. The output of this process is the generation of an AI model that predicts the optimal placement of employees.

[0630] Step 3:

[0631] The server generates placement suggestions using a trained AI model. The AI ​​model analyzes employee skills and the company's required competency based on collected data. The server inputs the employee's current status into the AI ​​model, obtaining the optimal department and position as output. This output is then prepared as an appropriate placement suggestion for each employee.

[0632] Step 4:

[0633] The terminal presents employees with appropriate placement suggestions sent from the server. The terminal visually represents, through its user interface, how well the employee's skill set matches the suggested placement. Based on this input data, it outputs information about necessary training and any missing skills. Specifically, it displays the data visually using bar graphs and charts, and provides links to learning resources via pop-up notifications.

[0634] Step 5:

[0635] Based on the information provided by the device, the user reconsiders their career plan. They review the proposed placements and learning methods, and select specific training courses to improve their skills. Based on this input information, a new action plan for skill acquisition is formulated as output. Specific actions include online course registration and scheduling.

[0636] (Application Example 1)

[0637] 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".

[0638] In today's production environment, it is crucial to maximize the capabilities of human resources within factories and improve operational efficiency. However, accurately assigning workers to roles that reflect their individual skills and past experience is difficult, resulting in wasted resources and situations where human potential is not fully realized. Solving this problem and achieving efficient human resource management with the right people in the right positions is essential.

[0639] 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.

[0640] In this invention, the server includes means for collecting personnel competence information and work evaluation information, means for training a generative AI model using the collected information, and means for proposing the optimal placement of personnel using the generative AI model. This enables the optimization of personnel roles within the factory, improving operational efficiency and maximizing the potential of personnel.

[0641] "Human resources" refers to individual workers who possess specific skills and experience and are engaged in work within an organization.

[0642] "Competency information" refers to data on the skills and expertise that workers possess, and serves as the basis for evaluating their usefulness in the workplace.

[0643] "Performance evaluation information" refers to data used to evaluate a worker's performance based on the results and efficiency of tasks they have performed in the past.

[0644] A "generative AI model" refers to artificial intelligence that is trained based on collected data and capable of performing specific tasks or making predictions.

[0645] "Optimal placement" refers to assigning individual workers to positions where they can perform most effectively, based on their abilities and the requirements of their work.

[0646] A "factory" refers to a production site equipped with specialized equipment and facilities for manufacturing and assembling products.

[0647] "Business efficiency" refers to the effectiveness of work in order to minimize the time and effort required to perform tasks and maximize results.

[0648] "Resources" is a general term for the personnel, equipment, time, and information available within an organization, and refers to the elements necessary for carrying out business operations.

[0649] A description of the embodiment for carrying out the invention will be provided.

[0650] This invention is a system for achieving optimal personnel allocation within a factory. The server collects personnel ability information and performance evaluation information, and uses this information to train a generative AI model. The generative AI model is built using machine learning libraries such as PyTorch and TensorFlow, which are based on Python. The data collected by the server is centrally stored in a database (e.g., SQLite or Google Firebase) and used as material for analysis. This analysis allows for the prediction of the optimal allocation of each personnel.

[0651] The server sends the optimal placement determined by the generated AI model to the terminal, and factory managers can receive this suggestion using their smartphones or tablets. Managers refer to the suggestion and make changes or reassignments to personnel within the factory. Based on the suggested placement, the system also provides specific training courses and online resources for the skills that each employee needs to acquire.

[0652] The administrator, as the user, can optimize operations based on the provided plan. For example, to distribute the workload on a factory production line, highly skilled workers can be assigned to the highest-demand work stations to improve efficiency.

[0653] An example of a prompt message is: "For departmental placement planning, use employee work experience and evaluation data from the past three months to predict the optimal placement. Please propose a plan considering employee ID, job ID, and skill evaluation." This system enables the optimal use of resources in the factory and leads to efficient operation.

[0654] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0655] Step 1:

[0656] The server collects personnel competency information and performance evaluation information into a database. Inputs include workers' skill sets and past project data. The server integrates and stores this data. SQLite or Google Firebase are used for the database, and the accumulated data is then used for subsequent processing.

[0657] Step 2:

[0658] The server uses the collected information to train a generative AI model. The input here is the data collected and stored in Step 1. The server uses Python, leveraging the PyTorch and TensorFlow libraries to build and train the AI ​​model. This model forms the basis for predicting the optimal placement of personnel. The output is an inference model for placement prediction.

[0659] Step 3:

[0660] The server uses a trained AI model to predict the optimal placement of personnel. In this step, the AI ​​model obtained in step 2 and real-time work requests are used as input to estimate the appropriate location and role for each personnel. The output is a proposal for the optimal placement for each personnel.

[0661] Step 4:

[0662] The terminal receives optimal placement suggestions sent from the server and displays them to the factory manager. The input is the placement suggestion data generated in step 3. The terminal converts this data into a readable format and presents it in a way that is easy for the manager to understand. Specifically, it displays it graphically on a smartphone or tablet interface.

[0663] Step 5:

[0664] The user, the factory manager, evaluates the presented optimal placement proposal and implements the placement plan as needed. The manager provides feedback to the system based on the prompts to support improvements to future proposals. This feedback is then returned to step 1 and repeatedly used as data to train a newly generated AI model.

[0665] 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.

[0666] This invention is a system that combines emotion recognition functionality with a system designed to promote employee career development. This enables the system to optimize suggestions and interfaces while considering the user's emotional state.

[0667] The server collects employee experience data and performance evaluation data, and uses this to train a generative AI model. Based on the insights gained from this training, it suggests the most suitable department and position for each employee. Furthermore, if an employee lacks the necessary skills for appropriate placement, it provides guidance on how to acquire those skills.

[0668] In addition, the emotion engine analyzes the user's facial expressions and voice data during interactions to infer their emotional state. This allows the system to adjust the career plan it proposes to make it more relatable. For example, if it determines that the user is stressed, it can make suggestions more cautious or include encouraging messages.

[0669] The device not only displays suggestions and learning methods sent from the server, but also recognizes the user's emotions and provides a flexible interface based on them. The system's emotionally responsive approach allows users to make more conscious choices about their career path.

[0670] For example, if an employee is feeling anxious about moving to a new department, the emotion engine will detect this anxiety, and the terminal will display the system's suggestions in a reassuring tone. It can also present specific success stories and messages from senior employees. This makes users more likely to accept the suggestions.

[0671] This structure makes it possible to provide flexible and emotionally responsive career development support to each individual employee.

[0672] The following describes the processing flow.

[0673] Step 1:

[0674] The terminal provides an interface for users to input information about their past work experience, current skills, and performance evaluations. Users input this information and send the data to the server.

[0675] Step 2:

[0676] The server stores user data sent from the terminal in a database. The server then organizes the stored data for training a generating AI model.

[0677] Step 3:

[0678] The server trains a generative AI model to determine suitable career placements for employees. This model improves its accuracy by learning from past success stories and skill patterns.

[0679] Step 4:

[0680] The terminal presents the user with carrier placement suggestions generated by an AI model sent from the server. This suggestion includes the skills required for the suggested placement and how to acquire them.

[0681] Step 5:

[0682] The emotion engine analyzes user facial expressions and voice data in real time to determine the user's emotional state. The server collects this emotion data and uses it to adjust suggestions and the interface.

[0683] Step 6:

[0684] The server adjusts the content presented based on the results of the emotion engine, generating a career plan that takes the user's emotions into consideration. If necessary, it changes the tone and content of the messages displayed on the device.

[0685] Step 7:

[0686] The user reviews the optimized plan and sends feedback to the server via their device. The server readjusts the career plan based on the feedback and makes further suggestions that take their emotional state into consideration.

[0687] (Example 2)

[0688] 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".

[0689] Conventional career development support systems often fail to consider the user's emotional state when making suggestions, resulting in ineffective acceptance of those suggestions. Furthermore, beyond simply presenting methods for acquiring technical skills, a function is needed to adjust the interface based on the user's emotions. The challenge lies in effectively supporting career development while alleviating user anxiety and stress.

[0690] 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.

[0691] In this invention, the server includes means for collecting employee work data, means for training a machine learning model, and means for acquiring and analyzing user facial and voice data to infer emotional state. This makes it possible to suggest appropriate job roles and adjust the interface according to the user's emotional state.

[0692] "Employee work data" refers to digital data that includes each employee's work history and performance evaluation information within a company.

[0693] A "machine learning model" is a model constructed using algorithms that learn patterns based on data, and is used for prediction and classification.

[0694] "Job assignment" is a concept that refers to assigning employees to appropriate jobs within an organization based on their individual abilities and aptitudes.

[0695] "Methods of acquiring skills" refers to information that describes the methods and means of obtaining the abilities and knowledge necessary to perform a specific job.

[0696] "User facial and voice data" refers to digital information including the user's facial movements and changes in voice, and is used to analyze their emotional state.

[0697] "Means for inferring emotional state" refers to technologies and methods for analyzing facial and voice data obtained from users to determine their psychological state and emotions.

[0698] "Interface adjustment" is a technique that optimizes the user's operating environment and screen display by changing them to match the user's situation and emotions.

[0699] This invention is a system designed to support employees' career development, utilizing emotion recognition to provide users with optimal suggestions. The system operates according to the following procedure.

[0700] First, the server collects employee work data from the company's database. This data includes employees' past work experience, performance evaluations, and skill sets, and is retrieved using SQL queries in the database management system (DBMS).

[0701] Next, the server uses the collected business data to train generative AI models using machine learning frameworks such as TensorFlow and PyTorch. These models learn trends related to employees' career development and are used to predict which positions or departments are suitable for them.

[0702] The device also uses a camera and microphone to capture the user's facial expressions and voice in real time. This data is analyzed by an emotion engine to infer the user's emotional state. This process utilizes OpenCV and other voice analysis technologies.

[0703] When a user uses this system, the terminal not only displays job assignment suggestions sent from the server, but also adjusts the interface based on the user's emotions. For example, if the user expresses anxiety, the terminal can present the suggestions in a calmer tone and display encouraging messages and relevant success stories.

[0704] For example, a user might be stressed after receiving a proposal to transfer to a new department. In this case, if the emotion engine detects the user's stress level, the device can present the proposal in a way that makes it easier to accept and display a reassuring message.

[0705] Furthermore, an example of a prompt given to the generating AI model might be something like, "Based on the employee's past work performance and current skills, please suggest the most suitable next career step."

[0706] Therefore, this system can provide career support that takes the emotions of each individual employee into consideration.

[0707] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0708] Step 1:

[0709] The server retrieves employee work data from the company's database. Employee IDs and necessary data items are provided as input. The server extracts data from the database using SQL queries, obtaining employee experience and evaluation information as output. Specifically, it performs data cleansing to remove noisy data.

[0710] Step 2:

[0711] The server trains a machine learning model using the business data collected in Step 1. The input is pre-processed employee business data. The server builds a generative AI model using TensorFlow or PyTorch and optimizes the model's parameters. The output is a trained model related to employee career development. Specifically, the data is split into a training set and a validation set, and the generalization performance of the model is evaluated.

[0712] Step 3:

[0713] The server uses a trained model to generate suggestions for suitable job placements for employees. The inputs are the trained generative AI model and the employees' latest work data. Based on these inputs, the server performs data calculations to suggest the most suitable job and department for each employee. The output is a suggestion for appropriate job placement. Specifically, the generative AI model is given the prompt "Suggest the optimal career path based on the employee's work history."

[0714] Step 4:

[0715] The terminal receives job assignment suggestions sent from the server and displays them to the user. The input is the suggestion information sent from the server. The terminal displays the received information on a user interface such as the screen. The output is a career suggestion presented to the user on the screen. Specifically, the information is arranged in a visually easy-to-understand format using GUI widgets.

[0716] Step 5:

[0717] The device acquires the user's facial expressions and voice, and begins processing to analyze their emotional state. The input is real-time data from the user obtained through the camera and microphone. The device sends this information to an emotion engine, which analyzes emotions using speech recognition and image processing technologies. The output is the user's emotional state, expressed as a numerical value or category. Specifically, the device adjusts the color scheme or message content of the user interface based on the analysis results.

[0718] Step 6:

[0719] The server optimizes the presentation of career suggestions using emotion analysis results. The input consists of emotional state data and suggested job placements. Based on this, the server calculates appropriate interface changes and generates optimized presentation content as output. Specifically, if the user is feeling anxious, the server makes adjustments such as adding encouraging words to the suggestion message.

[0720] (Application Example 2)

[0721] 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".

[0722] In today's work environment, there is a need to accurately understand the individual career needs and emotional states of each employee and provide them with the optimal career plan based on that understanding. However, existing systems often fail to address employees' emotions and end up offering uniform proposals. As a result, employees feel anxiety and resistance to the proposals, and problems arise in the smooth progress of their career development. This invention aims to solve these problems and more effectively support employees' career development.

[0723] 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.

[0724] In this invention, the server includes means for collecting employee experience information and performance evaluation information, means for training a predictive model using the collected information, means for proposing appropriate employee placement using the predictive model, means for emotion recognition for analyzing facial and voice data and inferring the user's emotional state, and means for adjusting the method of presenting career plans according to the user's emotional state. This makes it possible to provide flexible career plans based on the employee's emotional state.

[0725] "Employee experience information" refers to the history of skills, knowledge, and experience that an employee has acquired through their work.

[0726] "Performance evaluation information" refers to evaluations and feedback regarding employees' work results and performance.

[0727] A "predictive model" is the structure of artificial intelligence trained to predict future outcomes based on collected data.

[0728] "Appropriate placement" refers to assigning employees to jobs and roles that are best suited to their abilities and characteristics.

[0729] "Methods for acquiring missing skills" refers to the means and training methods for employees to efficiently learn the necessary skills.

[0730] "Facial and voice data" refers to digital information that shows the user's facial movements and voice tone.

[0731] "Emotion recognition methods" refer to technologies that analyze facial expressions and voice data and use that information to infer an individual's emotional state.

[0732] "Adjusting the presentation method of career plans" refers to changing the form and content of the proposed career plan to suit the individual's emotional state.

[0733] This invention is a system designed to support the individual career development of employees, and in particular, enables flexible career plan proposals tailored to the user's emotional state.

[0734] The server collects employee experience and performance evaluation information and uses it to train a predictive model. The collected information is stored in a database and analyzed using a specific algorithm (e.g., deep learning). The predictive model, as a generative AI model, is used to evaluate employees' skills and aptitudes and propose optimal placements and required skills. For emotion recognition, a camera (e.g., a high-resolution webcam) is used to acquire user facial data in real time, and a voice input device (e.g., a microphone) is used to analyze voice data. This utilizes facial recognition libraries such as OpenCV and the Google Cloud Speech-to-Text API. This allows the system to infer user emotions and present career plans in an emotionally responsive manner.

[0735] The device displays career plans and learning methods suggested by the server and dynamically adjusts the interface based on the user's emotions. Specifically, if the user shows anxiety, it displays encouraging messages on the screen and provides guidance in a reassuring tone. This functionality is achieved through the display and speaker installed in the device.

[0736] Users can interact naturally with the suggestions provided through this system. For example, if a user is feeling anxious about a new job, the emotion engine will detect this anxiety and provide a message such as, "Your experience will be valuable in your new role. Please refer to past success stories." In this way, emotion-based suggestions allow users to engage more proactively in their career development.

[0737] An example of a prompt would be, "Generate an encouraging message that takes into account the user's feelings of anxiety about their promotion." This allows the generative AI model to generate an appropriate message and provide the user with the most relevant information.

[0738] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0739] Step 1:

[0740] The server collects employee experience and performance evaluation information from a database. This input includes employee work history, past performance evaluations, and acquired skills. Based on this data, the server performs appropriate data cleaning and normalization, and processes it into a format that can be applied to training predictive models.

[0741] Step 2:

[0742] The server uses the collected information to train a generative AI model. Here, the data is used as input, and a deep learning algorithm forms a predictive model. As output, it generates a list of optimal placements and areas where skills are lacking for each employee. This results in personalized predictions for each employee.

[0743] Step 3:

[0744] The server presents the user's device with prediction results along with instructions on how to acquire the necessary skills. At this stage, the server sends the results of the already formed prediction model to the device and suggests the next action the user should take. Specifically, this may include links to online courses or training programs.

[0745] Step 4:

[0746] The device transmits facial and voice data acquired from the user to the server. The input here is biometric data obtained through the camera and microphone. Based on this, the server performs emotion recognition using OpenCV or the Google Cloud Speech-to-Text API.

[0747] Step 5:

[0748] The server uses the emotion recognition results to generate prompt messages through a generative AI model, adjusting the career plan presentation to suit the user's emotional state. The data calculations here include message generation that reflects the emotional state through the prompt messages. The output is an emotionally resonant suggestion message that is easily accepted by the user.

[0749] Step 6:

[0750] Users receive a career plan presented via their device and provide feedback. Based on this input, the device sends feedback data to the server. This feedback data is used to improve the proposed plan and contribute to further enhancing the accuracy of the model.

[0751] 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.

[0752] 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.

[0753] 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.

[0754] 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.

[0755] 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.

[0756] 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.

[0757] 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.

[0758] 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.

[0759] 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."

[0760] 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.

[0761] 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.

[0762] 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.

[0763] 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.

[0764] 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.

[0765] 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.

[0766] 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.

[0767] 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.

[0768] 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.

[0769] 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.

[0770] 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.

[0771] All documents, patent applications, and technical standards described herein are incorporated by reference to the same extent as if each individual document, patent application, and technical standard were specifically and individually noted as being incorporated by reference.

[0772] The following is further disclosed regarding the embodiments described above.

[0773] (Claim 1)

[0774] Means for collecting employee experience data and performance evaluation data,

[0775] A means for training a generative AI model using the aforementioned collected data,

[0776] A means of proposing the appropriate placement of employees using the aforementioned AI model,

[0777] A means of presenting methods for acquiring the skills that are lacking based on the above proposal,

[0778] A system that includes this.

[0779] (Claim 2)

[0780] The system according to claim 1, further comprising means for generating employee career plans based on the above proposal.

[0781] (Claim 3)

[0782] The system according to claim 1, further comprising means for receiving feedback from the aforementioned employee and adjusting the proposed plan.

[0783] "Example 1"

[0784] (Claim 1)

[0785] A means of aggregating information on personnel's work experience and evaluation information,

[0786] A means for constructing a generative machine learning model using the aggregated information,

[0787] A means for proposing the optimal allocation of personnel using the aforementioned generative machine learning model,

[0788] A means of providing a means of acquiring the necessary skills based on the above proposal,

[0789] A means of visually presenting a proposal using an information processing device,

[0790] A system that includes this.

[0791] (Claim 2)

[0792] The system according to claim 1, further comprising means for constructing a career plan for personnel based on the above proposal.

[0793] (Claim 3)

[0794] The system according to claim 1, further comprising means for receiving a response from the aforementioned personnel and adjusting the proposed plan.

[0795] "Application Example 1"

[0796] (Claim 1)

[0797] A means of collecting personnel competency information and performance evaluation information,

[0798] A means for training a generative AI model using the collected information,

[0799] A means for proposing the optimal allocation of personnel using the aforementioned generation AI model,

[0800] A means of presenting a method for acquiring the skills that are lacking based on the above proposal,

[0801] A means of presenting layout proposals to optimize factory operational efficiency,

[0802] A system that includes this.

[0803] (Claim 2)

[0804] The system according to claim 1, further comprising means for generating a work plan for personnel based on the above proposal.

[0805] (Claim 3)

[0806] The system according to claim 1, further comprising means for receiving opinions from the aforementioned personnel and adjusting the proposed plan.

[0807] "Example 2 of combining an emotion engine"

[0808] (Claim 1)

[0809] Means of collecting employee work data,

[0810] A means for training a machine learning model using the aforementioned collected data,

[0811] A means for proposing appropriate job assignments for employees using the aforementioned machine learning model,

[0812] A means of presenting a method for acquiring the skills that are lacking based on the above proposal,

[0813] A means of acquiring and analyzing the user's facial expressions and voice data to infer their emotional state,

[0814] Means for adjusting the display format of the proposal based on the aforementioned emotional state,

[0815] A system that includes this.

[0816] (Claim 2)

[0817] The system according to claim 1, further comprising means for generating an employee's work plan based on the aforementioned emotional state.

[0818] (Claim 3)

[0819] The system according to claim 1, further comprising means for receiving responses from employees who have received the aforementioned proposal and adjusting the job plan accordingly.

[0820] "Application example 2 when combining with an emotional engine"

[0821] (Claim 1)

[0822] Means for collecting employee experience information and performance evaluation information,

[0823] A means for training a predictive model using the collected information,

[0824] A means of proposing the appropriate placement of employees using the aforementioned predictive model,

[0825] A means of presenting a method for acquiring the missing skills based on the above proposal,

[0826] An emotion recognition means that analyzes facial expressions and voice data to infer the user's emotional state,

[0827] A means of adjusting the way career plans are presented according to the user's emotional state,

[0828] A system that includes this.

[0829] (Claim 2)

[0830] The system according to claim 1, further comprising means for generating employee career paths based on the above proposal.

[0831] (Claim 3)

[0832] The system according to claim 1, further comprising means for receiving feedback from the aforementioned employees and adjusting the proposed plan. [Explanation of Symbols]

[0833] 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. A means of collecting personnel competency information and performance evaluation information, A means for training a generative AI model using the collected information, A means for proposing the optimal allocation of personnel using the aforementioned generation AI model, A means of presenting a method for acquiring the skills that are lacking based on the above proposal, A means of presenting layout proposals to optimize factory operational efficiency, A system that includes this.

2. The system according to claim 1, further comprising means for generating employee job plans based on the above proposal.

3. The system according to claim 1, further comprising means for receiving opinions from the aforementioned personnel and adjusting the proposed plan.

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