system

Abstract

We provide the system. [Solution] Means for collecting information from emails, communication platforms, and digital documents within an organization, A means for inputting the collected information into a natural language processing model to extract job content and skills information, A means for summarizing the extracted work content and skills information and registering it in a database, A system including means for providing an interface that allows users to view and modify the registered information.

Description

Technical Field

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

Background Art

[0002] Patent Document 1 discloses a persona chatbot control method performed by at least one processor, including steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to an explanation of a chatbot character, encoding the prompt, and inputting the encoded prompt into a language model to generate a chatbot utterance as a 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] Conventional human resource visualization systems have the problem that employees need to manually input their work content and possessed skills individually, and due to the labor and time involved, accurate and up-to-date information cannot be maintained. In addition, there is a need to solve the problem that the potential skills possessed by employees are overlooked, making it difficult to make judgments regarding optimal human resource allocation and skill development.

Means for Solving the Problems

[0005] This invention provides a means for collecting information from emails, communication platforms, and digital documents within an organization, and automatically extracting employee job content and skills information using natural language processing. Furthermore, by summarizing the extracted information, registering it in a database, and providing an interface that allows users to review and modify it, it enables the sharing of accurate and up-to-date personnel information within the organization while saving effort.

[0006] "Email" is a digital communication method used to send and receive text and attachments over the internet or other computer networks.

[0007] A "communication platform" is a digital service or application that provides functions such as messaging, conversation, and collaboration.

[0008] A "digital document" is an electronic file containing text and images that is created, edited, stored, and displayed on a computer.

[0009] "Natural language processing" refers to the technologies and methods used to enable computers to understand and appropriately process human language.

[0010] "Job description" refers to the set of tasks, responsibilities, and activities that an employee performs in a specific job.

[0011] "Skills information" refers to data about an individual's specific knowledge and skills, which are used effectively in performing their job duties.

[0012] A "database" is a system for efficiently organizing, managing, and searching structured information.

[0013] An "interface" refers to the screen or means by which a user interacts with a computer system. [Brief explanation of the drawing]

[0014] [Figure 1] It is a conceptual diagram showing an example of the configuration of a data processing system according to the first embodiment. [Figure 2] It is a conceptual diagram showing an example of the main functions of a data processing device and a smart device according to the first embodiment. [Figure 3] It is a conceptual diagram showing an example of the configuration of a data processing system according to the second embodiment. [Figure 4] It is a conceptual diagram showing an example of the main functions of a data processing device and smart glasses according to the second embodiment. [Figure 5] It is a conceptual diagram showing an example of the configuration of a data processing system according to the third embodiment. [Figure 6] It is a conceptual diagram showing an example of the main functions of a data processing device and a headset-type terminal according to the third embodiment. [Figure 7] It is a conceptual diagram showing an example of the configuration of a data processing system according to the fourth embodiment. [Figure 8] It is a conceptual diagram showing an example of the main functions of a data processing device and a robot according to the fourth embodiment. [Figure 9] It shows an emotion map to which a plurality of emotions are mapped. [Figure 10] It shows an emotion map to which a plurality of emotions are mapped. [Figure 11] It is a sequence diagram showing the processing flow of the data processing system in Example 1. [Figure 12] It is a sequence diagram showing the processing flow of the data processing system in Application Example 1. [Figure 13] It is a sequence diagram showing the processing flow of the data processing system in Example 2 when an emotion engine is combined. [Figure 14] It is a sequence diagram showing the processing flow of the data processing system in Application Example 2 when an emotion engine is combined.

MODE FOR CARRYING OUT THE INVENTION

[0015] An example of an embodiment of the system according to the technology of the present disclosure will be described below with reference to the accompanying drawings.

[0016] First, the terms used in the following description will be explained.

[0017] In the following embodiments, the numbered processor (hereinafter simply referred to as "processor") may be a single arithmetic unit or a combination of multiple arithmetic units. Also, the processor may be a single type of arithmetic unit or a combination of multiple types of arithmetic units. Examples of arithmetic units include a CPU (Central Processing Unit), a GPU (Graphics Processing Unit), a GPGPU (General-Purpose computing on Graphics Processing Units), an APU (Accelerated Processing Unit), and the like.

[0018] In the following embodiments, the numbered RAM (Random Access Memory) is a memory in which information is temporarily stored and is used as a work memory by the processor.

[0019] In the following embodiments, the numbered storage is one or more non-volatile storage devices that store various programs and various parameters, etc. Examples of non-volatile storage devices include flash memory (SSD (Solid State Drive)), magnetic disks (e.g., hard disks), or magnetic tapes, etc.

[0020] 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).

[0021] 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."

[0022] [First Embodiment]

[0023] Figure 1 shows an example of the configuration of the data processing system 10 according to the first embodiment.

[0024] 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.

[0025] 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).

[0026] 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.

[0027] 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.

[0028] 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.

[0029] 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.

[0030] Figure 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0031] 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.

[0032] 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.

[0033] 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.

[0034] 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".

[0035] This invention aims to build a system for efficiently and accurately collecting and visualizing human resource information within an organization. This system is operated through the cooperation of servers, terminals, and users.

[0036] Processing performed by the server

[0037] The server first periodically collects emails, message histories from communication platforms, and digital documents within the organization. Since this requires data transmission from each terminal, the server includes an interface for automatically receiving this data.

[0038] Next, the server preprocesses the collected data. Preprocessing includes normalizing, tokenizing, and removing irrelevant information from the data to prepare it for smooth natural language processing.

[0039] The server inputs the formatted data into a natural language processing model. The model used here is pre-trained and designed to identify job duties and skills. The server analyzes the output from the model and extracts each employee's job duties and skills.

[0040] Next, the server summarizes the extracted information, organizes it clearly, and registers it in the database. This data is then shared within the organization as the latest personnel information.

[0041] Terminal and user roles

[0042] The terminal is a device used by users to check instructions and results from the server. Users can always verify that their information is accurately reflected and submit correction requests to the server as needed. These correction requests can be easily submitted through the interface, thereby improving user convenience.

[0043] Specific example

[0044] For example, suppose employee B communicates with the team through numerous emails and chats while managing a project. In this case, the server collects all of these communication records and extracts information related to project management skills and team leadership.

[0045] As a result, the server automatically registers information indicating that employee B has project management experience and strong leadership skills. This information is also used for appropriate personnel placement in job placement projects.

[0046] This invention enables the rapid and accurate digitization of employee information, optimizing the utilization of skills across the entire organization.

[0047] The following describes the processing flow.

[0048] Step 1:

[0049] The server collects emails, communication platform logs, and digital documents from each terminal. This includes scheduling that data is automatically sent to the server periodically.

[0050] Step 2:

[0051] The server preprocesses the collected data. Unnecessary headers and footers are removed from emails, and chat logs are similarly tokenized to remove noise. This prepares data suitable for natural language processing models.

[0052] Step 3:

[0053] The server feeds the prepared data into a natural language processing model. The model recognizes keywords and phrases in the text and analyzes them in relation to the work content. In this process, information linked to specific skills, roles, and responsibilities is extracted.

[0054] Step 4:

[0055] The server organizes and summarizes the information obtained through analysis. The server prioritizes the information, considering its importance and frequency of occurrence, before registering it in the personnel database. This data is stored as individual employee profiles.

[0056] Step 5:

[0057] Users can view their profile information on their device. The interface presents a summary of their job responsibilities and skills, allowing users to verify the accuracy of the information.

[0058] Step 6:

[0059] If a user determines that their profile information needs correction, they send a correction request to the server via their device. The server uses this feedback to update the database and improve the accuracy of the information by incorporating it into the next data analysis.

[0060] (Example 1)

[0061] 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."

[0062] Managing personnel information within an organization presents challenges, including the time-consuming process of collecting and organizing data, and the significant effort required to extract job descriptions and skills information. Furthermore, maintaining the accuracy of the collected information and updating it promptly can be difficult. Therefore, there is a need for a system that efficiently and accurately collects and analyzes personnel information within an organization, and allows users to easily view and modify that information.

[0063] 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.

[0064] In this invention, the server includes means for acquiring data from information communication means and document management means within the organization; means for preprocessing the acquired data and converting it into a format suitable for natural language processing; means for inputting the converted data into a generation AI model to identify job content and skills information; means for summarizing the identified job content and skills information and storing it in an information storage device; and means for providing a viewing device that allows users to view and modify the stored information. This makes it possible to efficiently collect personnel information within the organization, provide highly accurate information, and enable users to make rapid corrections.

[0065] "Information and communication means" refers to technologies and systems used within an organization for sending and receiving information, such as email and messaging platforms.

[0066] "Means of document management" refers to technologies and systems for storing, classifying, and searching documents and digital files.

[0067] "Means of acquiring data" refers to technologies and systems for automatically collecting necessary data from diverse sources.

[0068] "Preprocessing" refers to the process of converting raw data into a format suitable for analysis or model input, and includes noise reduction and data normalization.

[0069] "Natural language processing" refers to the ability of computers to understand and process human language, and is a technology used for text analysis and information extraction.

[0070] A "generative AI model" refers to an artificial intelligence model that uses a pre-trained algorithm to generate meaningful information from input data.

[0071] "Job description" refers to information that encompasses the tasks and responsibilities performed by a specific position or person within an organization.

[0072] "Skills information" refers to information about the knowledge and skills an individual possesses when performing their job duties.

[0073] "Information storage device" refers to a physical or digital storage medium used to store and manage data as needed.

[0074] A "browsing device" refers to a device or interface used by users to display and interact with digital information.

[0075] The present invention aims to accurately grasp job content and skills information by providing a system for efficiently collecting and analyzing information within an organization. To achieve this, the following hardware and software are used.

[0076] The server automatically retrieves data from information and communication methods and document management systems within the organization. At this stage, data can be collected, for example, through APIs or dedicated interfaces. Next, the server normalizes and removes noise from the data using Python's NLTK library or similar tools during the preprocessing stage.

[0077] Next, the server analyzes the pre-processed data using a pre-trained natural language processing technique (e.g., the BERT model) as a generative AI model. This process identifies job descriptions and skills information. The processed results are summarized and stored in a database, which is an information storage device. For example, a database such as PostgreSQL is used for storing and managing the information.

[0078] Users can access information stored in the database using a browsing device provided by the server via their device. If the information needs correction, they can submit a correction request through a dedicated interface. This interface is typically implemented as a front-end application accessible via a web browser.

[0079] For example, if employee A receives a large volume of emails related to a project, this system can be used to analyze that email data on the server, identify employee A's project management skills, and reflect this in the latest employee information.

[0080] An example of a prompt message that can be input into the generating AI model is, "Based on internal communication records, extract the project management capabilities and leadership skills of a specific employee."

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

[0082] Step 1:

[0083] The server retrieves data from information and communication methods within the organization (e.g., email, messaging platforms). This retrieval process automatically collects data periodically using a dedicated API. The input is raw communication data. The output is a collection of raw data aggregated on the server side. Specifically, the server accesses each data source at specified time intervals and retrieves updated information.

[0084] Step 2:

[0085] The server preprocesses the acquired data. The input is the raw data collected in Step 1. The server normalizes and tokenizes this data to standardize its format and remove unnecessary noise. It uses the Python NLTK library to cleanse the data and convert it into a format suitable for analysis. The output is a preprocessed and structured dataset. Specifically, the server divides each data entry into words and filters out less important information.

[0086] Step 3:

[0087] The server inputs pre-processed data into a generating AI model to extract job description and skills information. The input is the output dataset from step 2. The server uses natural language processing techniques, such as a pre-trained BERT model, to analyze the meaning of the data. The output is a list of extracted job description and skills information. Specifically, the server sends data to the AI ​​model and analyzes the results output by the model.

[0088] Step 4:

[0089] The server summarizes the extracted information and stores it in an information storage device. The input is the information list obtained from step 3. The server uses a summarization algorithm to organize the information concisely and registers it in a PostgreSQL database. The output is an information storage device organized by employee. Specifically, the server stores the summarized information in the database with an index to improve searchability.

[0090] Step 5:

[0091] The user accesses information via a viewing device provided by the server using a terminal. The input consists of information registered in a database. The terminal displays an interface for the user to view the information and request corrections if errors are found. The output is either the confirmed information or the submission of a correction request. Specifically, the user accesses the interface using a web browser and requests updates through the information viewing or correction fields.

[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] In modern manufacturing environments, effectively utilizing worker skill information and assigning tasks optimally is essential for improving production efficiency. However, traditional methods often failed to fully leverage workers' experience and abilities. In particular, there was a lack of systems that automatically analyzed worker skill information and assigned tasks appropriately. This made it difficult to allocate personnel effectively, leading to decreased production efficiency.

[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 collecting information on electronic communications and communication platforms and digital documents within an organization; means for extracting work content and skill information from the collected information using natural language processing technology; and means for generating and presenting optimal work assignments based on the characteristics of workers. This enables efficient work allocation that utilizes the skills of workers.

[0097] "Internal electronic communications" refers to the general term for information transmission methods such as email and instant communication services used internally within companies and organizations.

[0098] A "communication platform" is an online service or application for exchanging messages and data, facilitating communication both within and outside an organization.

[0099] "Digital documents" refer to documents and files created and stored electronically, including reports and presentation materials.

[0100] "Natural language processing technology" refers to the technology that enables computers to understand and process human language, and is used for text analysis and meaning extraction.

[0101] "Job description and skills information" refers to information about an individual's activities related to their job and the skills and abilities they possess.

[0102] A "large-scale information storage medium" is a digital storage system for efficiently storing large amounts of data, and cloud storage is an example of this.

[0103] "Means of providing a display device" refers to an interface or device that allows a user to visually confirm information.

[0104] A "means for generating work assignments" refers to a system that plans and proposes the optimal division of work based on the skill data of the workers.

[0105] To implement this invention, the server must first collect information from the organization's electronic communication and communication platforms, as well as from digital documents. The server periodically collects this information and converts it into a suitable format for analysis using natural language processing techniques. Specifically, the server performs text normalization, removal of irrelevant information, and tokenization as data preprocessing.

[0106] Next, the server uses natural language processing technology to extract job content and skills information from the collected data. This process utilizes a trained generative AI model to analyze information about each worker's skills. Through this technology, it is possible to effectively understand what kinds of tasks workers excel at and what skills they possess.

[0107] Furthermore, the terminal device uses information obtained from the server to visually present it to the user. A user interface is used for this, allowing the user to review their information and request corrections as needed. Such interfaces play a crucial role in streamlining the workflow.

[0108] Users can use a smartphone application to review suggested work assignments and contribute to their optimization. For example, a factory manager can use their smartphone to analyze workers' skills in real time, automatically receive efficient work assignments, and quickly implement work assignments.

[0109] An example of a prompt for a generative AI model would be, "To improve the efficiency of robot operations, please suggest the optimal work assignment based on worker skill information." This is expected to maximize productivity on the factory floor.

[0110] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0111] Step 1:

[0112] The server collects message data from the organization's electronic communications and communication platforms. In this step, data from each endpoint is received via an interface and temporarily stored in a database. The input is email and message logs, and the output is the temporarily stored dataset.

[0113] Step 2:

[0114] The server preprocesses the data collected in Step 1. Specifically, it performs processes such as data normalization, filtering of unnecessary information, and tokenization. The input for this step is raw message data, and the output is formatted text data. This process converts the data into a format suitable for the generative AI model.

[0115] Step 3:

[0116] The server inputs pre-processed data into a generative AI model to extract job content and skills information. The input is the formatted text data obtained in step 2, and the output is the analyzed job and skills metadata. This generative AI model uses a pre-trained model to convert natural language text into semantically structured data.

[0117] Step 4:

[0118] The server creates skill profiles for each worker based on the extracted information and registers them in a large-scale data storage medium. The input is metadata obtained in step 3, and the output is a database entry that can be shared across the entire organization. This information is organized to allow for easy understanding of each worker's strengths and aptitudes.

[0119] Step 5:

[0120] The terminal visually displays the worker's skill information and job description through a user interface. Input is database entries retrieved from the server, and output is informed data displayed on the display device. Users can review this information and request corrections as needed.

[0121] Step 6:

[0122] Users use their smartphones to view suggested work assignments and incorporate them into their actual operations. Input is skill information provided by the terminal, and output is the optimal placement plan for the worker. This leads to increased efficiency in on-site operations.

[0123] 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.

[0124] This invention further develops conventional personnel information visualization systems by incorporating an emotion engine and also incorporating user emotion information, thereby providing a more comprehensive employee profile. This system operates based on the interaction between the server, terminals, and users.

[0125] Processing performed by the server

[0126] The server first periodically collects information from email, communication platforms, and digital documents within the organization. This includes a configurable filtering function that can be customized to the user's preferences, allowing only critical, business-related information to be collected.

[0127] Next, the server processes this information through a natural language processing model and an emotion engine to simultaneously extract job content, skills information, and emotional information. While the natural language processing model analyzes job content and skills information, the emotion engine determines emotional states from the tone and expressions within the text. For example, emotions such as stress and satisfaction are analyzed from emails and chat messages.

[0128] The server then summarizes this information and generates an integrated profile in the database. This profile includes not only specific job skills information but also emotional states, making it useful for improving employee mental health and the work environment.

[0129] Terminal and user roles

[0130] The device provides an interface that allows users to view their individual profile information. Through the device, users can check their own profile, which includes work skills and emotional information. This profile information is updated regularly, allowing users to provide feedback for self-improvement.

[0131] Specific example

[0132] For example, if the server collects emails about project meetings that employee C frequently attends, it can not only understand C's project management skills but also obtain emotional information related to stress from the meeting content. Employee C, as the user, can then check their own profile through their terminal and take action to seek support within the company if necessary.

[0133] This invention enables organizations to perform comprehensive human resource management using both employee skill information and emotional data, contributing to improved teamwork and individual performance.

[0134] The following describes the processing flow.

[0135] Step 1:

[0136] The server collects emails, communication platform messages, and digital documents from each terminal. The server uses filtering functions to select work-related information and protects the data with privacy and security in mind.

[0137] Step 2:

[0138] The server preprocesses the collected data. Specifically, it removes unnecessary information from emails, tokenizes text data to convert it into a format suitable for natural language processing, removes data noise, and formats it into a format that is easy for the sentiment engine to process.

[0139] Step 3:

[0140] The server inputs pre-processed data into a natural language processing model and an emotion engine for analysis. The natural language processing model extracts job content and skills information, while the emotion engine recognizes the user's emotions from the tone and keywords of the text. For example, it identifies emotions such as "nervous" or "satisfied."

[0141] Step 4:

[0142] The server integrates extracted job description, skills information, and emotional information, and registers it in the database as a comprehensive employee profile. This profile is regularly updated to ensure it provides the most up-to-date information.

[0143] Step 5:

[0144] Users can view their own profiles on their devices. The information displayed through the interface includes not only work skills but also recent emotional tendencies, allowing users to understand their own condition and work environment.

[0145] Step 6:

[0146] Users can choose actions to further utilize their profile information. For example, they can request support for stress management or skill improvement based on emotional feedback. They can also notify the server of correction requests if there are any errors.

[0147] (Example 2)

[0148] 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".

[0149] There is a need to provide an information system that comprehensively understands the work performance capabilities and emotional state of employees within an organization, enabling efficient human resource management and individualized mental health support. However, conventional systems cannot simultaneously collect and analyze work information and emotional information, resulting in a lack of crucial information for human resource management.

[0150] 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.

[0151] In this invention, the server includes means for collecting data from internal information sources within the organization, means for analyzing the collected data and extracting business information and technical information using a natural language processing model, and means for determining the emotional state of text via an emotion analysis engine. This makes it possible to generate an integrated profile of employees' business information and emotional state, enabling comprehensive human resource management and mental health support.

[0152] "Means of data collection" refers to methods and devices for obtaining necessary information from sources such as email, communication platforms, and digital documents within an organization.

[0153] A "natural language processing model" is an artificial intelligence technology used to extract business and technical information from text data. It analyzes language and extracts information in a format that humans can understand.

[0154] An "emotion analysis engine" is software that determines the emotional state of a person based on the tone and expression within text data.

[0155] "Means of profile generation" refers to methods or devices for integrating analyzed business information and emotional information and storing them in a database as a single record.

[0156] "Means of providing an interface" refers to methods and technologies that provide screens or operating systems that allow users to view profile information and provide feedback for improvement.

[0157] "Contributing to mental health management" means using analyzed emotional state information to help organizations take measures to improve the mental health of their employees.

[0158] This invention is a system for simultaneously understanding business information and emotional states within an organization. This system is implemented through the interaction of a server, terminals, and users.

[0159] The server collects data from email, communication platforms, and digital documents within the organization. The server uses a natural language processing model and sentiment analysis engine on the collected data. The natural language processing model performs robust text analysis, extracting business and technical information. The sentiment analysis engine, at the same time, determines emotional states from the same dataset, revealing emotional indices such as stress and satisfaction. The analyzed information is integrated by the server and stored in a database. This database provides a profile of each employee, combining business skills and emotional information.

[0160] The device provides users with an interface to view this profile information. This interface is designed to allow users to check their own work skills and emotional state in real time. For example, when a user checks their profile, they may notice that their stress level is high and decide to seek help from workplace support. In this way, the profile information provides users with valuable feedback that encourages problem-solving and improvement actions.

[0161] As an example of a prompt, the AI ​​model might be instructed to "Analyze employee stress and satisfaction levels from internal email and communication chats. Consider specific job skills as well, and generate an integrated profile." This prompt clearly demonstrates how the system actually works.

[0162] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0163] Step 1:

[0164] The server periodically scans emails, communication platforms, and digital documents within the organization to collect data. During the collection process, filtering based on user preferences is applied to limit the information to business-related data. Input is unorganized text data, and output is a data stream organized for analysis. Specifically, it connects to email servers and communication tools via APIs to retrieve data.

[0165] Step 2:

[0166] The server sends the collected data to a natural language processing model for analysis. This model performs keyword extraction and semantic analysis of sentences. The input is the text data obtained in step 1, and the output is a list of business information and technical information. Specifically, tokenization and syntactic analysis are performed for each data entry.

[0167] Step 3:

[0168] The server processes the same dataset through an emotion analysis engine to determine the emotional state within the text. This process assigns emotion tags such as positive and negative. The input is the text data obtained in step 1, and the output is a list of emotion tags indicating the emotional state. Specifically, the emotion analysis algorithm is executed based on tokens and syntactic information obtained through natural language processing.

[0169] Step 4:

[0170] The server integrates business, technical, and emotional information to generate profiles for individual users. The input is a list, which is the output of steps 2 and 3, and the output is the integrated profile stored in the database. Specifically, it aggregates data for each user and forms structured records.

[0171] Step 5:

[0172] The device provides an interface that allows the user to view their own profile. The input is the profile data generated in step 4, and the output is the information displayed on the user's screen. Specifically, it presents information through an intuitive UI using a web application or mobile application.

[0173] Step 6:

[0174] The user analyzes profile information through the device, receives feedback, and decides on improvement actions. The input is the profile information displayed on the interface, and the output is a specific list of improvement actions and feedback. Specifically, the user interprets the displayed data and chooses to contact internal support organizations or other relevant parties as needed.

[0175] (Application Example 2)

[0176] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as the "server," and the smart device 14 will be referred to as the "terminal."

[0177] In modern manufacturing environments, worker efficiency and satisfaction significantly impact productivity. However, understanding the emotional state of individual workers and implementing appropriate work assignments and environmental improvements is challenging. Conventional systems focused on visualizing skills, failing to incorporate emotional states into comprehensive human resource management. This invention aims to solve the problem of improving organizational management and the work environment by monitoring workers' emotional states in real time.

[0178] 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.

[0179] In this invention, the server includes means for collecting information from an information and communication medium, means for inputting the collected information into natural language processing technology to extract business-related information and technical information, and means for analyzing emotional information together with the extracted business-related information and technical information. This enables real-time monitoring of workers' emotional states and allows for optimal work assignments and environmental improvements.

[0180] "Information and communication media" is a general term for various channels used to transmit and collect data, such as email, communication platforms, and digital documents.

[0181] "Natural language processing technology" is a technology that allows computers to understand, analyze, and generate human language, and is a method that can extract meaning and structure from text.

[0182] "Work-related information" refers to information related to specific tasks or projects that workers are currently working on, including their content and progress.

[0183] "Technical information" refers to technical information such as the skills and knowledge possessed by workers, and serves as a standard for evaluating their ability to perform their jobs.

[0184] "Emotional information" refers to information about the psychological state and emotions of workers, and includes a variety of emotional data, such as stress and satisfaction levels.

[0185] "Real-time monitoring" refers to a state where information can be analyzed immediately and the current situation can be quickly grasped.

[0186] "Integrating into a database" refers to a method of centrally gathering different information and storing it in data storage that allows for easy searching and referencing.

[0187] "Means for users to view and evaluate information" refers to a user interface that allows workers and administrators to review collected and analyzed information and consider improvements.

[0188] This invention is a system for improving operational efficiency and the work environment in factories and offices by analyzing employee emotional information. In this system, a server plays a central role.

[0189] The server periodically collects information from information and communication media such as email and chat. This requires the ability to convert various data formats into formats suitable for natural language processing technology, and also performs preprocessing for analysis of the collected information. In this process, libraries such as TextBlob and NLTK are used as natural language processing technologies to extract business-related information, technical information, and even sentiment information from the text.

[0190] The emotional information obtained is analyzed in real time, and the current psychological state of employees is understood through an emotional engine. This information is integrated into a database to support managers in making appropriate improvements to the factory environment and working conditions. For example, if an employee's stress level is high, task redistribution or encouraging breaks may be considered. This process improves the overall work efficiency of the organization.

[0191] Users can view and evaluate the analyzed information through an interface provided via their device. This interface is an important tool for employees to understand their own emotional state and, if necessary, request action from their managers.

[0192] An example of a prompt statement generated using a generative AI model is shown below.

[0193] "Analyze the emotional sentiment of the following communication data from an employee in a manufacturing environment. Provide insights for improving their work condition based on the analysis."

[0194] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0195] Step 1:

[0196] The server collects data from information and communication media such as email and chat. It uses information acquired from various devices within the organization, with text data being the primary input. This allows for the creation of a dataset that reflects the real-time communication situation within the organization.

[0197] Step 2:

[0198] The server preprocesses the collected text data and converts it into a format suitable for natural language processing techniques. It filters out unnecessary parts from the text data and provides a unified text format as output. This process includes text normalization and tokenization.

[0199] Step 3:

[0200] The server inputs pre-processed text data into natural language processing technology and an emotion engine to extract business-related information, technical information, and emotion information. Specifically, it uses libraries such as TextBlob and NLTK to derive specific keywords and emotion scores from the input text. This allows it to output a set of extracted information.

[0201] Step 4:

[0202] The server stores the extracted information in an integrated database. This database forms work-related profiles for each employee, making it easy to search and refer to information. The received analytical information is converted into a database format and integrated into the database storage.

[0203] Step 5:

[0204] Users, specifically administrators, view and evaluate integrated profiles via their devices. The interface on the device visualizes sentiment scores and metrics of work efficiency, allowing administrators to instantly grasp the situation. This enables them to formulate environmental improvement measures based on the outputted information.

[0205] 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.

[0206] 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.

[0207] 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.

[0208] [Second Embodiment]

[0209] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0210] 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.

[0211] 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).

[0212] 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.

[0213] 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.

[0214] 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).

[0215] 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.

[0216] 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.

[0217] 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.

[0218] 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.

[0219] 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.

[0220] 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".

[0221] This invention aims to build a system for efficiently and accurately collecting and visualizing human resource information within an organization. This system is operated through the cooperation of servers, terminals, and users.

[0222] Processing performed by the server

[0223] The server first periodically collects emails, message histories from communication platforms, and digital documents within the organization. Since this requires data transmission from each terminal, the server includes an interface for automatically receiving this data.

[0224] Next, the server preprocesses the collected data. Preprocessing includes normalizing, tokenizing, and removing irrelevant information from the data to prepare it for smooth natural language processing.

[0225] The server inputs the formatted data into a natural language processing model. The model used here is pre-trained and designed to identify job duties and skills. The server analyzes the output from the model and extracts each employee's job duties and skills.

[0226] Next, the server summarizes the extracted information, organizes it clearly, and registers it in the database. This data is then shared within the organization as the latest personnel information.

[0227] Terminal and user roles

[0228] The terminal is a device used by users to check instructions and results from the server. Users can always verify that their information is accurately reflected and submit correction requests to the server as needed. These correction requests can be easily submitted through the interface, thereby improving user convenience.

[0229] Specific example

[0230] For example, suppose employee B communicates with the team through numerous emails and chats while managing a project. In this case, the server collects all of these communication records and extracts information related to project management skills and team leadership.

[0231] As a result, the server automatically registers information indicating that employee B has project management experience and strong leadership skills. This information is also used for appropriate personnel placement in job placement projects.

[0232] This invention enables the rapid and accurate digitization of employee information, optimizing the utilization of skills across the entire organization.

[0233] The following describes the processing flow.

[0234] Step 1:

[0235] The server collects emails, communication platform logs, and digital documents from each terminal. This includes scheduling that data is automatically sent to the server periodically.

[0236] Step 2:

[0237] The server preprocesses the collected data. Unnecessary headers and footers are removed from emails, and chat logs are similarly tokenized to remove noise. This prepares data suitable for natural language processing models.

[0238] Step 3:

[0239] The server feeds the prepared data into a natural language processing model. The model recognizes keywords and phrases in the text and analyzes them in relation to the work content. In this process, information linked to specific skills, roles, and responsibilities is extracted.

[0240] Step 4:

[0241] The server organizes and summarizes the information obtained through analysis. The server prioritizes the information, considering its importance and frequency of occurrence, before registering it in the personnel database. This data is stored as individual employee profiles.

[0242] Step 5:

[0243] Users can view their profile information on their device. The interface presents a summary of their job responsibilities and skills, allowing users to verify the accuracy of the information.

[0244] Step 6:

[0245] If a user determines that their profile information needs correction, they send a correction request to the server via their device. The server uses this feedback to update the database and improve the accuracy of the information by incorporating it into the next data analysis.

[0246] (Example 1)

[0247] 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."

[0248] Managing personnel information within an organization presents challenges, including the time-consuming process of collecting and organizing data, and the significant effort required to extract job descriptions and skills information. Furthermore, maintaining the accuracy of the collected information and updating it promptly can be difficult. Therefore, there is a need for a system that efficiently and accurately collects and analyzes personnel information within an organization, and allows users to easily view and modify that information.

[0249] 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.

[0250] In this invention, the server includes means for acquiring data from information communication means and document management means within the organization; means for preprocessing the acquired data and converting it into a format suitable for natural language processing; means for inputting the converted data into a generation AI model to identify job content and skills information; means for summarizing the identified job content and skills information and storing it in an information storage device; and means for providing a viewing device that allows users to view and modify the stored information. This makes it possible to efficiently collect personnel information within the organization, provide highly accurate information, and enable users to make rapid corrections.

[0251] "Information and communication means" refers to technologies and systems used within an organization for sending and receiving information, such as email and messaging platforms.

[0252] "Means of document management" refers to technologies and systems for storing, classifying, and searching documents and digital files.

[0253] "Means of acquiring data" refers to technologies and systems for automatically collecting necessary data from diverse sources.

[0254] "Preprocessing" refers to the process of converting raw data into a format suitable for analysis or model input, and includes noise reduction and data normalization.

[0255] "Natural language processing" refers to the ability of computers to understand and process human language, and is a technology used for text analysis and information extraction.

[0256] A "generative AI model" refers to an artificial intelligence model that uses a pre-trained algorithm to generate meaningful information from input data.

[0257] "Job description" refers to information that encompasses the tasks and responsibilities performed by a specific position or person within an organization.

[0258] "Skills information" refers to information about the knowledge and skills an individual possesses when performing their job duties.

[0259] "Information storage device" refers to a physical or digital storage medium used to store and manage data as needed.

[0260] A "browsing device" refers to a device or interface used by users to display and interact with digital information.

[0261] The present invention aims to accurately grasp job content and skills information by providing a system for efficiently collecting and analyzing information within an organization. To achieve this, the following hardware and software are used.

[0262] The server automatically retrieves data from information and communication methods and document management systems within the organization. At this stage, data can be collected, for example, through APIs or dedicated interfaces. Next, the server normalizes and removes noise from the data using Python's NLTK library or similar tools during the preprocessing stage.

[0263] Next, the server analyzes the pre-processed data using a pre-trained natural language processing technique (e.g., the BERT model) as a generative AI model. This process identifies job descriptions and skills information. The processed results are summarized and stored in a database, which is an information storage device. For example, a database such as PostgreSQL is used for storing and managing the information.

[0264] Users can access information stored in the database using a browsing device provided by the server via their device. If the information needs correction, they can submit a correction request through a dedicated interface. This interface is typically implemented as a front-end application accessible via a web browser.

[0265] For example, if employee A receives a large volume of emails related to a project, this system can be used to analyze that email data on the server, identify employee A's project management skills, and reflect this in the latest employee information.

[0266] An example of a prompt message that can be input into the generating AI model is, "Based on internal communication records, extract the project management capabilities and leadership skills of a specific employee."

[0267] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0268] Step 1:

[0269] The server retrieves data from information and communication methods within the organization (e.g., email, messaging platforms). This retrieval process automatically collects data periodically using a dedicated API. The input is raw communication data. The output is a collection of raw data aggregated on the server side. Specifically, the server accesses each data source at specified time intervals and retrieves updated information.

[0270] Step 2:

[0271] The server preprocesses the acquired data. The input is the raw data collected in Step 1. The server normalizes and tokenizes this data to standardize its format and remove unnecessary noise. It uses the Python NLTK library to cleanse the data and convert it into a format suitable for analysis. The output is a preprocessed and structured dataset. Specifically, the server divides each data entry into words and filters out less important information.

[0272] Step 3:

[0273] The server inputs pre-processed data into a generating AI model to extract job description and skills information. The input is the output dataset from step 2. The server uses natural language processing techniques, such as a pre-trained BERT model, to analyze the meaning of the data. The output is a list of extracted job description and skills information. Specifically, the server sends data to the AI ​​model and analyzes the results output by the model.

[0274] Step 4:

[0275] The server summarizes the extracted information and stores it in an information storage device. The input is the information list obtained from step 3. The server uses a summarization algorithm to organize the information concisely and registers it in a PostgreSQL database. The output is an information storage device organized by employee. Specifically, the server stores the summarized information in the database with an index to improve searchability.

[0276] Step 5:

[0277] The user accesses information via a viewing device provided by the server using a terminal. The input consists of information registered in a database. The terminal displays an interface for the user to view the information and request corrections if errors are found. The output is either the confirmed information or the submission of a correction request. Specifically, the user accesses the interface using a web browser and requests updates through the information viewing or correction fields.

[0278] (Application Example 1)

[0279] 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."

[0280] In modern manufacturing environments, effectively utilizing worker skill information and assigning tasks optimally is essential for improving production efficiency. However, traditional methods often failed to fully leverage workers' experience and abilities. In particular, there was a lack of systems that automatically analyzed worker skill information and assigned tasks appropriately. This made it difficult to allocate personnel effectively, leading to decreased production efficiency.

[0281] 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.

[0282] In this invention, the server includes means for collecting information on electronic communications and communication platform digital documents within an organization, means for extracting business content and skill information from the collected information by natural language processing technology, and means for generating and presenting an optimal work assignment based on the characteristics of workers. As a result, an efficient work arrangement that utilizes the skills of workers becomes possible.

[0283] "Electronic communications within an organization" is a general term for information transmission means such as e-mails and instant communication services used internally in enterprises and organizations.

[0284] "Communication platform" is an online service or application for exchanging messages and data, which facilitates smooth communication inside and outside the organization.

[0285] "Digital document" refers to documents and files created and stored electronically, including reports and presentation materials.

[0286] "Natural language processing technology" is a technology for computers to understand and process human language, and is used for text analysis and meaning extraction.

[0287] "Business content and skill information" refers to information on the activities related to an individual's duties and the technologies and abilities that the person possesses.

[0288] "Large-scale information storage medium" is a digital storage system for efficiently storing a large amount of data, and cloud storage is an example of it.

[0289] "Means for providing a display device" refers to an interface or device that enables a user to visually confirm information.

[0290] A "means for generating work assignments" refers to a system that plans and proposes the optimal division of work based on the skill data of the workers.

[0291] To implement this invention, the server must first collect information from the organization's electronic communication and communication platforms, as well as from digital documents. The server periodically collects this information and converts it into a suitable format for analysis using natural language processing techniques. Specifically, the server performs text normalization, removal of irrelevant information, and tokenization as data preprocessing.

[0292] Next, the server uses natural language processing technology to extract job content and skills information from the collected data. This process utilizes a trained generative AI model to analyze information about each worker's skills. Through this technology, it is possible to effectively understand what kinds of tasks workers excel at and what skills they possess.

[0293] Furthermore, the terminal device uses information obtained from the server to visually present it to the user. A user interface is used for this, allowing the user to review their information and request corrections as needed. Such interfaces play a crucial role in streamlining the workflow.

[0294] Users can use a smartphone application to review suggested work assignments and contribute to their optimization. For example, a factory manager can use their smartphone to analyze workers' skills in real time, automatically receive efficient work assignments, and quickly implement work assignments.

[0295] An example of a prompt for a generative AI model would be, "To improve the efficiency of robot operations, please suggest the optimal work assignment based on worker skill information." This is expected to maximize productivity on the factory floor.

[0296] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0297] Step 1:

[0298] The server collects message data from the organization's electronic communications and communication platforms. In this step, data from each endpoint is received via an interface and temporarily stored in a database. The input is email and message logs, and the output is the temporarily stored dataset.

[0299] Step 2:

[0300] The server preprocesses the data collected in Step 1. Specifically, it performs processes such as data normalization, filtering of unnecessary information, and tokenization. The input for this step is raw message data, and the output is formatted text data. This process converts the data into a format suitable for the generative AI model.

[0301] Step 3:

[0302] The server inputs pre-processed data into a generative AI model to extract job content and skills information. The input is the formatted text data obtained in step 2, and the output is the analyzed job and skills metadata. This generative AI model uses a pre-trained model to convert natural language text into semantically structured data.

[0303] Step 4:

[0304] The server creates skill profiles for each worker based on the extracted information and registers them in a large-scale data storage medium. The input is metadata obtained in step 3, and the output is a database entry that can be shared across the entire organization. This information is organized to allow for easy understanding of each worker's strengths and aptitudes.

[0305] Step 5:

[0306] The terminal visually displays the operator's skill information and work content through the user interface. The input is a database entry obtained from the server, and the output is informed data on the display device. The user can view this information and request corrections if necessary.

[0307] Step 6:

[0308] The user uses a smartphone to view the proposed work assignment and reflect it in the actual operation. The input is the skill information provided by the terminal, and the output is the optimal arrangement plan for the operator. This realizes the improvement of work efficiency on-site.

[0309] Furthermore, an emotion engine for estimating the user's emotion may be combined. That is, the specific processing unit 290 may estimate the user's emotion using the emotion identification model 59 and perform specific processing using the user's emotion.

[0310] The present invention further develops the conventional human resource information visualization system and incorporates an emotion engine, and is a system that provides a more comprehensive employee profile by incorporating the user's emotion information. This system is operated based on the interaction between the server, the terminal, and the user.

[0311] Processing performed by the server

[0312] The server first regularly collects information from electronic mails, communication platforms, and digital documents within the organization. This incorporates a filter function that can be set according to the user's preferences, and only important information related to work can be collected.

[0313] Next, the server processes this information through a natural language processing model and an emotion engine to simultaneously extract job content, skills information, and emotional information. While the natural language processing model analyzes job content and skills information, the emotion engine determines emotional states from the tone and expressions within the text. For example, emotions such as stress and satisfaction are analyzed from emails and chat messages.

[0314] The server then summarizes this information and generates an integrated profile in the database. This profile includes not only specific job skills information but also emotional states, making it useful for improving employee mental health and the work environment.

[0315] Terminal and user roles

[0316] The device provides an interface that allows users to view their individual profile information. Through the device, users can check their own profile, which includes work skills and emotional information. This profile information is updated regularly, allowing users to provide feedback for self-improvement.

[0317] Specific example

[0318] For example, if the server collects emails about project meetings that employee C frequently attends, it can not only understand C's project management skills but also obtain emotional information related to stress from the meeting content. Employee C, as the user, can then check their own profile through their terminal and take action to seek support within the company if necessary.

[0319] This invention enables organizations to perform comprehensive human resource management using both employee skill information and emotional data, contributing to improved teamwork and individual performance.

[0320] The following describes the processing flow.

[0321] Step 1:

[0322] The server collects emails, communication platform messages, and digital documents from each terminal. The server uses filtering functions to select work-related information and protects the data with privacy and security in mind.

[0323] Step 2:

[0324] The server preprocesses the collected data. Specifically, it removes unnecessary information from emails, tokenizes text data to convert it into a format suitable for natural language processing, removes data noise, and formats it into a format that is easy for the sentiment engine to process.

[0325] Step 3:

[0326] The server inputs pre-processed data into a natural language processing model and an emotion engine for analysis. The natural language processing model extracts job content and skills information, while the emotion engine recognizes the user's emotions from the tone and keywords of the text. For example, it identifies emotions such as "nervous" or "satisfied."

[0327] Step 4:

[0328] The server integrates extracted job description, skills information, and emotional information, and registers it in the database as a comprehensive employee profile. This profile is regularly updated to ensure it provides the most up-to-date information.

[0329] Step 5:

[0330] Users can view their own profiles on their devices. The information displayed through the interface includes not only work skills but also recent emotional tendencies, allowing users to understand their own condition and work environment.

[0331] Step 6:

[0332] Users can choose actions to further utilize their profile information. For example, they can request support for stress management or skill improvement based on emotional feedback. They can also notify the server of correction requests if there are any errors.

[0333] (Example 2)

[0334] 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".

[0335] There is a need to provide an information system that comprehensively understands the work performance capabilities and emotional state of employees within an organization, enabling efficient human resource management and individualized mental health support. However, conventional systems cannot simultaneously collect and analyze work information and emotional information, resulting in a lack of crucial information for human resource management.

[0336] 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.

[0337] In this invention, the server includes means for collecting data from internal information sources within the organization, means for analyzing the collected data and extracting business information and technical information using a natural language processing model, and means for determining the emotional state of text via an emotion analysis engine. This makes it possible to generate an integrated profile of employees' business information and emotional state, enabling comprehensive human resource management and mental health support.

[0338] "Means of data collection" refers to methods and devices for obtaining necessary information from sources such as email, communication platforms, and digital documents within an organization.

[0339] A "natural language processing model" is an artificial intelligence technology used to extract business and technical information from text data. It analyzes language and extracts information in a format that humans can understand.

[0340] An "emotion analysis engine" is software that determines the emotional state of a person based on the tone and expression within text data.

[0341] "Means of profile generation" refers to methods or devices for integrating analyzed business information and emotional information and storing them in a database as a single record.

[0342] "Means of providing an interface" refers to methods and technologies that provide screens or operating systems that allow users to view profile information and provide feedback for improvement.

[0343] "Contributing to mental health management" means using analyzed emotional state information to help organizations take measures to improve the mental health of their employees.

[0344] This invention is a system for simultaneously understanding business information and emotional states within an organization. This system is implemented through the interaction of a server, terminals, and users.

[0345] The server collects data from email, communication platforms, and digital documents within the organization. The server uses a natural language processing model and sentiment analysis engine on the collected data. The natural language processing model performs robust text analysis, extracting business and technical information. The sentiment analysis engine, at the same time, determines emotional states from the same dataset, revealing emotional indices such as stress and satisfaction. The analyzed information is integrated by the server and stored in a database. This database provides a profile of each employee, combining business skills and emotional information.

[0346] The device provides users with an interface to view this profile information. This interface is designed to allow users to check their own work skills and emotional state in real time. For example, when a user checks their profile, they may notice that their stress level is high and decide to seek help from workplace support. In this way, the profile information provides users with valuable feedback that encourages problem-solving and improvement actions.

[0347] As an example of a prompt, the AI ​​model might be instructed to "Analyze employee stress and satisfaction levels from internal email and communication chats. Consider specific job skills as well, and generate an integrated profile." This prompt clearly demonstrates how the system actually works.

[0348] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0349] Step 1:

[0350] The server periodically scans emails, communication platforms, and digital documents within the organization to collect data. During the collection process, filtering based on user preferences is applied to limit the information to business-related data. Input is unorganized text data, and output is a data stream organized for analysis. Specifically, it connects to email servers and communication tools via APIs to retrieve data.

[0351] Step 2:

[0352] The server sends the collected data to a natural language processing model for analysis. This model performs keyword extraction and semantic analysis of sentences. The input is the text data obtained in step 1, and the output is a list of business information and technical information. Specifically, tokenization and syntactic analysis are performed for each data entry.

[0353] Step 3:

[0354] The server processes the same dataset through an emotion analysis engine to determine the emotional state within the text. This process assigns emotion tags such as positive and negative. The input is the text data obtained in step 1, and the output is a list of emotion tags indicating the emotional state. Specifically, the emotion analysis algorithm is executed based on tokens and syntactic information obtained through natural language processing.

[0355] Step 4:

[0356] The server integrates business, technical, and emotional information to generate profiles for individual users. The input is a list, which is the output of steps 2 and 3, and the output is the integrated profile stored in the database. Specifically, it aggregates data for each user and forms structured records.

[0357] Step 5:

[0358] The device provides an interface that allows the user to view their own profile. The input is the profile data generated in step 4, and the output is the information displayed on the user's screen. Specifically, it presents information through an intuitive UI using a web application or mobile application.

[0359] Step 6:

[0360] The user analyzes profile information through the device, receives feedback, and decides on improvement actions. The input is the profile information displayed on the interface, and the output is a specific list of improvement actions and feedback. Specifically, the user interprets the displayed data and chooses to contact internal support organizations or other relevant parties as needed.

[0361] (Application Example 2)

[0362] 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."

[0363] In modern manufacturing environments, worker efficiency and satisfaction significantly impact productivity. However, understanding the emotional state of individual workers and implementing appropriate work assignments and environmental improvements is challenging. Conventional systems focused on visualizing skills, failing to incorporate emotional states into comprehensive human resource management. This invention aims to solve the problem of improving organizational management and the work environment by monitoring workers' emotional states in real time.

[0364] 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.

[0365] In this invention, the server includes means for collecting information from an information and communication medium, means for inputting the collected information into natural language processing technology to extract business-related information and technical information, and means for analyzing emotional information together with the extracted business-related information and technical information. This enables real-time monitoring of workers' emotional states and allows for optimal work assignments and environmental improvements.

[0366] "Information and communication media" is a general term for various channels used to transmit and collect data, such as email, communication platforms, and digital documents.

[0367] "Natural language processing technology" is a technology that allows computers to understand, analyze, and generate human language, and is a method that can extract meaning and structure from text.

[0368] "Work-related information" refers to information related to specific tasks or projects that workers are currently working on, including their content and progress.

[0369] "Technical information" refers to technical information such as the skills and knowledge possessed by workers, and serves as a standard for evaluating their ability to perform their jobs.

[0370] "Emotional information" refers to information about the psychological state and emotions of workers, and includes a variety of emotional data, such as stress and satisfaction levels.

[0371] "Real-time monitoring" refers to a state where information can be analyzed immediately and the current situation can be quickly grasped.

[0372] "Integrating into a database" refers to a method of centrally gathering different information and storing it in data storage that allows for easy searching and referencing.

[0373] "Means for users to view and evaluate information" refers to a user interface that allows workers and administrators to review collected and analyzed information and consider improvements.

[0374] This invention is a system for improving operational efficiency and the work environment in factories and offices by analyzing employee emotional information. In this system, a server plays a central role.

[0375] The server periodically collects information from information and communication media such as email and chat. This requires the ability to convert various data formats into formats suitable for natural language processing technology, and also performs preprocessing for analysis of the collected information. In this process, libraries such as TextBlob and NLTK are used as natural language processing technologies to extract business-related information, technical information, and even sentiment information from the text.

[0376] The emotional information obtained is analyzed in real time, and the current psychological state of employees is understood through an emotional engine. This information is integrated into a database to support managers in making appropriate improvements to the factory environment and working conditions. For example, if an employee's stress level is high, task redistribution or encouraging breaks may be considered. This process improves the overall work efficiency of the organization.

[0377] Users can view and evaluate the analyzed information through an interface provided via their device. This interface is an important tool for employees to understand their own emotional state and, if necessary, request action from their managers.

[0378] An example of a prompt statement generated using a generative AI model is shown below.

[0379] "Analyze the emotional sentiment of the following communication data from an employee in a manufacturing environment. Provide insights for improving their work condition based on the analysis."

[0380] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0381] Step 1:

[0382] The server collects data from information and communication media such as email and chat. It uses information acquired from various devices within the organization, with text data being the primary input. This allows for the creation of a dataset that reflects the real-time communication situation within the organization.

[0383] Step 2:

[0384] The server preprocesses the collected text data and converts it into a format suitable for natural language processing techniques. It filters out unnecessary parts from the text data and provides a unified text format as output. This process includes text normalization and tokenization.

[0385] Step 3:

[0386] The server inputs pre-processed text data into natural language processing technology and an emotion engine to extract business-related information, technical information, and emotion information. Specifically, it uses libraries such as TextBlob and NLTK to derive specific keywords and emotion scores from the input text. This allows it to output a set of extracted information.

[0387] Step 4:

[0388] The server stores the extracted information in an integrated database. This database forms work-related profiles for each employee, making it easy to search and refer to information. The received analytical information is converted into a database format and integrated into the database storage.

[0389] Step 5:

[0390] Users, specifically administrators, view and evaluate integrated profiles via their devices. The interface on the device visualizes sentiment scores and metrics of work efficiency, allowing administrators to instantly grasp the situation. This enables them to formulate environmental improvement measures based on the outputted information.

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

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

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

[0394] [Third Embodiment]

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

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

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

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

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

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

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

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

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

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

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

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

[0407] This invention aims to build a system for efficiently and accurately collecting and visualizing human resource information within an organization. This system is operated through the cooperation of servers, terminals, and users.

[0408] Processing performed by the server

[0409] The server first periodically collects emails, message histories from communication platforms, and digital documents within the organization. Since this requires data transmission from each terminal, the server includes an interface for automatically receiving this data.

[0410] Next, the server preprocesses the collected data. Preprocessing includes normalizing, tokenizing, and removing irrelevant information from the data to prepare it for smooth natural language processing.

[0411] The server inputs the formatted data into a natural language processing model. The model used here is pre-trained and designed to identify job duties and skills. The server analyzes the output from the model and extracts each employee's job duties and skills.

[0412] Next, the server summarizes the extracted information, organizes it clearly, and registers it in the database. This data is then shared within the organization as the latest personnel information.

[0413] Terminal and user roles

[0414] The terminal is a device used by users to check instructions and results from the server. Users can always verify that their information is accurately reflected and submit correction requests to the server as needed. These correction requests can be easily submitted through the interface, thereby improving user convenience.

[0415] Specific example

[0416] For example, suppose employee B communicates with the team through numerous emails and chats while managing a project. In this case, the server collects all of these communication records and extracts information related to project management skills and team leadership.

[0417] As a result, the server automatically registers information indicating that employee B has project management experience and strong leadership skills. This information is also used for appropriate personnel placement in job placement projects.

[0418] This invention enables the rapid and accurate digitization of employee information, optimizing the utilization of skills across the entire organization.

[0419] The following describes the processing flow.

[0420] Step 1:

[0421] The server collects emails, communication platform logs, and digital documents from each terminal. This includes scheduling that data is automatically sent to the server periodically.

[0422] Step 2:

[0423] The server preprocesses the collected data. Unnecessary headers and footers are removed from emails, and chat logs are similarly tokenized to remove noise. This prepares data suitable for natural language processing models.

[0424] Step 3:

[0425] The server feeds the prepared data into a natural language processing model. The model recognizes keywords and phrases in the text and analyzes them in relation to the work content. In this process, information linked to specific skills, roles, and responsibilities is extracted.

[0426] Step 4:

[0427] The server organizes and summarizes the information obtained through analysis. The server prioritizes the information, considering its importance and frequency of occurrence, before registering it in the personnel database. This data is stored as individual employee profiles.

[0428] Step 5:

[0429] Users can view their profile information on their device. The interface presents a summary of their job responsibilities and skills, allowing users to verify the accuracy of the information.

[0430] Step 6:

[0431] If a user determines that their profile information needs correction, they send a correction request to the server via their device. The server uses this feedback to update the database and improve the accuracy of the information by incorporating it into the next data analysis.

[0432] (Example 1)

[0433] 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."

[0434] Managing personnel information within an organization presents challenges, including the time-consuming process of collecting and organizing data, and the significant effort required to extract job descriptions and skills information. Furthermore, maintaining the accuracy of the collected information and updating it promptly can be difficult. Therefore, there is a need for a system that efficiently and accurately collects and analyzes personnel information within an organization, and allows users to easily view and modify that information.

[0435] 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.

[0436] In this invention, the server includes means for acquiring data from information communication means and document management means within the organization; means for preprocessing the acquired data and converting it into a format suitable for natural language processing; means for inputting the converted data into a generation AI model to identify job content and skills information; means for summarizing the identified job content and skills information and storing it in an information storage device; and means for providing a viewing device that allows users to view and modify the stored information. This makes it possible to efficiently collect personnel information within the organization, provide highly accurate information, and enable users to make rapid corrections.

[0437] "Information and communication means" refers to technologies and systems used within an organization for sending and receiving information, such as email and messaging platforms.

[0438] "Means of document management" refers to technologies and systems for storing, classifying, and searching documents and digital files.

[0439] "Means of acquiring data" refers to technologies and systems for automatically collecting necessary data from diverse sources.

[0440] "Preprocessing" refers to the process of converting raw data into a format suitable for analysis or model input, and includes noise reduction and data normalization.

[0441] "Natural language processing" refers to the ability of computers to understand and process human language, and is a technology used for text analysis and information extraction.

[0442] A "generative AI model" refers to an artificial intelligence model that uses a pre-trained algorithm to generate meaningful information from input data.

[0443] "Job description" refers to information that encompasses the tasks and responsibilities performed by a specific position or person within an organization.

[0444] "Skills information" refers to information about the knowledge and skills an individual possesses when performing their job duties.

[0445] "Information storage device" refers to a physical or digital storage medium used to store and manage data as needed.

[0446] A "browsing device" refers to a device or interface used by users to display and interact with digital information.

[0447] The present invention aims to accurately grasp job content and skills information by providing a system for efficiently collecting and analyzing information within an organization. To achieve this, the following hardware and software are used.

[0448] The server automatically retrieves data from information and communication methods and document management systems within the organization. At this stage, data can be collected, for example, through APIs or dedicated interfaces. Next, the server normalizes and removes noise from the data using Python's NLTK library or similar tools during the preprocessing stage.

[0449] Next, the server analyzes the pre-processed data using a pre-trained natural language processing technique (e.g., the BERT model) as a generative AI model. This process identifies job descriptions and skills information. The processed results are summarized and stored in a database, which is an information storage device. For example, a database such as PostgreSQL is used for storing and managing the information.

[0450] Users can access information stored in the database using a browsing device provided by the server via their device. If the information needs correction, they can submit a correction request through a dedicated interface. This interface is typically implemented as a front-end application accessible via a web browser.

[0451] For example, if employee A receives a large volume of emails related to a project, this system can be used to analyze that email data on the server, identify employee A's project management skills, and reflect this in the latest employee information.

[0452] An example of a prompt message that can be input into the generating AI model is, "Based on internal communication records, extract the project management capabilities and leadership skills of a specific employee."

[0453] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0454] Step 1:

[0455] The server retrieves data from information and communication methods within the organization (e.g., email, messaging platforms). This retrieval process automatically collects data periodically using a dedicated API. The input is raw communication data. The output is a collection of raw data aggregated on the server side. Specifically, the server accesses each data source at specified time intervals and retrieves updated information.

[0456] Step 2:

[0457] The server preprocesses the acquired data. The input is the raw data collected in Step 1. The server normalizes and tokenizes this data to standardize its format and remove unnecessary noise. It uses the Python NLTK library to cleanse the data and convert it into a format suitable for analysis. The output is a preprocessed and structured dataset. Specifically, the server divides each data entry into words and filters out less important information.

[0458] Step 3:

[0459] The server inputs pre-processed data into a generating AI model to extract job description and skills information. The input is the output dataset from step 2. The server uses natural language processing techniques, such as a pre-trained BERT model, to analyze the meaning of the data. The output is a list of extracted job description and skills information. Specifically, the server sends data to the AI ​​model and analyzes the results output by the model.

[0460] Step 4:

[0461] The server summarizes the extracted information and stores it in an information storage device. The input is the information list obtained from step 3. The server uses a summarization algorithm to organize the information concisely and registers it in a PostgreSQL database. The output is an information storage device organized by employee. Specifically, the server stores the summarized information in the database with an index to improve searchability.

[0462] Step 5:

[0463] The user accesses information via a viewing device provided by the server using a terminal. The input consists of information registered in a database. The terminal displays an interface for the user to view the information and request corrections if errors are found. The output is either the confirmed information or the submission of a correction request. Specifically, the user accesses the interface using a web browser and requests updates through the information viewing or correction fields.

[0464] (Application Example 1)

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

[0466] In modern manufacturing environments, effectively utilizing worker skill information and assigning tasks optimally is essential for improving production efficiency. However, traditional methods often failed to fully leverage workers' experience and abilities. In particular, there was a lack of systems that automatically analyzed worker skill information and assigned tasks appropriately. This made it difficult to allocate personnel effectively, leading to decreased production efficiency.

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

[0468] In this invention, the server includes means for collecting information on electronic communications and communication platforms and digital documents within an organization; means for extracting work content and skill information from the collected information using natural language processing technology; and means for generating and presenting optimal work assignments based on the characteristics of workers. This enables efficient work allocation that utilizes the skills of workers.

[0469] "Internal electronic communications" refers to the general term for information transmission methods such as email and instant communication services used internally within companies and organizations.

[0470] A "communication platform" is an online service or application for exchanging messages and data, facilitating communication both within and outside an organization.

[0471] "Digital documents" refer to documents and files created and stored electronically, including reports and presentation materials.

[0472] "Natural language processing technology" refers to the technology that enables computers to understand and process human language, and is used for text analysis and meaning extraction.

[0473] "Job description and skills information" refers to information about an individual's activities related to their job and the skills and abilities they possess.

[0474] A "large-scale information storage medium" is a digital storage system for efficiently storing large amounts of data, and cloud storage is an example of this.

[0475] "Means of providing a display device" refers to an interface or device that allows a user to visually confirm information.

[0476] A "means for generating work assignments" refers to a system that plans and proposes the optimal division of work based on the skill data of the workers.

[0477] To implement this invention, the server must first collect information from the organization's electronic communication and communication platforms, as well as from digital documents. The server periodically collects this information and converts it into a suitable format for analysis using natural language processing techniques. Specifically, the server performs text normalization, removal of irrelevant information, and tokenization as data preprocessing.

[0478] Next, the server uses natural language processing technology to extract job content and skills information from the collected data. This process utilizes a trained generative AI model to analyze information about each worker's skills. Through this technology, it is possible to effectively understand what kinds of tasks workers excel at and what skills they possess.

[0479] Furthermore, the terminal device uses information obtained from the server to visually present it to the user. A user interface is used for this, allowing the user to review their information and request corrections as needed. Such interfaces play a crucial role in streamlining the workflow.

[0480] Users can use a smartphone application to review suggested work assignments and contribute to their optimization. For example, a factory manager can use their smartphone to analyze workers' skills in real time, automatically receive efficient work assignments, and quickly implement work assignments.

[0481] An example of a prompt for a generative AI model would be, "To improve the efficiency of robot operations, please suggest the optimal work assignment based on worker skill information." This is expected to maximize productivity on the factory floor.

[0482] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0483] Step 1:

[0484] The server collects message data from the organization's electronic communications and communication platforms. In this step, data from each endpoint is received via an interface and temporarily stored in a database. The input is email and message logs, and the output is the temporarily stored dataset.

[0485] Step 2:

[0486] The server preprocesses the data collected in Step 1. Specifically, it performs processes such as data normalization, filtering of unnecessary information, and tokenization. The input for this step is raw message data, and the output is formatted text data. This process converts the data into a format suitable for the generative AI model.

[0487] Step 3:

[0488] The server inputs pre-processed data into a generative AI model to extract job content and skills information. The input is the formatted text data obtained in step 2, and the output is the analyzed job and skills metadata. This generative AI model uses a pre-trained model to convert natural language text into semantically structured data.

[0489] Step 4:

[0490] The server creates skill profiles for each worker based on the extracted information and registers them in a large-scale data storage medium. The input is metadata obtained in step 3, and the output is a database entry that can be shared across the entire organization. This information is organized to allow for easy understanding of each worker's strengths and aptitudes.

[0491] Step 5:

[0492] The terminal visually displays the worker's skill information and job description through a user interface. Input is database entries retrieved from the server, and output is informed data displayed on the display device. Users can review this information and request corrections as needed.

[0493] Step 6:

[0494] Users use their smartphones to view suggested work assignments and incorporate them into their actual operations. Input is skill information provided by the terminal, and output is the optimal placement plan for the worker. This leads to increased efficiency in on-site operations.

[0495] 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.

[0496] This invention further develops conventional personnel information visualization systems by incorporating an emotion engine and also incorporating user emotion information, thereby providing a more comprehensive employee profile. This system operates based on the interaction between the server, terminals, and users.

[0497] Processing performed by the server

[0498] The server first periodically collects information from email, communication platforms, and digital documents within the organization. This includes a configurable filtering function that can be customized to the user's preferences, allowing only critical, business-related information to be collected.

[0499] Next, the server processes this information through a natural language processing model and an emotion engine to simultaneously extract job content, skills information, and emotional information. While the natural language processing model analyzes job content and skills information, the emotion engine determines emotional states from the tone and expressions within the text. For example, emotions such as stress and satisfaction are analyzed from emails and chat messages.

[0500] The server then summarizes this information and generates an integrated profile in the database. This profile includes not only specific job skills information but also emotional states, making it useful for improving employee mental health and the work environment.

[0501] Terminal and user roles

[0502] The device provides an interface that allows users to view their individual profile information. Through the device, users can check their own profile, which includes work skills and emotional information. This profile information is updated regularly, allowing users to provide feedback for self-improvement.

[0503] Specific example

[0504] For example, if the server collects emails about project meetings that employee C frequently attends, it can not only understand C's project management skills but also obtain emotional information related to stress from the meeting content. Employee C, as the user, can then check their own profile through their terminal and take action to seek support within the company if necessary.

[0505] This invention enables organizations to perform comprehensive human resource management using both employee skill information and emotional data, contributing to improved teamwork and individual performance.

[0506] The following describes the processing flow.

[0507] Step 1:

[0508] The server collects emails, communication platform messages, and digital documents from each terminal. The server uses filtering functions to select work-related information and protects the data with privacy and security in mind.

[0509] Step 2:

[0510] The server preprocesses the collected data. Specifically, it removes unnecessary information from emails, tokenizes text data to convert it into a format suitable for natural language processing, removes data noise, and formats it into a format that is easy for the sentiment engine to process.

[0511] Step 3:

[0512] The server inputs pre-processed data into a natural language processing model and an emotion engine for analysis. The natural language processing model extracts job content and skills information, while the emotion engine recognizes the user's emotions from the tone and keywords of the text. For example, it identifies emotions such as "nervous" or "satisfied."

[0513] Step 4:

[0514] The server integrates extracted job description, skills information, and emotional information, and registers it in the database as a comprehensive employee profile. This profile is regularly updated to ensure it provides the most up-to-date information.

[0515] Step 5:

[0516] Users can view their own profiles on their devices. The information displayed through the interface includes not only work skills but also recent emotional tendencies, allowing users to understand their own condition and work environment.

[0517] Step 6:

[0518] Users can choose actions to further utilize their profile information. For example, they can request support for stress management or skill improvement based on emotional feedback. They can also notify the server of correction requests if there are any errors.

[0519] (Example 2)

[0520] 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."

[0521] There is a need to provide an information system that comprehensively understands the work performance capabilities and emotional state of employees within an organization, enabling efficient human resource management and individualized mental health support. However, conventional systems cannot simultaneously collect and analyze work information and emotional information, resulting in a lack of crucial information for human resource management.

[0522] 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.

[0523] In this invention, the server includes means for collecting data from internal information sources within the organization, means for analyzing the collected data and extracting business information and technical information using a natural language processing model, and means for determining the emotional state of text via an emotion analysis engine. This makes it possible to generate an integrated profile of employees' business information and emotional state, enabling comprehensive human resource management and mental health support.

[0524] "Means of data collection" refers to methods and devices for obtaining necessary information from sources such as email, communication platforms, and digital documents within an organization.

[0525] A "natural language processing model" is an artificial intelligence technology used to extract business and technical information from text data. It analyzes language and extracts information in a format that humans can understand.

[0526] An "emotion analysis engine" is software that determines the emotional state of a person based on the tone and expression within text data.

[0527] "Means of profile generation" refers to methods or devices for integrating analyzed business information and emotional information and storing them in a database as a single record.

[0528] "Means of providing an interface" refers to methods and technologies that provide screens or operating systems that allow users to view profile information and provide feedback for improvement.

[0529] "Contributing to mental health management" means using analyzed emotional state information to help organizations take measures to improve the mental health of their employees.

[0530] This invention is a system for simultaneously understanding business information and emotional states within an organization. This system is implemented through the interaction of a server, terminals, and users.

[0531] The server collects data from email, communication platforms, and digital documents within the organization. The server uses a natural language processing model and sentiment analysis engine on the collected data. The natural language processing model performs robust text analysis, extracting business and technical information. The sentiment analysis engine, at the same time, determines emotional states from the same dataset, revealing emotional indices such as stress and satisfaction. The analyzed information is integrated by the server and stored in a database. This database provides a profile of each employee, combining business skills and emotional information.

[0532] The device provides users with an interface to view this profile information. This interface is designed to allow users to check their own work skills and emotional state in real time. For example, when a user checks their profile, they may notice that their stress level is high and decide to seek help from workplace support. In this way, the profile information provides users with valuable feedback that encourages problem-solving and improvement actions.

[0533] As an example of a prompt, the AI ​​model might be instructed to "Analyze employee stress and satisfaction levels from internal email and communication chats. Consider specific job skills as well, and generate an integrated profile." This prompt clearly demonstrates how the system actually works.

[0534] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0535] Step 1:

[0536] The server periodically scans emails, communication platforms, and digital documents within the organization to collect data. During the collection process, filtering based on user preferences is applied to limit the information to business-related data. Input is unorganized text data, and output is a data stream organized for analysis. Specifically, it connects to email servers and communication tools via APIs to retrieve data.

[0537] Step 2:

[0538] The server sends the collected data to a natural language processing model for analysis. This model performs keyword extraction and semantic analysis of sentences. The input is the text data obtained in step 1, and the output is a list of business information and technical information. Specifically, tokenization and syntactic analysis are performed for each data entry.

[0539] Step 3:

[0540] The server processes the same dataset through an emotion analysis engine to determine the emotional state within the text. This process assigns emotion tags such as positive and negative. The input is the text data obtained in step 1, and the output is a list of emotion tags indicating the emotional state. Specifically, the emotion analysis algorithm is executed based on tokens and syntactic information obtained through natural language processing.

[0541] Step 4:

[0542] The server integrates business, technical, and emotional information to generate profiles for individual users. The input is a list, which is the output of steps 2 and 3, and the output is the integrated profile stored in the database. Specifically, it aggregates data for each user and forms structured records.

[0543] Step 5:

[0544] The device provides an interface that allows the user to view their own profile. The input is the profile data generated in step 4, and the output is the information displayed on the user's screen. Specifically, it presents information through an intuitive UI using a web application or mobile application.

[0545] Step 6:

[0546] The user analyzes profile information through the device, receives feedback, and decides on improvement actions. The input is the profile information displayed on the interface, and the output is a specific list of improvement actions and feedback. Specifically, the user interprets the displayed data and chooses to contact internal support organizations or other relevant parties as needed.

[0547] (Application Example 2)

[0548] 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."

[0549] In modern manufacturing environments, worker efficiency and satisfaction significantly impact productivity. However, understanding the emotional state of individual workers and implementing appropriate work assignments and environmental improvements is challenging. Conventional systems focused on visualizing skills, failing to incorporate emotional states into comprehensive human resource management. This invention aims to solve the problem of improving organizational management and the work environment by monitoring workers' emotional states in real time.

[0550] 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.

[0551] In this invention, the server includes means for collecting information from an information and communication medium, means for inputting the collected information into natural language processing technology to extract business-related information and technical information, and means for analyzing emotional information together with the extracted business-related information and technical information. This enables real-time monitoring of workers' emotional states and allows for optimal work assignments and environmental improvements.

[0552] "Information and communication media" is a general term for various channels used to transmit and collect data, such as email, communication platforms, and digital documents.

[0553] "Natural language processing technology" is a technology that allows computers to understand, analyze, and generate human language, and is a method that can extract meaning and structure from text.

[0554] "Work-related information" refers to information related to specific tasks or projects that workers are currently working on, including their content and progress.

[0555] "Technical information" refers to technical information such as the skills and knowledge possessed by workers, and serves as a standard for evaluating their ability to perform their jobs.

[0556] "Emotional information" refers to information about the psychological state and emotions of workers, and includes a variety of emotional data, such as stress and satisfaction levels.

[0557] "Real-time monitoring" refers to a state where information can be analyzed immediately and the current situation can be quickly grasped.

[0558] "Integrating into a database" refers to a method of centrally gathering different information and storing it in data storage that allows for easy searching and referencing.

[0559] "Means for users to view and evaluate information" refers to a user interface that allows workers and administrators to review collected and analyzed information and consider improvements.

[0560] This invention is a system for improving operational efficiency and the work environment in factories and offices by analyzing employee emotional information. In this system, a server plays a central role.

[0561] The server periodically collects information from information and communication media such as email and chat. This requires the ability to convert various data formats into formats suitable for natural language processing technology, and also performs preprocessing for analysis of the collected information. In this process, libraries such as TextBlob and NLTK are used as natural language processing technologies to extract business-related information, technical information, and even sentiment information from the text.

[0562] The emotional information obtained is analyzed in real time, and the current psychological state of employees is understood through an emotional engine. This information is integrated into a database to support managers in making appropriate improvements to the factory environment and working conditions. For example, if an employee's stress level is high, task redistribution or encouraging breaks may be considered. This process improves the overall work efficiency of the organization.

[0563] Users can view and evaluate the analyzed information through an interface provided via their device. This interface is an important tool for employees to understand their own emotional state and, if necessary, request action from their managers.

[0564] An example of a prompt statement generated using a generative AI model is shown below.

[0565] "Analyze the emotional sentiment of the following communication data from an employee in a manufacturing environment. Provide insights for improving their work condition based on the analysis."

[0566] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0567] Step 1:

[0568] The server collects data from information and communication media such as email and chat. It uses information acquired from various devices within the organization, with text data being the primary input. This allows for the creation of a dataset that reflects the real-time communication situation within the organization.

[0569] Step 2:

[0570] The server preprocesses the collected text data and converts it into a format suitable for natural language processing techniques. It filters out unnecessary parts from the text data and provides a unified text format as output. This process includes text normalization and tokenization.

[0571] Step 3:

[0572] The server inputs pre-processed text data into natural language processing technology and an emotion engine to extract business-related information, technical information, and emotion information. Specifically, it uses libraries such as TextBlob and NLTK to derive specific keywords and emotion scores from the input text. This allows it to output a set of extracted information.

[0573] Step 4:

[0574] The server stores the extracted information in an integrated database. This database forms work-related profiles for each employee, making it easy to search and refer to information. The received analytical information is converted into a database format and integrated into the database storage.

[0575] Step 5:

[0576] Users, specifically administrators, view and evaluate integrated profiles via their devices. The interface on the device visualizes sentiment scores and metrics of work efficiency, allowing administrators to instantly grasp the situation. This enables them to formulate environmental improvement measures based on the outputted information.

[0577] 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.

[0578] 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.

[0579] 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.

[0580] [Fourth Embodiment]

[0581] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0582] 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.

[0583] 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).

[0584] 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.

[0585] 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.

[0586] 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).

[0587] 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.

[0588] 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.

[0589] 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.

[0590] 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.

[0591] 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.

[0592] 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.

[0593] 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".

[0594] This invention aims to build a system for efficiently and accurately collecting and visualizing human resource information within an organization. This system is operated through the cooperation of servers, terminals, and users.

[0595] Processing performed by the server

[0596] The server first periodically collects emails, message histories from communication platforms, and digital documents within the organization. Since this requires data transmission from each terminal, the server includes an interface for automatically receiving this data.

[0597] Next, the server preprocesses the collected data. Preprocessing includes normalizing, tokenizing, and removing irrelevant information from the data to prepare it for smooth natural language processing.

[0598] The server inputs the formatted data into a natural language processing model. The model used here is pre-trained and designed to identify job duties and skills. The server analyzes the output from the model and extracts each employee's job duties and skills.

[0599] Next, the server summarizes the extracted information, organizes it clearly, and registers it in the database. This data is then shared within the organization as the latest personnel information.

[0600] Terminal and user roles

[0601] The terminal is a device used by users to check instructions and results from the server. Users can always verify that their information is accurately reflected and submit correction requests to the server as needed. These correction requests can be easily submitted through the interface, thereby improving user convenience.

[0602] Specific example

[0603] For example, suppose employee B communicates with the team through numerous emails and chats while managing a project. In this case, the server collects all of these communication records and extracts information related to project management skills and team leadership.

[0604] As a result, the server automatically registers information indicating that employee B has project management experience and strong leadership skills. This information is also used for appropriate personnel placement in job placement projects.

[0605] This invention enables the rapid and accurate digitization of employee information, optimizing the utilization of skills across the entire organization.

[0606] The following describes the processing flow.

[0607] Step 1:

[0608] The server collects emails, communication platform logs, and digital documents from each terminal. This includes scheduling that data is automatically sent to the server periodically.

[0609] Step 2:

[0610] The server preprocesses the collected data. Unnecessary headers and footers are removed from emails, and chat logs are similarly tokenized to remove noise. This prepares data suitable for natural language processing models.

[0611] Step 3:

[0612] The server feeds the prepared data into a natural language processing model. The model recognizes keywords and phrases in the text and analyzes them in relation to the work content. In this process, information linked to specific skills, roles, and responsibilities is extracted.

[0613] Step 4:

[0614] The server organizes and summarizes the information obtained through analysis. The server prioritizes the information, considering its importance and frequency of occurrence, before registering it in the personnel database. This data is stored as individual employee profiles.

[0615] Step 5:

[0616] Users can view their profile information on their device. The interface presents a summary of their job responsibilities and skills, allowing users to verify the accuracy of the information.

[0617] Step 6:

[0618] If a user determines that their profile information needs correction, they send a correction request to the server via their device. The server uses this feedback to update the database and improve the accuracy of the information by incorporating it into the next data analysis.

[0619] (Example 1)

[0620] 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".

[0621] Managing personnel information within an organization presents challenges, including the time-consuming process of collecting and organizing data, and the significant effort required to extract job descriptions and skills information. Furthermore, maintaining the accuracy of the collected information and updating it promptly can be difficult. Therefore, there is a need for a system that efficiently and accurately collects and analyzes personnel information within an organization, and allows users to easily view and modify that information.

[0622] 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.

[0623] In this invention, the server includes means for acquiring data from information communication means and document management means within the organization; means for preprocessing the acquired data and converting it into a format suitable for natural language processing; means for inputting the converted data into a generation AI model to identify job content and skills information; means for summarizing the identified job content and skills information and storing it in an information storage device; and means for providing a viewing device that allows users to view and modify the stored information. This makes it possible to efficiently collect personnel information within the organization, provide highly accurate information, and enable users to make rapid corrections.

[0624] "Information and communication means" refers to technologies and systems used within an organization for sending and receiving information, such as email and messaging platforms.

[0625] "Means of document management" refers to technologies and systems for storing, classifying, and searching documents and digital files.

[0626] "Means of acquiring data" refers to technologies and systems for automatically collecting necessary data from diverse sources.

[0627] "Preprocessing" refers to the process of converting raw data into a format suitable for analysis or model input, and includes noise reduction and data normalization.

[0628] "Natural language processing" refers to the ability of computers to understand and process human language, and is a technology used for text analysis and information extraction.

[0629] A "generative AI model" refers to an artificial intelligence model that uses a pre-trained algorithm to generate meaningful information from input data.

[0630] "Job description" refers to information that encompasses the tasks and responsibilities performed by a specific position or person within an organization.

[0631] "Skills information" refers to information about the knowledge and skills an individual possesses when performing their job duties.

[0632] "Information storage device" refers to a physical or digital storage medium used to store and manage data as needed.

[0633] A "browsing device" refers to a device or interface used by users to display and interact with digital information.

[0634] The present invention aims to accurately grasp job content and skills information by providing a system for efficiently collecting and analyzing information within an organization. To achieve this, the following hardware and software are used.

[0635] The server automatically retrieves data from information and communication methods and document management systems within the organization. At this stage, data can be collected, for example, through APIs or dedicated interfaces. Next, the server normalizes and removes noise from the data using Python's NLTK library or similar tools during the preprocessing stage.

[0636] Next, the server analyzes the pre-processed data using a pre-trained natural language processing technique (e.g., the BERT model) as a generative AI model. This process identifies job descriptions and skills information. The processed results are summarized and stored in a database, which is an information storage device. For example, a database such as PostgreSQL is used for storing and managing the information.

[0637] Users can access information stored in the database using a browsing device provided by the server via their device. If the information needs correction, they can submit a correction request through a dedicated interface. This interface is typically implemented as a front-end application accessible via a web browser.

[0638] For example, if employee A receives a large volume of emails related to a project, this system can be used to analyze that email data on the server, identify employee A's project management skills, and reflect this in the latest employee information.

[0639] An example of a prompt message that can be input into the generating AI model is, "Based on internal communication records, extract the project management capabilities and leadership skills of a specific employee."

[0640] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0641] Step 1:

[0642] The server retrieves data from information and communication methods within the organization (e.g., email, messaging platforms). This retrieval process automatically collects data periodically using a dedicated API. The input is raw communication data. The output is a collection of raw data aggregated on the server side. Specifically, the server accesses each data source at specified time intervals and retrieves updated information.

[0643] Step 2:

[0644] The server preprocesses the acquired data. The input is the raw data collected in Step 1. The server normalizes and tokenizes this data to standardize its format and remove unnecessary noise. It uses the Python NLTK library to cleanse the data and convert it into a format suitable for analysis. The output is a preprocessed and structured dataset. Specifically, the server divides each data entry into words and filters out less important information.

[0645] Step 3:

[0646] The server inputs pre-processed data into a generating AI model to extract job description and skills information. The input is the output dataset from step 2. The server uses natural language processing techniques, such as a pre-trained BERT model, to analyze the meaning of the data. The output is a list of extracted job description and skills information. Specifically, the server sends data to the AI ​​model and analyzes the results output by the model.

[0647] Step 4:

[0648] The server summarizes the extracted information and stores it in an information storage device. The input is the information list obtained from step 3. The server uses a summarization algorithm to organize the information concisely and registers it in a PostgreSQL database. The output is an information storage device organized by employee. Specifically, the server stores the summarized information in the database with an index to improve searchability.

[0649] Step 5:

[0650] The user accesses information via a viewing device provided by the server using a terminal. The input consists of information registered in a database. The terminal displays an interface for the user to view the information and request corrections if errors are found. The output is either the confirmed information or the submission of a correction request. Specifically, the user accesses the interface using a web browser and requests updates through the information viewing or correction fields.

[0651] (Application Example 1)

[0652] 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".

[0653] In modern manufacturing environments, effectively utilizing worker skill information and assigning tasks optimally is essential for improving production efficiency. However, traditional methods often failed to fully leverage workers' experience and abilities. In particular, there was a lack of systems that automatically analyzed worker skill information and assigned tasks appropriately. This made it difficult to allocate personnel effectively, leading to decreased production efficiency.

[0654] 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.

[0655] In this invention, the server includes means for collecting information on electronic communications and communication platforms and digital documents within an organization; means for extracting work content and skill information from the collected information using natural language processing technology; and means for generating and presenting optimal work assignments based on the characteristics of workers. This enables efficient work allocation that utilizes the skills of workers.

[0656] "Internal electronic communications" refers to the general term for information transmission methods such as email and instant communication services used internally within companies and organizations.

[0657] A "communication platform" is an online service or application for exchanging messages and data, facilitating communication both within and outside an organization.

[0658] "Digital documents" refer to documents and files created and stored electronically, including reports and presentation materials.

[0659] "Natural language processing technology" refers to the technology that enables computers to understand and process human language, and is used for text analysis and meaning extraction.

[0660] "Job description and skills information" refers to information about an individual's activities related to their job and the skills and abilities they possess.

[0661] A "large-scale information storage medium" is a digital storage system for efficiently storing large amounts of data, and cloud storage is an example of this.

[0662] "Means of providing a display device" refers to an interface or device that allows a user to visually confirm information.

[0663] A "means for generating work assignments" refers to a system that plans and proposes the optimal division of work based on the skill data of the workers.

[0664] To implement this invention, the server must first collect information from the organization's electronic communication and communication platforms, as well as from digital documents. The server periodically collects this information and converts it into a suitable format for analysis using natural language processing techniques. Specifically, the server performs text normalization, removal of irrelevant information, and tokenization as data preprocessing.

[0665] Next, the server uses natural language processing technology to extract job content and skills information from the collected data. This process utilizes a trained generative AI model to analyze information about each worker's skills. Through this technology, it is possible to effectively understand what kinds of tasks workers excel at and what skills they possess.

[0666] Furthermore, the terminal device uses information obtained from the server to visually present it to the user. A user interface is used for this, allowing the user to review their information and request corrections as needed. Such interfaces play a crucial role in streamlining the workflow.

[0667] Users can use a smartphone application to review suggested work assignments and contribute to their optimization. For example, a factory manager can use their smartphone to analyze workers' skills in real time, automatically receive efficient work assignments, and quickly implement work assignments.

[0668] An example of a prompt for a generative AI model would be, "To improve the efficiency of robot operations, please suggest the optimal work assignment based on worker skill information." This is expected to maximize productivity on the factory floor.

[0669] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0670] Step 1:

[0671] The server collects message data from the organization's electronic communications and communication platforms. In this step, data from each endpoint is received via an interface and temporarily stored in a database. The input is email and message logs, and the output is the temporarily stored dataset.

[0672] Step 2:

[0673] The server preprocesses the data collected in Step 1. Specifically, it performs processes such as data normalization, filtering of unnecessary information, and tokenization. The input for this step is raw message data, and the output is formatted text data. This process converts the data into a format suitable for the generative AI model.

[0674] Step 3:

[0675] The server inputs pre-processed data into a generative AI model to extract job content and skills information. The input is the formatted text data obtained in step 2, and the output is the analyzed job and skills metadata. This generative AI model uses a pre-trained model to convert natural language text into semantically structured data.

[0676] Step 4:

[0677] The server creates skill profiles for each worker based on the extracted information and registers them in a large-scale data storage medium. The input is metadata obtained in step 3, and the output is a database entry that can be shared across the entire organization. This information is organized to allow for easy understanding of each worker's strengths and aptitudes.

[0678] Step 5:

[0679] The terminal visually displays the worker's skill information and job description through a user interface. Input is database entries retrieved from the server, and output is informed data displayed on the display device. Users can review this information and request corrections as needed.

[0680] Step 6:

[0681] Users use their smartphones to view suggested work assignments and incorporate them into their actual operations. Input is skill information provided by the terminal, and output is the optimal placement plan for the worker. This leads to increased efficiency in on-site operations.

[0682] 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.

[0683] This invention further develops conventional personnel information visualization systems by incorporating an emotion engine and also incorporating user emotion information, thereby providing a more comprehensive employee profile. This system operates based on the interaction between the server, terminals, and users.

[0684] Processing performed by the server

[0685] The server first periodically collects information from email, communication platforms, and digital documents within the organization. This includes a configurable filtering function that can be customized to the user's preferences, allowing only critical, business-related information to be collected.

[0686] Next, the server processes this information through a natural language processing model and an emotion engine to simultaneously extract job content, skills information, and emotional information. While the natural language processing model analyzes job content and skills information, the emotion engine determines emotional states from the tone and expressions within the text. For example, emotions such as stress and satisfaction are analyzed from emails and chat messages.

[0687] The server then summarizes this information and generates an integrated profile in the database. This profile includes not only specific job skills information but also emotional states, making it useful for improving employee mental health and the work environment.

[0688] Terminal and user roles

[0689] The device provides an interface that allows users to view their individual profile information. Through the device, users can check their own profile, which includes work skills and emotional information. This profile information is updated regularly, allowing users to provide feedback for self-improvement.

[0690] Specific example

[0691] For example, if the server collects emails about project meetings that employee C frequently attends, it can not only understand C's project management skills but also obtain emotional information related to stress from the meeting content. Employee C, as the user, can then check their own profile through their terminal and take action to seek support within the company if necessary.

[0692] This invention enables organizations to perform comprehensive human resource management using both employee skill information and emotional data, contributing to improved teamwork and individual performance.

[0693] The following describes the processing flow.

[0694] Step 1:

[0695] The server collects emails, communication platform messages, and digital documents from each terminal. The server uses filtering functions to select work-related information and protects the data with privacy and security in mind.

[0696] Step 2:

[0697] The server preprocesses the collected data. Specifically, it removes unnecessary information from emails, tokenizes text data to convert it into a format suitable for natural language processing, removes data noise, and formats it into a format that is easy for the sentiment engine to process.

[0698] Step 3:

[0699] The server inputs pre-processed data into a natural language processing model and an emotion engine for analysis. The natural language processing model extracts job content and skills information, while the emotion engine recognizes the user's emotions from the tone and keywords of the text. For example, it identifies emotions such as "nervous" or "satisfied."

[0700] Step 4:

[0701] The server integrates extracted job description, skills information, and emotional information, and registers it in the database as a comprehensive employee profile. This profile is regularly updated to ensure it provides the most up-to-date information.

[0702] Step 5:

[0703] Users can view their own profiles on their devices. The information displayed through the interface includes not only work skills but also recent emotional tendencies, allowing users to understand their own condition and work environment.

[0704] Step 6:

[0705] Users can choose actions to further utilize their profile information. For example, they can request support for stress management or skill improvement based on emotional feedback. They can also notify the server of correction requests if there are any errors.

[0706] (Example 2)

[0707] 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".

[0708] There is a need to provide an information system that comprehensively understands the work performance capabilities and emotional state of employees within an organization, enabling efficient human resource management and individualized mental health support. However, conventional systems cannot simultaneously collect and analyze work information and emotional information, resulting in a lack of crucial information for human resource management.

[0709] 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.

[0710] In this invention, the server includes means for collecting data from internal information sources within the organization, means for analyzing the collected data and extracting business information and technical information using a natural language processing model, and means for determining the emotional state of text via an emotion analysis engine. This makes it possible to generate an integrated profile of employees' business information and emotional state, enabling comprehensive human resource management and mental health support.

[0711] "Means of data collection" refers to methods and devices for obtaining necessary information from sources such as email, communication platforms, and digital documents within an organization.

[0712] A "natural language processing model" is an artificial intelligence technology used to extract business and technical information from text data. It analyzes language and extracts information in a format that humans can understand.

[0713] An "emotion analysis engine" is software that determines the emotional state of a person based on the tone and expression within text data.

[0714] "Means of profile generation" refers to methods or devices for integrating analyzed business information and emotional information and storing them in a database as a single record.

[0715] "Means of providing an interface" refers to methods and technologies that provide screens or operating systems that allow users to view profile information and provide feedback for improvement.

[0716] "Contributing to mental health management" means using analyzed emotional state information to help organizations take measures to improve the mental health of their employees.

[0717] This invention is a system for simultaneously understanding business information and emotional states within an organization. This system is implemented through the interaction of a server, terminals, and users.

[0718] The server collects data from email, communication platforms, and digital documents within the organization. The server uses a natural language processing model and sentiment analysis engine on the collected data. The natural language processing model performs robust text analysis, extracting business and technical information. The sentiment analysis engine, at the same time, determines emotional states from the same dataset, revealing emotional indices such as stress and satisfaction. The analyzed information is integrated by the server and stored in a database. This database provides a profile of each employee, combining business skills and emotional information.

[0719] The device provides users with an interface to view this profile information. This interface is designed to allow users to check their own work skills and emotional state in real time. For example, when a user checks their profile, they may notice that their stress level is high and decide to seek help from workplace support. In this way, the profile information provides users with valuable feedback that encourages problem-solving and improvement actions.

[0720] As an example of a prompt, the AI ​​model might be instructed to "Analyze employee stress and satisfaction levels from internal email and communication chats. Consider specific job skills as well, and generate an integrated profile." This prompt clearly demonstrates how the system actually works.

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

[0722] Step 1:

[0723] The server periodically scans emails, communication platforms, and digital documents within the organization to collect data. During the collection process, filtering based on user preferences is applied to limit the information to business-related data. Input is unorganized text data, and output is a data stream organized for analysis. Specifically, it connects to email servers and communication tools via APIs to retrieve data.

[0724] Step 2:

[0725] The server sends the collected data to a natural language processing model for analysis. This model performs keyword extraction and semantic analysis of sentences. The input is the text data obtained in step 1, and the output is a list of business information and technical information. Specifically, tokenization and syntactic analysis are performed for each data entry.

[0726] Step 3:

[0727] The server processes the same dataset through an emotion analysis engine to determine the emotional state within the text. This process assigns emotion tags such as positive and negative. The input is the text data obtained in step 1, and the output is a list of emotion tags indicating the emotional state. Specifically, the emotion analysis algorithm is executed based on tokens and syntactic information obtained through natural language processing.

[0728] Step 4:

[0729] The server integrates business, technical, and emotional information to generate profiles for individual users. The input is a list, which is the output of steps 2 and 3, and the output is the integrated profile stored in the database. Specifically, it aggregates data for each user and forms structured records.

[0730] Step 5:

[0731] The device provides an interface that allows the user to view their own profile. The input is the profile data generated in step 4, and the output is the information displayed on the user's screen. Specifically, it presents information through an intuitive UI using a web application or mobile application.

[0732] Step 6:

[0733] The user analyzes profile information through the device, receives feedback, and decides on improvement actions. The input is the profile information displayed on the interface, and the output is a specific list of improvement actions and feedback. Specifically, the user interprets the displayed data and chooses to contact internal support organizations or other relevant parties as needed.

[0734] (Application Example 2)

[0735] 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".

[0736] In modern manufacturing environments, worker efficiency and satisfaction significantly impact productivity. However, understanding the emotional state of individual workers and implementing appropriate work assignments and environmental improvements is challenging. Conventional systems focused on visualizing skills, failing to incorporate emotional states into comprehensive human resource management. This invention aims to solve the problem of improving organizational management and the work environment by monitoring workers' emotional states in real time.

[0737] 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.

[0738] In this invention, the server includes means for collecting information from an information and communication medium, means for inputting the collected information into natural language processing technology to extract business-related information and technical information, and means for analyzing emotional information together with the extracted business-related information and technical information. This enables real-time monitoring of workers' emotional states and allows for optimal work assignments and environmental improvements.

[0739] "Information and communication media" is a general term for various channels used to transmit and collect data, such as email, communication platforms, and digital documents.

[0740] "Natural language processing technology" is a technology that allows computers to understand, analyze, and generate human language, and is a method that can extract meaning and structure from text.

[0741] "Work-related information" refers to information related to specific tasks or projects that workers are currently working on, including their content and progress.

[0742] "Technical information" refers to technical information such as the skills and knowledge possessed by workers, and serves as a standard for evaluating their ability to perform their jobs.

[0743] "Emotional information" refers to information about the psychological state and emotions of workers, and includes a variety of emotional data, such as stress and satisfaction levels.

[0744] "Real-time monitoring" refers to a state where information can be analyzed immediately and the current situation can be quickly grasped.

[0745] "Integrating into a database" refers to a method of centrally gathering different information and storing it in data storage that allows for easy searching and referencing.

[0746] "Means for users to view and evaluate information" refers to a user interface that allows workers and administrators to review collected and analyzed information and consider improvements.

[0747] This invention is a system for improving operational efficiency and the work environment in factories and offices by analyzing employee emotional information. In this system, a server plays a central role.

[0748] The server periodically collects information from information and communication media such as email and chat. This requires the ability to convert various data formats into formats suitable for natural language processing technology, and also performs preprocessing for analysis of the collected information. In this process, libraries such as TextBlob and NLTK are used as natural language processing technologies to extract business-related information, technical information, and even sentiment information from the text.

[0749] The emotional information obtained is analyzed in real time, and the current psychological state of employees is understood through an emotional engine. This information is integrated into a database to support managers in making appropriate improvements to the factory environment and working conditions. For example, if an employee's stress level is high, task redistribution or encouraging breaks may be considered. This process improves the overall work efficiency of the organization.

[0750] Users can view and evaluate the analyzed information through an interface provided via their device. This interface is an important tool for employees to understand their own emotional state and, if necessary, request action from their managers.

[0751] An example of a prompt statement generated using a generative AI model is shown below.

[0752] "Analyze the emotional sentiment of the following communication data from an employee in a manufacturing environment. Provide insights for improving their work condition based on the analysis."

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

[0754] Step 1:

[0755] The server collects data from information and communication media such as email and chat. It uses information acquired from various devices within the organization, with text data being the primary input. This allows for the creation of a dataset that reflects the real-time communication situation within the organization.

[0756] Step 2:

[0757] The server preprocesses the collected text data and converts it into a format suitable for natural language processing techniques. It filters out unnecessary parts from the text data and provides a unified text format as output. This process includes text normalization and tokenization.

[0758] Step 3:

[0759] The server inputs pre-processed text data into natural language processing technology and an emotion engine to extract business-related information, technical information, and emotion information. Specifically, it uses libraries such as TextBlob and NLTK to derive specific keywords and emotion scores from the input text. This allows it to output a set of extracted information.

[0760] Step 4:

[0761] The server stores the extracted information in an integrated database. This database forms work-related profiles for each employee, making it easy to search and refer to information. The received analytical information is converted into a database format and integrated into the database storage.

[0762] Step 5:

[0763] Users, specifically administrators, view and evaluate integrated profiles via their devices. The interface on the device visualizes sentiment scores and metrics of work efficiency, allowing administrators to instantly grasp the situation. This enables them to formulate environmental improvement measures based on the outputted information.

[0764] 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.

[0765] 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.

[0766] 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.

[0767] 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.

[0768] 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.

[0769] 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.

[0770] 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.

[0771] 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.

[0772] 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."

[0773] 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.

[0774] 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.

[0775] 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.

[0776] 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.

[0777] 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.

[0778] 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.

[0779] 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.

[0780] 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.

[0781] 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.

[0782] 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.

[0783] 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.

[0784] All documents, patent applications, and technical standards described herein are incorporated by reference to the same extent as if each individual document, patent application, and technical standard were specifically and individually noted as being incorporated by reference.

[0785] The following is further disclosed regarding the embodiments described above.

[0786] (Claim 1)

[0787] Means for collecting information from emails, communication platforms, and digital documents within an organization,

[0788] A means for inputting the collected information into a natural language processing model to extract job content and skills information,

[0789] A means for summarizing the extracted work content and skills information and registering it in a database,

[0790] A system including means for providing an interface that allows users to view and modify the registered information.

[0791] (Claim 2)

[0792] The system according to claim 1, further comprising means for preprocessing information from emails, communication platforms, and digital documents within the organization and converting it into a format suitable for a language model.

[0793] (Claim 3)

[0794] The system according to claim 1, further comprising means for organizing information obtained from the natural language processing model based on importance and frequency, and generating a final information summary.

[0795] "Example 1"

[0796] (Claim 1)

[0797] From means of information and communication within an organization and means of document management, to means of acquiring data,

[0798] The acquired data is preprocessed and converted into a format suitable for natural language processing,

[0799] The means for inputting the converted data into a generating AI model to identify job content and skills information,

[0800] Means for summarizing the identified job content and skills information and storing it in an information storage device,

[0801] A system including means for providing a browsing device that allows a user to view and modify the aforementioned stored information.

[0802] (Claim 2)

[0803] The system according to claim 1, further comprising means for organizing the information obtained from the generating AI model according to its importance and frequency of occurrence, and creating a final information summary.

[0804] (Claim 3)

[0805] The system according to claim 1, further comprising means for processing a user's request to modify information and updating the information stored in the information storage device.

[0806] "Application Example 1"

[0807] (Claim 1)

[0808] Means for collecting information on electronic communications and communication platforms and digital documents within an organization,

[0809] The aforementioned collected information is used as a means to extract job content and skills information using natural language processing technology.

[0810] A means for summarizing the extracted work content and skills information and recording it in a large-scale information storage medium,

[0811] Means for providing a display device that allows a user to confirm and modify the recorded information,

[0812] A system that includes means for generating and presenting optimal work assignments based on the characteristics of the workers.

[0813] (Claim 2)

[0814] The system according to claim 1, further comprising means for preprocessing information from electronic communications and communication platforms and digital documents within the said organization and converting it into a format suitable for a generated AI model.

[0815] (Claim 3)

[0816] The system according to claim 1, further comprising means for analyzing the skills of workers and proposing efficient work assignments based on information obtained from the aforementioned natural language processing technology.

[0817] "Example 2 of combining an emotion engine"

[0818] (Claim 1)

[0819] Means of collecting data from internal organizational sources,

[0820] The aforementioned collected data is analyzed, and means are used to extract business information and technical information using a natural language processing model.

[0821] A means of determining the emotional state of text via an emotion analysis engine,

[0822] A means for generating a profile that integrates the extracted information and emotional state and stores it in a database,

[0823] A system including means for providing an interface that allows users to view the saved profile information and provide feedback for improvement.

[0824] (Claim 2)

[0825] The system according to claim 1, further comprising means for making the determination of emotional state by the emotion analysis engine available as a contribution to the organization's mental health management.

[0826] (Claim 3)

[0827] The system according to claim 1, further comprising means for enabling the user to update profile information at any time on the interface viewed by the user.

[0828] "Application example 2 when combining with an emotional engine"

[0829] (Claim 1)

[0830] Means for collecting information from information and communication media,

[0831] The aforementioned collected information is input into natural language processing technology to extract business-related information and technical information.

[0832] A means for analyzing emotional information together with the extracted business-related information and technical information,

[0833] A means of monitoring emotional states in real time and generating recommendations to optimize work assignments,

[0834] A means for organizing the analyzed information and integrating it into a database,

[0835] A system that provides a means for users to view and evaluate the aforementioned integrated information.

[0836] (Claim 2)

[0837] The system according to claim 1, further comprising means for preprocessing information in the aforementioned information and communication medium and converting it into a format suitable for natural language processing technology.

[0838] (Claim 3)

[0839] The system according to claim 1, further comprising means for suggesting measures to improve the work environment based on the results of the analysis of the aforementioned emotional information. [Explanation of Symbols]

[0840] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots< / url:> < / url:> < / url:> < / url:>

Claims

1. Means for collecting information from emails, communication platforms, and digital documents within an organization, A means for inputting the collected information into a natural language processing model to extract job content and skills information, A means for summarizing the extracted work content and skills information and registering it in a database, A system including means for providing an interface that allows users to view and modify the registered information.

2. The system according to claim 1, further comprising means for preprocessing information from emails, communication platforms, and digital documents within the organization and converting it into a format suitable for a language model.

3. The system according to claim 1, further comprising means for organizing information obtained from the natural language processing model based on importance and frequency, and generating a final information summary.

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