system

Abstract

We provide the system. [Solution] A means for analyzing the work history and skill data of workers obtained from data storage and generating a learning plan optimized for said worker, A means for monitoring learning progress in real time based on the aforementioned learning plan and providing necessary information, A means for generating a report to evaluate the work output after the completion of learning and to report the effects, A means for workers to visualize their learning progress on their personal devices and make immediate feedback and adjustments to their learning plans, A system that includes this.

Description

Technical Field

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

Background Art

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

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the conventional personnel training process in enterprises, it is difficult to set individual training plans according to the skill levels and job proficiency of each employee, and as a result, there is a problem that efficient and effective training cannot be carried out. In addition, providing the optimal training content for each employee within limited time and costs has limitations in manpower, and particularly with the deepening of the labor shortage, improvements in enterprise productivity and employee retention rate are required.

Means for Solving the Problems

[0005] This invention provides a system that automatically generates personalized training plans by acquiring employee work history and skill data from a database and analyzing it using AI. This system includes a function to monitor each employee's real-time training progress and provide feedback as needed. Furthermore, after training is completed, it evaluates its effectiveness, compiles it into a report, and contributes to improving the efficiency and productivity of talent development across the entire company. As a result, it is possible to provide optimal training for each employee at a low cost, thereby enhancing the company's competitiveness.

[0006] A "database" is an electronic recording system for systematically organizing, storing, and managing information and data.

[0007] An "employee" refers to an individual who belongs to a company or organization and is employed to perform its duties.

[0008] "Work history" refers to information that records the content and results of the work that each employee has performed to date.

[0009] "Skill data" refers to information that indicates an employee's technical abilities and knowledge, and usually includes information related to a specific job or field.

[0010] "AI" is an abbreviation for artificial intelligence, a technology that simulates human intellectual activity and performs automated analysis and decision-making.

[0011] A "training plan" refers to a plan of education or training designed to achieve a specific objective.

[0012] "Real-time" refers to processing or reacting to an event the very moment it occurs.

[0013] "Feedback" is information that conveys an evaluation or suggestions for improvement regarding a particular action or result.

[0014] A "report" is a document that compiles data and analysis results about a specific subject, and is created for the purpose of reporting information. [Brief explanation of the drawing]

[0015] [Figure 1] This is a conceptual diagram showing an example of the configuration of a data processing system according to the first embodiment. [Figure 2] This is a conceptual diagram showing an example of the essential functions of a data processing device and a smart device according to the first embodiment. [Figure 3] This is a conceptual diagram showing an example of the configuration of a data processing system according to the second embodiment. [Figure 4] This 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] This is a conceptual diagram showing an example of the configuration of a data processing system according to the third embodiment. [Figure 6] This 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] This is a conceptual diagram showing an example of the configuration of a data processing system according to the fourth embodiment. [Figure 8] This 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] This shows an emotion map where multiple emotions are mapped. [Figure 10] This shows an emotion map where multiple emotions are mapped. [Figure 11] This is a sequence diagram showing the processing flow of the data processing system in Example 1. [Figure 12] This is a sequence diagram showing the processing flow of the data processing system in Application Example 1. [Figure 13] This is a sequence diagram showing the processing flow of the data processing system in Example 2, which incorporates an emotion engine. [Figure 14]It is a sequence diagram showing the processing flow of a data processing system in Application Example 2 when a sentiment engine is combined.

Embodiments for Carrying Out the Invention

[0016] 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.

[0017] First, the terms used in the following description will be explained.

[0018] In the following embodiments, a numbered processor (hereinafter simply referred to as "processor") may be a single arithmetic unit or a combination of multiple arithmetic units. Also, the processor may be a single type of arithmetic unit or a combination of multiple types of arithmetic units. Examples of arithmetic units include a CPU (Central Processing Unit), a GPU (Graphics Processing Unit), a GPGPU (General-Purpose computing on Graphics Processing Units), an APU (Accelerated Processing Unit), and the like.

[0019] In the following embodiments, a numbered RAM (Random Access Memory) is a memory in which information is temporarily stored and is used as a work memory by the processor.

[0020] In the following embodiments, a numbered 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.

[0021] In the following embodiments, the signed communication interface (I / F) is an interface that includes a communication processor and an antenna, etc. The communication interface manages communication between multiple computers. Examples of communication standards applicable to the communication interface include wireless communication standards such as 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), or Bluetooth (registered trademark).

[0022] 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."

[0023] [First Embodiment]

[0024] Figure 1 shows an example of the configuration of the data processing system 10 according to the first embodiment.

[0025] 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.

[0026] 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).

[0027] 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.

[0028] 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.

[0029] 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.

[0030] 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.

[0031] Figure 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0032] 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.

[0033] 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.

[0034] 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.

[0035] 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".

[0036] The system of this invention automatically generates optimal training plans tailored to the individual skills and work proficiency levels of employees in sales departments, thereby improving employee productivity. To this end, the system includes the following elements.

[0037] First, the server retrieves employees' past performance data and existing skill data from the company's database. Furthermore, users input their own skills and abilities using a terminal and send this information to the server. The server integrates this data and uses an AI algorithm to evaluate the employee's skill level and job proficiency.

[0038] Next, the server generates an individually optimized training plan for each user based on the evaluation data. This plan includes online learning courses and role-playing exercises, presented in a way that is easy for employees to implement.

[0039] The device notifies the user of this training plan, and each user proceeds with the training accordingly. Users can check their daily progress within the system, allowing them to objectively understand their own learning.

[0040] Furthermore, the server monitors training progress in real time and generates feedback tailored to the user's learning status. The terminal immediately notifies the user of this feedback and suggests modifications to the training plan as needed. In this way, users can efficiently improve their skills.

[0041] Furthermore, after the training is completed, the server collects new work performance data and evaluates the training's effectiveness. Here, it analyzes employee productivity and skill improvement, and generates a report based on the results. The terminal provides this report to users and administrators, serving as a foundation for continuous talent development.

[0042] To give a specific example, employee "A" at a certain retail store was evaluated as having "intermediate customer service skills" based on past sales performance. Therefore, the AI ​​provides A with training plans for "advanced customer service" and "enhanced problem-solving skills," which are presented to A in an online format that can be accessed via a terminal. Through this system, A can more effectively improve their skills and contribute to increasing the store's sales.

[0043] The following describes the processing flow.

[0044] Step 1:

[0045] Users input their skill information and desired learning content through a terminal. The terminal digitizes the input information and sends it to the server.

[0046] Step 2:

[0047] The server accesses the company's database to retrieve users' past work history and skill data. This allows for the collection of detailed information about users' performance and experience.

[0048] Step 3:

[0049] The server integrates user input data with work history data from the database. AI algorithms are used to evaluate the user's skill level and work proficiency. Numerical indicators are used for this evaluation, and objective analysis is performed.

[0050] Step 4:

[0051] Based on the evaluated data, the server automatically generates an optimized training plan for each user. This plan includes relevant online learning modules and practical exercises.

[0052] Step 5:

[0053] The server sends the generated training plan to the terminal. The terminal then presents this information to the user, prompts them to log in, and starts the training.

[0054] Step 6:

[0055] Users learn by following the training plan presented on their device. Activities and progress are automatically recorded.

[0056] Step 7:

[0057] The server monitors the user's learning progress in real time. It analyzes the monitoring data and evaluates whether the progress is proceeding as planned.

[0058] Step 8:

[0059] The server generates feedback based on progress and evaluation results. This feedback includes important action points for the user and is provided to the user through their terminal.

[0060] Step 9:

[0061] After training is complete, the server collects new operational performance data and analyzes how much user productivity has improved. The results are quantified, and areas for improvement are identified.

[0062] Step 10:

[0063] The server generates a report based on the analysis results and sends it to the user and their administrator via the terminal. This report is used to optimize future training plans.

[0064] (Example 1)

[0065] 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."

[0066] In today's business environment, improving worker capabilities and performance are crucial challenges. However, manually creating and managing individualized training plans requires significant effort and time. Furthermore, ineffective skill development is hindered because related feedback and performance evaluations are not conducted in a timely manner. Therefore, there is a need for a system that automatically generates individually tailored training plans and efficiently manages their progress and results.

[0067] 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.

[0068] In this invention, the server includes means for analyzing the worker's performance history and ability data acquired from a data storage device and creating a training plan adapted to the worker; means for immediately tracking the progress of learning based on the training plan and providing the necessary responses; and means for determining the work results after the training is completed and generating records for reporting the results. This enables training optimized for each individual worker, allowing for efficient and sustainable skill improvement and performance enhancement.

[0069] A "data storage device" is an information management device that stores performance and capability information collected within a company or organization and provides it in a usable format as needed.

[0070] "Worker" refers to an individual who performs a specific job or task, and includes employees and workers whose abilities and performance are subject to evaluation and guidance planning.

[0071] "Performance history" refers to data that records, in chronological order, the performance and achievements of workers in their past work.

[0072] "Competency data" refers to numerical information that quantifies a worker's skills, expertise, and experience level, and serves as the basis for evaluation and training plans.

[0073] A "training plan" is a document that outlines the overall structure of individually customized training and learning activities aimed at improving the skills and performance of workers.

[0074] "Analysis" is the process of applying statistical methods and algorithms to collected data to derive meaningful insights and conclusions.

[0075] "Real-time tracking" is a management activity aimed at monitoring the progress of learning or work in real time and quickly understanding the situation.

[0076] "Responding" means providing necessary instructions and information based on learning progress and work status.

[0077] "Records" refer to documents or digital data that contain information about the progress and results of work, saved for later evaluation and analysis.

[0078] The embodiments for carrying out the present invention will be described below.

[0079] The server, at the heart of the invention, directly acquires worker performance history and capability data from the data storage device. This server is equipped with multi-purpose database management software, enabling efficient extraction and analysis of performance indicators and capability information. Specifically, data is retrieved using SQL queries, and analysis is performed using Python with machine learning frameworks (e.g., Tensorflow®, PyTorch).

[0080] Users input their own ability data via a terminal. The terminal is equipped with a user-friendly interface, and the input data is immediately sent to the server. The use of a REST API ensures the reliability and integrity of the data. The data entered by the user is integrated by the server's AI algorithm, contributing to the generation of new instructional plans.

[0081] The server uses a generative AI model based on integrated data to automatically generate instructional plans tailored to individual workers. This process leverages open AI APIs to generate prompts and guide the AI ​​in creating instructional plans. The generated plans include online learning courses and simulation-based assignments, which are delivered to users through the company's training management system (e.g., Adobe Captivate Prime, SAP Litmos).

[0082] The device has a means of notifying the user of the generated instruction plan. Push technology is used for notifications, allowing the user to proceed with the training accordingly. The user can use the device to check their learning progress at any time, enabling self-assessment and maintenance of motivation.

[0083] Furthermore, the server monitors learning progress in real time and provides dynamic feedback through a generative AI model. This feedback is communicated to the user in a timely manner at the application layer, and adjustments to the instruction plan are suggested as needed. This flexibility ensures an optimal learning environment tailored to the learner's needs.

[0084] As a concrete example, by inputting a prompt such as, "Please provide a training plan necessary to improve customer service skills in sales," into the AI ​​model, a training plan tailored to strengthening specific skills will be generated. This system supports efficient and sustainable capability improvement and performance enhancement across the entire company through appropriate feedback and evaluation.

[0085] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0086] Step 1:

[0087] The server retrieves worker performance history and capability data from the data storage device. It accesses the database using SQL queries as input and generates a dataset for each worker as output. This dataset includes past work performance and evaluation information.

[0088] Step 2:

[0089] Users input data about their abilities using their devices. They use web forms or mobile apps to specifically describe their skills and experience. The device sends this information to the server via a REST API, which adds it to the dataset as new data. The integrated dataset is then updated as output.

[0090] Step 3:

[0091] The server analyzes the integrated dataset and feeds it into a generative AI model. The entire dataset is supplied as input to the AI ​​algorithm, and prompts are used to generate guidance on lesson plans. The output is an individually optimized lesson plan, including specific online courses and simulation assignments.

[0092] Step 4:

[0093] The server registers the lesson plans on the enterprise learning platform and formats them so that each user can access them. The generated lesson plans are imported into the learning platform as input, and training courses linked to the user ID are prepared as output.

[0094] Step 5:

[0095] The terminal notifies the user of the generated lesson plan. It receives notification data from the server as input and displays it to the user in push notification format as output. The user then begins the training based on this notification.

[0096] Step 6:

[0097] Users progress through their learning according to the instructional plan while checking their progress on their devices. They use the device interface as input to provide progress data. Daily learning outcomes are stored in a database as output.

[0098] Step 7:

[0099] The server monitors progress data in real time and generates feedback using an AI model as needed. It receives progress data as input and performs AI analysis. As output, it sends feedback useful for individual instruction and suggestions for plan revisions to the terminal.

[0100] Step 8:

[0101] Once training is complete, the server collects new performance data and evaluates work results. It analyzes the updated performance database as input and generates reports using a generative AI model. The output is a detailed evaluation report summarizing worker skill improvements and work outcomes, which is provided to users and administrators.

[0102] (Application Example 1)

[0103] 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."

[0104] To improve productivity by providing optimal training plans tailored to each employee's individual skills and work proficiency, it is necessary to create an environment where employees can learn efficiently. However, current systems lack sufficient real-time progress management and adaptive feedback, making it difficult to present learning plans optimized for each employee.

[0105] 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.

[0106] This invention includes a server that includes means for analyzing an worker's work history and skill data obtained from data storage and generating a learning plan optimized for the worker; means for monitoring the learning progress based on the learning plan in real time and providing necessary information; means for evaluating work results after the completion of learning and generating a report to report the effects; and means for enabling the worker to visualize their learning progress on their personal device and perform immediate feedback and adjustments to the learning plan. This allows employees to receive training in a learning environment optimized according to their individual skill level, contributing to increased productivity.

[0107] "Data storage" refers to a storage device for saving and managing information, and it plays a role in holding workers' work history and skill data.

[0108] "Worker" refers to an individual who performs a specific task, and is the employee who is the target of the learning plan in this invention.

[0109] "Work history" refers to data that records what tasks an employee has performed in the past, and is used for skill assessment and the generation of learning plans.

[0110] "Skill data" refers to information about the specific skills and abilities that a worker possesses, and serves as the foundational data for generating individually optimized learning plans.

[0111] A "learning plan" is an educational program optimized according to the worker's skill level, and includes online learning and simulation-based assignments.

[0112] "Real-time monitoring" refers to a state where learning progress can be checked at any time, and corrective measures can be taken immediately as needed.

[0113] "Information provision" refers to the act of improving learning effectiveness by notifying learners of their current progress and areas for improvement.

[0114] A "report" is a document that summarizes the results of evaluating the worker's performance after the completion of the learning plan.

[0115] "Personal devices" refer to communication terminals that are individually owned and used by workers, such as smartphones and smart glasses.

[0116] "Feedback" refers to the act of providing guidance and advice based on learning progress and achievements, and plays a role in supporting learners' improvement.

[0117] "Adjusting the learning plan" means dynamically changing the learning content and approach in response to the worker's learning progress and feedback.

[0118] The system for realizing this invention is programmed as follows: First, the server retrieves the worker's work history and skill data from data storage. In this process, the server uses Python to analyze the data and prepare to generate an optimal learning plan. Next, a generative AI model using TensorFlow generates a learning plan that is individually optimized for each worker based on this data.

[0119] The devices are smartphones or smart glasses personally owned by the workers, allowing them to visualize their individual learning progress in real time. An application built with React Native is installed on the device, and through this application, workers can check their learning progress at any time and receive real-time feedback.

[0120] Furthermore, the server uses the Firebase database to monitor each worker's learning progress in real time and adjust the learning plan as needed. This adjustment allows for the provision of an optimal learning environment tailored to each worker's skill level.

[0121] As a concrete example, imagine a retail store employee using this system to improve their sales skills. They can efficiently improve their skills by checking their learning progress in real time via their smartphone after each day's work and receiving AI-powered feedback. For instance, if an employee wants to improve their product description skills, the system would provide online learning resources linked to a specialized learning plan tailored to that goal and track their progress.

[0122] By utilizing a generative AI model, the program can be executed using the following example prompt: "Generate an optimal learning plan based on the worker's past work data and self-registered skills. The goal is to improve product description skills."

[0123] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0124] Step 1:

[0125] The server retrieves worker work history and skills data from data storage. This data includes past work performance and self-reported information. The input data is in JSON format, and the server parses it to extract basic information about each worker. The resulting output data is converted into a format suitable for analysis by the AI ​​model.

[0126] Step 2:

[0127] The server inputs the acquired data into a generative AI model using TensorFlow to perform a skill assessment for each worker. The AI ​​model analyzes the worker's past activity data and formulates a learning plan optimized for that worker. Based on this prompt, the generated learning plan is output.

[0128] Step 3:

[0129] The terminal displays the learning plan received from the server on the worker's smartphone or smart glasses. The terminal visualizes the learning plan and displays progress via an application built with React Native. The input is the generated learning plan, and the output is the visualized learning content.

[0130] Step 4:

[0131] Users progress through their own devices and send progress data to the server in real time. User input consists of progress status and self-assessment data, which are uploaded to the server periodically. Output includes updated progress data and learning feedback.

[0132] Step 5:

[0133] The server monitors the user's progress via Firebase and adjusts the learning plan as needed. Input data includes the user's progress rate and feedback; the server updates the database based on this data and suggests new learning directions. The output is a revised learning plan based on adaptive feedback, provided to the user.

[0134] 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.

[0135] The system of this invention incorporates an emotion engine to maximize the individualized training effect for employees. This emotion engine recognizes the user's emotional state in real time during the training process and provides an appropriate learning environment and motivation enhancement measures.

[0136] Specifically, the server collects employee work history and skill data from the company's database and analyzes it using an AI algorithm. Based on this analysis, the server generates an optimal training plan for the user and prepares the learning content based on that plan.

[0137] The emotion engine uses the device's camera and sensors to analyze the user's facial expressions and tone of voice, recognizing the emotions being learned. For example, if the user is stressed, the server adjusts the training plan based on that information, providing a relaxing environment or generating more supportive feedback.

[0138] Users follow this training plan via their devices, performing online learning and role-playing exercises. User emotional data is transmitted to the server in real time, and its correlation with learning progress is evaluated. Based on this evaluation, the server provides users with motivational support via their devices to maximize their performance.

[0139] As a concrete example, consider a case where a retail employee "B" takes training using an emotion engine. The server analyzes B's past work data, determines that "strengthening customer service" is necessary, and generates a training plan to improve customer service skills. During the training, if the terminal's camera detects anxiety from B's facial expressions, the server immediately provides feedback on relaxation techniques. In this way, B can continue the training with peace of mind and effectively improve their skills.

[0140] This system allows each employee to receive optimal learning support tailored to their emotional state, thereby improving overall business efficiency within the company.

[0141] The following describes the processing flow.

[0142] Step 1:

[0143] Users input their skill information and learning preferences through their device. This information is then sent to the server in an appropriate format.

[0144] Step 2:

[0145] The server retrieves users' past work history and skill data from the company's database. The retrieved data is then analyzed using an AI algorithm.

[0146] Step 3:

[0147] The server generates a training plan optimized for the user based on the analysis results. This plan includes online learning modules and role-playing exercises.

[0148] Step 4:

[0149] The device notifies the user of the generated training plan and prompts them to log in. The user then reviews the plan on the device and begins training.

[0150] Step 5:

[0151] The emotion engine uses the camera and microphone connected to the device to analyze the user's facial expressions and voice tone in real time. The analysis results are sent to the server as emotion information.

[0152] Step 6:

[0153] The server integrates and analyzes the received emotional information and learning progress to evaluate the user's emotional state. Based on the evaluation results, it generates necessary feedback and motivational measures.

[0154] Step 7:

[0155] The server sends the generated feedback and motivational measures to the device. The device immediately notifies the user and modifies the training method as needed.

[0156] Step 8:

[0157] After receiving feedback through their device, the user continues training. The user's progress is monitored again in real time, and the server intervenes as needed.

[0158] Step 9:

[0159] After training is complete, the server collects new operational performance data and evaluates the results, taking into account sentiment data from the training period. Based on these results, the server generates a detailed report.

[0160] Step 10:

[0161] The server distributes the generated reports to users and their administrators via terminals. The reports provide visualized performance data that can be used to inform future training strategies.

[0162] (Example 2)

[0163] 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".

[0164] In today's work environment, there is a need to implement effective training that takes into account the diverse emotional states of employees. However, traditional methods have made it difficult to provide training optimized for individual employees, and in particular, they have been unable to adequately provide real-time feedback based on employees' emotional states.

[0165] 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.

[0166] In this invention, the server includes means for analyzing the work history and skills information of an employee obtained from an information management device and generating a training plan optimized for the employee; means for recognizing the employee's emotional state via a terminal device and adjusting the training plan in real time; and means for monitoring learning progress based on the training plan in real time and providing necessary support information. This makes it possible to provide effective and individualized training that is adapted to the emotional state of the employee.

[0167] An "information management device" is a device that acquires and stores employee work history and skills information, and provides the data necessary for analysis.

[0168] "Employee work history" refers to information about the work performed by an employee to date and the results of that work.

[0169] "Skills information" refers to information about an employee's specific job performance abilities and specialized knowledge.

[0170] A "training plan" is a plan that specifies the content and methods of training aimed at improving the skills of employees.

[0171] A "terminal device" is a device that a user directly operates to input data and receive feedback.

[0172] "Emotional state" refers to the emotional state an employee exhibits during learning or work, and is primarily expressed through facial expressions and tone of voice.

[0173] "Real-time monitoring" refers to a situation where information is collected and analyzed at that moment, and immediate action can be taken based on the results.

[0174] "Support information" refers to feedback and advice provided for the purpose of assisting in learning or performing tasks.

[0175] The present invention aims to provide employees with optimized training plans using information management devices, terminal devices, and servers, thereby maximizing their effectiveness.

[0176] The server collects employee work history and skills information from the information management device. This involves using database management systems and SQL queries to efficiently retrieve the necessary data. This information is analyzed by an AI algorithm to generate an optimal training plan for each employee. The generated AI model is used in this process to customize the training content and create the schedule.

[0177] The terminal device uses its built-in camera and microphone to analyze the user's emotional state. This analysis is performed in real time, utilizing facial recognition technology and voice analysis software (e.g., OpenCV, Affectiva). Once the user begins training, the terminal device continuously monitors the user's state and transmits the data to the server.

[0178] The server dynamically adjusts the training plan based on the received emotional data. For example, if the user is not concentrating, the training content can be adjusted to improve learning effectiveness. It also sends support information tailored to the user's state to the terminal device, providing the user with appropriate feedback.

[0179] As a concrete example, consider a scenario where a retail employee is undergoing training to improve their customer service skills. The server analyzes the employee's past interaction history and generates a training plan focused on specific skills. If the terminal device detects user anxiety, the server immediately sends feedback on relaxation techniques to the terminal device, helping the employee improve their skills while remaining relaxed.

[0180] This system allows each employee to receive optimal learning support tailored to their emotional state, supporting individual growth and improving overall work efficiency.

[0181] An example of a prompt message might be: "Generate an optimal training plan that takes into account the employee's work history and emotional state."

[0182] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0183] Step 1:

[0184] The server retrieves employee work history and skills information from the database within the information management device using SQL queries. The input is the employee's identification information, and the output is a set of work history and skills information related to that employee. This information is used in subsequent analysis steps.

[0185] Step 2:

[0186] The server inputs the acquired work history and skills information into an AI algorithm. This AI algorithm uses a machine learning model to analyze the elements of an optimal training plan. The input is the employee's work history and skills information, and the output is recommendations and a schedule for the training plan.

[0187] Step 3:

[0188] The server automatically generates a customized training plan based on the analysis results using a generative AI model. This generation utilizes natural language generation technology to construct specific learning tasks and training content. The input is the output of the AI ​​algorithm, and the output is a prompt sentence containing the specific training plan.

[0189] Step 4:

[0190] The device monitors the user who has started training via camera and microphone, and recognizes their emotional state. This process utilizes facial recognition technology and voice analysis software. The input is the user's real-time facial and voice data, and the output is an evaluation of the user's emotional state.

[0191] Step 5:

[0192] The server receives emotional state data transmitted from the terminal and dynamically adjusts the training plan. For example, it might adjust the training schedule by adding breaks for users who are assessed as lacking concentration. The input is emotional state data, and the output is the adjusted training plan.

[0193] Step 6:

[0194] Users follow a training plan provided via their device, engaging in designated online learning and simulation exercises. During this time, the device reports learning progress to the server in real time. Inputs are user operation data during the training plan's implementation, while outputs include the number of completed tasks and performance evaluation data.

[0195] Step 7:

[0196] The server evaluates learning progress, emotional state, and their correlation, and generates feedback and reports to improve motivation as needed. Inputs are learning progress data and emotional state data, while outputs are opinions and encouraging messages to motivate the user for the next training session.

[0197] (Application Example 2)

[0198] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as a "server" and the smart device 14 as a "terminal".

[0199] Traditional worker training systems lack learning support that takes into account the emotional state of individual workers, making efficient and effective skill acquisition difficult. Furthermore, training content is uniform and not sufficiently customized to the aptitudes and circumstances of individual workers. As a result, the efficiency of skill acquisition decreases, potentially negatively impacting the overall productivity of the company.

[0200] 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.

[0201] In this invention, the server includes means for analyzing the worker's work history and skills data obtained from data storage and generating a training plan optimized for the worker; means for monitoring the learning progress based on the training plan in real time and providing necessary feedback; and means for recognizing the worker's emotional state during training and providing a learning environment and motivational enhancement measures according to that emotional state. This makes it possible to provide optimal training support according to the emotional state of each individual worker and to acquire work skills efficiently and effectively.

[0202] "Data storage" refers to an information storage device that stores workers' work history and skill data, and allows for quick retrieval as needed.

[0203] A "training plan" is a learning program optimized based on an individual's work history and skills data to ensure that workers acquire skills appropriately.

[0204] "Learning progress" is a factor that indicates the extent to which a worker has acquired skills according to the training plan, and it is evaluated in real time.

[0205] "Feedback" refers to guidance and improvement suggestions provided according to learning progress, and is adjusted as appropriate according to the worker's level of understanding and emotional state.

[0206] "Emotional state" refers to the mental and psychological state that a worker exhibits during training, and is perceived through facial expressions and tone of voice.

[0207] "Motivation enhancement measures" refer to methods of creating an environment and providing support that enable workers to achieve maximum results in training, and are provided based on their emotional state.

[0208] The system implementing this invention mainly consists of a server, a terminal, and worker users. The server retrieves the worker's work history and skill data from data storage and applies an AI algorithm to analyze this information. Based on the results of the analysis, it generates a training plan optimized for each worker.

[0209] When executing the training plan, the terminal is equipped with a camera and microphone to sense the worker's facial expressions and tone of voice, recognizing their emotional state in real time. A TensorFlow model is used for this emotion recognition. The recognized emotional state is sent to a server. Based on this data, the server adjusts the learning environment and provides appropriate feedback.

[0210] Furthermore, the server monitors the worker's progress in real time and sends necessary information to the terminal. For example, if a worker shows anxiety during the process of acquiring a particular skill, the server immediately provides guidance on how to relax and suggests measures to improve motivation. Specific environmental adjustments could include playing music or nature videos.

[0211] Workers follow a provided training plan, receiving online learning and situational simulation training. They can adaptively improve their skills based on feedback received during the training. The generative AI model assisting this process uses prompts such as: "Please give me advice on how to reduce stress during today's robot operation training."

[0212] This system utilizes Apache® Kafka to implement real-time data processing and aims to provide efficient and individually optimized training. As a result, workers can learn in the best possible environment for their situation, accelerating skill acquisition.

[0213] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0214] Step 1:

[0215] The server retrieves worker work history and skills data from data storage. Based on this input data, it performs analysis using an AI algorithm. As a result, it generates a training plan optimized for the worker. The output is a training plan tailored to the individual worker.

[0216] Step 2:

[0217] The server prepares the corresponding online learning content based on the generated training plan and sends it to the terminal. The input here is the training plan, and the output is the dataset for content delivery. The terminal receives this dataset and begins learning by displaying it to the user.

[0218] Step 3:

[0219] The device uses a camera and microphone to sense the user's facial expressions and voice tone in real time, and inputs this data into a TensorFlow model. The model uses this data to recognize the emotional state and sends the result to the server. The input is live data from the sensors, and the output is the analyzed emotional state.

[0220] Step 4:

[0221] The server analyzes the received emotional state data and adjusts the training plan as needed. For example, if high stress levels are detected, it immediately generates and sends feedback on relaxation methods to the terminal. This feedback is provided to the user via screen display and audio. The input is emotional state data, and the output is the adjusted training plan and feedback.

[0222] Step 5:

[0223] Users improve their skills by continuing their training based on feedback provided from their devices. The user's progress is periodically sent from the device to the server, which analyzes this data and reports the final training outcome. The input is progress data, and the output is provided as a report.

[0224] Through these steps, workers can receive optimal training tailored to their emotional state.

[0225] 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.

[0226] Data generation model 58 is a type of 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.

[0227] 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.

[0228] [Second Embodiment]

[0229] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0230] 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.

[0231] 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).

[0232] 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.

[0233] 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.

[0234] 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).

[0235] 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.

[0236] 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.

[0237] 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.

[0238] 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.

[0239] 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.

[0240] 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".

[0241] The system of this invention automatically generates optimal training plans tailored to the individual skills and work proficiency levels of employees in sales departments, thereby improving employee productivity. To this end, the system includes the following elements.

[0242] First, the server retrieves employees' past performance data and existing skill data from the company's database. Furthermore, users input their own skills and abilities using a terminal and send this information to the server. The server integrates this data and uses an AI algorithm to evaluate the employee's skill level and job proficiency.

[0243] Next, the server generates an individually optimized training plan for each user based on the evaluation data. This plan includes online learning courses and role-playing exercises, presented in a way that is easy for employees to implement.

[0244] The device notifies the user of this training plan, and each user proceeds with the training accordingly. Users can check their daily progress within the system, allowing them to objectively understand their own learning.

[0245] Furthermore, the server monitors training progress in real time and generates feedback tailored to the user's learning status. The terminal immediately notifies the user of this feedback and suggests modifications to the training plan as needed. In this way, users can efficiently improve their skills.

[0246] Furthermore, after the training is completed, the server collects new work performance data and evaluates the training's effectiveness. Here, it analyzes employee productivity and skill improvement, and generates a report based on the results. The terminal provides this report to users and administrators, serving as a foundation for continuous talent development.

[0247] To give a specific example, employee "A" at a certain retail store was evaluated as having "intermediate customer service skills" based on past sales performance. Therefore, the AI ​​provides A with training plans for "advanced customer service" and "enhanced problem-solving skills," which are presented to A in an online format that can be accessed via a terminal. Through this system, A can more effectively improve their skills and contribute to increasing the store's sales.

[0248] The following describes the processing flow.

[0249] Step 1:

[0250] Users input their skill information and desired learning content through a terminal. The terminal digitizes the input information and sends it to the server.

[0251] Step 2:

[0252] The server accesses the company's database to retrieve users' past work history and skill data. This allows for the collection of detailed information about users' performance and experience.

[0253] Step 3:

[0254] The server integrates user input data with work history data from the database. AI algorithms are used to evaluate the user's skill level and work proficiency. Numerical indicators are used for this evaluation, and objective analysis is performed.

[0255] Step 4:

[0256] Based on the evaluated data, the server automatically generates an optimized training plan for each user. This plan includes relevant online learning modules and practical exercises.

[0257] Step 5:

[0258] The server sends the generated training plan to the terminal. The terminal then presents this information to the user, prompts them to log in, and starts the training.

[0259] Step 6:

[0260] Users learn by following the training plan presented on their device. Activities and progress are automatically recorded.

[0261] Step 7:

[0262] The server monitors the user's learning progress in real time. It analyzes the monitoring data and evaluates whether the progress is proceeding as planned.

[0263] Step 8:

[0264] The server generates feedback based on progress and evaluation results. This feedback includes important action points for the user and is provided to the user through their terminal.

[0265] Step 9:

[0266] After training is complete, the server collects new operational performance data and analyzes how much user productivity has improved. The results are quantified, and areas for improvement are identified.

[0267] Step 10:

[0268] The server generates a report based on the analysis results and sends it to the user and their administrator via the terminal. This report is used to optimize future training plans.

[0269] (Example 1)

[0270] 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."

[0271] In today's business environment, improving worker capabilities and performance are crucial challenges. However, manually creating and managing individualized training plans requires significant effort and time. Furthermore, ineffective skill development is hindered because related feedback and performance evaluations are not conducted in a timely manner. Therefore, there is a need for a system that automatically generates individually tailored training plans and efficiently manages their progress and results.

[0272] 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.

[0273] In this invention, the server includes means for analyzing the worker's performance history and ability data acquired from a data storage device and creating a training plan adapted to the worker; means for immediately tracking the progress of learning based on the training plan and providing the necessary responses; and means for determining the work results after the training is completed and generating records for reporting the results. This enables training optimized for each individual worker, allowing for efficient and sustainable skill improvement and performance enhancement.

[0274] A "data storage device" is an information management device that stores performance and capability information collected within a company or organization and provides it in a usable format as needed.

[0275] "Worker" refers to an individual who performs a specific job or task, and includes employees and workers whose abilities and performance are subject to evaluation and guidance planning.

[0276] "Performance history" refers to data that records, in chronological order, the performance and achievements of workers in their past work.

[0277] "Competency data" refers to numerical information that quantifies a worker's skills, expertise, and experience level, and serves as the basis for evaluation and training plans.

[0278] A "training plan" is a document that outlines the overall structure of individually customized training and learning activities aimed at improving the skills and performance of workers.

[0279] "Analysis" is the process of applying statistical methods and algorithms to collected data to derive meaningful insights and conclusions.

[0280] "Real-time tracking" is a management activity aimed at monitoring the progress of learning or work in real time and quickly understanding the situation.

[0281] "Responding" means providing necessary instructions and information based on learning progress and work status.

[0282] "Record" refers to documents or digital data that store information related to the progress and results of operations for later evaluation and analysis.

[0283] The embodiments for implementing the present invention will be described below.

[0284] As the core of the invention, the server directly obtains the performance history and ability data of workers from the data storage device. This server is equipped with multi-purpose database management software, enabling efficient extraction and analysis of performance indicators and ability information. Specifically, SQL queries are used to call data, and machine learning frameworks (e.g., TensorFlow, PyTorch) are applied using Python for analysis.

[0285] The user inputs their own ability data via the terminal. The terminal is equipped with a user-friendly interface, and the input data is immediately sent to the server. At this time, by using the REST API, the reliability and consistency of the data are ensured. The data input by the user is integrated by the server's AI algorithm and contributes to the generation of a new guidance plan.

[0286] Based on the integrated data, the server uses the generated AI model to automatically generate a guidance plan tailored to each individual worker. In this process, the API of OpenAI is utilized to generate prompt texts, providing guidelines for the AI to create the guidance plan. The generated plan includes online learning courses and simulation-based tasks, which are provided to users through the in-company training management system (e.g., Adobe Captivate Prime, SAP Litmos).

[0287] The terminal has a means to notify the user of this generated guidance plan. Push technology is used for the notification, and the user can proceed with the training accordingly. The user can use the terminal to check the progress of learning at any time, enabling self-evaluation and maintaining motivation.

[0288] Furthermore, the server monitors learning progress in real time and provides dynamic feedback through a generative AI model. This feedback is communicated to the user in a timely manner at the application layer, and adjustments to the instruction plan are suggested as needed. This flexibility ensures an optimal learning environment tailored to the learner's needs.

[0289] As a concrete example, by inputting a prompt such as, "Please provide a training plan necessary to improve customer service skills in sales," into the AI ​​model, a training plan tailored to strengthening specific skills will be generated. This system supports efficient and sustainable capability improvement and performance enhancement across the entire company through appropriate feedback and evaluation.

[0290] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0291] Step 1:

[0292] The server retrieves worker performance history and capability data from the data storage device. It accesses the database using SQL queries as input and generates a dataset for each worker as output. This dataset includes past work performance and evaluation information.

[0293] Step 2:

[0294] Users input data about their abilities using their devices. They use web forms or mobile apps to specifically describe their skills and experience. The device sends this information to the server via a REST API, which adds it to the dataset as new data. The integrated dataset is then updated as output.

[0295] Step 3:

[0296] The server analyzes the integrated dataset and feeds it into a generative AI model. The entire dataset is supplied as input to the AI ​​algorithm, and prompts are used to generate guidance on lesson plans. The output is an individually optimized lesson plan, including specific online courses and simulation assignments.

[0297] Step 4:

[0298] The server registers the lesson plans on the enterprise learning platform and formats them so that each user can access them. The generated lesson plans are imported into the learning platform as input, and training courses linked to the user ID are prepared as output.

[0299] Step 5:

[0300] The terminal notifies the user of the generated lesson plan. It receives notification data from the server as input and displays it to the user in push notification format as output. The user then begins the training based on this notification.

[0301] Step 6:

[0302] Users progress through their learning according to the instructional plan while checking their progress on their devices. They use the device interface as input to provide progress data. Daily learning outcomes are stored in a database as output.

[0303] Step 7:

[0304] The server monitors progress data in real time and generates feedback using an AI model as needed. It receives progress data as input and performs AI analysis. As output, it sends feedback useful for individual instruction and suggestions for plan revisions to the terminal.

[0305] Step 8:

[0306] When the training is completed, the server collects new performance data and evaluates the business results. It analyzes the updated performance database as input and generates a report using the generated AI model. The output is a detailed evaluation report summarizing the operator's skill improvement and business results, which is provided to users and administrators.

[0307] (Application Example 1)

[0308] Next, Application Example 1 will be described. In the following description, the data processing device 12 is referred to as the "server", and the smart glasses 214 are referred to as the "terminal".

[0309] In order to achieve productivity improvement by providing an optimal training plan according to the individual skills and business proficiency of employees, it is necessary to create an environment in which employees can learn efficiently. However, the current system has problems such as insufficient real-time progress management and adaptive feedback, and it is difficult to present a learning plan optimized for each employee.

[0310] The specific processing by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0311] In this invention, the server includes means for analyzing the work history and skill data of an operator obtained from a data storage and generating a learning plan optimized for the operator, means for monitoring the learning progress in real time based on the learning plan and providing necessary information, means for evaluating the work results after learning and generating a report for reporting the effects, and means for allowing the operator to visualize the learning progress on a personal device and execute immediate feedback and adjustment of the learning plan. As a result, employees can receive training in an optimized learning environment according to their individual skill levels, contributing to productivity improvement.

[0312] The "data storage" is a storage device for storing and managing information and plays a role in holding the work history and skill data of an operator.

[0313] "Worker" refers to an individual who performs a specific task, and is the employee who is the target of the learning plan in this invention.

[0314] "Work history" refers to data that records what tasks an employee has performed in the past, and is used for skill assessment and the generation of learning plans.

[0315] "Skill data" refers to information about the specific skills and abilities that a worker possesses, and serves as the foundational data for generating individually optimized learning plans.

[0316] A "learning plan" is an educational program optimized according to the worker's skill level, and includes online learning and simulation-based assignments.

[0317] "Real-time monitoring" refers to a state where learning progress can be checked at any time, and corrective measures can be taken immediately as needed.

[0318] "Information provision" refers to the act of improving learning effectiveness by notifying learners of their current progress and areas for improvement.

[0319] A "report" is a document that summarizes the results of evaluating the worker's performance after the completion of the learning plan.

[0320] "Personal devices" refer to communication terminals that are individually owned and used by workers, such as smartphones and smart glasses.

[0321] "Feedback" refers to the act of providing guidance and advice based on learning progress and achievements, and plays a role in supporting learners' improvement.

[0322] "Adjusting the learning plan" means dynamically changing the learning content and approach in response to the worker's learning progress and feedback.

[0323] The system for realizing this invention is programmed as follows: First, the server retrieves the worker's work history and skill data from data storage. In this process, the server uses Python to analyze the data and prepare to generate an optimal learning plan. Next, a generative AI model using TensorFlow generates a learning plan that is individually optimized for each worker based on this data.

[0324] The devices are smartphones or smart glasses personally owned by the workers, allowing them to visualize their individual learning progress in real time. An application built with React Native is installed on the device, and through this application, workers can check their learning progress at any time and receive real-time feedback.

[0325] Furthermore, the server uses the Firebase database to monitor each worker's learning progress in real time and adjust the learning plan as needed. This adjustment allows for the provision of an optimal learning environment tailored to each worker's skill level.

[0326] As a concrete example, imagine a retail store employee using this system to improve their sales skills. They can efficiently improve their skills by checking their learning progress in real time via their smartphone after each day's work and receiving AI-powered feedback. For instance, if an employee wants to improve their product description skills, the system would provide online learning resources linked to a specialized learning plan tailored to that goal and track their progress.

[0327] By utilizing a generative AI model, the program can be executed using the following example prompt: "Generate an optimal learning plan based on the worker's past work data and self-registered skills. The goal is to improve product description skills."

[0328] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0329] Step 1:

[0330] The server retrieves worker work history and skills data from data storage. This data includes past work performance and self-reported information. The input data is in JSON format, and the server parses it to extract basic information about each worker. The resulting output data is converted into a format suitable for analysis by the AI ​​model.

[0331] Step 2:

[0332] The server inputs the acquired data into a generative AI model using TensorFlow to perform a skill assessment for each worker. The AI ​​model analyzes the worker's past activity data and formulates a learning plan optimized for that worker. Based on this prompt, the generated learning plan is output.

[0333] Step 3:

[0334] The terminal displays the learning plan received from the server on the worker's smartphone or smart glasses. The terminal visualizes the learning plan and displays progress via an application built with React Native. The input is the generated learning plan, and the output is the visualized learning content.

[0335] Step 4:

[0336] Users progress through their own devices and send progress data to the server in real time. User input consists of progress status and self-assessment data, which are uploaded to the server periodically. Output includes updated progress data and learning feedback.

[0337] Step 5:

[0338] The server monitors the user's progress via Firebase and adjusts the learning plan as needed. Input data includes the user's progress rate and feedback; the server updates the database based on this data and suggests new learning directions. The output is a revised learning plan based on adaptive feedback, provided to the user.

[0339] 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.

[0340] The system of this invention incorporates an emotion engine to maximize the individualized training effect for employees. This emotion engine recognizes the user's emotional state in real time during the training process and provides an appropriate learning environment and motivation enhancement measures.

[0341] Specifically, the server collects employee work history and skill data from the company's database and analyzes it using an AI algorithm. Based on this analysis, the server generates an optimal training plan for the user and prepares the learning content based on that plan.

[0342] The emotion engine uses the device's camera and sensors to analyze the user's facial expressions and tone of voice, recognizing the emotions being learned. For example, if the user is stressed, the server adjusts the training plan based on that information, providing a relaxing environment or generating more supportive feedback.

[0343] Users follow this training plan via their devices, performing online learning and role-playing exercises. User emotional data is transmitted to the server in real time, and its correlation with learning progress is evaluated. Based on this evaluation, the server provides users with motivational support via their devices to maximize their performance.

[0344] As a concrete example, consider a case where a retail employee "B" takes training using an emotion engine. The server analyzes B's past work data, determines that "strengthening customer service" is necessary, and generates a training plan to improve customer service skills. During the training, if the terminal's camera detects anxiety from B's facial expressions, the server immediately provides feedback on relaxation techniques. In this way, B can continue the training with peace of mind and effectively improve their skills.

[0345] This system allows each employee to receive optimal learning support tailored to their emotional state, thereby improving overall business efficiency within the company.

[0346] The following describes the processing flow.

[0347] Step 1:

[0348] Users input their skill information and learning preferences through their device. This information is then sent to the server in an appropriate format.

[0349] Step 2:

[0350] The server retrieves users' past work history and skill data from the company's database. The retrieved data is then analyzed using an AI algorithm.

[0351] Step 3:

[0352] The server generates a training plan optimized for the user based on the analysis results. This plan includes online learning modules and role-playing exercises.

[0353] Step 4:

[0354] The device notifies the user of the generated training plan and prompts them to log in. The user then reviews the plan on the device and begins training.

[0355] Step 5:

[0356] The emotion engine uses the camera and microphone connected to the device to analyze the user's facial expressions and voice tone in real time. The analysis results are sent to the server as emotion information.

[0357] Step 6:

[0358] The server integrates and analyzes the received emotional information and learning progress to evaluate the user's emotional state. Based on the evaluation results, it generates necessary feedback and motivational measures.

[0359] Step 7:

[0360] The server sends the generated feedback and motivational measures to the device. The device immediately notifies the user and modifies the training method as needed.

[0361] Step 8:

[0362] After receiving feedback through their device, the user continues training. The user's progress is monitored again in real time, and the server intervenes as needed.

[0363] Step 9:

[0364] After training is complete, the server collects new operational performance data and evaluates the results, taking into account sentiment data from the training period. Based on these results, the server generates a detailed report.

[0365] Step 10:

[0366] The server distributes the generated reports to users and their administrators via terminals. The reports provide visualized performance data that can be used to inform future training strategies.

[0367] (Example 2)

[0368] 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".

[0369] In today's work environment, there is a need to implement effective training that takes into account the diverse emotional states of employees. However, traditional methods have made it difficult to provide training optimized for individual employees, and in particular, they have been unable to adequately provide real-time feedback based on employees' emotional states.

[0370] 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.

[0371] In this invention, the server includes means for analyzing the work history and skills information of an employee obtained from an information management device and generating a training plan optimized for the employee; means for recognizing the employee's emotional state via a terminal device and adjusting the training plan in real time; and means for monitoring learning progress based on the training plan in real time and providing necessary support information. This makes it possible to provide effective and individualized training that is adapted to the emotional state of the employee.

[0372] An "information management device" is a device that acquires and stores employee work history and skills information, and provides the data necessary for analysis.

[0373] "Employee work history" refers to information about the work performed by an employee to date and the results of that work.

[0374] "Skills information" refers to information about an employee's specific job performance abilities and specialized knowledge.

[0375] A "training plan" is a plan that specifies the content and methods of training aimed at improving the skills of employees.

[0376] A "terminal device" is a device that a user directly operates to input data and receive feedback.

[0377] "Emotional state" refers to the emotional state an employee exhibits during learning or work, and is primarily expressed through facial expressions and tone of voice.

[0378] "Real-time monitoring" refers to a situation where information is collected and analyzed at that moment, and immediate action can be taken based on the results.

[0379] "Support information" refers to feedback and advice provided for the purpose of assisting in learning or performing tasks.

[0380] The present invention aims to provide employees with optimized training plans using information management devices, terminal devices, and servers, thereby maximizing their effectiveness.

[0381] The server collects employee work history and skills information from the information management device. This involves using database management systems and SQL queries to efficiently retrieve the necessary data. This information is analyzed by an AI algorithm to generate an optimal training plan for each employee. The generated AI model is used in this process to customize the training content and create the schedule.

[0382] The terminal device uses its built-in camera and microphone to analyze the user's emotional state. This analysis is performed in real time, utilizing facial recognition technology and voice analysis software (e.g., OpenCV, Affectiva). Once the user begins training, the terminal device continuously monitors the user's state and transmits the data to the server.

[0383] The server dynamically adjusts the training plan based on the received emotional data. For example, if the user is not concentrating, the training content can be adjusted to improve learning effectiveness. It also sends support information tailored to the user's state to the terminal device, providing the user with appropriate feedback.

[0384] As a concrete example, consider a scenario where a retail employee is undergoing training to improve their customer service skills. The server analyzes the employee's past interaction history and generates a training plan focused on specific skills. If the terminal device detects user anxiety, the server immediately sends feedback on relaxation techniques to the terminal device, helping the employee improve their skills while remaining relaxed.

[0385] This system allows each employee to receive optimal learning support tailored to their emotional state, supporting individual growth and improving overall work efficiency.

[0386] An example of a prompt message might be: "Generate an optimal training plan that takes into account the employee's work history and emotional state."

[0387] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0388] Step 1:

[0389] The server retrieves employee work history and skills information from the database within the information management device using SQL queries. The input is the employee's identification information, and the output is a set of work history and skills information related to that employee. This information is used in subsequent analysis steps.

[0390] Step 2:

[0391] The server inputs the acquired work history and skills information into an AI algorithm. This AI algorithm uses a machine learning model to analyze the elements of an optimal training plan. The input is the employee's work history and skills information, and the output is recommendations and a schedule for the training plan.

[0392] Step 3:

[0393] The server automatically generates a customized training plan based on the analysis results using a generative AI model. This generation utilizes natural language generation technology to construct specific learning tasks and training content. The input is the output of the AI ​​algorithm, and the output is a prompt sentence containing the specific training plan.

[0394] Step 4:

[0395] The device monitors the user who has started training via camera and microphone, and recognizes their emotional state. This process utilizes facial recognition technology and voice analysis software. The input is the user's real-time facial and voice data, and the output is an evaluation of the user's emotional state.

[0396] Step 5:

[0397] The server receives emotional state data transmitted from the terminal and dynamically adjusts the training plan. For example, it might adjust the training schedule by adding breaks for users who are assessed as lacking concentration. The input is emotional state data, and the output is the adjusted training plan.

[0398] Step 6:

[0399] Users follow a training plan provided via their device, engaging in designated online learning and simulation exercises. During this time, the device reports learning progress to the server in real time. Inputs are user operation data during the training plan's implementation, while outputs include the number of completed tasks and performance evaluation data.

[0400] Step 7:

[0401] The server evaluates learning progress, emotional state, and their correlation, and generates feedback and reports to improve motivation as needed. Inputs are learning progress data and emotional state data, while outputs are opinions and encouraging messages to motivate the user for the next training session.

[0402] (Application Example 2)

[0403] 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."

[0404] Traditional worker training systems lack learning support that takes into account the emotional state of individual workers, making efficient and effective skill acquisition difficult. Furthermore, training content is uniform and not sufficiently customized to the aptitudes and circumstances of individual workers. As a result, the efficiency of skill acquisition decreases, potentially negatively impacting the overall productivity of the company.

[0405] 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.

[0406] In this invention, the server includes means for analyzing the worker's work history and skills data obtained from data storage and generating a training plan optimized for the worker; means for monitoring the learning progress based on the training plan in real time and providing necessary feedback; and means for recognizing the worker's emotional state during training and providing a learning environment and motivational enhancement measures according to that emotional state. This makes it possible to provide optimal training support according to the emotional state of each individual worker and to acquire work skills efficiently and effectively.

[0407] "Data storage" refers to an information storage device that stores workers' work history and skill data, and allows for quick retrieval as needed.

[0408] A "training plan" is a learning program optimized based on an individual's work history and skills data to ensure that workers acquire skills appropriately.

[0409] "Learning progress" is a factor that indicates the extent to which a worker has acquired skills according to the training plan, and it is evaluated in real time.

[0410] "Feedback" refers to guidance and improvement suggestions provided according to learning progress, and is adjusted as appropriate according to the worker's level of understanding and emotional state.

[0411] "Emotional state" refers to the mental and psychological state that a worker exhibits during training, and is perceived through facial expressions and tone of voice.

[0412] "Motivation enhancement measures" refer to methods of creating an environment and providing support that enable workers to achieve maximum results in training, and are provided based on their emotional state.

[0413] The system implementing this invention mainly consists of a server, a terminal, and worker users. The server retrieves the worker's work history and skill data from data storage and applies an AI algorithm to analyze this information. Based on the results of the analysis, it generates a training plan optimized for each worker.

[0414] When executing the training plan, the terminal is equipped with a camera and microphone to sense the worker's facial expressions and tone of voice, recognizing their emotional state in real time. A TensorFlow model is used for this emotion recognition. The recognized emotional state is sent to a server. Based on this data, the server adjusts the learning environment and provides appropriate feedback.

[0415] Furthermore, the server monitors the worker's progress in real time and sends necessary information to the terminal. For example, if a worker shows anxiety during the process of acquiring a particular skill, the server immediately provides guidance on how to relax and suggests measures to improve motivation. Specific environmental adjustments could include playing music or nature videos.

[0416] Workers follow a provided training plan, receiving online learning and situational simulation training. They can adaptively improve their skills based on feedback received during the training. The generative AI model assisting this process uses prompts such as: "Please give me advice on how to reduce stress during today's robot operation training."

[0417] This system utilizes Apache Kafka to implement real-time data processing and aims to provide efficient and individually optimized training. As a result, workers can learn in the best possible environment for their situation, accelerating skill acquisition.

[0418] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0419] Step 1:

[0420] The server retrieves worker work history and skills data from data storage. Based on this input data, it performs analysis using an AI algorithm. As a result, it generates a training plan optimized for the worker. The output is a training plan tailored to the individual worker.

[0421] Step 2:

[0422] The server prepares the corresponding online learning content based on the generated training plan and sends it to the terminal. The input here is the training plan, and the output is the dataset for content delivery. The terminal receives this dataset and begins learning by displaying it to the user.

[0423] Step 3:

[0424] The device uses a camera and microphone to sense the user's facial expressions and voice tone in real time, and inputs this data into a TensorFlow model. The model uses this data to recognize the emotional state and sends the result to the server. The input is live data from the sensors, and the output is the analyzed emotional state.

[0425] Step 4:

[0426] The server analyzes the received emotional state data and adjusts the training plan as needed. For example, if high stress levels are detected, it immediately generates and sends feedback on relaxation methods to the terminal. This feedback is provided to the user via screen display and audio. The input is emotional state data, and the output is the adjusted training plan and feedback.

[0427] Step 5:

[0428] Users improve their skills by continuing their training based on feedback provided from their devices. The user's progress is periodically sent from the device to the server, which analyzes this data and reports the final training outcome. The input is progress data, and the output is provided as a report.

[0429] Through these steps, workers can receive optimal training tailored to their emotional state.

[0430] 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.

[0431] 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.

[0432] 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.

[0433] [Third Embodiment]

[0434] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0435] 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.

[0436] 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).

[0437] 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.

[0438] 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.

[0439] 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).

[0440] 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.

[0441] 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.

[0442] 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.

[0443] 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.

[0444] 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.

[0445] 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".

[0446] The system of this invention automatically generates optimal training plans tailored to the individual skills and work proficiency levels of employees in sales departments, thereby improving employee productivity. To this end, the system includes the following elements.

[0447] First, the server retrieves employees' past performance data and existing skill data from the company's database. Furthermore, users input their own skills and abilities using a terminal and send this information to the server. The server integrates this data and uses an AI algorithm to evaluate the employee's skill level and job proficiency.

[0448] Next, the server generates an individually optimized training plan for each user based on the evaluation data. This plan includes online learning courses and role-playing exercises, presented in a way that is easy for employees to implement.

[0449] The device notifies the user of this training plan, and each user proceeds with the training accordingly. Users can check their daily progress within the system, allowing them to objectively understand their own learning.

[0450] Furthermore, the server monitors training progress in real time and generates feedback tailored to the user's learning status. The terminal immediately notifies the user of this feedback and suggests modifications to the training plan as needed. In this way, users can efficiently improve their skills.

[0451] Furthermore, after the training is completed, the server collects new work performance data and evaluates the training's effectiveness. Here, it analyzes employee productivity and skill improvement, and generates a report based on the results. The terminal provides this report to users and administrators, serving as a foundation for continuous talent development.

[0452] To give a specific example, employee "A" at a certain retail store was evaluated as having "intermediate customer service skills" based on past sales performance. Therefore, the AI ​​provides A with training plans for "advanced customer service" and "enhanced problem-solving skills," which are presented to A in an online format that can be accessed via a terminal. Through this system, A can more effectively improve their skills and contribute to increasing the store's sales.

[0453] The following describes the processing flow.

[0454] Step 1:

[0455] Users input their skill information and desired learning content through a terminal. The terminal digitizes the input information and sends it to the server.

[0456] Step 2:

[0457] The server accesses the company's database to retrieve users' past work history and skill data. This allows for the collection of detailed information about users' performance and experience.

[0458] Step 3:

[0459] The server integrates user input data with work history data from the database. AI algorithms are used to evaluate the user's skill level and work proficiency. Numerical indicators are used for this evaluation, and objective analysis is performed.

[0460] Step 4:

[0461] Based on the evaluated data, the server automatically generates an optimized training plan for each user. This plan includes relevant online learning modules and practical exercises.

[0462] Step 5:

[0463] The server sends the generated training plan to the terminal. The terminal then presents this information to the user, prompts them to log in, and starts the training.

[0464] Step 6:

[0465] Users learn by following the training plan presented on their device. Activities and progress are automatically recorded.

[0466] Step 7:

[0467] The server monitors the user's learning progress in real time. It analyzes the monitoring data and evaluates whether the progress is proceeding as planned.

[0468] Step 8:

[0469] The server generates feedback based on progress and evaluation results. This feedback includes important action points for the user and is provided to the user through their terminal.

[0470] Step 9:

[0471] After training is complete, the server collects new operational performance data and analyzes how much user productivity has improved. The results are quantified, and areas for improvement are identified.

[0472] Step 10:

[0473] The server generates a report based on the analysis results and sends it to the user and their administrator via the terminal. This report is used to optimize future training plans.

[0474] (Example 1)

[0475] 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."

[0476] In today's business environment, improving worker capabilities and performance are crucial challenges. However, manually creating and managing individualized training plans requires significant effort and time. Furthermore, ineffective skill development is hindered because related feedback and performance evaluations are not conducted in a timely manner. Therefore, there is a need for a system that automatically generates individually tailored training plans and efficiently manages their progress and results.

[0477] 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.

[0478] In this invention, the server includes means for analyzing the worker's performance history and ability data acquired from a data storage device and creating a training plan adapted to the worker; means for immediately tracking the progress of learning based on the training plan and providing the necessary responses; and means for determining the work results after the training is completed and generating records for reporting the results. This enables training optimized for each individual worker, allowing for efficient and sustainable skill improvement and performance enhancement.

[0479] A "data storage device" is an information management device that stores performance and capability information collected within a company or organization and provides it in a usable format as needed.

[0480] "Worker" refers to an individual who performs a specific job or task, and includes employees and workers whose abilities and performance are subject to evaluation and guidance planning.

[0481] "Performance history" refers to data that records, in chronological order, the performance and achievements of workers in their past work.

[0482] "Competency data" refers to numerical information that quantifies a worker's skills, expertise, and experience level, and serves as the basis for evaluation and training plans.

[0483] A "training plan" is a document that outlines the overall structure of individually customized training and learning activities aimed at improving the skills and performance of workers.

[0484] "Analysis" is the process of applying statistical methods and algorithms to collected data to derive meaningful insights and conclusions.

[0485] "Real-time tracking" is a management activity aimed at monitoring the progress of learning or work in real time and quickly understanding the situation.

[0486] "Responding" means providing necessary instructions and information based on learning progress and work status.

[0487] "Records" refer to documents or digital data that contain information about the progress and results of work, saved for later evaluation and analysis.

[0488] The embodiments for carrying out the present invention will be described below.

[0489] The server, at the heart of the invention, directly acquires worker performance history and capability data from the data storage device. This server is equipped with multipurpose database management software, enabling efficient extraction and analysis of performance indicators and capability information. Specifically, data is retrieved using SQL queries, and machine learning frameworks (e.g., TensorFlow, PyTorch) are applied to the analysis using Python.

[0490] Users input their own ability data via a terminal. The terminal is equipped with a user-friendly interface, and the input data is immediately sent to the server. The use of a REST API ensures the reliability and integrity of the data. The data entered by the user is integrated by the server's AI algorithm, contributing to the generation of new instructional plans.

[0491] The server uses a generative AI model based on integrated data to automatically generate instructional plans tailored to individual workers. This process leverages open AI APIs to generate prompts and guide the AI ​​in creating instructional plans. The generated plans include online learning courses and simulation-based assignments, which are delivered to users through the company's training management system (e.g., Adobe Captivate Prime, SAP Litmos).

[0492] The device has a means of notifying the user of the generated instruction plan. Push technology is used for notifications, allowing the user to proceed with the training accordingly. The user can use the device to check their learning progress at any time, enabling self-assessment and maintenance of motivation.

[0493] Furthermore, the server monitors learning progress in real time and provides dynamic feedback through a generative AI model. This feedback is communicated to the user in a timely manner at the application layer, and adjustments to the instruction plan are suggested as needed. This flexibility ensures an optimal learning environment tailored to the learner's needs.

[0494] As a concrete example, by inputting a prompt such as, "Please provide a training plan necessary to improve customer service skills in sales," into the AI ​​model, a training plan tailored to strengthening specific skills will be generated. This system supports efficient and sustainable capability improvement and performance enhancement across the entire company through appropriate feedback and evaluation.

[0495] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0496] Step 1:

[0497] The server retrieves worker performance history and capability data from the data storage device. It accesses the database using SQL queries as input and generates a dataset for each worker as output. This dataset includes past work performance and evaluation information.

[0498] Step 2:

[0499] Users input data about their abilities using their devices. They use web forms or mobile apps to specifically describe their skills and experience. The device sends this information to the server via a REST API, which adds it to the dataset as new data. The integrated dataset is then updated as output.

[0500] Step 3:

[0501] The server analyzes the integrated dataset and feeds it into a generative AI model. The entire dataset is supplied as input to the AI ​​algorithm, and prompts are used to generate guidance on lesson plans. The output is an individually optimized lesson plan, including specific online courses and simulation assignments.

[0502] Step 4:

[0503] The server registers the lesson plans on the enterprise learning platform and formats them so that each user can access them. The generated lesson plans are imported into the learning platform as input, and training courses linked to the user ID are prepared as output.

[0504] Step 5:

[0505] The terminal notifies the user of the generated lesson plan. It receives notification data from the server as input and displays it to the user in push notification format as output. The user then begins the training based on this notification.

[0506] Step 6:

[0507] Users progress through their learning according to the instructional plan while checking their progress on their devices. They use the device interface as input to provide progress data. Daily learning outcomes are stored in a database as output.

[0508] Step 7:

[0509] The server monitors progress data in real time and generates feedback using an AI model as needed. It receives progress data as input and performs AI analysis. As output, it sends feedback useful for individual instruction and suggestions for plan revisions to the terminal.

[0510] Step 8:

[0511] Once training is complete, the server collects new performance data and evaluates work results. It analyzes the updated performance database as input and generates reports using a generative AI model. The output is a detailed evaluation report summarizing worker skill improvements and work outcomes, which is provided to users and administrators.

[0512] (Application Example 1)

[0513] 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."

[0514] To improve productivity by providing optimal training plans tailored to each employee's individual skills and work proficiency, it is necessary to create an environment where employees can learn efficiently. However, current systems lack sufficient real-time progress management and adaptive feedback, making it difficult to present learning plans optimized for each employee.

[0515] 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.

[0516] This invention includes a server that includes means for analyzing an worker's work history and skill data obtained from data storage and generating a learning plan optimized for the worker; means for monitoring the learning progress based on the learning plan in real time and providing necessary information; means for evaluating work results after the completion of learning and generating a report to report the effects; and means for enabling the worker to visualize their learning progress on their personal device and perform immediate feedback and adjustments to the learning plan. This allows employees to receive training in a learning environment optimized according to their individual skill level, contributing to increased productivity.

[0517] "Data storage" refers to a storage device for saving and managing information, and it plays a role in holding workers' work history and skill data.

[0518] "Worker" refers to an individual who performs a specific task, and is the employee who is the target of the learning plan in this invention.

[0519] "Work history" refers to data that records what tasks an employee has performed in the past, and is used for skill assessment and the generation of learning plans.

[0520] "Skill data" refers to information about the specific skills and abilities that a worker possesses, and serves as the foundational data for generating individually optimized learning plans.

[0521] A "learning plan" is an educational program optimized according to the worker's skill level, and includes online learning and simulation-based assignments.

[0522] "Real-time monitoring" refers to a state where learning progress can be checked at any time, and corrective measures can be taken immediately as needed.

[0523] "Information provision" refers to the act of improving learning effectiveness by notifying learners of their current progress and areas for improvement.

[0524] A "report" is a document that summarizes the results of evaluating the worker's performance after the completion of the learning plan.

[0525] "Personal devices" refer to communication terminals that are individually owned and used by workers, such as smartphones and smart glasses.

[0526] "Feedback" refers to the act of providing guidance and advice based on learning progress and achievements, and plays a role in supporting learners' improvement.

[0527] "Adjusting the learning plan" means dynamically changing the learning content and approach in response to the worker's learning progress and feedback.

[0528] The system for realizing this invention is programmed as follows: First, the server retrieves the worker's work history and skill data from data storage. In this process, the server uses Python to analyze the data and prepare to generate an optimal learning plan. Next, a generative AI model using TensorFlow generates a learning plan that is individually optimized for each worker based on this data.

[0529] The devices are smartphones or smart glasses personally owned by the workers, allowing them to visualize their individual learning progress in real time. An application built with React Native is installed on the device, and through this application, workers can check their learning progress at any time and receive real-time feedback.

[0530] Furthermore, the server uses the Firebase database to monitor each worker's learning progress in real time and adjust the learning plan as needed. This adjustment allows for the provision of an optimal learning environment tailored to each worker's skill level.

[0531] As a concrete example, imagine a retail store employee using this system to improve their sales skills. They can efficiently improve their skills by checking their learning progress in real time via their smartphone after each day's work and receiving AI-powered feedback. For instance, if an employee wants to improve their product description skills, the system would provide online learning resources linked to a specialized learning plan tailored to that goal and track their progress.

[0532] By utilizing a generative AI model, the program can be executed using the following example prompt: "Generate an optimal learning plan based on the worker's past work data and self-registered skills. The goal is to improve product description skills."

[0533] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0534] Step 1:

[0535] The server retrieves worker work history and skills data from data storage. This data includes past work performance and self-reported information. The input data is in JSON format, and the server parses it to extract basic information about each worker. The resulting output data is converted into a format suitable for analysis by the AI ​​model.

[0536] Step 2:

[0537] The server inputs the acquired data into a generative AI model using TensorFlow to perform a skill assessment for each worker. The AI ​​model analyzes the worker's past activity data and formulates a learning plan optimized for that worker. Based on this prompt, the generated learning plan is output.

[0538] Step 3:

[0539] The terminal displays the learning plan received from the server on the worker's smartphone or smart glasses. The terminal visualizes the learning plan and displays progress via an application built with React Native. The input is the generated learning plan, and the output is the visualized learning content.

[0540] Step 4:

[0541] Users progress through their own devices and send progress data to the server in real time. User input consists of progress status and self-assessment data, which are uploaded to the server periodically. Output includes updated progress data and learning feedback.

[0542] Step 5:

[0543] The server monitors the user's progress via Firebase and adjusts the learning plan as needed. Input data includes the user's progress rate and feedback; the server updates the database based on this data and suggests new learning directions. The output is a revised learning plan based on adaptive feedback, provided to the user.

[0544] 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.

[0545] The system of this invention incorporates an emotion engine to maximize the individualized training effect for employees. This emotion engine recognizes the user's emotional state in real time during the training process and provides an appropriate learning environment and motivation enhancement measures.

[0546] Specifically, the server collects employee work history and skill data from the company's database and analyzes it using an AI algorithm. Based on this analysis, the server generates an optimal training plan for the user and prepares the learning content based on that plan.

[0547] The emotion engine uses the device's camera and sensors to analyze the user's facial expressions and tone of voice, recognizing the emotions being learned. For example, if the user is stressed, the server adjusts the training plan based on that information, providing a relaxing environment or generating more supportive feedback.

[0548] Users follow this training plan via their devices, performing online learning and role-playing exercises. User emotional data is transmitted to the server in real time, and its correlation with learning progress is evaluated. Based on this evaluation, the server provides users with motivational support via their devices to maximize their performance.

[0549] As a concrete example, consider a case where a retail employee "B" takes training using an emotion engine. The server analyzes B's past work data, determines that "strengthening customer service" is necessary, and generates a training plan to improve customer service skills. During the training, if the terminal's camera detects anxiety from B's facial expressions, the server immediately provides feedback on relaxation techniques. In this way, B can continue the training with peace of mind and effectively improve their skills.

[0550] This system allows each employee to receive optimal learning support tailored to their emotional state, thereby improving overall business efficiency within the company.

[0551] The following describes the processing flow.

[0552] Step 1:

[0553] Users input their skill information and learning preferences through their device. This information is then sent to the server in an appropriate format.

[0554] Step 2:

[0555] The server retrieves users' past work history and skill data from the company's database. The retrieved data is then analyzed using an AI algorithm.

[0556] Step 3:

[0557] The server generates a training plan optimized for the user based on the analysis results. This plan includes online learning modules and role-playing exercises.

[0558] Step 4:

[0559] The device notifies the user of the generated training plan and prompts them to log in. The user then reviews the plan on the device and begins training.

[0560] Step 5:

[0561] The emotion engine uses the camera and microphone connected to the device to analyze the user's facial expressions and voice tone in real time. The analysis results are sent to the server as emotion information.

[0562] Step 6:

[0563] The server integrates and analyzes the received emotional information and learning progress to evaluate the user's emotional state. Based on the evaluation results, it generates necessary feedback and motivational measures.

[0564] Step 7:

[0565] The server sends the generated feedback and motivational measures to the device. The device immediately notifies the user and modifies the training method as needed.

[0566] Step 8:

[0567] After receiving feedback through their device, the user continues training. The user's progress is monitored again in real time, and the server intervenes as needed.

[0568] Step 9:

[0569] After training is complete, the server collects new operational performance data and evaluates the results, taking into account sentiment data from the training period. Based on these results, the server generates a detailed report.

[0570] Step 10:

[0571] The server distributes the generated reports to users and their administrators via terminals. The reports provide visualized performance data that can be used to inform future training strategies.

[0572] (Example 2)

[0573] 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."

[0574] In today's work environment, there is a need to implement effective training that takes into account the diverse emotional states of employees. However, traditional methods have made it difficult to provide training optimized for individual employees, and in particular, they have been unable to adequately provide real-time feedback based on employees' emotional states.

[0575] 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.

[0576] In this invention, the server includes means for analyzing the work history and skills information of an employee obtained from an information management device and generating a training plan optimized for the employee; means for recognizing the employee's emotional state via a terminal device and adjusting the training plan in real time; and means for monitoring learning progress based on the training plan in real time and providing necessary support information. This makes it possible to provide effective and individualized training that is adapted to the emotional state of the employee.

[0577] An "information management device" is a device that acquires and stores employee work history and skills information, and provides the data necessary for analysis.

[0578] "Employee work history" refers to information about the work performed by an employee to date and the results of that work.

[0579] "Skills information" refers to information about an employee's specific job performance abilities and specialized knowledge.

[0580] A "training plan" is a plan that specifies the content and methods of training aimed at improving the skills of employees.

[0581] A "terminal device" is a device that a user directly operates to input data and receive feedback.

[0582] "Emotional state" refers to the emotional state an employee exhibits during learning or work, and is primarily expressed through facial expressions and tone of voice.

[0583] "Real-time monitoring" refers to a situation where information is collected and analyzed at that moment, and immediate action can be taken based on the results.

[0584] "Support information" refers to feedback and advice provided for the purpose of assisting in learning or performing tasks.

[0585] The present invention aims to provide employees with optimized training plans using information management devices, terminal devices, and servers, thereby maximizing their effectiveness.

[0586] The server collects employee work history and skills information from the information management device. This involves using database management systems and SQL queries to efficiently retrieve the necessary data. This information is analyzed by an AI algorithm to generate an optimal training plan for each employee. The generated AI model is used in this process to customize the training content and create the schedule.

[0587] The terminal device uses its built-in camera and microphone to analyze the user's emotional state. This analysis is performed in real time, utilizing facial recognition technology and voice analysis software (e.g., OpenCV, Affectiva). Once the user begins training, the terminal device continuously monitors the user's state and transmits the data to the server.

[0588] The server dynamically adjusts the training plan based on the received emotional data. For example, if the user is not concentrating, the training content can be adjusted to improve learning effectiveness. It also sends support information tailored to the user's state to the terminal device, providing the user with appropriate feedback.

[0589] As a concrete example, consider a scenario where a retail employee is undergoing training to improve their customer service skills. The server analyzes the employee's past interaction history and generates a training plan focused on specific skills. If the terminal device detects user anxiety, the server immediately sends feedback on relaxation techniques to the terminal device, helping the employee improve their skills while remaining relaxed.

[0590] This system allows each employee to receive optimal learning support tailored to their emotional state, supporting individual growth and improving overall work efficiency.

[0591] An example of a prompt message might be: "Generate an optimal training plan that takes into account the employee's work history and emotional state."

[0592] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0593] Step 1:

[0594] The server retrieves employee work history and skills information from the database within the information management device using SQL queries. The input is the employee's identification information, and the output is a set of work history and skills information related to that employee. This information is used in subsequent analysis steps.

[0595] Step 2:

[0596] The server inputs the acquired work history and skills information into an AI algorithm. This AI algorithm uses a machine learning model to analyze the elements of an optimal training plan. The input is the employee's work history and skills information, and the output is recommendations and a schedule for the training plan.

[0597] Step 3:

[0598] The server automatically generates a customized training plan based on the analysis results using a generative AI model. This generation utilizes natural language generation technology to construct specific learning tasks and training content. The input is the output of the AI ​​algorithm, and the output is a prompt sentence containing the specific training plan.

[0599] Step 4:

[0600] The device monitors the user who has started training via camera and microphone, and recognizes their emotional state. This process utilizes facial recognition technology and voice analysis software. The input is the user's real-time facial and voice data, and the output is an evaluation of the user's emotional state.

[0601] Step 5:

[0602] The server receives emotional state data transmitted from the terminal and dynamically adjusts the training plan. For example, it might adjust the training schedule by adding breaks for users who are assessed as lacking concentration. The input is emotional state data, and the output is the adjusted training plan.

[0603] Step 6:

[0604] Users follow a training plan provided via their device, engaging in designated online learning and simulation exercises. During this time, the device reports learning progress to the server in real time. Inputs are user operation data during the training plan's implementation, while outputs include the number of completed tasks and performance evaluation data.

[0605] Step 7:

[0606] The server evaluates learning progress, emotional state, and their correlation, and generates feedback and reports to improve motivation as needed. Inputs are learning progress data and emotional state data, while outputs are opinions and encouraging messages to motivate the user for the next training session.

[0607] (Application Example 2)

[0608] 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."

[0609] Traditional worker training systems lack learning support that takes into account the emotional state of individual workers, making efficient and effective skill acquisition difficult. Furthermore, training content is uniform and not sufficiently customized to the aptitudes and circumstances of individual workers. As a result, the efficiency of skill acquisition decreases, potentially negatively impacting the overall productivity of the company.

[0610] 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.

[0611] In this invention, the server includes means for analyzing the worker's work history and skills data obtained from data storage and generating a training plan optimized for the worker; means for monitoring the learning progress based on the training plan in real time and providing necessary feedback; and means for recognizing the worker's emotional state during training and providing a learning environment and motivational enhancement measures according to that emotional state. This makes it possible to provide optimal training support according to the emotional state of each individual worker and to acquire work skills efficiently and effectively.

[0612] "Data storage" refers to an information storage device that stores workers' work history and skill data, and allows for quick retrieval as needed.

[0613] A "training plan" is a learning program optimized based on an individual's work history and skills data to ensure that workers acquire skills appropriately.

[0614] "Learning progress" is a factor that indicates the extent to which a worker has acquired skills according to the training plan, and it is evaluated in real time.

[0615] "Feedback" refers to guidance and improvement suggestions provided according to learning progress, and is adjusted as appropriate according to the worker's level of understanding and emotional state.

[0616] "Emotional state" refers to the mental and psychological state that a worker exhibits during training, and is perceived through facial expressions and tone of voice.

[0617] "Motivation enhancement measures" refer to methods of creating an environment and providing support that enable workers to achieve maximum results in training, and are provided based on their emotional state.

[0618] The system implementing this invention mainly consists of a server, a terminal, and worker users. The server retrieves the worker's work history and skill data from data storage and applies an AI algorithm to analyze this information. Based on the results of the analysis, it generates a training plan optimized for each worker.

[0619] When executing the training plan, the terminal is equipped with a camera and microphone to sense the worker's facial expressions and tone of voice, recognizing their emotional state in real time. A TensorFlow model is used for this emotion recognition. The recognized emotional state is sent to a server. Based on this data, the server adjusts the learning environment and provides appropriate feedback.

[0620] Furthermore, the server monitors the worker's progress in real time and sends necessary information to the terminal. For example, if a worker shows anxiety during the process of acquiring a particular skill, the server immediately provides guidance on how to relax and suggests measures to improve motivation. Specific environmental adjustments could include playing music or nature videos.

[0621] Workers follow a provided training plan, receiving online learning and situational simulation training. They can adaptively improve their skills based on feedback received during the training. The generative AI model assisting this process uses prompts such as: "Please give me advice on how to reduce stress during today's robot operation training."

[0622] This system utilizes Apache Kafka to implement real-time data processing and aims to provide efficient and individually optimized training. As a result, workers can learn in the best possible environment for their situation, accelerating skill acquisition.

[0623] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0624] Step 1:

[0625] The server retrieves worker work history and skills data from data storage. Based on this input data, it performs analysis using an AI algorithm. As a result, it generates a training plan optimized for the worker. The output is a training plan tailored to the individual worker.

[0626] Step 2:

[0627] The server prepares the corresponding online learning content based on the generated training plan and sends it to the terminal. The input here is the training plan, and the output is the dataset for content delivery. The terminal receives this dataset and begins learning by displaying it to the user.

[0628] Step 3:

[0629] The device uses a camera and microphone to sense the user's facial expressions and voice tone in real time, and inputs this data into a TensorFlow model. The model uses this data to recognize the emotional state and sends the result to the server. The input is live data from the sensors, and the output is the analyzed emotional state.

[0630] Step 4:

[0631] The server analyzes the received emotional state data and adjusts the training plan as needed. For example, if high stress levels are detected, it immediately generates and sends feedback on relaxation methods to the terminal. This feedback is provided to the user via screen display and audio. The input is emotional state data, and the output is the adjusted training plan and feedback.

[0632] Step 5:

[0633] Users improve their skills by continuing their training based on feedback provided from their devices. The user's progress is periodically sent from the device to the server, which analyzes this data and reports the final training outcome. The input is progress data, and the output is provided as a report.

[0634] Through these steps, workers can receive optimal training tailored to their emotional state.

[0635] 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.

[0636] 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.

[0637] 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.

[0638] [Fourth Embodiment]

[0639] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0640] 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.

[0641] 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).

[0642] 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.

[0643] 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.

[0644] 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).

[0645] 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.

[0646] 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.

[0647] 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.

[0648] 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.

[0649] 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.

[0650] 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.

[0651] 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".

[0652] The system of this invention automatically generates optimal training plans tailored to the individual skills and work proficiency levels of employees in sales departments, thereby improving employee productivity. To this end, the system includes the following elements.

[0653] First, the server retrieves employees' past performance data and existing skill data from the company's database. Furthermore, users input their own skills and abilities using a terminal and send this information to the server. The server integrates this data and uses an AI algorithm to evaluate the employee's skill level and job proficiency.

[0654] Next, the server generates an individually optimized training plan for each user based on the evaluation data. This plan includes online learning courses and role-playing exercises, presented in a way that is easy for employees to implement.

[0655] The device notifies the user of this training plan, and each user proceeds with the training accordingly. Users can check their daily progress within the system, allowing them to objectively understand their own learning.

[0656] Furthermore, the server monitors training progress in real time and generates feedback tailored to the user's learning status. The terminal immediately notifies the user of this feedback and suggests modifications to the training plan as needed. In this way, users can efficiently improve their skills.

[0657] Furthermore, after the training is completed, the server collects new work performance data and evaluates the training's effectiveness. Here, it analyzes employee productivity and skill improvement, and generates a report based on the results. The terminal provides this report to users and administrators, serving as a foundation for continuous talent development.

[0658] To give a specific example, employee "A" at a certain retail store was evaluated as having "intermediate customer service skills" based on past sales performance. Therefore, the AI ​​provides A with training plans for "advanced customer service" and "enhanced problem-solving skills," which are presented to A in an online format that can be accessed via a terminal. Through this system, A can more effectively improve their skills and contribute to increasing the store's sales.

[0659] The following describes the processing flow.

[0660] Step 1:

[0661] Users input their skill information and desired learning content through a terminal. The terminal digitizes the input information and sends it to the server.

[0662] Step 2:

[0663] The server accesses the company's database to retrieve users' past work history and skill data. This allows for the collection of detailed information about users' performance and experience.

[0664] Step 3:

[0665] The server integrates user input data with work history data from the database. AI algorithms are used to evaluate the user's skill level and work proficiency. Numerical indicators are used for this evaluation, and objective analysis is performed.

[0666] Step 4:

[0667] Based on the evaluated data, the server automatically generates an optimized training plan for each user. This plan includes relevant online learning modules and practical exercises.

[0668] Step 5:

[0669] The server sends the generated training plan to the terminal. The terminal then presents this information to the user, prompts them to log in, and starts the training.

[0670] Step 6:

[0671] Users learn by following the training plan presented on their device. Activities and progress are automatically recorded.

[0672] Step 7:

[0673] The server monitors the user's learning progress in real time. It analyzes the monitoring data and evaluates whether the progress is proceeding as planned.

[0674] Step 8:

[0675] The server generates feedback based on progress and evaluation results. This feedback includes important action points for the user and is provided to the user through their terminal.

[0676] Step 9:

[0677] After training is complete, the server collects new operational performance data and analyzes how much user productivity has improved. The results are quantified, and areas for improvement are identified.

[0678] Step 10:

[0679] The server generates a report based on the analysis results and sends it to the user and their administrator via the terminal. This report is used to optimize future training plans.

[0680] (Example 1)

[0681] 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".

[0682] In today's business environment, improving worker capabilities and performance are crucial challenges. However, manually creating and managing individualized training plans requires significant effort and time. Furthermore, ineffective skill development is hindered because related feedback and performance evaluations are not conducted in a timely manner. Therefore, there is a need for a system that automatically generates individually tailored training plans and efficiently manages their progress and results.

[0683] 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.

[0684] In this invention, the server includes means for analyzing the worker's performance history and ability data acquired from a data storage device and creating a training plan adapted to the worker; means for immediately tracking the progress of learning based on the training plan and providing the necessary responses; and means for determining the work results after the training is completed and generating records for reporting the results. This enables training optimized for each individual worker, allowing for efficient and sustainable skill improvement and performance enhancement.

[0685] A "data storage device" is an information management device that stores performance and capability information collected within a company or organization and provides it in a usable format as needed.

[0686] "Worker" refers to an individual who performs a specific job or task, and includes employees and workers whose abilities and performance are subject to evaluation and guidance planning.

[0687] "Performance history" refers to data that records, in chronological order, the performance and achievements of workers in their past work.

[0688] "Competency data" refers to numerical information that quantifies a worker's skills, expertise, and experience level, and serves as the basis for evaluation and training plans.

[0689] A "training plan" is a document that outlines the overall structure of individually customized training and learning activities aimed at improving the skills and performance of workers.

[0690] "Analysis" is the process of applying statistical methods and algorithms to collected data to derive meaningful insights and conclusions.

[0691] "Real-time tracking" is a management activity aimed at monitoring the progress of learning or work in real time and quickly understanding the situation.

[0692] "Responding" means providing necessary instructions and information based on learning progress and work status.

[0693] "Records" refer to documents or digital data that contain information about the progress and results of work, saved for later evaluation and analysis.

[0694] The embodiments for carrying out the present invention will be described below.

[0695] The server, at the heart of the invention, directly acquires worker performance history and capability data from the data storage device. This server is equipped with multipurpose database management software, enabling efficient extraction and analysis of performance indicators and capability information. Specifically, data is retrieved using SQL queries, and machine learning frameworks (e.g., TensorFlow, PyTorch) are applied to the analysis using Python.

[0696] Users input their own ability data via a terminal. The terminal is equipped with a user-friendly interface, and the input data is immediately sent to the server. The use of a REST API ensures the reliability and integrity of the data. The data entered by the user is integrated by the server's AI algorithm, contributing to the generation of new instructional plans.

[0697] The server uses a generative AI model based on integrated data to automatically generate instructional plans tailored to individual workers. This process leverages open AI APIs to generate prompts and guide the AI ​​in creating instructional plans. The generated plans include online learning courses and simulation-based assignments, which are delivered to users through the company's training management system (e.g., Adobe Captivate Prime, SAP Litmos).

[0698] The device has a means of notifying the user of the generated instruction plan. Push technology is used for notifications, allowing the user to proceed with the training accordingly. The user can use the device to check their learning progress at any time, enabling self-assessment and maintenance of motivation.

[0699] Furthermore, the server monitors learning progress in real time and provides dynamic feedback through a generative AI model. This feedback is communicated to the user in a timely manner at the application layer, and adjustments to the instruction plan are suggested as needed. This flexibility ensures an optimal learning environment tailored to the learner's needs.

[0700] As a concrete example, by inputting a prompt such as, "Please provide a training plan necessary to improve customer service skills in sales," into the AI ​​model, a training plan tailored to strengthening specific skills will be generated. This system supports efficient and sustainable capability improvement and performance enhancement across the entire company through appropriate feedback and evaluation.

[0701] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0702] Step 1:

[0703] The server retrieves worker performance history and capability data from the data storage device. It accesses the database using SQL queries as input and generates a dataset for each worker as output. This dataset includes past work performance and evaluation information.

[0704] Step 2:

[0705] Users input data about their abilities using their devices. They use web forms or mobile apps to specifically describe their skills and experience. The device sends this information to the server via a REST API, which adds it to the dataset as new data. The integrated dataset is then updated as output.

[0706] Step 3:

[0707] The server analyzes the integrated dataset and feeds it into a generative AI model. The entire dataset is supplied as input to the AI ​​algorithm, and prompts are used to generate guidance on lesson plans. The output is an individually optimized lesson plan, including specific online courses and simulation assignments.

[0708] Step 4:

[0709] The server registers the lesson plans on the enterprise learning platform and formats them so that each user can access them. The generated lesson plans are imported into the learning platform as input, and training courses linked to the user ID are prepared as output.

[0710] Step 5:

[0711] The terminal notifies the user of the generated lesson plan. It receives notification data from the server as input and displays it to the user in push notification format as output. The user then begins the training based on this notification.

[0712] Step 6:

[0713] Users progress through their learning according to the instructional plan while checking their progress on their devices. They use the device interface as input to provide progress data. Daily learning outcomes are stored in a database as output.

[0714] Step 7:

[0715] The server monitors progress data in real time and generates feedback using an AI model as needed. It receives progress data as input and performs AI analysis. As output, it sends feedback useful for individual instruction and suggestions for plan revisions to the terminal.

[0716] Step 8:

[0717] Once training is complete, the server collects new performance data and evaluates work results. It analyzes the updated performance database as input and generates reports using a generative AI model. The output is a detailed evaluation report summarizing worker skill improvements and work outcomes, which is provided to users and administrators.

[0718] (Application Example 1)

[0719] 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".

[0720] To improve productivity by providing optimal training plans tailored to each employee's individual skills and work proficiency, it is necessary to create an environment where employees can learn efficiently. However, current systems lack sufficient real-time progress management and adaptive feedback, making it difficult to present learning plans optimized for each employee.

[0721] 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.

[0722] This invention includes a server that includes means for analyzing an worker's work history and skill data obtained from data storage and generating a learning plan optimized for the worker; means for monitoring the learning progress based on the learning plan in real time and providing necessary information; means for evaluating work results after the completion of learning and generating a report to report the effects; and means for enabling the worker to visualize their learning progress on their personal device and perform immediate feedback and adjustments to the learning plan. This allows employees to receive training in a learning environment optimized according to their individual skill level, contributing to increased productivity.

[0723] "Data storage" refers to a storage device for saving and managing information, and it plays a role in holding workers' work history and skill data.

[0724] "Worker" refers to an individual who performs a specific task, and is the employee who is the target of the learning plan in this invention.

[0725] "Work history" refers to data that records what tasks an employee has performed in the past, and is used for skill assessment and the generation of learning plans.

[0726] "Skill data" refers to information about the specific skills and abilities that a worker possesses, and serves as the foundational data for generating individually optimized learning plans.

[0727] A "learning plan" is an educational program optimized according to the worker's skill level, and includes online learning and simulation-based assignments.

[0728] "Real-time monitoring" refers to a state where learning progress can be checked at any time, and corrective measures can be taken immediately as needed.

[0729] "Information provision" refers to the act of improving learning effectiveness by notifying learners of their current progress and areas for improvement.

[0730] A "report" is a document that summarizes the results of evaluating the worker's performance after the completion of the learning plan.

[0731] "Personal devices" refer to communication terminals that are individually owned and used by workers, such as smartphones and smart glasses.

[0732] "Feedback" refers to the act of providing guidance and advice based on learning progress and achievements, and plays a role in supporting learners' improvement.

[0733] "Adjusting the learning plan" means dynamically changing the learning content and approach in response to the worker's learning progress and feedback.

[0734] The system for realizing this invention is programmed as follows: First, the server retrieves the worker's work history and skill data from data storage. In this process, the server uses Python to analyze the data and prepare to generate an optimal learning plan. Next, a generative AI model using TensorFlow generates a learning plan that is individually optimized for each worker based on this data.

[0735] The devices are smartphones or smart glasses personally owned by the workers, allowing them to visualize their individual learning progress in real time. An application built with React Native is installed on the device, and through this application, workers can check their learning progress at any time and receive real-time feedback.

[0736] Furthermore, the server uses the Firebase database to monitor each worker's learning progress in real time and adjust the learning plan as needed. This adjustment allows for the provision of an optimal learning environment tailored to each worker's skill level.

[0737] As a concrete example, imagine a retail store employee using this system to improve their sales skills. They can efficiently improve their skills by checking their learning progress in real time via their smartphone after each day's work and receiving AI-powered feedback. For instance, if an employee wants to improve their product description skills, the system would provide online learning resources linked to a specialized learning plan tailored to that goal and track their progress.

[0738] By utilizing a generative AI model, the program can be executed using the following example prompt: "Generate an optimal learning plan based on the worker's past work data and self-registered skills. The goal is to improve product description skills."

[0739] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0740] Step 1:

[0741] The server retrieves worker work history and skills data from data storage. This data includes past work performance and self-reported information. The input data is in JSON format, and the server parses it to extract basic information about each worker. The resulting output data is converted into a format suitable for analysis by the AI ​​model.

[0742] Step 2:

[0743] The server inputs the acquired data into a generative AI model using TensorFlow to perform a skill assessment for each worker. The AI ​​model analyzes the worker's past activity data and formulates a learning plan optimized for that worker. Based on this prompt, the generated learning plan is output.

[0744] Step 3:

[0745] The terminal displays the learning plan received from the server on the worker's smartphone or smart glasses. The terminal visualizes the learning plan and displays progress via an application built with React Native. The input is the generated learning plan, and the output is the visualized learning content.

[0746] Step 4:

[0747] Users progress through their own devices and send progress data to the server in real time. User input consists of progress status and self-assessment data, which are uploaded to the server periodically. Output includes updated progress data and learning feedback.

[0748] Step 5:

[0749] The server monitors the user's progress via Firebase and adjusts the learning plan as needed. Input data includes the user's progress rate and feedback; the server updates the database based on this data and suggests new learning directions. The output is a revised learning plan based on adaptive feedback, provided to the user.

[0750] 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.

[0751] The system of this invention incorporates an emotion engine to maximize the individualized training effect for employees. This emotion engine recognizes the user's emotional state in real time during the training process and provides an appropriate learning environment and motivation enhancement measures.

[0752] Specifically, the server collects employee work history and skill data from the company's database and analyzes it using an AI algorithm. Based on this analysis, the server generates an optimal training plan for the user and prepares the learning content based on that plan.

[0753] The emotion engine uses the device's camera and sensors to analyze the user's facial expressions and tone of voice, recognizing the emotions being learned. For example, if the user is stressed, the server adjusts the training plan based on that information, providing a relaxing environment or generating more supportive feedback.

[0754] Users follow this training plan via their devices, performing online learning and role-playing exercises. User emotional data is transmitted to the server in real time, and its correlation with learning progress is evaluated. Based on this evaluation, the server provides users with motivational support via their devices to maximize their performance.

[0755] As a concrete example, consider a case where a retail employee "B" takes training using an emotion engine. The server analyzes B's past work data, determines that "strengthening customer service" is necessary, and generates a training plan to improve customer service skills. During the training, if the terminal's camera detects anxiety from B's facial expressions, the server immediately provides feedback on relaxation techniques. In this way, B can continue the training with peace of mind and effectively improve their skills.

[0756] This system allows each employee to receive optimal learning support tailored to their emotional state, thereby improving overall business efficiency within the company.

[0757] The following describes the processing flow.

[0758] Step 1:

[0759] Users input their skill information and learning preferences through their device. This information is then sent to the server in an appropriate format.

[0760] Step 2:

[0761] The server retrieves users' past work history and skill data from the company's database. The retrieved data is then analyzed using an AI algorithm.

[0762] Step 3:

[0763] The server generates a training plan optimized for the user based on the analysis results. This plan includes online learning modules and role-playing exercises.

[0764] Step 4:

[0765] The device notifies the user of the generated training plan and prompts them to log in. The user then reviews the plan on the device and begins training.

[0766] Step 5:

[0767] The emotion engine uses the camera and microphone connected to the device to analyze the user's facial expressions and voice tone in real time. The analysis results are sent to the server as emotion information.

[0768] Step 6:

[0769] The server integrates and analyzes the received emotional information and learning progress to evaluate the user's emotional state. Based on the evaluation results, it generates necessary feedback and motivational measures.

[0770] Step 7:

[0771] The server sends the generated feedback and motivational measures to the device. The device immediately notifies the user and modifies the training method as needed.

[0772] Step 8:

[0773] After receiving feedback through their device, the user continues training. The user's progress is monitored again in real time, and the server intervenes as needed.

[0774] Step 9:

[0775] After training is complete, the server collects new operational performance data and evaluates the results, taking into account sentiment data from the training period. Based on these results, the server generates a detailed report.

[0776] Step 10:

[0777] The server distributes the generated reports to users and their administrators via terminals. The reports provide visualized performance data that can be used to inform future training strategies.

[0778] (Example 2)

[0779] 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".

[0780] In today's work environment, there is a need to implement effective training that takes into account the diverse emotional states of employees. However, traditional methods have made it difficult to provide training optimized for individual employees, and in particular, they have been unable to adequately provide real-time feedback based on employees' emotional states.

[0781] 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.

[0782] In this invention, the server includes means for analyzing the work history and skills information of an employee obtained from an information management device and generating a training plan optimized for the employee; means for recognizing the employee's emotional state via a terminal device and adjusting the training plan in real time; and means for monitoring learning progress based on the training plan in real time and providing necessary support information. This makes it possible to provide effective and individualized training that is adapted to the emotional state of the employee.

[0783] An "information management device" is a device that acquires and stores employee work history and skills information, and provides the data necessary for analysis.

[0784] "Employee work history" refers to information about the work performed by an employee to date and the results of that work.

[0785] "Skills information" refers to information about an employee's specific job performance abilities and specialized knowledge.

[0786] A "training plan" is a plan that specifies the content and methods of training aimed at improving the skills of employees.

[0787] A "terminal device" is a device that a user directly operates to input data and receive feedback.

[0788] "Emotional state" refers to the emotional state an employee exhibits during learning or work, and is primarily expressed through facial expressions and tone of voice.

[0789] "Real-time monitoring" refers to a situation where information is collected and analyzed at that moment, and immediate action can be taken based on the results.

[0790] "Support information" refers to feedback and advice provided for the purpose of assisting in learning or performing tasks.

[0791] The present invention aims to provide employees with optimized training plans using information management devices, terminal devices, and servers, thereby maximizing their effectiveness.

[0792] The server collects employee work history and skills information from the information management device. This involves using database management systems and SQL queries to efficiently retrieve the necessary data. This information is analyzed by an AI algorithm to generate an optimal training plan for each employee. The generated AI model is used in this process to customize the training content and create the schedule.

[0793] The terminal device uses its built-in camera and microphone to analyze the user's emotional state. This analysis is performed in real time, utilizing facial recognition technology and voice analysis software (e.g., OpenCV, Affectiva). Once the user begins training, the terminal device continuously monitors the user's state and transmits the data to the server.

[0794] The server dynamically adjusts the training plan based on the received emotional data. For example, if the user is not concentrating, the training content can be adjusted to improve learning effectiveness. It also sends support information tailored to the user's state to the terminal device, providing the user with appropriate feedback.

[0795] As a concrete example, consider a scenario where a retail employee is undergoing training to improve their customer service skills. The server analyzes the employee's past interaction history and generates a training plan focused on specific skills. If the terminal device detects user anxiety, the server immediately sends feedback on relaxation techniques to the terminal device, helping the employee improve their skills while remaining relaxed.

[0796] This system allows each employee to receive optimal learning support tailored to their emotional state, supporting individual growth and improving overall work efficiency.

[0797] An example of a prompt message might be: "Generate an optimal training plan that takes into account the employee's work history and emotional state."

[0798] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0799] Step 1:

[0800] The server retrieves employee work history and skills information from the database within the information management device using SQL queries. The input is the employee's identification information, and the output is a set of work history and skills information related to that employee. This information is used in subsequent analysis steps.

[0801] Step 2:

[0802] The server inputs the acquired work history and skills information into an AI algorithm. This AI algorithm uses a machine learning model to analyze the elements of an optimal training plan. The input is the employee's work history and skills information, and the output is recommendations and a schedule for the training plan.

[0803] Step 3:

[0804] The server automatically generates a customized training plan based on the analysis results using a generative AI model. This generation utilizes natural language generation technology to construct specific learning tasks and training content. The input is the output of the AI ​​algorithm, and the output is a prompt sentence containing the specific training plan.

[0805] Step 4:

[0806] The device monitors the user who has started training via camera and microphone, and recognizes their emotional state. This process utilizes facial recognition technology and voice analysis software. The input is the user's real-time facial and voice data, and the output is an evaluation of the user's emotional state.

[0807] Step 5:

[0808] The server receives emotional state data transmitted from the terminal and dynamically adjusts the training plan. For example, it might adjust the training schedule by adding breaks for users who are assessed as lacking concentration. The input is emotional state data, and the output is the adjusted training plan.

[0809] Step 6:

[0810] Users follow a training plan provided via their device, engaging in designated online learning and simulation exercises. During this time, the device reports learning progress to the server in real time. Inputs are user operation data during the training plan's implementation, while outputs include the number of completed tasks and performance evaluation data.

[0811] Step 7:

[0812] The server evaluates learning progress, emotional state, and their correlation, and generates feedback and reports to improve motivation as needed. Inputs are learning progress data and emotional state data, while outputs are opinions and encouraging messages to motivate the user for the next training session.

[0813] (Application Example 2)

[0814] 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".

[0815] Traditional worker training systems lack learning support that takes into account the emotional state of individual workers, making efficient and effective skill acquisition difficult. Furthermore, training content is uniform and not sufficiently customized to the aptitudes and circumstances of individual workers. As a result, the efficiency of skill acquisition decreases, potentially negatively impacting the overall productivity of the company.

[0816] 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.

[0817] In this invention, the server includes means for analyzing the worker's work history and skills data obtained from data storage and generating a training plan optimized for the worker; means for monitoring the learning progress based on the training plan in real time and providing necessary feedback; and means for recognizing the worker's emotional state during training and providing a learning environment and motivational enhancement measures according to that emotional state. This makes it possible to provide optimal training support according to the emotional state of each individual worker and to acquire work skills efficiently and effectively.

[0818] "Data storage" refers to an information storage device that stores workers' work history and skill data, and allows for quick retrieval as needed.

[0819] A "training plan" is a learning program optimized based on an individual's work history and skills data to ensure that workers acquire skills appropriately.

[0820] "Learning progress" is a factor that indicates the extent to which a worker has acquired skills according to the training plan, and it is evaluated in real time.

[0821] "Feedback" refers to guidance and improvement suggestions provided according to learning progress, and is adjusted as appropriate according to the worker's level of understanding and emotional state.

[0822] "Emotional state" refers to the mental and psychological state that a worker exhibits during training, and is perceived through facial expressions and tone of voice.

[0823] "Motivation enhancement measures" refer to methods of creating an environment and providing support that enable workers to achieve maximum results in training, and are provided based on their emotional state.

[0824] The system implementing this invention mainly consists of a server, a terminal, and worker users. The server retrieves the worker's work history and skill data from data storage and applies an AI algorithm to analyze this information. Based on the results of the analysis, it generates a training plan optimized for each worker.

[0825] When executing the training plan, the terminal is equipped with a camera and microphone to sense the worker's facial expressions and tone of voice, recognizing their emotional state in real time. A TensorFlow model is used for this emotion recognition. The recognized emotional state is sent to a server. Based on this data, the server adjusts the learning environment and provides appropriate feedback.

[0826] Furthermore, the server monitors the worker's progress in real time and sends necessary information to the terminal. For example, if a worker shows anxiety during the process of acquiring a particular skill, the server immediately provides guidance on how to relax and suggests measures to improve motivation. Specific environmental adjustments could include playing music or nature videos.

[0827] Workers follow a provided training plan, receiving online learning and situational simulation training. They can adaptively improve their skills based on feedback received during the training. The generative AI model assisting this process uses prompts such as: "Please give me advice on how to reduce stress during today's robot operation training."

[0828] This system utilizes Apache Kafka to implement real-time data processing and aims to provide efficient and individually optimized training. As a result, workers can learn in the best possible environment for their situation, accelerating skill acquisition.

[0829] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0830] Step 1:

[0831] The server retrieves worker work history and skills data from data storage. Based on this input data, it performs analysis using an AI algorithm. As a result, it generates a training plan optimized for the worker. The output is a training plan tailored to the individual worker.

[0832] Step 2:

[0833] The server prepares the corresponding online learning content based on the generated training plan and sends it to the terminal. The input here is the training plan, and the output is the dataset for content delivery. The terminal receives this dataset and begins learning by displaying it to the user.

[0834] Step 3:

[0835] The device uses a camera and microphone to sense the user's facial expressions and voice tone in real time, and inputs this data into a TensorFlow model. The model uses this data to recognize the emotional state and sends the result to the server. The input is live data from the sensors, and the output is the analyzed emotional state.

[0836] Step 4:

[0837] The server analyzes the received emotional state data and adjusts the training plan as needed. For example, if high stress levels are detected, it immediately generates and sends feedback on relaxation methods to the terminal. This feedback is provided to the user via screen display and audio. The input is emotional state data, and the output is the adjusted training plan and feedback.

[0838] Step 5:

[0839] Users improve their skills by continuing their training based on feedback provided from their devices. The user's progress is periodically sent from the device to the server, which analyzes this data and reports the final training outcome. The input is progress data, and the output is provided as a report.

[0840] Through these steps, workers can receive optimal training tailored to their emotional state.

[0841] 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.

[0842] 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.

[0843] 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 robot 414.

[0844] 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.

[0845] 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.

[0846] 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.

[0847] 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.

[0848] 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.

[0849] 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."

[0850] 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.

[0851] 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.

[0852] 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.

[0853] 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.

[0854] 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.

[0855] 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.

[0856] 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.

[0857] 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.

[0858] 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.

[0859] 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.

[0860] 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.

[0861] All documents, patent applications, and technical standards described herein are incorporated by reference to the same extent as if each individual document, patent application, and technical standard were specifically and individually noted to be incorporated by reference.

[0862] The following is further disclosed regarding the embodiments described above.

[0863] (Claim 1)

[0864] A means for analyzing employee work history and skill data obtained from a database and generating an optimized training plan for the employee,

[0865] A means for monitoring learning progress based on the aforementioned training plan in real time and providing necessary feedback,

[0866] A means of generating a report to evaluate work performance after training completion and to report on its effectiveness,

[0867] A system that includes this.

[0868] (Claim 2)

[0869] The system according to claim 1, further comprising means for having employees input their own skill information and integrating it with the information in the database.

[0870] (Claim 3)

[0871] The system according to claim 1, further comprising means for including online learning and role-playing assignments in the training plan.

[0872] "Example 1"

[0873] (Claim 1)

[0874] A means for analyzing the performance history and ability data of workers acquired from a data storage device and creating a training plan tailored to the worker,

[0875] A means for immediately tracking the progress of learning based on the aforementioned instructional plan and providing the necessary responses,

[0876] A means for determining the work results after the completion of instruction and generating records for reporting the results,

[0877] A system that includes this.

[0878] (Claim 2)

[0879] The system according to claim 1, further comprising means for having an operator input their own ability information and combining it with the information of the data storage device.

[0880] (Claim 3)

[0881] The system according to claim 1, further comprising means for including online training and simulation-based assignments in the instruction plan.

[0882] "Application Example 1"

[0883] (Claim 1)

[0884] A means for analyzing the work history and skill data of workers obtained from data storage and generating a learning plan optimized for said worker,

[0885] A means for monitoring learning progress in real time based on the aforementioned learning plan and providing necessary information,

[0886] A means for generating a report to evaluate the work output after the completion of learning and to report the effects,

[0887] A means for workers to visualize their learning progress on their personal devices and make immediate feedback and adjustments to their learning plans,

[0888] A system that includes this.

[0889] (Claim 2)

[0890] The system according to claim 1, further comprising means for having an operator input their own skill information and integrating it with the information in the data storage.

[0891] (Claim 3)

[0892] The system according to claim 1, further comprising means for including online learning and simulation-based assignments in the learning plan.

[0893] "Example 2 of combining an emotion engine"

[0894] (Claim 1)

[0895] A means for analyzing employee work history and skills information obtained from an information management device and generating a training plan optimized for the employee,

[0896] The aforementioned terminal device provides means for recognizing the emotional state of employees and adjusting the training plan in real time,

[0897] A means for monitoring learning progress in real time based on the aforementioned training plan and providing necessary support information,

[0898] A means for evaluating work performance after training completion and generating reports to report the results,

[0899] A system that includes this.

[0900] (Claim 2)

[0901] The system according to claim 1, further comprising means for having employees input their own skill information and integrating it with data from the information management device.

[0902] (Claim 3)

[0903] The system according to claim 1, further comprising means for including distance learning and simulated exercises in the training plan.

[0904] "Application example 2 when combining with an emotional engine"

[0905] (Claim 1)

[0906] A means for analyzing the work history and skills data of workers obtained from data storage and generating a training plan optimized for said worker,

[0907] A means for monitoring learning progress in real time based on the aforementioned training plan and providing necessary feedback,

[0908] A means for recognizing the emotional state of workers during training and providing a learning environment and motivational enhancement measures that correspond to that emotional state,

[0909] A means for generating a report to evaluate performance after training completion and report on its effectiveness,

[0910] A system that includes this.

[0911] (Claim 2)

[0912] The system according to claim 1, further comprising means for having an operator input their own skill information and integrating it with the information in the data storage.

[0913] (Claim 3)

[0914] The system according to claim 1, further comprising means of including online learning and situational reenactment tasks in the training plan. [Explanation of Symbols]

[0915] 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 for analyzing the work history and skill data of workers obtained from data storage and generating a learning plan optimized for said worker, A means for monitoring learning progress in real time based on the aforementioned learning plan and providing necessary information, A means for generating a report to evaluate the work output after the completion of learning and to report the effects, A means for workers to visualize their learning progress on their personal devices and make immediate feedback and adjustments to their learning plans, A system that includes this.

2. The system according to claim 1, further comprising means for having workers input their own skill information and integrating it with the information in the data storage.

3. The system according to claim 1, further comprising means for including online learning and simulation-based assignments in the learning plan.

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