System

Abstract

A system is provided.SOLUTION: This system is provided with a means for extracting the knowledge or skill of an employee by using interactive artificial intelligence, a means for visualizing the knowledge or skill based on third person evaluation, a means for storing the knowledge or skill in a database together with the evaluation result, a means for retrieving the stored knowledge or skill, and a means for displaying the retrieved knowledge or skill.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

[0003] [Patent Document 1] Japanese Patent Publication No. 2022-180282 Summary of the Invention [Problem to be solved by the invention]

[0004] Many companies face the problem of inefficient sharing of the valuable knowledge and experience of veteran employees throughout the organization. New employees and employees seeking to improve their skills are unable to obtain the information they need, which can delay their growth. Therefore, traditional knowledge sharing methods are insufficient, and a new system that enables efficient sharing of know-how is needed. [Means for solving the problem]

[0005] This invention is a system that includes a means for extracting employee knowledge and skills using interactive AI, a means for visualizing knowledge and skills based on third-party evaluations, a means for storing the knowledge and skills in a database along with the evaluation results, a means for making the stored knowledge and skills searchable, and a means for displaying the searched knowledge and skills. The interactive AI extracts knowledge and skills using natural language processing technology, and the evaluation results are reflected in personnel evaluations, thereby enabling the efficient sharing of employees' valuable knowledge and skills and promoting productivity and growth throughout the organization.

[0006] "Conversational AI" is an AI system that uses natural language processing technology to have natural conversations with users and extract necessary information.

[0007] "Natural language processing technology" is a technology that enables computers to recognize, process, and understand human language.

[0008] "Knowledge and skill extraction" is the process by which conversational AI identifies and extracts important knowledge and specific skills from the information it obtains during a conversation.

[0009] "Third-party evaluation" means evaluating the knowledge and skills extracted by interactive AI based on objective criteria.

[0010] "Visualization" means displaying evaluation results and extracted information in a format that is visually easy to understand, such as a graph or score.

[0011] "Storage in a database" means systematically storing evaluated knowledge and skill information and making it available for retrieval as needed.

[0012] A "means of making something searchable" is a mechanism for efficiently searching for information stored in a database based on specific keywords or conditions.

[0013] "Displaying search results" means visually presenting the information obtained by the search to the user.

[0014] "Reflecting in personnel evaluation" means incorporating the evaluated knowledge and skill information into the personnel evaluation criteria of employees and using it for employee evaluation and promotion. [Brief explanation of the drawings]

[0015] [Figure 1] 1 is a conceptual diagram showing an example of the configuration of a data processing system according to a first embodiment. [Figure 2] 1 is a conceptual diagram showing an example of main functions of a data processing device and a smart device according to a first embodiment. [Figure 3] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a second embodiment. [Figure 4] FIG. 10 is a conceptual diagram showing an example of main functions of a data processing device and smart glasses according to a second embodiment. [Figure 5] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a third embodiment. [Figure 6] FIG. 11 is a conceptual diagram showing an example of main functions of a data processing device and a headset-type terminal according to a third embodiment. [Figure 7] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a fourth embodiment. [Figure 8] FIG. 10 is a conceptual diagram showing an example of main functions of a data processing device and a robot according to a fourth embodiment. [Figure 9] 1 shows an emotion map onto which multiple emotions are mapped. [Figure 10] 1 shows an emotion map onto which multiple emotions are mapped. [Figure 11] FIG. 3 is a sequence diagram showing a processing flow of the data processing system according to the first embodiment. [Figure 12] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system in Application Example 1. [Figure 13] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system according to the second embodiment when an emotion engine is combined. [Figure 14]FIG. 10 is a sequence diagram showing the flow of processing in the data processing system in Application Example 2 when an emotion engine is combined. DETAILED DESCRIPTION OF THE INVENTION

[0016] An example of an embodiment of a system according to the technology of the present disclosure will be described below with reference to the accompanying drawings.

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

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

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

[0020] In the following embodiments, the coded storage is one or more non-volatile storage devices that store various programs, various parameters, etc. Examples of non-volatile storage devices include flash memory (SSD (Solid State Drive)), magnetic disks (e.g., hard disks), and magnetic tapes.

[0021] In the following embodiments, a communication I / F (Interface) with a symbol is an interface including a communication processor, an antenna, etc. The communication I / F controls communication between multiple computers. Examples of communication standards applied to the communication I / F include wireless communication standards including 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), Bluetooth (registered trademark), etc.

[0022] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B." In other words, "A and / or B" means that it may be only A, only B, or a combination of A and B. Furthermore, in this specification, the same concept as "A and / or B" is also applied when three or more things are expressed connected by "and / or."

[0023] [First embodiment]

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

[0025] 1, a data processing system 10 includes a data processing device 12 and a smart device 14. An example of the data processing device 12 is a server.

[0026] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0027] The smart device 14 includes a computer 36, a reception device 38, an output device 40, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The reception device 38, the output device 40, and the camera 42 are also connected to the bus 52.

[0028] The reception device 38 includes a touch panel 38A, a microphone 38B, and the like, and receives user input. The touch panel 38A detects contact with an indicator (for example, a pen or a finger) to receive user input by the touch of the indicator. The microphone 38B detects the user's voice to receive user input by voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the data indicating the user input.

[0029] The output device 40 includes a display 40A and a speaker 40B, and presents data to the user 20 by outputting the data in a form of expression that the user 20 can perceive (for example, audio and / or text). The display 40A displays visible information such as text and images in accordance with instructions from the processor 46. The speaker 40B outputs audio in accordance with instructions from the processor 46. The camera 42 is a compact digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0030] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 control the exchange of various information between the processor 46 and the processor 28 via the network 54.

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

[0032] 2, in the data processing device 12, a specific process 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" according to the technology of the present 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 process 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.

[0033] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0034] In the smart device 14, the processor 46 performs the reception output process. The storage 50 stores a reception output program 60. The reception output program 60 is used in conjunction with the 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 process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[0035] Next, a description will be given of the specific processing performed by the specific processing unit 290 of the data processing device 12. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0036] This invention is a system that uses interactive artificial intelligence to extract employee knowledge and skills, visualizes them based on third-party evaluations, and stores them in a database. Specific embodiments of this system are described below.

[0037] System configuration

[0038] The system mainly consists of the following components:

[0039] Terminal: Provides an interface for users to interact with conversational artificial intelligence.

[0040] Server: Performs interactive AI processing, database management, and evaluation / visualization processing.

[0041] Database: Stores employee knowledge, skills, and evaluation results.

[0042] Overview of program processing

[0043] In this system, employees (users) log in via their terminals, interact with the conversational AI, and the knowledge and skills acquired in the process are extracted and evaluated. The evaluation results are stored in a database and can be searched and used by other users. The evaluation results are also reflected in personnel evaluations.

[0044] Extracting user knowledge and skills

[0045] 1. Login and authentication: The user logs in by entering their employee ID and password into the terminal. The server authenticates the user information, and if authentication is successful, a session with the conversational AI begins.

[0046] 2. Dialogue and knowledge extraction: The user interacts with the conversational AI in natural language on the terminal. The server analyzes the content of this dialogue using natural language processing technology and extracts the user's knowledge and skills.

[0047] 3. Evaluation and visualization: The server performs a third-party evaluation of the extracted knowledge and skills and visualizes the evaluation results.

[0048] Database storage and search

[0049] 1. Storage of evaluation results: The knowledge and skill information for which evaluation has been completed is stored in a database along with the evaluation results.

[0050] 2. Information search: When other users search for the information they need, they use the search interface on their device. The server extracts information that matches the search keywords from the database and displays it on the device.

[0051] Specific examples

[0052] Example 1: Knowledge sharing among veteran employees

[0053] The terminal provides a screen for the veteran employee to log in. Once the user logs in, a session with the conversational artificial intelligence begins.

[0054] As users talk about project management best practices, the server uses natural language processing techniques to extract key knowledge.

[0055] The server performs a third-party evaluation of this knowledge and visualizes the results, which are then stored in a database and made available for other employees to search.

[0056] Example 2: Improving the skills of new employees

[0057] If a new employee logs in and wants to search for knowledge about project management, he or she will type "project management" into the search interface on the terminal.

[0058] The server retrieves relevant information from a database and displays it on the terminal, which is best practices and skills shared by veteran employees.

[0059] New employees can improve their skills based on the information displayed.

[0060] This will enable employees to share their knowledge and skills efficiently, which is expected to promote productivity and growth throughout the organization. This system will ensure that the wealth of experience of veteran employees is not lost, and will make it easily accessible to new employees and other employees, thereby promoting the effective use of knowledge across the entire company.

[0061] The processing flow will be explained below.

[0062] Step 1:

[0063] The terminal displays a login screen to the user, who then enters their employee ID and password.

[0064] Step 2:

[0065] The server receives the employee ID and password entered by the user. The server performs authentication in the database and, if successful, generates a session ID. The server then sends the session ID to the terminal.

[0066] Step 3:

[0067] The device displays a conversational AI interface to the user, who is given instructions such as "Talk about your project management experience."

[0068] Step 4:

[0069] The user speaks to the conversational AI in natural language about their knowledge and skills, and the device captures the user's voice or text input and sends it to the server.

[0070] Step 5:

[0071] The server analyzes the received dialogue data using natural language processing technology, performing tokenization, grammatical analysis, and semantic analysis to extract the user's knowledge and skills.

[0072] Step 6:

[0073] The server performs a third-party evaluation of the extracted knowledge and skills. The evaluation criteria are depth of knowledge, accuracy of content, and practicality. The evaluation results are visualized and displayed in the form of graphs and scores.

[0074] Step 7:

[0075] The terminal displays the evaluation results to the user, who can then check them and make corrections or additions as necessary.

[0076] Step 8:

[0077] The server stores the evaluated information in a database, including knowledge entities, evaluation results, and a summary of the dialogue content.

[0078] Step 9:

[0079] The terminal provides a search interface, where other users can input search keywords to search for the information they need.

[0080] Step 10:

[0081] The server processes the search query, retrieves information from the database that matches the search keywords, organizes the search results, and sends them to the device.

[0082] Step 11:

[0083] The device displays the search results, and the user checks the displayed information and applies the necessary knowledge and skills.

[0084] Step 12:

[0085] The user provides feedback on the information they searched for. The device collects the feedback and sends it to the server.

[0086] Step 13:

[0087] The server updates the database based on user feedback, making corrections to improve the accuracy and usefulness of the information.

[0088] Example 1

[0089] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0090] In modern companies, the knowledge and skills of employees are often not shared sufficiently, resulting in lower productivity throughout the organization and insufficient utilization of knowledge. In particular, there is a problem in which the valuable knowledge and skills accumulated by veteran employees are not effectively conveyed to new employees and other employees. Furthermore, there is a lack of a way to fairly evaluate employees' knowledge and skills and visualize the results, which also affects personnel evaluations. A method to solve these issues is needed.

[0091] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[0092] In this invention, the server includes means for extracting employee knowledge and abilities using interactive AI, means for visualizing knowledge and abilities based on third-party evaluation, means for storing the knowledge and abilities in a database along with the evaluation results, means for a user to input authentication information and the server to perform authentication, means for the interactive AI to analyze the content of the dialogue using a natural language processing tool and extract knowledge and abilities, and means for visualizing the evaluation results in graphs and charts and storing them in a database. This makes it possible to efficiently extract, objectively evaluate, and visualize employee knowledge and abilities, enabling knowledge sharing throughout the organization and improving the accuracy of personnel evaluations.

[0093] "Employee knowledge and abilities" refers to the specialized knowledge, skills, experience, and ability to apply those skills possessed by individual employees within an organization.

[0094] "Conversational artificial intelligence" refers to an artificial intelligence system that elicits information from a user through natural language dialogue and generates appropriate responses.

[0095] "Third-party evaluation" refers to the process of objectively evaluating the knowledge and abilities extracted by interactive artificial intelligence from a third-party perspective.

[0096] "Visualization means" refers to methods and devices for displaying extracted knowledge and abilities in a visual format such as graphs or charts, and expressing them in an easy-to-understand manner.

[0097] A "database" refers to a collection of data that stores accumulated knowledge, skills, and evaluation results and makes them searchable as needed.

[0098] "Authentication information" refers to the information required for a user to access a system (e.g., employee ID and password).

[0099] "Natural language processing tools" refer to software and algorithms that analyze input natural language and extract meaning.

[0100] "Graphs and charts" refers to figures and diagrams that visually represent evaluation results.

[0101] "Searchability" refers to the interfaces and algorithms that allow users to search for information in a database.

[0102] The "display means" refers to a method or device for displaying the searched information on a terminal so that the user can confirm it.

[0103] "User" refers to an individual who uses the system to extract, evaluate, search, and display knowledge and capabilities.

[0104] "Server" refers to a computer system that handles interactive artificial intelligence processing, database management, and evaluation / visualization processing.

[0105] This invention is a system that uses interactive artificial intelligence to extract employees' knowledge and abilities, visualize them based on third-party evaluations, and store them in a database.

[0106] System configuration

[0107] The system consists of the following components:

[0108] Terminal

[0109] It provides an interface for users to interact with the conversational AI. Users log in to the system by entering their employee ID and password and begin the conversation.

[0110] server

[0111] It handles interactive AI processing, database management, and evaluation / visualization. The server uses natural language processing tools (e.g., OpenAI's GPT-3) to analyze the user's dialogue and extract knowledge and abilities. It also performs a third-party evaluation of the extracted knowledge and abilities and stores the results in a database. The evaluation results are visualized using graphs and charts.

[0112] Database

[0113] Employee knowledge, skills, and evaluation results are stored and can be searched as needed. Users can use their devices to search the database and view the stored knowledge and skills.

[0114] Overview of program processing

[0115] The terminal, server, and database work together. Users log in through their terminal and interact with the conversational AI. The content of the conversation is sent to the server, where knowledge and abilities are extracted using natural language processing technology. After the evaluation is completed, the results are stored in the database and displayed on the terminal as visualized information.

[0116] Usage example

[0117] Here we will show a specific example of use.

[0118] Example 1: Knowledge sharing among veteran employees

[0119] The terminal provides a screen for the veteran employee to log in. Once the user logs in, a session with the conversational artificial intelligence begins.

[0120] As users talk about project management best practices, the server uses natural language processing techniques to extract key knowledge.

[0121] The server performs a third-party evaluation of this knowledge and visualizes the results, which are then stored in a database that can be searched and used by other employees.

[0122] Example prompt sentence:

[0123] Please enter your employee ID and password to log in.

[0124] "Tell me about your project management experience."

[0125] "Rate this knowledge on a scale of 1 to 5."

[0126] "Knowledge storage complete."

[0127] "Search for project management best practices."

[0128] Example 2: Improving the skills of new employees

[0129] If a new employee logs in and wants to search for knowledge about project management, he or she will type "project management" into the search interface on the terminal.

[0130] The server retrieves relevant information from a database and displays it on the terminal, which is best practices and skills shared by veteran employees.

[0131] New employees can improve their skills based on the information displayed.

[0132] Example prompt sentence:

[0133] "Search for project management best practices."

[0134] In this way, by creating a system that allows employees' knowledge and abilities to be shared efficiently, it is expected that productivity and growth will be promoted throughout the organization.

[0135] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0136] Step 1: User Login and Authentication

[0137] explanation:

[0138] Input: The user enters their employee ID and password into the terminal.

[0139] Specific operation: The terminal displays the login screen, and the user enters the employee ID "12345" and password "password123."

[0140] Data processing: The server checks the entered employee ID and password against the database.

[0141] Output: Returns the authentication result (success or failure) to the terminal.

[0142] Specific operation: The server queries the database for user information, and if authentication is successful, displays "Login successful."

[0143] Step 2: Dialogue and knowledge extraction

[0144] explanation:

[0145] Input: The user inputs the dialogue content into the conversational artificial intelligence through the terminal.

[0146] What happens: The device displays a dialogue interface and the user types, "What are some best practices for project management?"

[0147] Data processing: The server sends the dialogue content to a natural language processing tool (e.g., GPT-3) to extract knowledge and abilities.

[0148] Output: The extracted knowledge and capabilities (e.g., "risk management is important") are returned to the server.

[0149] Specific operation: The server uses GPT-3 to analyze the content of the conversation and extract knowledge such as "risk management is important."

[0150] Step 3: Third-party evaluation and visualization

[0151] explanation:

[0152] Input: Extracted knowledge and skills (e.g., "Risk management is important")

[0153] Specific operation: The server performs a third-party evaluation of the extracted knowledge and abilities.

[0154] Data processing: The server conducts third-party evaluations (e.g., evaluations by other employees or AI) and visualizes the results.

[0155] Output: Generate evaluation results (e.g., "very useful") in visual form (e.g., graphs, charts).

[0156] Specific operation: The server converts the evaluation results into a pie chart and visualizes them.

[0157] Step 4: Database storage

[0158] explanation:

[0159] Input: Knowledge and abilities extracted along with the evaluation results

[0160] Specific operation: The server stores the evaluation results and knowledge and ability information in a database.

[0161] Data processing: The server inserts the evaluation results and knowledge and ability information into a database table.

[0162] Output: Entries saved in the database

[0163] Specific operation: The server records knowledge about "risk management" and its evaluation results in a database.

[0164] Step 5: Find and view information

[0165] explanation:

[0166] Input: The user types a search keyword into the device (e.g., "project management")

[0167] Specific behavior: The device displays a search interface, and the user types "project management."

[0168] Data processing: The server searches the database and extracts information that matches the keywords.

[0169] Output: Return search results (e.g., "Risk management is important") to the terminal.

[0170] Specific operation: The server searches the database and displays the relevant information on the terminal, which then displays the search results to the user.

[0171] (Application example 1)

[0172] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0173] In the past, it was difficult to effectively draw out the knowledge and skills of employees and share them throughout the organization. In particular, there was a lack of means to quickly incorporate the knowledge and know-how of experienced engineers into factory automation equipment, which hindered improvements in production efficiency. Another challenge was properly evaluating employee skills, visualizing them, storing them in a database, and making them easily accessible to other employees.

[0174] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[0175] In this invention, the server includes a means for extracting employee knowledge and skills using interactive AI, a means for visualizing the knowledge and skills based on third-party evaluation, and a means for storing the knowledge and skills together with the evaluation results in a database, which allows the evaluated knowledge and skill information to be imported into automated equipment in the factory.

[0176] An "employee" is an individual who belongs to an organization or company and performs certain duties.

[0177] "Knowledge" is the sum of information and understanding gained through experience and learning.

[0178] A "skill" is a technique or ability to perform a specific task or work.

[0179] "Conversational artificial intelligence" is an artificial intelligence system that can converse with humans in natural language.

[0180] "Third-party evaluation" is an objective evaluation process based on specific criteria and evaluation methods.

[0181] "Visualization" is the process of visually representing data or information to make it easier to understand.

[0182] A "database" is a system that systematically stores and manages data and makes it easily accessible.

[0183] "Searching" is the process of locating data in a database based on specific criteria.

[0184] "Display" is the act of visually presenting data or information to a user.

[0185] "Factory automation equipment" refers to machinery and systems used to automate work within a factory.

[0186] An "interface" is a means or protocol that allows information to be exchanged between different systems or devices.

[0187] "Natural language processing technology" is a technology that enables computers to understand and manipulate human language.

[0188] "Human resource evaluation" is a process for evaluating employees' performance and abilities and ensuring fair treatment and placement.

[0189] The system embodying this invention uses interactive artificial intelligence to extract employee knowledge and skills, evaluate them from a third-party perspective, visualize them, and store them in a database. The system is mainly composed of the following components.

[0190] System configuration

[0191] The system consists of three main parts: terminals, servers, and databases, allowing employees' knowledge and skills to be efficiently shared and incorporated into the factory's automated equipment.

[0192] Terminal

[0193] The terminal is a device that provides an interface for engineers and employees to interact with the conversational AI. Specifically, a smartphone or tablet is used.

[0194] server

[0195] The server handles interactive AI processing, database management, evaluation and visualization. The AI ​​part includes a generative AI model, and OpenAI's GPT model is used as an example.

[0196] Database

[0197] The database is a system that stores employee knowledge, skills, and evaluation results. A lightweight database such as SQLite is used.

[0198] Interface

[0199] An interface is provided that allows factory automation equipment to incorporate assessed knowledge and skill information.

[0200] Program processing overview

[0201] Login and Authentication

[0202] Employees log in by entering their user ID and password from their smartphone or tablet. The server collates the user information and performs authentication, and if authentication is successful, a session with the conversational artificial intelligence begins.

[0203] Knowledge extraction and evaluation

[0204] Employees interact with the conversational AI through their devices, and the knowledge and skills acquired in the process are analyzed and extracted by the server using natural language processing technology. The extracted knowledge is then evaluated by a third party using a generative AI model.

[0205] Data visualization and accumulation

[0206] The evaluation results are visualized and stored in a database, allowing other engineers and employees to easily search the database and access the information they need. The evaluated knowledge and skill information is also incorporated into factory robots.

[0207] Example of program generation

[0208] For example, if an engineer wants to teach a robot how to optimize the welding process, here's a sample prompt:

[0209] text

[0210] Rate the following skills:

[0211] Learn best practices for optimizing your welding process, from job preparation to finishing. We share details on accurate measurements, proper material selection, temperature control, and time allocation.

[0212] The generative AI model returns the following evaluation results:

[0213] text

[0214] This technique is highly specialized and useful in many fields. Accurate dimensional measurements and appropriate material selection are particularly important. Temperature control and time allocation are also explained in detail, which is highly praised.

[0215] The evaluation results are stored in a database, and new robots can learn from this information, which is expected to lead to efficient knowledge sharing within factories and improved productivity.

[0216] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0217] Step 1:

[0218] A user logs in using a terminal by entering their employee ID and password. The employee ID and password are required as input, and the server receives them and checks them against the authentication information in the database. If authentication is successful, the server starts a session and provides the user with an interface with the conversational artificial intelligence. As output, the user is shown a message confirming successful login.

[0219] Step 2:

[0220] The user interacts with the conversational AI in natural language via a terminal. The content of the user's conversation (natural language) is required as input, and the server receives this. The server analyzes this content of the conversation using natural language processing technology and extracts information about the user's knowledge and skills. Data processing involves text analysis of the content of the conversation and formatting of related information, and knowledge and skills are extracted. The analysis results are then saved on the server as output.

[0221] Step 3:

[0222] The server uses a generative AI model to evaluate the extracted knowledge and skill information. The extracted information (natural language text) is required as input, and the server sends this to the generative AI model. The generative AI model evaluates the information based on the prompt sentence and generates an evaluation result. As data calculations, the generative AI model generates text and calculates an evaluation score. As output, the evaluation result is returned to the server.

[0223] Step 4:

[0224] Based on the evaluation results, the server visualizes knowledge and skill information and saves it in a database. The evaluation results are required as input, and the server receives them and visualizes them graphically. This makes the information easier to understand visually. Data processing involves generating graphs of the evaluation results and formatting them into tables. The visualized data is then saved in a database as output.

[0225] Step 5:

[0226] Other users or factory robots retrieve the information they need from the database via a search interface. Search keywords are required as input, and the user or robot enters a search query from their terminal. The server searches the database and extracts the relevant knowledge and skill information. As a data calculation, a search algorithm is used to identify and extract matching information. As an output, the search results are displayed on the terminal or robot.

[0227] Step 6:

[0228] The factory's automation equipment takes in the assessed knowledge and skill information and applies it to work. The retrieved knowledge and skill information is required as input, and is received by factory robots and other automation equipment. The automation equipment optimizes the work process based on the taken-in information. As data processing, the received information is integrated into the equipment's control algorithm and the operating procedure is adjusted. The optimized work process is realized as output.

[0229] Furthermore, an emotion engine that estimates the user's emotion may be combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0230] This invention is a system that uses interactive artificial intelligence to extract employee knowledge and skills, visualizes them based on third-party evaluations, and stores them in a database. In addition, by combining this with an emotion engine, it recognizes the user's emotions and reflects that information in the evaluations. A specific embodiment of this system is described below.

[0231] System configuration

[0232] The system mainly consists of the following components:

[0233] Terminal: Provides an interface for users to interact with the conversational artificial intelligence and emotion engine.

[0234] Server: Performs interactive AI processing, emotion recognition, database management, and evaluation / visualization processing.

[0235] Database: Stores employee knowledge, skills, evaluation results, and emotional data.

[0236] Overview of program processing

[0237] This system allows employees (users) to log in via a terminal, converse with a conversational AI, extract the knowledge and skills acquired in the process, and use an emotion engine to recognize the user's emotions to evaluate them. The evaluation results are stored in a database and can be searched and used by other users. The evaluation results and emotion data are also reflected in personnel evaluations.

[0238] Extracting user knowledge and skills and recognizing emotions

[0239] 1. Login and authentication: The user logs in by entering their employee ID and password into the terminal. The server authenticates the user information, and if authentication is successful, a session with the conversational artificial intelligence and emotion engine begins.

[0240] 2. Dialogue and knowledge extraction: The user interacts with the conversational AI in natural language on the terminal. The server analyzes the content of this dialogue using natural language processing technology and extracts the user's knowledge and skills.

[0241] 3. Emotion Recognition: During a conversation, the emotion engine analyzes the user's emotions from their voice, facial expressions, and text, and tracks the results in real time.

[0242] Evaluation and visualization

[0243] 1. Evaluation: The server performs a third-party evaluation of the extracted knowledge and skills. The evaluation criteria are depth of knowledge, accuracy of content, usefulness, and user emotional data.

[0244] 2. Visualization: The evaluation results are visualized and presented in an easy-to-understand format (graphs and scores). Emotional data is also displayed.

[0245] Database storage and search

[0246] 1. Storing evaluation results: The knowledge, skill information, and emotional data for which evaluation has been completed are stored in a database.

[0247] 2. Information search: When other users search for the information they need, they use the search interface on their device. The server extracts information that matches the search keywords from the database and displays it on the device.

[0248] Specific examples

[0249] Example 1: Knowledge sharing and emotion recognition among veteran employees

[0250] The terminal provides a screen for the veteran employee to log in. Once the user logs in, a session with the conversational artificial intelligence and emotion engine begins.

[0251] As users talk about project management best practices, the server uses natural language processing techniques to extract key knowledge, and an emotion engine analyzes the user's emotions in the process.

[0252] The server performs a third-party evaluation of this knowledge, visualizes the evaluation results and emotional data, and stores the results in a database that can be searched and used by other employees.

[0253] Example 2: Skill development for new employees and emotional feedback

[0254] If a new employee logs in and wants to search for knowledge about project management, he or she will type "project management" into the search interface on the terminal.

[0255] The server retrieves relevant information from the database and displays it on the device, including best practices and skills shared by veteran employees, as well as emotional data.

[0256] New employees can improve their skills based on the displayed information, and their emotional feedback during use is accumulated as an evaluation.

[0257] This is expected to enable efficient sharing of employee knowledge and skills, promoting productivity and growth throughout the organization. This system will prevent the wealth of experience of veteran employees from being buried, making it easily accessible to new employees and other employees, and by taking user emotions into consideration, it will enable deeper understanding and appropriate evaluation.

[0258] The processing flow will be explained below.

[0259] Step 1:

[0260] The terminal displays a login screen to the user, who then enters their employee ID and password.

[0261] Step 2:

[0262] The server receives the employee ID and password entered by the user. The server performs authentication processing in the database, and if successful, generates a session ID and sends it to the terminal.

[0263] Step 3:

[0264] The device displays the interface of the conversational artificial intelligence and emotion engine to the user, who is given instructions such as "Talk about your experience in project management."

[0265] Step 4:

[0266] The user speaks to the conversational AI in natural language about their knowledge and skills, and the device captures the user's voice or text input and sends it to the server.

[0267] Step 5:

[0268] The server analyzes the received dialogue data using natural language processing technology, performing tokenization, grammatical analysis, and semantic analysis to extract the user's knowledge and skills.

[0269] Step 6:

[0270] The emotion engine analyzes emotions from the user's voice, facial expressions, and text during a conversation and collects the data in real time. The emotion engine identifies emotions such as joy, sadness, and anger.

[0271] Step 7:

[0272] The server performs a third-party evaluation based on the extracted knowledge, skills, and emotional data. The evaluation criteria are depth of knowledge, accuracy of content, practicality, and emotional data. The evaluation results are visualized and displayed in the form of graphs and scores.

[0273] Step 8:

[0274] The device displays the evaluation results and emotion data to the user, who can then review the results and make corrections or additions as necessary.

[0275] Step 9:

[0276] The server stores the evaluated information and emotion data in a database, including knowledge entities, evaluation results, dialogue summaries, and emotion data.

[0277] Step 10:

[0278] The terminal provides a search interface, where other users can input search keywords to search for the information they need.

[0279] Step 11:

[0280] The server processes the search query, retrieves information from the database that matches the search keywords, organizes the search results, and sends them to the device.

[0281] Step 12:

[0282] The device displays the search results. The user can review the displayed information and utilize the necessary knowledge and skills. Emotional data is also displayed for the user to refer to.

[0283] Step 13:

[0284] The user provides feedback on the information they searched for. The device collects the feedback and sends it to the server.

[0285] Step 14:

[0286] The server updates the database based on user feedback, making corrections to improve the accuracy and usefulness of the information.

[0287] Example 2

[0288] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0289] Conventional systems only use conversational AI to extract employee knowledge and skills, but evaluations tend to be subjective. Furthermore, because they do not take emotions into account, the user's psychological state is not reflected in the evaluation, which can lead to inaccurate evaluations. Furthermore, the system has low search efficiency for accumulated knowledge and skills, making it difficult for new employees and other employees to quickly obtain the information they need. This has led to a need for improved knowledge sharing and personnel evaluations across the organization.

[0290] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[0291] In this invention, the server includes means for extracting employee knowledge and skills using interactive artificial intelligence, means for visualizing knowledge and skills based on third-party evaluations, means for storing the knowledge and skills in a database along with the evaluation results, means for making the stored knowledge and skills searchable, means for displaying the searched knowledge and skills, means for recognizing user emotions, and means for reflecting emotion data in the evaluation. This allows for fairer and more reliable evaluations by reflecting emotions along with accurate evaluations of users' knowledge and skills, and further enables knowledge sharing and skill improvement throughout the organization through appropriate information retrieval.

[0292] "Means of extracting employee knowledge and skills using interactive AI" is a function that extracts the knowledge and skills of a user by having the user interact with the interactive AI in natural language.

[0293] "A means of visualizing knowledge and skills based on third-party evaluation" is a function that evaluates extracted knowledge and skills using objective criteria and presents the evaluation results in a format that can be intuitively understood.

[0294] "Means for storing knowledge and skills together with evaluation results in a database" refers to a function for storing data on evaluated knowledge and skills and the evaluation results in a database in order to centrally manage them.

[0295] "Means for making accumulated knowledge and skills searchable" refers to a function that enables efficient searching of information stored in a database.

[0296] The "means for displaying searched knowledge and skills" is a function for displaying information obtained by a search to the user.

[0297] "Means for recognizing user emotions" refers to a function for identifying and analyzing the user's emotions during a dialogue or interaction, and determines emotions based on tone of voice, facial expressions, and text content.

[0298] The "means for reflecting emotional data in evaluation" is a function for correcting and amending the evaluation results of knowledge and skills based on the results of user emotion recognition, thereby enabling more accurate evaluation.

[0299] This invention is a system that uses interactive artificial intelligence to extract employee knowledge and skills, visualizes them based on third-party evaluations, and stores the results in a database. Furthermore, by combining it with an emotion engine, it recognizes the user's emotions and reflects that information in the evaluations. A specific embodiment of the invention will be described.

[0300] System configuration

[0301] The system mainly consists of the following components:

[0302] Terminal: Provides an interface for users to interact with the conversational artificial intelligence and emotion engine.

[0303] Server: Performs interactive AI processing, emotion recognition, database management, and evaluation / visualization processing.

[0304] Database: Stores employee knowledge, skills, evaluation results, and emotional data.

[0305] Hardware and software used

[0306] 1. Terminal: A device such as a PC, tablet, or smartphone. These devices provide a user interface and a means for users to interact with the conversational artificial intelligence and use the emotion engine.

[0307] 2. Server: A high-performance computer system that provides the execution environment for conversational artificial intelligence (e.g., generative AI models), natural language processing technologies (e.g., Python's NLTK or Google Cloud Natural Language API), emotion recognition engines (e.g., Microsoft Azure Emotion API), etc.

[0308] 3. Database: A relational database system (e.g., MySQL, PostgreSQL) or a NoSQL database (e.g., MongoDB) to efficiently store and search evaluation results, user knowledge and skill data, and sentiment data.

[0309] Specific examples

[0310] Example 1: Knowledge sharing and emotion recognition among veteran employees

[0311] The terminal provides a screen for the veteran employee to log in. Once the user logs in, a session with the conversational artificial intelligence and emotion engine begins.

[0312] As users talk about project management best practices, the server uses natural language processing techniques to extract key knowledge, and an emotion engine analyzes the user's emotions in the process.

[0313] The server performs a third-party evaluation of this knowledge, visualizes the evaluation results and emotional data, and stores the results in a database that can be searched and used by other employees.

[0314] Example 2: Skill development for new employees and emotional feedback

[0315] If a new employee logs in and wants to search for knowledge about project management, he or she will type "project management" into the search interface on the terminal.

[0316] The server retrieves relevant information from the database and displays it on the device, including best practices and skills shared by veteran employees, as well as emotional data.

[0317] Users can improve their skills based on the displayed information, and their emotional feedback during use is accumulated as an evaluation.

[0318] Prompt Sentence Examples

[0319] "What are some important points about project management?"

[0320] Please rate this skill.

[0321] "Please tell me the results of your recent sentiment analysis."

[0322] This invention enables fairer and more reliable evaluations by accurately assessing employees' knowledge and skills while also reflecting their emotions, and promotes knowledge sharing and skill improvement throughout the organization through appropriate information retrieval.

[0323] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0324] Step 1:

[0325] Login and Authentication

[0326] The user enters their employee ID and password into the login screen of the device.

[0327] Input: Employee ID and password

[0328] The terminal transmits the input data to the server.

[0329] Based on the received data, the server checks the authentication information of the corresponding user in the database.

[0330] Data processing: Authentication by database matching

[0331] Output: Authentication result (success or failure)

[0332] If the authentication is successful, the server starts the session and prepares the conversational artificial intelligence and emotion engine.

[0333] Specific working example:

[0334] The user opens the login page in a browser, enters the employee ID "user123" and password "pass123", and clicks the "Login" button.

[0335] The server verifies this information and displays the home screen if authentication is successful.

[0336] Step 2:

[0337] Dialogue and Knowledge Extraction

[0338] The user begins a conversation in natural language with a conversational artificial intelligence (generative AI model) on the device.

[0339] Input: Natural language questions and conversations

[0340] The terminal transmits the user's input text to the server in real time.

[0341] The server uses natural language processing technology (e.g., Python's NLTK or Google Cloud Natural Language API) to analyze the conversation and extract the user's knowledge and skills.

[0342] Data processing: Text analysis using natural language processing technology

[0343] Output: Extracted knowledge and skills

[0344] Specific working example:

[0345] User types, "What are some project management best practices?"

[0346] The server analyzes this text and extracts "project management" and "best practices" as key concepts.

[0347] Step 3:

[0348] emotion recognition

[0349] While the user is interacting, the emotion engine analyzes emotions from the user's voice, facial expressions, and text.

[0350] Input: Voice data, facial expression data, text data

[0351] The device uses voice input and a camera to extract voice and facial expression data and transmits it to a server.

[0352] The server analyzes this data using an emotion engine (e.g., Microsoft Azure Emotion API) to track emotional states in real time.

[0353] Data processing: Emotion recognition through voice and facial expression analysis

[0354] Output: Emotion data (e.g. percentage of happy, surprised, sad)

[0355] Specific working example:

[0356] The user shows expressions of joy and surprise during the interaction.

[0357] The server records the emotional data as "Happiness: 80%" and "Surprise: 60%."

[0358] Step 4:

[0359] Conducting the evaluation

[0360] The server performs a third-party evaluation of the extracted knowledge and skills.

[0361] The evaluation criteria are depth of knowledge, accuracy of content, usefulness, and user sentiment data.

[0362] Input: Extracted knowledge, skills, and emotion data

[0363] The server combines these multiple factors to generate an overall rating score.

[0364] Data processing: Criteria-based scoring

[0365] Output: Overall evaluation score

[0366] Specific working example:

[0367] The server evaluates the accuracy and practicality of the knowledge about "project management best practices" entered by the user and provides an "evaluation score: 85 points."

[0368] Step 5:

[0369] Visualization of evaluation results

[0370] The server visualizes the evaluation results and displays them in an easy-to-understand format.

[0371] Input: Evaluation scores, emotion data

[0372] The server generates visualization data and sends it to the terminal.

[0373] The terminal provides an interface for displaying the evaluation results in the form of graphs, scores, etc.

[0374] Data processing: Converting evaluation results into visualized data

[0375] Output: Visualized data (graphs, scores)

[0376] Specific working example:

[0377] The server generates data to display the evaluation score of "85 points" in a pie chart or bar graph.

[0378] The terminal receives this, visualizes the evaluation results, and displays them to the user.

[0379] Step 6:

[0380] Saving evaluation results

[0381] The server stores the evaluated knowledge, skill information and emotion data in a database.

[0382] Input: Evaluation results, emotion data

[0383] A database organizes and stores this information for future retrieval and reference.

[0384] Data processing: Inserting data into the database

[0385] Output: Accumulated data

[0386] Specific working example:

[0387] The server saves the evaluation results and emotion data in the database using an INSERT statement.

[0388] Step 7:

[0389] Searching for information

[0390] When a user searches for the information they need, they use the search interface on their device.

[0391] Input: Search keyword

[0392] The server extracts information that matches the search keywords from the database and sends it to the terminal.

[0393] Data processing: Data extraction based on search keywords

[0394] Output: Search results

[0395] Specific working example:

[0396] A new employee types in "project management" and clicks the search button.

[0397] The server retrieves the relevant information from the database and displays the appropriate information on the terminal.

[0398] (Application example 2)

[0399] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0400] Conventional systems only extracted employee knowledge and skills through conversational AI, making it difficult to properly reflect the evaluation in personnel evaluations. Furthermore, the system did not reflect the user's emotional state, making accurate evaluations and motivation management difficult. In particular, in factories, the emotions and stress levels of workers significantly affect the quality of their work, so the development of a system that takes this into account was needed.

[0401] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 2 is realized by the following means.

[0402] In this invention, the server includes means for extracting employee knowledge and skills using interactive artificial intelligence, means for visualizing knowledge and skills based on third-party evaluations, means for storing the knowledge and skills in a database along with the evaluation results, means for making the stored knowledge and skills searchable, means for displaying the searched knowledge and skills, and emotion recognition means for recognizing the user's emotions and reflecting that information in the evaluation. This enables appropriate evaluations and knowledge sharing that take into account the emotions of employees.

[0403] An "employee" is someone who belongs to a company or organization and provides labor for its activities.

[0404] "Knowledge" is an abstract concept that refers to information, understanding, and experiential understanding in a particular field.

[0405] "Skills" are the practical abilities and techniques required to effectively carry out a particular task or activity.

[0406] "Conversational AI" refers to an AI system designed to mimic natural human interactions and uses natural language processing techniques.

[0407] "Third-party evaluation" is an objective evaluation method conducted from an independent standpoint other than the person being evaluated.

[0408] "Visualization" is the act of presenting abstract data such as knowledge, skills, and evaluation results in a format that is visually easy to understand.

[0409] A "database" is a system that organizes and stores information so that it can be searched and used efficiently later.

[0410] "Emotion recognition" is a technology that automatically identifies a user's emotional state at any given time by analyzing their voice, facial expressions, text, etc.

[0411] "Evaluation results" are the results of evaluation of knowledge and skills obtained through interactive artificial intelligence or third-party evaluation.

[0412] A "server" is a computer that provides services such as data processing and storage to other computers over a network.

[0413] "Searchable" means having the ability to efficiently find specific information from the accumulated data.

[0414] This invention is a system that uses interactive artificial intelligence to extract employee knowledge and skills, visualizes the evaluation results, and stores them in a database. Furthermore, by combining it with emotion recognition technology, the system can reflect the user's emotional state in the evaluation. To implement this invention, the following specific configuration and procedures are required.

[0415] System configuration

[0416] The system mainly consists of the following components:

[0417] 1. Server: Performs interactive AI processing, emotion recognition, database management, and evaluation / visualization processing.

[0418] 2. Terminal: Provides an interface for users to interact with the conversational artificial intelligence and emotion engine.

[0419] 3. Database: Stores employee knowledge, skills, evaluation results, and emotional data.

[0420] Overview of program processing

[0421] This system allows users to log in via a terminal, converse with a conversational AI, extract the knowledge and skills gained in the process, and use an emotion engine to recognize the user's emotions to evaluate them. The evaluation results are stored in a database and can be searched and used by other users. The evaluation results and emotion data are also reflected in personnel evaluations.

[0422] Extracting user knowledge and skills and recognizing emotions

[0423] 1. Login and authentication: The user logs in by entering their employee ID and password into the terminal. The server authenticates the user information, and if authentication is successful, a session with the conversational artificial intelligence and emotion engine begins.

[0424] 2. Dialogue and knowledge extraction: The user interacts with the conversational AI in natural language on the terminal. The server analyzes the content of this dialogue using natural language processing technology and extracts the user's knowledge and skills.

[0425] 3. Emotion Recognition: During a conversation, the emotion engine analyzes the user's emotions from their voice, facial expressions, and text, and tracks the results in real time.

[0426] Evaluation and visualization

[0427] 1. Evaluation: The server performs a third-party evaluation of the extracted knowledge and skills. The evaluation criteria are depth of knowledge, accuracy of content, usefulness, and user emotional data.

[0428] 2. Visualization: The evaluation results are visualized and presented in an easy-to-understand format (graphs and scores). Emotional data is also displayed.

[0429] Database storage and search

[0430] 1. Storing evaluation results: The knowledge, skill information, and emotional data for which evaluation has been completed are stored in a database.

[0431] 2. Information search: When other users search for the information they need, they use the search interface on their device. The server extracts information that matches the search keywords from the database and displays it on the device.

[0432] Hardware and software used

[0433] 1. Smart glasses: Used during maintenance work on factory robots, they capture the faces and voices of workers and perform emotion recognition.

[0434] 2. EmotionEngine: Analyzes emotions in real time and sends the data to a server.

[0435] 3. Dialogue AI: Uses natural language processing technology to analyze the content of user dialogue and extract knowledge and skills.

[0436] Specific examples

[0437] Example 1: Knowledge sharing and emotion recognition among veteran employees

[0438] 1. The terminal provides a screen for the veteran employee to log in. Once the user logs in, a session with the conversational artificial intelligence and emotion engine begins.

[0439] 2. As users talk about project management best practices, the server uses natural language processing technology to extract key knowledge, and an emotion engine analyzes the user's emotions in the process.

[0440] 3. The server performs a third-party evaluation of this knowledge, visualizes the evaluation results and emotional data, and stores the results in a database where other employees can search and use them.

[0441] Example prompt sentences:

[0442] Please tell me how to replace the parts.

[0443] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0444] Step 1:

[0445] The user logs in by entering their employee ID and password into the terminal.

[0446] Input: Employee ID, Password

[0447] Data processing: The server checks the user information against the database and performs authentication.

[0448] Output: If authentication is successful, a session with the conversational artificial intelligence and emotion engine is initiated.

[0449] Specific operation: When a user enters their employee ID and password into the login screen on their device and presses the "Login" button, an authentication request is sent to the server. The server checks the authentication information against the database and, if correct, starts a session.

[0450] Step 2:

[0451] The user interacts with the conversational artificial intelligence on the terminal in natural language.

[0452] Input: User question or comment (natural language text)

[0453] Data processing: The server uses natural language processing technology to analyze the content of the dialogue and extract the user's knowledge and skills.

[0454] Output: Information about knowledge and skills

[0455] Specific operation: When a user uses the conversational artificial intelligence interface on the terminal to input "Please tell me how to replace this part," the server passes the text data to a natural language processing engine, which extracts knowledge about part replacement as an analysis result.

[0456] Step 3:

[0457] During a conversation, the emotion engine analyzes emotions from the user's voice, facial expressions, and text.

[0458] Input: Camera video, audio data, text data

[0459] Data processing: EmotionEngine analyzes this data and determines the emotional state in real time.

[0460] Output: Emotional state (e.g., happy, surprised, angry, etc.)

[0461] Specific operation: The device's camera and microphone are used to capture the user's face and voice, and this data is sent to EmotionEngine, which analyzes the data and determines and displays the user's emotional state in real time.

[0462] Step 4:

[0463] The server performs a third-party evaluation of the extracted knowledge and skills.

[0464] Input: Extracted knowledge, skills, and emotion data

[0465] Data processing: Evaluation is carried out based on evaluation criteria (depth of knowledge, accuracy of content, usefulness, and sentiment data).

[0466] Output: Evaluation results (score, comments, etc.)

[0467] Specific operation: The server inputs information about knowledge and skills and emotional data into the evaluation algorithm and generates an evaluation result based on multiple criteria. For example, if the knowledge is detailed, accurate, and useful, a high evaluation score is assigned.

[0468] Step 5:

[0469] The evaluation results are visualized and presented in an easy-to-understand format (graphs and scores).

[0470] Input: Evaluation results, emotion data

[0471] Data processing: Transformation for visual display in graphs and scores

[0472] Output: Visualized evaluation results and emotion data

[0473] Specific operation: The server obtains the evaluation results and emotion data, and uses a visualization engine to display them on the terminal in the form of bar graphs, pie charts, score displays, etc.

[0474] Step 6:

[0475] The evaluation results and emotional data are stored in a database.

[0476] Input: Visualized evaluation results and emotion data

[0477] Data processing: Add as a new record to the database

[0478] Output: Evaluation results and emotion data stored in a database

[0479] Specific action: Add a new entry of the emotion data and evaluation results to the database so that other users can access it later.

[0480] Step 7:

[0481] When other users search for the information they need, they use the search interface on their device.

[0482] Input: Search keyword

[0483] Data processing: Extract information that matches keywords from the database

[0484] Output: Related knowledge and skill information

[0485] Specific operation: A keyword is entered into the terminal's search interface, and the server searches for related information from the database and displays it on the terminal.

[0486] Step 8:

[0487] Emotional feedback is provided as the user works based on the search results.

[0488] Input: Use of search results, emotional state during work

[0489] Data processing: Emotion engine analyzes emotions in real time while working

[0490] Output: Emotional feedback data during the task

[0491] Specific operation: A user uses smart glasses while working, and the emotion engine continuously monitors their emotional state, generating and storing feedback data.

[0492] 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 a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the 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.

[0493] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0494] In the above embodiment, an example in which the specific process is performed by the data processing device 12 has been given, but the technology of the present disclosure is not limited to this, and the specific process may be performed by the smart device 14.

[0495] [Second embodiment]

[0496] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0497] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0498] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0499] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, and the camera 42 are also connected to the bus 52.

[0500] The microphone 238 receives instructions and the like from the user 20 by receiving voice uttered by the user 20. The microphone 238 captures the voice uttered by the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio in accordance with instructions from the processor 46.

[0501] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the surroundings of user 20 (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0502] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0503] Fig. 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Fig. 4, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0504] The specific processing program 56 is an example of a "program" according to the technology of the present 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.

[0505] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0506] In the smart glasses 214, the reception output process is performed by the processor 46. A reception output program 60 is stored in the storage 50. 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 process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[0507] Next, a description will be given of the identification process performed by the identification processing unit 290 of the data processing device 12. 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."

[0508] This invention is a system that uses interactive artificial intelligence to extract employee knowledge and skills, visualizes them based on third-party evaluations, and stores them in a database. Specific embodiments of this system are described below.

[0509] System configuration

[0510] The system mainly consists of the following components:

[0511] Terminal: Provides an interface for users to interact with conversational artificial intelligence.

[0512] Server: Performs interactive AI processing, database management, and evaluation / visualization processing.

[0513] Database: Stores employee knowledge, skills, and evaluation results.

[0514] Overview of program processing

[0515] In this system, employees (users) log in via their terminals, interact with the conversational AI, and the knowledge and skills acquired in the process are extracted and evaluated. The evaluation results are stored in a database and can be searched and used by other users. The evaluation results are also reflected in personnel evaluations.

[0516] Extracting user knowledge and skills

[0517] 1. Login and authentication: The user logs in by entering their employee ID and password into the terminal. The server authenticates the user information, and if authentication is successful, a session with the conversational AI begins.

[0518] 2. Dialogue and knowledge extraction: The user interacts with the conversational AI in natural language on the terminal. The server analyzes the content of this dialogue using natural language processing technology and extracts the user's knowledge and skills.

[0519] 3. Evaluation and visualization: The server performs a third-party evaluation of the extracted knowledge and skills and visualizes the evaluation results.

[0520] Database storage and search

[0521] 1. Storage of evaluation results: The knowledge and skill information for which evaluation has been completed is stored in a database along with the evaluation results.

[0522] 2. Information search: When other users search for the information they need, they use the search interface on their device. The server extracts information that matches the search keywords from the database and displays it on the device.

[0523] Specific examples

[0524] Example 1: Knowledge sharing among veteran employees

[0525] The terminal provides a screen for the veteran employee to log in. Once the user logs in, a session with the conversational artificial intelligence begins.

[0526] As users talk about project management best practices, the server uses natural language processing techniques to extract key knowledge.

[0527] The server performs a third-party evaluation of this knowledge and visualizes the results, which are then stored in a database and made available for other employees to search.

[0528] Example 2: Improving the skills of new employees

[0529] If a new employee logs in and wants to search for knowledge about project management, he or she will type "project management" into the search interface on the terminal.

[0530] The server retrieves relevant information from a database and displays it on the terminal, which is best practices and skills shared by veteran employees.

[0531] New employees can improve their skills based on the information displayed.

[0532] This will enable employees to share their knowledge and skills efficiently, which is expected to promote productivity and growth throughout the organization. This system will ensure that the wealth of experience of veteran employees is not lost, and will make it easily accessible to new employees and other employees, thereby promoting the effective use of knowledge across the entire company.

[0533] The processing flow will be explained below.

[0534] Step 1:

[0535] The terminal displays a login screen to the user, who then enters their employee ID and password.

[0536] Step 2:

[0537] The server receives the employee ID and password entered by the user. The server performs authentication in the database and, if successful, generates a session ID. The server then sends the session ID to the terminal.

[0538] Step 3:

[0539] The device displays a conversational AI interface to the user, who is given instructions such as "Talk about your project management experience."

[0540] Step 4:

[0541] The user speaks to the conversational AI in natural language about their knowledge and skills, and the device captures the user's voice or text input and sends it to the server.

[0542] Step 5:

[0543] The server analyzes the received dialogue data using natural language processing technology, performing tokenization, grammatical analysis, and semantic analysis to extract the user's knowledge and skills.

[0544] Step 6:

[0545] The server performs a third-party evaluation of the extracted knowledge and skills. The evaluation criteria are depth of knowledge, accuracy of content, and practicality. The evaluation results are visualized and displayed in the form of graphs and scores.

[0546] Step 7:

[0547] The terminal displays the evaluation results to the user, who can then check them and make corrections or additions as necessary.

[0548] Step 8:

[0549] The server stores the evaluated information in a database, including knowledge entities, evaluation results, and a summary of the dialogue content.

[0550] Step 9:

[0551] The terminal provides a search interface, where other users can input search keywords to search for the information they need.

[0552] Step 10:

[0553] The server processes the search query, retrieves information from the database that matches the search keywords, organizes the search results, and sends them to the device.

[0554] Step 11:

[0555] The device displays the search results, and the user checks the displayed information and applies the necessary knowledge and skills.

[0556] Step 12:

[0557] The user provides feedback on the information they searched for. The device collects the feedback and sends it to the server.

[0558] Step 13:

[0559] The server updates the database based on user feedback, making corrections to improve the accuracy and usefulness of the information.

[0560] Example 1

[0561] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0562] In modern companies, the knowledge and skills of employees are often not shared sufficiently, resulting in lower productivity throughout the organization and insufficient utilization of knowledge. In particular, there is a problem in which the valuable knowledge and skills accumulated by veteran employees are not effectively conveyed to new employees and other employees. Furthermore, there is a lack of a way to fairly evaluate employees' knowledge and skills and visualize the results, which also affects personnel evaluations. A method to solve these issues is needed.

[0563] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[0564] In this invention, the server includes means for extracting employee knowledge and abilities using interactive AI, means for visualizing knowledge and abilities based on third-party evaluation, means for storing the knowledge and abilities in a database along with the evaluation results, means for a user to input authentication information and the server to perform authentication, means for the interactive AI to analyze the content of the dialogue using a natural language processing tool and extract knowledge and abilities, and means for visualizing the evaluation results in graphs and charts and storing them in a database. This makes it possible to efficiently extract, objectively evaluate, and visualize employee knowledge and abilities, enabling knowledge sharing throughout the organization and improving the accuracy of personnel evaluations.

[0565] "Employee knowledge and abilities" refers to the specialized knowledge, skills, experience, and ability to apply those skills possessed by individual employees within an organization.

[0566] "Conversational artificial intelligence" refers to an artificial intelligence system that elicits information from a user through natural language dialogue and generates appropriate responses.

[0567] "Third-party evaluation" refers to the process of objectively evaluating the knowledge and abilities extracted by interactive artificial intelligence from a third-party perspective.

[0568] "Visualization means" refers to methods and devices for displaying extracted knowledge and abilities in a visual format such as graphs or charts, and expressing them in an easy-to-understand manner.

[0569] A "database" refers to a collection of data that stores accumulated knowledge, skills, and evaluation results and makes them searchable as needed.

[0570] "Authentication information" refers to the information required for a user to access a system (e.g., employee ID and password).

[0571] "Natural language processing tools" refer to software and algorithms that analyze input natural language and extract meaning.

[0572] "Graphs and charts" refers to figures and diagrams that visually represent evaluation results.

[0573] "Searchability" refers to the interfaces and algorithms that allow users to search for information in a database.

[0574] The "display means" refers to a method or device for displaying the searched information on a terminal so that the user can confirm it.

[0575] "User" refers to an individual who uses the system to extract, evaluate, search, and display knowledge and capabilities.

[0576] "Server" refers to a computer system that handles interactive artificial intelligence processing, database management, and evaluation / visualization processing.

[0577] This invention is a system that uses interactive artificial intelligence to extract employees' knowledge and abilities, visualize them based on third-party evaluations, and store them in a database.

[0578] System configuration

[0579] The system consists of the following components:

[0580] Terminal

[0581] It provides an interface for users to interact with the conversational AI. Users log in to the system by entering their employee ID and password and begin the conversation.

[0582] server

[0583] It handles interactive AI processing, database management, and evaluation / visualization. The server uses natural language processing tools (e.g., OpenAI's GPT-3) to analyze the user's dialogue and extract knowledge and abilities. It also performs a third-party evaluation of the extracted knowledge and abilities and stores the results in a database. The evaluation results are visualized using graphs and charts.

[0584] Database

[0585] Employee knowledge, skills, and evaluation results are stored and can be searched as needed. Users can use their devices to search the database and view the stored knowledge and skills.

[0586] Overview of program processing

[0587] The terminal, server, and database work together. Users log in through their terminal and interact with the conversational AI. The content of the conversation is sent to the server, where knowledge and abilities are extracted using natural language processing technology. After the evaluation is completed, the results are stored in the database and displayed on the terminal as visualized information.

[0588] Usage example

[0589] Here we will show a specific example of use.

[0590] Example 1: Knowledge sharing among veteran employees

[0591] The terminal provides a screen for the veteran employee to log in. Once the user logs in, a session with the conversational artificial intelligence begins.

[0592] As users talk about project management best practices, the server uses natural language processing techniques to extract key knowledge.

[0593] The server performs a third-party evaluation of this knowledge and visualizes the results, which are then stored in a database that can be searched and used by other employees.

[0594] Example prompt sentence:

[0595] Please enter your employee ID and password to log in.

[0596] "Tell me about your project management experience."

[0597] "Rate this knowledge on a scale of 1 to 5."

[0598] "Knowledge storage complete."

[0599] "Search for project management best practices."

[0600] Example 2: Improving the skills of new employees

[0601] If a new employee logs in and wants to search for knowledge about project management, he or she will type "project management" into the search interface on the terminal.

[0602] The server retrieves relevant information from a database and displays it on the terminal, which is best practices and skills shared by veteran employees.

[0603] New employees can improve their skills based on the information displayed.

[0604] Example prompt sentence:

[0605] "Search for project management best practices."

[0606] In this way, by creating a system that allows employees' knowledge and abilities to be shared efficiently, it is expected that productivity and growth will be promoted throughout the organization.

[0607] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0608] Step 1: User Login and Authentication

[0609] explanation:

[0610] Input: The user enters their employee ID and password into the terminal.

[0611] Specific operation: The terminal displays the login screen, and the user enters the employee ID "12345" and password "password123."

[0612] Data processing: The server checks the entered employee ID and password against the database.

[0613] Output: Returns the authentication result (success or failure) to the terminal.

[0614] Specific operation: The server queries the database for user information, and if authentication is successful, displays "Login successful."

[0615] Step 2: Dialogue and knowledge extraction

[0616] explanation:

[0617] Input: The user inputs the dialogue content into the conversational artificial intelligence through the terminal.

[0618] What happens: The device displays a dialogue interface and the user types, "What are some best practices for project management?"

[0619] Data processing: The server sends the dialogue content to a natural language processing tool (e.g., GPT-3) to extract knowledge and abilities.

[0620] Output: The extracted knowledge and capabilities (e.g., "risk management is important") are returned to the server.

[0621] Specific operation: The server uses GPT-3 to analyze the content of the conversation and extract knowledge such as "risk management is important."

[0622] Step 3: Third-party evaluation and visualization

[0623] explanation:

[0624] Input: Extracted knowledge and skills (e.g., "Risk management is important")

[0625] Specific operation: The server performs a third-party evaluation of the extracted knowledge and abilities.

[0626] Data processing: The server conducts third-party evaluations (e.g., evaluations by other employees or AI) and visualizes the results.

[0627] Output: Generate evaluation results (e.g., "very useful") in visual form (e.g., graphs, charts).

[0628] Specific operation: The server converts the evaluation results into a pie chart and visualizes them.

[0629] Step 4: Database storage

[0630] explanation:

[0631] Input: Knowledge and abilities extracted along with the evaluation results

[0632] Specific operation: The server stores the evaluation results and knowledge and ability information in a database.

[0633] Data processing: The server inserts the evaluation results and knowledge and ability information into a database table.

[0634] Output: Entries saved in the database

[0635] Specific operation: The server records knowledge about "risk management" and its evaluation results in a database.

[0636] Step 5: Find and view information

[0637] explanation:

[0638] Input: The user types a search keyword into the device (e.g., "project management")

[0639] Specific behavior: The device displays a search interface, and the user types "project management."

[0640] Data processing: The server searches the database and extracts information that matches the keywords.

[0641] Output: Return search results (e.g., "Risk management is important") to the terminal.

[0642] Specific operation: The server searches the database and displays the relevant information on the terminal, which then displays the search results to the user.

[0643] (Application example 1)

[0644] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0645] In the past, it was difficult to effectively draw out the knowledge and skills of employees and share them throughout the organization. In particular, there was a lack of means to quickly incorporate the knowledge and know-how of experienced engineers into factory automation equipment, which hindered improvements in production efficiency. Another challenge was properly evaluating employee skills, visualizing them, storing them in a database, and making them easily accessible to other employees.

[0646] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[0647] In this invention, the server includes a means for extracting employee knowledge and skills using interactive AI, a means for visualizing the knowledge and skills based on third-party evaluation, and a means for storing the knowledge and skills together with the evaluation results in a database, which allows the evaluated knowledge and skill information to be imported into automated equipment in the factory.

[0648] An "employee" is an individual who belongs to an organization or company and performs certain duties.

[0649] "Knowledge" is the sum of information and understanding gained through experience and learning.

[0650] A "skill" is a technique or ability to perform a specific task or work.

[0651] "Conversational artificial intelligence" is an artificial intelligence system that can converse with humans in natural language.

[0652] "Third-party evaluation" is an objective evaluation process based on specific criteria and evaluation methods.

[0653] "Visualization" is the process of visually representing data or information to make it easier to understand.

[0654] A "database" is a system that systematically stores and manages data and makes it easily accessible.

[0655] "Searching" is the process of locating data in a database based on specific criteria.

[0656] "Display" is the act of visually presenting data or information to a user.

[0657] "Factory automation equipment" refers to machinery and systems used to automate work within a factory.

[0658] An "interface" is a means or protocol that allows information to be exchanged between different systems or devices.

[0659] "Natural language processing technology" is a technology that enables computers to understand and manipulate human language.

[0660] "Human resource evaluation" is a process for evaluating employees' performance and abilities and ensuring fair treatment and placement.

[0661] The system embodying this invention uses interactive artificial intelligence to extract employee knowledge and skills, evaluate them from a third-party perspective, visualize them, and store them in a database. The system is mainly composed of the following components.

[0662] System configuration

[0663] The system consists of three main parts: terminals, servers, and databases, allowing employees' knowledge and skills to be efficiently shared and incorporated into the factory's automated equipment.

[0664] Terminal

[0665] The terminal is a device that provides an interface for engineers and employees to interact with the conversational AI. Specifically, a smartphone or tablet is used.

[0666] server

[0667] The server handles interactive AI processing, database management, evaluation and visualization. The AI ​​part includes a generative AI model, and OpenAI's GPT model is used as an example.

[0668] Database

[0669] The database is a system that stores employee knowledge, skills, and evaluation results. A lightweight database such as SQLite is used.

[0670] Interface

[0671] An interface is provided that allows factory automation equipment to incorporate assessed knowledge and skill information.

[0672] Program processing overview

[0673] Login and Authentication

[0674] Employees log in by entering their user ID and password from their smartphone or tablet. The server collates the user information and performs authentication, and if authentication is successful, a session with the conversational artificial intelligence begins.

[0675] Knowledge extraction and evaluation

[0676] Employees interact with the conversational AI through their devices, and the knowledge and skills acquired in the process are analyzed and extracted by the server using natural language processing technology. The extracted knowledge is then evaluated by a third party using a generative AI model.

[0677] Data visualization and accumulation

[0678] The evaluation results are visualized and stored in a database, allowing other engineers and employees to easily search the database and access the information they need. The evaluated knowledge and skill information is also incorporated into factory robots.

[0679] Example of program generation

[0680] For example, if an engineer wants to teach a robot how to optimize the welding process, here's a sample prompt:

[0681] text

[0682] Rate the following skills:

[0683] Learn best practices for optimizing your welding process, from job preparation to finishing. We share details on accurate measurements, proper material selection, temperature control, and time allocation.

[0684] The generative AI model returns the following evaluation results:

[0685] text

[0686] This technique is highly specialized and useful in many fields. Accurate dimensional measurements and appropriate material selection are particularly important. Temperature control and time allocation are also explained in detail, which is highly praised.

[0687] The evaluation results are stored in a database, and new robots can learn from this information, which is expected to lead to efficient knowledge sharing within factories and improved productivity.

[0688] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0689] Step 1:

[0690] A user logs in using a terminal by entering their employee ID and password. The employee ID and password are required as input, and the server receives them and checks them against the authentication information in the database. If authentication is successful, the server starts a session and provides the user with an interface with the conversational artificial intelligence. As output, the user is shown a message confirming successful login.

[0691] Step 2:

[0692] The user interacts with the conversational AI in natural language via a terminal. The content of the user's conversation (natural language) is required as input, and the server receives this. The server analyzes this content of the conversation using natural language processing technology and extracts information about the user's knowledge and skills. Data processing involves text analysis of the content of the conversation and formatting of related information, and knowledge and skills are extracted. The analysis results are then saved on the server as output.

[0693] Step 3:

[0694] The server uses a generative AI model to evaluate the extracted knowledge and skill information. The extracted information (natural language text) is required as input, and the server sends this to the generative AI model. The generative AI model evaluates the information based on the prompt sentence and generates an evaluation result. As data calculations, the generative AI model generates text and calculates an evaluation score. As output, the evaluation result is returned to the server.

[0695] Step 4:

[0696] Based on the evaluation results, the server visualizes knowledge and skill information and saves it in a database. The evaluation results are required as input, and the server receives them and visualizes them graphically. This makes the information easier to understand visually. Data processing involves generating graphs of the evaluation results and formatting them into tables. The visualized data is then saved in a database as output.

[0697] Step 5:

[0698] Other users or factory robots retrieve the information they need from the database via a search interface. Search keywords are required as input, and the user or robot enters a search query from their terminal. The server searches the database and extracts the relevant knowledge and skill information. As a data calculation, a search algorithm is used to identify and extract matching information. As an output, the search results are displayed on the terminal or robot.

[0699] Step 6:

[0700] The factory's automation equipment takes in the assessed knowledge and skill information and applies it to work. The retrieved knowledge and skill information is required as input, and is received by factory robots and other automation equipment. The automation equipment optimizes the work process based on the taken-in information. As data processing, the received information is integrated into the equipment's control algorithm and the operating procedure is adjusted. The optimized work process is realized as output.

[0701] Furthermore, an emotion engine that estimates the user's emotion may be further combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59, and perform identification processing using the user's emotion.

[0702] This invention is a system that uses interactive artificial intelligence to extract employee knowledge and skills, visualizes them based on third-party evaluations, and stores them in a database. In addition, by combining this with an emotion engine, it recognizes the user's emotions and reflects that information in the evaluations. A specific embodiment of this system is described below.

[0703] System configuration

[0704] The system mainly consists of the following components:

[0705] Terminal: Provides an interface for users to interact with the conversational artificial intelligence and emotion engine.

[0706] Server: Performs interactive AI processing, emotion recognition, database management, and evaluation / visualization processing.

[0707] Database: Stores employee knowledge, skills, evaluation results, and emotional data.

[0708] Overview of program processing

[0709] This system allows employees (users) to log in via a terminal, converse with a conversational AI, extract the knowledge and skills acquired in the process, and use an emotion engine to recognize the user's emotions to evaluate them. The evaluation results are stored in a database and can be searched and used by other users. The evaluation results and emotion data are also reflected in personnel evaluations.

[0710] Extracting user knowledge and skills and recognizing emotions

[0711] 1. Login and authentication: The user logs in by entering their employee ID and password into the terminal. The server authenticates the user information, and if authentication is successful, a session with the conversational artificial intelligence and emotion engine begins.

[0712] 2. Dialogue and knowledge extraction: The user interacts with the conversational AI in natural language on the terminal. The server analyzes the content of this dialogue using natural language processing technology and extracts the user's knowledge and skills.

[0713] 3. Emotion Recognition: During a conversation, the emotion engine analyzes the user's emotions from their voice, facial expressions, and text, and tracks the results in real time.

[0714] Evaluation and visualization

[0715] 1. Evaluation: The server performs a third-party evaluation of the extracted knowledge and skills. The evaluation criteria are depth of knowledge, accuracy of content, usefulness, and user emotional data.

[0716] 2. Visualization: The evaluation results are visualized and presented in an easy-to-understand format (graphs and scores). Emotional data is also displayed.

[0717] Database storage and search

[0718] 1. Storing evaluation results: The knowledge, skill information, and emotional data for which evaluation has been completed are stored in a database.

[0719] 2. Information search: When other users search for the information they need, they use the search interface on their device. The server extracts information that matches the search keywords from the database and displays it on the device.

[0720] Specific examples

[0721] Example 1: Knowledge sharing and emotion recognition among veteran employees

[0722] The terminal provides a screen for the veteran employee to log in. Once the user logs in, a session with the conversational artificial intelligence and emotion engine begins.

[0723] As users talk about project management best practices, the server uses natural language processing techniques to extract key knowledge, and an emotion engine analyzes the user's emotions in the process.

[0724] The server performs a third-party evaluation of this knowledge, visualizes the evaluation results and emotional data, and stores the results in a database that can be searched and used by other employees.

[0725] Example 2: Skill development for new employees and emotional feedback

[0726] If a new employee logs in and wants to search for knowledge about project management, he or she will type "project management" into the search interface on the terminal.

[0727] The server retrieves relevant information from the database and displays it on the device, including best practices and skills shared by veteran employees, as well as emotional data.

[0728] New employees can improve their skills based on the displayed information, and their emotional feedback during use is accumulated as an evaluation.

[0729] This is expected to enable efficient sharing of employee knowledge and skills, promoting productivity and growth throughout the organization. This system will prevent the wealth of experience of veteran employees from being buried, making it easily accessible to new employees and other employees, and by taking user emotions into consideration, it will enable deeper understanding and appropriate evaluation.

[0730] The processing flow will be explained below.

[0731] Step 1:

[0732] The terminal displays a login screen to the user, who then enters their employee ID and password.

[0733] Step 2:

[0734] The server receives the employee ID and password entered by the user. The server performs authentication processing in the database, and if successful, generates a session ID and sends it to the terminal.

[0735] Step 3:

[0736] The device displays the interface of the conversational artificial intelligence and emotion engine to the user, who is given instructions such as "Talk about your experience in project management."

[0737] Step 4:

[0738] The user speaks to the conversational AI in natural language about their knowledge and skills, and the device captures the user's voice or text input and sends it to the server.

[0739] Step 5:

[0740] The server analyzes the received dialogue data using natural language processing technology, performing tokenization, grammatical analysis, and semantic analysis to extract the user's knowledge and skills.

[0741] Step 6:

[0742] The emotion engine analyzes emotions from the user's voice, facial expressions, and text during a conversation and collects the data in real time. The emotion engine identifies emotions such as joy, sadness, and anger.

[0743] Step 7:

[0744] The server performs a third-party evaluation based on the extracted knowledge, skills, and emotional data. The evaluation criteria are depth of knowledge, accuracy of content, practicality, and emotional data. The evaluation results are visualized and displayed in the form of graphs and scores.

[0745] Step 8:

[0746] The device displays the evaluation results and emotion data to the user, who can then review the results and make corrections or additions as necessary.

[0747] Step 9:

[0748] The server stores the evaluated information and emotion data in a database, including knowledge entities, evaluation results, dialogue summaries, and emotion data.

[0749] Step 10:

[0750] The terminal provides a search interface, where other users can input search keywords to search for the information they need.

[0751] Step 11:

[0752] The server processes the search query, retrieves information from the database that matches the search keywords, organizes the search results, and sends them to the device.

[0753] Step 12:

[0754] The device displays the search results. The user can review the displayed information and utilize the necessary knowledge and skills. Emotional data is also displayed for the user to refer to.

[0755] Step 13:

[0756] The user provides feedback on the information they searched for. The device collects the feedback and sends it to the server.

[0757] Step 14:

[0758] The server updates the database based on user feedback, making corrections to improve the accuracy and usefulness of the information.

[0759] Example 2

[0760] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0761] Conventional systems only use conversational AI to extract employee knowledge and skills, but evaluations tend to be subjective. Furthermore, because they do not take emotions into account, the user's psychological state is not reflected in the evaluation, which can lead to inaccurate evaluations. Furthermore, the system has low search efficiency for accumulated knowledge and skills, making it difficult for new employees and other employees to quickly obtain the information they need. This has led to a need for improved knowledge sharing and personnel evaluations across the organization.

[0762] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[0763] In this invention, the server includes means for extracting employee knowledge and skills using interactive artificial intelligence, means for visualizing knowledge and skills based on third-party evaluations, means for storing the knowledge and skills in a database along with the evaluation results, means for making the stored knowledge and skills searchable, means for displaying the searched knowledge and skills, means for recognizing user emotions, and means for reflecting emotion data in the evaluation. This allows for fairer and more reliable evaluations by reflecting emotions along with accurate evaluations of users' knowledge and skills, and further enables knowledge sharing and skill improvement throughout the organization through appropriate information retrieval.

[0764] "Means of extracting employee knowledge and skills using interactive AI" is a function that extracts the knowledge and skills of a user by having the user interact with the interactive AI in natural language.

[0765] "A means of visualizing knowledge and skills based on third-party evaluation" is a function that evaluates extracted knowledge and skills using objective criteria and presents the evaluation results in a format that can be intuitively understood.

[0766] "Means for storing knowledge and skills together with evaluation results in a database" refers to a function for storing data on evaluated knowledge and skills and the evaluation results in a database in order to centrally manage them.

[0767] "Means for making accumulated knowledge and skills searchable" refers to a function that enables efficient searching of information stored in a database.

[0768] The "means for displaying searched knowledge and skills" is a function for displaying information obtained by a search to the user.

[0769] "Means for recognizing user emotions" refers to a function for identifying and analyzing the user's emotions during a dialogue or interaction, and determines emotions based on tone of voice, facial expressions, and text content.

[0770] The "means for reflecting emotional data in evaluation" is a function for correcting and amending the evaluation results of knowledge and skills based on the results of user emotion recognition, thereby enabling more accurate evaluation.

[0771] This invention is a system that uses interactive artificial intelligence to extract employee knowledge and skills, visualizes them based on third-party evaluations, and stores the results in a database. Furthermore, by combining it with an emotion engine, it recognizes the user's emotions and reflects that information in the evaluations. A specific embodiment of the invention will be described.

[0772] System configuration

[0773] The system mainly consists of the following components:

[0774] Terminal: Provides an interface for users to interact with the conversational artificial intelligence and emotion engine.

[0775] Server: Performs interactive AI processing, emotion recognition, database management, and evaluation / visualization processing.

[0776] Database: Stores employee knowledge, skills, evaluation results, and emotional data.

[0777] Hardware and software used

[0778] 1. Terminal: A device such as a PC, tablet, or smartphone. These devices provide a user interface and a means for users to interact with the conversational artificial intelligence and use the emotion engine.

[0779] 2. Server: A high-performance computer system that provides the execution environment for conversational artificial intelligence (e.g., generative AI models), natural language processing technologies (e.g., Python's NLTK or Google Cloud Natural Language API), emotion recognition engines (e.g., Microsoft Azure Emotion API), etc.

[0780] 3. Database: A relational database system (e.g., MySQL, PostgreSQL) or a NoSQL database (e.g., MongoDB) to efficiently store and search evaluation results, user knowledge and skill data, and sentiment data.

[0781] Specific examples

[0782] Example 1: Knowledge sharing and emotion recognition among veteran employees

[0783] The terminal provides a screen for the veteran employee to log in. Once the user logs in, a session with the conversational artificial intelligence and emotion engine begins.

[0784] As users talk about project management best practices, the server uses natural language processing techniques to extract key knowledge, and an emotion engine analyzes the user's emotions in the process.

[0785] The server performs a third-party evaluation of this knowledge, visualizes the evaluation results and emotional data, and stores the results in a database that can be searched and used by other employees.

[0786] Example 2: Skill development for new employees and emotional feedback

[0787] If a new employee logs in and wants to search for knowledge about project management, he or she will type "project management" into the search interface on the terminal.

[0788] The server retrieves relevant information from the database and displays it on the device, including best practices and skills shared by veteran employees, as well as emotional data.

[0789] Users can improve their skills based on the displayed information, and their emotional feedback during use is accumulated as an evaluation.

[0790] Prompt Sentence Examples

[0791] "What are some important points about project management?"

[0792] Please rate this skill.

[0793] "Please tell me the results of your recent sentiment analysis."

[0794] This invention enables fairer and more reliable evaluations by accurately assessing employees' knowledge and skills while also reflecting their emotions, and promotes knowledge sharing and skill improvement throughout the organization through appropriate information retrieval.

[0795] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0796] Step 1:

[0797] Login and Authentication

[0798] The user enters their employee ID and password into the login screen of the device.

[0799] Input: Employee ID and password

[0800] The terminal transmits the input data to the server.

[0801] Based on the received data, the server checks the authentication information of the corresponding user in the database.

[0802] Data processing: Authentication by database matching

[0803] Output: Authentication result (success or failure)

[0804] If the authentication is successful, the server starts the session and prepares the conversational artificial intelligence and emotion engine.

[0805] Specific working example:

[0806] The user opens the login page in a browser, enters the employee ID "user123" and password "pass123", and clicks the "Login" button.

[0807] The server verifies this information and displays the home screen if authentication is successful.

[0808] Step 2:

[0809] Dialogue and Knowledge Extraction

[0810] The user begins a conversation in natural language with a conversational artificial intelligence (generative AI model) on the device.

[0811] Input: Natural language questions and conversations

[0812] The terminal transmits the user's input text to the server in real time.

[0813] The server uses natural language processing technology (e.g., Python's NLTK or Google Cloud Natural Language API) to analyze the conversation and extract the user's knowledge and skills.

[0814] Data processing: Text analysis using natural language processing technology

[0815] Output: Extracted knowledge and skills

[0816] Specific working example:

[0817] User types, "What are some project management best practices?"

[0818] The server analyzes this text and extracts "project management" and "best practices" as key concepts.

[0819] Step 3:

[0820] emotion recognition

[0821] While the user is interacting, the emotion engine analyzes emotions from the user's voice, facial expressions, and text.

[0822] Input: Voice data, facial expression data, text data

[0823] The device uses voice input and a camera to extract voice and facial expression data and transmits it to a server.

[0824] The server analyzes this data using an emotion engine (e.g., Microsoft Azure Emotion API) to track emotional states in real time.

[0825] Data processing: Emotion recognition through voice and facial expression analysis

[0826] Output: Emotion data (e.g. percentage of happy, surprised, sad)

[0827] Specific working example:

[0828] The user shows expressions of joy and surprise during the interaction.

[0829] The server records the emotional data as "Happiness: 80%" and "Surprise: 60%."

[0830] Step 4:

[0831] Conducting the evaluation

[0832] The server performs a third-party evaluation of the extracted knowledge and skills.

[0833] The evaluation criteria are depth of knowledge, accuracy of content, usefulness, and user sentiment data.

[0834] Input: Extracted knowledge, skills, and emotion data

[0835] The server combines these multiple factors to generate an overall rating score.

[0836] Data processing: Criteria-based scoring

[0837] Output: Overall evaluation score

[0838] Specific working example:

[0839] The server evaluates the accuracy and practicality of the knowledge about "project management best practices" entered by the user and provides an "evaluation score: 85 points."

[0840] Step 5:

[0841] Visualization of evaluation results

[0842] The server visualizes the evaluation results and displays them in an easy-to-understand format.

[0843] Input: Evaluation scores, emotion data

[0844] The server generates visualization data and sends it to the terminal.

[0845] The terminal provides an interface for displaying the evaluation results in the form of graphs, scores, etc.

[0846] Data processing: Converting evaluation results into visualized data

[0847] Output: Visualized data (graphs, scores)

[0848] Specific working example:

[0849] The server generates data to display the evaluation score of "85 points" in a pie chart or bar graph.

[0850] The terminal receives this, visualizes the evaluation results, and displays them to the user.

[0851] Step 6:

[0852] Saving evaluation results

[0853] The server stores the evaluated knowledge, skill information and emotion data in a database.

[0854] Input: Evaluation results, emotion data

[0855] A database organizes and stores this information for future retrieval and reference.

[0856] Data processing: Inserting data into the database

[0857] Output: Accumulated data

[0858] Specific working example:

[0859] The server saves the evaluation results and emotion data in the database using an INSERT statement.

[0860] Step 7:

[0861] Searching for information

[0862] When a user searches for the information they need, they use the search interface on their device.

[0863] Input: Search keyword

[0864] The server extracts information that matches the search keywords from the database and sends it to the terminal.

[0865] Data processing: Data extraction based on search keywords

[0866] Output: Search results

[0867] Specific working example:

[0868] A new employee types in "project management" and clicks the search button.

[0869] The server retrieves the relevant information from the database and displays the appropriate information on the terminal.

[0870] (Application example 2)

[0871] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0872] Conventional systems only extracted employee knowledge and skills through conversational AI, making it difficult to properly reflect the evaluation in personnel evaluations. Furthermore, the system did not reflect the user's emotional state, making accurate evaluations and motivation management difficult. In particular, in factories, the emotions and stress levels of workers significantly affect the quality of their work, so the development of a system that takes this into account was needed.

[0873] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 2 is realized by the following means.

[0874] In this invention, the server includes means for extracting employee knowledge and skills using interactive artificial intelligence, means for visualizing knowledge and skills based on third-party evaluations, means for storing the knowledge and skills in a database along with the evaluation results, means for making the stored knowledge and skills searchable, means for displaying the searched knowledge and skills, and emotion recognition means for recognizing the user's emotions and reflecting that information in the evaluation. This enables appropriate evaluations and knowledge sharing that take into account the emotions of employees.

[0875] An "employee" is someone who belongs to a company or organization and provides labor for its activities.

[0876] "Knowledge" is an abstract concept that refers to information, understanding, and experiential understanding in a particular field.

[0877] "Skills" are the practical abilities and techniques required to effectively carry out a particular task or activity.

[0878] "Conversational AI" refers to an AI system designed to mimic natural human interactions and uses natural language processing techniques.

[0879] "Third-party evaluation" is an objective evaluation method conducted from an independent standpoint other than the person being evaluated.

[0880] "Visualization" is the act of presenting abstract data such as knowledge, skills, and evaluation results in a format that is visually easy to understand.

[0881] A "database" is a system that organizes and stores information so that it can be searched and used efficiently later.

[0882] "Emotion recognition" is a technology that automatically identifies a user's emotional state at any given time by analyzing their voice, facial expressions, text, etc.

[0883] "Evaluation results" are the results of evaluation of knowledge and skills obtained through interactive artificial intelligence or third-party evaluation.

[0884] A "server" is a computer that provides services such as data processing and storage to other computers over a network.

[0885] "Searchable" means having the ability to efficiently find specific information from the accumulated data.

[0886] This invention is a system that uses interactive artificial intelligence to extract employee knowledge and skills, visualizes the evaluation results, and stores them in a database. Furthermore, by combining it with emotion recognition technology, the system can reflect the user's emotional state in the evaluation. To implement this invention, the following specific configuration and procedures are required.

[0887] System configuration

[0888] The system mainly consists of the following components:

[0889] 1. Server: Performs interactive AI processing, emotion recognition, database management, and evaluation / visualization processing.

[0890] 2. Terminal: Provides an interface for users to interact with the conversational artificial intelligence and emotion engine.

[0891] 3. Database: Stores employee knowledge, skills, evaluation results, and emotional data.

[0892] Overview of program processing

[0893] This system allows users to log in via a terminal, converse with a conversational AI, extract the knowledge and skills gained in the process, and use an emotion engine to recognize the user's emotions to evaluate them. The evaluation results are stored in a database and can be searched and used by other users. The evaluation results and emotion data are also reflected in personnel evaluations.

[0894] Extracting user knowledge and skills and recognizing emotions

[0895] 1. Login and authentication: The user logs in by entering their employee ID and password into the terminal. The server authenticates the user information, and if authentication is successful, a session with the conversational artificial intelligence and emotion engine begins.

[0896] 2. Dialogue and knowledge extraction: The user interacts with the conversational AI in natural language on the terminal. The server analyzes the content of this dialogue using natural language processing technology and extracts the user's knowledge and skills.

[0897] 3. Emotion Recognition: During a conversation, the emotion engine analyzes the user's emotions from their voice, facial expressions, and text, and tracks the results in real time.

[0898] Evaluation and visualization

[0899] 1. Evaluation: The server performs a third-party evaluation of the extracted knowledge and skills. The evaluation criteria are depth of knowledge, accuracy of content, usefulness, and user emotional data.

[0900] 2. Visualization: The evaluation results are visualized and presented in an easy-to-understand format (graphs and scores). Emotional data is also displayed.

[0901] Database storage and search

[0902] 1. Storing evaluation results: The knowledge, skill information, and emotional data for which evaluation has been completed are stored in a database.

[0903] 2. Information search: When other users search for the information they need, they use the search interface on their device. The server extracts information that matches the search keywords from the database and displays it on the device.

[0904] Hardware and software used

[0905] 1. Smart glasses: Used during maintenance work on factory robots, they capture the faces and voices of workers and perform emotion recognition.

[0906] 2. EmotionEngine: Analyzes emotions in real time and sends the data to a server.

[0907] 3. Dialogue AI: Uses natural language processing technology to analyze the content of user dialogue and extract knowledge and skills.

[0908] Specific examples

[0909] Example 1: Knowledge sharing and emotion recognition among veteran employees

[0910] 1. The terminal provides a screen for the veteran employee to log in. Once the user logs in, a session with the conversational artificial intelligence and emotion engine begins.

[0911] 2. As users talk about project management best practices, the server uses natural language processing technology to extract key knowledge, and an emotion engine analyzes the user's emotions in the process.

[0912] 3. The server performs a third-party evaluation of this knowledge, visualizes the evaluation results and emotional data, and stores the results in a database where other employees can search and use them.

[0913] Example prompt sentences:

[0914] Please tell me how to replace the parts.

[0915] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0916] Step 1:

[0917] The user logs in by entering their employee ID and password into the terminal.

[0918] Input: Employee ID, Password

[0919] Data processing: The server checks the user information against the database and performs authentication.

[0920] Output: If authentication is successful, a session with the conversational artificial intelligence and emotion engine is initiated.

[0921] Specific operation: When a user enters their employee ID and password into the login screen on their device and presses the "Login" button, an authentication request is sent to the server. The server checks the authentication information against the database and, if correct, starts a session.

[0922] Step 2:

[0923] The user interacts with the conversational artificial intelligence on the terminal in natural language.

[0924] Input: User question or comment (natural language text)

[0925] Data processing: The server uses natural language processing technology to analyze the content of the dialogue and extract the user's knowledge and skills.

[0926] Output: Information about knowledge and skills

[0927] Specific operation: When a user uses the conversational artificial intelligence interface on the terminal to input "Please tell me how to replace this part," the server passes the text data to a natural language processing engine, which extracts knowledge about part replacement as an analysis result.

[0928] Step 3:

[0929] During a conversation, the emotion engine analyzes emotions from the user's voice, facial expressions, and text.

[0930] Input: Camera video, audio data, text data

[0931] Data processing: EmotionEngine analyzes this data and determines the emotional state in real time.

[0932] Output: Emotional state (e.g., happy, surprised, angry, etc.)

[0933] Specific operation: The device's camera and microphone are used to capture the user's face and voice, and this data is sent to EmotionEngine, which analyzes the data and determines and displays the user's emotional state in real time.

[0934] Step 4:

[0935] The server performs a third-party evaluation of the extracted knowledge and skills.

[0936] Input: Extracted knowledge, skills, and emotion data

[0937] Data processing: Evaluation is carried out based on evaluation criteria (depth of knowledge, accuracy of content, usefulness, and sentiment data).

[0938] Output: Evaluation results (score, comments, etc.)

[0939] Specific operation: The server inputs information about knowledge and skills and emotional data into the evaluation algorithm and generates an evaluation result based on multiple criteria. For example, if the knowledge is detailed, accurate, and useful, a high evaluation score is assigned.

[0940] Step 5:

[0941] The evaluation results are visualized and presented in an easy-to-understand format (graphs and scores).

[0942] Input: Evaluation results, emotion data

[0943] Data processing: Transformation for visual display in graphs and scores

[0944] Output: Visualized evaluation results and emotion data

[0945] Specific operation: The server obtains the evaluation results and emotion data, and uses a visualization engine to display them on the terminal in the form of bar graphs, pie charts, score displays, etc.

[0946] Step 6:

[0947] The evaluation results and emotional data are stored in a database.

[0948] Input: Visualized evaluation results and emotion data

[0949] Data processing: Add as a new record to the database

[0950] Output: Evaluation results and emotion data stored in a database

[0951] Specific action: Add a new entry of the emotion data and evaluation results to the database so that other users can access it later.

[0952] Step 7:

[0953] When other users search for the information they need, they use the search interface on their device.

[0954] Input: Search keyword

[0955] Data processing: Extract information that matches keywords from the database

[0956] Output: Related knowledge and skill information

[0957] Specific operation: A keyword is entered into the terminal's search interface, and the server searches for related information from the database and displays it on the terminal.

[0958] Step 8:

[0959] Emotional feedback is provided as the user works based on the search results.

[0960] Input: Use of search results, emotional state during work

[0961] Data processing: Emotion engine analyzes emotions in real time while working

[0962] Output: Emotional feedback data during the task

[0963] Specific operation: A user uses smart glasses while working, and the emotion engine continuously monitors their emotional state, generating and storing feedback data.

[0964] 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 a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0965] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0966] In the above embodiment, an example in which the specific processing is performed by the data processing device 12 has been given, but the technology of the present disclosure is not limited to this, and the specific processing may be performed by the smart glasses 214.

[0967] [Third embodiment]

[0968] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0969] 5, the data processing system 310 includes the data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0970] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0971] The headset type terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a display 343. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the display 343 are also connected to the bus 52.

[0972] The microphone 238 receives instructions and the like from the user 20 by receiving voice uttered by the user 20. The microphone 238 captures the voice uttered by the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio in accordance with instructions from the processor 46.

[0973] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the surroundings of user 20 (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0974] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0975] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset type terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0976] The specific processing program 56 is an example of a "program" according to the technology of the present 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.

[0977] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0978] In the headset type terminal 314, a reception output process is performed by the processor 46. A reception output program 60 is stored in the storage 50. 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 process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[0979] Next, a description will be given of the identification process performed by the identification processing unit 290 of the data processing device 12. 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."

[0980] This invention is a system that uses interactive artificial intelligence to extract employee knowledge and skills, visualizes them based on third-party evaluations, and stores them in a database. Specific embodiments of this system are described below.

[0981] System configuration

[0982] The system mainly consists of the following components:

[0983] Terminal: Provides an interface for users to interact with conversational artificial intelligence.

[0984] Server: Performs interactive AI processing, database management, and evaluation / visualization processing.

[0985] Database: Stores employee knowledge, skills, and evaluation results.

[0986] Overview of program processing

[0987] In this system, employees (users) log in via their terminals, interact with the conversational AI, and the knowledge and skills acquired in the process are extracted and evaluated. The evaluation results are stored in a database and can be searched and used by other users. The evaluation results are also reflected in personnel evaluations.

[0988] Extracting user knowledge and skills

[0989] 1. Login and authentication: The user logs in by entering their employee ID and password into the terminal. The server authenticates the user information, and if authentication is successful, a session with the conversational AI begins.

[0990] 2. Dialogue and knowledge extraction: The user interacts with the conversational AI in natural language on the terminal. The server analyzes the content of this dialogue using natural language processing technology and extracts the user's knowledge and skills.

[0991] 3. Evaluation and visualization: The server performs a third-party evaluation of the extracted knowledge and skills and visualizes the evaluation results.

[0992] Database storage and search

[0993] 1. Storage of evaluation results: The knowledge and skill information for which evaluation has been completed is stored in a database along with the evaluation results.

[0994] 2. Information search: When other users search for the information they need, they use the search interface on their device. The server extracts information that matches the search keywords from the database and displays it on the device.

[0995] Specific examples

[0996] Example 1: Knowledge sharing among veteran employees

[0997] The terminal provides a screen for the veteran employee to log in. Once the user logs in, a session with the conversational artificial intelligence begins.

[0998] As users talk about project management best practices, the server uses natural language processing techniques to extract key knowledge.

[0999] The server performs a third-party evaluation of this knowledge and visualizes the results, which are then stored in a database and made available for other employees to search.

[1000] Example 2: Improving the skills of new employees

[1001] If a new employee logs in and wants to search for knowledge about project management, he or she will type "project management" into the search interface on the terminal.

[1002] The server retrieves relevant information from a database and displays it on the terminal, which is best practices and skills shared by veteran employees.

[1003] New employees can improve their skills based on the information displayed.

[1004] This will enable employees to share their knowledge and skills efficiently, which is expected to promote productivity and growth throughout the organization. This system will ensure that the wealth of experience of veteran employees is not lost, and will make it easily accessible to new employees and other employees, thereby promoting the effective use of knowledge across the entire company.

[1005] The processing flow will be explained below.

[1006] Step 1:

[1007] The terminal displays a login screen to the user, who then enters their employee ID and password.

[1008] Step 2:

[1009] The server receives the employee ID and password entered by the user. The server performs authentication in the database and, if successful, generates a session ID. The server then sends the session ID to the terminal.

[1010] Step 3:

[1011] The device displays a conversational AI interface to the user, who is given instructions such as "Talk about your project management experience."

[1012] Step 4:

[1013] The user speaks to the conversational AI in natural language about their knowledge and skills, and the device captures the user's voice or text input and sends it to the server.

[1014] Step 5:

[1015] The server analyzes the received dialogue data using natural language processing technology, performing tokenization, grammatical analysis, and semantic analysis to extract the user's knowledge and skills.

[1016] Step 6:

[1017] The server performs a third-party evaluation of the extracted knowledge and skills. The evaluation criteria are depth of knowledge, accuracy of content, and practicality. The evaluation results are visualized and displayed in the form of graphs and scores.

[1018] Step 7:

[1019] The terminal displays the evaluation results to the user, who can then check them and make corrections or additions as necessary.

[1020] Step 8:

[1021] The server stores the evaluated information in a database, including knowledge entities, evaluation results, and a summary of the dialogue content.

[1022] Step 9:

[1023] The terminal provides a search interface, where other users can input search keywords to search for the information they need.

[1024] Step 10:

[1025] The server processes the search query, retrieves information from the database that matches the search keywords, organizes the search results, and sends them to the device.

[1026] Step 11:

[1027] The device displays the search results, and the user checks the displayed information and applies the necessary knowledge and skills.

[1028] Step 12:

[1029] The user provides feedback on the information they searched for. The device collects the feedback and sends it to the server.

[1030] Step 13:

[1031] The server updates the database based on user feedback, making corrections to improve the accuracy and usefulness of the information.

[1032] Example 1

[1033] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[1034] In modern companies, the knowledge and skills of employees are often not shared sufficiently, resulting in lower productivity throughout the organization and insufficient utilization of knowledge. In particular, there is a problem in which the valuable knowledge and skills accumulated by veteran employees are not effectively conveyed to new employees and other employees. Furthermore, there is a lack of a way to fairly evaluate employees' knowledge and skills and visualize the results, which also affects personnel evaluations. A method to solve these issues is needed.

[1035] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[1036] In this invention, the server includes means for extracting employee knowledge and abilities using interactive AI, means for visualizing knowledge and abilities based on third-party evaluation, means for storing the knowledge and abilities in a database along with the evaluation results, means for a user to input authentication information and the server to perform authentication, means for the interactive AI to analyze the content of the dialogue using a natural language processing tool and extract knowledge and abilities, and means for visualizing the evaluation results in graphs and charts and storing them in a database. This makes it possible to efficiently extract, objectively evaluate, and visualize employee knowledge and abilities, enabling knowledge sharing throughout the organization and improving the accuracy of personnel evaluations.

[1037] "Employee knowledge and abilities" refers to the specialized knowledge, skills, experience, and ability to apply those skills possessed by individual employees within an organization.

[1038] "Conversational artificial intelligence" refers to an artificial intelligence system that elicits information from a user through natural language dialogue and generates appropriate responses.

[1039] "Third-party evaluation" refers to the process of objectively evaluating the knowledge and abilities extracted by interactive artificial intelligence from a third-party perspective.

[1040] "Visualization means" refers to methods and devices for displaying extracted knowledge and abilities in a visual format such as graphs or charts, and expressing them in an easy-to-understand manner.

[1041] A "database" refers to a collection of data that stores accumulated knowledge, skills, and evaluation results and makes them searchable as needed.

[1042] "Authentication information" refers to the information required for a user to access a system (e.g., employee ID and password).

[1043] "Natural language processing tools" refer to software and algorithms that analyze input natural language and extract meaning.

[1044] "Graphs and charts" refers to figures and diagrams that visually represent evaluation results.

[1045] "Searchability" refers to the interfaces and algorithms that allow users to search for information in a database.

[1046] The "display means" refers to a method or device for displaying the searched information on a terminal so that the user can confirm it.

[1047] "User" refers to an individual who uses the system to extract, evaluate, search, and display knowledge and capabilities.

[1048] "Server" refers to a computer system that handles interactive artificial intelligence processing, database management, and evaluation / visualization processing.

[1049] This invention is a system that uses interactive artificial intelligence to extract employees' knowledge and abilities, visualize them based on third-party evaluations, and store them in a database.

[1050] System configuration

[1051] The system consists of the following components:

[1052] Terminal

[1053] It provides an interface for users to interact with the conversational AI. Users log in to the system by entering their employee ID and password and begin the conversation.

[1054] server

[1055] It handles interactive AI processing, database management, and evaluation / visualization. The server uses natural language processing tools (e.g., OpenAI's GPT-3) to analyze the user's dialogue and extract knowledge and abilities. It also performs a third-party evaluation of the extracted knowledge and abilities and stores the results in a database. The evaluation results are visualized using graphs and charts.

[1056] Database

[1057] Employee knowledge, skills, and evaluation results are stored and can be searched as needed. Users can use their devices to search the database and view the stored knowledge and skills.

[1058] Overview of program processing

[1059] The terminal, server, and database work together. Users log in through their terminal and interact with the conversational AI. The content of the conversation is sent to the server, where knowledge and abilities are extracted using natural language processing technology. After the evaluation is completed, the results are stored in the database and displayed on the terminal as visualized information.

[1060] Usage example

[1061] Here we will show a specific example of use.

[1062] Example 1: Knowledge sharing among veteran employees

[1063] The terminal provides a screen for the veteran employee to log in. Once the user logs in, a session with the conversational artificial intelligence begins.

[1064] As users talk about project management best practices, the server uses natural language processing techniques to extract key knowledge.

[1065] The server performs a third-party evaluation of this knowledge and visualizes the results, which are then stored in a database that can be searched and used by other employees.

[1066] Example prompt sentence:

[1067] Please enter your employee ID and password to log in.

[1068] "Tell me about your project management experience."

[1069] "Rate this knowledge on a scale of 1 to 5."

[1070] "Knowledge storage complete."

[1071] "Search for project management best practices."

[1072] Example 2: Improving the skills of new employees

[1073] If a new employee logs in and wants to search for knowledge about project management, he or she will type "project management" into the search interface on the terminal.

[1074] The server retrieves relevant information from a database and displays it on the terminal, which is best practices and skills shared by veteran employees.

[1075] New employees can improve their skills based on the information displayed.

[1076] Example prompt sentence:

[1077] "Search for project management best practices."

[1078] In this way, by creating a system that allows employees' knowledge and abilities to be shared efficiently, it is expected that productivity and growth will be promoted throughout the organization.

[1079] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1080] Step 1: User Login and Authentication

[1081] explanation:

[1082] Input: The user enters their employee ID and password into the terminal.

[1083] Specific operation: The terminal displays the login screen, and the user enters the employee ID "12345" and password "password123."

[1084] Data processing: The server checks the entered employee ID and password against the database.

[1085] Output: Returns the authentication result (success or failure) to the terminal.

[1086] Specific operation: The server queries the database for user information, and if authentication is successful, displays "Login successful."

[1087] Step 2: Dialogue and knowledge extraction

[1088] explanation:

[1089] Input: The user inputs the dialogue content into the conversational artificial intelligence through the terminal.

[1090] What happens: The device displays a dialogue interface and the user types, "What are some best practices for project management?"

[1091] Data processing: The server sends the dialogue content to a natural language processing tool (e.g., GPT-3) to extract knowledge and abilities.

[1092] Output: The extracted knowledge and capabilities (e.g., "risk management is important") are returned to the server.

[1093] Specific operation: The server uses GPT-3 to analyze the content of the conversation and extract knowledge such as "risk management is important."

[1094] Step 3: Third-party evaluation and visualization

[1095] explanation:

[1096] Input: Extracted knowledge and skills (e.g., "Risk management is important")

[1097] Specific operation: The server performs a third-party evaluation of the extracted knowledge and abilities.

[1098] Data processing: The server conducts third-party evaluations (e.g., evaluations by other employees or AI) and visualizes the results.

[1099] Output: Generate evaluation results (e.g., "very useful") in visual form (e.g., graphs, charts).

[1100] Specific operation: The server converts the evaluation results into a pie chart and visualizes them.

[1101] Step 4: Database storage

[1102] explanation:

[1103] Input: Knowledge and abilities extracted along with the evaluation results

[1104] Specific operation: The server stores the evaluation results and knowledge and ability information in a database.

[1105] Data processing: The server inserts the evaluation results and knowledge and ability information into a database table.

[1106] Output: Entries saved in the database

[1107] Specific operation: The server records knowledge about "risk management" and its evaluation results in a database.

[1108] Step 5: Find and view information

[1109] explanation:

[1110] Input: The user types a search keyword into the device (e.g., "project management")

[1111] Specific behavior: The device displays a search interface, and the user types "project management."

[1112] Data processing: The server searches the database and extracts information that matches the keywords.

[1113] Output: Return search results (e.g., "Risk management is important") to the terminal.

[1114] Specific operation: The server searches the database and displays the relevant information on the terminal, which then displays the search results to the user.

[1115] (Application example 1)

[1116] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[1117] In the past, it was difficult to effectively draw out the knowledge and skills of employees and share them throughout the organization. In particular, there was a lack of means to quickly incorporate the knowledge and know-how of experienced engineers into factory automation equipment, which hindered improvements in production efficiency. Another challenge was properly evaluating employee skills, visualizing them, storing them in a database, and making them easily accessible to other employees.

[1118] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[1119] In this invention, the server includes a means for extracting employee knowledge and skills using interactive AI, a means for visualizing the knowledge and skills based on third-party evaluation, and a means for storing the knowledge and skills together with the evaluation results in a database, which allows the evaluated knowledge and skill information to be imported into automated equipment in the factory.

[1120] An "employee" is an individual who belongs to an organization or company and performs certain duties.

[1121] "Knowledge" is the sum of information and understanding gained through experience and learning.

[1122] A "skill" is a technique or ability to perform a specific task or work.

[1123] "Conversational artificial intelligence" is an artificial intelligence system that can converse with humans in natural language.

[1124] "Third-party evaluation" is an objective evaluation process based on specific criteria and evaluation methods.

[1125] "Visualization" is the process of visually representing data or information to make it easier to understand.

[1126] A "database" is a system that systematically stores and manages data and makes it easily accessible.

[1127] "Searching" is the process of locating data in a database based on specific criteria.

[1128] "Display" is the act of visually presenting data or information to a user.

[1129] "Factory automation equipment" refers to machinery and systems used to automate work within a factory.

[1130] An "interface" is a means or protocol that allows information to be exchanged between different systems or devices.

[1131] "Natural language processing technology" is a technology that enables computers to understand and manipulate human language.

[1132] "Human resource evaluation" is a process for evaluating employees' performance and abilities and ensuring fair treatment and placement.

[1133] The system embodying this invention uses interactive artificial intelligence to extract employee knowledge and skills, evaluate them from a third-party perspective, visualize them, and store them in a database. The system is mainly composed of the following components.

[1134] System configuration

[1135] The system consists of three main parts: terminals, servers, and databases, allowing employees' knowledge and skills to be efficiently shared and incorporated into the factory's automated equipment.

[1136] Terminal

[1137] The terminal is a device that provides an interface for engineers and employees to interact with the conversational AI. Specifically, a smartphone or tablet is used.

[1138] server

[1139] The server handles interactive AI processing, database management, evaluation and visualization. The AI ​​part includes a generative AI model, and OpenAI's GPT model is used as an example.

[1140] Database

[1141] The database is a system that stores employee knowledge, skills, and evaluation results. A lightweight database such as SQLite is used.

[1142] Interface

[1143] An interface is provided that allows factory automation equipment to incorporate assessed knowledge and skill information.

[1144] Program processing overview

[1145] Login and Authentication

[1146] Employees log in by entering their user ID and password from their smartphone or tablet. The server collates the user information and performs authentication, and if authentication is successful, a session with the conversational artificial intelligence begins.

[1147] Knowledge extraction and evaluation

[1148] Employees interact with the conversational AI through their devices, and the knowledge and skills acquired in the process are analyzed and extracted by the server using natural language processing technology. The extracted knowledge is then evaluated by a third party using a generative AI model.

[1149] Data visualization and accumulation

[1150] The evaluation results are visualized and stored in a database, allowing other engineers and employees to easily search the database and access the information they need. The evaluated knowledge and skill information is also incorporated into factory robots.

[1151] Example of program generation

[1152] For example, if an engineer wants to teach a robot how to optimize the welding process, here's a sample prompt:

[1153] text

[1154] Rate the following skills:

[1155] Learn best practices for optimizing your welding process, from job preparation to finishing. We share details on accurate measurements, proper material selection, temperature control, and time allocation.

[1156] The generative AI model returns the following evaluation results:

[1157] text

[1158] This technique is highly specialized and useful in many fields. Accurate dimensional measurements and appropriate material selection are particularly important. Temperature control and time allocation are also explained in detail, which is highly praised.

[1159] The evaluation results are stored in a database, and new robots can learn from this information, which is expected to lead to efficient knowledge sharing within factories and improved productivity.

[1160] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1161] Step 1:

[1162] A user logs in using a terminal by entering their employee ID and password. The employee ID and password are required as input, and the server receives them and checks them against the authentication information in the database. If authentication is successful, the server starts a session and provides the user with an interface with the conversational artificial intelligence. As output, the user is shown a message confirming successful login.

[1163] Step 2:

[1164] The user interacts with the conversational AI in natural language via a terminal. The content of the user's conversation (natural language) is required as input, and the server receives this. The server analyzes this content of the conversation using natural language processing technology and extracts information about the user's knowledge and skills. Data processing involves text analysis of the content of the conversation and formatting of related information, and knowledge and skills are extracted. The analysis results are then saved on the server as output.

[1165] Step 3:

[1166] The server uses a generative AI model to evaluate the extracted knowledge and skill information. The extracted information (natural language text) is required as input, and the server sends this to the generative AI model. The generative AI model evaluates the information based on the prompt sentence and generates an evaluation result. As data calculations, the generative AI model generates text and calculates an evaluation score. As output, the evaluation result is returned to the server.

[1167] Step 4:

[1168] Based on the evaluation results, the server visualizes knowledge and skill information and saves it in a database. The evaluation results are required as input, and the server receives them and visualizes them graphically. This makes the information easier to understand visually. Data processing involves generating graphs of the evaluation results and formatting them into tables. The visualized data is then saved in a database as output.

[1169] Step 5:

[1170] Other users or factory robots retrieve the information they need from the database via a search interface. Search keywords are required as input, and the user or robot enters a search query from their terminal. The server searches the database and extracts the relevant knowledge and skill information. As a data calculation, a search algorithm is used to identify and extract matching information. As an output, the search results are displayed on the terminal or robot.

[1171] Step 6:

[1172] The factory's automation equipment takes in the assessed knowledge and skill information and applies it to work. The retrieved knowledge and skill information is required as input, and is received by factory robots and other automation equipment. The automation equipment optimizes the work process based on the taken-in information. As data processing, the received information is integrated into the equipment's control algorithm and the operating procedure is adjusted. The optimized work process is realized as output.

[1173] Furthermore, an emotion engine that estimates the user's emotion may be further combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59, and perform identification processing using the user's emotion.

[1174] This invention is a system that uses interactive artificial intelligence to extract employee knowledge and skills, visualizes them based on third-party evaluations, and stores them in a database. In addition, by combining this with an emotion engine, it recognizes the user's emotions and reflects that information in the evaluations. A specific embodiment of this system is described below.

[1175] System configuration

[1176] The system mainly consists of the following components:

[1177] Terminal: Provides an interface for users to interact with the conversational artificial intelligence and emotion engine.

[1178] Server: Performs interactive AI processing, emotion recognition, database management, and evaluation / visualization processing.

[1179] Database: Stores employee knowledge, skills, evaluation results, and emotional data.

[1180] Overview of program processing

[1181] This system allows employees (users) to log in via a terminal, converse with a conversational AI, extract the knowledge and skills acquired in the process, and use an emotion engine to recognize the user's emotions to evaluate them. The evaluation results are stored in a database and can be searched and used by other users. The evaluation results and emotion data are also reflected in personnel evaluations.

[1182] Extracting user knowledge and skills and recognizing emotions

[1183] 1. Login and authentication: The user logs in by entering their employee ID and password into the terminal. The server authenticates the user information, and if authentication is successful, a session with the conversational artificial intelligence and emotion engine begins.

[1184] 2. Dialogue and knowledge extraction: The user interacts with the conversational AI in natural language on the terminal. The server analyzes the content of this dialogue using natural language processing technology and extracts the user's knowledge and skills.

[1185] 3. Emotion Recognition: During a conversation, the emotion engine analyzes the user's emotions from their voice, facial expressions, and text, and tracks the results in real time.

[1186] Evaluation and visualization

[1187] 1. Evaluation: The server performs a third-party evaluation of the extracted knowledge and skills. The evaluation criteria are depth of knowledge, accuracy of content, usefulness, and user emotional data.

[1188] 2. Visualization: The evaluation results are visualized and presented in an easy-to-understand format (graphs and scores). Emotional data is also displayed.

[1189] Database storage and search

[1190] 1. Storing evaluation results: The knowledge, skill information, and emotional data for which evaluation has been completed are stored in a database.

[1191] 2. Information search: When other users search for the information they need, they use the search interface on their device. The server extracts information that matches the search keywords from the database and displays it on the device.

[1192] Specific examples

[1193] Example 1: Knowledge sharing and emotion recognition among veteran employees

[1194] The terminal provides a screen for the veteran employee to log in. Once the user logs in, a session with the conversational artificial intelligence and emotion engine begins.

[1195] As users talk about project management best practices, the server uses natural language processing techniques to extract key knowledge, and an emotion engine analyzes the user's emotions in the process.

[1196] The server performs a third-party evaluation of this knowledge, visualizes the evaluation results and emotional data, and stores the results in a database that can be searched and used by other employees.

[1197] Example 2: Skill development for new employees and emotional feedback

[1198] If a new employee logs in and wants to search for knowledge about project management, he or she will type "project management" into the search interface on the terminal.

[1199] The server retrieves relevant information from the database and displays it on the device, including best practices and skills shared by veteran employees, as well as emotional data.

[1200] New employees can improve their skills based on the displayed information, and their emotional feedback during use is accumulated as an evaluation.

[1201] This is expected to enable efficient sharing of employee knowledge and skills, promoting productivity and growth throughout the organization. This system will prevent the wealth of experience of veteran employees from being buried, making it easily accessible to new employees and other employees, and by taking user emotions into consideration, it will enable deeper understanding and appropriate evaluation.

[1202] The processing flow will be explained below.

[1203] Step 1:

[1204] The terminal displays a login screen to the user, who then enters their employee ID and password.

[1205] Step 2:

[1206] The server receives the employee ID and password entered by the user. The server performs authentication processing in the database, and if successful, generates a session ID and sends it to the terminal.

[1207] Step 3:

[1208] The device displays the interface of the conversational artificial intelligence and emotion engine to the user, who is given instructions such as "Talk about your experience in project management."

[1209] Step 4:

[1210] The user speaks to the conversational AI in natural language about their knowledge and skills, and the device captures the user's voice or text input and sends it to the server.

[1211] Step 5:

[1212] The server analyzes the received dialogue data using natural language processing technology, performing tokenization, grammatical analysis, and semantic analysis to extract the user's knowledge and skills.

[1213] Step 6:

[1214] The emotion engine analyzes emotions from the user's voice, facial expressions, and text during a conversation and collects the data in real time. The emotion engine identifies emotions such as joy, sadness, and anger.

[1215] Step 7:

[1216] The server performs a third-party evaluation based on the extracted knowledge, skills, and emotional data. The evaluation criteria are depth of knowledge, accuracy of content, practicality, and emotional data. The evaluation results are visualized and displayed in the form of graphs and scores.

[1217] Step 8:

[1218] The device displays the evaluation results and emotion data to the user, who can then review the results and make corrections or additions as necessary.

[1219] Step 9:

[1220] The server stores the evaluated information and emotion data in a database, including knowledge entities, evaluation results, dialogue summaries, and emotion data.

[1221] Step 10:

[1222] The terminal provides a search interface, where other users can input search keywords to search for the information they need.

[1223] Step 11:

[1224] The server processes the search query, retrieves information from the database that matches the search keywords, organizes the search results, and sends them to the device.

[1225] Step 12:

[1226] The device displays the search results. The user can review the displayed information and utilize the necessary knowledge and skills. Emotional data is also displayed for the user to refer to.

[1227] Step 13:

[1228] The user provides feedback on the information they searched for. The device collects the feedback and sends it to the server.

[1229] Step 14:

[1230] The server updates the database based on user feedback, making corrections to improve the accuracy and usefulness of the information.

[1231] Example 2

[1232] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[1233] Conventional systems only use conversational AI to extract employee knowledge and skills, but evaluations tend to be subjective. Furthermore, because they do not take emotions into account, the user's psychological state is not reflected in the evaluation, which can lead to inaccurate evaluations. Furthermore, the system has low search efficiency for accumulated knowledge and skills, making it difficult for new employees and other employees to quickly obtain the information they need. This has led to a need for improved knowledge sharing and personnel evaluations across the organization.

[1234] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[1235] In this invention, the server includes means for extracting employee knowledge and skills using interactive artificial intelligence, means for visualizing knowledge and skills based on third-party evaluations, means for storing the knowledge and skills in a database along with the evaluation results, means for making the stored knowledge and skills searchable, means for displaying the searched knowledge and skills, means for recognizing user emotions, and means for reflecting emotion data in the evaluation. This allows for fairer and more reliable evaluations by reflecting emotions along with accurate evaluations of users' knowledge and skills, and further enables knowledge sharing and skill improvement throughout the organization through appropriate information retrieval.

[1236] "Means of extracting employee knowledge and skills using interactive AI" is a function that extracts the knowledge and skills of a user by having the user interact with the interactive AI in natural language.

[1237] "A means of visualizing knowledge and skills based on third-party evaluation" is a function that evaluates extracted knowledge and skills using objective criteria and presents the evaluation results in a format that can be intuitively understood.

[1238] "Means for storing knowledge and skills together with evaluation results in a database" refers to a function for storing data on evaluated knowledge and skills and the evaluation results in a database in order to centrally manage them.

[1239] "Means for making accumulated knowledge and skills searchable" refers to a function that enables efficient searching of information stored in a database.

[1240] The "means for displaying searched knowledge and skills" is a function for displaying information obtained by a search to the user.

[1241] "Means for recognizing user emotions" refers to a function for identifying and analyzing the user's emotions during a dialogue or interaction, and determines emotions based on tone of voice, facial expressions, and text content.

[1242] The "means for reflecting emotional data in evaluation" is a function for correcting and amending the evaluation results of knowledge and skills based on the results of user emotion recognition, thereby enabling more accurate evaluation.

[1243] This invention is a system that uses interactive artificial intelligence to extract employee knowledge and skills, visualizes them based on third-party evaluations, and stores the results in a database. Furthermore, by combining it with an emotion engine, it recognizes the user's emotions and reflects that information in the evaluations. A specific embodiment of the invention will be described.

[1244] System configuration

[1245] The system mainly consists of the following components:

[1246] Terminal: Provides an interface for users to interact with the conversational artificial intelligence and emotion engine.

[1247] Server: Performs interactive AI processing, emotion recognition, database management, and evaluation / visualization processing.

[1248] Database: Stores employee knowledge, skills, evaluation results, and emotional data.

[1249] Hardware and software used

[1250] 1. Terminal: A device such as a PC, tablet, or smartphone. These devices provide a user interface and a means for users to interact with the conversational artificial intelligence and use the emotion engine.

[1251] 2. Server: A high-performance computer system that provides the execution environment for conversational artificial intelligence (e.g., generative AI models), natural language processing technologies (e.g., Python's NLTK or Google Cloud Natural Language API), emotion recognition engines (e.g., Microsoft Azure Emotion API), etc.

[1252] 3. Database: A relational database system (e.g., MySQL, PostgreSQL) or a NoSQL database (e.g., MongoDB) to efficiently store and search evaluation results, user knowledge and skill data, and sentiment data.

[1253] Specific examples

[1254] Example 1: Knowledge sharing and emotion recognition among veteran employees

[1255] The terminal provides a screen for the veteran employee to log in. Once the user logs in, a session with the conversational artificial intelligence and emotion engine begins.

[1256] As users talk about project management best practices, the server uses natural language processing techniques to extract key knowledge, and an emotion engine analyzes the user's emotions in the process.

[1257] The server performs a third-party evaluation of this knowledge, visualizes the evaluation results and emotional data, and stores the results in a database that can be searched and used by other employees.

[1258] Example 2: Skill development for new employees and emotional feedback

[1259] If a new employee logs in and wants to search for knowledge about project management, he or she will type "project management" into the search interface on the terminal.

[1260] The server retrieves relevant information from the database and displays it on the device, including best practices and skills shared by veteran employees, as well as emotional data.

[1261] Users can improve their skills based on the displayed information, and their emotional feedback during use is accumulated as an evaluation.

[1262] Prompt Sentence Examples

[1263] "What are some important points about project management?"

[1264] Please rate this skill.

[1265] "Please tell me the results of your recent sentiment analysis."

[1266] This invention enables fairer and more reliable evaluations by accurately assessing employees' knowledge and skills while also reflecting their emotions, and promotes knowledge sharing and skill improvement throughout the organization through appropriate information retrieval.

[1267] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1268] Step 1:

[1269] Login and Authentication

[1270] The user enters their employee ID and password into the login screen of the device.

[1271] Input: Employee ID and password

[1272] The terminal transmits the input data to the server.

[1273] Based on the received data, the server checks the authentication information of the corresponding user in the database.

[1274] Data processing: Authentication by database matching

[1275] Output: Authentication result (success or failure)

[1276] If the authentication is successful, the server starts the session and prepares the conversational artificial intelligence and emotion engine.

[1277] Specific working example:

[1278] The user opens the login page in a browser, enters the employee ID "user123" and password "pass123", and clicks the "Login" button.

[1279] The server verifies this information and displays the home screen if authentication is successful.

[1280] Step 2:

[1281] Dialogue and Knowledge Extraction

[1282] The user begins a conversation in natural language with a conversational artificial intelligence (generative AI model) on the device.

[1283] Input: Natural language questions and conversations

[1284] The terminal transmits the user's input text to the server in real time.

[1285] The server uses natural language processing technology (e.g., Python's NLTK or Google Cloud Natural Language API) to analyze the conversation and extract the user's knowledge and skills.

[1286] Data processing: Text analysis using natural language processing technology

[1287] Output: Extracted knowledge and skills

[1288] Specific working example:

[1289] User types, "What are some project management best practices?"

[1290] The server analyzes this text and extracts "project management" and "best practices" as key concepts.

[1291] Step 3:

[1292] emotion recognition

[1293] While the user is interacting, the emotion engine analyzes emotions from the user's voice, facial expressions, and text.

[1294] Input: Voice data, facial expression data, text data

[1295] The device uses voice input and a camera to extract voice and facial expression data and transmits it to a server.

[1296] The server analyzes this data using an emotion engine (e.g., Microsoft Azure Emotion API) to track emotional states in real time.

[1297] Data processing: Emotion recognition through voice and facial expression analysis

[1298] Output: Emotion data (e.g. percentage of happy, surprised, sad)

[1299] Specific working example:

[1300] The user shows expressions of joy and surprise during the interaction.

[1301] The server records the emotional data as "Happiness: 80%" and "Surprise: 60%."

[1302] Step 4:

[1303] Conducting the evaluation

[1304] The server performs a third-party evaluation of the extracted knowledge and skills.

[1305] The evaluation criteria are depth of knowledge, accuracy of content, usefulness, and user sentiment data.

[1306] Input: Extracted knowledge, skills, and emotion data

[1307] The server combines these multiple factors to generate an overall rating score.

[1308] Data processing: Criteria-based scoring

[1309] Output: Overall evaluation score

[1310] Specific working example:

[1311] The server evaluates the accuracy and practicality of the knowledge about "project management best practices" entered by the user and provides an "evaluation score: 85 points."

[1312] Step 5:

[1313] Visualization of evaluation results

[1314] The server visualizes the evaluation results and displays them in an easy-to-understand format.

[1315] Input: Evaluation scores, emotion data

[1316] The server generates visualization data and sends it to the terminal.

[1317] The terminal provides an interface for displaying the evaluation results in the form of graphs, scores, etc.

[1318] Data processing: Converting evaluation results into visualized data

[1319] Output: Visualized data (graphs, scores)

[1320] Specific working example:

[1321] The server generates data to display the evaluation score of "85 points" in a pie chart or bar graph.

[1322] The terminal receives this, visualizes the evaluation results, and displays them to the user.

[1323] Step 6:

[1324] Saving evaluation results

[1325] The server stores the evaluated knowledge, skill information and emotion data in a database.

[1326] Input: Evaluation results, emotion data

[1327] A database organizes and stores this information for future retrieval and reference.

[1328] Data processing: Inserting data into the database

[1329] Output: Accumulated data

[1330] Specific working example:

[1331] The server saves the evaluation results and emotion data in the database using an INSERT statement.

[1332] Step 7:

[1333] Searching for information

[1334] When a user searches for the information they need, they use the search interface on their device.

[1335] Input: Search keyword

[1336] The server extracts information that matches the search keywords from the database and sends it to the terminal.

[1337] Data processing: Data extraction based on search keywords

[1338] Output: Search results

[1339] Specific working example:

[1340] A new employee types in "project management" and clicks the search button.

[1341] The server retrieves the relevant information from the database and displays the appropriate information on the terminal.

[1342] (Application example 2)

[1343] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[1344] Conventional systems only extracted employee knowledge and skills through conversational AI, making it difficult to properly reflect the evaluation in personnel evaluations. Furthermore, the system did not reflect the user's emotional state, making accurate evaluations and motivation management difficult. In particular, in factories, the emotions and stress levels of workers significantly affect the quality of their work, so the development of a system that takes this into account was needed.

[1345] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 2 is realized by the following means.

[1346] In this invention, the server includes means for extracting employee knowledge and skills using interactive artificial intelligence, means for visualizing knowledge and skills based on third-party evaluations, means for storing the knowledge and skills in a database along with the evaluation results, means for making the stored knowledge and skills searchable, means for displaying the searched knowledge and skills, and emotion recognition means for recognizing the user's emotions and reflecting that information in the evaluation. This enables appropriate evaluations and knowledge sharing that take into account the emotions of employees.

[1347] An "employee" is someone who belongs to a company or organization and provides labor for its activities.

[1348] "Knowledge" is an abstract concept that refers to information, understanding, and experiential understanding in a particular field.

[1349] "Skills" are the practical abilities and techniques required to effectively carry out a particular task or activity.

[1350] "Conversational AI" refers to an AI system designed to mimic natural human interactions and uses natural language processing techniques.

[1351] "Third-party evaluation" is an objective evaluation method conducted from an independent standpoint other than the person being evaluated.

[1352] "Visualization" is the act of presenting abstract data such as knowledge, skills, and evaluation results in a format that is visually easy to understand.

[1353] A "database" is a system that organizes and stores information so that it can be searched and used efficiently later.

[1354] "Emotion recognition" is a technology that automatically identifies a user's emotional state at any given time by analyzing their voice, facial expressions, text, etc.

[1355] "Evaluation results" are the results of evaluation of knowledge and skills obtained through interactive artificial intelligence or third-party evaluation.

[1356] A "server" is a computer that provides services such as data processing and storage to other computers over a network.

[1357] "Searchable" means having the ability to efficiently find specific information from the accumulated data.

[1358] This invention is a system that uses interactive artificial intelligence to extract employee knowledge and skills, visualizes the evaluation results, and stores them in a database. Furthermore, by combining it with emotion recognition technology, the system can reflect the user's emotional state in the evaluation. To implement this invention, the following specific configuration and procedures are required.

[1359] System configuration

[1360] The system mainly consists of the following components:

[1361] 1. Server: Performs interactive AI processing, emotion recognition, database management, and evaluation / visualization processing.

[1362] 2. Terminal: Provides an interface for users to interact with the conversational artificial intelligence and emotion engine.

[1363] 3. Database: Stores employee knowledge, skills, evaluation results, and emotional data.

[1364] Overview of program processing

[1365] This system allows users to log in via a terminal, converse with a conversational AI, extract the knowledge and skills gained in the process, and use an emotion engine to recognize the user's emotions to evaluate them. The evaluation results are stored in a database and can be searched and used by other users. The evaluation results and emotion data are also reflected in personnel evaluations.

[1366] Extracting user knowledge and skills and recognizing emotions

[1367] 1. Login and authentication: The user logs in by entering their employee ID and password into the terminal. The server authenticates the user information, and if authentication is successful, a session with the conversational artificial intelligence and emotion engine begins.

[1368] 2. Dialogue and knowledge extraction: The user interacts with the conversational AI in natural language on the terminal. The server analyzes the content of this dialogue using natural language processing technology and extracts the user's knowledge and skills.

[1369] 3. Emotion Recognition: During a conversation, the emotion engine analyzes the user's emotions from their voice, facial expressions, and text, and tracks the results in real time.

[1370] Evaluation and visualization

[1371] 1. Evaluation: The server performs a third-party evaluation of the extracted knowledge and skills. The evaluation criteria are depth of knowledge, accuracy of content, usefulness, and user emotional data.

[1372] 2. Visualization: The evaluation results are visualized and presented in an easy-to-understand format (graphs and scores). Emotional data is also displayed.

[1373] Database storage and search

[1374] 1. Storing evaluation results: The knowledge, skill information, and emotional data for which evaluation has been completed are stored in a database.

[1375] 2. Information search: When other users search for the information they need, they use the search interface on their device. The server extracts information that matches the search keywords from the database and displays it on the device.

[1376] Hardware and software used

[1377] 1. Smart glasses: Used during maintenance work on factory robots, they capture the faces and voices of workers and perform emotion recognition.

[1378] 2. EmotionEngine: Analyzes emotions in real time and sends the data to a server.

[1379] 3. Dialogue AI: Uses natural language processing technology to analyze the content of user dialogue and extract knowledge and skills.

[1380] Specific examples

[1381] Example 1: Knowledge sharing and emotion recognition among veteran employees

[1382] 1. The terminal provides a screen for the veteran employee to log in. Once the user logs in, a session with the conversational artificial intelligence and emotion engine begins.

[1383] 2. As users talk about project management best practices, the server uses natural language processing technology to extract key knowledge, and an emotion engine analyzes the user's emotions in the process.

[1384] 3. The server performs a third-party evaluation of this knowledge, visualizes the evaluation results and emotional data, and stores the results in a database where other employees can search and use them.

[1385] Example prompt sentences:

[1386] Please tell me how to replace the parts.

[1387] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1388] Step 1:

[1389] The user logs in by entering their employee ID and password into the terminal.

[1390] Input: Employee ID, Password

[1391] Data processing: The server checks the user information against the database and performs authentication.

[1392] Output: If authentication is successful, a session with the conversational artificial intelligence and emotion engine is initiated.

[1393] Specific operation: When a user enters their employee ID and password into the login screen on their device and presses the "Login" button, an authentication request is sent to the server. The server checks the authentication information against the database and, if correct, starts a session.

[1394] Step 2:

[1395] The user interacts with the conversational artificial intelligence on the terminal in natural language.

[1396] Input: User question or comment (natural language text)

[1397] Data processing: The server uses natural language processing technology to analyze the content of the dialogue and extract the user's knowledge and skills.

[1398] Output: Information about knowledge and skills

[1399] Specific operation: When a user uses the conversational artificial intelligence interface on the terminal to input "Please tell me how to replace this part," the server passes the text data to a natural language processing engine, which extracts knowledge about part replacement as an analysis result.

[1400] Step 3:

[1401] During a conversation, the emotion engine analyzes emotions from the user's voice, facial expressions, and text.

[1402] Input: Camera video, audio data, text data

[1403] Data processing: EmotionEngine analyzes this data and determines the emotional state in real time.

[1404] Output: Emotional state (e.g., happy, surprised, angry, etc.)

[1405] Specific operation: The device's camera and microphone are used to capture the user's face and voice, and this data is sent to EmotionEngine, which analyzes the data and determines and displays the user's emotional state in real time.

[1406] Step 4:

[1407] The server performs a third-party evaluation of the extracted knowledge and skills.

[1408] Input: Extracted knowledge, skills, and emotion data

[1409] Data processing: Evaluation is carried out based on evaluation criteria (depth of knowledge, accuracy of content, usefulness, and sentiment data).

[1410] Output: Evaluation results (score, comments, etc.)

[1411] Specific operation: The server inputs information about knowledge and skills and emotional data into the evaluation algorithm and generates an evaluation result based on multiple criteria. For example, if the knowledge is detailed, accurate, and useful, a high evaluation score is assigned.

[1412] Step 5:

[1413] The evaluation results are visualized and presented in an easy-to-understand format (graphs and scores).

[1414] Input: Evaluation results, emotion data

[1415] Data processing: Transformation for visual display in graphs and scores

[1416] Output: Visualized evaluation results and emotion data

[1417] Specific operation: The server obtains the evaluation results and emotion data, and uses a visualization engine to display them on the terminal in the form of bar graphs, pie charts, score displays, etc.

[1418] Step 6:

[1419] The evaluation results and emotional data are stored in a database.

[1420] Input: Visualized evaluation results and emotion data

[1421] Data processing: Add as a new record to the database

[1422] Output: Evaluation results and emotion data stored in a database

[1423] Specific action: Add a new entry of the emotion data and evaluation results to the database so that other users can access it later.

[1424] Step 7:

[1425] When other users search for the information they need, they use the search interface on their device.

[1426] Input: Search keyword

[1427] Data processing: Extract information that matches keywords from the database

[1428] Output: Related knowledge and skill information

[1429] Specific operation: A keyword is entered into the terminal's search interface, and the server searches for related information from the database and displays it on the terminal.

[1430] Step 8:

[1431] Emotional feedback is provided as the user works based on the search results.

[1432] Input: Use of search results, emotional state during work

[1433] Data processing: Emotion engine analyzes emotions in real time while working

[1434] Output: Emotional feedback data during the task

[1435] Specific operation: A user uses smart glasses while working, and the emotion engine continuously monitors their emotional state, generating and storing feedback data.

[1436] The specific processing unit 290 transmits the result of the specific processing to the headset type terminal 314. In the headset type terminal 314, the control unit 46A causes the speaker 240 and the display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[1437] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[1438] 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 the present disclosure is not limited to this, and the specific processing may be performed by the headset type terminal 314.

[1439] [Fourth embodiment]

[1440] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[1441] 7, a 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.

[1442] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[1443] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a control target 443. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the control target 443 are also connected to the bus 52.

[1444] The microphone 238 receives instructions and the like from the user 20 by receiving voice uttered by the user 20. The microphone 238 captures the voice uttered by the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio in accordance with instructions from the processor 46.

[1445] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the surroundings of user 20 (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[1446] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[1447] The control object 443 includes a display device, LEDs in the eyes, and motors for driving 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 emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

[1448] Fig. 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Fig. 8, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[1449] The specific processing program 56 is an example of a "program" according to the technology of the present 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.

[1450] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[1451] In the robot 414, the processor 46 performs the reception output process. A reception output program 60 is stored in the storage 50. 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 process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[1452] Next, a description will be given of the specific processing performed by the specific processing unit 290 of the data processing device 12. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1453] This invention is a system that uses interactive artificial intelligence to extract employee knowledge and skills, visualizes them based on third-party evaluations, and stores them in a database. Specific embodiments of this system are described below.

[1454] System configuration

[1455] The system mainly consists of the following components:

[1456] Terminal: Provides an interface for users to interact with conversational artificial intelligence.

[1457] Server: Performs interactive AI processing, database management, and evaluation / visualization processing.

[1458] Database: Stores employee knowledge, skills, and evaluation results.

[1459] Overview of program processing

[1460] In this system, employees (users) log in via their terminals, interact with the conversational AI, and the knowledge and skills acquired in the process are extracted and evaluated. The evaluation results are stored in a database and can be searched and used by other users. The evaluation results are also reflected in personnel evaluations.

[1461] Extracting user knowledge and skills

[1462] 1. Login and authentication: The user logs in by entering their employee ID and password into the terminal. The server authenticates the user information, and if authentication is successful, a session with the conversational AI begins.

[1463] 2. Dialogue and knowledge extraction: The user interacts with the conversational AI in natural language on the terminal. The server analyzes the content of this dialogue using natural language processing technology and extracts the user's knowledge and skills.

[1464] 3. Evaluation and visualization: The server performs a third-party evaluation of the extracted knowledge and skills and visualizes the evaluation results.

[1465] Database storage and search

[1466] 1. Storage of evaluation results: The knowledge and skill information for which evaluation has been completed is stored in a database along with the evaluation results.

[1467] 2. Information search: When other users search for the information they need, they use the search interface on their device. The server extracts information that matches the search keywords from the database and displays it on the device.

[1468] Specific examples

[1469] Example 1: Knowledge sharing among veteran employees

[1470] The terminal provides a screen for the veteran employee to log in. Once the user logs in, a session with the conversational artificial intelligence begins.

[1471] As users talk about project management best practices, the server uses natural language processing techniques to extract key knowledge.

[1472] The server performs a third-party evaluation of this knowledge and visualizes the results, which are then stored in a database and made available for other employees to search.

[1473] Example 2: Improving the skills of new employees

[1474] If a new employee logs in and wants to search for knowledge about project management, he or she will type "project management" into the search interface on the terminal.

[1475] The server retrieves relevant information from a database and displays it on the terminal, which is best practices and skills shared by veteran employees.

[1476] New employees can improve their skills based on the information displayed.

[1477] This will enable employees to share their knowledge and skills efficiently, which is expected to promote productivity and growth throughout the organization. This system will ensure that the wealth of experience of veteran employees is not lost, and will make it easily accessible to new employees and other employees, thereby promoting the effective use of knowledge across the entire company.

[1478] The processing flow will be explained below.

[1479] Step 1:

[1480] The terminal displays a login screen to the user, who then enters their employee ID and password.

[1481] Step 2:

[1482] The server receives the employee ID and password entered by the user. The server performs authentication in the database and, if successful, generates a session ID. The server then sends the session ID to the terminal.

[1483] Step 3:

[1484] The device displays a conversational AI interface to the user, who is given instructions such as "Talk about your project management experience."

[1485] Step 4:

[1486] The user speaks to the conversational AI in natural language about their knowledge and skills, and the device captures the user's voice or text input and sends it to the server.

[1487] Step 5:

[1488] The server analyzes the received dialogue data using natural language processing technology, performing tokenization, grammatical analysis, and semantic analysis to extract the user's knowledge and skills.

[1489] Step 6:

[1490] The server performs a third-party evaluation of the extracted knowledge and skills. The evaluation criteria are depth of knowledge, accuracy of content, and practicality. The evaluation results are visualized and displayed in the form of graphs and scores.

[1491] Step 7:

[1492] The terminal displays the evaluation results to the user, who can then check them and make corrections or additions as necessary.

[1493] Step 8:

[1494] The server stores the evaluated information in a database, including knowledge entities, evaluation results, and a summary of the dialogue content.

[1495] Step 9:

[1496] The terminal provides a search interface, where other users can input search keywords to search for the information they need.

[1497] Step 10:

[1498] The server processes the search query, retrieves information from the database that matches the search keywords, organizes the search results, and sends them to the device.

[1499] Step 11:

[1500] The device displays the search results, and the user checks the displayed information and applies the necessary knowledge and skills.

[1501] Step 12:

[1502] The user provides feedback on the information they searched for. The device collects the feedback and sends it to the server.

[1503] Step 13:

[1504] The server updates the database based on user feedback, making corrections to improve the accuracy and usefulness of the information.

[1505] Example 1

[1506] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1507] In modern companies, the knowledge and skills of employees are often not shared sufficiently, resulting in lower productivity throughout the organization and insufficient utilization of knowledge. In particular, there is a problem in which the valuable knowledge and skills accumulated by veteran employees are not effectively conveyed to new employees and other employees. Furthermore, there is a lack of a way to fairly evaluate employees' knowledge and skills and visualize the results, which also affects personnel evaluations. A method to solve these issues is needed.

[1508] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[1509] In this invention, the server includes means for extracting employee knowledge and abilities using interactive AI, means for visualizing knowledge and abilities based on third-party evaluation, means for storing the knowledge and abilities in a database along with the evaluation results, means for a user to input authentication information and the server to perform authentication, means for the interactive AI to analyze the content of the dialogue using a natural language processing tool and extract knowledge and abilities, and means for visualizing the evaluation results in graphs and charts and storing them in a database. This makes it possible to efficiently extract, objectively evaluate, and visualize employee knowledge and abilities, enabling knowledge sharing throughout the organization and improving the accuracy of personnel evaluations.

[1510] "Employee knowledge and abilities" refers to the specialized knowledge, skills, experience, and ability to apply those skills possessed by individual employees within an organization.

[1511] "Conversational artificial intelligence" refers to an artificial intelligence system that elicits information from a user through natural language dialogue and generates appropriate responses.

[1512] "Third-party evaluation" refers to the process of objectively evaluating the knowledge and abilities extracted by interactive artificial intelligence from a third-party perspective.

[1513] "Visualization means" refers to methods and devices for displaying extracted knowledge and abilities in a visual format such as graphs or charts, and expressing them in an easy-to-understand manner.

[1514] A "database" refers to a collection of data that stores accumulated knowledge, skills, and evaluation results and makes them searchable as needed.

[1515] "Authentication information" refers to the information required for a user to access a system (e.g., employee ID and password).

[1516] "Natural language processing tools" refer to software and algorithms that analyze input natural language and extract meaning.

[1517] "Graphs and charts" refers to figures and diagrams that visually represent evaluation results.

[1518] "Searchability" refers to the interfaces and algorithms that allow users to search for information in a database.

[1519] The "display means" refers to a method or device for displaying the searched information on a terminal so that the user can confirm it.

[1520] "User" refers to an individual who uses the system to extract, evaluate, search, and display knowledge and capabilities.

[1521] "Server" refers to a computer system that handles interactive artificial intelligence processing, database management, and evaluation / visualization processing.

[1522] This invention is a system that uses interactive artificial intelligence to extract employees' knowledge and abilities, visualize them based on third-party evaluations, and store them in a database.

[1523] System configuration

[1524] The system consists of the following components:

[1525] Terminal

[1526] It provides an interface for users to interact with the conversational AI. Users log in to the system by entering their employee ID and password and begin the conversation.

[1527] server

[1528] It handles interactive AI processing, database management, and evaluation / visualization. The server uses natural language processing tools (e.g., OpenAI's GPT-3) to analyze the user's dialogue and extract knowledge and abilities. It also performs a third-party evaluation of the extracted knowledge and abilities and stores the results in a database. The evaluation results are visualized using graphs and charts.

[1529] Database

[1530] Employee knowledge, skills, and evaluation results are stored and can be searched as needed. Users can use their devices to search the database and view the stored knowledge and skills.

[1531] Overview of program processing

[1532] The terminal, server, and database work together. Users log in through their terminal and interact with the conversational AI. The content of the conversation is sent to the server, where knowledge and abilities are extracted using natural language processing technology. After the evaluation is completed, the results are stored in the database and displayed on the terminal as visualized information.

[1533] Usage example

[1534] Here we will show a specific example of use.

[1535] Example 1: Knowledge sharing among veteran employees

[1536] The terminal provides a screen for the veteran employee to log in. Once the user logs in, a session with the conversational artificial intelligence begins.

[1537] As users talk about project management best practices, the server uses natural language processing techniques to extract key knowledge.

[1538] The server performs a third-party evaluation of this knowledge and visualizes the results, which are then stored in a database that can be searched and used by other employees.

[1539] Example prompt sentence:

[1540] Please enter your employee ID and password to log in.

[1541] "Tell me about your project management experience."

[1542] "Rate this knowledge on a scale of 1 to 5."

[1543] "Knowledge storage complete."

[1544] "Search for project management best practices."

[1545] Example 2: Improving the skills of new employees

[1546] If a new employee logs in and wants to search for knowledge about project management, he or she will type "project management" into the search interface on the terminal.

[1547] The server retrieves relevant information from a database and displays it on the terminal, which is best practices and skills shared by veteran employees.

[1548] New employees can improve their skills based on the information displayed.

[1549] Example prompt sentence:

[1550] "Search for project management best practices."

[1551] In this way, by creating a system that allows employees' knowledge and abilities to be shared efficiently, it is expected that productivity and growth will be promoted throughout the organization.

[1552] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1553] Step 1: User Login and Authentication

[1554] explanation:

[1555] Input: The user enters their employee ID and password into the terminal.

[1556] Specific operation: The terminal displays the login screen, and the user enters the employee ID "12345" and password "password123."

[1557] Data processing: The server checks the entered employee ID and password against the database.

[1558] Output: Returns the authentication result (success or failure) to the terminal.

[1559] Specific operation: The server queries the database for user information, and if authentication is successful, displays "Login successful."

[1560] Step 2: Dialogue and knowledge extraction

[1561] explanation:

[1562] Input: The user inputs the dialogue content into the conversational artificial intelligence through the terminal.

[1563] What happens: The device displays a dialogue interface and the user types, "What are some best practices for project management?"

[1564] Data processing: The server sends the dialogue content to a natural language processing tool (e.g., GPT-3) to extract knowledge and abilities.

[1565] Output: The extracted knowledge and capabilities (e.g., "risk management is important") are returned to the server.

[1566] Specific operation: The server uses GPT-3 to analyze the content of the conversation and extract knowledge such as "risk management is important."

[1567] Step 3: Third-party evaluation and visualization

[1568] explanation:

[1569] Input: Extracted knowledge and skills (e.g., "Risk management is important")

[1570] Specific operation: The server performs a third-party evaluation of the extracted knowledge and abilities.

[1571] Data processing: The server conducts third-party evaluations (e.g., evaluations by other employees or AI) and visualizes the results.

[1572] Output: Generate evaluation results (e.g., "very useful") in visual form (e.g., graphs, charts).

[1573] Specific operation: The server converts the evaluation results into a pie chart and visualizes them.

[1574] Step 4: Database storage

[1575] explanation:

[1576] Input: Knowledge and abilities extracted along with the evaluation results

[1577] Specific operation: The server stores the evaluation results and knowledge and ability information in a database.

[1578] Data processing: The server inserts the evaluation results and knowledge and ability information into a database table.

[1579] Output: Entries saved in the database

[1580] Specific operation: The server records knowledge about "risk management" and its evaluation results in a database.

[1581] Step 5: Find and view information

[1582] explanation:

[1583] Input: The user types a search keyword into the device (e.g., "project management")

[1584] Specific behavior: The device displays a search interface, and the user types "project management."

[1585] Data processing: The server searches the database and extracts information that matches the keywords.

[1586] Output: Return search results (e.g., "Risk management is important") to the terminal.

[1587] Specific operation: The server searches the database and displays the relevant information on the terminal, which then displays the search results to the user.

[1588] (Application example 1)

[1589] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1590] In the past, it was difficult to effectively draw out the knowledge and skills of employees and share them throughout the organization. In particular, there was a lack of means to quickly incorporate the knowledge and know-how of experienced engineers into factory automation equipment, which hindered improvements in production efficiency. Another challenge was properly evaluating employee skills, visualizing them, storing them in a database, and making them easily accessible to other employees.

[1591] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[1592] In this invention, the server includes a means for extracting employee knowledge and skills using interactive AI, a means for visualizing the knowledge and skills based on third-party evaluation, and a means for storing the knowledge and skills together with the evaluation results in a database, which allows the evaluated knowledge and skill information to be imported into automated equipment in the factory.

[1593] An "employee" is an individual who belongs to an organization or company and performs certain duties.

[1594] "Knowledge" is the sum of information and understanding gained through experience and learning.

[1595] A "skill" is a technique or ability to perform a specific task or work.

[1596] "Conversational artificial intelligence" is an artificial intelligence system that can converse with humans in natural language.

[1597] "Third-party evaluation" is an objective evaluation process based on specific criteria and evaluation methods.

[1598] "Visualization" is the process of visually representing data or information to make it easier to understand.

[1599] A "database" is a system that systematically stores and manages data and makes it easily accessible.

[1600] "Searching" is the process of locating data in a database based on specific criteria.

[1601] "Display" is the act of visually presenting data or information to a user.

[1602] "Factory automation equipment" refers to machinery and systems used to automate work within a factory.

[1603] An "interface" is a means or protocol that allows information to be exchanged between different systems or devices.

[1604] "Natural language processing technology" is a technology that enables computers to understand and manipulate human language.

[1605] "Human resource evaluation" is a process for evaluating employees' performance and abilities and ensuring fair treatment and placement.

[1606] The system embodying this invention uses interactive artificial intelligence to extract employee knowledge and skills, evaluate them from a third-party perspective, visualize them, and store them in a database. The system is mainly composed of the following components.

[1607] System configuration

[1608] The system consists of three main parts: terminals, servers, and databases, allowing employees' knowledge and skills to be efficiently shared and incorporated into the factory's automated equipment.

[1609] Terminal

[1610] The terminal is a device that provides an interface for engineers and employees to interact with the conversational AI. Specifically, a smartphone or tablet is used.

[1611] server

[1612] The server handles interactive AI processing, database management, evaluation and visualization. The AI ​​part includes a generative AI model, and OpenAI's GPT model is used as an example.

[1613] Database

[1614] The database is a system that stores employee knowledge, skills, and evaluation results. A lightweight database such as SQLite is used.

[1615] Interface

[1616] An interface is provided that allows factory automation equipment to incorporate assessed knowledge and skill information.

[1617] Program processing overview

[1618] Login and Authentication

[1619] Employees log in by entering their user ID and password from their smartphone or tablet. The server collates the user information and performs authentication, and if authentication is successful, a session with the conversational artificial intelligence begins.

[1620] Knowledge extraction and evaluation

[1621] Employees interact with the conversational AI through their devices, and the knowledge and skills acquired in the process are analyzed and extracted by the server using natural language processing technology. The extracted knowledge is then evaluated by a third party using a generative AI model.

[1622] Data visualization and accumulation

[1623] The evaluation results are visualized and stored in a database, allowing other engineers and employees to easily search the database and access the information they need. The evaluated knowledge and skill information is also incorporated into factory robots.

[1624] Example of program generation

[1625] For example, if an engineer wants to teach a robot how to optimize the welding process, here's a sample prompt:

[1626] text

[1627] Rate the following skills:

[1628] Learn best practices for optimizing your welding process, from job preparation to finishing. We share details on accurate measurements, proper material selection, temperature control, and time allocation.

[1629] The generative AI model returns the following evaluation results:

[1630] text

[1631] This technique is highly specialized and useful in many fields. Accurate dimensional measurements and appropriate material selection are particularly important. Temperature control and time allocation are also explained in detail, which is highly praised.

[1632] The evaluation results are stored in a database, and new robots can learn from this information, which is expected to lead to efficient knowledge sharing within factories and improved productivity.

[1633] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1634] Step 1:

[1635] A user logs in using a terminal by entering their employee ID and password. The employee ID and password are required as input, and the server receives them and checks them against the authentication information in the database. If authentication is successful, the server starts a session and provides the user with an interface with the conversational artificial intelligence. As output, the user is shown a message confirming successful login.

[1636] Step 2:

[1637] The user interacts with the conversational AI in natural language via a terminal. The content of the user's conversation (natural language) is required as input, and the server receives this. The server analyzes this content of the conversation using natural language processing technology and extracts information about the user's knowledge and skills. Data processing involves text analysis of the content of the conversation and formatting of related information, and knowledge and skills are extracted. The analysis results are then saved on the server as output.

[1638] Step 3:

[1639] The server uses a generative AI model to evaluate the extracted knowledge and skill information. The extracted information (natural language text) is required as input, and the server sends this to the generative AI model. The generative AI model evaluates the information based on the prompt sentence and generates an evaluation result. As data calculations, the generative AI model generates text and calculates an evaluation score. As output, the evaluation result is returned to the server.

[1640] Step 4:

[1641] Based on the evaluation results, the server visualizes knowledge and skill information and saves it in a database. The evaluation results are required as input, and the server receives them and visualizes them graphically. This makes the information easier to understand visually. Data processing involves generating graphs of the evaluation results and formatting them into tables. The visualized data is then saved in a database as output.

[1642] Step 5:

[1643] Other users or factory robots retrieve the information they need from the database via a search interface. Search keywords are required as input, and the user or robot enters a search query from their terminal. The server searches the database and extracts the relevant knowledge and skill information. As a data calculation, a search algorithm is used to identify and extract matching information. As an output, the search results are displayed on the terminal or robot.

[1644] Step 6:

[1645] The factory's automation equipment takes in the assessed knowledge and skill information and applies it to work. The retrieved knowledge and skill information is required as input, and is received by factory robots and other automation equipment. The automation equipment optimizes the work process based on the taken-in information. As data processing, the received information is integrated into the equipment's control algorithm and the operating procedure is adjusted. The optimized work process is realized as output.

[1646] Furthermore, an emotion engine that estimates the user's emotion may be further combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59, and perform identification processing using the user's emotion.

[1647] This invention is a system that uses interactive artificial intelligence to extract employee knowledge and skills, visualizes them based on third-party evaluations, and stores them in a database. In addition, by combining this with an emotion engine, it recognizes the user's emotions and reflects that information in the evaluations. A specific embodiment of this system is described below.

[1648] System configuration

[1649] The system mainly consists of the following components:

[1650] Terminal: Provides an interface for users to interact with the conversational artificial intelligence and emotion engine.

[1651] Server: Performs interactive AI processing, emotion recognition, database management, and evaluation / visualization processing.

[1652] Database: Stores employee knowledge, skills, evaluation results, and emotional data.

[1653] Overview of program processing

[1654] This system allows employees (users) to log in via a terminal, converse with a conversational AI, extract the knowledge and skills acquired in the process, and use an emotion engine to recognize the user's emotions to evaluate them. The evaluation results are stored in a database and can be searched and used by other users. The evaluation results and emotion data are also reflected in personnel evaluations.

[1655] Extracting user knowledge and skills and recognizing emotions

[1656] 1. Login and authentication: The user logs in by entering their employee ID and password into the terminal. The server authenticates the user information, and if authentication is successful, a session with the conversational artificial intelligence and emotion engine begins.

[1657] 2. Dialogue and knowledge extraction: The user interacts with the conversational AI in natural language on the terminal. The server analyzes the content of this dialogue using natural language processing technology and extracts the user's knowledge and skills.

[1658] 3. Emotion Recognition: During a conversation, the emotion engine analyzes the user's emotions from their voice, facial expressions, and text, and tracks the results in real time.

[1659] Evaluation and visualization

[1660] 1. Evaluation: The server performs a third-party evaluation of the extracted knowledge and skills. The evaluation criteria are depth of knowledge, accuracy of content, usefulness, and user emotional data.

[1661] 2. Visualization: The evaluation results are visualized and presented in an easy-to-understand format (graphs and scores). Emotional data is also displayed.

[1662] Database storage and search

[1663] 1. Storing evaluation results: The knowledge, skill information, and emotional data for which evaluation has been completed are stored in a database.

[1664] 2. Information search: When other users search for the information they need, they use the search interface on their device. The server extracts information that matches the search keywords from the database and displays it on the device.

[1665] Specific examples

[1666] Example 1: Knowledge sharing and emotion recognition among veteran employees

[1667] The terminal provides a screen for the veteran employee to log in. Once the user logs in, a session with the conversational artificial intelligence and emotion engine begins.

[1668] As users talk about project management best practices, the server uses natural language processing techniques to extract key knowledge, and an emotion engine analyzes the user's emotions in the process.

[1669] The server performs a third-party evaluation of this knowledge, visualizes the evaluation results and emotional data, and stores the results in a database that can be searched and used by other employees.

[1670] Example 2: Skill development for new employees and emotional feedback

[1671] If a new employee logs in and wants to search for knowledge about project management, he or she will type "project management" into the search interface on the terminal.

[1672] The server retrieves relevant information from the database and displays it on the device, including best practices and skills shared by veteran employees, as well as emotional data.

[1673] New employees can improve their skills based on the displayed information, and their emotional feedback during use is accumulated as an evaluation.

[1674] This is expected to enable efficient sharing of employee knowledge and skills, promoting productivity and growth throughout the organization. This system will prevent the wealth of experience of veteran employees from being buried, making it easily accessible to new employees and other employees, and by taking user emotions into consideration, it will enable deeper understanding and appropriate evaluation.

[1675] The processing flow will be explained below.

[1676] Step 1:

[1677] The terminal displays a login screen to the user, who then enters their employee ID and password.

[1678] Step 2:

[1679] The server receives the employee ID and password entered by the user. The server performs authentication processing in the database, and if successful, generates a session ID and sends it to the terminal.

[1680] Step 3:

[1681] The device displays the interface of the conversational artificial intelligence and emotion engine to the user, who is given instructions such as "Talk about your experience in project management."

[1682] Step 4:

[1683] The user speaks to the conversational AI in natural language about their knowledge and skills, and the device captures the user's voice or text input and sends it to the server.

[1684] Step 5:

[1685] The server analyzes the received dialogue data using natural language processing technology, performing tokenization, grammatical analysis, and semantic analysis to extract the user's knowledge and skills.

[1686] Step 6:

[1687] The emotion engine analyzes emotions from the user's voice, facial expressions, and text during a conversation and collects the data in real time. The emotion engine identifies emotions such as joy, sadness, and anger.

[1688] Step 7:

[1689] The server performs a third-party evaluation based on the extracted knowledge, skills, and emotional data. The evaluation criteria are depth of knowledge, accuracy of content, practicality, and emotional data. The evaluation results are visualized and displayed in the form of graphs and scores.

[1690] Step 8:

[1691] The device displays the evaluation results and emotion data to the user, who can then review the results and make corrections or additions as necessary.

[1692] Step 9:

[1693] The server stores the evaluated information and emotion data in a database, including knowledge entities, evaluation results, dialogue summaries, and emotion data.

[1694] Step 10:

[1695] The terminal provides a search interface, where other users can input search keywords to search for the information they need.

[1696] Step 11:

[1697] The server processes the search query, retrieves information from the database that matches the search keywords, organizes the search results, and sends them to the device.

[1698] Step 12:

[1699] The device displays the search results. The user can review the displayed information and utilize the necessary knowledge and skills. Emotional data is also displayed for the user to refer to.

[1700] Step 13:

[1701] The user provides feedback on the information they searched for. The device collects the feedback and sends it to the server.

[1702] Step 14:

[1703] The server updates the database based on user feedback, making corrections to improve the accuracy and usefulness of the information.

[1704] Example 2

[1705] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1706] Conventional systems only use conversational AI to extract employee knowledge and skills, but evaluations tend to be subjective. Furthermore, because they do not take emotions into account, the user's psychological state is not reflected in the evaluation, which can lead to inaccurate evaluations. Furthermore, the system has low search efficiency for accumulated knowledge and skills, making it difficult for new employees and other employees to quickly obtain the information they need. This has led to a need for improved knowledge sharing and personnel evaluations across the organization.

[1707] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[1708] In this invention, the server includes means for extracting employee knowledge and skills using interactive artificial intelligence, means for visualizing knowledge and skills based on third-party evaluations, means for storing the knowledge and skills in a database along with the evaluation results, means for making the stored knowledge and skills searchable, means for displaying the searched knowledge and skills, means for recognizing user emotions, and means for reflecting emotion data in the evaluation. This allows for fairer and more reliable evaluations by reflecting emotions along with accurate evaluations of users' knowledge and skills, and further enables knowledge sharing and skill improvement throughout the organization through appropriate information retrieval.

[1709] "Means of extracting employee knowledge and skills using interactive AI" is a function that extracts the knowledge and skills of a user by having the user interact with the interactive AI in natural language.

[1710] "A means of visualizing knowledge and skills based on third-party evaluation" is a function that evaluates extracted knowledge and skills using objective criteria and presents the evaluation results in a format that can be intuitively understood.

[1711] "Means for storing knowledge and skills together with evaluation results in a database" refers to a function for storing data on evaluated knowledge and skills and the evaluation results in a database in order to centrally manage them.

[1712] "Means for making accumulated knowledge and skills searchable" refers to a function that enables efficient searching of information stored in a database.

[1713] The "means for displaying searched knowledge and skills" is a function for displaying information obtained by a search to the user.

[1714] "Means for recognizing user emotions" refers to a function for identifying and analyzing the user's emotions during a dialogue or interaction, and determines emotions based on tone of voice, facial expressions, and text content.

[1715] The "means for reflecting emotional data in evaluation" is a function for correcting and amending the evaluation results of knowledge and skills based on the results of user emotion recognition, thereby enabling more accurate evaluation.

[1716] This invention is a system that uses interactive artificial intelligence to extract employee knowledge and skills, visualizes them based on third-party evaluations, and stores the results in a database. Furthermore, by combining it with an emotion engine, it recognizes the user's emotions and reflects that information in the evaluations. A specific embodiment of the invention will be described.

[1717] System configuration

[1718] The system mainly consists of the following components:

[1719] Terminal: Provides an interface for users to interact with the conversational artificial intelligence and emotion engine.

[1720] Server: Performs interactive AI processing, emotion recognition, database management, and evaluation / visualization processing.

[1721] Database: Stores employee knowledge, skills, evaluation results, and emotional data.

[1722] Hardware and software used

[1723] 1. Terminal: A device such as a PC, tablet, or smartphone. These devices provide a user interface and a means for users to interact with the conversational artificial intelligence and use the emotion engine.

[1724] 2. Server: A high-performance computer system that provides the execution environment for conversational artificial intelligence (e.g., generative AI models), natural language processing technologies (e.g., Python's NLTK or Google Cloud Natural Language API), emotion recognition engines (e.g., Microsoft Azure Emotion API), etc.

[1725] 3. Database: A relational database system (e.g., MySQL, PostgreSQL) or a NoSQL database (e.g., MongoDB) to efficiently store and search evaluation results, user knowledge and skill data, and sentiment data.

[1726] Specific examples

[1727] Example 1: Knowledge sharing and emotion recognition among veteran employees

[1728] The terminal provides a screen for the veteran employee to log in. Once the user logs in, a session with the conversational artificial intelligence and emotion engine begins.

[1729] As users talk about project management best practices, the server uses natural language processing techniques to extract key knowledge, and an emotion engine analyzes the user's emotions in the process.

[1730] The server performs a third-party evaluation of this knowledge, visualizes the evaluation results and emotional data, and stores the results in a database that can be searched and used by other employees.

[1731] Example 2: Skill development for new employees and emotional feedback

[1732] If a new employee logs in and wants to search for knowledge about project management, he or she will type "project management" into the search interface on the terminal.

[1733] The server retrieves relevant information from the database and displays it on the device, including best practices and skills shared by veteran employees, as well as emotional data.

[1734] Users can improve their skills based on the displayed information, and their emotional feedback during use is accumulated as an evaluation.

[1735] Prompt Sentence Examples

[1736] "What are some important points about project management?"

[1737] Please rate this skill.

[1738] "Please tell me the results of your recent sentiment analysis."

[1739] This invention enables fairer and more reliable evaluations by accurately assessing employees' knowledge and skills while also reflecting their emotions, and promotes knowledge sharing and skill improvement throughout the organization through appropriate information retrieval.

[1740] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1741] Step 1:

[1742] Login and Authentication

[1743] The user enters their employee ID and password into the login screen of the device.

[1744] Input: Employee ID and password

[1745] The terminal transmits the input data to the server.

[1746] Based on the received data, the server checks the authentication information of the corresponding user in the database.

[1747] Data processing: Authentication by database matching

[1748] Output: Authentication result (success or failure)

[1749] If the authentication is successful, the server starts the session and prepares the conversational artificial intelligence and emotion engine.

[1750] Specific working example:

[1751] The user opens the login page in a browser, enters the employee ID "user123" and password "pass123", and clicks the "Login" button.

[1752] The server verifies this information and displays the home screen if authentication is successful.

[1753] Step 2:

[1754] Dialogue and Knowledge Extraction

[1755] The user begins a conversation in natural language with a conversational artificial intelligence (generative AI model) on the device.

[1756] Input: Natural language questions and conversations

[1757] The terminal transmits the user's input text to the server in real time.

[1758] The server uses natural language processing technology (e.g., Python's NLTK or Google Cloud Natural Language API) to analyze the conversation and extract the user's knowledge and skills.

[1759] Data processing: Text analysis using natural language processing technology

[1760] Output: Extracted knowledge and skills

[1761] Specific working example:

[1762] User types, "What are some project management best practices?"

[1763] The server analyzes this text and extracts "project management" and "best practices" as key concepts.

[1764] Step 3:

[1765] emotion recognition

[1766] While the user is interacting, the emotion engine analyzes emotions from the user's voice, facial expressions, and text.

[1767] Input: Voice data, facial expression data, text data

[1768] The device uses voice input and a camera to extract voice and facial expression data and transmits it to a server.

[1769] The server analyzes this data using an emotion engine (e.g., Microsoft Azure Emotion API) to track emotional states in real time.

[1770] Data processing: Emotion recognition through voice and facial expression analysis

[1771] Output: Emotion data (e.g. percentage of happy, surprised, sad)

[1772] Specific working example:

[1773] The user shows expressions of joy and surprise during the interaction.

[1774] The server records the emotional data as "Happiness: 80%" and "Surprise: 60%."

[1775] Step 4:

[1776] Conducting the evaluation

[1777] The server performs a third-party evaluation of the extracted knowledge and skills.

[1778] The evaluation criteria are depth of knowledge, accuracy of content, usefulness, and user sentiment data.

[1779] Input: Extracted knowledge, skills, and emotion data

[1780] The server combines these multiple factors to generate an overall rating score.

[1781] Data processing: Criteria-based scoring

[1782] Output: Overall evaluation score

[1783] Specific working example:

[1784] The server evaluates the accuracy and practicality of the knowledge about "project management best practices" entered by the user and provides an "evaluation score: 85 points."

[1785] Step 5:

[1786] Visualization of evaluation results

[1787] The server visualizes the evaluation results and displays them in an easy-to-understand format.

[1788] Input: Evaluation scores, emotion data

[1789] The server generates visualization data and sends it to the terminal.

[1790] The terminal provides an interface for displaying the evaluation results in the form of graphs, scores, etc.

[1791] Data processing: Converting evaluation results into visualized data

[1792] Output: Visualized data (graphs, scores)

[1793] Specific working example:

[1794] The server generates data to display the evaluation score of "85 points" in a pie chart or bar graph.

[1795] The terminal receives this, visualizes the evaluation results, and displays them to the user.

[1796] Step 6:

[1797] Saving evaluation results

[1798] The server stores the evaluated knowledge, skill information and emotion data in a database.

[1799] Input: Evaluation results, emotion data

[1800] A database organizes and stores this information for future retrieval and reference.

[1801] Data processing: Inserting data into the database

[1802] Output: Accumulated data

[1803] Specific working example:

[1804] The server saves the evaluation results and emotion data in the database using an INSERT statement.

[1805] Step 7:

[1806] Searching for information

[1807] When a user searches for the information they need, they use the search interface on their device.

[1808] Input: Search keyword

[1809] The server extracts information that matches the search keywords from the database and sends it to the terminal.

[1810] Data processing: Data extraction based on search keywords

[1811] Output: Search results

[1812] Specific working example:

[1813] A new employee types in "project management" and clicks the search button.

[1814] The server retrieves the relevant information from the database and displays the appropriate information on the terminal.

[1815] (Application example 2)

[1816] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1817] Conventional systems only extracted employee knowledge and skills through conversational AI, making it difficult to properly reflect the evaluation in personnel evaluations. Furthermore, the system did not reflect the user's emotional state, making accurate evaluations and motivation management difficult. In particular, in factories, the emotions and stress levels of workers significantly affect the quality of their work, so the development of a system that takes this into account was needed.

[1818] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 2 is realized by the following means.

[1819] In this invention, the server includes means for extracting employee knowledge and skills using interactive artificial intelligence, means for visualizing knowledge and skills based on third-party evaluations, means for storing the knowledge and skills in a database along with the evaluation results, means for making the stored knowledge and skills searchable, means for displaying the searched knowledge and skills, and emotion recognition means for recognizing the user's emotions and reflecting that information in the evaluation. This enables appropriate evaluations and knowledge sharing that take into account the emotions of employees.

[1820] An "employee" is someone who belongs to a company or organization and provides labor for its activities.

[1821] "Knowledge" is an abstract concept that refers to information, understanding, and experiential understanding in a particular field.

[1822] "Skills" are the practical abilities and techniques required to effectively carry out a particular task or activity.

[1823] "Conversational AI" refers to an AI system designed to mimic natural human interactions and uses natural language processing techniques.

[1824] "Third-party evaluation" is an objective evaluation method conducted from an independent standpoint other than the person being evaluated.

[1825] "Visualization" is the act of presenting abstract data such as knowledge, skills, and evaluation results in a format that is visually easy to understand.

[1826] A "database" is a system that organizes and stores information so that it can be searched and used efficiently later.

[1827] "Emotion recognition" is a technology that automatically identifies a user's emotional state at any given time by analyzing their voice, facial expressions, text, etc.

[1828] "Evaluation results" are the results of evaluation of knowledge and skills obtained through interactive artificial intelligence or third-party evaluation.

[1829] A "server" is a computer that provides services such as data processing and storage to other computers over a network.

[1830] "Searchable" means having the ability to efficiently find specific information from the accumulated data.

[1831] This invention is a system that uses interactive artificial intelligence to extract employee knowledge and skills, visualizes the evaluation results, and stores them in a database. Furthermore, by combining it with emotion recognition technology, the system can reflect the user's emotional state in the evaluation. To implement this invention, the following specific configuration and procedures are required.

[1832] System configuration

[1833] The system mainly consists of the following components:

[1834] 1. Server: Performs interactive AI processing, emotion recognition, database management, and evaluation / visualization processing.

[1835] 2. Terminal: Provides an interface for users to interact with the conversational artificial intelligence and emotion engine.

[1836] 3. Database: Stores employee knowledge, skills, evaluation results, and emotional data.

[1837] Overview of program processing

[1838] This system allows users to log in via a terminal, converse with a conversational AI, extract the knowledge and skills gained in the process, and use an emotion engine to recognize the user's emotions to evaluate them. The evaluation results are stored in a database and can be searched and used by other users. The evaluation results and emotion data are also reflected in personnel evaluations.

[1839] Extracting user knowledge and skills and recognizing emotions

[1840] 1. Login and authentication: The user logs in by entering their employee ID and password into the terminal. The server authenticates the user information, and if authentication is successful, a session with the conversational artificial intelligence and emotion engine begins.

[1841] 2. Dialogue and knowledge extraction: The user interacts with the conversational AI in natural language on the terminal. The server analyzes the content of this dialogue using natural language processing technology and extracts the user's knowledge and skills.

[1842] 3. Emotion Recognition: During a conversation, the emotion engine analyzes the user's emotions from their voice, facial expressions, and text, and tracks the results in real time.

[1843] Evaluation and visualization

[1844] 1. Evaluation: The server performs a third-party evaluation of the extracted knowledge and skills. The evaluation criteria are depth of knowledge, accuracy of content, usefulness, and user emotional data.

[1845] 2. Visualization: The evaluation results are visualized and presented in an easy-to-understand format (graphs and scores). Emotional data is also displayed.

[1846] Database storage and search

[1847] 1. Storing evaluation results: The knowledge, skill information, and emotional data for which evaluation has been completed are stored in a database.

[1848] 2. Information search: When other users search for the information they need, they use the search interface on their device. The server extracts information that matches the search keywords from the database and displays it on the device.

[1849] Hardware and software used

[1850] 1. Smart glasses: Used during maintenance work on factory robots, they capture the faces and voices of workers and perform emotion recognition.

[1851] 2. EmotionEngine: Analyzes emotions in real time and sends the data to a server.

[1852] 3. Dialogue AI: Uses natural language processing technology to analyze the content of user dialogue and extract knowledge and skills.

[1853] Specific examples

[1854] Example 1: Knowledge sharing and emotion recognition among veteran employees

[1855] 1. The terminal provides a screen for the veteran employee to log in. Once the user logs in, a session with the conversational artificial intelligence and emotion engine begins.

[1856] 2. As users talk about project management best practices, the server uses natural language processing technology to extract key knowledge, and an emotion engine analyzes the user's emotions in the process.

[1857] 3. The server performs a third-party evaluation of this knowledge, visualizes the evaluation results and emotional data, and stores the results in a database where other employees can search and use them.

[1858] Example prompt sentences:

[1859] Please tell me how to replace the parts.

[1860] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1861] Step 1:

[1862] The user logs in by entering their employee ID and password into the terminal.

[1863] Input: Employee ID, Password

[1864] Data processing: The server checks the user information against the database and performs authentication.

[1865] Output: If authentication is successful, a session with the conversational artificial intelligence and emotion engine is initiated.

[1866] Specific operation: When a user enters their employee ID and password into the login screen on their device and presses the "Login" button, an authentication request is sent to the server. The server checks the authentication information against the database and, if correct, starts a session.

[1867] Step 2:

[1868] The user interacts with the conversational artificial intelligence on the terminal in natural language.

[1869] Input: User question or comment (natural language text)

[1870] Data processing: The server uses natural language processing technology to analyze the content of the dialogue and extract the user's knowledge and skills.

[1871] Output: Information about knowledge and skills

[1872] Specific operation: When a user uses the conversational artificial intelligence interface on the terminal to input "Please tell me how to replace this part," the server passes the text data to a natural language processing engine, which extracts knowledge about part replacement as an analysis result.

[1873] Step 3:

[1874] During a conversation, the emotion engine analyzes emotions from the user's voice, facial expressions, and text.

[1875] Input: Camera video, audio data, text data

[1876] Data processing: EmotionEngine analyzes this data and determines the emotional state in real time.

[1877] Output: Emotional state (e.g., happy, surprised, angry, etc.)

[1878] Specific operation: The device's camera and microphone are used to capture the user's face and voice, and this data is sent to EmotionEngine, which analyzes the data and determines and displays the user's emotional state in real time.

[1879] Step 4:

[1880] The server performs a third-party evaluation of the extracted knowledge and skills.

[1881] Input: Extracted knowledge, skills, and emotion data

[1882] Data processing: Evaluation is carried out based on evaluation criteria (depth of knowledge, accuracy of content, usefulness, and sentiment data).

[1883] Output: Evaluation results (score, comments, etc.)

[1884] Specific operation: The server inputs information about knowledge and skills and emotional data into the evaluation algorithm and generates an evaluation result based on multiple criteria. For example, if the knowledge is detailed, accurate, and useful, a high evaluation score is assigned.

[1885] Step 5:

[1886] The evaluation results are visualized and presented in an easy-to-understand format (graphs and scores).

[1887] Input: Evaluation results, emotion data

[1888] Data processing: Transformation for visual display in graphs and scores

[1889] Output: Visualized evaluation results and emotion data

[1890] Specific operation: The server obtains the evaluation results and emotion data, and uses a visualization engine to display them on the terminal in the form of bar graphs, pie charts, score displays, etc.

[1891] Step 6:

[1892] The evaluation results and emotional data are stored in a database.

[1893] Input: Visualized evaluation results and emotion data

[1894] Data processing: Add as a new record to the database

[1895] Output: Evaluation results and emotion data stored in a database

[1896] Specific action: Add a new entry of the emotion data and evaluation results to the database so that other users can access it later.

[1897] Step 7:

[1898] When other users search for the information they need, they use the search interface on their device.

[1899] Input: Search keyword

[1900] Data processing: Extract information that matches keywords from the database

[1901] Output: Related knowledge and skill information

[1902] Specific operation: A keyword is entered into the terminal's search interface, and the server searches for related information from the database and displays it on the terminal.

[1903] Step 8:

[1904] Emotional feedback is provided as the user works based on the search results.

[1905] Input: Use of search results, emotional state during work

[1906] Data processing: Emotion engine analyzes emotions in real time while working

[1907] Output: Emotional feedback data during the task

[1908] Specific operation: A user uses smart glasses while working, and the emotion engine continuously monitors their emotional state, generating and storing feedback data.

[1909] 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 control target 443 to output the result of the specific processing. The microphone 238 acquires voice indicating a user input regarding the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[1910] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[1911] In the above embodiment, an example in which the specific processing is performed by the data processing device 12 has been given, but the technology of the present disclosure is not limited to this, and the specific processing may be performed by the robot 414.

[1912] The emotion identification model 59 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 an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[1913] FIG. 9 illustrates an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. Emotions closer to the center of the concentric circles are more primitive. Emotions representing states and behaviors arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion includes both affect and mental states. Emotions generally generated from reactions occurring in the brain are arranged on the left side of the concentric circles. Emotions generally induced by situational judgment are arranged on the right side of the concentric circles. Emotions generally generated from reactions occurring in the brain and induced by situational judgment are arranged on the upper and lower sides of the concentric circles. Furthermore, the emotion of "pleasure" is arranged on the upper side of the concentric circles, and the emotion of "discomfort" is arranged on the lower side. In this way, in the emotion map 400, multiple emotions are mapped based on the structure by which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[1914] These emotions are distributed in the 3 o'clock direction on emotion map 400, and typically fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

[1915] The inside of emotion map 400 represents what is going on in the mind, and the outside of emotion map 400 represents behavior, so the further you go outside emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[1916] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is indicated, and when they approach the ideal, a state of pleasure is indicated. Emotions can also be created for robots, automobiles, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is indicated, and when they approach the ideal, a state of pleasure is indicated. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on Voice Emotion Recognition and Emotional Brain Physiological Signal Analysis Systems, Tokushima University, Doctoral Dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the "reaction" domain, where sensation is dominant. The right half of the emotion map lists emotions belonging to the "situation" domain, where situational awareness is dominant.

[1917] The emotion map defines two emotions that promote learning. One is a negative emotion on the situation side, around the middle of "repentance" or "reflection." In other words, this occurs 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 a positive emotion on the response side, around "desire." In other words, this occurs when the robot experiences positive feelings such as "I want more" or "I want to know more."

[1918] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple pieces of training data that are combinations of user input and emotion values ​​indicating each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions that are located close to each other have similar values, as in the emotion map 900 shown in FIG. 10. FIG. 10 shows an example in which multiple emotions, "relieved," "calm," and "reassuring," have similar emotion values.

[1919] The system according to the present disclosure has been described above mainly with respect to the functions of the data processing device 12, but the system according to the present disclosure is not necessarily implemented on a server. The system according to the present disclosure may be implemented as a general information processing system. The present disclosure may be implemented, for example, as a software program running on a personal computer or an application running on a smartphone, etc. The method according to the present disclosure may be provided to users in the form of SaaS (Software as a Service).

[1920] In the above embodiment, an example was given in which the specific processing is performed by one computer 22, but the technology of the present disclosure is not limited to this, and the specific processing may be distributed and performed by a plurality of computers including the computer 22. For example, the data generation model 58 may be provided in an external device of the data processing device 12, and data may be generated in the external device in accordance with input data.

[1921] In the above embodiment, an example in which the specific processing program 56 is stored in the storage 32 has been described, but the technology of the present disclosure is not limited to this. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-transitory storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-transitory storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes the specific processing in accordance with the specific processing program 56.

[1922] 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.

[1923] It is not necessary to store all 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 all of the specific processing program 56 in the storage 32; only a portion of the specific processing program 56 may be stored.

[1924] The hardware resource for executing a specific process can be any of the following processors: An example of a processor is a CPU, which is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. Another example of a processor is a dedicated electrical circuit, such as an FPGA (Field-Programmable Gate Array), a PLD (Programmable Logic Device), or an ASIC (Application Specific Integrated Circuit), which is a processor with a circuit configuration designed specifically for executing a specific process. Each processor has built-in or connected memory, and each processor uses the memory to execute the specific process.

[1925] The hardware resource that executes the specific processing may be configured with one of these various processors, or may be configured with 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). Also, the hardware resource that executes the specific processing may be a single processor.

[1926] As an example of a system configured with a single processor, first, one processor is configured by combining one or more CPUs and software, and this processor functions as a hardware resource that executes a specific process. Second, there is a system that uses a processor that realizes the functions of an entire system including multiple hardware resources that execute a specific process on a single IC chip, as typified by SoC (System-on-a-chip). In this way, a specific process is realized using one or more of the above-mentioned various processors as hardware resources.

[1927] Furthermore, the hardware structure of these various processors can be, more specifically, an electric circuit that combines circuit elements such as semiconductor devices. The specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps may be deleted, new steps may be added, or the processing order may be rearranged, without departing from the spirit of the invention.

[1928] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, to avoid confusion and facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[1929] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference.

[1930] The following is further disclosed regarding the above embodiment.

[1931] (Claim 1)

[1932] A means of drawing out employee knowledge and skills using conversational artificial intelligence,

[1933] A means to visualize knowledge and skills based on third-party evaluation,

[1934] A means of storing knowledge and skills in a database along with the evaluation results;

[1935] A means to make accumulated knowledge and skills searchable,

[1936] A means for displaying the retrieved knowledge and skills;

[1937] A system including:

[1938] (Claim 2)

[1939] 2. The system of claim 1, wherein the interactive artificial intelligence includes means for extracting knowledge and skills using natural language processing techniques.

[1940] (Claim 3)

[1941] 2. The system according to claim 1, wherein the evaluation results are reflected in personnel evaluations.

[1942] "Example 1"

[1943] (Claim 1)

[1944] A means of drawing out employees' knowledge and abilities using interactive artificial intelligence,

[1945] A means to visualize knowledge and abilities based on third-party evaluation,

[1946] A means of storing knowledge and skills together with the evaluation results in a database;

[1947] A means of making accumulated knowledge and capabilities searchable;

[1948] a means for displaying the retrieved knowledge and capabilities;

[1949] a means for the user to input authentication information and for the server to perform authentication;

[1950] A means for conversational AI to analyze the content of conversations using natural language processing tools and extract knowledge and capabilities;

[1951] A means to visualize the results of the evaluation in graphs and charts and store them in a database,

[1952] A system including:

[1953] (Claim 2)

[1954] 2. The system of claim 1, wherein the interactive artificial intelligence includes means for extracting knowledge and capabilities using natural language processing techniques.

[1955] (Claim 3)

[1956] 2. The system according to claim 1, wherein the evaluation results are reflected in personnel evaluations.

[1957] "Application Example 1"

[1958] (Claim 1)

[1959] A means of drawing out employee knowledge and skills using conversational artificial intelligence,

[1960] A means to visualize knowledge and skills based on third-party evaluation,

[1961] A means of storing knowledge and skills in a database along with the evaluation results;

[1962] A means to make accumulated knowledge and skills searchable,

[1963] A means for displaying the retrieved knowledge and skills;

[1964] a means for providing an interface for factory automation equipment to capture the assessed knowledge and skill information;

[1965] A system including:

[1966] (Claim 2)

[1967] 2. The system of claim 1, wherein the interactive artificial intelligence includes means for extracting knowledge and skills using natural language processing techniques.

[1968] (Claim 3)

[1969] 2. The system according to claim 1, wherein the evaluation results are reflected in personnel evaluations.

[1970] "Example 2: Combining Emotion Engines"

[1971] (Claim 1)

[1972] A means of drawing out employee knowledge and skills using conversational artificial intelligence,

[1973] A means to visualize knowledge and skills based on third-party evaluation,

[1974] A means of storing knowledge and skills in a database along with the evaluation results;

[1975] A means to make accumulated knowledge and skills searchable,

[1976] A means for displaying the retrieved knowledge and skills;

[1977] means for recognizing a user's emotion;

[1978] A means for reflecting emotion data in evaluation;

[1979] A system including:

[1980] (Claim 2)

[1981] 2. The system of claim 1, wherein the interactive artificial intelligence includes means for extracting knowledge and skills using natural language processing techniques.

[1982] (Claim 3)

[1983] 2. The system according to claim 1, wherein the evaluation results are reflected in personnel evaluations.

[1984] "Application example 2 when combining emotion engines"

[1985] (Claim 1)

[1986] A means of drawing out employee knowledge and skills using conversational artificial intelligence,

[1987] A means to visualize knowledge and skills based on third-party evaluation,

[1988] A means of storing knowledge and skills in a database along with the evaluation results;

[1989] A means to make accumulated knowledge and skills searchable,

[1990] A means for displaying the retrieved knowledge and skills;

[1991] emotion recognition means for recognizing the user's emotion and reflecting the information in the evaluation;

[1992] A system including:

[1993] (Claim 2)

[1994] 2. The system of claim 1, wherein the interactive artificial intelligence includes means for extracting knowledge and skills using natural language processing techniques.

[1995] (Claim 3)

[1996] 2. The system according to claim 1, wherein the evaluation results are reflected in personnel evaluations. [Explanation of symbols]

[1997] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot< / url:> < / url:> < / url:> < / url:>

Claims

1. A means of drawing out employee knowledge and skills using conversational artificial intelligence, A means to visualize knowledge and skills based on third-party evaluation, A means of storing knowledge and skills in a database along with the evaluation results; A means to make accumulated knowledge and skills searchable, A means for displaying the retrieved knowledge and skills; A system including:

2. 2. The system of claim 1, wherein the interactive artificial intelligence includes means for extracting knowledge and skills using natural language processing techniques.

3. 2. The system according to claim 1, wherein the evaluation results are reflected in personnel evaluations.

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