system

Abstract

We provide the system. [Solution] A means of aggregating and centrally managing employee information from multiple data sources, A means of analyzing employee skills and performance information using machine learning algorithms, A means to automatically generate optimal personnel placement and training plans for each employee, A means of notifying users of the generated plan and receiving feedback, A means of improving machine learning algorithms using feedback, 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 method for controlling a persona chatbot, which is 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] It is often difficult for large-scale organizations to achieve strategic human resource management that effectively utilizes employee information. In many companies, the integration and analysis of employee information scattered across different data sources are cumbersome, which hinders appropriate personnel allocation and development. Also, real-time data updates to support rapid decision-making and prediction of future human resource needs have not been fully realized yet. There is a need to provide a system that solves these problems and improves the productivity and employee satisfaction of the company.

Means for Solving the Problems

[0005] This invention provides a system that aggregates and centrally manages employee information from multiple data sources, analyzes employee skills and performance information using machine learning algorithms, and automatically generates optimal personnel placement and training plans for each employee. This system includes means for notifying users of the generated plans and receiving user feedback. Furthermore, it features the ability to continuously improve the machine learning algorithms using feedback, interact with users in natural language, and provide information in response to inquiries. It also includes the ability to update data in real time and predict future personnel needs, thereby supporting strategic human resource management across the entire company.

[0006] "Employee information" refers to personal or professional data about individual employees belonging to a company, including work history, skill set, performance evaluations, and promotion history.

[0007] A "data source" refers to the source of information that a system uses as a basis for aggregating data, and this includes HR systems, employee portals, attendance management systems, etc.

[0008] A "machine learning algorithm" is a mathematical model that analyzes data, recognizes patterns, and makes predictions, and its performance can be improved based on empirical data.

[0009] "Personnel allocation" refers to appropriately assigning the right people to each position within an organization, with the aim of maximizing the organization's efficiency and productivity.

[0010] A "development plan" refers to an education and training program designed to promote the growth and development of individual employees based on their skills and career goals.

[0011] "Updating data in real time" refers to the process of immediately reflecting information in the database as soon as it is generated or changed.

[0012] A "user" refers to a company employee whose role is to use the system to acquire, analyze, and report information related to employee management.

[0013] "Natural language interaction" refers to the process of communicating with a computer system using the language and sentences that humans normally use, enabling users to intuitively utilize the system.

[0014] "Feedback" refers to users' evaluations and opinions on system suggestions and outputs, and is used to improve and adjust the system's performance.

[0015] "Personnel needs forecasting" refers to estimating the skills and number of personnel needed to meet the organization's future operational requirements, and then developing a plan based on that estimate. [Brief explanation of the drawing]

[0016] [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]Shows an emotion map to which a plurality of emotions are mapped. [Figure 10] Shows an emotion map to which a plurality of emotions are mapped. [Figure 11] It is a sequence diagram showing the processing flow of the data processing system in Embodiment 1. [Figure 12] It is a sequence diagram showing the processing flow of the data processing system in Application Example 1. [Figure 13] It is a sequence diagram showing the processing flow of the data processing system in Embodiment 2 when an emotion engine is combined. [Figure 14] It is a sequence diagram showing the processing flow of the data processing system in Application Example 2 when an emotion engine is combined.

Mode for Carrying Out the Invention

[0017] Hereinafter, an example of an embodiment of a system according to the technology of the present disclosure will be described according to the accompanying drawings.

[0018] First, the language used in the following description will be described.

[0019] In the following embodiments, the labeled processor (hereinafter simply referred to as "processor") may be one arithmetic unit or a combination of a plurality of arithmetic units. Also, the processor may be one type of arithmetic unit or a combination of a plurality of 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.

[0020] In the following embodiments, signed RAM (Random Access Memory) is a memory that temporarily stores information and is used as work memory by the processor.

[0021] In the following embodiments, the signed storage is one or more non-volatile storage devices that store various programs and various parameters. Examples of non-volatile storage devices include flash memory (SSD (Solid State Drive)), magnetic disks (e.g., hard disks), or magnetic tapes.

[0022] 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).

[0023] 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."

[0024] [First Embodiment]

[0025] Figure 1 shows an example of the configuration of the data processing system 10 according to the first embodiment.

[0026] 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.

[0027] 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).

[0028] 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.

[0029] 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.

[0030] 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.

[0031] 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.

[0032] Figure 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0033] 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.

[0034] 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.

[0035] 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.

[0036] 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".

[0037] This invention provides a system that effectively utilizes employee information and enables strategic human resource management. This system operates through the collaborative efforts of its server, terminal, and user components.

[0038] The server first aggregates employee information from numerous data sources. This information includes employee skills, work history, and performance evaluations. The server centrally manages this data and uses machine learning algorithms to create optimal talent placement and development plans for each employee. These algorithms analyze each employee's past performance data and skill set.

[0039] For example, the server matches a specific employee's skill information and analyzes whether that person is suitable to be the next project leader. In this process, the server takes into account the employee's past project performance and evaluations to suggest the optimal placement. Furthermore, based on the analysis results, the server uses predictive algorithms to forecast future talent needs. This allows companies to plan for developing personnel with the necessary skill sets to prepare for future business expansion.

[0040] The terminal provides users with information and an interface that enables natural language interaction. Through the terminal, users can view analysis results and proposed personnel placement plans within the system.

[0041] For example, if a user asks the terminal, "Who is the most suitable person for the next project?", the terminal will provide a result based on data analyzed by the server. Furthermore, the user can input feedback into the terminal, and this feedback is sent to the server and used to improve the algorithm.

[0042] This system supports strategic human resource management within companies and provides an implementation that aims to improve overall organizational productivity and employee satisfaction.

[0043] The following describes the processing flow.

[0044] Step 1:

[0045] The server periodically collects employee information from various data sources. This includes retrieving information from the HR system via APIs and importing it into the database. The server also formats this information and ensures data integrity.

[0046] Step 2:

[0047] The server integrates the collected data into a database for centralized management. This process involves data cleansing, correcting duplicates and errors. Data is then merged using specific employee IDs.

[0048] Step 3:

[0049] The server uses machine learning algorithms to analyze employee data. This analysis includes a process that uses each employee's skills and work history as input to generate optimal personnel placement and training plans.

[0050] Step 4:

[0051] The server generates a report to notify the user of the analysis results. This report includes recommended personnel placement and training plans, and is provided to the user in a visualized format.

[0052] Step 5:

[0053] The device provides an interface for interacting with the user and responds to inquiries in natural language. Through the device, users can ask specific questions and view AI-generated suggestions.

[0054] Step 6:

[0055] Users provide feedback on the proposed plan and layout. This feedback is sent from the terminal to the server and used to improve future algorithms.

[0056] Step 7:

[0057] The server updates data in real time and predicts future talent needs. At this stage, trend analysis generates proposals for planning the skills and personnel that will be needed in the future.

[0058] (Example 1)

[0059] 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."

[0060] For businesses, effectively utilizing employee information and conducting strategic talent management is crucial. However, individually managing employee skills and performance information and formulating optimal placement and development plans is not easy. Furthermore, rapid prediction of talent needs and effective information sharing methods with users are also required. The development of a system that meets these requirements is necessary.

[0061] 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.

[0062] In this invention, the server includes means for collecting and integrating employee information from multiple sources, means for analyzing employee skills and work performance information using machine learning, and means for automatically generating appropriate personnel placement and growth plans for each employee. This enables companies to strategically and efficiently utilize human resources and predict future demand.

[0063] "Employee information" is a general term for information about individual employees, such as skills, work history, and performance evaluations, and is valuable data for an organization.

[0064] "Information source" refers to the source from which data is obtained, and includes databases, external APIs, and so on.

[0065] "Integrated management" means organizing and integrating information collected from different sources and handling it in a unified manner.

[0066] "Machine learning" is a technique that analyzes large amounts of data and extracts patterns and rules from it, and includes algorithms for building predictive models.

[0067] "Analysis" means using given data to reveal the characteristics and patterns contained within it.

[0068] "Personnel allocation" is the process of placing each employee within an organization in the appropriate job or position to allow them to utilize their abilities to the fullest.

[0069] A "growth plan" is a plan that includes specific goals and steps to enhance employees' skills and careers.

[0070] "Automatically generated" refers to a system creating information and plans autonomously with little to no manual intervention from humans.

[0071] "Opinions" refer to the thoughts and feedback that users have regarding the information and plans provided by the system.

[0072] "Visualization" is the process of representing data and results using visual means to make them easier to understand.

[0073] This invention is a system for effectively utilizing employee information and realizing strategic human resource management. Specific embodiments of this system are described below.

[0074] Server role:

[0075] The server collects employee information from multiple sources both inside and outside the company and manages it centrally using a database management system. Specifically, it uses databases such as MySQL® or PostgreSQL to store data in a structured format. The server uses Python to preprocess data with the pandas library and implements machine learning algorithms using Scikit-learn and TENSORFLOW®. This allows the server to analyze employees' skills and work performance and automatically generate optimal personnel placement and growth plans for each individual.

[0076] Terminal role:

[0077] The terminal provides an interface for visually displaying information provided by the server to the user. This interface utilizes a web browser or mobile app and displays information in natural language using HTML or JavaScript (registered trademark). The terminal works in conjunction with the server to collect user feedback and send it to the server.

[0078] User roles:

[0079] Users make strategic decisions based on the information displayed through their devices. For example, users might query information from their devices, such as "Who is the most suitable person for the next project?", and provide feedback based on the results. This allows the algorithms to be continuously improved, leading to an overall increase in system accuracy.

[0080] Thus, the present invention represents a useful form that streamlines the human resource management process within a company and supports the overall human resource strategy of the organization.

[0081] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0082] Step 1:

[0083] The server collects employee information from multiple sources. These sources include databases from various departments within the company and external business data services. The server periodically retrieves the necessary information from each source using APIs and stores it as input in the database. In this process, the data format is standardized and missing values ​​are imputed. The output is organized employee information.

[0084] Step 2:

[0085] The server prepares organized employee information as input data for a machine learning model. Specifically, it uses the Python pandas library to construct a data frame, extracts features, and performs normalization. This unifies data from different scales, making it possible to analyze it with machine learning algorithms. As output, it generates a dataset suitable as input for a machine learning model.

[0086] Step 3:

[0087] The server analyzes the data using machine learning algorithms. In this step, Scikit-learn and TensorFlow are used to predict the optimal placement of employees based on past performance and skills. The input is the dataset generated in step 2, and the algorithm calculates the optimal placement and training plan for each employee.

[0088] Step 4:

[0089] The terminal visually displays the analysis results provided by the server to the user. The interface of the terminal used by the user is developed using HTML and JavaScript, and presents the results in an easily interpretable format using natural language. The input is the analysis results from the server, and the output is an information display in a format that is easy for the user to understand.

[0090] Step 5:

[0091] Users provide feedback based on information obtained through the terminal interface. For example, a user might input an evaluation such as, "The proposed deployment plan is appropriate." This feedback is then sent back to the server and used as input to update and improve the machine learning model. The output is feedback, which leads to improved prediction accuracy in the next cycle.

[0092] (Application Example 1)

[0093] 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."

[0094] Traditional human resource management systems often fail to adequately utilize employee information, making it difficult to create optimal personnel placement and training plans. This is particularly problematic in manufacturing environments where real-time optimization of work instructions and analysis of the work environment are required. In this context, strategic human resource management based on the skills and performance of individual employees is essential.

[0095] 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.

[0096] In this invention, the server includes means for aggregating and centrally managing employee information from multiple sources, means for analyzing employee ability and performance information using machine learning algorithms, means for automatically generating optimal personnel allocation and training plans for each employee, means for providing work support devices with optimized work instructions in real time, and means for analyzing the work environment using data collected from work support devices. This enables efficient personnel allocation and optimization of work instructions even in manufacturing sites, making it possible to improve overall productivity and employee satisfaction.

[0097] "Information sources" refer to multiple sources or databases that provide data related to employees.

[0098] "Ability" refers to the skills, knowledge, and experience that an employee possesses in order to perform their job duties.

[0099] "Performance information" refers to data that includes the results and evaluations of tasks that employees have completed in the past.

[0100] A "machine learning algorithm" is an automated processing method that analyzes data to discover patterns and regularities, and then makes predictions and decisions.

[0101] "Work support devices" refer to tools and electronic devices used by employees to help them perform their tasks smoothly.

[0102] "Analysis of the work environment" refers to data analysis aimed at improving work efficiency by understanding the conditions of the workplace and the behavior of employees.

[0103] As an embodiment of this invention, a personnel management and work support system for manufacturing sites is proposed. This system mainly consists of three main components: a server, a work support device, and a user interface.

[0104] The server aggregates and centrally manages employee information obtained from numerous sources. This includes employee skills and performance data, as well as historical data from the production line. The server analyzes this data using machine learning algorithms and automatically generates optimal personnel placement and training plans for each employee. Machine learning frameworks such as TensorFlow are used for this analysis.

[0105] Work support devices, such as smart glasses and other wearable devices, receive optimized work instructions and training guides from a server in real time and provide them to the user. This data is available via Bluetooth or Wi-Fi. The work support devices also collect data from the work environment and return it to the server for analysis. This enables efficient operations on-site.

[0106] Users can submit feedback on employee assignments and work instructions via their devices. This feedback is analyzed by the server and used to further improve machine learning algorithms.

[0107] As a concrete example, in a newly introduced production line, when a worker wearing smart glasses performs their duties, the glasses visually display the next steps to be taken in real time. This reduces work errors and improves overall productivity. Furthermore, in response to a prompt such as "Tell me the optimal work tasks for the next production line operation," the server can instantly provide optimized data.

[0108] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0109] Step 1:

[0110] The server aggregates data, including employee skills and performance information, from numerous sources. Input is raw data obtained from various databases and sensor devices. This data is normalized and formatted into a centrally manageable format. Output is an analyzable, integrated dataset.

[0111] Step 2:

[0112] The server feeds the aggregated data into a machine learning algorithm. The input is the integrated dataset generated in Step 1. Based on this data, TensorFlow and other tools are used to analyze each employee's abilities and aptitudes, and an optimal personnel placement and training plan is automatically generated. The output is the proposed plan for each employee.

[0113] Step 3:

[0114] The server sends the generated optimal placement and training plans to the work support device. The input is the plan output in step 2. This information is transmitted in real time via Bluetooth or WiFi and displayed on the work support device (e.g., smart glasses). The output is a visualized work instruction on the user's device.

[0115] Step 4:

[0116] The user executes work instructions received through the work support device. The input is work instructions received in real time. The user performs the work according to the instructions and feeds back data about the work status from the device to the server. The output is work progress data and performance information.

[0117] Step 5:

[0118] The server analyzes user feedback data and continuously improves the machine learning algorithm. The input is the feedback data obtained in step 4. This data is used to retrain the model and tune parameters to improve accuracy and efficiency. The output is the improved next proposed plan and algorithm.

[0119] 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.

[0120] This invention provides a system that enables strategic human resource management that takes user emotions into account while utilizing employee information. This system operates through the collaborative efforts of a server, terminal, user, and emotion engine.

[0121] The server aggregates employee information from diverse data sources and integrates it into an organized database. Using machine learning algorithms, the server analyzes each employee's skills and performance information and automatically generates optimal personnel placement and training plans. These plans are backed by objective, data-driven judgments, supporting companies in efficiently utilizing their human resources.

[0122] The device interacts with the user and enables natural language dialogue. An emotion engine is used to detect emotions in real time from the user's facial expressions, tone of voice, and entered text. For example, if a user provides dissatisfied feedback, the emotion engine recognizes that emotion, and the device can offer a more detailed explanation or suggest additional support.

[0123] Furthermore, users not only input their feedback into their devices, but this feedback is also evaluated along with emotional bias and sent to the server. This feedback helps improve the server's algorithms, contributing to the generation of more accurate personnel placement and training plans. The server also has the ability to report an overview of the team's emotional state to administrators based on the collected emotional data. This makes it possible to monitor the organization's health from an emotional perspective as well.

[0124] Therefore, the present invention provides a comprehensive human resource management system that can effectively utilize employee data while taking emotions into consideration to provide users with optimal information and decision-making support.

[0125] The following describes the processing flow.

[0126] Step 1:

[0127] The server collects employee information from numerous data sources. This includes retrieving employee skills, work history, and performance information via APIs and integrating it into a database.

[0128] Step 2:

[0129] The server inputs integrated data into machine learning algorithms to perform employee skill matching and performance predictions. Based on these results, it generates optimal personnel placement and training plans for each employee.

[0130] Step 3:

[0131] The device notifies the user in natural language of recommended personnel placement and training plans. During this process, it can also answer questions and provide additional information through dialogue with the user.

[0132] Step 4:

[0133] An emotion engine is built into the device to detect the user's emotional state during interactions. For example, if a user expresses dissatisfaction with a question, the emotion engine identifies that emotion and adjusts the response accordingly.

[0134] Step 5:

[0135] Users input their feedback into their device. This feedback includes emotional data analyzed by the emotion engine and is sent to the server.

[0136] Step 6:

[0137] The server uses user feedback and emotional state data to improve its machine learning algorithms. As a result, it improves the accuracy of future personnel placement and training plans.

[0138] Step 7:

[0139] The server analyzes emotional data to assess the emotional state of the entire organization and provides a dashboard that reports to administrators. This allows for a comprehensive understanding of the health of teams within the organization.

[0140] (Example 2)

[0141] 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".

[0142] Effectively utilizing employee data and implementing talent management that considers individual emotions is a crucial challenge for companies. However, traditional systems often struggle to adequately consider emotional aspects, which can prevent the development of optimal personnel placement and training plans. Furthermore, there is a lack of means to monitor and respond to the emotional health of the entire organization in real time.

[0143] 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.

[0144] In this invention, the server includes means for aggregating and centrally managing employee information from multiple sources, means for analyzing employee technical and work performance information using machine learning methods, means for automatically generating optimal personnel placement and training plans for each employee, and means for evaluating emotions and dynamically adjusting responses based on user feedback. This enables strategic personnel management that takes emotions into account and allows for effective monitoring of the emotional health of the entire organization.

[0145] "Employee information" refers to data about individual employees belonging to a company, including skills, work history, and performance evaluations.

[0146] "Information sources" refer to the starting points or systems for collecting data, such as internal databases or external APIs.

[0147] "Machine learning techniques" are a collection of algorithms that enable computers to learn from data and perform specific tasks.

[0148] "Personnel allocation" refers to assigning the most suitable employees to specific projects or positions.

[0149] A "development plan" is a specific growth strategy or program designed to improve employees' skills and careers.

[0150] "Emotional assessment" is the process of identifying and analyzing a user's emotional state, using emotional indicators such as facial expressions and voice.

[0151] "Feedback" refers to the opinions and evaluations that users provide to a system, and is used to improve and adjust the system's performance.

[0152] "Real-time" refers to a processing method in which a system processes data sequentially and provides results immediately.

[0153] This invention provides a system for leveraging employee information to achieve strategic human resource management that takes emotions into account. The system operates with a server, terminals, and users working together to collect and analyze data in real time and provide results.

[0154] The server collects employee information from multiple sources and integrates it into a database. These sources include internal databases and external data services. The collected information is kept consistent using a Python-based data cleansing tool and analyzed using machine learning algorithms with the Scikit-learn library. This analysis evaluates employees' technical skills and performance, and creates optimal personnel placement and training plans. These plans are stored on the server in data format such as JSON.

[0155] The device provides an interactive interface to the user. Using an emotion engine, the device analyzes the user's facial expressions and voice tone to assess their emotional state in real time. This includes information acquisition through the camera and microphone. Based on these results, it can provide appropriate responses when the user provides feedback. For example, it can effectively collect user feedback by using prompts such as, "Please share your thoughts on the current project."

[0156] Users input feedback into the terminal using natural language. This feedback is analyzed by natural language processing tools and evaluated along with emotional bias. This allows the server's algorithm to continuously improve, increasing the accuracy of emotionally balanced plan suggestions.

[0157] As a concrete example, suppose a user provides feedback such as, "I was given a presentation about the introduction of a new tool at a recent meeting, but I'm not confident in my ability to use it." In this case, the system senses this anxiety and suggests appropriate learning resources or a connection to a support team. In this way, it is possible to provide responses that take employee feelings into consideration.

[0158] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0159] Step 1:

[0160] The server collects employee information from internal and external sources. This involves obtaining data on skills, work history, and performance through internal databases and external APIs. The server then uses data cleansing techniques to format this data and integrate it into a centralized database. The output is a dataset in a unified format.

[0161] Step 2:

[0162] The server uses machine learning algorithms to analyze the collected data. The input is the dataset integrated in Step 1. The server utilizes the Scikit-learn library, employing random forest models and support vector machines to predict skill assessments and work performance for each employee. The output generates data for optimal personnel placement and training plans for each employee.

[0163] Step 3:

[0164] The device processes real-time data collected through user interaction. Inputs include the user's facial expressions, tone of voice, and text feedback. The device utilizes an emotion engine to analyze emotions in real time. Specifically, it evaluates the emotional state using an emotion analysis API based on input from the camera and microphone. Output is a feedback response based on the user's emotions.

[0165] Step 4:

[0166] The user enters feedback into the device. This input includes free-form text such as "Thoughts on recent projects." The device analyzes this feedback using natural language processing tools to assess emotional bias. Specifically, it analyzes the input text using a scoring algorithm to determine the positive or negative tendency of the emotion. The output is the individual feedback evaluation result.

[0167] Step 5:

[0168] The server collects user feedback data and uses it to improve the machine learning algorithm. The input is the feedback evaluation results generated in step 4. The server uses this to adjust the algorithm's hyperparameters and improve prediction accuracy. The output is a new version of the improved personnel allocation and training plan. This enables continuous system improvement through feedback.

[0169] (Application Example 2)

[0170] 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".

[0171] Modern organizations are required to effectively manage employee skills and performance, but in addition, it is crucial to consider employee emotions and mental health when planning staffing and development programs. However, traditional systems have made it difficult to analyze emotional data in real time and reflect it in organizational management. This has led to problems such as increased employee stress and decreased work efficiency.

[0172] 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.

[0173] In this invention, the server includes means for aggregating and centrally managing employee information from multiple sources, means for analyzing employee skills and work performance information using machine learning techniques, and means for analyzing employees' emotional states using emotion recognition functions and proposing appropriate personnel assignments and work plans. This enables efficient personnel management that takes employee emotions into consideration.

[0174] "Employee information" refers to information relating to individual employees within an organization, including data such as skills, performance, roles, and emotional state.

[0175] "Information sources" refer to the multiple data providers and platforms used to collect employee information.

[0176] "Centralized management" refers to organizing information gathered from multiple sources in a unified manner and managing it within a single system.

[0177] "Skills" refers to the abilities and knowledge required for an employee to perform a specific task or job.

[0178] "Work results" refer to the performance and evaluation results achieved by employees through their actual work.

[0179] "Machine learning techniques" are algorithmic technologies used to identify data patterns and make predictions, and are used in the analysis of employee information.

[0180] "Emotion recognition function" refers to technology that detects and analyzes emotions from employees' facial expressions and voices.

[0181] "Personnel allocation" refers to the act of improving organizational efficiency by assigning employees to appropriate roles and tasks.

[0182] A "work plan" refers to a schedule of specific activities and tasks formulated to achieve an organization's goals.

[0183] To realize this application, the server aggregates employee information from multiple sources. This involves using a database management system and integrating it with emotion data from facial recognition cameras and microphones. Subsequently, machine learning techniques are used to analyze employee skills and work performance information, and the system automatically generates optimal personnel placement and training plans.

[0184] The terminal interacts with users (employees and administrators) using natural language and provides appropriate information in response to inquiries. Emotion recognition is used in this process. Specifically, the camera and microphone on the terminal detect the user's facial expressions and voice tone in real time, and software that analyzes emotions processes this data. The analyzed emotion data is sent to a server and used for further data analysis.

[0185] Users can input feedback on their work situation and emotions through their devices, and this feedback contributes to improving machine learning methods. In addition, personnel allocation and work plans generated by the server are notified to users, supporting the efficient operation of the organization.

[0186] For example, on a factory line, stressed employees can be identified through emotion recognition, and the system can then reassign them to tasks that reduce their burden. This functionality allows organizations to improve work efficiency while maintaining the emotional well-being of their employees.

[0187] An example of an input prompt for the generating AI model would be a sentence like, "Please generate an algorithm that analyzes the emotional data of employees in a factory and proposes the optimal staffing arrangement to reduce stress."

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

[0189] Step 1:

[0190] The server collects employee information from multiple sources. Inputs include each employee's skills, performance, and emotional data. This data is aggregated and stored in a unified database.

[0191] Step 2:

[0192] The server analyzes the aggregated data using machine learning techniques. It uses integrated employee information as input. This allows it to evaluate employee skills and work performance, and generate an output that creates an appropriate personnel allocation plan.

[0193] Step 3:

[0194] The terminal interacts with the user using natural language. Input includes user inquiries and feedback. Based on this, it generates output, providing the user with deployment plans and related information generated by the server.

[0195] Step 4:

[0196] The device uses emotion recognition to detect the user's facial expressions and voice tone in real time. It uses emotion data from the camera and microphone as input. This allows it to analyze the user's emotional state and obtain output to send to the server.

[0197] Step 5:

[0198] The server analyzes user feedback and sentiment data to improve its machine learning methods. Sentiment data and feedback are used as input. This data is analyzed to optimize the learning algorithm, resulting in output aimed at further improving the accuracy of personnel placement.

[0199] Step 6:

[0200] The server uses a generative AI model to apply the generated staffing plan across the entire organization. The input is an optimized staffing plan. This results in an output that implements efficient work allocation while considering emotional well-being.

[0201] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0202] Data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of data generation model 58 is ChatGPT (registered trademark) (Internet search).<URL: https: / / openai.com / blog / chatgpt> ), Gemini (registered trademark) (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0203] In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and the specific processing may also be performed by the smart device 14.

[0204] [Second Embodiment]

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

[0206] As shown in Figure 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0207] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0208] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication interface 44. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, and camera 42 are also connected to the bus 52.

[0209] The microphone 238 receives voice signals from the user 20 and receives instructions from the user 20. The microphone 238 captures the voice signals from the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0210] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the area around the user 20 (for example, an imaging range defined by a field of view equivalent to the width of a typical healthy person's field of vision).

[0211] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0212] Figure 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Figure 4, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0213] The specific processing program 56 is an example of a "program" relating to the technology of this disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0214] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0215] In the smart glasses 214, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0216] Next, the identification processing performed by the identification processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the smart glasses 214 will be referred to as the "terminal".

[0217] This invention provides a system that effectively utilizes employee information and enables strategic human resource management. This system operates through the collaborative efforts of its server, terminal, and user components.

[0218] The server first aggregates employee information from numerous data sources. This information includes employee skills, work history, and performance evaluations. The server centrally manages this data and uses machine learning algorithms to create optimal talent placement and development plans for each employee. These algorithms analyze each employee's past performance data and skill set.

[0219] For example, the server matches a specific employee's skill information and analyzes whether that person is suitable to be the next project leader. In this process, the server takes into account the employee's past project performance and evaluations to suggest the optimal placement. Furthermore, based on the analysis results, the server uses predictive algorithms to forecast future talent needs. This allows companies to plan for developing personnel with the necessary skill sets to prepare for future business expansion.

[0220] The terminal provides users with information and an interface that enables natural language interaction. Through the terminal, users can view analysis results and proposed personnel placement plans within the system.

[0221] For example, if a user asks the terminal, "Who is the most suitable person for the next project?", the terminal will provide a result based on data analyzed by the server. Furthermore, the user can input feedback into the terminal, and this feedback is sent to the server and used to improve the algorithm.

[0222] This system supports strategic human resource management within companies and provides an implementation that aims to improve overall organizational productivity and employee satisfaction.

[0223] The following describes the processing flow.

[0224] Step 1:

[0225] The server periodically collects employee information from various data sources. This includes retrieving information from the HR system via APIs and importing it into the database. The server also formats this information and ensures data integrity.

[0226] Step 2:

[0227] The server integrates the collected data into a database for centralized management. This process involves data cleansing, correcting duplicates and errors. Data is then merged using specific employee IDs.

[0228] Step 3:

[0229] The server uses machine learning algorithms to analyze employee data. This analysis includes a process that uses each employee's skills and work history as input to generate optimal personnel placement and training plans.

[0230] Step 4:

[0231] The server generates a report to notify the user of the analysis results. This report includes recommended personnel placement and training plans, and is provided to the user in a visualized format.

[0232] Step 5:

[0233] The device provides an interface for interacting with the user and responds to inquiries in natural language. Through the device, users can ask specific questions and view AI-generated suggestions.

[0234] Step 6:

[0235] Users provide feedback on the proposed plan and layout. This feedback is sent from the terminal to the server and used to improve future algorithms.

[0236] Step 7:

[0237] The server updates data in real time and predicts future talent needs. At this stage, trend analysis generates proposals for planning the skills and personnel that will be needed in the future.

[0238] (Example 1)

[0239] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server," and the smart glasses 214 will be referred to as the "terminal."

[0240] For businesses, effectively utilizing employee information and conducting strategic talent management is crucial. However, individually managing employee skills and performance information and formulating optimal placement and development plans is not easy. Furthermore, rapid prediction of talent needs and effective information sharing methods with users are also required. The development of a system that meets these requirements is necessary.

[0241] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0242] In this invention, the server includes means for collecting and integrating employee information from multiple sources, means for analyzing employee skills and work performance information using machine learning, and means for automatically generating appropriate personnel placement and growth plans for each employee. This enables companies to strategically and efficiently utilize human resources and predict future demand.

[0243] "Employee information" is a general term for information about individual employees, such as skills, work history, and performance evaluations, and is valuable data for an organization.

[0244] "Information source" refers to the source from which data is obtained, and includes databases, external APIs, and so on.

[0245] "Integrated management" means organizing and integrating information collected from different sources and handling it in a unified manner.

[0246] "Machine learning" is a technique that analyzes large amounts of data and extracts patterns and rules from it, and includes algorithms for building predictive models.

[0247] "Analysis" means using given data to reveal the characteristics and patterns contained within it.

[0248] "Personnel allocation" is the process of placing each employee within an organization in the appropriate job or position to allow them to utilize their abilities to the fullest.

[0249] A "growth plan" is a plan that includes specific goals and steps to enhance employees' skills and careers.

[0250] "Automatically generated" refers to a system creating information and plans autonomously with little to no manual intervention from humans.

[0251] "Opinions" refer to the thoughts and feedback that users have regarding the information and plans provided by the system.

[0252] "Visualization" is the process of representing data and results using visual means to make them easier to understand.

[0253] This invention is a system for effectively utilizing employee information and realizing strategic human resource management. Specific embodiments of this system are described below.

[0254] Server role:

[0255] The server collects employee information from multiple sources both inside and outside the company and manages it centrally using a database management system. Specifically, it uses MySQL or PostgreSQL, for example, to store the data in a structured format. The server uses Python to preprocess the data with the pandas library and implements machine learning algorithms using Scikit-learn or TensorFlow. This allows the server to analyze employees' skills and work performance and automatically generate individually optimized personnel placement and growth plans.

[0256] Terminal role:

[0257] The device provides an interface for visually displaying information provided by the server to the user. This interface utilizes a web browser or mobile app, displaying information in natural language using HTML or JavaScript. The device also works with the server to collect user feedback and send it to the server.

[0258] User roles:

[0259] Users make strategic decisions based on the information displayed through their devices. For example, users might query information from their devices, such as "Who is the most suitable person for the next project?", and provide feedback based on the results. This allows the algorithms to be continuously improved, leading to an overall increase in system accuracy.

[0260] Thus, the present invention represents a useful form that streamlines the human resource management process within a company and supports the overall human resource strategy of the organization.

[0261] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0262] Step 1:

[0263] The server collects employee information from multiple sources. These sources include databases from various departments within the company and external business data services. The server periodically retrieves the necessary information from each source using APIs and stores it as input in the database. In this process, the data format is standardized and missing values ​​are imputed. The output is organized employee information.

[0264] Step 2:

[0265] The server prepares organized employee information as input data for a machine learning model. Specifically, it uses the Python pandas library to construct a data frame, extracts features, and performs normalization. This unifies data from different scales, making it possible to analyze it with machine learning algorithms. As output, it generates a dataset suitable as input for a machine learning model.

[0266] Step 3:

[0267] The server analyzes the data using machine learning algorithms. In this step, Scikit-learn and TensorFlow are used to predict the optimal placement of employees based on past performance and skills. The input is the dataset generated in step 2, and the algorithm calculates the optimal placement and training plan for each employee.

[0268] Step 4:

[0269] The terminal visually displays the analysis results provided by the server to the user. The interface of the terminal used by the user is developed using HTML and JavaScript, and presents the results in an easily interpretable format using natural language. The input is the analysis results from the server, and the output is an information display in a format that is easy for the user to understand.

[0270] Step 5:

[0271] Users provide feedback based on information obtained through the terminal interface. For example, a user might input an evaluation such as, "The proposed deployment plan is appropriate." This feedback is then sent back to the server and used as input to update and improve the machine learning model. The output is feedback, which leads to improved prediction accuracy in the next cycle.

[0272] (Application Example 1)

[0273] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server," and the smart glasses 214 will be referred to as the "terminal."

[0274] Traditional human resource management systems often fail to adequately utilize employee information, making it difficult to create optimal personnel placement and training plans. This is particularly problematic in manufacturing environments where real-time optimization of work instructions and analysis of the work environment are required. In this context, strategic human resource management based on the skills and performance of individual employees is essential.

[0275] 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.

[0276] In this invention, the server includes means for aggregating and centrally managing employee information from multiple sources, means for analyzing employee ability and performance information using machine learning algorithms, means for automatically generating optimal personnel allocation and training plans for each employee, means for providing work support devices with optimized work instructions in real time, and means for analyzing the work environment using data collected from work support devices. This enables efficient personnel allocation and optimization of work instructions even in manufacturing sites, making it possible to improve overall productivity and employee satisfaction.

[0277] "Information sources" refer to multiple sources or databases that provide data related to employees.

[0278] "Ability" refers to the skills, knowledge, and experience that an employee possesses in order to perform their job duties.

[0279] "Performance information" refers to data that includes the results and evaluations of tasks that employees have completed in the past.

[0280] A "machine learning algorithm" is an automated processing method that analyzes data to discover patterns and regularities, and then makes predictions and decisions.

[0281] The "work support device" refers to tools and electronic devices used by employees to perform their work smoothly.

[0282] The "analysis of the work environment" refers to data analysis for grasping the state of the workplace and the actions of employees in order to improve work efficiency.

[0283] As a form for implementing this invention, a personnel management and work support system in a manufacturing site is proposed. This system is mainly composed of three main components: a server, a work support device, and a user interface.

[0284] The server aggregates and centrally manages employee information obtained from multiple information sources. This includes employees' ability and achievement information, and historical data on the production line. The server analyzes these data using machine learning algorithms and automatically generates an optimal personnel allocation and training plan for each employee. Machine learning frameworks such as TensorFlow are used for this analysis.

[0285] The work support device, such as smart glasses and other wearable devices, receives optimized work instructions and training guides from the server in real time and provides them to the user. This data is available via Bluetooth or WiFi. Also, the work support device collects data from the work environment, returns it to the server, and conducts analysis of the work environment. Thereby, efficient operation at the site is realized.

[0286] The user can send feedback regarding employee allocation and work instructions through a terminal. This feedback is analyzed by the server and used for further improvement of the machine learning algorithms.

[0287] As a concrete example, in a newly introduced production line, when a worker wearing smart glasses performs their duties, the glasses visually display the next steps to be taken in real time. This reduces work errors and improves overall productivity. Furthermore, in response to a prompt such as "Tell me the optimal work tasks for the next production line operation," the server can instantly provide optimized data.

[0288] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0289] Step 1:

[0290] The server aggregates data, including employee skills and performance information, from numerous sources. Input is raw data obtained from various databases and sensor devices. This data is normalized and formatted into a centrally manageable format. Output is an analyzable, integrated dataset.

[0291] Step 2:

[0292] The server feeds the aggregated data into a machine learning algorithm. The input is the integrated dataset generated in Step 1. Based on this data, TensorFlow and other tools are used to analyze each employee's abilities and aptitudes, and an optimal personnel placement and training plan is automatically generated. The output is the proposed plan for each employee.

[0293] Step 3:

[0294] The server sends the generated optimal placement and training plans to the work support device. The input is the plan output in step 2. This information is transmitted in real time via Bluetooth or WiFi and displayed on the work support device (e.g., smart glasses). The output is a visualized work instruction on the user's device.

[0295] Step 4:

[0296] The user executes work instructions received through the work support device. The input is work instructions received in real time. The user performs the work according to the instructions and feeds back data about the work status from the device to the server. The output is work progress data and performance information.

[0297] Step 5:

[0298] The server analyzes user feedback data and continuously improves the machine learning algorithm. The input is the feedback data obtained in step 4. This data is used to retrain the model and tune parameters to improve accuracy and efficiency. The output is the improved next proposed plan and algorithm.

[0299] 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.

[0300] This invention provides a system that enables strategic human resource management that takes user emotions into account while utilizing employee information. This system operates through the collaborative efforts of a server, terminal, user, and emotion engine.

[0301] The server aggregates employee information from diverse data sources and integrates it into an organized database. Using machine learning algorithms, the server analyzes each employee's skills and performance information and automatically generates optimal personnel placement and training plans. These plans are backed by objective, data-driven judgments, supporting companies in efficiently utilizing their human resources.

[0302] The terminal interacts with the user and enables dialogue using natural language. At this time, the emotion engine is utilized to detect emotions in real time from the user's expression, voice tone, and input text. For example, when the user inputs feedback with dissatisfaction, the emotion engine recognizes the emotion, and the terminal can provide a more detailed explanation or propose additional support.

[0303] Furthermore, not only does the user input their feedback to the terminal, but it is also evaluated with emotional bias and sent to the server. This feedback helps improve the server's algorithms and contributes to the generation of more accurate talent placement and training plans. Additionally, the server has a function to report to the administrator an overview of the emotional state of the entire team based on the collected emotional data. Thus, it is possible to monitor the health of the organization from an emotional aspect.

[0304] Therefore, the present invention provides a comprehensive human resource management system that can effectively utilize employee data while taking emotions into account to provide optimal information and decision-making support to users.

[0305] The processing flow will be described below.

[0306] Step 1:

[0307] The server collects employee information from multiple data sources. This includes operations to obtain employees' skills, work histories, and evaluation information through APIs and integrate them into the database.

[0308] Step 2:

[0309] The server inputs the integrated data into machine learning algorithms to perform employee skill matching and performance prediction. Based on this result, an optimal talent placement and training plan for each employee is generated.

[0310] Step 3:

[0311] The device notifies the user in natural language of recommended personnel placement and training plans. During this process, it can also answer questions and provide additional information through dialogue with the user.

[0312] Step 4:

[0313] An emotion engine is built into the device to detect the user's emotional state during interactions. For example, if a user expresses dissatisfaction with a question, the emotion engine identifies that emotion and adjusts the response accordingly.

[0314] Step 5:

[0315] Users input their feedback into their device. This feedback includes emotional data analyzed by the emotion engine and is sent to the server.

[0316] Step 6:

[0317] The server uses user feedback and emotional state data to improve its machine learning algorithms. As a result, it improves the accuracy of future personnel placement and training plans.

[0318] Step 7:

[0319] The server analyzes emotional data to assess the emotional state of the entire organization and provides a dashboard that reports to administrators. This allows for a comprehensive understanding of the health of teams within the organization.

[0320] (Example 2)

[0321] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server" and the smart glasses 214 will be referred to as the "terminal".

[0322] Effectively utilizing employee data and implementing talent management that considers individual emotions is a crucial challenge for companies. However, traditional systems often struggle to adequately consider emotional aspects, which can prevent the development of optimal personnel placement and training plans. Furthermore, there is a lack of means to monitor and respond to the emotional health of the entire organization in real time.

[0323] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0324] In this invention, the server includes means for aggregating and centrally managing employee information from multiple sources, means for analyzing employee technical and work performance information using machine learning methods, means for automatically generating optimal personnel placement and training plans for each employee, and means for evaluating emotions and dynamically adjusting responses based on user feedback. This enables strategic personnel management that takes emotions into account and allows for effective monitoring of the emotional health of the entire organization.

[0325] "Employee information" refers to data about individual employees belonging to a company, including skills, work history, and performance evaluations.

[0326] "Information sources" refer to the starting points or systems for collecting data, such as internal databases or external APIs.

[0327] "Machine learning techniques" are a collection of algorithms that enable computers to learn from data and perform specific tasks.

[0328] "Personnel allocation" refers to assigning the most suitable employees to specific projects or positions.

[0329] A "development plan" is a specific growth strategy or program designed to improve employees' skills and careers.

[0330] "Emotional assessment" is the process of identifying and analyzing a user's emotional state, using emotional indicators such as facial expressions and voice.

[0331] "Feedback" refers to the opinions and evaluations that users provide to a system, and is used to improve and adjust the system's performance.

[0332] "Real-time" refers to a processing method in which a system processes data sequentially and provides results immediately.

[0333] This invention provides a system for leveraging employee information to achieve strategic human resource management that takes emotions into account. The system operates with a server, terminals, and users working together to collect and analyze data in real time and provide results.

[0334] The server collects employee information from multiple sources and integrates it into a database. These sources include internal databases and external data services. The collected information is kept consistent using a Python-based data cleansing tool and analyzed using machine learning algorithms with the Scikit-learn library. This analysis evaluates employees' technical skills and performance, and creates optimal personnel placement and training plans. These plans are stored on the server in data format such as JSON.

[0335] The device provides an interactive interface to the user. Using an emotion engine, the device analyzes the user's facial expressions and voice tone to assess their emotional state in real time. This includes information acquisition through the camera and microphone. Based on these results, it can provide appropriate responses when the user provides feedback. For example, it can effectively collect user feedback by using prompts such as, "Please share your thoughts on the current project."

[0336] Users input feedback into the terminal using natural language. This feedback is analyzed by natural language processing tools and evaluated along with emotional bias. This allows the server's algorithm to continuously improve, increasing the accuracy of emotionally balanced plan suggestions.

[0337] As a concrete example, suppose a user provides feedback such as, "I was given a presentation about the introduction of a new tool at a recent meeting, but I'm not confident in my ability to use it." In this case, the system senses this anxiety and suggests appropriate learning resources or a connection to a support team. In this way, it is possible to provide responses that take employee feelings into consideration.

[0338] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0339] Step 1:

[0340] The server collects employee information from internal and external sources. This involves obtaining data on skills, work history, and performance through internal databases and external APIs. The server then uses data cleansing techniques to format this data and integrate it into a centralized database. The output is a dataset in a unified format.

[0341] Step 2:

[0342] The server uses machine learning algorithms to analyze the collected data. The input is the dataset integrated in Step 1. The server utilizes the Scikit-learn library, employing random forest models and support vector machines to predict skill assessments and work performance for each employee. The output generates data for optimal personnel placement and training plans for each employee.

[0343] Step 3:

[0344] The device processes real-time data collected through user interaction. Inputs include the user's facial expressions, tone of voice, and text feedback. The device utilizes an emotion engine to analyze emotions in real time. Specifically, it evaluates the emotional state using an emotion analysis API based on input from the camera and microphone. Output is a feedback response based on the user's emotions.

[0345] Step 4:

[0346] The user enters feedback into the device. This input includes free-form text such as "Thoughts on recent projects." The device analyzes this feedback using natural language processing tools to assess emotional bias. Specifically, it analyzes the input text using a scoring algorithm to determine the positive or negative tendency of the emotion. The output is the individual feedback evaluation result.

[0347] Step 5:

[0348] The server collects user feedback data and uses it to improve the machine learning algorithm. The input is the feedback evaluation results generated in step 4. The server uses this to adjust the algorithm's hyperparameters and improve prediction accuracy. The output is a new version of the improved personnel allocation and training plan. This enables continuous system improvement through feedback.

[0349] (Application Example 2)

[0350] 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."

[0351] Modern organizations are required to effectively manage employee skills and performance, but in addition, it is crucial to consider employee emotions and mental health when planning staffing and development programs. However, traditional systems have made it difficult to analyze emotional data in real time and reflect it in organizational management. This has led to problems such as increased employee stress and decreased work efficiency.

[0352] 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.

[0353] In this invention, the server includes means for aggregating and centrally managing employee information from multiple sources, means for analyzing employee skills and work performance information using machine learning techniques, and means for analyzing employees' emotional states using emotion recognition functions and proposing appropriate personnel assignments and work plans. This enables efficient personnel management that takes employee emotions into consideration.

[0354] "Employee information" refers to information relating to individual employees within an organization, including data such as skills, performance, roles, and emotional state.

[0355] "Information sources" refer to the multiple data providers and platforms used to collect employee information.

[0356] "Centralized management" refers to organizing information gathered from multiple sources in a unified manner and managing it within a single system.

[0357] "Skills" refers to the abilities and knowledge required for an employee to perform a specific task or job.

[0358] "Work results" refer to the performance and evaluation results achieved by employees through their actual work.

[0359] "Machine learning techniques" are algorithmic technologies used to identify data patterns and make predictions, and are used in the analysis of employee information.

[0360] "Emotion recognition function" refers to technology that detects and analyzes emotions from employees' facial expressions and voices.

[0361] "Personnel allocation" refers to the act of improving organizational efficiency by assigning employees to appropriate roles and tasks.

[0362] A "work plan" refers to a schedule of specific activities and tasks formulated to achieve an organization's goals.

[0363] To realize this application, the server aggregates employee information from multiple sources. This involves using a database management system and integrating it with emotion data from facial recognition cameras and microphones. Subsequently, machine learning techniques are used to analyze employee skills and work performance information, and the system automatically generates optimal personnel placement and training plans.

[0364] The terminal interacts with users (employees and administrators) using natural language and provides appropriate information in response to inquiries. Emotion recognition is used in this process. Specifically, the camera and microphone on the terminal detect the user's facial expressions and voice tone in real time, and software that analyzes emotions processes this data. The analyzed emotion data is sent to a server and used for further data analysis.

[0365] Users can input feedback on their work situation and emotions through their devices, and this feedback contributes to improving machine learning methods. In addition, personnel allocation and work plans generated by the server are notified to users, supporting the efficient operation of the organization.

[0366] For example, on a factory line, stressed employees can be identified through emotion recognition, and the system can then reassign them to tasks that reduce their burden. This functionality allows organizations to improve work efficiency while maintaining the emotional well-being of their employees.

[0367] An example of an input prompt for the generating AI model would be a sentence like, "Please generate an algorithm that analyzes the emotional data of employees in a factory and proposes the optimal staffing arrangement to reduce stress."

[0368] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0369] Step 1:

[0370] The server collects employee information from multiple sources. Inputs include each employee's skills, performance, and emotional data. This data is aggregated and stored in a unified database.

[0371] Step 2:

[0372] The server analyzes the aggregated data using machine learning techniques. It uses integrated employee information as input. This allows it to evaluate employee skills and work performance, and generate an output that creates an appropriate personnel allocation plan.

[0373] Step 3:

[0374] The terminal interacts with the user using natural language. Input includes user inquiries and feedback. Based on this, it generates output, providing the user with deployment plans and related information generated by the server.

[0375] Step 4:

[0376] The device uses emotion recognition to detect the user's facial expressions and voice tone in real time. It uses emotion data from the camera and microphone as input. This allows it to analyze the user's emotional state and obtain output to send to the server.

[0377] Step 5:

[0378] The server analyzes user feedback and sentiment data to improve its machine learning methods. Sentiment data and feedback are used as input. This data is analyzed to optimize the learning algorithm, resulting in output aimed at further improving the accuracy of personnel placement.

[0379] Step 6:

[0380] The server uses a generative AI model to apply the generated staffing plan across the entire organization. The input is an optimized staffing plan. This results in an output that implements efficient work allocation while considering emotional well-being.

[0381] 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.

[0382] 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.

[0383] 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.

[0384] [Third Embodiment]

[0385] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0386] 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.

[0387] 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).

[0388] 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.

[0389] 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.

[0390] 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).

[0391] 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.

[0392] 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.

[0393] 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.

[0394] 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.

[0395] 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.

[0396] 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".

[0397] This invention provides a system that effectively utilizes employee information and enables strategic human resource management. This system operates through the collaborative efforts of its server, terminal, and user components.

[0398] The server first aggregates employee information from numerous data sources. This information includes employee skills, work history, and performance evaluations. The server centrally manages this data and uses machine learning algorithms to create optimal talent placement and development plans for each employee. These algorithms analyze each employee's past performance data and skill set.

[0399] For example, the server matches a specific employee's skill information and analyzes whether that person is suitable to be the next project leader. In this process, the server takes into account the employee's past project performance and evaluations to suggest the optimal placement. Furthermore, based on the analysis results, the server uses predictive algorithms to forecast future talent needs. This allows companies to plan for developing personnel with the necessary skill sets to prepare for future business expansion.

[0400] The terminal provides users with information and an interface that enables natural language interaction. Through the terminal, users can view analysis results and proposed personnel placement plans within the system.

[0401] For example, if a user asks the terminal, "Who is the most suitable person for the next project?", the terminal will provide a result based on data analyzed by the server. Furthermore, the user can input feedback into the terminal, and this feedback is sent to the server and used to improve the algorithm.

[0402] This system supports strategic human resource management within companies and provides an implementation that aims to improve overall organizational productivity and employee satisfaction.

[0403] The following describes the processing flow.

[0404] Step 1:

[0405] The server periodically collects employee information from various data sources. This includes retrieving information from the HR system via APIs and importing it into the database. The server also formats this information and ensures data integrity.

[0406] Step 2:

[0407] The server integrates the collected data into a database for centralized management. This process involves data cleansing, correcting duplicates and errors. Data is then merged using specific employee IDs.

[0408] Step 3:

[0409] The server uses machine learning algorithms to analyze employee data. This analysis includes a process that uses each employee's skills and work history as input to generate optimal personnel placement and training plans.

[0410] Step 4:

[0411] The server generates a report to notify the user of the analysis results. This report includes recommended personnel placement and training plans, and is provided to the user in a visualized format.

[0412] Step 5:

[0413] The device provides an interface for interacting with the user and responds to inquiries in natural language. Through the device, users can ask specific questions and view AI-generated suggestions.

[0414] Step 6:

[0415] Users provide feedback on the proposed plan and layout. This feedback is sent from the terminal to the server and used to improve future algorithms.

[0416] Step 7:

[0417] The server updates data in real time and predicts future talent needs. At this stage, trend analysis generates proposals for planning the skills and personnel that will be needed in the future.

[0418] (Example 1)

[0419] 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."

[0420] For businesses, effectively utilizing employee information and conducting strategic talent management is crucial. However, individually managing employee skills and performance information and formulating optimal placement and development plans is not easy. Furthermore, rapid prediction of talent needs and effective information sharing methods with users are also required. The development of a system that meets these requirements is necessary.

[0421] 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.

[0422] In this invention, the server includes means for collecting and integrating employee information from multiple sources, means for analyzing employee skills and work performance information using machine learning, and means for automatically generating appropriate personnel placement and growth plans for each employee. This enables companies to strategically and efficiently utilize human resources and predict future demand.

[0423] "Employee information" is a general term for information about individual employees, such as skills, work history, and performance evaluations, and is valuable data for an organization.

[0424] "Information source" refers to the source from which data is obtained, and includes databases, external APIs, and so on.

[0425] "Integrated management" means organizing and integrating information collected from different sources and handling it in a unified manner.

[0426] "Machine learning" is a technique that analyzes large amounts of data and extracts patterns and rules from it, and includes algorithms for building predictive models.

[0427] "Analysis" means using given data to reveal the characteristics and patterns contained within it.

[0428] "Personnel allocation" is the process of placing each employee within an organization in the appropriate job or position to allow them to utilize their abilities to the fullest.

[0429] A "growth plan" is a plan that includes specific goals and steps to enhance employees' skills and careers.

[0430] "Automatically generated" refers to a system creating information and plans autonomously with little to no manual intervention from humans.

[0431] "Opinions" refer to the thoughts and feedback that users have regarding the information and plans provided by the system.

[0432] "Visualization" is the process of representing data and results using visual means to make them easier to understand.

[0433] This invention is a system for effectively utilizing employee information and realizing strategic human resource management. Specific embodiments of this system are described below.

[0434] Server role:

[0435] The server collects employee information from multiple sources both inside and outside the company and manages it centrally using a database management system. Specifically, it uses MySQL or PostgreSQL, for example, to store the data in a structured format. The server uses Python to preprocess the data with the pandas library and implements machine learning algorithms using Scikit-learn or TensorFlow. This allows the server to analyze employees' skills and work performance and automatically generate individually optimized personnel placement and growth plans.

[0436] Terminal role:

[0437] The device provides an interface for visually displaying information provided by the server to the user. This interface utilizes a web browser or mobile app, displaying information in natural language using HTML or JavaScript. The device also works with the server to collect user feedback and send it to the server.

[0438] User roles:

[0439] Users make strategic decisions based on the information displayed through their devices. For example, users might query information from their devices, such as "Who is the most suitable person for the next project?", and provide feedback based on the results. This allows the algorithms to be continuously improved, leading to an overall increase in system accuracy.

[0440] Thus, the present invention represents a useful form that streamlines the human resource management process within a company and supports the overall human resource strategy of the organization.

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

[0442] Step 1:

[0443] The server collects employee information from multiple sources. These sources include databases from various departments within the company and external business data services. The server periodically retrieves the necessary information from each source using APIs and stores it as input in the database. In this process, the data format is standardized and missing values ​​are imputed. The output is organized employee information.

[0444] Step 2:

[0445] The server prepares organized employee information as input data for a machine learning model. Specifically, it uses the Python pandas library to construct a data frame, extracts features, and performs normalization. This unifies data from different scales, making it possible to analyze it with machine learning algorithms. As output, it generates a dataset suitable as input for a machine learning model.

[0446] Step 3:

[0447] The server analyzes the data using machine learning algorithms. In this step, Scikit-learn and TensorFlow are used to predict the optimal placement of employees based on past performance and skills. The input is the dataset generated in step 2, and the algorithm calculates the optimal placement and training plan for each employee.

[0448] Step 4:

[0449] The terminal visually displays the analysis results provided by the server to the user. The interface of the terminal used by the user is developed using HTML and JavaScript, and presents the results in an easily interpretable format using natural language. The input is the analysis results from the server, and the output is an information display in a format that is easy for the user to understand.

[0450] Step 5:

[0451] Users provide feedback based on information obtained through the terminal interface. For example, a user might input an evaluation such as, "The proposed deployment plan is appropriate." This feedback is then sent back to the server and used as input to update and improve the machine learning model. The output is feedback, which leads to improved prediction accuracy in the next cycle.

[0452] (Application Example 1)

[0453] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server," and the headset-type terminal 314 will be referred to as the "terminal."

[0454] Traditional human resource management systems often fail to adequately utilize employee information, making it difficult to create optimal personnel placement and training plans. This is particularly problematic in manufacturing environments where real-time optimization of work instructions and analysis of the work environment are required. In this context, strategic human resource management based on the skills and performance of individual employees is essential.

[0455] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0456] In this invention, the server includes means for aggregating and centrally managing employee information from multiple sources, means for analyzing employee ability and performance information using machine learning algorithms, means for automatically generating optimal personnel allocation and training plans for each employee, means for providing work support devices with optimized work instructions in real time, and means for analyzing the work environment using data collected from work support devices. This enables efficient personnel allocation and optimization of work instructions even in manufacturing sites, making it possible to improve overall productivity and employee satisfaction.

[0457] "Information sources" refer to multiple sources or databases that provide data related to employees.

[0458] "Ability" refers to the skills, knowledge, and experience that an employee possesses in order to perform their job duties.

[0459] "Performance information" refers to data that includes the results and evaluations of tasks that employees have completed in the past.

[0460] A "machine learning algorithm" is an automated processing method that analyzes data to discover patterns and regularities, and then makes predictions and decisions.

[0461] "Work support devices" refer to tools and electronic devices used by employees to help them perform their tasks smoothly.

[0462] "Analysis of the work environment" refers to data analysis aimed at improving work efficiency by understanding the conditions of the workplace and the behavior of employees.

[0463] As an embodiment of this invention, a personnel management and work support system for manufacturing sites is proposed. This system mainly consists of three main components: a server, a work support device, and a user interface.

[0464] The server aggregates and centrally manages employee information obtained from numerous sources. This includes employee skills and performance data, as well as historical data from the production line. The server analyzes this data using machine learning algorithms and automatically generates optimal personnel placement and training plans for each employee. Machine learning frameworks such as TensorFlow are used for this analysis.

[0465] Work support devices, such as smart glasses and other wearable devices, receive optimized work instructions and training guides from a server in real time and provide them to the user. This data is available via Bluetooth or Wi-Fi. The work support devices also collect data from the work environment and return it to the server for analysis. This enables efficient operations on-site.

[0466] Users can submit feedback on employee assignments and work instructions via their devices. This feedback is analyzed by the server and used to further improve machine learning algorithms.

[0467] As a concrete example, in a newly introduced production line, when a worker wearing smart glasses performs their duties, the glasses visually display the next steps to be taken in real time. This reduces work errors and improves overall productivity. Furthermore, in response to a prompt such as "Tell me the optimal work tasks for the next production line operation," the server can instantly provide optimized data.

[0468] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0469] Step 1:

[0470] The server aggregates data, including employee skills and performance information, from numerous sources. Input is raw data obtained from various databases and sensor devices. This data is normalized and formatted into a centrally manageable format. Output is an analyzable, integrated dataset.

[0471] Step 2:

[0472] The server feeds the aggregated data into a machine learning algorithm. The input is the integrated dataset generated in Step 1. Based on this data, TensorFlow and other tools are used to analyze each employee's abilities and aptitudes, and an optimal personnel placement and training plan is automatically generated. The output is the proposed plan for each employee.

[0473] Step 3:

[0474] The server sends the generated optimal placement and training plans to the work support device. The input is the plan output in step 2. This information is transmitted in real time via Bluetooth or WiFi and displayed on the work support device (e.g., smart glasses). The output is a visualized work instruction on the user's device.

[0475] Step 4:

[0476] The user executes work instructions received through the work support device. The input is work instructions received in real time. The user performs the work according to the instructions and feeds back data about the work status from the device to the server. The output is work progress data and performance information.

[0477] Step 5:

[0478] The server analyzes user feedback data and continuously improves the machine learning algorithm. The input is the feedback data obtained in step 4. This data is used to retrain the model and tune parameters to improve accuracy and efficiency. The output is the improved next proposed plan and algorithm.

[0479] 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.

[0480] This invention provides a system that enables strategic human resource management that takes user emotions into account while utilizing employee information. This system operates through the collaborative efforts of a server, terminal, user, and emotion engine.

[0481] The server aggregates employee information from diverse data sources and integrates it into an organized database. Using machine learning algorithms, the server analyzes each employee's skills and performance information and automatically generates optimal personnel placement and training plans. These plans are backed by objective, data-driven judgments, supporting companies in efficiently utilizing their human resources.

[0482] The device interacts with the user and enables natural language dialogue. An emotion engine is used to detect emotions in real time from the user's facial expressions, tone of voice, and entered text. For example, if a user provides dissatisfied feedback, the emotion engine recognizes that emotion, and the device can offer a more detailed explanation or suggest additional support.

[0483] Furthermore, users not only input their feedback into their devices, but this feedback is also evaluated along with emotional bias and sent to the server. This feedback helps improve the server's algorithms, contributing to the generation of more accurate personnel placement and training plans. The server also has the ability to report an overview of the team's emotional state to administrators based on the collected emotional data. This makes it possible to monitor the organization's health from an emotional perspective as well.

[0484] Therefore, the present invention provides a comprehensive human resource management system that can effectively utilize employee data while taking emotions into consideration to provide users with optimal information and decision-making support.

[0485] The following describes the processing flow.

[0486] Step 1:

[0487] The server collects employee information from numerous data sources. This includes retrieving employee skills, work history, and performance information via APIs and integrating it into a database.

[0488] Step 2:

[0489] The server inputs integrated data into machine learning algorithms to perform employee skill matching and performance predictions. Based on these results, it generates optimal personnel placement and training plans for each employee.

[0490] Step 3:

[0491] The device notifies the user in natural language of recommended personnel placement and training plans. During this process, it can also answer questions and provide additional information through dialogue with the user.

[0492] Step 4:

[0493] An emotion engine is built into the device to detect the user's emotional state during interactions. For example, if a user expresses dissatisfaction with a question, the emotion engine identifies that emotion and adjusts the response accordingly.

[0494] Step 5:

[0495] Users input their feedback into their device. This feedback includes emotional data analyzed by the emotion engine and is sent to the server.

[0496] Step 6:

[0497] The server uses user feedback and emotional state data to improve its machine learning algorithms. As a result, it improves the accuracy of future personnel placement and training plans.

[0498] Step 7:

[0499] The server analyzes emotional data to assess the emotional state of the entire organization and provides a dashboard that reports to administrators. This allows for a comprehensive understanding of the health of teams within the organization.

[0500] (Example 2)

[0501] 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."

[0502] Effectively utilizing employee data and implementing talent management that considers individual emotions is a crucial challenge for companies. However, traditional systems often struggle to adequately consider emotional aspects, which can prevent the development of optimal personnel placement and training plans. Furthermore, there is a lack of means to monitor and respond to the emotional health of the entire organization in real time.

[0503] 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.

[0504] In this invention, the server includes means for aggregating and centrally managing employee information from multiple sources, means for analyzing employee technical and work performance information using machine learning methods, means for automatically generating optimal personnel placement and training plans for each employee, and means for evaluating emotions and dynamically adjusting responses based on user feedback. This enables strategic personnel management that takes emotions into account and allows for effective monitoring of the emotional health of the entire organization.

[0505] "Employee information" refers to data about individual employees belonging to a company, including skills, work history, and performance evaluations.

[0506] "Information sources" refer to the starting points or systems for collecting data, such as internal databases or external APIs.

[0507] "Machine learning techniques" are a collection of algorithms that enable computers to learn from data and perform specific tasks.

[0508] "Personnel allocation" refers to assigning the most suitable employees to specific projects or positions.

[0509] A "development plan" is a specific growth strategy or program designed to improve employees' skills and careers.

[0510] "Emotional assessment" is the process of identifying and analyzing a user's emotional state, using emotional indicators such as facial expressions and voice.

[0511] "Feedback" refers to the opinions and evaluations that users provide to a system, and is used to improve and adjust the system's performance.

[0512] "Real-time" refers to a processing method in which a system processes data sequentially and provides results immediately.

[0513] This invention provides a system for leveraging employee information to achieve strategic human resource management that takes emotions into account. The system operates with a server, terminals, and users working together to collect and analyze data in real time and provide results.

[0514] The server collects employee information from multiple sources and integrates it into a database. These sources include internal databases and external data services. The collected information is kept consistent using a Python-based data cleansing tool and analyzed using machine learning algorithms with the Scikit-learn library. This analysis evaluates employees' technical skills and performance, and creates optimal personnel placement and training plans. These plans are stored on the server in data format such as JSON.

[0515] The device provides an interactive interface to the user. Using an emotion engine, the device analyzes the user's facial expressions and voice tone to assess their emotional state in real time. This includes information acquisition through the camera and microphone. Based on these results, it can provide appropriate responses when the user provides feedback. For example, it can effectively collect user feedback by using prompts such as, "Please share your thoughts on the current project."

[0516] Users input feedback into the terminal using natural language. This feedback is analyzed by natural language processing tools and evaluated along with emotional bias. This allows the server's algorithm to continuously improve, increasing the accuracy of emotionally balanced plan suggestions.

[0517] As a concrete example, suppose a user provides feedback such as, "I was given a presentation about the introduction of a new tool at a recent meeting, but I'm not confident in my ability to use it." In this case, the system senses this anxiety and suggests appropriate learning resources or a connection to a support team. In this way, it is possible to provide responses that take employee feelings into consideration.

[0518] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0519] Step 1:

[0520] The server collects employee information from internal and external sources. This involves obtaining data on skills, work history, and performance through internal databases and external APIs. The server then uses data cleansing techniques to format this data and integrate it into a centralized database. The output is a dataset in a unified format.

[0521] Step 2:

[0522] The server uses machine learning algorithms to analyze the collected data. The input is the dataset integrated in Step 1. The server utilizes the Scikit-learn library, employing random forest models and support vector machines to predict skill assessments and work performance for each employee. The output generates data for optimal personnel placement and training plans for each employee.

[0523] Step 3:

[0524] The device processes real-time data collected through user interaction. Inputs include the user's facial expressions, tone of voice, and text feedback. The device utilizes an emotion engine to analyze emotions in real time. Specifically, it evaluates the emotional state using an emotion analysis API based on input from the camera and microphone. Output is a feedback response based on the user's emotions.

[0525] Step 4:

[0526] The user enters feedback into the device. This input includes free-form text such as "Thoughts on recent projects." The device analyzes this feedback using natural language processing tools to assess emotional bias. Specifically, it analyzes the input text using a scoring algorithm to determine the positive or negative tendency of the emotion. The output is the individual feedback evaluation result.

[0527] Step 5:

[0528] The server collects user feedback data and uses it to improve the machine learning algorithm. The input is the feedback evaluation results generated in step 4. The server uses this to adjust the algorithm's hyperparameters and improve prediction accuracy. The output is a new version of the improved personnel allocation and training plan. This enables continuous system improvement through feedback.

[0529] (Application Example 2)

[0530] 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."

[0531] Modern organizations are required to effectively manage employee skills and performance, but in addition, it is crucial to consider employee emotions and mental health when planning staffing and development programs. However, traditional systems have made it difficult to analyze emotional data in real time and reflect it in organizational management. This has led to problems such as increased employee stress and decreased work efficiency.

[0532] 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.

[0533] In this invention, the server includes means for aggregating and centrally managing employee information from multiple sources, means for analyzing employee skills and work performance information using machine learning techniques, and means for analyzing employees' emotional states using emotion recognition functions and proposing appropriate personnel assignments and work plans. This enables efficient personnel management that takes employee emotions into consideration.

[0534] "Employee information" refers to information relating to individual employees within an organization, including data such as skills, performance, roles, and emotional state.

[0535] "Information sources" refer to the multiple data providers and platforms used to collect employee information.

[0536] "Centralized management" refers to organizing information gathered from multiple sources in a unified manner and managing it within a single system.

[0537] "Skills" refers to the abilities and knowledge required for an employee to perform a specific task or job.

[0538] "Work results" refer to the performance and evaluation results achieved by employees through their actual work.

[0539] "Machine learning techniques" are algorithmic technologies used to identify data patterns and make predictions, and are used in the analysis of employee information.

[0540] "Emotion recognition function" refers to technology that detects and analyzes emotions from employees' facial expressions and voices.

[0541] "Personnel allocation" refers to the act of improving organizational efficiency by assigning employees to appropriate roles and tasks.

[0542] A "work plan" refers to a schedule of specific activities and tasks formulated to achieve an organization's goals.

[0543] To realize this application, the server aggregates employee information from multiple sources. This involves using a database management system and integrating it with emotion data from facial recognition cameras and microphones. Subsequently, machine learning techniques are used to analyze employee skills and work performance information, and the system automatically generates optimal personnel placement and training plans.

[0544] The terminal interacts with users (employees and administrators) using natural language and provides appropriate information in response to inquiries. Emotion recognition is used in this process. Specifically, the camera and microphone on the terminal detect the user's facial expressions and voice tone in real time, and software that analyzes emotions processes this data. The analyzed emotion data is sent to a server and used for further data analysis.

[0545] Users can input feedback on their work situation and emotions through their devices, and this feedback contributes to improving machine learning methods. In addition, personnel allocation and work plans generated by the server are notified to users, supporting the efficient operation of the organization.

[0546] For example, on a factory line, stressed employees can be identified through emotion recognition, and the system can then reassign them to tasks that reduce their burden. This functionality allows organizations to improve work efficiency while maintaining the emotional well-being of their employees.

[0547] An example of an input prompt for the generating AI model would be a sentence like, "Please generate an algorithm that analyzes the emotional data of employees in a factory and proposes the optimal staffing arrangement to reduce stress."

[0548] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0549] Step 1:

[0550] The server collects employee information from multiple sources. Inputs include each employee's skills, performance, and emotional data. This data is aggregated and stored in a unified database.

[0551] Step 2:

[0552] The server analyzes the aggregated data using machine learning techniques. It uses integrated employee information as input. This allows it to evaluate employee skills and work performance, and generate an output that creates an appropriate personnel allocation plan.

[0553] Step 3:

[0554] The terminal interacts with the user using natural language. Input includes user inquiries and feedback. Based on this, it generates output, providing the user with deployment plans and related information generated by the server.

[0555] Step 4:

[0556] The device uses emotion recognition to detect the user's facial expressions and voice tone in real time. It uses emotion data from the camera and microphone as input. This allows it to analyze the user's emotional state and obtain output to send to the server.

[0557] Step 5:

[0558] The server analyzes user feedback and sentiment data to improve its machine learning methods. Sentiment data and feedback are used as input. This data is analyzed to optimize the learning algorithm, resulting in output aimed at further improving the accuracy of personnel placement.

[0559] Step 6:

[0560] The server uses a generative AI model to apply the generated staffing plan across the entire organization. The input is an optimized staffing plan. This results in an output that implements efficient work allocation while considering emotional well-being.

[0561] 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.

[0562] 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.

[0563] 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.

[0564] [Fourth Embodiment]

[0565] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0566] 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.

[0567] 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).

[0568] 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.

[0569] 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.

[0570] 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).

[0571] 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.

[0572] 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.

[0573] 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.

[0574] 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.

[0575] 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.

[0576] 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.

[0577] 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".

[0578] This invention provides a system that effectively utilizes employee information and enables strategic human resource management. This system operates through the collaborative efforts of its server, terminal, and user components.

[0579] The server first aggregates employee information from numerous data sources. This information includes employee skills, work history, and performance evaluations. The server centrally manages this data and uses machine learning algorithms to create optimal talent placement and development plans for each employee. These algorithms analyze each employee's past performance data and skill set.

[0580] For example, the server matches a specific employee's skill information and analyzes whether that person is suitable to be the next project leader. In this process, the server takes into account the employee's past project performance and evaluations to suggest the optimal placement. Furthermore, based on the analysis results, the server uses predictive algorithms to forecast future talent needs. This allows companies to plan for developing personnel with the necessary skill sets to prepare for future business expansion.

[0581] The terminal provides users with information and an interface that enables natural language interaction. Through the terminal, users can view analysis results and proposed personnel placement plans within the system.

[0582] For example, if a user asks the terminal, "Who is the most suitable person for the next project?", the terminal will provide a result based on data analyzed by the server. Furthermore, the user can input feedback into the terminal, and this feedback is sent to the server and used to improve the algorithm.

[0583] This system supports strategic human resource management within companies and provides an implementation that aims to improve overall organizational productivity and employee satisfaction.

[0584] The following describes the processing flow.

[0585] Step 1:

[0586] The server periodically collects employee information from various data sources. This includes retrieving information from the HR system via APIs and importing it into the database. The server also formats this information and ensures data integrity.

[0587] Step 2:

[0588] The server integrates the collected data into a database for centralized management. This process involves data cleansing, correcting duplicates and errors. Data is then merged using specific employee IDs.

[0589] Step 3:

[0590] The server uses machine learning algorithms to analyze employee data. This analysis includes a process that uses each employee's skills and work history as input to generate optimal personnel placement and training plans.

[0591] Step 4:

[0592] The server generates a report to notify the user of the analysis results. This report includes recommended personnel placement and training plans, and is provided to the user in a visualized format.

[0593] Step 5:

[0594] The device provides an interface for interacting with the user and responds to inquiries in natural language. Through the device, users can ask specific questions and view AI-generated suggestions.

[0595] Step 6:

[0596] Users provide feedback on the proposed plan and layout. This feedback is sent from the terminal to the server and used to improve future algorithms.

[0597] Step 7:

[0598] The server updates data in real time and predicts future talent needs. At this stage, trend analysis generates proposals for planning the skills and personnel that will be needed in the future.

[0599] (Example 1)

[0600] 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".

[0601] For businesses, effectively utilizing employee information and conducting strategic talent management is crucial. However, individually managing employee skills and performance information and formulating optimal placement and development plans is not easy. Furthermore, rapid prediction of talent needs and effective information sharing methods with users are also required. The development of a system that meets these requirements is necessary.

[0602] 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.

[0603] In this invention, the server includes means for collecting and integrating employee information from multiple sources, means for analyzing employee skills and work performance information using machine learning, and means for automatically generating appropriate personnel placement and growth plans for each employee. This enables companies to strategically and efficiently utilize human resources and predict future demand.

[0604] "Employee information" is a general term for information about individual employees, such as skills, work history, and performance evaluations, and is valuable data for an organization.

[0605] "Information source" refers to the source from which data is obtained, and includes databases, external APIs, and so on.

[0606] "Integrated management" means organizing and integrating information collected from different sources and handling it in a unified manner.

[0607] "Machine learning" is a technique that analyzes large amounts of data and extracts patterns and rules from it, and includes algorithms for building predictive models.

[0608] "Analysis" means using given data to reveal the characteristics and patterns contained within it.

[0609] "Personnel allocation" is the process of placing each employee within an organization in the appropriate job or position to allow them to utilize their abilities to the fullest.

[0610] A "growth plan" is a plan that includes specific goals and steps to enhance employees' skills and careers.

[0611] "Automatically generated" refers to a system creating information and plans autonomously with little to no manual intervention from humans.

[0612] "Opinions" refer to the thoughts and feedback that users have regarding the information and plans provided by the system.

[0613] "Visualization" is the process of representing data and results using visual means to make them easier to understand.

[0614] This invention is a system for effectively utilizing employee information and realizing strategic human resource management. Specific embodiments of this system are described below.

[0615] Server role:

[0616] The server collects employee information from multiple sources both inside and outside the company and manages it centrally using a database management system. Specifically, it uses MySQL or PostgreSQL, for example, to store the data in a structured format. The server uses Python to preprocess the data with the pandas library and implements machine learning algorithms using Scikit-learn or TensorFlow. This allows the server to analyze employees' skills and work performance and automatically generate individually optimized personnel placement and growth plans.

[0617] Terminal role:

[0618] The device provides an interface for visually displaying information provided by the server to the user. This interface utilizes a web browser or mobile app, displaying information in natural language using HTML or JavaScript. The device also works with the server to collect user feedback and send it to the server.

[0619] User roles:

[0620] Users make strategic decisions based on the information displayed through their devices. For example, users might query information from their devices, such as "Who is the most suitable person for the next project?", and provide feedback based on the results. This allows the algorithms to be continuously improved, leading to an overall increase in system accuracy.

[0621] Thus, the present invention represents a useful form that streamlines the human resource management process within a company and supports the overall human resource strategy of the organization.

[0622] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0623] Step 1:

[0624] The server collects employee information from multiple sources. These sources include databases from various departments within the company and external business data services. The server periodically retrieves the necessary information from each source using APIs and stores it as input in the database. In this process, the data format is standardized and missing values ​​are imputed. The output is organized employee information.

[0625] Step 2:

[0626] The server prepares organized employee information as input data for a machine learning model. Specifically, it uses the Python pandas library to construct a data frame, extracts features, and performs normalization. This unifies data from different scales, making it possible to analyze it with machine learning algorithms. As output, it generates a dataset suitable as input for a machine learning model.

[0627] Step 3:

[0628] The server analyzes the data using machine learning algorithms. In this step, Scikit-learn and TensorFlow are used to predict the optimal placement of employees based on past performance and skills. The input is the dataset generated in step 2, and the algorithm calculates the optimal placement and training plan for each employee.

[0629] Step 4:

[0630] The terminal visually displays the analysis results provided by the server to the user. The interface of the terminal used by the user is developed using HTML and JavaScript, and presents the results in an easily interpretable format using natural language. The input is the analysis results from the server, and the output is an information display in a format that is easy for the user to understand.

[0631] Step 5:

[0632] Users provide feedback based on information obtained through the terminal interface. For example, a user might input an evaluation such as, "The proposed deployment plan is appropriate." This feedback is then sent back to the server and used as input to update and improve the machine learning model. The output is feedback, which leads to improved prediction accuracy in the next cycle.

[0633] (Application Example 1)

[0634] 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".

[0635] Traditional human resource management systems often fail to adequately utilize employee information, making it difficult to create optimal personnel placement and training plans. This is particularly problematic in manufacturing environments where real-time optimization of work instructions and analysis of the work environment are required. In this context, strategic human resource management based on the skills and performance of individual employees is essential.

[0636] 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.

[0637] In this invention, the server includes means for aggregating and centrally managing employee information from multiple sources, means for analyzing employee ability and performance information using machine learning algorithms, means for automatically generating optimal personnel allocation and training plans for each employee, means for providing work support devices with optimized work instructions in real time, and means for analyzing the work environment using data collected from work support devices. This enables efficient personnel allocation and optimization of work instructions even in manufacturing sites, making it possible to improve overall productivity and employee satisfaction.

[0638] "Information sources" refer to multiple sources or databases that provide data related to employees.

[0639] "Ability" refers to the skills, knowledge, and experience that an employee possesses in order to perform their job duties.

[0640] "Performance information" refers to data that includes the results and evaluations of tasks that employees have completed in the past.

[0641] A "machine learning algorithm" is an automated processing method that analyzes data to discover patterns and regularities, and then makes predictions and decisions.

[0642] "Work support devices" refer to tools and electronic devices used by employees to help them perform their tasks smoothly.

[0643] "Analysis of the work environment" refers to data analysis aimed at improving work efficiency by understanding the conditions of the workplace and the behavior of employees.

[0644] As an embodiment of this invention, a personnel management and work support system for manufacturing sites is proposed. This system mainly consists of three main components: a server, a work support device, and a user interface.

[0645] The server aggregates and centrally manages employee information obtained from numerous sources. This includes employee skills and performance data, as well as historical data from the production line. The server analyzes this data using machine learning algorithms and automatically generates optimal personnel placement and training plans for each employee. Machine learning frameworks such as TensorFlow are used for this analysis.

[0646] Work support devices, such as smart glasses and other wearable devices, receive optimized work instructions and training guides from a server in real time and provide them to the user. This data is available via Bluetooth or Wi-Fi. The work support devices also collect data from the work environment and return it to the server for analysis. This enables efficient operations on-site.

[0647] Users can submit feedback on employee assignments and work instructions via their devices. This feedback is analyzed by the server and used to further improve machine learning algorithms.

[0648] As a concrete example, in a newly introduced production line, when a worker wearing smart glasses performs their duties, the glasses visually display the next steps to be taken in real time. This reduces work errors and improves overall productivity. Furthermore, in response to a prompt such as "Tell me the optimal work tasks for the next production line operation," the server can instantly provide optimized data.

[0649] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0650] Step 1:

[0651] The server aggregates data, including employee skills and performance information, from numerous sources. Input is raw data obtained from various databases and sensor devices. This data is normalized and formatted into a centrally manageable format. Output is an analyzable, integrated dataset.

[0652] Step 2:

[0653] The server feeds the aggregated data into a machine learning algorithm. The input is the integrated dataset generated in Step 1. Based on this data, TensorFlow and other tools are used to analyze each employee's abilities and aptitudes, and an optimal personnel placement and training plan is automatically generated. The output is the proposed plan for each employee.

[0654] Step 3:

[0655] The server sends the generated optimal placement and training plans to the work support device. The input is the plan output in step 2. This information is transmitted in real time via Bluetooth or WiFi and displayed on the work support device (e.g., smart glasses). The output is a visualized work instruction on the user's device.

[0656] Step 4:

[0657] The user executes work instructions received through the work support device. The input is work instructions received in real time. The user performs the work according to the instructions and feeds back data about the work status from the device to the server. The output is work progress data and performance information.

[0658] Step 5:

[0659] The server analyzes user feedback data and continuously improves the machine learning algorithm. The input is the feedback data obtained in step 4. This data is used to retrain the model and tune parameters to improve accuracy and efficiency. The output is the improved next proposed plan and algorithm.

[0660] 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.

[0661] This invention provides a system that enables strategic human resource management that takes user emotions into account while utilizing employee information. This system operates through the collaborative efforts of a server, terminal, user, and emotion engine.

[0662] The server aggregates employee information from diverse data sources and integrates it into an organized database. Using machine learning algorithms, the server analyzes each employee's skills and performance information and automatically generates optimal personnel placement and training plans. These plans are backed by objective, data-driven judgments, supporting companies in efficiently utilizing their human resources.

[0663] The device interacts with the user and enables natural language dialogue. An emotion engine is used to detect emotions in real time from the user's facial expressions, tone of voice, and entered text. For example, if a user provides dissatisfied feedback, the emotion engine recognizes that emotion, and the device can offer a more detailed explanation or suggest additional support.

[0664] Furthermore, users not only input their feedback into their devices, but this feedback is also evaluated along with emotional bias and sent to the server. This feedback helps improve the server's algorithms, contributing to the generation of more accurate personnel placement and training plans. The server also has the ability to report an overview of the team's emotional state to administrators based on the collected emotional data. This makes it possible to monitor the organization's health from an emotional perspective as well.

[0665] Therefore, the present invention provides a comprehensive human resource management system that can effectively utilize employee data while taking emotions into consideration to provide users with optimal information and decision-making support.

[0666] The following describes the processing flow.

[0667] Step 1:

[0668] The server collects employee information from numerous data sources. This includes retrieving employee skills, work history, and performance information via APIs and integrating it into a database.

[0669] Step 2:

[0670] The server inputs integrated data into machine learning algorithms to perform employee skill matching and performance predictions. Based on these results, it generates optimal personnel placement and training plans for each employee.

[0671] Step 3:

[0672] The device notifies the user in natural language of recommended personnel placement and training plans. During this process, it can also answer questions and provide additional information through dialogue with the user.

[0673] Step 4:

[0674] An emotion engine is built into the device to detect the user's emotional state during interactions. For example, if a user expresses dissatisfaction with a question, the emotion engine identifies that emotion and adjusts the response accordingly.

[0675] Step 5:

[0676] Users input their feedback into their device. This feedback includes emotional data analyzed by the emotion engine and is sent to the server.

[0677] Step 6:

[0678] The server uses user feedback and emotional state data to improve its machine learning algorithms. As a result, it improves the accuracy of future personnel placement and training plans.

[0679] Step 7:

[0680] The server analyzes emotional data to assess the emotional state of the entire organization and provides a dashboard that reports to administrators. This allows for a comprehensive understanding of the health of teams within the organization.

[0681] (Example 2)

[0682] 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".

[0683] Effectively utilizing employee data and implementing talent management that considers individual emotions is a crucial challenge for companies. However, traditional systems often struggle to adequately consider emotional aspects, which can prevent the development of optimal personnel placement and training plans. Furthermore, there is a lack of means to monitor and respond to the emotional health of the entire organization in real time.

[0684] 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.

[0685] In this invention, the server includes means for aggregating and centrally managing employee information from multiple sources, means for analyzing employee technical and work performance information using machine learning methods, means for automatically generating optimal personnel placement and training plans for each employee, and means for evaluating emotions and dynamically adjusting responses based on user feedback. This enables strategic personnel management that takes emotions into account and allows for effective monitoring of the emotional health of the entire organization.

[0686] "Employee information" refers to data about individual employees belonging to a company, including skills, work history, and performance evaluations.

[0687] "Information sources" refer to the starting points or systems for collecting data, such as internal databases or external APIs.

[0688] "Machine learning techniques" are a collection of algorithms that enable computers to learn from data and perform specific tasks.

[0689] "Personnel allocation" refers to assigning the most suitable employees to specific projects or positions.

[0690] A "development plan" is a specific growth strategy or program designed to improve employees' skills and careers.

[0691] "Emotional assessment" is the process of identifying and analyzing a user's emotional state, using emotional indicators such as facial expressions and voice.

[0692] "Feedback" refers to the opinions and evaluations that users provide to a system, and is used to improve and adjust the system's performance.

[0693] "Real-time" refers to a processing method in which a system processes data sequentially and provides results immediately.

[0694] This invention provides a system for leveraging employee information to achieve strategic human resource management that takes emotions into account. The system operates with a server, terminals, and users working together to collect and analyze data in real time and provide results.

[0695] The server collects employee information from multiple sources and integrates it into a database. These sources include internal databases and external data services. The collected information is kept consistent using a Python-based data cleansing tool and analyzed using machine learning algorithms with the Scikit-learn library. This analysis evaluates employees' technical skills and performance, and creates optimal personnel placement and training plans. These plans are stored on the server in data format such as JSON.

[0696] The device provides an interactive interface to the user. Using an emotion engine, the device analyzes the user's facial expressions and voice tone to assess their emotional state in real time. This includes information acquisition through the camera and microphone. Based on these results, it can provide appropriate responses when the user provides feedback. For example, it can effectively collect user feedback by using prompts such as, "Please share your thoughts on the current project."

[0697] Users input feedback into the terminal using natural language. This feedback is analyzed by natural language processing tools and evaluated along with emotional bias. This allows the server's algorithm to continuously improve, increasing the accuracy of emotionally balanced plan suggestions.

[0698] As a concrete example, suppose a user provides feedback such as, "I was given a presentation about the introduction of a new tool at a recent meeting, but I'm not confident in my ability to use it." In this case, the system senses this anxiety and suggests appropriate learning resources or a connection to a support team. In this way, it is possible to provide responses that take employee feelings into consideration.

[0699] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0700] Step 1:

[0701] The server collects employee information from internal and external sources. This involves obtaining data on skills, work history, and performance through internal databases and external APIs. The server then uses data cleansing techniques to format this data and integrate it into a centralized database. The output is a dataset in a unified format.

[0702] Step 2:

[0703] The server uses machine learning algorithms to analyze the collected data. The input is the dataset integrated in Step 1. The server utilizes the Scikit-learn library, employing random forest models and support vector machines to predict skill assessments and work performance for each employee. The output generates data for optimal personnel placement and training plans for each employee.

[0704] Step 3:

[0705] The device processes real-time data collected through user interaction. Inputs include the user's facial expressions, tone of voice, and text feedback. The device utilizes an emotion engine to analyze emotions in real time. Specifically, it evaluates the emotional state using an emotion analysis API based on input from the camera and microphone. Output is a feedback response based on the user's emotions.

[0706] Step 4:

[0707] The user enters feedback into the device. This input includes free-form text such as "Thoughts on recent projects." The device analyzes this feedback using natural language processing tools to assess emotional bias. Specifically, it analyzes the input text using a scoring algorithm to determine the positive or negative tendency of the emotion. The output is the individual feedback evaluation result.

[0708] Step 5:

[0709] The server collects user feedback data and uses it to improve the machine learning algorithm. The input is the feedback evaluation results generated in step 4. The server uses this to adjust the algorithm's hyperparameters and improve prediction accuracy. The output is a new version of the improved personnel allocation and training plan. This enables continuous system improvement through feedback.

[0710] (Application Example 2)

[0711] 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".

[0712] Modern organizations are required to effectively manage employee skills and performance, but in addition, it is crucial to consider employee emotions and mental health when planning staffing and development programs. However, traditional systems have made it difficult to analyze emotional data in real time and reflect it in organizational management. This has led to problems such as increased employee stress and decreased work efficiency.

[0713] 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.

[0714] In this invention, the server includes means for aggregating and centrally managing employee information from multiple sources, means for analyzing employee skills and work performance information using machine learning techniques, and means for analyzing employees' emotional states using emotion recognition functions and proposing appropriate personnel assignments and work plans. This enables efficient personnel management that takes employee emotions into consideration.

[0715] "Employee information" refers to information relating to individual employees within an organization, including data such as skills, performance, roles, and emotional state.

[0716] "Information sources" refer to the multiple data providers and platforms used to collect employee information.

[0717] "Centralized management" refers to organizing information gathered from multiple sources in a unified manner and managing it within a single system.

[0718] "Skills" refers to the abilities and knowledge required for an employee to perform a specific task or job.

[0719] "Work results" refer to the performance and evaluation results achieved by employees through their actual work.

[0720] "Machine learning techniques" are algorithmic technologies used to identify data patterns and make predictions, and are used in the analysis of employee information.

[0721] "Emotion recognition function" refers to technology that detects and analyzes emotions from employees' facial expressions and voices.

[0722] "Personnel allocation" refers to the act of improving organizational efficiency by assigning employees to appropriate roles and tasks.

[0723] A "work plan" refers to a schedule of specific activities and tasks formulated to achieve an organization's goals.

[0724] To realize this application, the server aggregates employee information from multiple sources. This involves using a database management system and integrating it with emotion data from facial recognition cameras and microphones. Subsequently, machine learning techniques are used to analyze employee skills and work performance information, and the system automatically generates optimal personnel placement and training plans.

[0725] The terminal interacts with users (employees and administrators) using natural language and provides appropriate information in response to inquiries. Emotion recognition is used in this process. Specifically, the camera and microphone on the terminal detect the user's facial expressions and voice tone in real time, and software that analyzes emotions processes this data. The analyzed emotion data is sent to a server and used for further data analysis.

[0726] Users can input feedback on their work situation and emotions through their devices, and this feedback contributes to improving machine learning methods. In addition, personnel allocation and work plans generated by the server are notified to users, supporting the efficient operation of the organization.

[0727] For example, on a factory line, stressed employees can be identified through emotion recognition, and the system can then reassign them to tasks that reduce their burden. This functionality allows organizations to improve work efficiency while maintaining the emotional well-being of their employees.

[0728] An example of an input prompt for the generating AI model would be a sentence like, "Please generate an algorithm that analyzes the emotional data of employees in a factory and proposes the optimal staffing arrangement to reduce stress."

[0729] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0730] Step 1:

[0731] The server collects employee information from multiple sources. Inputs include each employee's skills, performance, and emotional data. This data is aggregated and stored in a unified database.

[0732] Step 2:

[0733] The server analyzes the aggregated data using machine learning techniques. It uses integrated employee information as input. This allows it to evaluate employee skills and work performance, and generate an output that creates an appropriate personnel allocation plan.

[0734] Step 3:

[0735] The terminal interacts with the user using natural language. Input includes user inquiries and feedback. Based on this, it generates output, providing the user with deployment plans and related information generated by the server.

[0736] Step 4:

[0737] The device uses emotion recognition to detect the user's facial expressions and voice tone in real time. It uses emotion data from the camera and microphone as input. This allows it to analyze the user's emotional state and obtain output to send to the server.

[0738] Step 5:

[0739] The server analyzes user feedback and sentiment data to improve its machine learning methods. Sentiment data and feedback are used as input. This data is analyzed to optimize the learning algorithm, resulting in output aimed at further improving the accuracy of personnel placement.

[0740] Step 6:

[0741] The server uses a generative AI model to apply the generated staffing plan across the entire organization. The input is an optimized staffing plan. This results in an output that implements efficient work allocation while considering emotional well-being.

[0742] 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.

[0743] 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.

[0744] In the above embodiment, an example was given in which the specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and the specific processing may also be performed by the robot 414.

[0745] 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.

[0746] 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.

[0747] 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.

[0748] 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.

[0749] 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.

[0750] 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."

[0751] 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.

[0752] 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.

[0753] 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.

[0754] 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.

[0755] 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.

[0756] 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.

[0757] 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.

[0758] 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.

[0759] 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.

[0760] 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.

[0761] 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.

[0762] 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.

[0763] The following is further disclosed regarding the embodiments described above.

[0764] (Claim 1)

[0765] A means of aggregating and centrally managing employee information from multiple data sources,

[0766] A means of analyzing employee skills and performance information using machine learning algorithms,

[0767] A means to automatically generate optimal personnel placement and training plans for each employee,

[0768] A means of notifying users of the generated plan and receiving feedback,

[0769] A means of improving machine learning algorithms using feedback,

[0770] A system that includes this.

[0771] (Claim 2)

[0772] The system according to claim 1, further comprising means for interacting with a user in natural language and providing information in response to an inquiry.

[0773] (Claim 3)

[0774] The system according to claim 1, further comprising means for updating data in real time and predicting future personnel needs.

[0775] "Example 1"

[0776] (Claim 1)

[0777] A means of collecting and integrating employee information from multiple sources,

[0778] A means of analyzing employee skills and work performance information using machine learning,

[0779] A means for automatically generating appropriate personnel placement and growth plans for each employee,

[0780] A means of providing the generated plan to users and collecting their opinions,

[0781] Methods for improving machine learning based on feedback,

[0782] Means of visualizing information,

[0783] A system that includes this.

[0784] (Claim 2)

[0785] The system according to claim 1, further comprising means for interacting with a user in natural language and displaying information based on a question.

[0786] (Claim 3)

[0787] The system according to claim 1, further comprising means for continuously updating information and predicting future talent demand.

[0788] "Application Example 1"

[0789] (Claim 1)

[0790] A means of aggregating and centrally managing employee information from multiple sources,

[0791] A means of analyzing employee capabilities and performance information using machine learning algorithms,

[0792] A means to automatically generate optimal personnel placement and training plans for each employee,

[0793] A means of notifying users of the generated plan and receiving their feedback,

[0794] A means of improving machine learning algorithms using feedback,

[0795] A means for providing work instructions optimized in real time to a work support device,

[0796] A means of analyzing the work environment using data collected from work support devices,

[0797] A system that includes this.

[0798] (Claim 2)

[0799] The system according to claim 1, further comprising means for interacting with the user in natural language and providing information in response to inquiries.

[0800] (Claim 3)

[0801] The system according to claim 1, further comprising means for updating data in real time and predicting future personnel needs.

[0802] "Example 2 of combining an emotion engine"

[0803] (Claim 1)

[0804] A means of aggregating and centrally managing employee information from multiple sources,

[0805] A means of analyzing employee technical and work performance information using machine learning methods,

[0806] A means for automatically generating optimal personnel allocation and training plans for each employee,

[0807] A means of notifying users of the generated plan and receiving their feedback,

[0808] A means of improving machine learning methods using feedback,

[0809] A means of evaluating emotions and dynamically adjusting responses based on user feedback,

[0810] A system that includes this.

[0811] (Claim 2)

[0812] The system according to claim 1, further comprising means for interacting with a user in natural language, providing information in response to inquiries, and analyzing emotions.

[0813] (Claim 3)

[0814] The system according to claim 1, further comprising means for updating data in real time, predicting future personnel needs, and monitoring the emotional health of the organization based on collected emotional information.

[0815] "Application example 2 when combining with an emotional engine"

[0816] (Claim 1)

[0817] A means of aggregating and centrally managing employee information from multiple sources,

[0818] A means of analyzing employee skills and work performance information using machine learning methods,

[0819] A means for automatically generating optimal personnel allocation and training plans for each employee,

[0820] A means of notifying users of the generated plan and receiving feedback that takes emotions into consideration,

[0821] A means of improving machine learning methods using feedback and sentiment data,

[0822] A method for analyzing employees' emotional states using emotion recognition functions and proposing appropriate personnel allocation and work plans,

[0823] A system that includes this.

[0824] (Claim 2)

[0825] The system according to claim 1, further comprising means for interacting with users in natural language and providing information in response to inquiries.

[0826] (Claim 3)

[0827] The system according to claim 1, further comprising means for updating current information and predicting future personnel demand. [Explanation of symbols]

[0828] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots< / url:> < / url:> < / url:> < / url:>

Claims

1. A means of aggregating and centrally managing employee information from multiple data sources, A means of analyzing employee skills and performance information using machine learning algorithms, A means to automatically generate optimal personnel placement and training plans for each employee, A means of notifying users of the generated plan and receiving feedback, A means of improving machine learning algorithms using feedback, A system that includes this.

2. The system according to claim 1, further comprising means for interacting with a user in natural language and providing information in response to an inquiry.

3. The system according to claim 1, further comprising means for updating data in real time and predicting future personnel needs.

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