System

Abstract

A system is provided.SOLUTION: A system, comprising: means for inputting a question from a terminal by a new employee or a middle employee; means for transmitting the input question to a server; means for analyzing the received question and generating an answer using a natural language processing model by the server; means for transmitting the generated answer to the terminal and displaying the generated answer; and means for storing the measured growth degree in a database and displaying the measured growth degree on a dashboard of an elder or a senior.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] New employees and mid-career employees often lack a thorough understanding of basic work procedures and company terminology, resulting in work delays and reduced efficiency. Furthermore, even if they want to ask questions, they often find it difficult to receive appropriate support if senior employees are busy or in an atmosphere where it is difficult to ask. Furthermore, because it is difficult to visualize their own growth, motivation declines and employees find it difficult to realize their own personal growth. It is also difficult for elders and superiors to grasp the specific progress and questions of new employees, making it difficult to provide appropriate evaluations and feedback. [Means for solving the problem]

[0005] The present invention provides a system in which new employees and mid-career employees input questions from their terminals and send them to a server. The server analyzes the received questions, generates answers using a natural language processing model, and sends the generated answers to the terminals for display. New employees and mid-career employees also upload their daily work details from their terminals to the server, which analyzes the uploaded work details and measures their level of growth. The measured level of growth is stored in a database and displayed on a dashboard for elders or superiors. This system enables quick answers to questions, visualization of growth levels, and appropriate feedback, thereby enabling new employees to smoothly carry out their work and support their growth.

[0006] "Device" refers to the electronic device used by users to enter questions and upload work details, including PCs and smartphones.

[0007] "Server" refers to the central computer system that receives, analyzes, and processes questions and business content sent from the terminal.

[0008] "Question" refers to a statement that a new employee or mid-career employee types into a terminal to resolve a work-related question.

[0009] A "natural language processing model" refers to an artificial intelligence technology used to analyze input questions and generate optimal answers.

[0010] "Answer" refers to a response to a question generated using a natural language processing model.

[0011] "Work content" refers to information about the progress of work, such as the tasks that new or mid-career employees perform on a daily basis and the documents they create.

[0012] "Growth level" refers to an indicator that evaluates the improvement in skills and knowledge of new or mid-career employees based on an analysis of their work content.

[0013] "Database" refers to a system in which a server stores and manages information such as analysis results and growth rate.

[0014] A "dashboard" refers to an interface that allows elders and superiors to visually check the progress and other work-related information of new or mid-career employees.

[0015] "Elder" refers to a senior employee who is responsible for educating and supporting new or mid-career employees.

[0016] "Superior" refers to a higher-ranking employee who supervises and evaluates the work performance of new or mid-career employees. [Brief explanation of the drawings]

[0017] [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

[0018] 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.

[0019] First, the terms used in the following description will be explained.

[0020] 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).

[0021] 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.

[0022] 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.

[0023] 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.

[0024] 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."

[0025] [First embodiment]

[0026] FIG. 1 shows an example of the configuration of a data processing system 10 according to the first embodiment.

[0027] 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.

[0028] 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).

[0029] 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.

[0030] 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.

[0031] 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.

[0032] 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.

[0033] FIG. 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0034] 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.

[0035] 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.

[0036] 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.

[0037] 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."

[0038] The present invention is an educational AI system that provides work assistance and growth measurement for new and mid-career employees. This system is composed of a terminal, a server, and software for linking these. The specific operation of the system of the present invention is described below.

[0039] 1. User enters question:

[0040] New employees or mid-career employees (hereafter referred to as users) access the web portal using a device (PC or smartphone) and enter a question about their work. For example, they might enter, "Please tell me how to proceed with this project."

[0041] 2. Submitting and analyzing questions:

[0042] The device sends the input question to the server, which then passes the received question to a natural language processing model (e.g., GPT-4 (registered trademark)) for analysis.

[0043] 3. Generate and display answers:

[0044] The server's natural language processing model generates the best answer based on the question. For example, in response to the question above, it might generate an answer such as, "Project progress is carried out in the following five steps: 1. Requirements definition, 2. Planning, 3. Implementation, 4. Testing, 5. Release." The server then sends the generated answer to the device, which then displays it on the user interface.

[0045] 4. Upload your job description:

[0046] Users upload their daily work details and documents they have created to the server via their terminals. For example, they upload meeting documents they have created in PDF format.

[0047] 5. Job Analysis and Growth Measurement:

[0048] The server analyzes the uploaded materials and measures the growth of new or mid-career employees. The growth measurement engine evaluates the content, quality, and format consistency of the materials to generate a growth score. For example, a "growth score: 85" could be generated based on the depth and consistency of the material's content.

[0049] 6. Storage and display of growth data:

[0050] The server stores the generated growth score in a database. Elders and superiors can check the user's growth score in real time through the dashboard. For example, the dashboard might show "User A: Score 85."

[0051] 7. Providing Feedback:

[0052] The elder or superior provides appropriate feedback to the user based on the growth data in the dashboard. For example, feedback such as "User A's document creation skills have improved" can be posted on the web portal.

[0053] In this way, the system of the present invention provides functions that improve the efficiency of work performance for new employees and mid-career employees and visualize their growth by providing quick answers to user questions and measuring growth levels through analysis of work content. This allows elders and superiors to provide appropriate evaluations and feedback, realizing smooth work performance and growth support for new employees.

[0054] The processing flow will be explained below.

[0055] Step 1:

[0056] A user accesses the web portal from a terminal and enters a question into the inquiry form. For example, the user might enter, "Please tell me how to proceed with this project."

[0057] Step 2:

[0058] The device sends the question data to the server. Specifically, the question content is sent to the server as JSON format data via a POST request.

[0059] Step 3:

[0060] The server receives the POST request and passes the question to the question answering engine for processing. Specifically, the server extracts and prepares the question text from the JSON data.

[0061] Step 4:

[0062] The server's question-answering engine uses a natural language processing model (such as GPT-4) to analyze the question and generate the optimal answer. For example, if asked "How do you proceed with a project?", it will generate an answer such as "Project progress is carried out in the following five steps: 1. Requirements definition, 2. Planning, 3. Implementation, 4. Testing, and 5. Release."

[0063] Step 5:

[0064] The server converts the generated response into JSON format and sends it to the terminal as an HTTP response.

[0065] Step 6:

[0066] The terminal receives the response data from the server and displays it on the user interface. Specifically, the answer "The project will proceed in the following steps..." is displayed on the web page.

[0067] Step 7:

[0068] Users upload their daily work details and documents they have created from their devices to the server. For example, they upload meeting materials they have created from a web portal.

[0069] Step 8:

[0070] The terminal sends data on the business content to the server. Specifically, the created documents are sent to the server in a format such as PDF.

[0071] Step 9:

[0072] The server passes the received business content data to the growth measurement engine for analysis, which evaluates the content, quality, and format consistency of the materials.

[0073] Step 10:

[0074] The server stores the growth score generated by the growth measurement engine in a database, for example, "Growth score of user A's meeting materials: 85."

[0075] Step 11:

[0076] The server updates the growth data to the dashboard of the elders and superiors, who can view the user's growth score in real time on the dashboard.

[0077] Step 12:

[0078] The elder or superior checks the growth data on the dashboard and provides appropriate feedback to the user. For example, the feedback could be posted on the web portal, saying, "User A's document creation skills have improved."

[0079] Through the above steps, the system of the present invention improves the efficiency of work execution for new employees and mid-career employees and effectively supports their growth.

[0080] Example 1

[0081] 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."

[0082] In the conventional training system, new employees and mid-career employees were unable to efficiently resolve their work-related questions, making it difficult to immediately grasp the improvement of their work skills. In addition, there were limited means for superiors and managers to accurately measure the skill growth of new employees and provide appropriate feedback.

[0083] 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.

[0084] In this invention, the server includes means for inputting a question from a terminal, means for transmitting the input question to a computer system, means for the computer system to analyze the received question and generate an answer using a generative model, means for transmitting the generated answer to the terminal and displaying it, means for uploading work content from the terminal to the computer system, means for the computer system to analyze the uploaded work content and measure an evaluation score, and means for saving the measured evaluation score in a data store and displaying it on a display screen for an administrator. This enables new employees and mid-career employees to receive quick and appropriate answers to questions about their work, and makes it possible to measure the growth of their work skills in real time and provide appropriate feedback based on that data.

[0085] A "terminal" is an information processing device used by a user, and includes a personal computer (PC) or a smartphone.

[0086] A "computer system" is a group of information processing devices, including servers and cloud-based computing resources, that transmit, receive, and analyze data.

[0087] A "generative model" is an algorithm for natural language processing, and refers to an advanced generative AI model such as GPT-4.

[0088] A "data store" is a storage system for saving and managing data, including databases and cloud storage.

[0089] The "means for inputting a question" is an interface for a user to input a question in text format, and includes an input form on a web portal.

[0090] "Means for sending a question to a computer system" refers to a communication protocol and its implementation for transferring a question entered by a user to a server over a network.

[0091] "Means for analyzing questions and generating answers using a generative model" refers to a mechanism for analyzing received questions using natural language processing technology and automatically generating appropriate answers.

[0092] "Means for transmitting the answer to the terminal and displaying it" refers to an interface and its implementation for transmitting the generated answer to the terminal and visually presenting it to the user.

[0093] "Means for uploading business content from a terminal to a computer system" refers to the function for sending documents created by users to a server in file format.

[0094] "Means for analyzing uploaded work content and measuring evaluation scores" refers to the algorithm and its implementation for analyzing uploaded materials and data and quantifying the degree of growth based on their quality and content.

[0095] "Administrator display screen" refers to the user interface that allows elders or superiors to check growth data and feedback.

[0096] The present invention relates to an education system that provides work support and growth measurement for new employees and mid-career employees. This system is composed of a terminal, a computer system, and software for linking these.

[0097] First, a user accesses a web portal using a device (e.g., a PC or smartphone) and inputs a question related to their work. The device then sends the user's input to a computer system. The computer system then passes the received question to a generative AI model (e.g., GPT-4), which analyzes the question using natural language processing and generates the optimal answer.

[0098] As a specific example, suppose a user types, "Tell me how to proceed with this project." The generative AI model in the computer system analyzes the question and generates an appropriate answer. For example, it might generate an answer such as, "The five steps in project progression are: 1. Requirements definition, 2. Planning, 3. Implementation, 4. Testing, and 5. Release." The server sends the generated answer to the device, which then displays it on the user interface.

[0099] Furthermore, users upload their daily work details and documents they have created to the computer system via their terminals. The computer system analyzes the uploaded documents and measures the user's level of growth. The growth measurement engine evaluates the content, quality, and format consistency of the documents and generates a growth score. For example, a "growth score of 85" is generated based on the depth and consistency of the document's content.

[0100] The generated growth scores are stored in a data store, and elders and superiors can check them in real time via the dashboard. For example, the dashboard might display "User A: Score 85." The elder or superior can provide appropriate feedback to the user based on this growth data. For example, they could post feedback such as "User A's document creation skills are improving" on the web portal.

[0101] Examples of prompts include:

[0102] "Tell me how to proceed with this project."

[0103] "What are some best practices for dealing with customers?"

[0104] In this way, this system improves the efficiency of work execution for new and mid-career employees, provides a function that visualizes their growth, and makes it easier for elders and superiors to provide appropriate evaluations and feedback, thereby enabling new employees to smoothly carry out their work and support their growth.

[0105] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0106] Step 1: User enters question

[0107] A user accesses a web portal using a terminal and inputs a question about their work. The input question is processed as string data within the terminal and prepared for transmission. For example, a user might input "Please tell me how to proceed with this project." This input data is converted to JSON format data for subsequent processing and prepared as an HTTP request.

[0108] Step 2: Submit your question

[0109] The device sends the entered question data to the server. This is done using an HTTP POST request. The input data includes the question content and the user ID. The server receives the question data and prepares it for analysis.

[0110] Step 3: Parsing the Question

[0111] The server passes the received question data to a generative AI model (e.g., GPT-4) for analysis. At this time, the server converts the question data into an appropriate format and sends it to the generative AI model's API. The input data is the user's question, and the output data is the generated answer. The server obtains the answer returned by the generative AI model and prepares it for the next process.

[0112] Step 4: Generate an answer

[0113] The generative AI model on the server analyzes the question and generates the optimal answer. For example, it might generate an answer such as, "The five steps in project progress are: 1. Requirements definition, 2. Planning, 3. Implementation, 4. Testing, 5. Release." The input is the user's question data, and the output is the generated answer data.

[0114] Step 5: Submit and view your responses

[0115] The server sends the generated answer data to the terminal as an HTTP response. The terminal analyzes the received answer data and displays it on the user interface. The input data is the generated answer, and the output is the visual answer information displayed to the user.

[0116] Step 6: Upload your work

[0117] Users upload their daily work details and documents they have created to the server via their terminal. The terminal sends the user's work data (e.g., PDF files) to the server as an HTTP POST request. The input data is a file containing the work details, and the output is the file saved on the server.

[0118] Step 7: Analyze work and measure growth

[0119] The server analyzes the uploaded materials and measures their growth. Specifically, it uses text mining tools to analyze the data and evaluates its content, quality, format consistency, etc. The input data is the uploaded materials, and the output data is the calculated growth score. For example, a "Growth Score: 85" is generated.

[0120] Step 8: Save and display growth data

[0121] The server stores the generated growth score in a data store and displays it through a dashboard. The input data is the growth score, and the output is the information stored in the database and displayed on the dashboard. Elders and superiors can access the dashboard and see "User A: Score 85."

[0122] Step 9: Provide feedback

[0123] An elder or superior provides feedback based on the growth data displayed on the dashboard. The feedback is sent from the terminal to the server and saved in the user's profile. For example, a comment such as "User A's document creation skills have improved" is entered. The input data is the feedback, and the output is the feedback information saved in the user's profile.

[0124] (Application example 1)

[0125] 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."

[0126] Conventional training systems for new employees and mid-career hires make it difficult to provide immediate answers to work-related questions or measure the degree of growth. In particular, there was a lack of means to quickly and effectively train factory engineers on robot operation and maintenance. This resulted in a decline in the quality and efficiency of training, and problems such as an inability to visualize the degree of growth of engineers or provide appropriate feedback.

[0127] 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.

[0128] In this invention, the server includes: means for a new employee or mid-career employee to input a question from an information terminal; means for transmitting the input question to a computer; means for the computer to analyze the received question and generate an answer using a natural language analysis model; means for transmitting the generated answer to the information terminal and displaying it; means for a new employee or mid-career employee to upload daily work content from the information terminal to a computer; means for the computer to analyze the uploaded work content and measure the degree of growth; means for storing the measured degree of growth in a storage device and displaying it on a control panel for an instructor or manager; means for a technician to input a question about robot operation or maintenance via visual wear; means for the visual wear to recognize the question by voice and transmit it to a computer; means for the computer to generate an answer using a natural language analysis model based on the received question and display it on the visual wear; means for the technician to upload work reports or maintenance records via visual wear; means for the computer to analyze the uploaded data and measure the technician's degree of growth; and means for providing feedback to the technician based on the degree of growth. This allows new employees, mid-career hires, and factory technicians to receive real-time training support, measure their progress, and provide appropriate feedback.

[0129] "New employee" refers to an employee who has been newly hired by a company or organization.

[0130] "Mid-career employees" refer to employees who have been newly hired after transferring from another company or organization.

[0131] "Information terminal" refers to a computer device that can connect to the Internet, such as a desktop computer, laptop, tablet, or smartphone.

[0132] "Computer" refers to a server, cloud, or other computer system with computing power.

[0133] "Natural language analysis model" refers to artificial intelligence technology for understanding and analyzing human language and generating appropriate responses. Specifically, it refers to large-scale language models such as GPT-4.

[0134] "Growth" refers to a measure of how much an educated employee or technician has improved their skills and knowledge.

[0135] "Storage device" refers to hardware for storing digital data, such as a hard disk drive (HDD) or solid-state drive (SSD).

[0136] "Leaders" refer to supervisors and trainers who are responsible for educating and guiding new employees and mid-career hires.

[0137] "Manager" refers to a person who has the authority to manage the work performance and development of employees and engineers.

[0138] A "control panel" is software or hardware with a user interface, and refers to a screen or device for displaying and operating data.

[0139] "Visual wear" refers to wearable devices such as smart glasses and head-mounted displays that can display real-time information and enable interactive operation.

[0140] "Work reports" refer to documents and data used by employees and technicians to record and report on their daily work activities and progress.

[0141] "Maintenance records" refers to documents and data that record the progress and results of maintenance work on robots and equipment.

[0142] "Feedback" refers to the evaluation and advice given to employees and engineers based on their growth and performance.

[0143] The present invention relates to an education support system for training new employees, mid-career recruits, and factory engineers and measuring their growth. This system is composed of an information terminal, a computer (server), and software for linking these. The specific operation of the system of the present invention will be described below.

[0144] The system uses information terminals, computers (servers), natural language analysis models (such as GPT-4), visual wear (smart glasses or head-mounted displays), and storage devices. These hardware and software enable employees and engineers to receive real-time support and measure their progress.

[0145] Users, i.e., new employees or mid-career employees, can access a web portal using an information terminal (PC, tablet, smartphone, etc.) and input questions related to their work. For example, they might input, "Please tell me how to proceed with this project." The input question is sent from the terminal to a computer, which then passes the received question to a natural language analysis model for analysis. The computer generates an optimal answer as a result of the analysis, and the computer returns the generated answer to the terminal, which displays it on the user interface.

[0146] Furthermore, factory engineers can input questions about robot operation and maintenance via the visual wear. The visual wear is equipped with a voice recognition function and sends the questions to a computer. The computer analyzes the received questions using a natural language analysis model and sends the generated answers to the visual wear for display. This allows engineers to obtain the necessary information hands-free.

[0147] Furthermore, users can upload their daily work details and created documents to the computer via their information terminal. For example, meeting materials can be uploaded in PDF format. The computer analyzes the uploaded documents and measures the growth of new or mid-career employees. The growth measurement engine evaluates the content, quality, and format consistency of the documents to generate a growth score. The generated growth score is saved in a storage device, and leaders and managers can check it in real time through the control panel.

[0148] For example, if a technician wearing vision wear asks, "Tell me the robot maintenance procedure," the system will generate and present an answer such as, "Maintenance is performed in the following steps: 1. Turn off the robot. 2. Disassemble the parts. 3. Clean each part. 4. Reassemble. 5. Turn on the power and check operation." Additionally, when a technician uploads a work report, the computer analyzes the uploaded data, measures the technician's progress, and provides the results to the instructor or manager.

[0149] In this way, the system of the present invention provides a function that streamlines the training of new employees, mid-career employees, and in-house engineers and visualizes their growth by providing quick answers to user questions and measuring their growth through analysis of their work content, allowing instructors and managers to provide appropriate evaluations and feedback.

[0150] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0151] Step 1:

[0152] The user inputs a question using an information terminal. The user inputs a business-related question (e.g., "How do I proceed with this project?") into the input field of the web portal. This question becomes the initial input to the system.

[0153] Step 2:

[0154] The terminal sends the entered question to a computer (server), which then sends the question as text data to the server, preparing the data for analysis in the next step.

[0155] Step 3:

[0156] The server passes the received question to a natural language analysis model for analysis. The server inputs the question data into the natural language analysis model (e.g., GPT-4), and the model analyzes the intent of the question and generates the optimal answer. The input here is the text data of the question, and the output is the text data of the answer.

[0157] Step 4:

[0158] The server sends the generated answer to the terminal, which then uses the API to send it back to the terminal and display it on the screen for the user to check.

[0159] Step 5:

[0160] Users upload their daily work details and documents they have created to the server via their terminal. For example, they upload meeting materials they have created in PDF format through a web portal. This data becomes the input for the next analysis process.

[0161] Step 6:

[0162] The server analyzes the uploaded work and measures its growth. The server evaluates the content, quality, and format consistency of the uploaded material to generate a growth score. The analysis algorithm extracts specific elements of the material and evaluates them. The input here is the uploaded material, and the output is a growth score.

[0163] Step 7:

[0164] The server stores the measured growth in a storage device and displays it on the control panel for instructors and administrators. The generated growth scores are stored in a database and can be checked in real time on the dashboard, allowing instructors and administrators to provide specific evaluations and feedback.

[0165] Step 8:

[0166] The engineer inputs questions about robot operation and maintenance through the vision wear. The engineer inputs questions into the vision wear by voice (e.g., "Please tell me the robot maintenance procedure."). This voice data becomes the input for the next analysis process.

[0167] Step 9:

[0168] The visual ware recognizes the question by voice, converts it into text format, and sends it to the computer. The voice recognition module in the visual ware converts the voice data into text data, which is then sent to the server.

[0169] Step 10:

[0170] The server generates an answer based on the received question using a natural language analysis model and displays it on the visual wear. The server passes the question text data to the natural language analysis model and sends the generated answer to the visual wear so that the technician can check it hands-free.

[0171] Step 11:

[0172] Technicians upload work reports or maintenance records via vision wear. Technicians upload reports of maintenance work or other operations in digital format using vision wear, which becomes the input for the next analytical process.

[0173] Step 12:

[0174] The server analyzes the uploaded data and measures the technician's growth. The server evaluates the content and accuracy of the reported maintenance records and generates a growth score.

[0175] Step 13:

[0176] The server provides feedback to the technician based on the growth rate. The computer generates appropriate feedback to the technician based on the measured growth score and notifies the technician through the visual wear.

[0177] 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.

[0178] The present invention combines an emotion engine with an educational AI system that provides work assistance and growth measurement for new and mid-career employees. This system is composed of a terminal, a server, an emotion engine, and software for linking these. The specific operation of the system of the present invention is described below.

[0179] 1. User enters question:

[0180] New employees or mid-career employees (hereafter referred to as users) access the web portal using a device (PC or smartphone) and input a question about their work. For example, they might input, "Please tell me how to proceed with this project." The emotion engine analyzes the user's tone of voice and facial expressions to generate emotion data.

[0181] 2. Submitting and analyzing questions:

[0182] The device sends the question data and emotion data to the server. Specifically, the question content and emotion data are sent to the server as JSON format data via a POST request.

[0183] 3. Answer generation and sentiment analysis:

[0184] The server receives the POST request and passes the question and emotion data to the question-answering engine. The question-answering engine analyzes the question using a natural language processing model (such as GPT-4) and generates the optimal answer based on the emotion data. For example, if a user is asked "How to proceed with a project" and feels anxious, the engine generates an answer such as "The project will proceed in the following five steps. If you have any problems, please rest assured that we are always here to support you."

[0185] 4. Submitting and Viewing Answers:

[0186] The server converts the generated answer into JSON format and sends it to the terminal as an HTTP response. The terminal receives the response data from the server and displays it on the user interface. For example, the answer "The project will proceed in the following steps..." is displayed on a web page.

[0187] 5. Upload your job description:

[0188] Users upload their daily work details and documents they have created to the server via their terminals. For example, they upload meeting documents they have created from a web portal.

[0189] 6. Job Analysis and Growth Measurement:

[0190] The server analyzes the uploaded materials and measures the growth of new or mid-career employees. The growth measurement engine evaluates the content, quality, and format consistency of the materials to generate a growth score. For example, a "growth score: 85" could be generated based on the depth and consistency of the material's content.

[0191] 7. Storage and display of growth data:

[0192] The server stores the generated growth score in a database. Elders and superiors can view the user's growth score and emotional data through a dashboard. For example, the dashboard can display "User A: Score 85" along with the user's recent emotional trend.

[0193] 8. Providing Feedback:

[0194] An elder or superior can check the growth and emotion data on the dashboard and provide appropriate feedback to the user. For example, the feedback could be posted on the web portal, such as, "User A's document creation skills are improving. He also seems to be feeling anxious recently, so he may need some support."

[0195] In this way, the system of the present invention can provide more personalized support and improve the efficiency of work for new and mid-career employees by providing quick answers to user questions, measuring growth by analyzing work content, and analyzing emotional data. This allows elders and superiors to provide appropriate evaluations and feedback, enabling new employees to smoothly carry out their work and support their growth.

[0196] The processing flow will be explained below.

[0197] Step 1:

[0198] A user accesses the web portal from a device and enters a question into the inquiry form. For example, they might enter, "Please tell me how to proceed with this project." The emotion engine analyzes the user's tone of voice and facial expressions to generate emotion data.

[0199] Step 2:

[0200] The device sends the question data and emotion data to the server. Specifically, the question content and emotion data are sent to the server as JSON format data via a POST request.

[0201] Step 3:

[0202] The server receives the POST request and passes the question and emotion data to the question-answering engine, which extracts the question text and emotion data from the JSON data.

[0203] Step 4:

[0204] The server's question-answering engine analyzes the question using a natural language processing model (such as GPT-4) and generates the optimal answer based on emotional data. For example, if a user is asked "How to proceed with the project" and feels anxious, the server generates a response such as "The project will proceed in the following five steps. If you have any problems, please rest assured that we are always here to support you."

[0205] Step 5:

[0206] The server converts the generated answer into JSON format and sends it to the terminal as an HTTP response. The terminal receives the response data from the server.

[0207] Step 6:

[0208] The response data received by the terminal is displayed on the user interface. For example, a response such as "The project will proceed in the following steps..." is displayed on a web page.

[0209] Step 7:

[0210] Users upload their daily work details and documents they have created from their devices to the server. For example, they upload meeting materials they have created from a web portal.

[0211] Step 8:

[0212] The terminal sends data on the business content to the server. Specifically, the created documents are sent to the server in a format such as PDF.

[0213] Step 9:

[0214] The server passes the received business content data to the growth measurement engine for analysis, which evaluates the content, quality, and format consistency of the materials.

[0215] Step 10:

[0216] The server stores the growth score generated by the growth measurement engine in a database, for example, "Growth score of user A's meeting materials: 85."

[0217] Step 11:

[0218] The server updates the growth and emotional data on the dashboard of the elder or superior. The elder or superior can view the user's growth score and emotional data on the dashboard. For example, the dashboard will show "User A: Score 85" along with the recent emotional trend.

[0219] Step 12:

[0220] An elder or superior can check the growth and emotion data on the dashboard and provide appropriate feedback to the user. For example, the feedback could be posted on the web portal, such as, "User A's document creation skills are improving. He also seems to be feeling anxious recently, so he may need some support."

[0221] Through the above steps, the system of the present invention can improve the efficiency of work execution for new employees and mid-career employees, effectively support their growth, and provide personalized support using emotional data. This allows elders and superiors to provide appropriate evaluations and feedback, enabling smooth work execution and growth support for new employees.

[0222] Example 2

[0223] 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."

[0224] Conventional training systems make it difficult to consider the emotional state of new and mid-career employees when providing work support or measuring their growth, making it difficult to provide personalized support. This can lead to insufficient work efficiency and support for employee growth. Another issue is that it is difficult to provide appropriate feedback that takes into account the emotional state of employees.

[0225] 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.

[0226] In this invention, the server includes: means for a new employee or mid-career employee to input a question from a terminal; means for transmitting the input question to the server; means for the server to analyze the received question and generate an answer using a natural language processing model; means for transmitting the generated answer to the terminal and displaying it; means for the new employee or mid-career employee to upload daily work content from the terminal to the server; means for the server to analyze the uploaded work content and measure growth level; means for generating and analyzing emotional data of the new employee or mid-career employee using an emotion engine; and means for storing the measured growth level and emotional data in a database and displaying it on a dashboard for an elder or supervisor. This makes it possible to provide work assistance and growth measurement for employees, as well as personalized support that takes into account their emotional state.

[0227] "New employees or mid-career employees" refers to employees who have been newly hired by a company or organization, or employees who have transferred from another organization.

[0228] A "terminal" refers to a computer system operated by a user, such as a device such as a personal computer or smartphone.

[0229] A "question" is business-related information that a user inputs about something they are unsure about or want to confirm.

[0230] "Server" refers to a computer system that communicates with terminals via a network and processes, manages, and stores various data.

[0231] "Natural language processing model" refers to artificial intelligence technology for understanding, analyzing, and generating human language, and specifically includes generative AI models such as GPT-4.

[0232] "Answer" refers to information generated by a natural language processing model in response to a user's question.

[0233] "Business content" refers to activities and materials created related to a user's daily work.

[0234] "Emotion data" refers to information about emotions obtained by analyzing the user's tone of voice, facial expressions, etc.

[0235] "Degree of growth" refers to the degree of growth evaluated based on the user's work content.

[0236] "Database" refers to a system for storing and managing structured information.

[0237] A "dashboard" refers to an interface that elders and superiors can access to check users' growth and emotional data.

[0238] "Elder or superior" refers to a leader or supervisor who is responsible for training and managing new and mid-career employees.

[0239] "Feedback" refers to evaluations and advice provided by elders or superiors regarding a user's work performance and growth.

[0240] This invention combines an emotion engine with an educational AI system that provides work support and growth measurement for new and mid-career employees. This system is composed of a user, a terminal, a server, an emotion engine, and software for linking these elements.

[0241] A user accesses a web portal using a device (PC or smartphone) and inputs a question about work. For example, they input a prompt such as, "Please tell me how to proceed with this project." At this time, the emotion engine analyzes the user's tone of voice and facial expressions to generate emotion data. The emotion engine detects the user's emotions using, for example, voice analysis software or image analysis software.

[0242] The device sends question data and emotion data to the server. Specifically, it converts this data into JSON format and sends it to the server using an HTTP POST request. The server analyzes the received POST request and passes the question and emotion data to the question answering engine.

[0243] A question-answering engine analyzes questions using a natural language processing model (e.g., a generative AI model such as GPT-4) and generates the optimal answer based on emotional data. For example, if a user asks, "Please tell me how to proceed with the project," and the emotional data detects anxiety, the question-answering engine will generate an answer such as, "The project will proceed in the following five steps. If you run into any problems, don't worry, we're always here to help."

[0244] The generated answer is returned to the server, which converts it to JSON format and sends it to the device. The device parses the received JSON data and displays the answer on a web page, where the user can check the answer.

[0245] Furthermore, users upload their daily work details and documents they have created to the server via their devices. For example, they upload meeting materials they have created from a web portal. The server analyzes the uploaded materials and measures their growth by evaluating the work details, the quality of the materials, and the consistency of the format. The growth measurement engine generates a growth score based on this analytical data and stores it in a database.

[0246] Elders and superiors can check the user's growth score and emotional data through the dashboard. For example, the dashboard might show "User A: Score 85" along with the user's recent emotional trend. The elder or superior can use the dashboard to check the growth and emotional data and provide appropriate feedback on the web portal. For example, they might provide feedback such as, "User A's document creation skills are improving. He or she also seems to be feeling anxious recently, so he or she may need support."

[0247] This makes it possible for the system of the present invention to provide personalized support that takes into account the user's work assistance, growth measurement, and even emotional state. The entire system works in cooperation with each other to support the user's efficient work performance and growth.

[0248] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0249] Step 1:

[0250] Users access the web portal using a device (PC or smartphone) and input a question. At this time, the emotion engine analyzes the user's tone of voice and facial expressions to generate emotion data.

[0251] Input: Question (e.g., "Tell me how to proceed with this project"), voice tone, and facial expression data

[0252] Output: Question data, emotion data

[0253] Specific behavior:

[0254] A user accesses the web portal.

[0255] The user enters a question in the input field.

[0256] The emotion engine acquires emotion data using the device's camera and microphone.

[0257] Emotional data is processed using voice and image analysis algorithms.

[0258] Step 2:

[0259] The device sends question data and emotion data to the server, which converts the data into JSON format and sends it to the server via an HTTP POST request.

[0260] Input: Question data, emotion data

[0261] Output: HTTP POST request to the server

[0262] Specific behavior:

[0263] The terminal collects the input question data and emotion data.

[0264] Convert question data and sentiment data into JSON format.

[0265] Sends an HTTP POST request to the server.

[0266] Step 3:

[0267] The server receives the POST request and passes the question and emotion data to the question-answering engine, which then analyzes the question using a natural language processing model (such as GPT-4) and generates an optimal answer based on the emotion data.

[0268] Input: Question data, emotion data

[0269] Output: The generated answer

[0270] Specific behavior:

[0271] The server decodes the POST request and extracts the question data and sentiment data.

[0272] The question data and emotion data are passed to the question answering engine.

[0273] The question answering engine uses a natural language processing model to parse the question.

[0274] Generate optimal answers that take emotional data into account.

[0275] Step 4:

[0276] The server converts the generated answer into JSON format and sends it to the terminal as an HTTP response. The terminal receives the response data from the server and displays it on the user interface.

[0277] Input: Generated answer

[0278] Output: HTTP response to the device

[0279] Specific behavior:

[0280] The server converts the generated response into JSON format.

[0281] Sends JSON data to the terminal as an HTTP response.

[0282] The device analyzes the received JSON data and displays the answer on a web page.

[0283] Step 5:

[0284] Users upload their daily work and documents they have created to the server via their devices. The server analyzes the uploaded documents and evaluates the consistency of the work content, quality of the documents, and format to measure their growth.

[0285] Input: Business details and materials

[0286] Output: Upload data to the server

[0287] Specific behavior:

[0288] Users access the web portal using their terminal.

[0289] Use the upload function on the web portal to select and upload materials.

[0290] The server receives and stores the uploaded materials.

[0291] Step 6:

[0292] The server's growth measurement engine analyzes the uploaded materials and generates a growth score based on content, quality, and format consistency, which is stored in a database.

[0293] Input: Uploaded materials

[0294] Output: Growth score

[0295] Specific behavior:

[0296] A growth measurement engine analyzes the stored data.

[0297] Evaluate the consistency of content, quality, and format of materials.

[0298] A growth score is calculated based on the evaluation results and stored in a database.

[0299] Step 7:

[0300] Elders and superiors can view users' growth scores and sentiment data through a dashboard.

[0301] Input: Growth score, emotion data

[0302] Output: Dashboard display

[0303] Specific behavior:

[0304] Elders and superiors log in to the dashboard.

[0305] View growth scores and sentiment data at a glance on the dashboard.

[0306] Step 8:

[0307] Elders and superiors provide appropriate feedback to users based on the growth scores and emotional data they have confirmed, and the feedback is posted on the web portal.

[0308] Input: Growth score, emotion data

[0309] Output: Feedback

[0310] Specific behavior:

[0311] Elders and superiors review the dashboard data.

[0312] Generate appropriate feedback and enter it into the web portal.

[0313] Users can view feedback on the web portal.

[0314] (Application example 2)

[0315] 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."

[0316] In today's brick-and-mortar stores, new employees and mid-career recruits are expected to respond to customers and explain products immediately, but in reality, they often perform their work while harboring doubts and anxieties. This calls for a support system that enables staff to immediately resolve questions about their work and provide appropriate explanations and responses. Furthermore, while there is an expectation that staff growth can be objectively measured and that supervisors and training personnel can provide appropriate feedback based on that data, existing systems do not adequately address the emotional aspects of staff. The present invention aims to solve these issues and provide a system for improving staff performance.

[0317] 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.

[0318] In this invention, the server

[0319] A means for new employees or mid-career employees to input questions from a terminal;

[0320] means for transmitting the input question to a server;

[0321] means for the server to analyze the received question and generate an answer using a natural language processing model;

[0322] means for transmitting the generated answer to a terminal and displaying it;

[0323] A means for new employees or mid-career employees to upload their daily work details from their devices to the server,

[0324] A means for the server to analyze the uploaded business content and measure the growth degree;

[0325] A means for storing the measured growth level in a database and displaying it on a dashboard for a superior or a training officer;

[0326] means for analyzing the tone of voice and facial expressions of the staff member when the question is input, and an emotion engine for generating emotion data;

[0327] means for generating an optimal answer based on the emotion data;

[0328] This allows staff to instantly resolve work-related questions and receive appropriate support, including emotional support. Furthermore, superiors and training personnel can provide appropriate feedback based on staff growth and emotional data, making it possible to simultaneously improve staff performance and provide mental care.

[0329] A "terminal" is an electronic device that allows a user to enter questions and upload business details.

[0330] "Server" means a computer on a network that receives and analyzes entered questions or uploaded data and generates answers.

[0331] A "natural language processing model" is an artificial intelligence technology that analyzes the content of a user's question and generates an appropriate answer.

[0332] The "emotion engine" is a system that analyzes the user's tone of voice and facial expressions to generate emotion data.

[0333] "Growth level" is an index used to analyze the work content uploaded by the user and evaluate the quality and progress of the work.

[0334] The "dashboard" is a user interface that allows managers and training personnel to check staff growth and emotional data.

[0335] "Answer generation" is the process of generating optimal responses based on natural language processing models and sentiment data.

[0336] "Inputting a question" refers to the act of a user communicating an unclear point or question to the system via a terminal.

[0337] This invention is a system for supporting staff in brick-and-mortar stores, allowing new employees and mid-career recruits to instantly resolve questions about their work and measure their progress. This system is composed of a terminal, a server, an emotion engine, a natural language processing model, and software for linking these components.

[0338] Hardware and software used

[0339] A terminal is an electronic device (e.g., a smartphone or tablet) through which a user enters questions and uploads work content.

[0340] A server is a computer on a network that receives and analyzes questions and task data to generate answers and measure progress.

[0341] The emotion engine analyzes the tone of voice and facial expressions when a question is entered to generate emotion data.

[0342] A natural language processing model is an AI technology that analyzes questions and generates optimal answers. An example of this is GPT-4.

[0343] Specific operation of this system

[0344] Users access the system using a terminal and input business-related questions. Questions can be entered as text or as voice data. The emotion engine analyzes the voice tone and facial expressions entered and generates emotion data.

[0345] The device sends the question data and emotion data to the server in JSON format. The server then passes the received question data and emotion data to a natural language processing model to generate the optimal answer. For example, if a question about how to explain a new product is entered and the emotion data indicates anxiety, the system will generate an answer such as, "The explanation of the new product will be done in the following steps. Also, please rest assured that we are always available to provide support if you have any concerns."

[0346] The generated answer is converted to JSON format and sent to the device as an HTTP response. The device receives it and displays it visually to the user, for example, as text on a web page.

[0347] Users upload their daily work and documents they create to the server via their devices. This allows their work performance to be analyzed and their level of growth measured. Growth is evaluated based on the consistency of the content, quality, and format of the documents.

[0348] The generated growth data is stored in a database and displayed on a dashboard for supervisors and training personnel, allowing them to check staff growth and provide appropriate feedback.

[0349] Examples of concrete examples and prompts

[0350] For example, if a user asks, "Please tell me how to explain a new product," and the emotion engine analyzes the user's tone of voice and determines that the user is feeling anxious, the system will generate an answer like this: "The steps to explain a new product are as follows. Also, please rest assured that we are always here to support you if you feel anxious."

[0351] An example prompt is:

[0352] How do you explain a new product? I'm a little nervous.

[0353] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0354] Step 1:

[0355] A user accesses the system using a terminal and inputs a question about the business. For example, they input, "Please tell me how to explain a new product." Voice data may also be input at the same time.

[0356] Input: Question text, audio data

[0357] Output: Question text and sentiment data generation preparation

[0358] Step 2:

[0359] The device uses an emotion engine to analyze input voice data and facial expressions to generate emotion data, for example, analyzing the user's emotion, such as anxiety, from the tone of their voice.

[0360] Input: Audio data, question text

[0361] Data processing / calculation: Generating emotional data by analyzing voice tone and facial expressions

[0362] Output: Emotion data

[0363] Step 3:

[0364] The device sends the question text and generated emotion data to the server as JSON format data using a POST request.

[0365] Input: Question text, emotion data

[0366] Data processing / calculation: Converting questions and sentiment data into JSON format

[0367] Output: POST request sent to the server

[0368] Step 4:

[0369] The server parses the received POST request and passes the question text and sentiment data to the natural language processing model.

[0370] Input: Question text, emotion data

[0371] Data processing / calculation: Analysis of questions and sentiment data and input to natural language processing models

[0372] Output: Data passed to the natural language processing model

[0373] Step 5:

[0374] The server uses a natural language processing model to analyze the question and generate the optimal answer based on emotional data, such as "We'll explain the new product in the following steps. If you have any concerns, please rest assured that we're always here to help."

[0375] Input: Question text, sentiment data, natural language processing model

[0376] Data processing / calculation: Question analysis and answer generation

[0377] Output: The generated answer

[0378] Step 6:

[0379] The server converts the generated response into JSON format data and sends it to the terminal as an HTTP response.

[0380] Input: Generated Answer

[0381] Data processing / calculation: Converting answers to JSON format

[0382] Output: Sending HTTP response to the terminal

[0383] Step 7:

[0384] The terminal receives the response data from the server and visually displays it to the user. For example, a message such as "To explain the new product, follow the steps below..." is displayed on a web page.

[0385] Input: JSON format response data from the server

[0386] Data processing / calculation: Parsing JSON data and converting it to a display format

[0387] Output: The answer displayed in the user interface

[0388] Step 8:

[0389] Users upload their daily work details and documents they have created to the server via their terminals, for example, by uploading meeting materials they have created or customer service details.

[0390] Input: Business details data

[0391] Data processing / calculation: Formatting and uploading business data

[0392] Output: Business content data saved on the server

[0393] Step 9:

[0394] The server analyzes the uploaded work content and measures the degree of growth. For example, it evaluates the content and format of the material and generates a "Growth Score: 85."

[0395] Input: Business content data

[0396] Data processing / calculation: Analysis of work content and growth measurement

[0397] Output: Growth Score

[0398] Step 10:

[0399] The server stores the generated growth scores in a database and displays them on a dashboard for superiors and training personnel.

[0400] Input: Growth score, emotion data

[0401] Data processing / calculation: Saving growth scores and updating dashboard displays

[0402] Output: Growth scores and sentiment data displayed on a dashboard

[0403] Step 11:

[0404] Supervisors and training personnel can check the growth and emotional data on the dashboard and provide appropriate feedback, such as, "Your document creation skills are improving. You seem to be feeling anxious recently, so you may need some support."

[0405] Input: Growth score on the dashboard, sentiment data

[0406] Data processing / calculation: Feedback generation based on growth scores and emotion data

[0407] Output: Feedback provided to the user

[0408] 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.

[0409] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search engine).<URL: https: / / openai.com / blog / chatgpt> ), Gemini (registered trademark) (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.

[0410] 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.

[0411] [Second embodiment]

[0412] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0413] 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.

[0414] 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).

[0415] 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.

[0416] 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.

[0417] 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).

[0418] 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.

[0419] 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.

[0420] 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.

[0421] 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.

[0422] In the smart glasses 214, 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.

[0423] 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."

[0424] The present invention is an educational AI system that provides work assistance and growth measurement for new and mid-career employees. This system is composed of a terminal, a server, and software for linking these. The specific operation of the system of the present invention is described below.

[0425] 1. User enters question:

[0426] New employees or mid-career employees (hereafter referred to as users) access the web portal using a device (PC or smartphone) and enter a question about their work. For example, they might enter, "Please tell me how to proceed with this project."

[0427] 2. Submitting and analyzing questions:

[0428] The device sends the input question to the server, which then passes the received question to a natural language processing model (e.g., GPT-4) for analysis.

[0429] 3. Generate and display answers:

[0430] The server's natural language processing model generates the best answer based on the question. For example, in response to the question above, it might generate an answer such as, "Project progress is carried out in the following five steps: 1. Requirements definition, 2. Planning, 3. Implementation, 4. Testing, 5. Release." The server then sends the generated answer to the device, which then displays it on the user interface.

[0431] 4. Upload your job description:

[0432] Users upload their daily work details and documents they have created to the server via their terminals. For example, they upload meeting documents they have created in PDF format.

[0433] 5. Job Analysis and Growth Measurement:

[0434] The server analyzes the uploaded materials and measures the growth of new or mid-career employees. The growth measurement engine evaluates the content, quality, and format consistency of the materials to generate a growth score. For example, a "growth score: 85" could be generated based on the depth and consistency of the material's content.

[0435] 6. Storage and display of growth data:

[0436] The server stores the generated growth score in a database. Elders and superiors can check the user's growth score in real time through the dashboard. For example, the dashboard might show "User A: Score 85."

[0437] 7. Providing Feedback:

[0438] The elder or superior provides appropriate feedback to the user based on the growth data in the dashboard. For example, feedback such as "User A's document creation skills have improved" can be posted on the web portal.

[0439] In this way, the system of the present invention provides functions that improve the efficiency of work performance for new employees and mid-career employees and visualize their growth by providing quick answers to user questions and measuring growth levels through analysis of work content. This allows elders and superiors to provide appropriate evaluations and feedback, realizing smooth work performance and growth support for new employees.

[0440] The processing flow will be explained below.

[0441] Step 1:

[0442] A user accesses the web portal from a terminal and enters a question into the inquiry form. For example, the user might enter, "Please tell me how to proceed with this project."

[0443] Step 2:

[0444] The device sends the question data to the server. Specifically, the question content is sent to the server as JSON format data via a POST request.

[0445] Step 3:

[0446] The server receives the POST request and passes the question to the question answering engine for processing. Specifically, the server extracts and prepares the question text from the JSON data.

[0447] Step 4:

[0448] The server's question-answering engine uses a natural language processing model (such as GPT-4) to analyze the question and generate the optimal answer. For example, if asked "How do you proceed with a project?", it will generate an answer such as "Project progress is carried out in the following five steps: 1. Requirements definition, 2. Planning, 3. Implementation, 4. Testing, and 5. Release."

[0449] Step 5:

[0450] The server converts the generated response into JSON format and sends it to the terminal as an HTTP response.

[0451] Step 6:

[0452] The terminal receives the response data from the server and displays it on the user interface. Specifically, the answer "The project will proceed in the following steps..." is displayed on the web page.

[0453] Step 7:

[0454] Users upload their daily work details and documents they have created from their devices to the server. For example, they upload meeting materials they have created from a web portal.

[0455] Step 8:

[0456] The terminal sends data on the business content to the server. Specifically, the created documents are sent to the server in a format such as PDF.

[0457] Step 9:

[0458] The server passes the received business content data to the growth measurement engine for analysis, which evaluates the content, quality, and format consistency of the materials.

[0459] Step 10:

[0460] The server stores the growth score generated by the growth measurement engine in a database, for example, "Growth score of user A's meeting materials: 85."

[0461] Step 11:

[0462] The server updates the growth data to the dashboard of the elders and superiors, who can view the user's growth score in real time on the dashboard.

[0463] Step 12:

[0464] The elder or superior checks the growth data on the dashboard and provides appropriate feedback to the user. For example, the feedback could be posted on the web portal, saying, "User A's document creation skills have improved."

[0465] Through the above steps, the system of the present invention improves the efficiency of work execution for new employees and mid-career employees and effectively supports their growth.

[0466] Example 1

[0467] 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."

[0468] In the conventional training system, new employees and mid-career employees were unable to efficiently resolve their work-related questions, making it difficult to immediately grasp the improvement of their work skills. In addition, there were limited means for superiors and managers to accurately measure the skill growth of new employees and provide appropriate feedback.

[0469] 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.

[0470] In this invention, the server includes means for inputting a question from a terminal, means for transmitting the input question to a computer system, means for the computer system to analyze the received question and generate an answer using a generative model, means for transmitting the generated answer to the terminal and displaying it, means for uploading work content from the terminal to the computer system, means for the computer system to analyze the uploaded work content and measure an evaluation score, and means for saving the measured evaluation score in a data store and displaying it on a display screen for an administrator. This enables new employees and mid-career employees to receive quick and appropriate answers to questions about their work, and makes it possible to measure the growth of their work skills in real time and provide appropriate feedback based on that data.

[0471] A "terminal" is an information processing device used by a user, and includes a personal computer (PC) or a smartphone.

[0472] A "computer system" is a group of information processing devices, including servers and cloud-based computing resources, that transmit, receive, and analyze data.

[0473] A "generative model" is an algorithm for natural language processing, and refers to an advanced generative AI model such as GPT-4.

[0474] A "data store" is a storage system for saving and managing data, including databases and cloud storage.

[0475] The "means for inputting a question" is an interface for a user to input a question in text format, and includes an input form on a web portal.

[0476] "Means for sending a question to a computer system" refers to a communication protocol and its implementation for transferring a question entered by a user to a server over a network.

[0477] "Means for analyzing questions and generating answers using a generative model" refers to a mechanism for analyzing received questions using natural language processing technology and automatically generating appropriate answers.

[0478] "Means for transmitting the answer to the terminal and displaying it" refers to an interface and its implementation for transmitting the generated answer to the terminal and visually presenting it to the user.

[0479] "Means for uploading business content from a terminal to a computer system" refers to the function for sending documents created by users to a server in file format.

[0480] "Means for analyzing uploaded work content and measuring evaluation scores" refers to the algorithm and its implementation for analyzing uploaded materials and data and quantifying the degree of growth based on their quality and content.

[0481] "Administrator display screen" refers to the user interface that allows elders or superiors to check growth data and feedback.

[0482] The present invention relates to an education system that provides work support and growth measurement for new employees and mid-career employees. This system is composed of a terminal, a computer system, and software for linking these.

[0483] First, a user accesses a web portal using a device (e.g., a PC or smartphone) and inputs a question related to their work. The device then sends the user's input to a computer system. The computer system then passes the received question to a generative AI model (e.g., GPT-4), which analyzes the question using natural language processing and generates the optimal answer.

[0484] As a specific example, suppose a user types, "Tell me how to proceed with this project." The generative AI model in the computer system analyzes the question and generates an appropriate answer. For example, it might generate an answer such as, "The five steps in project progression are: 1. Requirements definition, 2. Planning, 3. Implementation, 4. Testing, and 5. Release." The server sends the generated answer to the device, which then displays it on the user interface.

[0485] Furthermore, users upload their daily work details and documents they have created to the computer system via their terminals. The computer system analyzes the uploaded documents and measures the user's level of growth. The growth measurement engine evaluates the content, quality, and format consistency of the documents and generates a growth score. For example, a "growth score of 85" is generated based on the depth and consistency of the document's content.

[0486] The generated growth scores are stored in a data store, and elders and superiors can check them in real time via the dashboard. For example, the dashboard might display "User A: Score 85." The elder or superior can provide appropriate feedback to the user based on this growth data. For example, they could post feedback such as "User A's document creation skills are improving" on the web portal.

[0487] Examples of prompts include:

[0488] "Tell me how to proceed with this project."

[0489] "What are some best practices for dealing with customers?"

[0490] In this way, this system improves the efficiency of work execution for new and mid-career employees, provides a function that visualizes their growth, and makes it easier for elders and superiors to provide appropriate evaluations and feedback, thereby enabling new employees to smoothly carry out their work and support their growth.

[0491] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0492] Step 1: User enters question

[0493] A user accesses a web portal using a terminal and inputs a question about their work. The input question is processed as string data within the terminal and prepared for transmission. For example, a user might input "Please tell me how to proceed with this project." This input data is converted to JSON format data for subsequent processing and prepared as an HTTP request.

[0494] Step 2: Submit your question

[0495] The device sends the entered question data to the server. This is done using an HTTP POST request. The input data includes the question content and the user ID. The server receives the question data and prepares it for analysis.

[0496] Step 3: Parsing the Question

[0497] The server passes the received question data to a generative AI model (e.g., GPT-4) for analysis. At this time, the server converts the question data into an appropriate format and sends it to the generative AI model's API. The input data is the user's question, and the output data is the generated answer. The server obtains the answer returned by the generative AI model and prepares it for the next process.

[0498] Step 4: Generate an answer

[0499] The generative AI model on the server analyzes the question and generates the optimal answer. For example, it might generate an answer such as, "The five steps in project progress are: 1. Requirements definition, 2. Planning, 3. Implementation, 4. Testing, 5. Release." The input is the user's question data, and the output is the generated answer data.

[0500] Step 5: Submit and view your responses

[0501] The server sends the generated answer data to the terminal as an HTTP response. The terminal analyzes the received answer data and displays it on the user interface. The input data is the generated answer, and the output is the visual answer information displayed to the user.

[0502] Step 6: Upload your work

[0503] Users upload their daily work details and documents they have created to the server via their terminal. The terminal sends the user's work data (e.g., PDF files) to the server as an HTTP POST request. The input data is a file containing the work details, and the output is the file saved on the server.

[0504] Step 7: Analyze work and measure growth

[0505] The server analyzes the uploaded materials and measures their growth. Specifically, it uses text mining tools to analyze the data and evaluates its content, quality, format consistency, etc. The input data is the uploaded materials, and the output data is the calculated growth score. For example, a "Growth Score: 85" is generated.

[0506] Step 8: Save and display growth data

[0507] The server stores the generated growth score in a data store and displays it through a dashboard. The input data is the growth score, and the output is the information stored in the database and displayed on the dashboard. Elders and superiors can access the dashboard and see "User A: Score 85."

[0508] Step 9: Provide feedback

[0509] An elder or superior provides feedback based on the growth data displayed on the dashboard. The feedback is sent from the terminal to the server and saved in the user's profile. For example, a comment such as "User A's document creation skills have improved" is entered. The input data is the feedback, and the output is the feedback information saved in the user's profile.

[0510] (Application example 1)

[0511] 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."

[0512] Conventional training systems for new employees and mid-career hires make it difficult to provide immediate answers to work-related questions or measure the degree of growth. In particular, there was a lack of means to quickly and effectively train factory engineers on robot operation and maintenance. This resulted in a decline in the quality and efficiency of training, and problems such as an inability to visualize the degree of growth of engineers or provide appropriate feedback.

[0513] 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.

[0514] In this invention, the server includes: means for a new employee or mid-career employee to input a question from an information terminal; means for transmitting the input question to a computer; means for the computer to analyze the received question and generate an answer using a natural language analysis model; means for transmitting the generated answer to the information terminal and displaying it; means for a new employee or mid-career employee to upload daily work content from the information terminal to a computer; means for the computer to analyze the uploaded work content and measure the degree of growth; means for storing the measured degree of growth in a storage device and displaying it on a control panel for an instructor or manager; means for a technician to input a question about robot operation or maintenance via visual wear; means for the visual wear to recognize the question by voice and transmit it to a computer; means for the computer to generate an answer using a natural language analysis model based on the received question and display it on the visual wear; means for the technician to upload work reports or maintenance records via visual wear; means for the computer to analyze the uploaded data and measure the technician's degree of growth; and means for providing feedback to the technician based on the degree of growth. This allows new employees, mid-career hires, and factory technicians to receive real-time training support, measure their progress, and provide appropriate feedback.

[0515] "New employee" refers to an employee who has been newly hired by a company or organization.

[0516] "Mid-career employees" refer to employees who have been newly hired after transferring from another company or organization.

[0517] "Information terminal" refers to a computer device that can connect to the Internet, such as a desktop computer, laptop, tablet, or smartphone.

[0518] "Computer" refers to a server, cloud, or other computer system with computing power.

[0519] "Natural language analysis model" refers to artificial intelligence technology for understanding and analyzing human language and generating appropriate responses. Specifically, it refers to large-scale language models such as GPT-4.

[0520] "Growth" refers to a measure of how much an educated employee or technician has improved their skills and knowledge.

[0521] "Storage device" refers to hardware for storing digital data, such as a hard disk drive (HDD) or solid-state drive (SSD).

[0522] "Leaders" refer to supervisors and trainers who are responsible for educating and guiding new employees and mid-career hires.

[0523] "Manager" refers to a person who has the authority to manage the work performance and development of employees and engineers.

[0524] A "control panel" is software or hardware with a user interface, and refers to a screen or device for displaying and operating data.

[0525] "Visual wear" refers to wearable devices such as smart glasses and head-mounted displays that can display real-time information and enable interactive operation.

[0526] "Work reports" refer to documents and data used by employees and technicians to record and report on their daily work activities and progress.

[0527] "Maintenance records" refers to documents and data that record the progress and results of maintenance work on robots and equipment.

[0528] "Feedback" refers to the evaluation and advice given to employees and engineers based on their growth and performance.

[0529] The present invention relates to an education support system for training new employees, mid-career recruits, and factory engineers and measuring their growth. This system is composed of an information terminal, a computer (server), and software for linking these. The specific operation of the system of the present invention will be described below.

[0530] The system uses information terminals, computers (servers), natural language analysis models (such as GPT-4), visual wear (smart glasses or head-mounted displays), and storage devices. These hardware and software enable employees and engineers to receive real-time support and measure their progress.

[0531] Users, i.e., new employees or mid-career employees, can access a web portal using an information terminal (PC, tablet, smartphone, etc.) and input questions related to their work. For example, they might input, "Please tell me how to proceed with this project." The input question is sent from the terminal to a computer, which then passes the received question to a natural language analysis model for analysis. The computer generates an optimal answer as a result of the analysis, and the computer returns the generated answer to the terminal, which displays it on the user interface.

[0532] Furthermore, factory engineers can input questions about robot operation and maintenance via the visual wear. The visual wear is equipped with a voice recognition function and sends the questions to a computer. The computer analyzes the received questions using a natural language analysis model and sends the generated answers to the visual wear for display. This allows engineers to obtain the necessary information hands-free.

[0533] Furthermore, users can upload their daily work details and created documents to the computer via their information terminal. For example, meeting materials can be uploaded in PDF format. The computer analyzes the uploaded documents and measures the growth of new or mid-career employees. The growth measurement engine evaluates the content, quality, and format consistency of the documents to generate a growth score. The generated growth score is saved in a storage device, and leaders and managers can check it in real time through the control panel.

[0534] For example, if a technician wearing vision wear asks, "Tell me the robot maintenance procedure," the system will generate and present an answer such as, "Maintenance is performed in the following steps: 1. Turn off the robot. 2. Disassemble the parts. 3. Clean each part. 4. Reassemble. 5. Turn on the power and check operation." Additionally, when a technician uploads a work report, the computer analyzes the uploaded data, measures the technician's progress, and provides the results to the instructor or manager.

[0535] In this way, the system of the present invention provides a function that streamlines the training of new employees, mid-career employees, and in-house engineers and visualizes their growth by providing quick answers to user questions and measuring their growth through analysis of their work content, allowing instructors and managers to provide appropriate evaluations and feedback.

[0536] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0537] Step 1:

[0538] The user inputs a question using an information terminal. The user inputs a business-related question (e.g., "How do I proceed with this project?") into the input field of the web portal. This question becomes the initial input to the system.

[0539] Step 2:

[0540] The terminal sends the entered question to a computer (server), which then sends the question as text data to the server, preparing the data for analysis in the next step.

[0541] Step 3:

[0542] The server passes the received question to a natural language analysis model for analysis. The server inputs the question data into the natural language analysis model (e.g., GPT-4), and the model analyzes the intent of the question and generates the optimal answer. The input here is the text data of the question, and the output is the text data of the answer.

[0543] Step 4:

[0544] The server sends the generated answer to the terminal, which then uses the API to send it back to the terminal and display it on the screen for the user to check.

[0545] Step 5:

[0546] Users upload their daily work details and documents they have created to the server via their terminal. For example, they upload meeting materials they have created in PDF format through a web portal. This data becomes the input for the next analysis process.

[0547] Step 6:

[0548] The server analyzes the uploaded work and measures its growth. The server evaluates the content, quality, and format consistency of the uploaded material to generate a growth score. The analysis algorithm extracts specific elements of the material and evaluates them. The input here is the uploaded material, and the output is a growth score.

[0549] Step 7:

[0550] The server stores the measured growth in a storage device and displays it on the control panel for instructors and administrators. The generated growth scores are stored in a database and can be checked in real time on the dashboard, allowing instructors and administrators to provide specific evaluations and feedback.

[0551] Step 8:

[0552] The engineer inputs questions about robot operation and maintenance through the vision wear. The engineer inputs questions into the vision wear by voice (e.g., "Please tell me the robot maintenance procedure."). This voice data becomes the input for the next analysis process.

[0553] Step 9:

[0554] The visual ware recognizes the question by voice, converts it into text format, and sends it to the computer. The voice recognition module in the visual ware converts the voice data into text data, which is then sent to the server.

[0555] Step 10:

[0556] The server generates an answer based on the received question using a natural language analysis model and displays it on the visual wear. The server passes the question text data to the natural language analysis model and sends the generated answer to the visual wear so that the technician can check it hands-free.

[0557] Step 11:

[0558] Technicians upload work reports or maintenance records via vision wear. Technicians upload reports of maintenance work or other operations in digital format using vision wear, which becomes the input for the next analytical process.

[0559] Step 12:

[0560] The server analyzes the uploaded data and measures the technician's growth. The server evaluates the content and accuracy of the reported maintenance records and generates a growth score.

[0561] Step 13:

[0562] The server provides feedback to the technician based on the growth rate. The computer generates appropriate feedback to the technician based on the measured growth score and notifies the technician through the visual wear.

[0563] 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.

[0564] The present invention combines an emotion engine with an educational AI system that provides work assistance and growth measurement for new and mid-career employees. This system is composed of a terminal, a server, an emotion engine, and software for linking these. The specific operation of the system of the present invention is described below.

[0565] 1. User enters question:

[0566] New employees or mid-career employees (hereafter referred to as users) access the web portal using a device (PC or smartphone) and input a question about their work. For example, they might input, "Please tell me how to proceed with this project." The emotion engine analyzes the user's tone of voice and facial expressions to generate emotion data.

[0567] 2. Submitting and analyzing questions:

[0568] The device sends the question data and emotion data to the server. Specifically, the question content and emotion data are sent to the server as JSON format data via a POST request.

[0569] 3. Answer generation and sentiment analysis:

[0570] The server receives the POST request and passes the question and emotion data to the question-answering engine. The question-answering engine analyzes the question using a natural language processing model (such as GPT-4) and generates the optimal answer based on the emotion data. For example, if a user is asked "How to proceed with a project" and feels anxious, the engine generates an answer such as "The project will proceed in the following five steps. If you have any problems, please rest assured that we are always here to support you."

[0571] 4. Submitting and Viewing Answers:

[0572] The server converts the generated answer into JSON format and sends it to the terminal as an HTTP response. The terminal receives the response data from the server and displays it on the user interface. For example, the answer "The project will proceed in the following steps..." is displayed on a web page.

[0573] 5. Upload your job description:

[0574] Users upload their daily work details and documents they have created to the server via their terminals. For example, they upload meeting documents they have created from a web portal.

[0575] 6. Job Analysis and Growth Measurement:

[0576] The server analyzes the uploaded materials and measures the growth of new or mid-career employees. The growth measurement engine evaluates the content, quality, and format consistency of the materials to generate a growth score. For example, a "growth score: 85" could be generated based on the depth and consistency of the material's content.

[0577] 7. Storage and display of growth data:

[0578] The server stores the generated growth score in a database. Elders and superiors can view the user's growth score and emotional data through a dashboard. For example, the dashboard can display "User A: Score 85" along with the user's recent emotional trend.

[0579] 8. Providing Feedback:

[0580] An elder or superior can check the growth and emotion data on the dashboard and provide appropriate feedback to the user. For example, the feedback could be posted on the web portal, such as, "User A's document creation skills are improving. He also seems to be feeling anxious recently, so he may need some support."

[0581] In this way, the system of the present invention can provide more personalized support and improve the efficiency of work for new and mid-career employees by providing quick answers to user questions, measuring growth by analyzing work content, and analyzing emotional data. This allows elders and superiors to provide appropriate evaluations and feedback, enabling new employees to smoothly carry out their work and support their growth.

[0582] The processing flow will be explained below.

[0583] Step 1:

[0584] A user accesses the web portal from a device and enters a question into the inquiry form. For example, they might enter, "Please tell me how to proceed with this project." The emotion engine analyzes the user's tone of voice and facial expressions to generate emotion data.

[0585] Step 2:

[0586] The device sends the question data and emotion data to the server. Specifically, the question content and emotion data are sent to the server as JSON format data via a POST request.

[0587] Step 3:

[0588] The server receives the POST request and passes the question and emotion data to the question-answering engine, which extracts the question text and emotion data from the JSON data.

[0589] Step 4:

[0590] The server's question-answering engine analyzes the question using a natural language processing model (such as GPT-4) and generates the optimal answer based on emotional data. For example, if a user is asked "How to proceed with the project" and feels anxious, the server generates a response such as "The project will proceed in the following five steps. If you have any problems, please rest assured that we are always here to support you."

[0591] Step 5:

[0592] The server converts the generated answer into JSON format and sends it to the terminal as an HTTP response. The terminal receives the response data from the server.

[0593] Step 6:

[0594] The response data received by the terminal is displayed on the user interface. For example, a response such as "The project will proceed in the following steps..." is displayed on a web page.

[0595] Step 7:

[0596] Users upload their daily work details and documents they have created from their devices to the server. For example, they upload meeting materials they have created from a web portal.

[0597] Step 8:

[0598] The terminal sends data on the business content to the server. Specifically, the created documents are sent to the server in a format such as PDF.

[0599] Step 9:

[0600] The server passes the received business content data to the growth measurement engine for analysis, which evaluates the content, quality, and format consistency of the materials.

[0601] Step 10:

[0602] The server stores the growth score generated by the growth measurement engine in a database, for example, "Growth score of user A's meeting materials: 85."

[0603] Step 11:

[0604] The server updates the growth and emotional data on the dashboard of the elder or superior. The elder or superior can view the user's growth score and emotional data on the dashboard. For example, the dashboard will show "User A: Score 85" along with the recent emotional trend.

[0605] Step 12:

[0606] An elder or superior can check the growth and emotion data on the dashboard and provide appropriate feedback to the user. For example, the feedback could be posted on the web portal, such as, "User A's document creation skills are improving. He also seems to be feeling anxious recently, so he may need some support."

[0607] Through the above steps, the system of the present invention can improve the efficiency of work execution for new employees and mid-career employees, effectively support their growth, and provide personalized support using emotional data. This allows elders and superiors to provide appropriate evaluations and feedback, enabling smooth work execution and growth support for new employees.

[0608] Example 2

[0609] 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."

[0610] Conventional training systems make it difficult to consider the emotional state of new and mid-career employees when providing work support or measuring their growth, making it difficult to provide personalized support. This can lead to insufficient work efficiency and support for employee growth. Another issue is that it is difficult to provide appropriate feedback that takes into account the emotional state of employees.

[0611] 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.

[0612] In this invention, the server includes: means for a new employee or mid-career employee to input a question from a terminal; means for transmitting the input question to the server; means for the server to analyze the received question and generate an answer using a natural language processing model; means for transmitting the generated answer to the terminal and displaying it; means for the new employee or mid-career employee to upload daily work content from the terminal to the server; means for the server to analyze the uploaded work content and measure growth level; means for generating and analyzing emotional data of the new employee or mid-career employee using an emotion engine; and means for storing the measured growth level and emotional data in a database and displaying it on a dashboard for an elder or supervisor. This makes it possible to provide work assistance and growth measurement for employees, as well as personalized support that takes into account their emotional state.

[0613] "New employees or mid-career employees" refers to employees who have been newly hired by a company or organization, or employees who have transferred from another organization.

[0614] A "terminal" refers to a computer system operated by a user, such as a device such as a personal computer or smartphone.

[0615] A "question" is business-related information that a user inputs about something they are unsure about or want to confirm.

[0616] "Server" refers to a computer system that communicates with terminals via a network and processes, manages, and stores various data.

[0617] "Natural language processing model" refers to artificial intelligence technology for understanding, analyzing, and generating human language, and specifically includes generative AI models such as GPT-4.

[0618] "Answer" refers to information generated by a natural language processing model in response to a user's question.

[0619] "Business content" refers to activities and materials created related to a user's daily work.

[0620] "Emotion data" refers to information about emotions obtained by analyzing the user's tone of voice, facial expressions, etc.

[0621] "Degree of growth" refers to the degree of growth evaluated based on the user's work content.

[0622] "Database" refers to a system for storing and managing structured information.

[0623] A "dashboard" refers to an interface that elders and superiors can access to check users' growth and emotional data.

[0624] "Elder or superior" refers to a leader or supervisor who is responsible for training and managing new and mid-career employees.

[0625] "Feedback" refers to evaluations and advice provided by elders or superiors regarding a user's work performance and growth.

[0626] This invention combines an emotion engine with an educational AI system that provides work support and growth measurement for new and mid-career employees. This system is composed of a user, a terminal, a server, an emotion engine, and software for linking these elements.

[0627] A user accesses a web portal using a device (PC or smartphone) and inputs a question about work. For example, they input a prompt such as, "Please tell me how to proceed with this project." At this time, the emotion engine analyzes the user's tone of voice and facial expressions to generate emotion data. The emotion engine detects the user's emotions using, for example, voice analysis software or image analysis software.

[0628] The device sends question data and emotion data to the server. Specifically, it converts this data into JSON format and sends it to the server using an HTTP POST request. The server analyzes the received POST request and passes the question and emotion data to the question answering engine.

[0629] A question-answering engine analyzes questions using a natural language processing model (e.g., a generative AI model such as GPT-4) and generates the optimal answer based on emotional data. For example, if a user asks, "Please tell me how to proceed with the project," and the emotional data detects anxiety, the question-answering engine will generate an answer such as, "The project will proceed in the following five steps. If you run into any problems, don't worry, we're always here to help."

[0630] The generated answer is returned to the server, which converts it to JSON format and sends it to the device. The device parses the received JSON data and displays the answer on a web page, where the user can check the answer.

[0631] Furthermore, users upload their daily work details and documents they have created to the server via their devices. For example, they upload meeting materials they have created from a web portal. The server analyzes the uploaded materials and measures their growth by evaluating the work details, the quality of the materials, and the consistency of the format. The growth measurement engine generates a growth score based on this analytical data and stores it in a database.

[0632] Elders and superiors can check the user's growth score and emotional data through the dashboard. For example, the dashboard might show "User A: Score 85" along with the user's recent emotional trend. The elder or superior can use the dashboard to check the growth and emotional data and provide appropriate feedback on the web portal. For example, they might provide feedback such as, "User A's document creation skills are improving. He or she also seems to be feeling anxious recently, so he or she may need support."

[0633] This makes it possible for the system of the present invention to provide personalized support that takes into account the user's work assistance, growth measurement, and even emotional state. The entire system works in cooperation with each other to support the user's efficient work performance and growth.

[0634] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0635] Step 1:

[0636] Users access the web portal using a device (PC or smartphone) and input a question. At this time, the emotion engine analyzes the user's tone of voice and facial expressions to generate emotion data.

[0637] Input: Question (e.g., "Tell me how to proceed with this project"), voice tone, and facial expression data

[0638] Output: Question data, emotion data

[0639] Specific behavior:

[0640] A user accesses the web portal.

[0641] The user enters a question in the input field.

[0642] The emotion engine acquires emotion data using the device's camera and microphone.

[0643] Emotional data is processed using voice and image analysis algorithms.

[0644] Step 2:

[0645] The device sends question data and emotion data to the server, which converts the data into JSON format and sends it to the server via an HTTP POST request.

[0646] Input: Question data, emotion data

[0647] Output: HTTP POST request to the server

[0648] Specific behavior:

[0649] The terminal collects the input question data and emotion data.

[0650] Convert question data and sentiment data into JSON format.

[0651] Sends an HTTP POST request to the server.

[0652] Step 3:

[0653] The server receives the POST request and passes the question and emotion data to the question-answering engine, which then analyzes the question using a natural language processing model (such as GPT-4) and generates an optimal answer based on the emotion data.

[0654] Input: Question data, emotion data

[0655] Output: The generated answer

[0656] Specific behavior:

[0657] The server decodes the POST request and extracts the question data and sentiment data.

[0658] The question data and emotion data are passed to the question answering engine.

[0659] The question answering engine uses a natural language processing model to parse the question.

[0660] Generate optimal answers that take emotional data into account.

[0661] Step 4:

[0662] The server converts the generated answer into JSON format and sends it to the terminal as an HTTP response. The terminal receives the response data from the server and displays it on the user interface.

[0663] Input: Generated answer

[0664] Output: HTTP response to the device

[0665] Specific behavior:

[0666] The server converts the generated response into JSON format.

[0667] Sends JSON data to the terminal as an HTTP response.

[0668] The device analyzes the received JSON data and displays the answer on a web page.

[0669] Step 5:

[0670] Users upload their daily work and documents they have created to the server via their devices. The server analyzes the uploaded documents and evaluates the consistency of the work content, quality of the documents, and format to measure their growth.

[0671] Input: Business details and materials

[0672] Output: Upload data to the server

[0673] Specific behavior:

[0674] Users access the web portal using their terminal.

[0675] Use the upload function on the web portal to select and upload materials.

[0676] The server receives and stores the uploaded materials.

[0677] Step 6:

[0678] The server's growth measurement engine analyzes the uploaded materials and generates a growth score based on content, quality, and format consistency, which is stored in a database.

[0679] Input: Uploaded materials

[0680] Output: Growth score

[0681] Specific behavior:

[0682] A growth measurement engine analyzes the stored data.

[0683] Evaluate the consistency of content, quality, and format of materials.

[0684] A growth score is calculated based on the evaluation results and stored in a database.

[0685] Step 7:

[0686] Elders and superiors can view users' growth scores and sentiment data through a dashboard.

[0687] Input: Growth score, emotion data

[0688] Output: Dashboard display

[0689] Specific behavior:

[0690] Elders and superiors log in to the dashboard.

[0691] View growth scores and sentiment data at a glance on the dashboard.

[0692] Step 8:

[0693] Elders and superiors provide appropriate feedback to users based on the growth scores and emotional data they have confirmed, and the feedback is posted on the web portal.

[0694] Input: Growth score, emotion data

[0695] Output: Feedback

[0696] Specific behavior:

[0697] Elders and superiors review the dashboard data.

[0698] Generate appropriate feedback and enter it into the web portal.

[0699] Users can view feedback on the web portal.

[0700] (Application example 2)

[0701] 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."

[0702] In today's brick-and-mortar stores, new employees and mid-career recruits are expected to respond to customers and explain products immediately, but in reality, they often perform their work while harboring doubts and anxieties. This calls for a support system that enables staff to immediately resolve questions about their work and provide appropriate explanations and responses. Furthermore, while there is an expectation that staff growth can be objectively measured and that supervisors and training personnel can provide appropriate feedback based on that data, existing systems do not adequately address the emotional aspects of staff. The present invention aims to solve these issues and provide a system for improving staff performance.

[0703] 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.

[0704] In this invention, the server

[0705] A means for new employees or mid-career employees to input questions from a terminal;

[0706] means for transmitting the input question to a server;

[0707] means for the server to analyze the received question and generate an answer using a natural language processing model;

[0708] means for transmitting the generated answer to a terminal and displaying it;

[0709] A means for new employees or mid-career employees to upload their daily work details from their devices to the server,

[0710] A means for the server to analyze the uploaded business content and measure the growth degree;

[0711] A means for storing the measured growth level in a database and displaying it on a dashboard for a superior or a training officer;

[0712] means for analyzing the tone of voice and facial expressions of the staff member when the question is input, and an emotion engine for generating emotion data;

[0713] means for generating an optimal answer based on the emotion data;

[0714] This allows staff to instantly resolve work-related questions and receive appropriate support, including emotional support. Furthermore, superiors and training personnel can provide appropriate feedback based on staff growth and emotional data, making it possible to simultaneously improve staff performance and provide mental care.

[0715] A "terminal" is an electronic device that allows a user to enter questions and upload business details.

[0716] "Server" means a computer on a network that receives and analyzes entered questions or uploaded data and generates answers.

[0717] A "natural language processing model" is an artificial intelligence technology that analyzes the content of a user's question and generates an appropriate answer.

[0718] The "emotion engine" is a system that analyzes the user's tone of voice and facial expressions to generate emotion data.

[0719] "Growth level" is an index used to analyze the work content uploaded by the user and evaluate the quality and progress of the work.

[0720] The "dashboard" is a user interface that allows managers and training personnel to check staff growth and emotional data.

[0721] "Answer generation" is the process of generating optimal responses based on natural language processing models and sentiment data.

[0722] "Inputting a question" refers to the act of a user communicating an unclear point or question to the system via a terminal.

[0723] This invention is a system for supporting staff in brick-and-mortar stores, allowing new employees and mid-career recruits to instantly resolve questions about their work and measure their progress. This system is composed of a terminal, a server, an emotion engine, a natural language processing model, and software for linking these components.

[0724] Hardware and software used

[0725] A terminal is an electronic device (e.g., a smartphone or tablet) through which a user enters questions and uploads work content.

[0726] A server is a computer on a network that receives and analyzes questions and task data to generate answers and measure progress.

[0727] The emotion engine analyzes the tone of voice and facial expressions when a question is entered to generate emotion data.

[0728] A natural language processing model is an AI technology that analyzes questions and generates optimal answers. An example of this is GPT-4.

[0729] Specific operation of this system

[0730] Users access the system using a terminal and input business-related questions. Questions can be entered as text or as voice data. The emotion engine analyzes the voice tone and facial expressions entered and generates emotion data.

[0731] The device sends the question data and emotion data to the server in JSON format. The server then passes the received question data and emotion data to a natural language processing model to generate the optimal answer. For example, if a question about how to explain a new product is entered and the emotion data indicates anxiety, the system will generate an answer such as, "The explanation of the new product will be done in the following steps. Also, please rest assured that we are always available to provide support if you have any concerns."

[0732] The generated answer is converted to JSON format and sent to the device as an HTTP response. The device receives it and displays it visually to the user, for example, as text on a web page.

[0733] Users upload their daily work and documents they create to the server via their devices. This allows their work performance to be analyzed and their level of growth measured. Growth is evaluated based on the consistency of the content, quality, and format of the documents.

[0734] The generated growth data is stored in a database and displayed on a dashboard for supervisors and training personnel, allowing them to check staff growth and provide appropriate feedback.

[0735] Examples of concrete examples and prompts

[0736] For example, if a user asks, "Please tell me how to explain a new product," and the emotion engine analyzes the user's tone of voice and determines that the user is feeling anxious, the system will generate an answer like this: "The steps to explain a new product are as follows. Also, please rest assured that we are always here to support you if you feel anxious."

[0737] An example prompt is:

[0738] How do you explain a new product? I'm a little nervous.

[0739] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0740] Step 1:

[0741] A user accesses the system using a terminal and inputs a question about the business. For example, they input, "Please tell me how to explain a new product." Voice data may also be input at the same time.

[0742] Input: Question text, audio data

[0743] Output: Question text and sentiment data generation preparation

[0744] Step 2:

[0745] The device uses an emotion engine to analyze input voice data and facial expressions to generate emotion data, for example, analyzing the user's emotion, such as anxiety, from the tone of their voice.

[0746] Input: Audio data, question text

[0747] Data processing / calculation: Generating emotional data by analyzing voice tone and facial expressions

[0748] Output: Emotion data

[0749] Step 3:

[0750] The device sends the question text and generated emotion data to the server as JSON format data using a POST request.

[0751] Input: Question text, emotion data

[0752] Data processing / calculation: Converting questions and sentiment data into JSON format

[0753] Output: POST request sent to the server

[0754] Step 4:

[0755] The server parses the received POST request and passes the question text and sentiment data to the natural language processing model.

[0756] Input: Question text, emotion data

[0757] Data processing / calculation: Analysis of questions and sentiment data and input to natural language processing models

[0758] Output: Data passed to the natural language processing model

[0759] Step 5:

[0760] The server uses a natural language processing model to analyze the question and generate the optimal answer based on emotional data, such as "We'll explain the new product in the following steps. If you have any concerns, please rest assured that we're always here to help."

[0761] Input: Question text, sentiment data, natural language processing model

[0762] Data processing / calculation: Question analysis and answer generation

[0763] Output: The generated answer

[0764] Step 6:

[0765] The server converts the generated response into JSON format data and sends it to the terminal as an HTTP response.

[0766] Input: Generated Answer

[0767] Data processing / calculation: Converting answers to JSON format

[0768] Output: Sending HTTP response to the terminal

[0769] Step 7:

[0770] The terminal receives the response data from the server and visually displays it to the user. For example, a message such as "To explain the new product, follow the steps below..." is displayed on a web page.

[0771] Input: JSON format response data from the server

[0772] Data processing / calculation: Parsing JSON data and converting it to a display format

[0773] Output: The answer displayed in the user interface

[0774] Step 8:

[0775] Users upload their daily work details and documents they have created to the server via their terminals, for example, by uploading meeting materials they have created or customer service details.

[0776] Input: Business details data

[0777] Data processing / calculation: Formatting and uploading business data

[0778] Output: Business content data saved on the server

[0779] Step 9:

[0780] The server analyzes the uploaded work content and measures the degree of growth. For example, it evaluates the content and format of the material and generates a "Growth Score: 85."

[0781] Input: Business content data

[0782] Data processing / calculation: Analysis of work content and growth measurement

[0783] Output: Growth Score

[0784] Step 10:

[0785] The server stores the generated growth scores in a database and displays them on a dashboard for superiors and training personnel.

[0786] Input: Growth score, emotion data

[0787] Data processing / calculation: Saving growth scores and updating dashboard displays

[0788] Output: Growth scores and sentiment data displayed on a dashboard

[0789] Step 11:

[0790] Supervisors and training personnel can check the growth and emotional data on the dashboard and provide appropriate feedback, such as, "Your document creation skills are improving. You seem to be feeling anxious recently, so you may need some support."

[0791] Input: Growth score on the dashboard, sentiment data

[0792] Data processing / calculation: Feedback generation based on growth scores and emotion data

[0793] Output: Feedback provided to the user

[0794] 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.

[0795] 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.

[0796] 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.

[0797] [Third embodiment]

[0798] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0799] 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.

[0800] 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).

[0801] 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.

[0802] 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.

[0803] 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).

[0804] 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.

[0805] 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.

[0806] 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.

[0807] 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.

[0808] 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.

[0809] 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."

[0810] The present invention is an educational AI system that provides work assistance and growth measurement for new and mid-career employees. This system is composed of a terminal, a server, and software for linking these. The specific operation of the system of the present invention is described below.

[0811] 1. User enters question:

[0812] New employees or mid-career employees (hereafter referred to as users) access the web portal using a device (PC or smartphone) and enter a question about their work. For example, they might enter, "Please tell me how to proceed with this project."

[0813] 2. Submitting and analyzing questions:

[0814] The device sends the input question to the server, which then passes the received question to a natural language processing model (e.g., GPT-4) for analysis.

[0815] 3. Generate and display answers:

[0816] The server's natural language processing model generates the best answer based on the question. For example, in response to the question above, it might generate an answer such as, "Project progress is carried out in the following five steps: 1. Requirements definition, 2. Planning, 3. Implementation, 4. Testing, 5. Release." The server then sends the generated answer to the device, which then displays it on the user interface.

[0817] 4. Upload your job description:

[0818] Users upload their daily work details and documents they have created to the server via their terminals. For example, they upload meeting documents they have created in PDF format.

[0819] 5. Job Analysis and Growth Measurement:

[0820] The server analyzes the uploaded materials and measures the growth of new or mid-career employees. The growth measurement engine evaluates the content, quality, and format consistency of the materials to generate a growth score. For example, a "growth score: 85" could be generated based on the depth and consistency of the material's content.

[0821] 6. Storage and display of growth data:

[0822] The server stores the generated growth score in a database. Elders and superiors can check the user's growth score in real time through the dashboard. For example, the dashboard might show "User A: Score 85."

[0823] 7. Providing Feedback:

[0824] The elder or superior provides appropriate feedback to the user based on the growth data in the dashboard. For example, feedback such as "User A's document creation skills have improved" can be posted on the web portal.

[0825] In this way, the system of the present invention provides functions that improve the efficiency of work performance for new employees and mid-career employees and visualize their growth by providing quick answers to user questions and measuring growth levels through analysis of work content. This allows elders and superiors to provide appropriate evaluations and feedback, realizing smooth work performance and growth support for new employees.

[0826] The processing flow will be explained below.

[0827] Step 1:

[0828] A user accesses the web portal from a terminal and enters a question into the inquiry form. For example, the user might enter, "Please tell me how to proceed with this project."

[0829] Step 2:

[0830] The device sends the question data to the server. Specifically, the question content is sent to the server as JSON format data via a POST request.

[0831] Step 3:

[0832] The server receives the POST request and passes the question to the question answering engine for processing. Specifically, the server extracts and prepares the question text from the JSON data.

[0833] Step 4:

[0834] The server's question-answering engine uses a natural language processing model (such as GPT-4) to analyze the question and generate the optimal answer. For example, if asked "How do you proceed with a project?", it will generate an answer such as "Project progress is carried out in the following five steps: 1. Requirements definition, 2. Planning, 3. Implementation, 4. Testing, and 5. Release."

[0835] Step 5:

[0836] The server converts the generated response into JSON format and sends it to the terminal as an HTTP response.

[0837] Step 6:

[0838] The terminal receives the response data from the server and displays it on the user interface. Specifically, the answer "The project will proceed in the following steps..." is displayed on the web page.

[0839] Step 7:

[0840] Users upload their daily work details and documents they have created from their devices to the server. For example, they upload meeting materials they have created from a web portal.

[0841] Step 8:

[0842] The terminal sends data on the business content to the server. Specifically, the created documents are sent to the server in a format such as PDF.

[0843] Step 9:

[0844] The server passes the received business content data to the growth measurement engine for analysis, which evaluates the content, quality, and format consistency of the materials.

[0845] Step 10:

[0846] The server stores the growth score generated by the growth measurement engine in a database, for example, "Growth score of user A's meeting materials: 85."

[0847] Step 11:

[0848] The server updates the growth data to the dashboard of the elders and superiors, who can view the user's growth score in real time on the dashboard.

[0849] Step 12:

[0850] The elder or superior checks the growth data on the dashboard and provides appropriate feedback to the user. For example, the feedback could be posted on the web portal, saying, "User A's document creation skills have improved."

[0851] Through the above steps, the system of the present invention improves the efficiency of work execution for new employees and mid-career employees and effectively supports their growth.

[0852] Example 1

[0853] 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."

[0854] In the conventional training system, new employees and mid-career employees were unable to efficiently resolve their work-related questions, making it difficult to immediately grasp the improvement of their work skills. In addition, there were limited means for superiors and managers to accurately measure the skill growth of new employees and provide appropriate feedback.

[0855] 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.

[0856] In this invention, the server includes means for inputting a question from a terminal, means for transmitting the input question to a computer system, means for the computer system to analyze the received question and generate an answer using a generative model, means for transmitting the generated answer to the terminal and displaying it, means for uploading work content from the terminal to the computer system, means for the computer system to analyze the uploaded work content and measure an evaluation score, and means for saving the measured evaluation score in a data store and displaying it on a display screen for an administrator. This enables new employees and mid-career employees to receive quick and appropriate answers to questions about their work, and makes it possible to measure the growth of their work skills in real time and provide appropriate feedback based on that data.

[0857] A "terminal" is an information processing device used by a user, and includes a personal computer (PC) or a smartphone.

[0858] A "computer system" is a group of information processing devices, including servers and cloud-based computing resources, that transmit, receive, and analyze data.

[0859] A "generative model" is an algorithm for natural language processing, and refers to an advanced generative AI model such as GPT-4.

[0860] A "data store" is a storage system for saving and managing data, including databases and cloud storage.

[0861] The "means for inputting a question" is an interface for a user to input a question in text format, and includes an input form on a web portal.

[0862] "Means for sending a question to a computer system" refers to a communication protocol and its implementation for transferring a question entered by a user to a server over a network.

[0863] "Means for analyzing questions and generating answers using a generative model" refers to a mechanism for analyzing received questions using natural language processing technology and automatically generating appropriate answers.

[0864] "Means for transmitting the answer to the terminal and displaying it" refers to an interface and its implementation for transmitting the generated answer to the terminal and visually presenting it to the user.

[0865] "Means for uploading business content from a terminal to a computer system" refers to the function for sending documents created by users to a server in file format.

[0866] "Means for analyzing uploaded work content and measuring evaluation scores" refers to the algorithm and its implementation for analyzing uploaded materials and data and quantifying the degree of growth based on their quality and content.

[0867] "Administrator display screen" refers to the user interface that allows elders or superiors to check growth data and feedback.

[0868] The present invention relates to an education system that provides work support and growth measurement for new employees and mid-career employees. This system is composed of a terminal, a computer system, and software for linking these.

[0869] First, a user accesses a web portal using a device (e.g., a PC or smartphone) and inputs a question related to their work. The device then sends the user's input to a computer system. The computer system then passes the received question to a generative AI model (e.g., GPT-4), which analyzes the question using natural language processing and generates the optimal answer.

[0870] As a specific example, suppose a user types, "Tell me how to proceed with this project." The generative AI model in the computer system analyzes the question and generates an appropriate answer. For example, it might generate an answer such as, "The five steps in project progression are: 1. Requirements definition, 2. Planning, 3. Implementation, 4. Testing, and 5. Release." The server sends the generated answer to the device, which then displays it on the user interface.

[0871] Furthermore, users upload their daily work details and documents they have created to the computer system via their terminals. The computer system analyzes the uploaded documents and measures the user's level of growth. The growth measurement engine evaluates the content, quality, and format consistency of the documents and generates a growth score. For example, a "growth score of 85" is generated based on the depth and consistency of the document's content.

[0872] The generated growth scores are stored in a data store, and elders and superiors can check them in real time via the dashboard. For example, the dashboard might display "User A: Score 85." The elder or superior can provide appropriate feedback to the user based on this growth data. For example, they could post feedback such as "User A's document creation skills are improving" on the web portal.

[0873] Examples of prompts include:

[0874] "Tell me how to proceed with this project."

[0875] "What are some best practices for dealing with customers?"

[0876] In this way, this system improves the efficiency of work execution for new and mid-career employees, provides a function that visualizes their growth, and makes it easier for elders and superiors to provide appropriate evaluations and feedback, thereby enabling new employees to smoothly carry out their work and support their growth.

[0877] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0878] Step 1: User enters question

[0879] A user accesses a web portal using a terminal and inputs a question about their work. The input question is processed as string data within the terminal and prepared for transmission. For example, a user might input "Please tell me how to proceed with this project." This input data is converted to JSON format data for subsequent processing and prepared as an HTTP request.

[0880] Step 2: Submit your question

[0881] The device sends the entered question data to the server. This is done using an HTTP POST request. The input data includes the question content and the user ID. The server receives the question data and prepares it for analysis.

[0882] Step 3: Parsing the Question

[0883] The server passes the received question data to a generative AI model (e.g., GPT-4) for analysis. At this time, the server converts the question data into an appropriate format and sends it to the generative AI model's API. The input data is the user's question, and the output data is the generated answer. The server obtains the answer returned by the generative AI model and prepares it for the next process.

[0884] Step 4: Generate an answer

[0885] The generative AI model on the server analyzes the question and generates the optimal answer. For example, it might generate an answer such as, "The five steps in project progress are: 1. Requirements definition, 2. Planning, 3. Implementation, 4. Testing, 5. Release." The input is the user's question data, and the output is the generated answer data.

[0886] Step 5: Submit and view your responses

[0887] The server sends the generated answer data to the terminal as an HTTP response. The terminal analyzes the received answer data and displays it on the user interface. The input data is the generated answer, and the output is the visual answer information displayed to the user.

[0888] Step 6: Upload your work

[0889] Users upload their daily work details and documents they have created to the server via their terminal. The terminal sends the user's work data (e.g., PDF files) to the server as an HTTP POST request. The input data is a file containing the work details, and the output is the file saved on the server.

[0890] Step 7: Analyze work and measure growth

[0891] The server analyzes the uploaded materials and measures their growth. Specifically, it uses text mining tools to analyze the data and evaluates its content, quality, format consistency, etc. The input data is the uploaded materials, and the output data is the calculated growth score. For example, a "Growth Score: 85" is generated.

[0892] Step 8: Save and display growth data

[0893] The server stores the generated growth score in a data store and displays it through a dashboard. The input data is the growth score, and the output is the information stored in the database and displayed on the dashboard. Elders and superiors can access the dashboard and see "User A: Score 85."

[0894] Step 9: Provide feedback

[0895] An elder or superior provides feedback based on the growth data displayed on the dashboard. The feedback is sent from the terminal to the server and saved in the user's profile. For example, a comment such as "User A's document creation skills have improved" is entered. The input data is the feedback, and the output is the feedback information saved in the user's profile.

[0896] (Application example 1)

[0897] 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."

[0898] Conventional training systems for new employees and mid-career hires make it difficult to provide immediate answers to work-related questions or measure the degree of growth. In particular, there was a lack of means to quickly and effectively train factory engineers on robot operation and maintenance. This resulted in a decline in the quality and efficiency of training, and problems such as an inability to visualize the degree of growth of engineers or provide appropriate feedback.

[0899] 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.

[0900] In this invention, the server includes: means for a new employee or mid-career employee to input a question from an information terminal; means for transmitting the input question to a computer; means for the computer to analyze the received question and generate an answer using a natural language analysis model; means for transmitting the generated answer to the information terminal and displaying it; means for a new employee or mid-career employee to upload daily work content from the information terminal to a computer; means for the computer to analyze the uploaded work content and measure the degree of growth; means for storing the measured degree of growth in a storage device and displaying it on a control panel for an instructor or manager; means for a technician to input a question about robot operation or maintenance via visual wear; means for the visual wear to recognize the question by voice and transmit it to a computer; means for the computer to generate an answer using a natural language analysis model based on the received question and display it on the visual wear; means for the technician to upload work reports or maintenance records via visual wear; means for the computer to analyze the uploaded data and measure the technician's degree of growth; and means for providing feedback to the technician based on the degree of growth. This allows new employees, mid-career hires, and factory technicians to receive real-time training support, measure their progress, and provide appropriate feedback.

[0901] "New employee" refers to an employee who has been newly hired by a company or organization.

[0902] "Mid-career employees" refer to employees who have been newly hired after transferring from another company or organization.

[0903] "Information terminal" refers to a computer device that can connect to the Internet, such as a desktop computer, laptop, tablet, or smartphone.

[0904] "Computer" refers to a server, cloud, or other computer system with computing power.

[0905] "Natural language analysis model" refers to artificial intelligence technology for understanding and analyzing human language and generating appropriate responses. Specifically, it refers to large-scale language models such as GPT-4.

[0906] "Growth" refers to a measure of how much an educated employee or technician has improved their skills and knowledge.

[0907] "Storage device" refers to hardware for storing digital data, such as a hard disk drive (HDD) or solid-state drive (SSD).

[0908] "Leaders" refer to supervisors and trainers who are responsible for educating and guiding new employees and mid-career hires.

[0909] "Manager" refers to a person who has the authority to manage the work performance and development of employees and engineers.

[0910] A "control panel" is software or hardware with a user interface, and refers to a screen or device for displaying and operating data.

[0911] "Visual wear" refers to wearable devices such as smart glasses and head-mounted displays that can display real-time information and enable interactive operation.

[0912] "Work reports" refer to documents and data used by employees and technicians to record and report on their daily work activities and progress.

[0913] "Maintenance records" refers to documents and data that record the progress and results of maintenance work on robots and equipment.

[0914] "Feedback" refers to the evaluation and advice given to employees and engineers based on their growth and performance.

[0915] The present invention relates to an education support system for training new employees, mid-career recruits, and factory engineers and measuring their growth. This system is composed of an information terminal, a computer (server), and software for linking these. The specific operation of the system of the present invention will be described below.

[0916] The system uses information terminals, computers (servers), natural language analysis models (such as GPT-4), visual wear (smart glasses or head-mounted displays), and storage devices. These hardware and software enable employees and engineers to receive real-time support and measure their progress.

[0917] Users, i.e., new employees or mid-career employees, can access a web portal using an information terminal (PC, tablet, smartphone, etc.) and input questions related to their work. For example, they might input, "Please tell me how to proceed with this project." The input question is sent from the terminal to a computer, which then passes the received question to a natural language analysis model for analysis. The computer generates an optimal answer as a result of the analysis, and the computer returns the generated answer to the terminal, which displays it on the user interface.

[0918] Furthermore, factory engineers can input questions about robot operation and maintenance via the visual wear. The visual wear is equipped with a voice recognition function and sends the questions to a computer. The computer analyzes the received questions using a natural language analysis model and sends the generated answers to the visual wear for display. This allows engineers to obtain the necessary information hands-free.

[0919] Furthermore, users can upload their daily work details and created documents to the computer via their information terminal. For example, meeting materials can be uploaded in PDF format. The computer analyzes the uploaded documents and measures the growth of new or mid-career employees. The growth measurement engine evaluates the content, quality, and format consistency of the documents to generate a growth score. The generated growth score is saved in a storage device, and leaders and managers can check it in real time through the control panel.

[0920] For example, if a technician wearing vision wear asks, "Tell me the robot maintenance procedure," the system will generate and present an answer such as, "Maintenance is performed in the following steps: 1. Turn off the robot. 2. Disassemble the parts. 3. Clean each part. 4. Reassemble. 5. Turn on the power and check operation." Additionally, when a technician uploads a work report, the computer analyzes the uploaded data, measures the technician's progress, and provides the results to the instructor or manager.

[0921] In this way, the system of the present invention provides a function that streamlines the training of new employees, mid-career employees, and in-house engineers and visualizes their growth by providing quick answers to user questions and measuring their growth through analysis of their work content, allowing instructors and managers to provide appropriate evaluations and feedback.

[0922] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0923] Step 1:

[0924] The user inputs a question using an information terminal. The user inputs a business-related question (e.g., "How do I proceed with this project?") into the input field of the web portal. This question becomes the initial input to the system.

[0925] Step 2:

[0926] The terminal sends the entered question to a computer (server), which then sends the question as text data to the server, preparing the data for analysis in the next step.

[0927] Step 3:

[0928] The server passes the received question to a natural language analysis model for analysis. The server inputs the question data into the natural language analysis model (e.g., GPT-4), and the model analyzes the intent of the question and generates the optimal answer. The input here is the text data of the question, and the output is the text data of the answer.

[0929] Step 4:

[0930] The server sends the generated answer to the terminal, which then uses the API to send it back to the terminal and display it on the screen for the user to check.

[0931] Step 5:

[0932] Users upload their daily work details and documents they have created to the server via their terminal. For example, they upload meeting materials they have created in PDF format through a web portal. This data becomes the input for the next analysis process.

[0933] Step 6:

[0934] The server analyzes the uploaded work and measures its growth. The server evaluates the content, quality, and format consistency of the uploaded material to generate a growth score. The analysis algorithm extracts specific elements of the material and evaluates them. The input here is the uploaded material, and the output is a growth score.

[0935] Step 7:

[0936] The server stores the measured growth in a storage device and displays it on the control panel for instructors and administrators. The generated growth scores are stored in a database and can be checked in real time on the dashboard, allowing instructors and administrators to provide specific evaluations and feedback.

[0937] Step 8:

[0938] The engineer inputs questions about robot operation and maintenance through the vision wear. The engineer inputs questions into the vision wear by voice (e.g., "Please tell me the robot maintenance procedure."). This voice data becomes the input for the next analysis process.

[0939] Step 9:

[0940] The visual ware recognizes the question by voice, converts it into text format, and sends it to the computer. The voice recognition module in the visual ware converts the voice data into text data, which is then sent to the server.

[0941] Step 10:

[0942] The server generates an answer based on the received question using a natural language analysis model and displays it on the visual wear. The server passes the question text data to the natural language analysis model and sends the generated answer to the visual wear so that the technician can check it hands-free.

[0943] Step 11:

[0944] Technicians upload work reports or maintenance records via vision wear. Technicians upload reports of maintenance work or other operations in digital format using vision wear, which becomes the input for the next analytical process.

[0945] Step 12:

[0946] The server analyzes the uploaded data and measures the technician's growth. The server evaluates the content and accuracy of the reported maintenance records and generates a growth score.

[0947] Step 13:

[0948] The server provides feedback to the technician based on the growth rate. The computer generates appropriate feedback to the technician based on the measured growth score and notifies the technician through the visual wear.

[0949] 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.

[0950] The present invention combines an emotion engine with an educational AI system that provides work assistance and growth measurement for new and mid-career employees. This system is composed of a terminal, a server, an emotion engine, and software for linking these. The specific operation of the system of the present invention is described below.

[0951] 1. User enters question:

[0952] New employees or mid-career employees (hereafter referred to as users) access the web portal using a device (PC or smartphone) and input a question about their work. For example, they might input, "Please tell me how to proceed with this project." The emotion engine analyzes the user's tone of voice and facial expressions to generate emotion data.

[0953] 2. Submitting and analyzing questions:

[0954] The device sends the question data and emotion data to the server. Specifically, the question content and emotion data are sent to the server as JSON format data via a POST request.

[0955] 3. Answer generation and sentiment analysis:

[0956] The server receives the POST request and passes the question and emotion data to the question-answering engine. The question-answering engine analyzes the question using a natural language processing model (such as GPT-4) and generates the optimal answer based on the emotion data. For example, if a user is asked "How to proceed with a project" and feels anxious, the engine generates an answer such as "The project will proceed in the following five steps. If you have any problems, please rest assured that we are always here to support you."

[0957] 4. Submitting and Viewing Answers:

[0958] The server converts the generated answer into JSON format and sends it to the terminal as an HTTP response. The terminal receives the response data from the server and displays it on the user interface. For example, the answer "The project will proceed in the following steps..." is displayed on a web page.

[0959] 5. Upload your job description:

[0960] Users upload their daily work details and documents they have created to the server via their terminals. For example, they upload meeting documents they have created from a web portal.

[0961] 6. Job Analysis and Growth Measurement:

[0962] The server analyzes the uploaded materials and measures the growth of new or mid-career employees. The growth measurement engine evaluates the content, quality, and format consistency of the materials to generate a growth score. For example, a "growth score: 85" could be generated based on the depth and consistency of the material's content.

[0963] 7. Storage and display of growth data:

[0964] The server stores the generated growth score in a database. Elders and superiors can view the user's growth score and emotional data through a dashboard. For example, the dashboard can display "User A: Score 85" along with the user's recent emotional trend.

[0965] 8. Providing Feedback:

[0966] An elder or superior can check the growth and emotion data on the dashboard and provide appropriate feedback to the user. For example, the feedback could be posted on the web portal, such as, "User A's document creation skills are improving. He also seems to be feeling anxious recently, so he may need some support."

[0967] In this way, the system of the present invention can provide more personalized support and improve the efficiency of work for new and mid-career employees by providing quick answers to user questions, measuring growth by analyzing work content, and analyzing emotional data. This allows elders and superiors to provide appropriate evaluations and feedback, enabling new employees to smoothly carry out their work and support their growth.

[0968] The processing flow will be explained below.

[0969] Step 1:

[0970] A user accesses the web portal from a device and enters a question into the inquiry form. For example, they might enter, "Please tell me how to proceed with this project." The emotion engine analyzes the user's tone of voice and facial expressions to generate emotion data.

[0971] Step 2:

[0972] The device sends the question data and emotion data to the server. Specifically, the question content and emotion data are sent to the server as JSON format data via a POST request.

[0973] Step 3:

[0974] The server receives the POST request and passes the question and emotion data to the question-answering engine, which extracts the question text and emotion data from the JSON data.

[0975] Step 4:

[0976] The server's question-answering engine analyzes the question using a natural language processing model (such as GPT-4) and generates the optimal answer based on emotional data. For example, if a user is asked "How to proceed with the project" and feels anxious, the server generates a response such as "The project will proceed in the following five steps. If you have any problems, please rest assured that we are always here to support you."

[0977] Step 5:

[0978] The server converts the generated answer into JSON format and sends it to the terminal as an HTTP response. The terminal receives the response data from the server.

[0979] Step 6:

[0980] The response data received by the terminal is displayed on the user interface. For example, a response such as "The project will proceed in the following steps..." is displayed on a web page.

[0981] Step 7:

[0982] Users upload their daily work details and documents they have created from their devices to the server. For example, they upload meeting materials they have created from a web portal.

[0983] Step 8:

[0984] The terminal sends data on the business content to the server. Specifically, the created documents are sent to the server in a format such as PDF.

[0985] Step 9:

[0986] The server passes the received business content data to the growth measurement engine for analysis, which evaluates the content, quality, and format consistency of the materials.

[0987] Step 10:

[0988] The server stores the growth score generated by the growth measurement engine in a database, for example, "Growth score of user A's meeting materials: 85."

[0989] Step 11:

[0990] The server updates the growth and emotional data on the dashboard of the elder or superior. The elder or superior can view the user's growth score and emotional data on the dashboard. For example, the dashboard will show "User A: Score 85" along with the recent emotional trend.

[0991] Step 12:

[0992] An elder or superior can check the growth and emotion data on the dashboard and provide appropriate feedback to the user. For example, the feedback could be posted on the web portal, such as, "User A's document creation skills are improving. He also seems to be feeling anxious recently, so he may need some support."

[0993] Through the above steps, the system of the present invention can improve the efficiency of work execution for new employees and mid-career employees, effectively support their growth, and provide personalized support using emotional data. This allows elders and superiors to provide appropriate evaluations and feedback, enabling smooth work execution and growth support for new employees.

[0994] Example 2

[0995] 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."

[0996] Conventional training systems make it difficult to consider the emotional state of new and mid-career employees when providing work support or measuring their growth, making it difficult to provide personalized support. This can lead to insufficient work efficiency and support for employee growth. Another issue is that it is difficult to provide appropriate feedback that takes into account the emotional state of employees.

[0997] 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.

[0998] In this invention, the server includes: means for a new employee or mid-career employee to input a question from a terminal; means for transmitting the input question to the server; means for the server to analyze the received question and generate an answer using a natural language processing model; means for transmitting the generated answer to the terminal and displaying it; means for the new employee or mid-career employee to upload daily work content from the terminal to the server; means for the server to analyze the uploaded work content and measure growth level; means for generating and analyzing emotional data of the new employee or mid-career employee using an emotion engine; and means for storing the measured growth level and emotional data in a database and displaying it on a dashboard for an elder or supervisor. This makes it possible to provide work assistance and growth measurement for employees, as well as personalized support that takes into account their emotional state.

[0999] "New employees or mid-career employees" refers to employees who have been newly hired by a company or organization, or employees who have transferred from another organization.

[1000] A "terminal" refers to a computer system operated by a user, such as a device such as a personal computer or smartphone.

[1001] A "question" is business-related information that a user inputs about something they are unsure about or want to confirm.

[1002] "Server" refers to a computer system that communicates with terminals via a network and processes, manages, and stores various data.

[1003] "Natural language processing model" refers to artificial intelligence technology for understanding, analyzing, and generating human language, and specifically includes generative AI models such as GPT-4.

[1004] "Answer" refers to information generated by a natural language processing model in response to a user's question.

[1005] "Business content" refers to activities and materials created related to a user's daily work.

[1006] "Emotion data" refers to information about emotions obtained by analyzing the user's tone of voice, facial expressions, etc.

[1007] "Degree of growth" refers to the degree of growth evaluated based on the user's work content.

[1008] "Database" refers to a system for storing and managing structured information.

[1009] A "dashboard" refers to an interface that elders and superiors can access to check users' growth and emotional data.

[1010] "Elder or superior" refers to a leader or supervisor who is responsible for training and managing new and mid-career employees.

[1011] "Feedback" refers to evaluations and advice provided by elders or superiors regarding a user's work performance and growth.

[1012] This invention combines an emotion engine with an educational AI system that provides work support and growth measurement for new and mid-career employees. This system is composed of a user, a terminal, a server, an emotion engine, and software for linking these elements.

[1013] A user accesses a web portal using a device (PC or smartphone) and inputs a question about work. For example, they input a prompt such as, "Please tell me how to proceed with this project." At this time, the emotion engine analyzes the user's tone of voice and facial expressions to generate emotion data. The emotion engine detects the user's emotions using, for example, voice analysis software or image analysis software.

[1014] The device sends question data and emotion data to the server. Specifically, it converts this data into JSON format and sends it to the server using an HTTP POST request. The server analyzes the received POST request and passes the question and emotion data to the question answering engine.

[1015] A question-answering engine analyzes questions using a natural language processing model (e.g., a generative AI model such as GPT-4) and generates the optimal answer based on emotional data. For example, if a user asks, "Please tell me how to proceed with the project," and the emotional data detects anxiety, the question-answering engine will generate an answer such as, "The project will proceed in the following five steps. If you run into any problems, don't worry, we're always here to help."

[1016] The generated answer is returned to the server, which converts it to JSON format and sends it to the device. The device parses the received JSON data and displays the answer on a web page, where the user can check the answer.

[1017] Furthermore, users upload their daily work details and documents they have created to the server via their devices. For example, they upload meeting materials they have created from a web portal. The server analyzes the uploaded materials and measures their growth by evaluating the work details, the quality of the materials, and the consistency of the format. The growth measurement engine generates a growth score based on this analytical data and stores it in a database.

[1018] Elders and superiors can check the user's growth score and emotional data through the dashboard. For example, the dashboard might show "User A: Score 85" along with the user's recent emotional trend. The elder or superior can use the dashboard to check the growth and emotional data and provide appropriate feedback on the web portal. For example, they might provide feedback such as, "User A's document creation skills are improving. He or she also seems to be feeling anxious recently, so he or she may need support."

[1019] This makes it possible for the system of the present invention to provide personalized support that takes into account the user's work assistance, growth measurement, and even emotional state. The entire system works in cooperation with each other to support the user's efficient work performance and growth.

[1020] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1021] Step 1:

[1022] Users access the web portal using a device (PC or smartphone) and input a question. At this time, the emotion engine analyzes the user's tone of voice and facial expressions to generate emotion data.

[1023] Input: Question (e.g., "Tell me how to proceed with this project"), voice tone, and facial expression data

[1024] Output: Question data, emotion data

[1025] Specific behavior:

[1026] A user accesses the web portal.

[1027] The user enters a question in the input field.

[1028] The emotion engine acquires emotion data using the device's camera and microphone.

[1029] Emotional data is processed using voice and image analysis algorithms.

[1030] Step 2:

[1031] The device sends question data and emotion data to the server, which converts the data into JSON format and sends it to the server via an HTTP POST request.

[1032] Input: Question data, emotion data

[1033] Output: HTTP POST request to the server

[1034] Specific behavior:

[1035] The terminal collects the input question data and emotion data.

[1036] Convert question data and sentiment data into JSON format.

[1037] Sends an HTTP POST request to the server.

[1038] Step 3:

[1039] The server receives the POST request and passes the question and emotion data to the question-answering engine, which then analyzes the question using a natural language processing model (such as GPT-4) and generates an optimal answer based on the emotion data.

[1040] Input: Question data, emotion data

[1041] Output: The generated answer

[1042] Specific behavior:

[1043] The server decodes the POST request and extracts the question data and sentiment data.

[1044] The question data and emotion data are passed to the question answering engine.

[1045] The question answering engine uses a natural language processing model to parse the question.

[1046] Generate optimal answers that take emotional data into account.

[1047] Step 4:

[1048] The server converts the generated answer into JSON format and sends it to the terminal as an HTTP response. The terminal receives the response data from the server and displays it on the user interface.

[1049] Input: Generated answer

[1050] Output: HTTP response to the device

[1051] Specific behavior:

[1052] The server converts the generated response into JSON format.

[1053] Sends JSON data to the terminal as an HTTP response.

[1054] The device analyzes the received JSON data and displays the answer on a web page.

[1055] Step 5:

[1056] Users upload their daily work and documents they have created to the server via their devices. The server analyzes the uploaded documents and evaluates the consistency of the work content, quality of the documents, and format to measure their growth.

[1057] Input: Business details and materials

[1058] Output: Upload data to the server

[1059] Specific behavior:

[1060] Users access the web portal using their terminal.

[1061] Use the upload function on the web portal to select and upload materials.

[1062] The server receives and stores the uploaded materials.

[1063] Step 6:

[1064] The server's growth measurement engine analyzes the uploaded materials and generates a growth score based on content, quality, and format consistency, which is stored in a database.

[1065] Input: Uploaded materials

[1066] Output: Growth score

[1067] Specific behavior:

[1068] A growth measurement engine analyzes the stored data.

[1069] Evaluate the consistency of content, quality, and format of materials.

[1070] A growth score is calculated based on the evaluation results and stored in a database.

[1071] Step 7:

[1072] Elders and superiors can view users' growth scores and sentiment data through a dashboard.

[1073] Input: Growth score, emotion data

[1074] Output: Dashboard display

[1075] Specific behavior:

[1076] Elders and superiors log in to the dashboard.

[1077] View growth scores and sentiment data at a glance on the dashboard.

[1078] Step 8:

[1079] Elders and superiors provide appropriate feedback to users based on the growth scores and emotional data they have confirmed, and the feedback is posted on the web portal.

[1080] Input: Growth score, emotion data

[1081] Output: Feedback

[1082] Specific behavior:

[1083] Elders and superiors review the dashboard data.

[1084] Generate appropriate feedback and enter it into the web portal.

[1085] Users can view feedback on the web portal.

[1086] (Application example 2)

[1087] 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."

[1088] In today's brick-and-mortar stores, new employees and mid-career recruits are expected to respond to customers and explain products immediately, but in reality, they often perform their work while harboring doubts and anxieties. This calls for a support system that enables staff to immediately resolve questions about their work and provide appropriate explanations and responses. Furthermore, while there is an expectation that staff growth can be objectively measured and that supervisors and training personnel can provide appropriate feedback based on that data, existing systems do not adequately address the emotional aspects of staff. The present invention aims to solve these issues and provide a system for improving staff performance.

[1089] 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.

[1090] In this invention, the server

[1091] A means for new employees or mid-career employees to input questions from a terminal;

[1092] means for transmitting the input question to a server;

[1093] means for the server to analyze the received question and generate an answer using a natural language processing model;

[1094] means for transmitting the generated answer to a terminal and displaying it;

[1095] A means for new employees or mid-career employees to upload their daily work details from their devices to the server,

[1096] A means for the server to analyze the uploaded business content and measure the growth degree;

[1097] A means for storing the measured growth level in a database and displaying it on a dashboard for a superior or a training officer;

[1098] means for analyzing the tone of voice and facial expressions of the staff member when the question is input, and an emotion engine for generating emotion data;

[1099] means for generating an optimal answer based on the emotion data;

[1100] This allows staff to instantly resolve work-related questions and receive appropriate support, including emotional support. Furthermore, superiors and training personnel can provide appropriate feedback based on staff growth and emotional data, making it possible to simultaneously improve staff performance and provide mental care.

[1101] A "terminal" is an electronic device that allows a user to enter questions and upload business details.

[1102] "Server" means a computer on a network that receives and analyzes entered questions or uploaded data and generates answers.

[1103] A "natural language processing model" is an artificial intelligence technology that analyzes the content of a user's question and generates an appropriate answer.

[1104] The "emotion engine" is a system that analyzes the user's tone of voice and facial expressions to generate emotion data.

[1105] "Growth level" is an index used to analyze the work content uploaded by the user and evaluate the quality and progress of the work.

[1106] The "dashboard" is a user interface that allows managers and training personnel to check staff growth and emotional data.

[1107] "Answer generation" is the process of generating optimal responses based on natural language processing models and sentiment data.

[1108] "Inputting a question" refers to the act of a user communicating an unclear point or question to the system via a terminal.

[1109] This invention is a system for supporting staff in brick-and-mortar stores, allowing new employees and mid-career recruits to instantly resolve questions about their work and measure their progress. This system is composed of a terminal, a server, an emotion engine, a natural language processing model, and software for linking these components.

[1110] Hardware and software used

[1111] A terminal is an electronic device (e.g., a smartphone or tablet) through which a user enters questions and uploads work content.

[1112] A server is a computer on a network that receives and analyzes questions and task data to generate answers and measure progress.

[1113] The emotion engine analyzes the tone of voice and facial expressions when a question is entered to generate emotion data.

[1114] A natural language processing model is an AI technology that analyzes questions and generates optimal answers. An example of this is GPT-4.

[1115] Specific operation of this system

[1116] Users access the system using a terminal and input business-related questions. Questions can be entered as text or as voice data. The emotion engine analyzes the voice tone and facial expressions entered and generates emotion data.

[1117] The device sends the question data and emotion data to the server in JSON format. The server then passes the received question data and emotion data to a natural language processing model to generate the optimal answer. For example, if a question about how to explain a new product is entered and the emotion data indicates anxiety, the system will generate an answer such as, "The explanation of the new product will be done in the following steps. Also, please rest assured that we are always available to provide support if you have any concerns."

[1118] The generated answer is converted to JSON format and sent to the device as an HTTP response. The device receives it and displays it visually to the user, for example, as text on a web page.

[1119] Users upload their daily work and documents they create to the server via their devices. This allows their work performance to be analyzed and their level of growth measured. Growth is evaluated based on the consistency of the content, quality, and format of the documents.

[1120] The generated growth data is stored in a database and displayed on a dashboard for supervisors and training personnel, allowing them to check staff growth and provide appropriate feedback.

[1121] Examples of concrete examples and prompts

[1122] For example, if a user asks, "Please tell me how to explain a new product," and the emotion engine analyzes the user's tone of voice and determines that the user is feeling anxious, the system will generate an answer like this: "The steps to explain a new product are as follows. Also, please rest assured that we are always here to support you if you feel anxious."

[1123] An example prompt is:

[1124] How do you explain a new product? I'm a little nervous.

[1125] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1126] Step 1:

[1127] A user accesses the system using a terminal and inputs a question about the business. For example, they input, "Please tell me how to explain a new product." Voice data may also be input at the same time.

[1128] Input: Question text, audio data

[1129] Output: Question text and sentiment data generation preparation

[1130] Step 2:

[1131] The device uses an emotion engine to analyze input voice data and facial expressions to generate emotion data, for example, analyzing the user's emotion, such as anxiety, from the tone of their voice.

[1132] Input: Audio data, question text

[1133] Data processing / calculation: Generating emotional data by analyzing voice tone and facial expressions

[1134] Output: Emotion data

[1135] Step 3:

[1136] The device sends the question text and generated emotion data to the server as JSON format data using a POST request.

[1137] Input: Question text, emotion data

[1138] Data processing / calculation: Converting questions and sentiment data into JSON format

[1139] Output: POST request sent to the server

[1140] Step 4:

[1141] The server parses the received POST request and passes the question text and sentiment data to the natural language processing model.

[1142] Input: Question text, emotion data

[1143] Data processing / calculation: Analysis of questions and sentiment data and input to natural language processing models

[1144] Output: Data passed to the natural language processing model

[1145] Step 5:

[1146] The server uses a natural language processing model to analyze the question and generate the optimal answer based on emotional data, such as "We'll explain the new product in the following steps. If you have any concerns, please rest assured that we're always here to help."

[1147] Input: Question text, sentiment data, natural language processing model

[1148] Data processing / calculation: Question analysis and answer generation

[1149] Output: The generated answer

[1150] Step 6:

[1151] The server converts the generated response into JSON format data and sends it to the terminal as an HTTP response.

[1152] Input: Generated Answer

[1153] Data processing / calculation: Converting answers to JSON format

[1154] Output: Sending HTTP response to the terminal

[1155] Step 7:

[1156] The terminal receives the response data from the server and visually displays it to the user. For example, a message such as "To explain the new product, follow the steps below..." is displayed on a web page.

[1157] Input: JSON format response data from the server

[1158] Data processing / calculation: Parsing JSON data and converting it to a display format

[1159] Output: The answer displayed in the user interface

[1160] Step 8:

[1161] Users upload their daily work details and documents they have created to the server via their terminals, for example, by uploading meeting materials they have created or customer service details.

[1162] Input: Business details data

[1163] Data processing / calculation: Formatting and uploading business data

[1164] Output: Business content data saved on the server

[1165] Step 9:

[1166] The server analyzes the uploaded work content and measures the degree of growth. For example, it evaluates the content and format of the material and generates a "Growth Score: 85."

[1167] Input: Business content data

[1168] Data processing / calculation: Analysis of work content and growth measurement

[1169] Output: Growth Score

[1170] Step 10:

[1171] The server stores the generated growth scores in a database and displays them on a dashboard for superiors and training personnel.

[1172] Input: Growth score, emotion data

[1173] Data processing / calculation: Saving growth scores and updating dashboard displays

[1174] Output: Growth scores and sentiment data displayed on a dashboard

[1175] Step 11:

[1176] Supervisors and training personnel can check the growth and emotional data on the dashboard and provide appropriate feedback, such as, "Your document creation skills are improving. You seem to be feeling anxious recently, so you may need some support."

[1177] Input: Growth score on the dashboard, sentiment data

[1178] Data processing / calculation: Feedback generation based on growth scores and emotion data

[1179] Output: Feedback provided to the user

[1180] 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.

[1181] 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.

[1182] 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.

[1183] [Fourth embodiment]

[1184] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[1185] 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.

[1186] 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).

[1187] 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.

[1188] 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.

[1189] 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).

[1190] 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.

[1191] 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.

[1192] 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.

[1193] 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.

[1194] 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.

[1195] 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.

[1196] 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."

[1197] The present invention is an educational AI system that provides work assistance and growth measurement for new and mid-career employees. This system is composed of a terminal, a server, and software for linking these. The specific operation of the system of the present invention is described below.

[1198] 1. User enters question:

[1199] New employees or mid-career employees (hereafter referred to as users) access the web portal using a device (PC or smartphone) and enter a question about their work. For example, they might enter, "Please tell me how to proceed with this project."

[1200] 2. Submitting and analyzing questions:

[1201] The device sends the input question to the server, which then passes the received question to a natural language processing model (e.g., GPT-4) for analysis.

[1202] 3. Generate and display answers:

[1203] The server's natural language processing model generates the best answer based on the question. For example, in response to the question above, it might generate an answer such as, "Project progress is carried out in the following five steps: 1. Requirements definition, 2. Planning, 3. Implementation, 4. Testing, 5. Release." The server then sends the generated answer to the device, which then displays it on the user interface.

[1204] 4. Upload your job description:

[1205] Users upload their daily work details and documents they have created to the server via their terminals. For example, they upload meeting documents they have created in PDF format.

[1206] 5. Job Analysis and Growth Measurement:

[1207] The server analyzes the uploaded materials and measures the growth of new or mid-career employees. The growth measurement engine evaluates the content, quality, and format consistency of the materials to generate a growth score. For example, a "growth score: 85" could be generated based on the depth and consistency of the material's content.

[1208] 6. Storage and display of growth data:

[1209] The server stores the generated growth score in a database. Elders and superiors can check the user's growth score in real time through the dashboard. For example, the dashboard might show "User A: Score 85."

[1210] 7. Providing Feedback:

[1211] The elder or superior provides appropriate feedback to the user based on the growth data in the dashboard. For example, feedback such as "User A's document creation skills have improved" can be posted on the web portal.

[1212] In this way, the system of the present invention provides functions that improve the efficiency of work performance for new employees and mid-career employees and visualize their growth by providing quick answers to user questions and measuring growth levels through analysis of work content. This allows elders and superiors to provide appropriate evaluations and feedback, realizing smooth work performance and growth support for new employees.

[1213] The processing flow will be explained below.

[1214] Step 1:

[1215] A user accesses the web portal from a terminal and enters a question into the inquiry form. For example, the user might enter, "Please tell me how to proceed with this project."

[1216] Step 2:

[1217] The device sends the question data to the server. Specifically, the question content is sent to the server as JSON format data via a POST request.

[1218] Step 3:

[1219] The server receives the POST request and passes the question to the question answering engine for processing. Specifically, the server extracts and prepares the question text from the JSON data.

[1220] Step 4:

[1221] The server's question-answering engine uses a natural language processing model (such as GPT-4) to analyze the question and generate the optimal answer. For example, if asked "How do you proceed with a project?", it will generate an answer such as "Project progress is carried out in the following five steps: 1. Requirements definition, 2. Planning, 3. Implementation, 4. Testing, and 5. Release."

[1222] Step 5:

[1223] The server converts the generated response into JSON format and sends it to the terminal as an HTTP response.

[1224] Step 6:

[1225] The terminal receives the response data from the server and displays it on the user interface. Specifically, the answer "The project will proceed in the following steps..." is displayed on the web page.

[1226] Step 7:

[1227] Users upload their daily work details and documents they have created from their devices to the server. For example, they upload meeting materials they have created from a web portal.

[1228] Step 8:

[1229] The terminal sends data on the business content to the server. Specifically, the created documents are sent to the server in a format such as PDF.

[1230] Step 9:

[1231] The server passes the received business content data to the growth measurement engine for analysis, which evaluates the content, quality, and format consistency of the materials.

[1232] Step 10:

[1233] The server stores the growth score generated by the growth measurement engine in a database, for example, "Growth score of user A's meeting materials: 85."

[1234] Step 11:

[1235] The server updates the growth data to the dashboard of the elders and superiors, who can view the user's growth score in real time on the dashboard.

[1236] Step 12:

[1237] The elder or superior checks the growth data on the dashboard and provides appropriate feedback to the user. For example, the feedback could be posted on the web portal, saying, "User A's document creation skills have improved."

[1238] Through the above steps, the system of the present invention improves the efficiency of work execution for new employees and mid-career employees and effectively supports their growth.

[1239] Example 1

[1240] 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."

[1241] In the conventional training system, new employees and mid-career employees were unable to efficiently resolve their work-related questions, making it difficult to immediately grasp the improvement of their work skills. In addition, there were limited means for superiors and managers to accurately measure the skill growth of new employees and provide appropriate feedback.

[1242] 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.

[1243] In this invention, the server includes means for inputting a question from a terminal, means for transmitting the input question to a computer system, means for the computer system to analyze the received question and generate an answer using a generative model, means for transmitting the generated answer to the terminal and displaying it, means for uploading work content from the terminal to the computer system, means for the computer system to analyze the uploaded work content and measure an evaluation score, and means for saving the measured evaluation score in a data store and displaying it on a display screen for an administrator. This enables new employees and mid-career employees to receive quick and appropriate answers to questions about their work, and makes it possible to measure the growth of their work skills in real time and provide appropriate feedback based on that data.

[1244] A "terminal" is an information processing device used by a user, and includes a personal computer (PC) or a smartphone.

[1245] A "computer system" is a group of information processing devices, including servers and cloud-based computing resources, that transmit, receive, and analyze data.

[1246] A "generative model" is an algorithm for natural language processing, and refers to an advanced generative AI model such as GPT-4.

[1247] A "data store" is a storage system for saving and managing data, including databases and cloud storage.

[1248] The "means for inputting a question" is an interface for a user to input a question in text format, and includes an input form on a web portal.

[1249] "Means for sending a question to a computer system" refers to a communication protocol and its implementation for transferring a question entered by a user to a server over a network.

[1250] "Means for analyzing questions and generating answers using a generative model" refers to a mechanism for analyzing received questions using natural language processing technology and automatically generating appropriate answers.

[1251] "Means for transmitting the answer to the terminal and displaying it" refers to an interface and its implementation for transmitting the generated answer to the terminal and visually presenting it to the user.

[1252] "Means for uploading business content from a terminal to a computer system" refers to the function for sending documents created by users to a server in file format.

[1253] "Means for analyzing uploaded work content and measuring evaluation scores" refers to the algorithm and its implementation for analyzing uploaded materials and data and quantifying the degree of growth based on their quality and content.

[1254] "Administrator display screen" refers to the user interface that allows elders or superiors to check growth data and feedback.

[1255] The present invention relates to an education system that provides work support and growth measurement for new employees and mid-career employees. This system is composed of a terminal, a computer system, and software for linking these.

[1256] First, a user accesses a web portal using a device (e.g., a PC or smartphone) and inputs a question related to their work. The device then sends the user's input to a computer system. The computer system then passes the received question to a generative AI model (e.g., GPT-4), which analyzes the question using natural language processing and generates the optimal answer.

[1257] As a specific example, suppose a user types, "Tell me how to proceed with this project." The generative AI model in the computer system analyzes the question and generates an appropriate answer. For example, it might generate an answer such as, "The five steps in project progression are: 1. Requirements definition, 2. Planning, 3. Implementation, 4. Testing, and 5. Release." The server sends the generated answer to the device, which then displays it on the user interface.

[1258] Furthermore, users upload their daily work details and documents they have created to the computer system via their terminals. The computer system analyzes the uploaded documents and measures the user's level of growth. The growth measurement engine evaluates the content, quality, and format consistency of the documents and generates a growth score. For example, a "growth score of 85" is generated based on the depth and consistency of the document's content.

[1259] The generated growth scores are stored in a data store, and elders and superiors can check them in real time via the dashboard. For example, the dashboard might display "User A: Score 85." The elder or superior can provide appropriate feedback to the user based on this growth data. For example, they could post feedback such as "User A's document creation skills are improving" on the web portal.

[1260] Examples of prompts include:

[1261] "Tell me how to proceed with this project."

[1262] "What are some best practices for dealing with customers?"

[1263] In this way, this system improves the efficiency of work execution for new and mid-career employees, provides a function that visualizes their growth, and makes it easier for elders and superiors to provide appropriate evaluations and feedback, thereby enabling new employees to smoothly carry out their work and support their growth.

[1264] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1265] Step 1: User enters question

[1266] A user accesses a web portal using a terminal and inputs a question about their work. The input question is processed as string data within the terminal and prepared for transmission. For example, a user might input "Please tell me how to proceed with this project." This input data is converted to JSON format data for subsequent processing and prepared as an HTTP request.

[1267] Step 2: Submit your question

[1268] The device sends the entered question data to the server. This is done using an HTTP POST request. The input data includes the question content and the user ID. The server receives the question data and prepares it for analysis.

[1269] Step 3: Parsing the Question

[1270] The server passes the received question data to a generative AI model (e.g., GPT-4) for analysis. At this time, the server converts the question data into an appropriate format and sends it to the generative AI model's API. The input data is the user's question, and the output data is the generated answer. The server obtains the answer returned by the generative AI model and prepares it for the next process.

[1271] Step 4: Generate an answer

[1272] The generative AI model on the server analyzes the question and generates the optimal answer. For example, it might generate an answer such as, "The five steps in project progress are: 1. Requirements definition, 2. Planning, 3. Implementation, 4. Testing, 5. Release." The input is the user's question data, and the output is the generated answer data.

[1273] Step 5: Submit and view your responses

[1274] The server sends the generated answer data to the terminal as an HTTP response. The terminal analyzes the received answer data and displays it on the user interface. The input data is the generated answer, and the output is the visual answer information displayed to the user.

[1275] Step 6: Upload your work

[1276] Users upload their daily work details and documents they have created to the server via their terminal. The terminal sends the user's work data (e.g., PDF files) to the server as an HTTP POST request. The input data is a file containing the work details, and the output is the file saved on the server.

[1277] Step 7: Analyze work and measure growth

[1278] The server analyzes the uploaded materials and measures their growth. Specifically, it uses text mining tools to analyze the data and evaluates its content, quality, format consistency, etc. The input data is the uploaded materials, and the output data is the calculated growth score. For example, a "Growth Score: 85" is generated.

[1279] Step 8: Save and display growth data

[1280] The server stores the generated growth score in a data store and displays it through a dashboard. The input data is the growth score, and the output is the information stored in the database and displayed on the dashboard. Elders and superiors can access the dashboard and see "User A: Score 85."

[1281] Step 9: Provide feedback

[1282] An elder or superior provides feedback based on the growth data displayed on the dashboard. The feedback is sent from the terminal to the server and saved in the user's profile. For example, a comment such as "User A's document creation skills have improved" is entered. The input data is the feedback, and the output is the feedback information saved in the user's profile.

[1283] (Application example 1)

[1284] 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."

[1285] Conventional training systems for new employees and mid-career hires make it difficult to provide immediate answers to work-related questions or measure the degree of growth. In particular, there was a lack of means to quickly and effectively train factory engineers on robot operation and maintenance. This resulted in a decline in the quality and efficiency of training, and problems such as an inability to visualize the degree of growth of engineers or provide appropriate feedback.

[1286] 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.

[1287] In this invention, the server includes: means for a new employee or mid-career employee to input a question from an information terminal; means for transmitting the input question to a computer; means for the computer to analyze the received question and generate an answer using a natural language analysis model; means for transmitting the generated answer to the information terminal and displaying it; means for a new employee or mid-career employee to upload daily work content from the information terminal to a computer; means for the computer to analyze the uploaded work content and measure the degree of growth; means for storing the measured degree of growth in a storage device and displaying it on a control panel for an instructor or manager; means for a technician to input a question about robot operation or maintenance via visual wear; means for the visual wear to recognize the question by voice and transmit it to a computer; means for the computer to generate an answer using a natural language analysis model based on the received question and display it on the visual wear; means for the technician to upload work reports or maintenance records via visual wear; means for the computer to analyze the uploaded data and measure the technician's degree of growth; and means for providing feedback to the technician based on the degree of growth. This allows new employees, mid-career hires, and factory technicians to receive real-time training support, measure their progress, and provide appropriate feedback.

[1288] "New employee" refers to an employee who has been newly hired by a company or organization.

[1289] "Mid-career employees" refer to employees who have been newly hired after transferring from another company or organization.

[1290] "Information terminal" refers to a computer device that can connect to the Internet, such as a desktop computer, laptop, tablet, or smartphone.

[1291] "Computer" refers to a server, cloud, or other computer system with computing power.

[1292] "Natural language analysis model" refers to artificial intelligence technology for understanding and analyzing human language and generating appropriate responses. Specifically, it refers to large-scale language models such as GPT-4.

[1293] "Growth" refers to a measure of how much an educated employee or technician has improved their skills and knowledge.

[1294] "Storage device" refers to hardware for storing digital data, such as a hard disk drive (HDD) or solid-state drive (SSD).

[1295] "Leaders" refer to supervisors and trainers who are responsible for educating and guiding new employees and mid-career hires.

[1296] "Manager" refers to a person who has the authority to manage the work performance and development of employees and engineers.

[1297] A "control panel" is software or hardware with a user interface, and refers to a screen or device for displaying and operating data.

[1298] "Visual wear" refers to wearable devices such as smart glasses and head-mounted displays that can display real-time information and enable interactive operation.

[1299] "Work reports" refer to documents and data used by employees and technicians to record and report on their daily work activities and progress.

[1300] "Maintenance records" refers to documents and data that record the progress and results of maintenance work on robots and equipment.

[1301] "Feedback" refers to the evaluation and advice given to employees and engineers based on their growth and performance.

[1302] The present invention relates to an education support system for training new employees, mid-career recruits, and factory engineers and measuring their growth. This system is composed of an information terminal, a computer (server), and software for linking these. The specific operation of the system of the present invention will be described below.

[1303] The system uses information terminals, computers (servers), natural language analysis models (such as GPT-4), visual wear (smart glasses or head-mounted displays), and storage devices. These hardware and software enable employees and engineers to receive real-time support and measure their progress.

[1304] Users, i.e., new employees or mid-career employees, can access a web portal using an information terminal (PC, tablet, smartphone, etc.) and input questions related to their work. For example, they might input, "Please tell me how to proceed with this project." The input question is sent from the terminal to a computer, which then passes the received question to a natural language analysis model for analysis. The computer generates an optimal answer as a result of the analysis, and the computer returns the generated answer to the terminal, which displays it on the user interface.

[1305] Furthermore, factory engineers can input questions about robot operation and maintenance via the visual wear. The visual wear is equipped with a voice recognition function and sends the questions to a computer. The computer analyzes the received questions using a natural language analysis model and sends the generated answers to the visual wear for display. This allows engineers to obtain the necessary information hands-free.

[1306] Furthermore, users can upload their daily work details and created documents to the computer via their information terminal. For example, meeting materials can be uploaded in PDF format. The computer analyzes the uploaded documents and measures the growth of new or mid-career employees. The growth measurement engine evaluates the content, quality, and format consistency of the documents to generate a growth score. The generated growth score is saved in a storage device, and leaders and managers can check it in real time through the control panel.

[1307] For example, if a technician wearing vision wear asks, "Tell me the robot maintenance procedure," the system will generate and present an answer such as, "Maintenance is performed in the following steps: 1. Turn off the robot. 2. Disassemble the parts. 3. Clean each part. 4. Reassemble. 5. Turn on the power and check operation." Additionally, when a technician uploads a work report, the computer analyzes the uploaded data, measures the technician's progress, and provides the results to the instructor or manager.

[1308] In this way, the system of the present invention provides a function that streamlines the training of new employees, mid-career employees, and in-house engineers and visualizes their growth by providing quick answers to user questions and measuring their growth through analysis of their work content, allowing instructors and managers to provide appropriate evaluations and feedback.

[1309] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1310] Step 1:

[1311] The user inputs a question using an information terminal. The user inputs a business-related question (e.g., "How do I proceed with this project?") into the input field of the web portal. This question becomes the initial input to the system.

[1312] Step 2:

[1313] The terminal sends the entered question to a computer (server), which then sends the question as text data to the server, preparing the data for analysis in the next step.

[1314] Step 3:

[1315] The server passes the received question to a natural language analysis model for analysis. The server inputs the question data into the natural language analysis model (e.g., GPT-4), and the model analyzes the intent of the question and generates the optimal answer. The input here is the text data of the question, and the output is the text data of the answer.

[1316] Step 4:

[1317] The server sends the generated answer to the terminal, which then uses the API to send it back to the terminal and display it on the screen for the user to check.

[1318] Step 5:

[1319] Users upload their daily work details and documents they have created to the server via their terminal. For example, they upload meeting materials they have created in PDF format through a web portal. This data becomes the input for the next analysis process.

[1320] Step 6:

[1321] The server analyzes the uploaded work and measures its growth. The server evaluates the content, quality, and format consistency of the uploaded material to generate a growth score. The analysis algorithm extracts specific elements of the material and evaluates them. The input here is the uploaded material, and the output is a growth score.

[1322] Step 7:

[1323] The server stores the measured growth in a storage device and displays it on the control panel for instructors and administrators. The generated growth scores are stored in a database and can be checked in real time on the dashboard, allowing instructors and administrators to provide specific evaluations and feedback.

[1324] Step 8:

[1325] The engineer inputs questions about robot operation and maintenance through the vision wear. The engineer inputs questions into the vision wear by voice (e.g., "Please tell me the robot maintenance procedure."). This voice data becomes the input for the next analysis process.

[1326] Step 9:

[1327] The visual ware recognizes the question by voice, converts it into text format, and sends it to the computer. The voice recognition module in the visual ware converts the voice data into text data, which is then sent to the server.

[1328] Step 10:

[1329] The server generates an answer based on the received question using a natural language analysis model and displays it on the visual wear. The server passes the question text data to the natural language analysis model and sends the generated answer to the visual wear so that the technician can check it hands-free.

[1330] Step 11:

[1331] Technicians upload work reports or maintenance records via vision wear. Technicians upload reports of maintenance work or other operations in digital format using vision wear, which becomes the input for the next analytical process.

[1332] Step 12:

[1333] The server analyzes the uploaded data and measures the technician's growth. The server evaluates the content and accuracy of the reported maintenance records and generates a growth score.

[1334] Step 13:

[1335] The server provides feedback to the technician based on the growth rate. The computer generates appropriate feedback to the technician based on the measured growth score and notifies the technician through the visual wear.

[1336] 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.

[1337] The present invention combines an emotion engine with an educational AI system that provides work assistance and growth measurement for new and mid-career employees. This system is composed of a terminal, a server, an emotion engine, and software for linking these. The specific operation of the system of the present invention is described below.

[1338] 1. User enters question:

[1339] New employees or mid-career employees (hereafter referred to as users) access the web portal using a device (PC or smartphone) and input a question about their work. For example, they might input, "Please tell me how to proceed with this project." The emotion engine analyzes the user's tone of voice and facial expressions to generate emotion data.

[1340] 2. Submitting and analyzing questions:

[1341] The device sends the question data and emotion data to the server. Specifically, the question content and emotion data are sent to the server as JSON format data via a POST request.

[1342] 3. Answer generation and sentiment analysis:

[1343] The server receives the POST request and passes the question and emotion data to the question-answering engine. The question-answering engine analyzes the question using a natural language processing model (such as GPT-4) and generates the optimal answer based on the emotion data. For example, if a user is asked "How to proceed with a project" and feels anxious, the engine generates an answer such as "The project will proceed in the following five steps. If you have any problems, please rest assured that we are always here to support you."

[1344] 4. Submitting and Viewing Answers:

[1345] The server converts the generated answer into JSON format and sends it to the terminal as an HTTP response. The terminal receives the response data from the server and displays it on the user interface. For example, the answer "The project will proceed in the following steps..." is displayed on a web page.

[1346] 5. Upload your job description:

[1347] Users upload their daily work details and documents they have created to the server via their terminals. For example, they upload meeting documents they have created from a web portal.

[1348] 6. Job Analysis and Growth Measurement:

[1349] The server analyzes the uploaded materials and measures the growth of new or mid-career employees. The growth measurement engine evaluates the content, quality, and format consistency of the materials to generate a growth score. For example, a "growth score: 85" could be generated based on the depth and consistency of the material's content.

[1350] 7. Storage and display of growth data:

[1351] The server stores the generated growth score in a database. Elders and superiors can view the user's growth score and emotional data through a dashboard. For example, the dashboard can display "User A: Score 85" along with the user's recent emotional trend.

[1352] 8. Providing Feedback:

[1353] An elder or superior can check the growth and emotion data on the dashboard and provide appropriate feedback to the user. For example, the feedback could be posted on the web portal, such as, "User A's document creation skills are improving. He also seems to be feeling anxious recently, so he may need some support."

[1354] In this way, the system of the present invention can provide more personalized support and improve the efficiency of work for new and mid-career employees by providing quick answers to user questions, measuring growth by analyzing work content, and analyzing emotional data. This allows elders and superiors to provide appropriate evaluations and feedback, enabling new employees to smoothly carry out their work and support their growth.

[1355] The processing flow will be explained below.

[1356] Step 1:

[1357] A user accesses the web portal from a device and enters a question into the inquiry form. For example, they might enter, "Please tell me how to proceed with this project." The emotion engine analyzes the user's tone of voice and facial expressions to generate emotion data.

[1358] Step 2:

[1359] The device sends the question data and emotion data to the server. Specifically, the question content and emotion data are sent to the server as JSON format data via a POST request.

[1360] Step 3:

[1361] The server receives the POST request and passes the question and emotion data to the question-answering engine, which extracts the question text and emotion data from the JSON data.

[1362] Step 4:

[1363] The server's question-answering engine analyzes the question using a natural language processing model (such as GPT-4) and generates the optimal answer based on emotional data. For example, if a user is asked "How to proceed with the project" and feels anxious, the server generates a response such as "The project will proceed in the following five steps. If you have any problems, please rest assured that we are always here to support you."

[1364] Step 5:

[1365] The server converts the generated answer into JSON format and sends it to the terminal as an HTTP response. The terminal receives the response data from the server.

[1366] Step 6:

[1367] The response data received by the terminal is displayed on the user interface. For example, a response such as "The project will proceed in the following steps..." is displayed on a web page.

[1368] Step 7:

[1369] Users upload their daily work details and documents they have created from their devices to the server. For example, they upload meeting materials they have created from a web portal.

[1370] Step 8:

[1371] The terminal sends data on the business content to the server. Specifically, the created documents are sent to the server in a format such as PDF.

[1372] Step 9:

[1373] The server passes the received business content data to the growth measurement engine for analysis, which evaluates the content, quality, and format consistency of the materials.

[1374] Step 10:

[1375] The server stores the growth score generated by the growth measurement engine in a database, for example, "Growth score of user A's meeting materials: 85."

[1376] Step 11:

[1377] The server updates the growth and emotional data on the dashboard of the elder or superior. The elder or superior can view the user's growth score and emotional data on the dashboard. For example, the dashboard will show "User A: Score 85" along with the recent emotional trend.

[1378] Step 12:

[1379] An elder or superior can check the growth and emotion data on the dashboard and provide appropriate feedback to the user. For example, the feedback could be posted on the web portal, such as, "User A's document creation skills are improving. He also seems to be feeling anxious recently, so he may need some support."

[1380] Through the above steps, the system of the present invention can improve the efficiency of work execution for new employees and mid-career employees, effectively support their growth, and provide personalized support using emotional data. This allows elders and superiors to provide appropriate evaluations and feedback, enabling smooth work execution and growth support for new employees.

[1381] Example 2

[1382] 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."

[1383] Conventional training systems make it difficult to consider the emotional state of new and mid-career employees when providing work support or measuring their growth, making it difficult to provide personalized support. This can lead to insufficient work efficiency and support for employee growth. Another issue is that it is difficult to provide appropriate feedback that takes into account the emotional state of employees.

[1384] 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.

[1385] In this invention, the server includes: means for a new employee or mid-career employee to input a question from a terminal; means for transmitting the input question to the server; means for the server to analyze the received question and generate an answer using a natural language processing model; means for transmitting the generated answer to the terminal and displaying it; means for the new employee or mid-career employee to upload daily work content from the terminal to the server; means for the server to analyze the uploaded work content and measure growth level; means for generating and analyzing emotional data of the new employee or mid-career employee using an emotion engine; and means for storing the measured growth level and emotional data in a database and displaying it on a dashboard for an elder or supervisor. This makes it possible to provide work assistance and growth measurement for employees, as well as personalized support that takes into account their emotional state.

[1386] "New employees or mid-career employees" refers to employees who have been newly hired by a company or organization, or employees who have transferred from another organization.

[1387] A "terminal" refers to a computer system operated by a user, such as a device such as a personal computer or smartphone.

[1388] A "question" is business-related information that a user inputs about something they are unsure about or want to confirm.

[1389] "Server" refers to a computer system that communicates with terminals via a network and processes, manages, and stores various data.

[1390] "Natural language processing model" refers to artificial intelligence technology for understanding, analyzing, and generating human language, and specifically includes generative AI models such as GPT-4.

[1391] "Answer" refers to information generated by a natural language processing model in response to a user's question.

[1392] "Business content" refers to activities and materials created related to a user's daily work.

[1393] "Emotion data" refers to information about emotions obtained by analyzing the user's tone of voice, facial expressions, etc.

[1394] "Degree of growth" refers to the degree of growth evaluated based on the user's work content.

[1395] "Database" refers to a system for storing and managing structured information.

[1396] A "dashboard" refers to an interface that elders and superiors can access to check users' growth and emotional data.

[1397] "Elder or superior" refers to a leader or supervisor who is responsible for training and managing new and mid-career employees.

[1398] "Feedback" refers to evaluations and advice provided by elders or superiors regarding a user's work performance and growth.

[1399] This invention combines an emotion engine with an educational AI system that provides work support and growth measurement for new and mid-career employees. This system is composed of a user, a terminal, a server, an emotion engine, and software for linking these elements.

[1400] A user accesses a web portal using a device (PC or smartphone) and inputs a question about work. For example, they input a prompt such as, "Please tell me how to proceed with this project." At this time, the emotion engine analyzes the user's tone of voice and facial expressions to generate emotion data. The emotion engine detects the user's emotions using, for example, voice analysis software or image analysis software.

[1401] The device sends question data and emotion data to the server. Specifically, it converts this data into JSON format and sends it to the server using an HTTP POST request. The server analyzes the received POST request and passes the question and emotion data to the question answering engine.

[1402] A question-answering engine analyzes questions using a natural language processing model (e.g., a generative AI model such as GPT-4) and generates the optimal answer based on emotional data. For example, if a user asks, "Please tell me how to proceed with the project," and the emotional data detects anxiety, the question-answering engine will generate an answer such as, "The project will proceed in the following five steps. If you run into any problems, don't worry, we're always here to help."

[1403] The generated answer is returned to the server, which converts it to JSON format and sends it to the device. The device parses the received JSON data and displays the answer on a web page, where the user can check the answer.

[1404] Furthermore, users upload their daily work details and documents they have created to the server via their devices. For example, they upload meeting materials they have created from a web portal. The server analyzes the uploaded materials and measures their growth by evaluating the work details, the quality of the materials, and the consistency of the format. The growth measurement engine generates a growth score based on this analytical data and stores it in a database.

[1405] Elders and superiors can check the user's growth score and emotional data through the dashboard. For example, the dashboard might show "User A: Score 85" along with the user's recent emotional trend. The elder or superior can use the dashboard to check the growth and emotional data and provide appropriate feedback on the web portal. For example, they might provide feedback such as, "User A's document creation skills are improving. He or she also seems to be feeling anxious recently, so he or she may need support."

[1406] This makes it possible for the system of the present invention to provide personalized support that takes into account the user's work assistance, growth measurement, and even emotional state. The entire system works in cooperation with each other to support the user's efficient work performance and growth.

[1407] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1408] Step 1:

[1409] Users access the web portal using a device (PC or smartphone) and input a question. At this time, the emotion engine analyzes the user's tone of voice and facial expressions to generate emotion data.

[1410] Input: Question (e.g., "Tell me how to proceed with this project"), voice tone, and facial expression data

[1411] Output: Question data, emotion data

[1412] Specific behavior:

[1413] A user accesses the web portal.

[1414] The user enters a question in the input field.

[1415] The emotion engine acquires emotion data using the device's camera and microphone.

[1416] Emotional data is processed using voice and image analysis algorithms.

[1417] Step 2:

[1418] The device sends question data and emotion data to the server, which converts the data into JSON format and sends it to the server via an HTTP POST request.

[1419] Input: Question data, emotion data

[1420] Output: HTTP POST request to the server

[1421] Specific behavior:

[1422] The terminal collects the input question data and emotion data.

[1423] Convert question data and sentiment data into JSON format.

[1424] Sends an HTTP POST request to the server.

[1425] Step 3:

[1426] The server receives the POST request and passes the question and emotion data to the question-answering engine, which then analyzes the question using a natural language processing model (such as GPT-4) and generates an optimal answer based on the emotion data.

[1427] Input: Question data, emotion data

[1428] Output: The generated answer

[1429] Specific behavior:

[1430] The server decodes the POST request and extracts the question data and sentiment data.

[1431] The question data and emotion data are passed to the question answering engine.

[1432] The question answering engine uses a natural language processing model to parse the question.

[1433] Generate optimal answers that take emotional data into account.

[1434] Step 4:

[1435] The server converts the generated answer into JSON format and sends it to the terminal as an HTTP response. The terminal receives the response data from the server and displays it on the user interface.

[1436] Input: Generated answer

[1437] Output: HTTP response to the device

[1438] Specific behavior:

[1439] The server converts the generated response into JSON format.

[1440] Sends JSON data to the terminal as an HTTP response.

[1441] The device analyzes the received JSON data and displays the answer on a web page.

[1442] Step 5:

[1443] Users upload their daily work and documents they have created to the server via their devices. The server analyzes the uploaded documents and evaluates the consistency of the work content, quality of the documents, and format to measure their growth.

[1444] Input: Business details and materials

[1445] Output: Upload data to the server

[1446] Specific behavior:

[1447] Users access the web portal using their terminal.

[1448] Use the upload function on the web portal to select and upload materials.

[1449] The server receives and stores the uploaded materials.

[1450] Step 6:

[1451] The server's growth measurement engine analyzes the uploaded materials and generates a growth score based on content, quality, and format consistency, which is stored in a database.

[1452] Input: Uploaded materials

[1453] Output: Growth score

[1454] Specific behavior:

[1455] A growth measurement engine analyzes the stored data.

[1456] Evaluate the consistency of content, quality, and format of materials.

[1457] A growth score is calculated based on the evaluation results and stored in a database.

[1458] Step 7:

[1459] Elders and superiors can view users' growth scores and sentiment data through a dashboard.

[1460] Input: Growth score, emotion data

[1461] Output: Dashboard display

[1462] Specific behavior:

[1463] Elders and superiors log in to the dashboard.

[1464] View growth scores and sentiment data at a glance on the dashboard.

[1465] Step 8:

[1466] Elders and superiors provide appropriate feedback to users based on the growth scores and emotional data they have confirmed, and the feedback is posted on the web portal.

[1467] Input: Growth score, emotion data

[1468] Output: Feedback

[1469] Specific behavior:

[1470] Elders and superiors review the dashboard data.

[1471] Generate appropriate feedback and enter it into the web portal.

[1472] Users can view feedback on the web portal.

[1473] (Application example 2)

[1474] 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."

[1475] In today's brick-and-mortar stores, new employees and mid-career recruits are expected to respond to customers and explain products immediately, but in reality, they often perform their work while harboring doubts and anxieties. This calls for a support system that enables staff to immediately resolve questions about their work and provide appropriate explanations and responses. Furthermore, while there is an expectation that staff growth can be objectively measured and that supervisors and training personnel can provide appropriate feedback based on that data, existing systems do not adequately address the emotional aspects of staff. The present invention aims to solve these issues and provide a system for improving staff performance.

[1476] 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.

[1477] In this invention, the server

[1478] A means for new employees or mid-career employees to input questions from a terminal;

[1479] means for transmitting the input question to a server;

[1480] means for the server to analyze the received question and generate an answer using a natural language processing model;

[1481] means for transmitting the generated answer to a terminal and displaying it;

[1482] A means for new employees or mid-career employees to upload their daily work details from their devices to the server,

[1483] A means for the server to analyze the uploaded business content and measure the growth degree;

[1484] A means for storing the measured growth level in a database and displaying it on a dashboard for a superior or a training officer;

[1485] means for analyzing the tone of voice and facial expressions of the staff member when the question is input, and an emotion engine for generating emotion data;

[1486] means for generating an optimal answer based on the emotion data;

[1487] This allows staff to instantly resolve work-related questions and receive appropriate support, including emotional support. Furthermore, superiors and training personnel can provide appropriate feedback based on staff growth and emotional data, making it possible to simultaneously improve staff performance and provide mental care.

[1488] A "terminal" is an electronic device that allows a user to enter questions and upload business details.

[1489] "Server" means a computer on a network that receives and analyzes entered questions or uploaded data and generates answers.

[1490] A "natural language processing model" is an artificial intelligence technology that analyzes the content of a user's question and generates an appropriate answer.

[1491] The "emotion engine" is a system that analyzes the user's tone of voice and facial expressions to generate emotion data.

[1492] "Growth level" is an index used to analyze the work content uploaded by the user and evaluate the quality and progress of the work.

[1493] The "dashboard" is a user interface that allows managers and training personnel to check staff growth and emotional data.

[1494] "Answer generation" is the process of generating optimal responses based on natural language processing models and sentiment data.

[1495] "Inputting a question" refers to the act of a user communicating an unclear point or question to the system via a terminal.

[1496] This invention is a system for supporting staff in brick-and-mortar stores, allowing new employees and mid-career recruits to instantly resolve questions about their work and measure their progress. This system is composed of a terminal, a server, an emotion engine, a natural language processing model, and software for linking these components.

[1497] Hardware and software used

[1498] A terminal is an electronic device (e.g., a smartphone or tablet) through which a user enters questions and uploads work content.

[1499] A server is a computer on a network that receives and analyzes questions and task data to generate answers and measure progress.

[1500] The emotion engine analyzes the tone of voice and facial expressions when a question is entered to generate emotion data.

[1501] A natural language processing model is an AI technology that analyzes questions and generates optimal answers. An example of this is GPT-4.

[1502] Specific operation of this system

[1503] Users access the system using a terminal and input business-related questions. Questions can be entered as text or as voice data. The emotion engine analyzes the voice tone and facial expressions entered and generates emotion data.

[1504] The device sends the question data and emotion data to the server in JSON format. The server then passes the received question data and emotion data to a natural language processing model to generate the optimal answer. For example, if a question about how to explain a new product is entered and the emotion data indicates anxiety, the system will generate an answer such as, "The explanation of the new product will be done in the following steps. Also, please rest assured that we are always available to provide support if you have any concerns."

[1505] The generated answer is converted to JSON format and sent to the device as an HTTP response. The device receives it and displays it visually to the user, for example, as text on a web page.

[1506] Users upload their daily work and documents they create to the server via their devices. This allows their work performance to be analyzed and their level of growth measured. Growth is evaluated based on the consistency of the content, quality, and format of the documents.

[1507] The generated growth data is stored in a database and displayed on a dashboard for supervisors and training personnel, allowing them to check staff growth and provide appropriate feedback.

[1508] Examples of concrete examples and prompts

[1509] For example, if a user asks, "Please tell me how to explain a new product," and the emotion engine analyzes the user's tone of voice and determines that the user is feeling anxious, the system will generate an answer like this: "The steps to explain a new product are as follows. Also, please rest assured that we are always here to support you if you feel anxious."

[1510] An example prompt is:

[1511] How do you explain a new product? I'm a little nervous.

[1512] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1513] Step 1:

[1514] A user accesses the system using a terminal and inputs a question about the business. For example, they input, "Please tell me how to explain a new product." Voice data may also be input at the same time.

[1515] Input: Question text, audio data

[1516] Output: Question text and sentiment data generation preparation

[1517] Step 2:

[1518] The device uses an emotion engine to analyze input voice data and facial expressions to generate emotion data, for example, analyzing the user's emotion, such as anxiety, from the tone of their voice.

[1519] Input: Audio data, question text

[1520] Data processing / calculation: Generating emotional data by analyzing voice tone and facial expressions

[1521] Output: Emotion data

[1522] Step 3:

[1523] The device sends the question text and generated emotion data to the server as JSON format data using a POST request.

[1524] Input: Question text, emotion data

[1525] Data processing / calculation: Converting questions and sentiment data into JSON format

[1526] Output: POST request sent to the server

[1527] Step 4:

[1528] The server parses the received POST request and passes the question text and sentiment data to the natural language processing model.

[1529] Input: Question text, emotion data

[1530] Data processing / calculation: Analysis of questions and sentiment data and input to natural language processing models

[1531] Output: Data passed to the natural language processing model

[1532] Step 5:

[1533] The server uses a natural language processing model to analyze the question and generate the optimal answer based on emotional data, such as "We'll explain the new product in the following steps. If you have any concerns, please rest assured that we're always here to help."

[1534] Input: Question text, sentiment data, natural language processing model

[1535] Data processing / calculation: Question analysis and answer generation

[1536] Output: The generated answer

[1537] Step 6:

[1538] The server converts the generated response into JSON format data and sends it to the terminal as an HTTP response.

[1539] Input: Generated Answer

[1540] Data processing / calculation: Converting answers to JSON format

[1541] Output: Sending HTTP response to the terminal

[1542] Step 7:

[1543] The terminal receives the response data from the server and visually displays it to the user. For example, a message such as "To explain the new product, follow the steps below..." is displayed on a web page.

[1544] Input: JSON format response data from the server

[1545] Data processing / calculation: Parsing JSON data and converting it to a display format

[1546] Output: The answer displayed in the user interface

[1547] Step 8:

[1548] Users upload their daily work details and documents they have created to the server via their terminals, for example, by uploading meeting materials they have created or customer service details.

[1549] Input: Business details data

[1550] Data processing / calculation: Formatting and uploading business data

[1551] Output: Business content data saved on the server

[1552] Step 9:

[1553] The server analyzes the uploaded work content and measures the degree of growth. For example, it evaluates the content and format of the material and generates a "Growth Score: 85."

[1554] Input: Business content data

[1555] Data processing / calculation: Analysis of work content and growth measurement

[1556] Output: Growth Score

[1557] Step 10:

[1558] The server stores the generated growth scores in a database and displays them on a dashboard for superiors and training personnel.

[1559] Input: Growth score, emotion data

[1560] Data processing / calculation: Saving growth scores and updating dashboard displays

[1561] Output: Growth scores and sentiment data displayed on a dashboard

[1562] Step 11:

[1563] Supervisors and training personnel can check the growth and emotional data on the dashboard and provide appropriate feedback, such as, "Your document creation skills are improving. You seem to be feeling anxious recently, so you may need some support."

[1564] Input: Growth score on the dashboard, sentiment data

[1565] Data processing / calculation: Feedback generation based on growth scores and emotion data

[1566] Output: Feedback provided to the user

[1567] 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.

[1568] 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.

[1569] 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.

[1570] 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.

[1571] FIG. 9 is a diagram illustrating 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 actions 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.

[1572] 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.

[1573] 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).

[1574] 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.

[1575] 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."

[1576] 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.

[1577] 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).

[1578] 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.

[1579] 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.

[1580] 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.

[1581] 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.

[1582] 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.

[1583] 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.

[1584] 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.

[1585] 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.

[1586] 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.

[1587] 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.

[1588] The following is further disclosed regarding the above embodiment.

[1589] (Claim 1)

[1590] A means for new employees or mid-career employees to input questions from a terminal;

[1591] means for transmitting the input question to a server;

[1592] means for the server to analyze the received question and generate an answer using a natural language processing model;

[1593] means for transmitting the generated answer to a terminal and displaying it;

[1594] A means for new employees or mid-career employees to upload their daily work details from their devices to the server,

[1595] A means for the server to analyze the uploaded business content and measure the growth degree;

[1596] A means for storing the measured growth rate in a database and displaying it on a dashboard for an elder or a superior;

[1597] A system including:

[1598] (Claim 2)

[1599] 2. The system according to claim 1, wherein when generating an answer to a question from a new employee or mid-career employee, the natural language processing model generates an optimal answer based on the content of the question.

[1600] (Claim 3)

[1601] The system of claim 1, which enables the elder or supervisor to check the growth of new or mid-career employees and provide appropriate feedback.

[1602] "Example 1"

[1603] (Claim 1)

[1604] a means for inputting a question from a terminal;

[1605] means for transmitting the entered question to a computer system;

[1606] means for analyzing a question received by the computer system and generating an answer using a generative model;

[1607] means for transmitting the generated answer to a terminal and displaying it;

[1608] a means for uploading work content from the terminal to a computer system;

[1609] A means for the computer system to analyze the uploaded work content and measure an evaluation score;

[1610] means for storing the measured evaluation score in a data store and displaying it on a display screen of an administrator;

[1611] A system including:

[1612] (Claim 2)

[1613] 2. The system according to claim 1, wherein when generating answers to questions posed by new employees or mid-career employees, the generative model generates optimal answers based on the content of the questions.

[1614] (Claim 3)

[1615] The system according to claim 1, wherein the manager is able to check the evaluation scores of new employees and mid-career employees and provide appropriate feedback.

[1616] "Application Example 1"

[1617] (Claim 1)

[1618] A means for new employees or mid-career employees to input questions from an information terminal;

[1619] means for transmitting the input question to a computer;

[1620] means for said computer to analyze the received question and generate an answer using a natural language analysis model;

[1621] means for transmitting the generated answer to an information terminal and displaying it;

[1622] A means for new employees or mid-career employees to upload their daily work details from an information terminal to a computer;

[1623] means for analyzing the work content uploaded by the computer and measuring the degree of growth;

[1624] means for storing the measured growth rate in a storage device and displaying it on a control panel of an instructor or administrator;

[1625] A means for technicians to input questions about robot operation and maintenance via vision wear;

[1626] means for the visual wear to recognize a question by voice and transmit it to a computer;

[1627] a means for generating an answer based on the received question by the computer using a natural language analysis model and displaying the answer on the visual display;

[1628] means for the technician to upload work reports or maintenance records via vision wear;

[1629] A means for analyzing the uploaded data by the computer and measuring the degree of growth of the engineer;

[1630] means for providing feedback to the engineer based on said progress;

[1631] A system including:

[1632] (Claim 2)

[1633] 2. The system according to claim 1, wherein when generating an answer to a question posed by a new employee or mid-career employee, the natural language analysis model generates an optimal answer based on the content of the question.

[1634] (Claim 3)

[1635] 2. The system according to claim 1, wherein the instructor or manager can check the progress of new or mid-career employees and engineers and provide appropriate feedback.

[1636] "Example 2: Combining Emotion Engines"

[1637] (Claim 1)

[1638] A means for new employees or mid-career employees to input questions from a terminal;

[1639] means for transmitting the input question to a server;

[1640] means for the server to analyze the received question and generate an answer using a natural language processing model;

[1641] means for transmitting the generated answer to a terminal and displaying it;

[1642] A means for new employees or mid-career employees to upload their daily work details from their devices to the server,

[1643] A means for the server to analyze the uploaded business content and measure the growth degree;

[1644] A means for generating and analyzing emotion data of new employees or mid-career employees using an emotion engine;

[1645] A means for storing the measured growth level and emotion data in a database and displaying it on a dashboard for an elder or a superior;

[1646] A system including:

[1647] (Claim 2)

[1648] 2. The system according to claim 1, wherein when generating an answer to a question from a new employee or mid-career employee, the natural language processing model generates an optimal answer based on the question content and emotion data.

[1649] (Claim 3)

[1650] The system of claim 1, wherein the elder or supervisor can check the growth and emotional data of new or mid-career employees and provide appropriate feedback.

[1651] "Application example 2 when combining emotion engines"

[1652] (Claim 1)

[1653] A means for new employees or mid-career employees to input questions from a terminal;

[1654] means for transmitting the input question to a server;

[1655] means for the server to analyze the received question and generate an answer using a natural language processing model;

[1656] means for transmitting the generated answer to a terminal and displaying it;

[1657] A means for new employees or mid-career employees to upload their daily work details from their devices to the server,

[1658] A means for the server to analyze the uploaded business content and measure the growth degree;

[1659] A means for storing the measured growth level in a database and displaying it on a dashboard for a superior or a training officer;

[1660] means for analyzing the tone of voice and facial expressions of the staff member when the question is input, and an emotion engine for generating emotion data;

[1661] means for generating an optimal answer based on the emotion data;

[1662] A system including:

[1663] (Claim 2)

[1664] 2. The system according to claim 1, wherein when generating an answer to a question from a new employee or mid-career employee, the natural language processing model generates an optimal answer based on the question content and emotion data.

[1665] (Claim 3)

[1666] 2. The system according to claim 1, wherein the supervisor or training officer can check the growth and emotional data of the new employee or mid-career employee and provide appropriate feedback. [Explanation of symbols]

[1667] 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 for new employees or mid-career employees to input questions from a terminal; means for transmitting the input question to a server; means for the server to analyze the received question and generate an answer using a natural language processing model; means for transmitting the generated answer to a terminal and displaying it; A means for new or mid-career employees to upload their daily work details from their devices to the server, A means for analyzing the uploaded business contents by the server and measuring the degree of growth; A means for storing the measured growth rate in a database and displaying it on a dashboard for an elder or a superior; A system including:

2. The system according to claim 1 , wherein when generating an answer to a question from a new employee or mid-career employee, the natural language processing model generates an optimal answer based on the content of the question.

3. The system according to claim 1, wherein the elder or supervisor can check the growth of new employees or mid-career employees and provide appropriate feedback.

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