System

Abstract

A system is provided.SOLUTION: A system comprising means for initial setting of a generative AI model for a new employee to learn business skills and company-specific rules, means for inputting and registering a profile information of the new employee, means for sending a generated email or presentation material to the generative AI model and obtaining feedback on improvement, means for sending a question to the generative AI model and obtaining an answer, and means for analyzing an activity log of the new employee and providing overall feedback.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] Traditional mentoring systems have several issues, such as compatibility issues between new employees and mentors, mentors being too busy to look after new employees, and age differences when there are few young employees in the workplace. For these reasons, there is a need for effective methods to ensure that new employees can smoothly commit to the workplace and prevent early resignation. [Means for solving the problem]

[0005] The present invention is a system that includes a means for initializing a generative AI model for new employees to learn business skills and company-specific rules, a means for inputting and registering new employee profile information, a means for sending created emails and presentation materials to the generative AI model and receiving feedback on areas for improvement, a means for sending questions to the generative AI model and receiving answers, and a means for analyzing the new employee's activity log and providing overall feedback. This allows new employees to easily consult the generative AI model and receive individual feedback. Furthermore, by having the generative AI model learn data on business skills and presentation skills, as well as the company's unique mindset and rules, new employees can share the company's values ​​and quickly adapt to the workplace.

[0006] "Generative AI models" are algorithms and systems that use machine learning and artificial intelligence techniques to generate appropriate answers and feedback for specific questions and challenges.

[0007] "Business skills" refers to a variety of abilities and techniques needed in the workplace, such as effective communication, presentation, leadership, and problem-solving skills.

[0008] "Rules" refer to rules, guidelines, and policies that must be followed within a company or organization.

[0009] "Profile information" is data that includes basic information about a new employee, such as name, job title, interests, skill set, and desired support.

[0010] An "email draft" is a draft of an email before it is officially sent, and refers to the state before any editing or corrections have been made.

[0011] "Presentation materials" are materials that include slides, documents, and design elements used in a presentation.

[0012] "Feedback" refers to improvements, advice, and guidance provided by a generative AI model.

[0013] "Questions" refer to inquiries or confirmations that new employees make to the generative AI model.

[0014] An "activity log" is data that records a new employee's actions and interactions on the system.

[0015] "Providing feedback" refers to the process of informing new employees of the feedback generated by the generative AI model. [Brief explanation of the drawings]

[0016] [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

[0017] 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.

[0018] First, the terms used in the following description will be explained.

[0019] 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).

[0020] 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.

[0021] 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.

[0022] 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.

[0023] 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."

[0024] [First embodiment]

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

[0026] 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.

[0027] 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).

[0028] 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.

[0029] 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.

[0030] The output device 40 includes a display 40A and a speaker 40B, and presents data to the user 20 by outputting the data in a form 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.

[0031] 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.

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

[0033] 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.

[0034] 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.

[0035] 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.

[0036] 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."

[0037] This invention relates to a generative AI mentoring system that complements traditional mentoring systems to help new employees commit smoothly to the workplace and prevent early turnover. This system provides an environment where new employees can easily seek advice and receive individual feedback.

[0038] Overall overview

[0039] This generative AI mentor system performs the following processes using a server, terminals, and generative AI models.

[0040] 1. Initial Setup

[0041] 2. New employee registration

[0042] 3. Correction of emails and presentation materials

[0043] 4. Communication support

[0044] 5. Providing Feedback

[0045] Program processing explanation

[0046] The program processing at each step will be explained below in order.

[0047] 1. Initial Setup

[0048] The server first obtains book data on business skills and presentation skills and trains the generative AI model.

[0049] The server also acquires data on the company's unique mindset and rules, which it then uses to train the generative AI model.

[0050] 2. New employee registration

[0051] The terminal displays a login screen for new employees, where the user (new employee) enters profile information such as name, job title, skill set, desired support content, etc.

[0052] The terminal transmits the entered profile information to the server.

[0053] The server analyzes the new employee's profile and assigns the most appropriate generative AI model.

[0054] 3. Correction of emails and presentation materials

[0055] The terminal displays an editor screen for the user, and the user creates a draft of a business email or presentation materials.

[0056] The terminal transmits the completed draft or document to the server.

[0057] The server sends the submitted materials to a generative AI model, which analyzes grammar and expressions and provides feedback on areas for improvement.

[0058] The server receives the feedback and provides it to the user.

[0059] The terminal displays the feedback to the user.

[0060] 4. Communication support

[0061] The terminal displays a consultation form for the user, and the user inputs a question.

[0062] The terminal sends a query to the server.

[0063] The server sends the question to a generative AI model, which generates an answer.

[0064] The server receives the generative AI model's answer and provides it to the user.

[0065] The terminal displays the provided answers to the user.

[0066] 5. Providing Feedback

[0067] The server records and analyzes a log of the user's activities on the system.

[0068] The server periodically retrieves the analysis results from the generated AI model and generates overall feedback.

[0069] The server provides feedback to the user.

[0070] The terminal displays the provided feedback to the user.

[0071] Specific examples

[0072] Example 1: Email editing

[0073] 1. A user types a draft of a business email into a terminal.

[0074] 2. The device sends the email draft to the server.

[0075] 3. The server sends the draft to the generative AI model for editing.

[0076] 4. The generative AI model suggests improvements to grammar, honorifics, and expressions.

[0077] 5. The server receives the feedback and sends it to the device.

[0078] 6. The device displays feedback to the user.

[0079] Example 2: Presentation correction

[0080] 1. The user uploads the presentation materials to the device.

[0081] 2. The device sends the materials to the server.

[0082] 3. The server sends the document to the generative AI model and requests corrections.

[0083] 4. The generative AI model suggests improvements to the content, design, and logical structure of your slides.

[0084] 5. The server receives the feedback and sends it to the device.

[0085] 6. The device displays feedback to the user.

[0086] The above is the details of the mode for carrying out the invention. This enables new employees to use the generative AI mentor system to smoothly adapt to the workplace and improve their skills.

[0087] The processing flow will be explained below.

[0088] 1. Initial Setup

[0089] Step 1:

[0090] The server acquires book data on business skills and presentation skills.

[0091] Step 2:

[0092] The data acquired by the server is trained into a generative AI model.

[0093] Step 3:

[0094] The server collects data on the company's unique mindset and rules.

[0095] Step 4:

[0096] The server trains the generative AI model on data about the company's mindset and rules.

[0097] 2. New employee registration

[0098] Step 1:

[0099] The terminal displays the login screen for new employees.

[0100] Step 2:

[0101] Users enter their profile information on the login screen and register the skills they need and the support they would like.

[0102] Step 3:

[0103] The device transmits the entered profile information to the server.

[0104] Step 4:

[0105] The server analyzes the new employee's profile and assigns an appropriate generative AI model.

[0106] 3. Correction of emails and presentation materials

[0107] Step 1:

[0108] The terminal displays the editor screen for the user.

[0109] Step 2:

[0110] The user creates a draft of a business email or a presentation in the editor.

[0111] Step 3:

[0112] The terminal transmits the drafts and documents created to the server.

[0113] Step 4:

[0114] The server sends the data to the generative AI model and requests it to analyze it.

[0115] Step 5:

[0116] The generative AI model analyzes the grammar and expressions of the document and suggests areas for improvement.

[0117] Step 6:

[0118] The server receives feedback from the generative AI model and sends it to the device.

[0119] Step 7:

[0120] The device displays the feedback to the user.

[0121] 4. Communication support

[0122] Step 1:

[0123] The terminal displays a consultation form for the user.

[0124] Step 2:

[0125] The user enters a question about workplace rules and business etiquette into a consultation form.

[0126] Step 3:

[0127] The terminal sends the entered question to the server.

[0128] Step 4:

[0129] The server sends the question to the generative AI model and requests an answer.

[0130] Step 5:

[0131] A generative AI model creates answers to questions.

[0132] Step 6:

[0133] The server receives the answer from the generated AI model and sends it to the device.

[0134] Step 7:

[0135] The terminal displays the provided answers to the user.

[0136] 5. Providing Feedback

[0137] Step 1:

[0138] The server logs the user's activity on the system.

[0139] Step 2:

[0140] The server analyzes the recorded activity logs and periodically generates feedback from the generative AI model.

[0141] Step 3:

[0142] The server sends the generated feedback to the user.

[0143] Step 4:

[0144] The terminal displays the notified feedback to the user.

[0145] These are the specific steps in the programming process of the generative AI mentor system, which allows new employees to efficiently improve their skills and adapt to the workplace.

[0146] Example 1

[0147] 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."

[0148] An appropriate support environment is necessary to enable new employees to smoothly adapt to the workplace and prevent early attrition. However, traditional mentoring systems have problems with providing individual feedback and making it difficult to carry out effective follow-up. Furthermore, when many new employees join the company at once, the burden on mentors increases, and as a result, there is a risk that high-quality support will not be provided. To solve these problems, a system that utilizes generative AI models to provide appropriate support and feedback to new employees is needed.

[0149] 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.

[0150] In this invention, the server includes means for initializing a generative AI model that has learned business skills and company-specific rules, means for inputting and registering new employee profile information, means for sending created documents to the generative AI model and receiving feedback on areas for improvement, means for sending questions to the generative AI model and receiving answers, means for analyzing the new employee's activity log and providing overall feedback, means for sending information input at the terminal to the server, and means for receiving analysis results from the generative AI model at the server and sending them to the terminal. This allows new employees to not only receive individual feedback, but also quickly receive specific advice on how to adapt to the workplace in an appropriate support environment.

[0151] "Business skills" refers to a set of skills required in the workplace, such as business etiquette, communication skills, problem-solving skills, and presentation skills.

[0152] "Company-specific rules" refer to the unique mindset, code of conduct, ethical guidelines, internal rules, etc. established by a particular company.

[0153] A "generative AI model" is a system that uses artificial intelligence technology to learn and generate, and refers to a model that uses natural language processing technology to generate feedback and answers for users.

[0154] "Initialization means" refers to the process of providing the necessary training data for a generative AI model and configuring the model to suit the specific needs of the company.

[0155] "Profile Information" refers to personal information entered by a user, such as name, job title, skill set, and desired support type.

[0156] "Document" refers to text data such as a business email draft or presentation material created by a user.

[0157] "Activity log" refers to data that records a user's actions and interactions when using a system.

[0158] "Feedback" refers to specific advice provided by the generative AI model on areas for improvement such as grammar, expression, pay, design, and logical structure.

[0159] "Means for obtaining an answer" refers to the process by which a user sends a question to a generative AI model and obtains an answer to that question from the generative AI model.

[0160] "Analysis results" refers to the results of the analysis performed by the generative AI model based on the user's input data.

[0161] This invention relates to a generative AI mentoring system that complements traditional mentoring systems to help new employees smoothly adapt to the workplace and prevent early turnover. The system provides an environment where new employees can easily seek advice and receive individual feedback.

[0162] Hardware and software used

[0163] This system operates using a server, a terminal, and a generative AI model. The specific hardware and software used are as follows:

[0164] Server: Manages and processes data to learn business skills and company-specific rules.

[0165] Software used: "Learning-Resource-Server", "Profile-Analyzer", "Grammar-Checker AI", "Pitch-Perfect AI", "Question-Responder AI", "Activity-Analyzer", "Activity-Logger", etc.

[0166] Terminal: Provides an interface for users (new employees) to enter information and receive feedback and responses.

[0167] Software used: "Employee-Portal", "Business-Editor", "Consultation-Form", etc.

[0168] Program processing explanation

[0169] The program for this system consists of multiple processing steps that mainly involve the server, terminal, and generative AI model working together. Each processing step is explained below in natural language.

[0170] Initial Setup

[0171] The server collects book data on business skills and presentation skills and trains the generative AI model via the "Learning-Resource-Server."

[0172] The server also collects document data about the company's unique mindset and rules, and similarly trains the generative AI model. This process allows the generative AI model to respond to the specific needs of the company.

[0173] New employee registration

[0174] When a user accesses the system for the first time, the terminal displays a login screen for new employees. This system uses the "Employee-Portal."

[0175] The user (new employee) enters profile information (name, position, skill set, desired support content, etc.) on the login screen.

[0176] The terminal transmits the profile information entered by the user to the server.

[0177] The server uses the "Profile-Analyzer" to analyze the profile information of new employees and assign the optimal generative AI model. At this time, the parameters of the generative AI model are adjusted according to the employee's skill set and desired support content.

[0178] Correction of emails and presentation materials

[0179] The terminal displays an editor screen where users can create business emails and presentation materials. This editor uses "Business-Editor."

[0180] The user creates a draft of a business email or a presentation document on the editor screen.

[0181] The terminal transmits the completed email draft or presentation materials to the server.

[0182] The server sends the received materials to generative AI models, such as "Grammar-Checker AI" and "Pitch-Perfect AI," and requests them to analyze grammar, expression, design, and logical structure.

[0183] The generative AI model provides feedback on grammatical errors, appropriate phrasing, and areas for improvement in the design and logical structure of the presentation materials.

[0184] The server receives the generated feedback and transmits it to the terminal.

[0185] The device displays feedback to the user, allowing them to specifically understand areas for improvement and make corrections.

[0186] Communication Support

[0187] The terminal displays a consultation form where the user can enter their questions. This form uses the "Consultation-Form".

[0188] The user inputs a question into the consultation form. Specifically, the user inputs a question such as "Please tell me how to approach a new project."

[0189] The terminal transmits the entered question to the server.

[0190] The server uses "Question-Responder AI" to have a generative AI model generate answers to questions.

[0191] Generative AI models generate specific advice and answers to questions.

[0192] The server receives the generated response and transmits it to the terminal.

[0193] The terminal displays the provided answers to the user, who can receive advice in real time.

[0194] Providing Feedback

[0195] The server records the user's actions and interactions on the system (for example, what questions they asked, what feedback they received), and uses the "Activity-Logger" as the recording system.

[0196] The server periodically analyzes the recorded activity log using "Activity-Analyzer."

[0197] The server sends the analysis results to a generative AI model to generate overall feedback.

[0198] The generative AI model provides feedback on the user's progress and areas for improvement, such as "Your communication in a recent project has improved" or "Your presentation slides are now more logically structured."

[0199] The server provides the generated feedback to the user.

[0200] The device displays rich feedback to users, helping them track their progress, and also displays a feedback dashboard.

[0201] Examples of concrete examples and prompts

[0202] Example 1: Email editing

[0203] 1. The user inputs a draft of a business email into the terminal, for example, using "Business-Editor."

[0204] 2. The device sends the email draft to the server.

[0205] 3. The server sends the draft to a generative AI model for correction, specifically using "Grammar-Checker AI."

[0206] 4. The generative AI model suggests improvements to grammar, honorifics, and expressions.

[0207] 5. The server receives the feedback and sends it to the device.

[0208] 6. The device displays feedback to the user.

[0209] Example 2: Presentation correction

[0210] 1. The user uploads the presentation materials to the device, for example, using "Business-Editor."

[0211] 2. The device sends the materials to the server.

[0212] 3. The server sends the document to a generative AI model and requests corrections. Specifically, it uses "Pitch-Perfect AI."

[0213] 4. The generative AI model suggests improvements to the content, design, and logical structure of your slides.

[0214] 5. The server receives the feedback and sends it to the device.

[0215] 6. The device displays feedback to the user.

[0216] Examples of prompts include:

[0217] How do you approach a new project?

[0218] "Please check the grammar of this business email."

[0219] "How can I improve the design of my presentation materials?"

[0220] This concludes the detailed description of the embodiments of the invention, which will enable new employees to make the most of the generative AI mentor system, allowing them to smoothly adapt to the workplace and improve their skills.

[0221] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0222] Step 1: Initial Setup

[0223] The server collects book data on business skills and presentation skills.

[0224] As input, business books and materials are collected from external databases and the Internet.

[0225] As part of the data processing, the collected data is analyzed via the "Learning-Resource-Server."

[0226] The output provides training data for the generative AI model.

[0227] The server also collects document data about the company's unique mindset and rules.

[0228] As input, company policy documents and manuals are collected.

[0229] As part of the data processing, these documents are tokenized and text analyzed.

[0230] The output provides training data for the generative AI model.

[0231] Step 2: Register new employees

[0232] The terminal displays a login screen for new employees.

[0233] As input, a user attempts to log in.

[0234] As a result, the terminal launches the "Employee-Portal" and displays the login screen.

[0235] As an output, a login form is provided to the user.

[0236] The user (new employee) enters profile information.

[0237] As input, the user enters their name, job title, skill set, and desired support type.

[0238] In operation, the user enters the required information into each information field.

[0239] The terminal transmits the entered profile information to the server.

[0240] As input, the terminal receives the user's profile data.

[0241] In operation, the terminal issues a request to send these data to the server.

[0242] As an output, the profile data is sent to a server.

[0243] The server analyzes the profile information of the new employee.

[0244] The profile data received by the server as input.

[0245] For data calculation, the server uses "Profile-Analyzer" to analyze the data.

[0246] As an output, the new employee is assigned the best suited generative AI model.

[0247] Step 3: Editing emails and presentation materials

[0248] The terminal displays an editor screen for the user.

[0249] As input, the user accesses the editor screen.

[0250] As an operation, the editor "Business-Editor" is launched and displayed to the user.

[0251] As an output, an editor screen is provided.

[0252] Users create drafts of business emails and presentation materials on the editor screen.

[0253] As input, the user enters text and presentation slides into the editor screen.

[0254] In action, the user creates a draft or slide.

[0255] The terminal transmits the completed document to the server.

[0256] As input, the editor sends the completed draft or material.

[0257] In operation, the terminal issues a request to transmit material data to the server.

[0258] As an output, the document data is sent to the server.

[0259] The server sends the submitted materials to the generative AI model and requests it to analyze them.

[0260] Source data received by the server as input.

[0261] To calculate the data, the server requests "Grammar-Checker AI" and "Pitch-Perfect AI" to analyze the materials.

[0262] As an output, the analysis results are produced.

[0263] The generative AI model provides feedback on grammatical errors and areas for improvement in design and logical structure.

[0264] As input, material data.

[0265] As a data calculation, the generative AI model analyzes the material and generates specific feedback.

[0266] As an output, feedback data is generated.

[0267] The server sends the feedback data to the terminal for providing to the user.

[0268] As input, feedback data from the generative AI model.

[0269] In operation, the server transmits feedback data to the terminal.

[0270] As an output, feedback is sent to the terminal.

[0271] The terminal displays feedback to the user.

[0272] As input, the received feedback data.

[0273] As an action, feedback is displayed to the user.

[0274] As an output, feedback content is provided to the user.

[0275] Step 4: Communication support

[0276] The terminal displays a consultation form for the user.

[0277] As input, the user accesses a consultation form.

[0278] As a result, the "Consultation-Form" is displayed.

[0279] As output, a consultation form is provided.

[0280] The user enters their question into the consultation form.

[0281] As input, the user enters the consultation content into the form.

[0282] In action, the user enters a specific question.

[0283] The terminal transmits the entered question to the server.

[0284] As input, the question data entered by the user.

[0285] In operation, the query data is sent to the server.

[0286] As an output, the query data is sent to the server.

[0287] The server sends the question to a generative AI model, which generates an answer.

[0288] The query data received by the server as input.

[0289] As a data calculation, the "Question-Responder AI" analyzes the question and generates an answer.

[0290] As an output, response data is generated.

[0291] The server sends the generated response to the terminal.

[0292] As input, the generated response data.

[0293] As an operation, the response data is sent to the terminal.

[0294] As an output, the answer data is provided to the terminal.

[0295] The terminal displays the answer to the user.

[0296] As input, the received response data.

[0297] As an action, the answer is displayed to the user.

[0298] As an output, the answer content is presented to the user.

[0299] Step 5: Provide feedback

[0300] The server records the user's actions and interactions on the system.

[0301] As input, user activity log data.

[0302] In operation, "Activity-Logger" records user actions.

[0303] As an output, the activity log data is saved.

[0304] The server periodically analyzes the recorded activity log.

[0305] As input, the saved activity log data.

[0306] For data calculation, the server performs analysis using "Activity-Analyzer."

[0307] As an output, analysis result data is generated.

[0308] The server sends the analysis results to a generative AI model to generate overall feedback.

[0309] As input, the analysis result data.

[0310] As a data calculation, the generative AI model generates feedback based on the analysis results.

[0311] As an output, feedback data is generated.

[0312] The server transmits the generated feedback to the terminal.

[0313] As input, the generated feedback data.

[0314] As an operation, feedback data is transmitted to the terminal.

[0315] As an output, feedback data is provided to the terminal.

[0316] The terminal displays feedback to the user.

[0317] As input, the received feedback data.

[0318] As an action, feedback is displayed to the user.

[0319] As an output, feedback content is presented to the user.

[0320] (Application example 1)

[0321] 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."

[0322] In order for new employees to adapt quickly to the factory floor and perform their work smoothly, they need immediate support and appropriate feedback. However, traditional mentoring systems alone may not be able to provide sufficient support for each new employee, making it difficult for them to efficiently acquire skills and perform work safely. Therefore, a system is needed that can properly teach the factory's unique procedures and safety guidelines and quickly respond to any questions new employees may have.

[0323] 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.

[0324] In this invention, the server includes a means for learning the factory-specific procedures and safety guidelines, a means for providing real-time support for new employees to carry out on-site work procedures and safety checks, and a means for checking reports and memos created by new employees in real time and providing feedback, thereby enabling new employees to quickly adapt to the factory floor and perform their work safely and efficiently.

[0325] A "generative AI model" is an artificial intelligence system that helps new employees learn the skills and knowledge they need to perform their jobs and generates feedback and answers.

[0326] "Initial setup" refers to the preparatory work of teaching the generative AI model the business skills that new employees should learn, the company's unique rules, and factory procedures and safety guidelines.

[0327] "Profile Information" refers to personal data such as a new hire's name, job title, skill set, and desired support type.

[0328] "Feedback on areas for improvement" involves the generative AI model analyzing emails and presentation materials created by new employees and providing feedback and suggestions regarding grammar, structure, expression, etc.

[0329] "Means of sending questions to a generative AI model and obtaining answers" refers to a system in which new employees send questions that arise during work to a generative AI model and obtain solutions or explanations.

[0330] An "activity log" is a record of all operations and actions performed by new employees on the system.

[0331] The "means of providing feedback" refers to a system that analyzes new employees' profile information and activity logs and regularly provides advice for growth and improvement.

[0332] "Reports and memos to be prepared" refers to documents that new employees use to record the status and results of their work.

[0333] "Means for providing real-time support" refers to a system that instantly displays and provides necessary information and instructions to new employees when they perform their work on-site.

[0334] A "procedure manual" is a document that describes the specific steps and methods for properly carrying out work.

[0335] "Safety guidelines" are documents that list safety measures and precautions to take at work sites such as factories.

[0336] This invention provides a generative AI mentoring system that helps new employees quickly adapt to the workplace and perform their work safely and efficiently. The system consists of the following elements:

[0337] 1. Initial Setup

[0338] The server acquires the factory's specific procedures and safety guidelines and trains the generative AI model. It also trains the model on data related to the company's unique mindset, rules, and business skills. In this way, the generative AI model acquires the knowledge necessary for on-site work.

[0339] 2. New employee registration

[0340] New employees use their devices to access a login screen and enter their profile information, such as their name, job title, skill set, and desired support content. This information is sent to a server, which analyzes the new employee's profile and assigns the optimal generative AI model.

[0341] 3. Business support and real-time support

[0342] As new employees work on-site, the devices display real-time information on factory procedures and safety checks. If a new employee has a question, they can type it into the device, which sends it to the generative AI model via the server. The answer is then provided to the device via the server, where the new employee can immediately check it.

[0343] 4. Correction of emails and presentation materials

[0344] Reports and memos written by new employees are sent to a server via their device. The server then feeds the documents into a generative AI model and receives feedback on improvements to grammar, expression, and structure. The feedback is displayed on the device for the new employee to review.

[0345] 5. Providing Feedback

[0346] The server records and analyzes the activity logs of new employees. Periodically, a generative AI model evaluates the log data and generates feedback for growth and improvement, allowing new employees to continuously improve their skills.

[0347] Hardware and software used

[0348] Hardware: Factory robots (such as Universal Robots' UR series), data servers, and terminals (PCs or tablets)

[0349] Software: Generative AI models (e.g., GPT-4 (registered trademark)), databases (e.g., MongoDB)

[0350] Specific examples

[0351] Example 1: New employee asking a question

[0352] Question: "I don't know what to do today. What exactly should I do?"

[0353] process:

[0354] 1. The new employee types a question into the terminal.

[0355] 2. The server sends the question to the generative AI model, which generates an answer.

[0356] 3. The server sends the generated answer to the terminal, where the new employee can view it.

[0357] 4. This process ensures that new employees are immediately aware of the steps they need to take.

[0358] Example 2: New employee submitting a report

[0359] Report: "Please correct today's work report. Please let me know if there are any mistakes."

[0360] process:

[0361] 1. The new employee uploads the report to the terminal.

[0362] 2. The server sends the report to the generative AI model for correction.

[0363] 3. The generative AI model generates feedback suggesting improvements to grammar and expression.

[0364] 4. The server sends the feedback to the terminal, where the new employee can check it.

[0365] 5. This feedback will enable the new employee to improve the quality of their reports.

[0366] This completes the description of the preferred embodiment of the present invention. This system allows new employees to quickly adapt to the factory floor and perform their jobs safely and efficiently.

[0367] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0368] Step 1: Initial Setup

[0369] The server acquires data related to the factory's specific procedures and safety guidelines, the company's unique mindset and rules, and business skills. This data is trained into a generative AI model. The input is data related to the factory's procedures, safety guidelines, mindset, rules, and business skills. The output is the knowledge learned by the generative AI model. The server takes in this data and performs data analysis and model training.

[0370] Step 2: Register new employees

[0371] The user (new employee) uses a terminal to access the login screen and enters profile information such as name, job title, skill set, and desired support content. The input is the new employee's profile information. The terminal sends this to the server. The server analyzes the profile information and assigns the optimal generative AI model. The output is the assigned generative AI model.

[0372] Step 3: Operational and real-time support

[0373] When new employees work on-site, the terminal displays information on factory work procedures and safety checks in real time. The input is data on work procedures and safety checks. The output is the information displayed on the terminal. The terminal receives various data necessary for on-site work from the server and presents it to the user in a timely manner.

[0374] Step 4: Submit your question and get an answer

[0375] When a user (new employee) has a question, they input it into the terminal. The input is the user's question. The terminal sends the question to the server. The server sends the question to the generative AI model and generates an answer. The output is the generated answer. The server sends the answer received from the generative AI model to the terminal, and the user confirms it.

[0376] Step 5: Editing emails and presentation materials

[0377] The user (new employee) uploads a report or memo they created to the device. The input is the report or memo. The device sends it to the server. The server sends the report or memo to a generative AI model and receives feedback on improvements to grammar, expression, and structure. The output is feedback. The server sends the feedback to the device so that the user can review it.

[0378] Step 6: Provide feedback

[0379] The server records new employee activity logs and periodically analyzes them. The input is the activity log. The server evaluates the log data using a generative AI model and generates feedback for growth and improvement. The output is feedback for growth and improvement. The server sends the generated feedback to a terminal, where the new employee can check it.

[0380] The above is the specific flow of operations at each processing step of this system, which enables new employees to quickly adapt to the factory floor and perform their work safely and efficiently.

[0381] 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.

[0382] This invention relates to a generative AI mentoring system that complements traditional mentoring systems to help new employees smoothly commit to the workplace. This system includes an emotion engine that recognizes the user's emotions, and can analyze the user's emotional state and reflect it in the feedback of the generative AI model.

[0383] Overall overview

[0384] This generative AI mentor system performs the following processes using a server, terminals, a generative AI model, and an emotion engine.

[0385] 1. Initial Setup

[0386] 2. New employee registration

[0387] 3. Correction of emails and presentation materials

[0388] 4. Communication support

[0389] 5. Providing Feedback

[0390] 6. Sentiment Analysis and Adaptive Feedback

[0391] Program processing explanation

[0392] The program processing at each step will be explained below in order.

[0393] 1. Initial Setup

[0394] The server retrieves book data on business skills and presentation skills and trains the generative AI model.

[0395] The server collects data on the company's unique mindset and rules and trains the generative AI model.

[0396] 2. New employee registration

[0397] The terminal displays a login screen for new employees, where the user enters their profile information and registers the skills they need and the support they would like to receive.

[0398] The device transmits the entered profile information to the server.

[0399] The server analyzes the new employee's profile and assigns an appropriate generative AI model.

[0400] 3. Correction of emails and presentation materials

[0401] The terminal displays an editor screen for the user, and the user creates a draft of a business email or presentation materials.

[0402] The terminal transmits the created materials to the server.

[0403] The server sends the data to the generative AI model and requests it to analyze it.

[0404] The generative AI model analyzes the grammar and expressions of the document and suggests areas for improvement.

[0405] The server receives feedback from the generative AI model and sends it to the device, where it is displayed to the user.

[0406] 4. Communication support

[0407] The terminal displays a consultation form for the user, and the user inputs questions about workplace rules and business etiquette.

[0408] The terminal sends a question to the server.

[0409] The server sends the question to the generative AI model and requests an answer.

[0410] A generative AI model creates answers to questions.

[0411] The server receives the answer from the generated AI model, sends it to the device, and the device displays it to the user.

[0412] 5. Providing Feedback

[0413] The server logs the user's activity on the system.

[0414] The server analyzes the recorded activity logs and periodically generates feedback from the generative AI model.

[0415] The server sends the generated feedback to the terminal, which displays it to the user.

[0416] 6. Sentiment Analysis and Adaptive Feedback

[0417] The device sends the user's input text and voice data to the emotion engine.

[0418] An emotion engine analyzes the user's emotional state.

[0419] The emotion engine sends the analysis results to the generative AI model, which then uses them as feedback.

[0420] The server receives the analysis results from the emotion engine and adaptively changes the feedback content of the generative AI model.

[0421] The server sends adaptive feedback to the terminal, which displays it to the user.

[0422] Specific examples

[0423] Example 1: Email editing and sentiment analysis

[0424] 1. A user types a draft of a business email into a terminal.

[0425] 2. The device sends the email draft to the server, which then sends it to the generative AI model for editing.

[0426] 3. The generative AI model suggests improvements to grammar, honorifics, and expressions.

[0427] 4. The device sends the input text to the emotion engine, which analyzes the emotional state.

[0428] 5. The server sends the emotion analysis results to the generative AI model and adaptively changes the feedback content.

[0429] 6. The server sends adaptive feedback to the terminal, which displays it to the user.

[0430] Example 2: Presentation correction and sentiment analysis

[0431] 1. The user uploads the presentation materials to the device.

[0432] 2. The device sends the document to the server, which then sends it to the generative AI model for correction.

[0433] 3. The generative AI model suggests improvements to the content, design, and logical structure of your slides.

[0434] 4. The device sends the input text or voice to the emotion engine, which analyzes the emotional state.

[0435] 5. The server sends the emotion analysis results to the generative AI model and adaptively changes the feedback content.

[0436] 6. The server sends adaptive feedback to the terminal, which displays it to the user.

[0437] The above is a detailed description of the embodiment of the invention of a generative AI mentor system that combines an emotion engine. This enables new employees to receive appropriate support based on their emotions, improve their skills more efficiently, and adapt to the workplace.

[0438] The processing flow will be explained below.

[0439] 1. Initial Setup

[0440] Step 1:

[0441] The server acquires book data on business skills and presentation skills.

[0442] Step 2:

[0443] The data acquired by the server is trained into a generative AI model.

[0444] Step 3:

[0445] The server collects data on the company's unique mindset and rules.

[0446] Step 4:

[0447] The server trains the generative AI model on data about the company's mindset and rules.

[0448] 2. New employee registration

[0449] Step 1:

[0450] The terminal displays the login screen for new employees.

[0451] Step 2:

[0452] Users enter their profile information on the login screen and register the skills they need and the support they would like.

[0453] Step 3:

[0454] The device transmits the entered profile information to the server.

[0455] Step 4:

[0456] The server analyzes the new employee's profile and assigns an appropriate generative AI model.

[0457] 3. Correction of emails and presentation materials

[0458] Step 1:

[0459] The terminal displays the editor screen for the user.

[0460] Step 2:

[0461] The user creates a draft of a business email or a presentation in the editor.

[0462] Step 3:

[0463] The terminal transmits the drafts and documents created to the server.

[0464] Step 4:

[0465] The server sends the data to the generative AI model and requests it to analyze it.

[0466] Step 5:

[0467] The generative AI model analyzes the grammar and expressions of the document and suggests areas for improvement.

[0468] Step 6:

[0469] The server receives feedback from the generative AI model and sends it to the device.

[0470] Step 7:

[0471] The device displays the feedback to the user.

[0472] 4. Communication support

[0473] Step 1:

[0474] The terminal displays a consultation form for the user.

[0475] Step 2:

[0476] The user enters a question about workplace rules and business etiquette into a consultation form.

[0477] Step 3:

[0478] The terminal sends the entered question to the server.

[0479] Step 4:

[0480] The server sends the question to the generative AI model and requests an answer.

[0481] Step 5:

[0482] A generative AI model creates answers to questions.

[0483] Step 6:

[0484] The server receives the answer from the generated AI model and sends it to the device.

[0485] Step 7:

[0486] The terminal displays the provided answers to the user.

[0487] 5. Providing Feedback

[0488] Step 1:

[0489] The server logs the user's activity on the system.

[0490] Step 2:

[0491] The server analyzes the recorded activity logs and periodically generates feedback from the generative AI model.

[0492] Step 3:

[0493] The server transmits the generated feedback to the terminal.

[0494] Step 4:

[0495] The terminal displays the notified feedback to the user.

[0496] 6. Sentiment Analysis and Adaptive Feedback

[0497] Step 1:

[0498] The device sends the user's input text and voice data to the emotion engine.

[0499] Step 2:

[0500] An emotion engine analyzes the user's emotional state.

[0501] Step 3:

[0502] The emotion engine sends the analysis results to the server.

[0503] Step 4:

[0504] The server sends the emotion analysis results to the generative AI model and requests it to adaptively change the feedback content.

[0505] Step 5:

[0506] The generative AI model adjusts the feedback content based on the results of sentiment analysis.

[0507] Step 6:

[0508] The server sends the adapted feedback to the terminal.

[0509] Step 7:

[0510] The terminal displays adaptive feedback to the user.

[0511] The above is a detailed description of the embodiment of the invention of a generative AI mentor system that combines an emotion engine. This enables new employees to receive appropriate support according to their emotions, improve their skills more efficiently, and adapt to the workplace.

[0512] Example 2

[0513] 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."

[0514] Traditional mentoring systems often lack sufficient support to help new employees smoothly adapt to the workplace. In particular, they face the problem of not only failing to quickly learn business skills and company-specific rules, but also failing to receive appropriate feedback tailored to their individual emotional state. As a result, there are concerns that new employees may not be able to adapt to the workplace well and may find it difficult to improve their skills efficiently.

[0515] 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.

[0516] In this invention, the server includes: means for initializing a generative AI model for learning business skills and company-specific rules; means for inputting and registering new employee profile information; means for sending created emails and presentation materials to the generative AI model and receiving feedback on areas for improvement; means for sending questions to the generative AI model and receiving answers; means for analyzing the new employee's activity log and providing overall feedback; emotion engine means for analyzing user input text and voice data to obtain emotional states; and means for reflecting the emotional states from the emotion engine in the generative AI model and providing adaptive feedback. This allows new employees to quickly learn business skills and company-specific rules, receive appropriate feedback according to their individual emotional states, and smoothly adapt to the workplace.

[0517] "Business skills" refers to the knowledge and skills required to carry out work in the workplace, including communication skills, problem-solving skills, leadership skills, and time management skills.

[0518] "Company-specific rules" refer to internal rules and guidelines established by a specific company, including compliance, ethics rules, and internal company manuals, and are guidelines for behavior and operating procedures that employees must follow.

[0519] A "generative AI model" is a machine learning model that is trained using artificial intelligence techniques to perform a specific task (e.g., sentence generation, grammar checking, data analysis, etc.).

[0520] An "emotion engine" refers to a program or system that has the ability to analyze user input data (text or voice) and determine the user's emotional state based on that data.

[0521] "Feedback" refers to the information and suggestions that a generative AI model provides as a result of its analysis and judgment, suggesting areas for improvement or next steps based on user-entered data and actions.

[0522] "Activity logs" refer to data that records the operations and inputs that users make on the system. These are important data for analyzing and generating feedback.

[0523] "Adaptive feedback" refers to personalized feedback that is adjusted according to the user's emotional state and behavioral history. Its distinctive feature is that the content changes according to the user's state and needs.

[0524] This invention relates to a generative AI mentoring system that complements traditional mentoring systems to help new employees smoothly commit to the workplace. This system is equipped with an emotion engine that recognizes the user's emotions, analyzes the user's emotional state, and reflects this in the feedback of the generative AI model.

[0525] Hardware and software used

[0526] This system consists of the following hardware and software:

[0527] Server: A high-performance computer for running the generative AI models and emotion engine, and managing user data.

[0528] Device: A device such as a computer, tablet, or smartphone that the new employee will have access to.

[0529] Generative AI model: A machine learning model that uses natural language processing technology to correct business emails and answer questions.

[0530] Emotion engine: Software that uses speech recognition and natural language processing techniques to analyze a user's emotional state.

[0531] Program processing overview

[0532] The system's processing consists mainly of the following components:

[0533] Initial Setup

[0534] The server acquires book data on business skills and presentation skills and trains the generative AI model. It also acquires data on the company's unique mindset and rules and trains the generative AI model.

[0535] New employee registration

[0536] The device displays a login screen for new employees, and the user enters their profile information. The information is sent to the server for analysis, and the server assigns an appropriate generative AI model based on the analysis results.

[0537] Correction of emails and presentation materials

[0538] The device provides an editor screen for the user, who then creates business emails and presentation materials. The created materials are sent to a server and analyzed by a generative AI model. The generative AI model then suggests improvements to grammar and expression, and sends this feedback to the device via the server.

[0539] Communication Support

[0540] The device provides a consultation form for users, and users input questions about workplace rules and business etiquette. The questions are sent via the server to a generative AI model, which generates answers. The generated answers are then displayed on the device.

[0541] Providing Feedback

[0542] The server records the user's activity on the system in real time. These logs are analyzed and sent to the generative AI model, which then periodically generates feedback that is displayed on the device via the server.

[0543] Sentiment Analysis and Adaptive Feedback

[0544] The device sends the user's input text and voice data to the emotion engine. The emotion engine analyzes the user's emotional state and reflects the results in the generative AI model. The generative AI model generates adaptive feedback based on the user's emotional state and displays it on the device via the server.

[0545] Specific examples

[0546] Example 1: Email editing and sentiment analysis

[0547] 1. A user types a draft of a business email into a terminal.

[0548] 2. The device sends the email draft to the server, which then sends it to the generative AI model for editing.

[0549] 3. The generative AI model suggests improvements to grammar, honorifics, and expressions.

[0550] 4. The device sends the input text to the emotion engine, which analyzes the emotional state.

[0551] 5. The server sends the emotion analysis results to the generative AI model and adaptively changes the feedback content.

[0552] 6. The server sends adaptive feedback to the terminal, which displays it to the user.

[0553] Example prompt sentence:

[0554] "Please review the business email below and suggest improvements to grammar, honorifics, and expressions. We'd also like you to perform sentiment analysis and provide adaptive feedback."

[0555] Example 2: Presentation correction and sentiment analysis

[0556] 1. The user uploads the presentation materials to the device.

[0557] 2. The device sends the document to the server, which then sends it to the generative AI model for correction.

[0558] 3. The generative AI model suggests improvements to the content, design, and logical structure of your slides.

[0559] 4. The device sends the input text or voice to the emotion engine, which analyzes the emotional state.

[0560] 5. The server sends the emotion analysis results to the generative AI model and adaptively changes the feedback content.

[0561] 6. The server sends adaptive feedback to the terminal, which displays it to the user.

[0562] Example prompt sentence:

[0563] "Please review the presentation below and suggest improvements to the content, design, and logical structure. Please also conduct a sentiment analysis and provide adaptive feedback."

[0564] The above is an embodiment of the invention of a generative AI mentor system that combines an emotion engine. This system allows new employees to receive appropriate support based on their emotions, allowing them to improve their skills more efficiently and adapt to the workplace.

[0565] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0566] Step 1: Initial Setup

[0567] The server acquires book data related to business skills and presentation skills. Specifically, it accesses and downloads corporate databases and external specialized materials. The acquired data is in document format (PDF or text file) or audio format (audio file). The server preprocesses this data, performing tokenization and data cleaning. The server then supplies the data to the generative AI model, which then performs learning. This allows the generative AI model to acquire knowledge related to business skills and presentation skills.

[0568] Input: Book data, corporate databases, external specialist materials

[0569] Output: Preprocessed data, learning results of generative AI model

[0570] Step 2: Register new employees

[0571] The device displays a login screen for new employees. The user accesses the login screen and enters profile information such as their name, department, desired skills, and desired support content. The entered profile information is sent from the device to the server. The server stores the received profile information in a database and analyzes it. Based on the analysis results, the server assigns an appropriate generative AI model to the user.

[0572] Input: New employee profile information

[0573] Output: Analysis results, assignment of generative AI model

[0574] Step 3: Editing emails and presentation materials

[0575] The device provides an editor screen for the user, who then creates business emails and presentation materials. The completed materials are sent from the device to the server. The server analyzes the received materials and generates prompts for the generative AI model. The server sends the materials and prompts to the generative AI model, requesting analysis. The generative AI model analyzes the grammar and expressions of the materials and suggests improvements. The server receives feedback from the generative AI model, reflects this in the document, and sends it to the device. The device displays the improved document and feedback to the user.

[0576] Input: business emails, presentation materials, prompts

[0577] Output: Feedback for generative AI models, improved documentation

[0578] Step 4: Communication support

[0579] The device provides a consultation form for users, and the user inputs questions about workplace rules and business etiquette. The device then sends the input question to the server. The server analyzes the question and, if necessary, sends the question content to the generative AI model as a prompt. The server receives the answer from the generative AI model and sends it to the device. The device then displays the appropriate answer to the user.

[0580] Input: User question, prompt

[0581] Output: The answer of the generative AI model, displayed to the user

[0582] Step 5: Provide feedback

[0583] The server records the user's activity log on the system in real time. The recorded activity log is analyzed, and the server sends the log data to the generative AI model, requesting it to generate feedback. The generative AI model analyzes the activity log and periodically generates feedback. The server sends the generated feedback to the device, which then displays it to the user.

[0584] Input: Activity log, prompt

[0585] Output: Feedback from the generative AI model, displayed to the user

[0586] Step 6: Sentiment Analysis and Adaptive Feedback

[0587] The device sends the user's input text and voice data to the emotion engine. The emotion engine analyzes the text and voice data and obtains the user's emotional state. The emotion engine's analysis results are sent to the server, which then sends an emotion-reflecting prompt to the generative AI model. The generative AI model generates adaptive feedback that takes the emotional state into account. The server sends the adaptive feedback to the device, which displays it to the user.

[0588] Input: User input text, voice data, and sentiment analysis results

[0589] Output: Analysis results of the emotion engine, adaptive feedback by the generative AI model, and display to the user

[0590] (Application example 2)

[0591] 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."

[0592] Traditional mentoring systems alone have limitations in helping new employees and new store associates quickly and effectively adapt to their workplace or store operations, particularly in terms of emotional support. This increases the likelihood of delayed workplace adaptation and work errors. Furthermore, it is difficult to obtain adaptive feedback in real time when dealing with customers or performing work tasks in a physical store. Therefore, there is a need for a system that provides adaptive learning support and real-time feedback that takes into account the emotional state of new employees and new store associates.

[0593] 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.

[0594] In this invention, the server includes: means for initializing a generative AI model for new employees and store clerks to learn business skills and company-specific rules; means for inputting and registering profile information for new employees and store clerks; means for sending created emails and presentation materials to the generative AI model and receiving feedback on areas for improvement; means for sending questions to the generative AI model and receiving answers; means for analyzing the activity logs of new employees and store clerks and providing overall feedback; means for store clerks to view and study store operations manuals; means for store clerks to perform customer interaction simulations; means for providing real-time work feedback; and means for analyzing the emotional state of store clerks using an emotion analysis engine and providing adaptive feedback. This allows new employees and store clerks to receive appropriate support according to their emotional state, allowing them to improve their skills more efficiently and quickly adapt to work in the workplace or store.

[0595] A "generative AI model" is an artificial intelligence technology that learns from large amounts of data and generates appropriate answers and feedback for specific tasks or questions.

[0596] "Profile information" refers to information such as an individual's basic attributes, skills, experience, and desired support content.

[0597] "Feedback" refers to the evaluation and advice that the generative AI model provides to new employees and store associates on their actions, materials, and questions.

[0598] An "activity log" is data that records the history of operations and actions performed on the system by new employees and store clerks.

[0599] An "emotion analysis engine" is software or a system that analyzes a user's emotional state from voice, text, facial expression data, etc.

[0600] "Adaptive feedback" refers to appropriate advice and assessments provided by a generative AI model based on the user's emotional state and situation.

[0601] A "store operations manual" is a guidebook that summarizes store operations procedures, customer service methods, rules, etc.

[0602] "Customer service simulation" refers to a system or program that allows store staff to practice simulating actual customer service situations.

[0603] "Real-time feedback" refers to feedback that store employees receive immediately while they are working.

[0604] Overall overview

[0605] The present invention is a system that helps new employees and new store associates quickly adapt to their workplace or store and efficiently improve their skills. This system uses a server, a terminal, a generative AI model, and a sentiment analysis engine. Specifically, it is implemented through the following steps.

[0606] System Configuration

[0607] The system of the present invention includes the following elements:

[0608] 1. Initial Setup

[0609] server:

[0610] Obtain book data or electronic data related to business skills and presentation skills and train a generative AI model.

[0611] The generative AI model is trained on data related to a company's unique mindset, rules, store operations manuals, and customer service skills.

[0612] 2. Registering new employees and store associates

[0613] Device:

[0614] A login screen for new employees and new store clerks is displayed, and users enter their profile information, including the required skills and desired support content.

[0615] The entered profile information is sent to the server, where it is analyzed and an appropriate generative AI model is assigned.

[0616] 3. Correction of emails and presentation materials

[0617] Device:

[0618] An editor screen is displayed, and the user creates a draft of a business email or a presentation document.

[0619] The created materials are sent to the server and then sent to the generative AI model for analysis.

[0620] Generative AI models:

[0621] Analyze the grammar and expressions of the material and suggest areas for improvement.

[0622] server:

[0623] The proposed feedback is sent to the terminal and displayed to the user.

[0624] 4. Communication support

[0625] Device:

[0626] A consultation form is displayed, and the user inputs a question about workplace rules and business etiquette.

[0627] server:

[0628] Send your questions to a generative AI model and ask for an answer.

[0629] Generative AI models:

[0630] Create an answer to the question and send it to the server.

[0631] server:

[0632] The answer is sent to the terminal and displayed to the user.

[0633] 5. Customer response simulation

[0634] Device:

[0635] It provides customer interaction simulations for users to practice.

[0636] Generative AI models:

[0637] The simulation content is analyzed in real time and feedback is provided.

[0638] 6. Providing Feedback

[0639] server:

[0640] Record and analyze user activity logs.

[0641] Based on the analysis results, the generative AI model provides regular feedback.

[0642] 7. Sentiment Analysis and Adaptive Feedback

[0643] Device:

[0644] The user's input text and voice data are sent to the sentiment analysis engine.

[0645] Sentiment Analysis Engine:

[0646] It analyzes the emotional state and sends the results to a generative AI model.

[0647] Generative AI models:

[0648] Generate adaptive feedback that takes emotional state into account.

[0649] server:

[0650] The provided adaptive feedback is sent to the terminal and displayed to the user.

[0651] Specific examples

[0652] Example 1: Customer interaction simulation

[0653] 1. The user begins a simulated customer interaction practice session on the smartphone app.

[0654] 2. The app uses voice and facial recognition technology to evaluate the user's reactions and responses.

[0655] 3. The emotion analysis engine analyzes the user's emotional state in real time, and the generative AI model provides real-time feedback such as "Take a deep breath and speak calmly."

[0656] Specific generative AI model prompt examples:

[0657] "A new employee is handling a customer complaint. Please rate the following response and suggest improvements. The user is feeling a bit anxious. Please take this into consideration when providing feedback. Here's what the user said:"

[0658] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0659] Step 1:

[0660] The server performs the initial setup.

[0661] Input: Data on business skills, presentation skills, company-specific rules, store operations manuals, and customer service skills.

[0662] Processing: The server trains the generative AI model on the collected book and electronic data, as well as the company's specific rules and mindset.

[0663] Output: A generative AI model with initial configuration completed.

[0664] Step 2:

[0665] The terminal registers new employees and store clerks.

[0666] Input: Profile information for new employees and store clerks (basic information, skills, experience, desired support).

[0667] Processing: The device displays a login screen, where the user enters their profile information. The information is sent to the server, which analyzes it and assigns an appropriate generative AI model.

[0668] Output: The assigned generative AI model and the user's profile data.

[0669] Step 3:

[0670] A user creates an email or presentation document and requests corrections.

[0671] Input: Business email drafts and presentation materials created by users.

[0672] Processing: The device displays the editor screen, the user creates a document, and sends it to the server. The server then sends the document to the generative AI model, which analyzes the document's grammar and expressions and suggests improvements.

[0673] Output: Suggested feedback.

[0674] Step 4:

[0675] Users can enter questions and receive communication support.

[0676] Input: User asks a question about workplace rules and business etiquette.

[0677] Processing: The device displays a consultation form, the user enters a question, and sends it to the server. The server sends the question to the generative AI model and requests an answer. The generative AI model creates an answer, which the server sends to the device.

[0678] Output: The generative AI model's answer to the question.

[0679] Step 5:

[0680] The user performs a customer interaction simulation.

[0681] Input: The command with which the user starts the simulation.

[0682] Processing: The terminal provides customer interaction simulations, and the generative AI model analyzes the simulation content in real time and provides appropriate feedback. It also uses an emotion analysis engine to analyze the user's emotional state.

[0683] Output: Feedback during the simulation.

[0684] Step 6:

[0685] The server provides feedback.

[0686] Input: User activity log.

[0687] Processing: The server records and analyzes the user's activity log, periodically generates feedback from the generative AI model, and sends the feedback to the device.

[0688] Output: Periodic feedback.

[0689] Step 7:

[0690] Providing adaptive feedback based on emotional state.

[0691] Input: User-entered text or voice data.

[0692] Processing: The device sends the input data to the emotion analysis engine to analyze the emotional state. The analysis results are sent to the generative AI model to generate adaptive feedback. The server sends the adaptive feedback to the device.

[0693] Output: Adaptive feedback taking into account emotional state.

[0694] 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.

[0695] 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.

[0696] 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.

[0697] [Second embodiment]

[0698] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0699] 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.

[0700] 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).

[0701] 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.

[0702] 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.

[0703] 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).

[0704] 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.

[0705] 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.

[0706] 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.

[0707] 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.

[0708] In the smart glasses 214, the reception output process is performed by the processor 46. A reception output program 60 is stored in the storage 50. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[0709] 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."

[0710] This invention relates to a generative AI mentoring system that complements traditional mentoring systems to help new employees commit smoothly to the workplace and prevent early turnover. This system provides an environment where new employees can easily seek advice and receive individual feedback.

[0711] Overall overview

[0712] This generative AI mentor system performs the following processes using a server, terminals, and generative AI models.

[0713] 1. Initial Setup

[0714] 2. New employee registration

[0715] 3. Correction of emails and presentation materials

[0716] 4. Communication support

[0717] 5. Providing Feedback

[0718] Program processing explanation

[0719] The program processing at each step will be explained below in order.

[0720] 1. Initial Setup

[0721] The server first obtains book data on business skills and presentation skills and trains the generative AI model.

[0722] The server also acquires data on the company's unique mindset and rules, which it then uses to train the generative AI model.

[0723] 2. New employee registration

[0724] The terminal displays a login screen for new employees, where the user (new employee) enters profile information such as name, job title, skill set, desired support content, etc.

[0725] The terminal transmits the entered profile information to the server.

[0726] The server analyzes the new employee's profile and assigns the most appropriate generative AI model.

[0727] 3. Correction of emails and presentation materials

[0728] The terminal displays an editor screen for the user, and the user creates a draft of a business email or presentation materials.

[0729] The terminal transmits the completed draft or document to the server.

[0730] The server sends the submitted materials to a generative AI model, which analyzes grammar and expressions and provides feedback on areas for improvement.

[0731] The server receives the feedback and provides it to the user.

[0732] The terminal displays the feedback to the user.

[0733] 4. Communication support

[0734] The terminal displays a consultation form for the user, and the user inputs a question.

[0735] The terminal sends a query to the server.

[0736] The server sends the question to a generative AI model, which generates an answer.

[0737] The server receives the generative AI model's answer and provides it to the user.

[0738] The terminal displays the provided answers to the user.

[0739] 5. Providing Feedback

[0740] The server records and analyzes a log of the user's activities on the system.

[0741] The server periodically retrieves the analysis results from the generated AI model and generates overall feedback.

[0742] The server provides feedback to the user.

[0743] The terminal displays the provided feedback to the user.

[0744] Specific examples

[0745] Example 1: Email editing

[0746] 1. A user types a draft of a business email into a terminal.

[0747] 2. The device sends the email draft to the server.

[0748] 3. The server sends the draft to the generative AI model for editing.

[0749] 4. The generative AI model suggests improvements to grammar, honorifics, and expressions.

[0750] 5. The server receives the feedback and sends it to the device.

[0751] 6. The device displays feedback to the user.

[0752] Example 2: Presentation correction

[0753] 1. The user uploads the presentation materials to the device.

[0754] 2. The device sends the materials to the server.

[0755] 3. The server sends the document to the generative AI model and requests corrections.

[0756] 4. The generative AI model suggests improvements to the content, design, and logical structure of your slides.

[0757] 5. The server receives the feedback and sends it to the device.

[0758] 6. The device displays feedback to the user.

[0759] The above is the details of the mode for carrying out the invention. This enables new employees to use the generative AI mentor system to smoothly adapt to the workplace and improve their skills.

[0760] The processing flow will be explained below.

[0761] 1. Initial Setup

[0762] Step 1:

[0763] The server acquires book data on business skills and presentation skills.

[0764] Step 2:

[0765] The data acquired by the server is trained into a generative AI model.

[0766] Step 3:

[0767] The server collects data on the company's unique mindset and rules.

[0768] Step 4:

[0769] The server trains the generative AI model on data about the company's mindset and rules.

[0770] 2. New employee registration

[0771] Step 1:

[0772] The terminal displays the login screen for new employees.

[0773] Step 2:

[0774] Users enter their profile information on the login screen and register the skills they need and the support they would like.

[0775] Step 3:

[0776] The device transmits the entered profile information to the server.

[0777] Step 4:

[0778] The server analyzes the new employee's profile and assigns an appropriate generative AI model.

[0779] 3. Correction of emails and presentation materials

[0780] Step 1:

[0781] The terminal displays the editor screen for the user.

[0782] Step 2:

[0783] The user creates a draft of a business email or a presentation in the editor.

[0784] Step 3:

[0785] The terminal transmits the drafts and documents created to the server.

[0786] Step 4:

[0787] The server sends the data to the generative AI model and requests it to analyze it.

[0788] Step 5:

[0789] The generative AI model analyzes the grammar and expressions of the document and suggests areas for improvement.

[0790] Step 6:

[0791] The server receives feedback from the generative AI model and sends it to the device.

[0792] Step 7:

[0793] The device displays the feedback to the user.

[0794] 4. Communication support

[0795] Step 1:

[0796] The terminal displays a consultation form for the user.

[0797] Step 2:

[0798] The user enters a question about workplace rules and business etiquette into a consultation form.

[0799] Step 3:

[0800] The terminal sends the entered question to the server.

[0801] Step 4:

[0802] The server sends the question to the generative AI model and requests an answer.

[0803] Step 5:

[0804] A generative AI model creates answers to questions.

[0805] Step 6:

[0806] The server receives the answer from the generated AI model and sends it to the device.

[0807] Step 7:

[0808] The terminal displays the provided answers to the user.

[0809] 5. Providing Feedback

[0810] Step 1:

[0811] The server logs the user's activity on the system.

[0812] Step 2:

[0813] The server analyzes the recorded activity logs and periodically generates feedback from the generative AI model.

[0814] Step 3:

[0815] The server sends the generated feedback to the user.

[0816] Step 4:

[0817] The terminal displays the notified feedback to the user.

[0818] These are the specific steps in the programming process of the generative AI mentor system, which allows new employees to efficiently improve their skills and adapt to the workplace.

[0819] Example 1

[0820] 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."

[0821] An appropriate support environment is necessary to enable new employees to smoothly adapt to the workplace and prevent early attrition. However, traditional mentoring systems have problems with providing individual feedback and making it difficult to carry out effective follow-up. Furthermore, when many new employees join the company at once, the burden on mentors increases, and as a result, there is a risk that high-quality support will not be provided. To solve these problems, a system that utilizes generative AI models to provide appropriate support and feedback to new employees is needed.

[0822] 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.

[0823] In this invention, the server includes means for initializing a generative AI model that has learned business skills and company-specific rules, means for inputting and registering new employee profile information, means for sending created documents to the generative AI model and receiving feedback on areas for improvement, means for sending questions to the generative AI model and receiving answers, means for analyzing the new employee's activity log and providing overall feedback, means for sending information input at the terminal to the server, and means for receiving analysis results from the generative AI model at the server and sending them to the terminal. This allows new employees to not only receive individual feedback, but also quickly receive specific advice on how to adapt to the workplace in an appropriate support environment.

[0824] "Business skills" refers to a set of skills required in the workplace, such as business etiquette, communication skills, problem-solving skills, and presentation skills.

[0825] "Company-specific rules" refer to the unique mindset, code of conduct, ethical guidelines, internal rules, etc. established by a particular company.

[0826] A "generative AI model" is a system that uses artificial intelligence technology to learn and generate, and refers to a model that uses natural language processing technology to generate feedback and answers for users.

[0827] "Initialization means" refers to the process of providing the necessary training data for a generative AI model and configuring the model to suit the specific needs of the company.

[0828] "Profile Information" refers to personal information entered by a user, such as name, job title, skill set, and desired support type.

[0829] "Document" refers to text data such as a business email draft or presentation material created by a user.

[0830] "Activity log" refers to data that records a user's actions and interactions when using a system.

[0831] "Feedback" refers to specific advice provided by the generative AI model on areas for improvement such as grammar, expression, pay, design, and logical structure.

[0832] "Means for obtaining an answer" refers to the process by which a user sends a question to a generative AI model and obtains an answer to that question from the generative AI model.

[0833] "Analysis results" refers to the results of the analysis performed by the generative AI model based on the user's input data.

[0834] This invention relates to a generative AI mentoring system that complements traditional mentoring systems to help new employees smoothly adapt to the workplace and prevent early turnover. The system provides an environment where new employees can easily seek advice and receive individual feedback.

[0835] Hardware and software used

[0836] This system operates using a server, a terminal, and a generative AI model. The specific hardware and software used are as follows:

[0837] Server: Manages and processes data to learn business skills and company-specific rules.

[0838] Software used: "Learning-Resource-Server", "Profile-Analyzer", "Grammar-Checker AI", "Pitch-Perfect AI", "Question-Responder AI", "Activity-Analyzer", "Activity-Logger", etc.

[0839] Terminal: Provides an interface for users (new employees) to enter information and receive feedback and responses.

[0840] Software used: "Employee-Portal", "Business-Editor", "Consultation-Form", etc.

[0841] Program processing explanation

[0842] The program for this system consists of multiple processing steps that mainly involve the server, terminal, and generative AI model working together. Each processing step is explained below in natural language.

[0843] Initial Setup

[0844] The server collects book data on business skills and presentation skills and trains the generative AI model via the "Learning-Resource-Server."

[0845] The server also collects document data about the company's unique mindset and rules, and similarly trains the generative AI model. This process allows the generative AI model to respond to the specific needs of the company.

[0846] New employee registration

[0847] When a user accesses the system for the first time, the terminal displays a login screen for new employees. This system uses the "Employee-Portal."

[0848] The user (new employee) enters profile information (name, position, skill set, desired support content, etc.) on the login screen.

[0849] The terminal transmits the profile information entered by the user to the server.

[0850] The server uses the "Profile-Analyzer" to analyze the profile information of new employees and assign the optimal generative AI model. At this time, the parameters of the generative AI model are adjusted according to the employee's skill set and desired support content.

[0851] Correction of emails and presentation materials

[0852] The terminal displays an editor screen where users can create business emails and presentation materials. This editor uses "Business-Editor."

[0853] The user creates a draft of a business email or a presentation document on the editor screen.

[0854] The terminal transmits the completed email draft or presentation materials to the server.

[0855] The server sends the received materials to generative AI models, such as "Grammar-Checker AI" and "Pitch-Perfect AI," and requests them to analyze grammar, expression, design, and logical structure.

[0856] The generative AI model provides feedback on grammatical errors, appropriate phrasing, and areas for improvement in the design and logical structure of the presentation materials.

[0857] The server receives the generated feedback and transmits it to the terminal.

[0858] The device displays feedback to the user, allowing them to specifically understand areas for improvement and make corrections.

[0859] Communication Support

[0860] The terminal displays a consultation form where the user can enter their questions. This form uses the "Consultation-Form".

[0861] The user inputs a question into the consultation form. Specifically, the user inputs a question such as "Please tell me how to approach a new project."

[0862] The terminal transmits the entered question to the server.

[0863] The server uses "Question-Responder AI" to have a generative AI model generate answers to questions.

[0864] Generative AI models generate specific advice and answers to questions.

[0865] The server receives the generated response and transmits it to the terminal.

[0866] The terminal displays the provided answers to the user, who can receive advice in real time.

[0867] Providing Feedback

[0868] The server records the user's actions and interactions on the system (for example, what questions they asked, what feedback they received), and uses the "Activity-Logger" as the recording system.

[0869] The server periodically analyzes the recorded activity log using "Activity-Analyzer."

[0870] The server sends the analysis results to a generative AI model to generate overall feedback.

[0871] The generative AI model provides feedback on the user's progress and areas for improvement, such as "Your communication in a recent project has improved" or "Your presentation slides are now more logically structured."

[0872] The server provides the generated feedback to the user.

[0873] The device displays rich feedback to users, helping them track their progress, and also displays a feedback dashboard.

[0874] Examples of concrete examples and prompts

[0875] Example 1: Email editing

[0876] 1. The user inputs a draft of a business email into the terminal, for example, using "Business-Editor."

[0877] 2. The device sends the email draft to the server.

[0878] 3. The server sends the draft to a generative AI model for correction, specifically using "Grammar-Checker AI."

[0879] 4. The generative AI model suggests improvements to grammar, honorifics, and expressions.

[0880] 5. The server receives the feedback and sends it to the device.

[0881] 6. The device displays feedback to the user.

[0882] Example 2: Presentation correction

[0883] 1. The user uploads the presentation materials to the device, for example, using "Business-Editor."

[0884] 2. The device sends the materials to the server.

[0885] 3. The server sends the document to a generative AI model and requests corrections. Specifically, it uses "Pitch-Perfect AI."

[0886] 4. The generative AI model suggests improvements to the content, design, and logical structure of your slides.

[0887] 5. The server receives the feedback and sends it to the device.

[0888] 6. The device displays feedback to the user.

[0889] Examples of prompts include:

[0890] How do you approach a new project?

[0891] "Please check the grammar of this business email."

[0892] "How can I improve the design of my presentation materials?"

[0893] This concludes the detailed description of the embodiments of the invention, which will enable new employees to make the most of the generative AI mentor system, allowing them to smoothly adapt to the workplace and improve their skills.

[0894] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0895] Step 1: Initial Setup

[0896] The server collects book data on business skills and presentation skills.

[0897] As input, business books and materials are collected from external databases and the Internet.

[0898] As part of the data processing, the collected data is analyzed via the "Learning-Resource-Server."

[0899] The output provides training data for the generative AI model.

[0900] The server also collects document data about the company's unique mindset and rules.

[0901] As input, company policy documents and manuals are collected.

[0902] As part of the data processing, these documents are tokenized and text analyzed.

[0903] The output provides training data for the generative AI model.

[0904] Step 2: Register new employees

[0905] The terminal displays a login screen for new employees.

[0906] As input, a user attempts to log in.

[0907] As a result, the terminal launches the "Employee-Portal" and displays the login screen.

[0908] As an output, a login form is provided to the user.

[0909] The user (new employee) enters profile information.

[0910] As input, the user enters their name, job title, skill set, and desired support type.

[0911] In operation, the user enters the required information into each information field.

[0912] The terminal transmits the entered profile information to the server.

[0913] As input, the terminal receives the user's profile data.

[0914] In operation, the terminal issues a request to send these data to the server.

[0915] As an output, the profile data is sent to a server.

[0916] The server analyzes the profile information of the new employee.

[0917] The profile data received by the server as input.

[0918] For data calculation, the server uses "Profile-Analyzer" to analyze the data.

[0919] As an output, the new employee is assigned the best suited generative AI model.

[0920] Step 3: Editing emails and presentation materials

[0921] The terminal displays an editor screen for the user.

[0922] As input, the user accesses the editor screen.

[0923] As an operation, the editor "Business-Editor" is launched and displayed to the user.

[0924] As an output, an editor screen is provided.

[0925] Users create drafts of business emails and presentation materials on the editor screen.

[0926] As input, the user enters text and presentation slides into the editor screen.

[0927] In action, the user creates a draft or slide.

[0928] The terminal transmits the completed document to the server.

[0929] As input, the editor sends the completed draft or material.

[0930] In operation, the terminal issues a request to transmit material data to the server.

[0931] As an output, the document data is sent to the server.

[0932] The server sends the submitted materials to the generative AI model and requests it to analyze them.

[0933] Source data received by the server as input.

[0934] To calculate the data, the server requests "Grammar-Checker AI" and "Pitch-Perfect AI" to analyze the materials.

[0935] As an output, the analysis results are produced.

[0936] The generative AI model provides feedback on grammatical errors and areas for improvement in design and logical structure.

[0937] As input, material data.

[0938] As a data calculation, the generative AI model analyzes the material and generates specific feedback.

[0939] As an output, feedback data is generated.

[0940] The server sends the feedback data to the terminal for providing to the user.

[0941] As input, feedback data from the generative AI model.

[0942] In operation, the server transmits feedback data to the terminal.

[0943] As an output, feedback is sent to the terminal.

[0944] The terminal displays feedback to the user.

[0945] As input, the received feedback data.

[0946] As an action, feedback is displayed to the user.

[0947] As an output, feedback content is provided to the user.

[0948] Step 4: Communication support

[0949] The terminal displays a consultation form for the user.

[0950] As input, the user accesses a consultation form.

[0951] As a result, the "Consultation-Form" is displayed.

[0952] As output, a consultation form is provided.

[0953] The user enters their question into the consultation form.

[0954] As input, the user enters the consultation content into the form.

[0955] In action, the user enters a specific question.

[0956] The terminal transmits the entered question to the server.

[0957] As input, the question data entered by the user.

[0958] In operation, the query data is sent to the server.

[0959] As an output, the query data is sent to the server.

[0960] The server sends the question to a generative AI model, which generates an answer.

[0961] The query data received by the server as input.

[0962] As a data calculation, the "Question-Responder AI" analyzes the question and generates an answer.

[0963] As an output, response data is generated.

[0964] The server sends the generated response to the terminal.

[0965] As input, the generated response data.

[0966] As an operation, the response data is sent to the terminal.

[0967] As an output, the answer data is provided to the terminal.

[0968] The terminal displays the answer to the user.

[0969] As input, the received response data.

[0970] As an action, the answer is displayed to the user.

[0971] As an output, the answer content is presented to the user.

[0972] Step 5: Provide feedback

[0973] The server records the user's actions and interactions on the system.

[0974] As input, user activity log data.

[0975] In operation, "Activity-Logger" records user actions.

[0976] As an output, the activity log data is saved.

[0977] The server periodically analyzes the recorded activity log.

[0978] As input, the saved activity log data.

[0979] For data calculation, the server performs analysis using "Activity-Analyzer."

[0980] As an output, analysis result data is generated.

[0981] The server sends the analysis results to a generative AI model to generate overall feedback.

[0982] As input, the analysis result data.

[0983] As a data calculation, the generative AI model generates feedback based on the analysis results.

[0984] As an output, feedback data is generated.

[0985] The server transmits the generated feedback to the terminal.

[0986] As input, the generated feedback data.

[0987] As an operation, feedback data is transmitted to the terminal.

[0988] As an output, feedback data is provided to the terminal.

[0989] The terminal displays feedback to the user.

[0990] As input, the received feedback data.

[0991] As an action, feedback is displayed to the user.

[0992] As an output, feedback content is presented to the user.

[0993] (Application example 1)

[0994] 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."

[0995] In order for new employees to adapt quickly to the factory floor and perform their work smoothly, they need immediate support and appropriate feedback. However, traditional mentoring systems alone may not be able to provide sufficient support for each new employee, making it difficult for them to efficiently acquire skills and perform work safely. Therefore, a system is needed that can properly teach the factory's unique procedures and safety guidelines and quickly respond to any questions new employees may have.

[0996] 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.

[0997] In this invention, the server includes a means for learning the factory-specific procedures and safety guidelines, a means for providing real-time support for new employees to carry out on-site work procedures and safety checks, and a means for checking reports and memos created by new employees in real time and providing feedback, thereby enabling new employees to quickly adapt to the factory floor and perform their work safely and efficiently.

[0998] A "generative AI model" is an artificial intelligence system that helps new employees learn the skills and knowledge they need to perform their jobs and generates feedback and answers.

[0999] "Initial setup" refers to the preparatory work of teaching the generative AI model the business skills that new employees should learn, the company's unique rules, and factory procedures and safety guidelines.

[1000] "Profile Information" refers to personal data such as a new hire's name, job title, skill set, and desired support type.

[1001] "Feedback on areas for improvement" involves the generative AI model analyzing emails and presentation materials created by new employees and providing feedback and suggestions regarding grammar, structure, expression, etc.

[1002] "Means of sending questions to a generative AI model and obtaining answers" refers to a system in which new employees send questions that arise during work to a generative AI model and obtain solutions or explanations.

[1003] An "activity log" is a record of all operations and actions performed by new employees on the system.

[1004] The "means of providing feedback" refers to a system that analyzes new employees' profile information and activity logs and regularly provides advice for growth and improvement.

[1005] "Reports and memos to be prepared" refers to documents that new employees use to record the status and results of their work.

[1006] "Means for providing real-time support" refers to a system that instantly displays and provides necessary information and instructions to new employees when they perform their work on-site.

[1007] A "procedure manual" is a document that describes the specific steps and methods for properly carrying out work.

[1008] "Safety guidelines" are documents that list safety measures and precautions to take at work sites such as factories.

[1009] This invention provides a generative AI mentoring system that helps new employees quickly adapt to the workplace and perform their work safely and efficiently. The system consists of the following elements:

[1010] 1. Initial Setup

[1011] The server acquires the factory's specific procedures and safety guidelines and trains the generative AI model. It also trains the model on data related to the company's unique mindset, rules, and business skills. In this way, the generative AI model acquires the knowledge necessary for on-site work.

[1012] 2. New employee registration

[1013] New employees use their devices to access a login screen and enter their profile information, such as their name, job title, skill set, and desired support content. This information is sent to a server, which analyzes the new employee's profile and assigns the optimal generative AI model.

[1014] 3. Business support and real-time support

[1015] As new employees work on-site, the devices display real-time information on factory procedures and safety checks. If a new employee has a question, they can type it into the device, which sends it to the generative AI model via the server. The answer is then provided to the device via the server, where the new employee can immediately check it.

[1016] 4. Correction of emails and presentation materials

[1017] Reports and memos written by new employees are sent to a server via their device. The server then feeds the documents into a generative AI model and receives feedback on improvements to grammar, expression, and structure. The feedback is displayed on the device for the new employee to review.

[1018] 5. Providing Feedback

[1019] The server records and analyzes the activity logs of new employees. Periodically, a generative AI model evaluates the log data and generates feedback for growth and improvement, allowing new employees to continuously improve their skills.

[1020] Hardware and software used

[1021] Hardware: Factory robots (such as Universal Robots' UR series), data servers, and terminals (PCs or tablets)

[1022] Software: Generative AI models (e.g., GPT-4), databases (e.g., MongoDB)

[1023] Specific examples

[1024] Example 1: New employee asking a question

[1025] Question: "I don't know what to do today. What exactly should I do?"

[1026] process:

[1027] 1. The new employee types a question into the terminal.

[1028] 2. The server sends the question to the generative AI model, which generates an answer.

[1029] 3. The server sends the generated answer to the terminal, where the new employee can view it.

[1030] 4. This process ensures that new employees are immediately aware of the steps they need to take.

[1031] Example 2: New employee submitting a report

[1032] Report: "Please correct today's work report. Please let me know if there are any mistakes."

[1033] process:

[1034] 1. The new employee uploads the report to the terminal.

[1035] 2. The server sends the report to the generative AI model for correction.

[1036] 3. The generative AI model generates feedback suggesting improvements to grammar and expression.

[1037] 4. The server sends the feedback to the terminal, where the new employee can check it.

[1038] 5. This feedback will enable the new employee to improve the quality of their reports.

[1039] This completes the description of the preferred embodiment of the present invention. This system allows new employees to quickly adapt to the factory floor and perform their jobs safely and efficiently.

[1040] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1041] Step 1: Initial Setup

[1042] The server acquires data related to the factory's specific procedures and safety guidelines, the company's unique mindset and rules, and business skills. This data is trained into a generative AI model. The input is data related to the factory's procedures, safety guidelines, mindset, rules, and business skills. The output is the knowledge learned by the generative AI model. The server takes in this data and performs data analysis and model training.

[1043] Step 2: Register new employees

[1044] The user (new employee) uses a terminal to access the login screen and enters profile information such as name, job title, skill set, and desired support content. The input is the new employee's profile information. The terminal sends this to the server. The server analyzes the profile information and assigns the optimal generative AI model. The output is the assigned generative AI model.

[1045] Step 3: Operational and real-time support

[1046] When new employees work on-site, the terminal displays information on factory work procedures and safety checks in real time. The input is data on work procedures and safety checks. The output is the information displayed on the terminal. The terminal receives various data necessary for on-site work from the server and presents it to the user in a timely manner.

[1047] Step 4: Submit your question and get an answer

[1048] When a user (new employee) has a question, they input it into the terminal. The input is the user's question. The terminal sends the question to the server. The server sends the question to the generative AI model and generates an answer. The output is the generated answer. The server sends the answer received from the generative AI model to the terminal, and the user confirms it.

[1049] Step 5: Editing emails and presentation materials

[1050] The user (new employee) uploads a report or memo they created to the device. The input is the report or memo. The device sends it to the server. The server sends the report or memo to a generative AI model and receives feedback on improvements to grammar, expression, and structure. The output is feedback. The server sends the feedback to the device so that the user can review it.

[1051] Step 6: Provide feedback

[1052] The server records new employee activity logs and periodically analyzes them. The input is the activity log. The server evaluates the log data using a generative AI model and generates feedback for growth and improvement. The output is feedback for growth and improvement. The server sends the generated feedback to a terminal, where the new employee can check it.

[1053] The above is the specific flow of operations at each processing step of this system, which enables new employees to quickly adapt to the factory floor and perform their work safely and efficiently.

[1054] 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.

[1055] This invention relates to a generative AI mentoring system that complements traditional mentoring systems to help new employees smoothly commit to the workplace. This system includes an emotion engine that recognizes the user's emotions, and can analyze the user's emotional state and reflect it in the feedback of the generative AI model.

[1056] Overall overview

[1057] This generative AI mentor system performs the following processes using a server, terminals, a generative AI model, and an emotion engine.

[1058] 1. Initial Setup

[1059] 2. New employee registration

[1060] 3. Correction of emails and presentation materials

[1061] 4. Communication support

[1062] 5. Providing Feedback

[1063] 6. Sentiment Analysis and Adaptive Feedback

[1064] Program processing explanation

[1065] The program processing at each step will be explained below in order.

[1066] 1. Initial Setup

[1067] The server retrieves book data on business skills and presentation skills and trains the generative AI model.

[1068] The server collects data on the company's unique mindset and rules and trains the generative AI model.

[1069] 2. New employee registration

[1070] The terminal displays a login screen for new employees, where the user enters their profile information and registers the skills they need and the support they would like to receive.

[1071] The device transmits the entered profile information to the server.

[1072] The server analyzes the new employee's profile and assigns an appropriate generative AI model.

[1073] 3. Correction of emails and presentation materials

[1074] The terminal displays an editor screen for the user, and the user creates a draft of a business email or presentation materials.

[1075] The terminal transmits the created materials to the server.

[1076] The server sends the data to the generative AI model and requests it to analyze it.

[1077] The generative AI model analyzes the grammar and expressions of the document and suggests areas for improvement.

[1078] The server receives feedback from the generative AI model and sends it to the device, where it is displayed to the user.

[1079] 4. Communication support

[1080] The terminal displays a consultation form for the user, and the user inputs questions about workplace rules and business etiquette.

[1081] The terminal sends a question to the server.

[1082] The server sends the question to the generative AI model and requests an answer.

[1083] A generative AI model creates answers to questions.

[1084] The server receives the answer from the generated AI model, sends it to the device, and the device displays it to the user.

[1085] 5. Providing Feedback

[1086] The server logs the user's activity on the system.

[1087] The server analyzes the recorded activity logs and periodically generates feedback from the generative AI model.

[1088] The server sends the generated feedback to the terminal, which displays it to the user.

[1089] 6. Sentiment Analysis and Adaptive Feedback

[1090] The device sends the user's input text and voice data to the emotion engine.

[1091] An emotion engine analyzes the user's emotional state.

[1092] The emotion engine sends the analysis results to the generative AI model, which then uses them as feedback.

[1093] The server receives the analysis results from the emotion engine and adaptively changes the feedback content of the generative AI model.

[1094] The server sends adaptive feedback to the terminal, which displays it to the user.

[1095] Specific examples

[1096] Example 1: Email editing and sentiment analysis

[1097] 1. A user types a draft of a business email into a terminal.

[1098] 2. The device sends the email draft to the server, which then sends it to the generative AI model for editing.

[1099] 3. The generative AI model suggests improvements to grammar, honorifics, and expressions.

[1100] 4. The device sends the input text to the emotion engine, which analyzes the emotional state.

[1101] 5. The server sends the emotion analysis results to the generative AI model and adaptively changes the feedback content.

[1102] 6. The server sends adaptive feedback to the terminal, which displays it to the user.

[1103] Example 2: Presentation correction and sentiment analysis

[1104] 1. The user uploads the presentation materials to the device.

[1105] 2. The device sends the document to the server, which then sends it to the generative AI model for correction.

[1106] 3. The generative AI model suggests improvements to the content, design, and logical structure of your slides.

[1107] 4. The device sends the input text or voice to the emotion engine, which analyzes the emotional state.

[1108] 5. The server sends the emotion analysis results to the generative AI model and adaptively changes the feedback content.

[1109] 6. The server sends adaptive feedback to the terminal, which displays it to the user.

[1110] The above is a detailed description of the embodiment of the invention of a generative AI mentor system that combines an emotion engine. This enables new employees to receive appropriate support based on their emotions, improve their skills more efficiently, and adapt to the workplace.

[1111] The processing flow will be explained below.

[1112] 1. Initial Setup

[1113] Step 1:

[1114] The server acquires book data on business skills and presentation skills.

[1115] Step 2:

[1116] The data acquired by the server is trained into a generative AI model.

[1117] Step 3:

[1118] The server collects data on the company's unique mindset and rules.

[1119] Step 4:

[1120] The server trains the generative AI model on data about the company's mindset and rules.

[1121] 2. New employee registration

[1122] Step 1:

[1123] The terminal displays the login screen for new employees.

[1124] Step 2:

[1125] Users enter their profile information on the login screen and register the skills they need and the support they would like.

[1126] Step 3:

[1127] The device transmits the entered profile information to the server.

[1128] Step 4:

[1129] The server analyzes the new employee's profile and assigns an appropriate generative AI model.

[1130] 3. Correction of emails and presentation materials

[1131] Step 1:

[1132] The terminal displays the editor screen for the user.

[1133] Step 2:

[1134] The user creates a draft of a business email or a presentation in the editor.

[1135] Step 3:

[1136] The terminal transmits the drafts and documents created to the server.

[1137] Step 4:

[1138] The server sends the data to the generative AI model and requests it to analyze it.

[1139] Step 5:

[1140] The generative AI model analyzes the grammar and expressions of the document and suggests areas for improvement.

[1141] Step 6:

[1142] The server receives feedback from the generative AI model and sends it to the device.

[1143] Step 7:

[1144] The device displays the feedback to the user.

[1145] 4. Communication support

[1146] Step 1:

[1147] The terminal displays a consultation form for the user.

[1148] Step 2:

[1149] The user enters a question about workplace rules and business etiquette into a consultation form.

[1150] Step 3:

[1151] The terminal sends the entered question to the server.

[1152] Step 4:

[1153] The server sends the question to the generative AI model and requests an answer.

[1154] Step 5:

[1155] A generative AI model creates answers to questions.

[1156] Step 6:

[1157] The server receives the answer from the generated AI model and sends it to the device.

[1158] Step 7:

[1159] The terminal displays the provided answers to the user.

[1160] 5. Providing Feedback

[1161] Step 1:

[1162] The server logs the user's activity on the system.

[1163] Step 2:

[1164] The server analyzes the recorded activity logs and periodically generates feedback from the generative AI model.

[1165] Step 3:

[1166] The server transmits the generated feedback to the terminal.

[1167] Step 4:

[1168] The terminal displays the notified feedback to the user.

[1169] 6. Sentiment Analysis and Adaptive Feedback

[1170] Step 1:

[1171] The device sends the user's input text and voice data to the emotion engine.

[1172] Step 2:

[1173] An emotion engine analyzes the user's emotional state.

[1174] Step 3:

[1175] The emotion engine sends the analysis results to the server.

[1176] Step 4:

[1177] The server sends the emotion analysis results to the generative AI model and requests it to adaptively change the feedback content.

[1178] Step 5:

[1179] The generative AI model adjusts the feedback content based on the results of sentiment analysis.

[1180] Step 6:

[1181] The server sends the adapted feedback to the terminal.

[1182] Step 7:

[1183] The terminal displays adaptive feedback to the user.

[1184] The above is a detailed description of the embodiment of the invention of a generative AI mentor system that combines an emotion engine. This enables new employees to receive appropriate support according to their emotions, improve their skills more efficiently, and adapt to the workplace.

[1185] Example 2

[1186] 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."

[1187] Traditional mentoring systems often lack sufficient support to help new employees smoothly adapt to the workplace. In particular, they face the problem of not only failing to quickly learn business skills and company-specific rules, but also failing to receive appropriate feedback tailored to their individual emotional state. As a result, there are concerns that new employees may not be able to adapt to the workplace well and may find it difficult to improve their skills efficiently.

[1188] 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.

[1189] In this invention, the server includes: means for initializing a generative AI model for learning business skills and company-specific rules; means for inputting and registering new employee profile information; means for sending created emails and presentation materials to the generative AI model and receiving feedback on areas for improvement; means for sending questions to the generative AI model and receiving answers; means for analyzing the new employee's activity log and providing overall feedback; emotion engine means for analyzing user input text and voice data to obtain emotional states; and means for reflecting the emotional states from the emotion engine in the generative AI model and providing adaptive feedback. This allows new employees to quickly learn business skills and company-specific rules, receive appropriate feedback according to their individual emotional states, and smoothly adapt to the workplace.

[1190] "Business skills" refers to the knowledge and skills required to carry out work in the workplace, including communication skills, problem-solving skills, leadership skills, and time management skills.

[1191] "Company-specific rules" refer to internal rules and guidelines established by a specific company, including compliance, ethics rules, and internal company manuals, and are guidelines for behavior and operating procedures that employees must follow.

[1192] A "generative AI model" is a machine learning model that is trained using artificial intelligence techniques to perform a specific task (e.g., sentence generation, grammar checking, data analysis, etc.).

[1193] An "emotion engine" refers to a program or system that has the ability to analyze user input data (text or voice) and determine the user's emotional state based on that data.

[1194] "Feedback" refers to the information and suggestions that a generative AI model provides as a result of its analysis and judgment, suggesting areas for improvement or next steps based on user-entered data and actions.

[1195] "Activity logs" refer to data that records the operations and inputs that users make on the system. These are important data for analyzing and generating feedback.

[1196] "Adaptive feedback" refers to personalized feedback that is adjusted according to the user's emotional state and behavioral history. Its distinctive feature is that the content changes according to the user's state and needs.

[1197] This invention relates to a generative AI mentoring system that complements traditional mentoring systems to help new employees smoothly commit to the workplace. This system is equipped with an emotion engine that recognizes the user's emotions, analyzes the user's emotional state, and reflects this in the feedback of the generative AI model.

[1198] Hardware and software used

[1199] This system consists of the following hardware and software:

[1200] Server: A high-performance computer for running the generative AI models and emotion engine, and managing user data.

[1201] Device: A device such as a computer, tablet, or smartphone that the new employee will have access to.

[1202] Generative AI model: A machine learning model that uses natural language processing technology to correct business emails and answer questions.

[1203] Emotion engine: Software that uses speech recognition and natural language processing techniques to analyze a user's emotional state.

[1204] Program processing overview

[1205] The system's processing consists mainly of the following components:

[1206] Initial Setup

[1207] The server acquires book data on business skills and presentation skills and trains the generative AI model. It also acquires data on the company's unique mindset and rules and trains the generative AI model.

[1208] New employee registration

[1209] The device displays a login screen for new employees, and the user enters their profile information. The information is sent to the server for analysis, and the server assigns an appropriate generative AI model based on the analysis results.

[1210] Correction of emails and presentation materials

[1211] The device provides an editor screen for the user, who then creates business emails and presentation materials. The created materials are sent to a server and analyzed by a generative AI model. The generative AI model then suggests improvements to grammar and expression, and sends this feedback to the device via the server.

[1212] Communication Support

[1213] The device provides a consultation form for users, and users input questions about workplace rules and business etiquette. The questions are sent via the server to a generative AI model, which generates answers. The generated answers are then displayed on the device.

[1214] Providing Feedback

[1215] The server records the user's activity on the system in real time. These logs are analyzed and sent to the generative AI model, which then periodically generates feedback that is displayed on the device via the server.

[1216] Sentiment Analysis and Adaptive Feedback

[1217] The device sends the user's input text and voice data to the emotion engine. The emotion engine analyzes the user's emotional state and reflects the results in the generative AI model. The generative AI model generates adaptive feedback based on the user's emotional state and displays it on the device via the server.

[1218] Specific examples

[1219] Example 1: Email editing and sentiment analysis

[1220] 1. A user types a draft of a business email into a terminal.

[1221] 2. The device sends the email draft to the server, which then sends it to the generative AI model for editing.

[1222] 3. The generative AI model suggests improvements to grammar, honorifics, and expressions.

[1223] 4. The device sends the input text to the emotion engine, which analyzes the emotional state.

[1224] 5. The server sends the emotion analysis results to the generative AI model and adaptively changes the feedback content.

[1225] 6. The server sends adaptive feedback to the terminal, which displays it to the user.

[1226] Example prompt sentence:

[1227] "Please review the business email below and suggest improvements to grammar, honorifics, and expressions. We'd also like you to perform sentiment analysis and provide adaptive feedback."

[1228] Example 2: Presentation correction and sentiment analysis

[1229] 1. The user uploads the presentation materials to the device.

[1230] 2. The device sends the document to the server, which then sends it to the generative AI model for correction.

[1231] 3. The generative AI model suggests improvements to the content, design, and logical structure of your slides.

[1232] 4. The device sends the input text or voice to the emotion engine, which analyzes the emotional state.

[1233] 5. The server sends the emotion analysis results to the generative AI model and adaptively changes the feedback content.

[1234] 6. The server sends adaptive feedback to the terminal, which displays it to the user.

[1235] Example prompt sentence:

[1236] "Please review the presentation below and suggest improvements to the content, design, and logical structure. Please also conduct a sentiment analysis and provide adaptive feedback."

[1237] The above is an embodiment of the invention of a generative AI mentor system that combines an emotion engine. This system allows new employees to receive appropriate support based on their emotions, allowing them to improve their skills more efficiently and adapt to the workplace.

[1238] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1239] Step 1: Initial Setup

[1240] The server acquires book data related to business skills and presentation skills. Specifically, it accesses and downloads corporate databases and external specialized materials. The acquired data is in document format (PDF or text file) or audio format (audio file). The server preprocesses this data, performing tokenization and data cleaning. The server then supplies the data to the generative AI model, which then performs learning. This allows the generative AI model to acquire knowledge related to business skills and presentation skills.

[1241] Input: Book data, corporate databases, external specialist materials

[1242] Output: Preprocessed data, learning results of generative AI model

[1243] Step 2: Register new employees

[1244] The device displays a login screen for new employees. The user accesses the login screen and enters profile information such as their name, department, desired skills, and desired support content. The entered profile information is sent from the device to the server. The server stores the received profile information in a database and analyzes it. Based on the analysis results, the server assigns an appropriate generative AI model to the user.

[1245] Input: New employee profile information

[1246] Output: Analysis results, assignment of generative AI model

[1247] Step 3: Editing emails and presentation materials

[1248] The device provides an editor screen for the user, who then creates business emails and presentation materials. The completed materials are sent from the device to the server. The server analyzes the received materials and generates prompts for the generative AI model. The server sends the materials and prompts to the generative AI model, requesting analysis. The generative AI model analyzes the grammar and expressions of the materials and suggests improvements. The server receives feedback from the generative AI model, reflects this in the document, and sends it to the device. The device displays the improved document and feedback to the user.

[1249] Input: business emails, presentation materials, prompts

[1250] Output: Feedback for generative AI models, improved documentation

[1251] Step 4: Communication support

[1252] The device provides a consultation form for users, and the user inputs questions about workplace rules and business etiquette. The device then sends the input question to the server. The server analyzes the question and, if necessary, sends the question content to the generative AI model as a prompt. The server receives the answer from the generative AI model and sends it to the device. The device then displays the appropriate answer to the user.

[1253] Input: User question, prompt

[1254] Output: The answer of the generative AI model, displayed to the user

[1255] Step 5: Provide feedback

[1256] The server records the user's activity log on the system in real time. The recorded activity log is analyzed, and the server sends the log data to the generative AI model, requesting it to generate feedback. The generative AI model analyzes the activity log and periodically generates feedback. The server sends the generated feedback to the device, which then displays it to the user.

[1257] Input: Activity log, prompt

[1258] Output: Feedback from the generative AI model, displayed to the user

[1259] Step 6: Sentiment Analysis and Adaptive Feedback

[1260] The device sends the user's input text and voice data to the emotion engine. The emotion engine analyzes the text and voice data and obtains the user's emotional state. The emotion engine's analysis results are sent to the server, which then sends an emotion-reflecting prompt to the generative AI model. The generative AI model generates adaptive feedback that takes the emotional state into account. The server sends the adaptive feedback to the device, which displays it to the user.

[1261] Input: User input text, voice data, and sentiment analysis results

[1262] Output: Analysis results of the emotion engine, adaptive feedback by the generative AI model, and display to the user

[1263] (Application example 2)

[1264] 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."

[1265] Traditional mentoring systems alone have limitations in helping new employees and new store associates quickly and effectively adapt to their workplace or store operations, particularly in terms of emotional support. This increases the likelihood of delayed workplace adaptation and work errors. Furthermore, it is difficult to obtain adaptive feedback in real time when dealing with customers or performing work tasks in a physical store. Therefore, there is a need for a system that provides adaptive learning support and real-time feedback that takes into account the emotional state of new employees and new store associates.

[1266] 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.

[1267] In this invention, the server includes: means for initializing a generative AI model for new employees and store clerks to learn business skills and company-specific rules; means for inputting and registering profile information for new employees and store clerks; means for sending created emails and presentation materials to the generative AI model and receiving feedback on areas for improvement; means for sending questions to the generative AI model and receiving answers; means for analyzing the activity logs of new employees and store clerks and providing overall feedback; means for store clerks to view and study store operations manuals; means for store clerks to perform customer interaction simulations; means for providing real-time work feedback; and means for analyzing the emotional state of store clerks using an emotion analysis engine and providing adaptive feedback. This allows new employees and store clerks to receive appropriate support according to their emotional state, allowing them to improve their skills more efficiently and quickly adapt to work in the workplace or store.

[1268] A "generative AI model" is an artificial intelligence technology that learns from large amounts of data and generates appropriate answers and feedback for specific tasks or questions.

[1269] "Profile information" refers to information such as an individual's basic attributes, skills, experience, and desired support content.

[1270] "Feedback" refers to the evaluation and advice that the generative AI model provides to new employees and store associates on their actions, materials, and questions.

[1271] An "activity log" is data that records the history of operations and actions performed on the system by new employees and store clerks.

[1272] An "emotion analysis engine" is software or a system that analyzes a user's emotional state from voice, text, facial expression data, etc.

[1273] "Adaptive feedback" refers to appropriate advice and assessments provided by a generative AI model based on the user's emotional state and situation.

[1274] A "store operations manual" is a guidebook that summarizes store operations procedures, customer service methods, rules, etc.

[1275] "Customer service simulation" refers to a system or program that allows store staff to practice simulating actual customer service situations.

[1276] "Real-time feedback" refers to feedback that store employees receive immediately while they are working.

[1277] Overall overview

[1278] The present invention is a system that helps new employees and new store associates quickly adapt to their workplace or store and efficiently improve their skills. This system uses a server, a terminal, a generative AI model, and a sentiment analysis engine. Specifically, it is implemented through the following steps.

[1279] System Configuration

[1280] The system of the present invention includes the following elements:

[1281] 1. Initial Setup

[1282] server:

[1283] Obtain book data or electronic data related to business skills and presentation skills and train a generative AI model.

[1284] The generative AI model is trained on data related to a company's unique mindset, rules, store operations manuals, and customer service skills.

[1285] 2. Registering new employees and store associates

[1286] Device:

[1287] A login screen for new employees and new store clerks is displayed, and users enter their profile information, including the required skills and desired support content.

[1288] The entered profile information is sent to the server, where it is analyzed and an appropriate generative AI model is assigned.

[1289] 3. Correction of emails and presentation materials

[1290] Device:

[1291] An editor screen is displayed, and the user creates a draft of a business email or a presentation document.

[1292] The created materials are sent to the server and then sent to the generative AI model for analysis.

[1293] Generative AI models:

[1294] Analyze the grammar and expressions of the material and suggest areas for improvement.

[1295] server:

[1296] The proposed feedback is sent to the terminal and displayed to the user.

[1297] 4. Communication support

[1298] Device:

[1299] A consultation form is displayed, and the user inputs a question about workplace rules and business etiquette.

[1300] server:

[1301] Send your questions to a generative AI model and ask for an answer.

[1302] Generative AI models:

[1303] Create an answer to the question and send it to the server.

[1304] server:

[1305] The answer is sent to the terminal and displayed to the user.

[1306] 5. Customer response simulation

[1307] Device:

[1308] It provides customer interaction simulations for users to practice.

[1309] Generative AI models:

[1310] The simulation content is analyzed in real time and feedback is provided.

[1311] 6. Providing Feedback

[1312] server:

[1313] Record and analyze user activity logs.

[1314] Based on the analysis results, the generative AI model provides regular feedback.

[1315] 7. Sentiment Analysis and Adaptive Feedback

[1316] Device:

[1317] The user's input text and voice data are sent to the sentiment analysis engine.

[1318] Sentiment Analysis Engine:

[1319] It analyzes the emotional state and sends the results to a generative AI model.

[1320] Generative AI models:

[1321] Generate adaptive feedback that takes emotional state into account.

[1322] server:

[1323] The provided adaptive feedback is sent to the terminal and displayed to the user.

[1324] Specific examples

[1325] Example 1: Customer interaction simulation

[1326] 1. The user begins a simulated customer interaction practice session on the smartphone app.

[1327] 2. The app uses voice and facial recognition technology to evaluate the user's reactions and responses.

[1328] 3. The emotion analysis engine analyzes the user's emotional state in real time, and the generative AI model provides real-time feedback such as "Take a deep breath and speak calmly."

[1329] Specific generative AI model prompt examples:

[1330] "A new employee is handling a customer complaint. Please rate the following response and suggest improvements. The user is feeling a bit anxious. Please take this into consideration when providing feedback. Here's what the user said:"

[1331] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1332] Step 1:

[1333] The server performs the initial setup.

[1334] Input: Data on business skills, presentation skills, company-specific rules, store operations manuals, and customer service skills.

[1335] Processing: The server trains the generative AI model on the collected book and electronic data, as well as the company's specific rules and mindset.

[1336] Output: A generative AI model with initial configuration completed.

[1337] Step 2:

[1338] The terminal registers new employees and store clerks.

[1339] Input: Profile information for new employees and store clerks (basic information, skills, experience, desired support).

[1340] Processing: The device displays a login screen, where the user enters their profile information. The information is sent to the server, which analyzes it and assigns an appropriate generative AI model.

[1341] Output: The assigned generative AI model and the user's profile data.

[1342] Step 3:

[1343] A user creates an email or presentation document and requests corrections.

[1344] Input: Business email drafts and presentation materials created by users.

[1345] Processing: The device displays the editor screen, the user creates a document, and sends it to the server. The server then sends the document to the generative AI model, which analyzes the document's grammar and expressions and suggests improvements.

[1346] Output: Suggested feedback.

[1347] Step 4:

[1348] Users can enter questions and receive communication support.

[1349] Input: User asks a question about workplace rules and business etiquette.

[1350] Processing: The device displays a consultation form, the user enters a question, and sends it to the server. The server sends the question to the generative AI model and requests an answer. The generative AI model creates an answer, which the server sends to the device.

[1351] Output: The generative AI model's answer to the question.

[1352] Step 5:

[1353] The user performs a customer interaction simulation.

[1354] Input: The command with which the user starts the simulation.

[1355] Processing: The terminal provides customer interaction simulations, and the generative AI model analyzes the simulation content in real time and provides appropriate feedback. It also uses an emotion analysis engine to analyze the user's emotional state.

[1356] Output: Feedback during the simulation.

[1357] Step 6:

[1358] The server provides feedback.

[1359] Input: User activity log.

[1360] Processing: The server records and analyzes the user's activity log, periodically generates feedback from the generative AI model, and sends the feedback to the device.

[1361] Output: Periodic feedback.

[1362] Step 7:

[1363] Providing adaptive feedback based on emotional state.

[1364] Input: User-entered text or voice data.

[1365] Processing: The device sends the input data to the emotion analysis engine to analyze the emotional state. The analysis results are sent to the generative AI model to generate adaptive feedback. The server sends the adaptive feedback to the device.

[1366] Output: Adaptive feedback taking into account emotional state.

[1367] 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.

[1368] 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.

[1369] 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.

[1370] [Third embodiment]

[1371] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[1372] 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.

[1373] 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).

[1374] 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.

[1375] 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.

[1376] 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).

[1377] 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.

[1378] 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.

[1379] 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.

[1380] 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.

[1381] 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.

[1382] 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."

[1383] This invention relates to a generative AI mentoring system that complements traditional mentoring systems to help new employees commit smoothly to the workplace and prevent early turnover. This system provides an environment where new employees can easily seek advice and receive individual feedback.

[1384] Overall overview

[1385] This generative AI mentor system performs the following processes using a server, terminals, and generative AI models.

[1386] 1. Initial Setup

[1387] 2. New employee registration

[1388] 3. Correction of emails and presentation materials

[1389] 4. Communication support

[1390] 5. Providing Feedback

[1391] Program processing explanation

[1392] The program processing at each step will be explained below in order.

[1393] 1. Initial Setup

[1394] The server first obtains book data on business skills and presentation skills and trains the generative AI model.

[1395] The server also acquires data on the company's unique mindset and rules, which it then uses to train the generative AI model.

[1396] 2. New employee registration

[1397] The terminal displays a login screen for new employees, where the user (new employee) enters profile information such as name, job title, skill set, desired support content, etc.

[1398] The terminal transmits the entered profile information to the server.

[1399] The server analyzes the new employee's profile and assigns the most appropriate generative AI model.

[1400] 3. Correction of emails and presentation materials

[1401] The terminal displays an editor screen for the user, and the user creates a draft of a business email or presentation materials.

[1402] The terminal transmits the completed draft or document to the server.

[1403] The server sends the submitted materials to a generative AI model, which analyzes grammar and expressions and provides feedback on areas for improvement.

[1404] The server receives the feedback and provides it to the user.

[1405] The terminal displays the feedback to the user.

[1406] 4. Communication support

[1407] The terminal displays a consultation form for the user, and the user inputs a question.

[1408] The terminal sends a query to the server.

[1409] The server sends the question to a generative AI model, which generates an answer.

[1410] The server receives the generative AI model's answer and provides it to the user.

[1411] The terminal displays the provided answers to the user.

[1412] 5. Providing Feedback

[1413] The server records and analyzes a log of the user's activities on the system.

[1414] The server periodically retrieves the analysis results from the generated AI model and generates overall feedback.

[1415] The server provides feedback to the user.

[1416] The terminal displays the provided feedback to the user.

[1417] Specific examples

[1418] Example 1: Email editing

[1419] 1. A user types a draft of a business email into a terminal.

[1420] 2. The device sends the email draft to the server.

[1421] 3. The server sends the draft to the generative AI model for editing.

[1422] 4. The generative AI model suggests improvements to grammar, honorifics, and expressions.

[1423] 5. The server receives the feedback and sends it to the device.

[1424] 6. The device displays feedback to the user.

[1425] Example 2: Presentation correction

[1426] 1. The user uploads the presentation materials to the device.

[1427] 2. The device sends the materials to the server.

[1428] 3. The server sends the document to the generative AI model and requests corrections.

[1429] 4. The generative AI model suggests improvements to the content, design, and logical structure of your slides.

[1430] 5. The server receives the feedback and sends it to the device.

[1431] 6. The device displays feedback to the user.

[1432] The above is the details of the mode for carrying out the invention. This enables new employees to use the generative AI mentor system to smoothly adapt to the workplace and improve their skills.

[1433] The processing flow will be explained below.

[1434] 1. Initial Setup

[1435] Step 1:

[1436] The server acquires book data on business skills and presentation skills.

[1437] Step 2:

[1438] The data acquired by the server is trained into a generative AI model.

[1439] Step 3:

[1440] The server collects data on the company's unique mindset and rules.

[1441] Step 4:

[1442] The server trains the generative AI model on data about the company's mindset and rules.

[1443] 2. New employee registration

[1444] Step 1:

[1445] The terminal displays the login screen for new employees.

[1446] Step 2:

[1447] Users enter their profile information on the login screen and register the skills they need and the support they would like.

[1448] Step 3:

[1449] The device transmits the entered profile information to the server.

[1450] Step 4:

[1451] The server analyzes the new employee's profile and assigns an appropriate generative AI model.

[1452] 3. Correction of emails and presentation materials

[1453] Step 1:

[1454] The terminal displays the editor screen for the user.

[1455] Step 2:

[1456] The user creates a draft of a business email or a presentation in the editor.

[1457] Step 3:

[1458] The terminal transmits the drafts and documents created to the server.

[1459] Step 4:

[1460] The server sends the data to the generative AI model and requests it to analyze it.

[1461] Step 5:

[1462] The generative AI model analyzes the grammar and expressions of the document and suggests areas for improvement.

[1463] Step 6:

[1464] The server receives feedback from the generative AI model and sends it to the device.

[1465] Step 7:

[1466] The device displays the feedback to the user.

[1467] 4. Communication support

[1468] Step 1:

[1469] The terminal displays a consultation form for the user.

[1470] Step 2:

[1471] The user enters a question about workplace rules and business etiquette into a consultation form.

[1472] Step 3:

[1473] The terminal sends the entered question to the server.

[1474] Step 4:

[1475] The server sends the question to the generative AI model and requests an answer.

[1476] Step 5:

[1477] A generative AI model creates answers to questions.

[1478] Step 6:

[1479] The server receives the answer from the generated AI model and sends it to the device.

[1480] Step 7:

[1481] The terminal displays the provided answers to the user.

[1482] 5. Providing Feedback

[1483] Step 1:

[1484] The server logs the user's activity on the system.

[1485] Step 2:

[1486] The server analyzes the recorded activity logs and periodically generates feedback from the generative AI model.

[1487] Step 3:

[1488] The server sends the generated feedback to the user.

[1489] Step 4:

[1490] The terminal displays the notified feedback to the user.

[1491] These are the specific steps in the programming process of the generative AI mentor system, which allows new employees to efficiently improve their skills and adapt to the workplace.

[1492] Example 1

[1493] 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."

[1494] An appropriate support environment is necessary to enable new employees to smoothly adapt to the workplace and prevent early attrition. However, traditional mentoring systems have problems with providing individual feedback and making it difficult to carry out effective follow-up. Furthermore, when many new employees join the company at once, the burden on mentors increases, and as a result, there is a risk that high-quality support will not be provided. To solve these problems, a system that utilizes generative AI models to provide appropriate support and feedback to new employees is needed.

[1495] 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.

[1496] In this invention, the server includes means for initializing a generative AI model that has learned business skills and company-specific rules, means for inputting and registering new employee profile information, means for sending created documents to the generative AI model and receiving feedback on areas for improvement, means for sending questions to the generative AI model and receiving answers, means for analyzing the new employee's activity log and providing overall feedback, means for sending information input at the terminal to the server, and means for receiving analysis results from the generative AI model at the server and sending them to the terminal. This allows new employees to not only receive individual feedback, but also quickly receive specific advice on how to adapt to the workplace in an appropriate support environment.

[1497] "Business skills" refers to a set of skills required in the workplace, such as business etiquette, communication skills, problem-solving skills, and presentation skills.

[1498] "Company-specific rules" refer to the unique mindset, code of conduct, ethical guidelines, internal rules, etc. established by a particular company.

[1499] A "generative AI model" is a system that uses artificial intelligence technology to learn and generate, and refers to a model that uses natural language processing technology to generate feedback and answers for users.

[1500] "Initialization means" refers to the process of providing the necessary training data for a generative AI model and configuring the model to suit the specific needs of the company.

[1501] "Profile Information" refers to personal information entered by a user, such as name, job title, skill set, and desired support type.

[1502] "Document" refers to text data such as a business email draft or presentation material created by a user.

[1503] "Activity log" refers to data that records a user's actions and interactions when using a system.

[1504] "Feedback" refers to specific advice provided by the generative AI model on areas for improvement such as grammar, expression, pay, design, and logical structure.

[1505] "Means for obtaining an answer" refers to the process by which a user sends a question to a generative AI model and obtains an answer to that question from the generative AI model.

[1506] "Analysis results" refers to the results of the analysis performed by the generative AI model based on the user's input data.

[1507] This invention relates to a generative AI mentoring system that complements traditional mentoring systems to help new employees smoothly adapt to the workplace and prevent early turnover. The system provides an environment where new employees can easily seek advice and receive individual feedback.

[1508] Hardware and software used

[1509] This system operates using a server, a terminal, and a generative AI model. The specific hardware and software used are as follows:

[1510] Server: Manages and processes data to learn business skills and company-specific rules.

[1511] Software used: "Learning-Resource-Server", "Profile-Analyzer", "Grammar-Checker AI", "Pitch-Perfect AI", "Question-Responder AI", "Activity-Analyzer", "Activity-Logger", etc.

[1512] Terminal: Provides an interface for users (new employees) to enter information and receive feedback and responses.

[1513] Software used: "Employee-Portal", "Business-Editor", "Consultation-Form", etc.

[1514] Program processing explanation

[1515] The program for this system consists of multiple processing steps that mainly involve the server, terminal, and generative AI model working together. Each processing step is explained below in natural language.

[1516] Initial Setup

[1517] The server collects book data on business skills and presentation skills and trains the generative AI model via the "Learning-Resource-Server."

[1518] The server also collects document data about the company's unique mindset and rules, and similarly trains the generative AI model. This process allows the generative AI model to respond to the specific needs of the company.

[1519] New employee registration

[1520] When a user accesses the system for the first time, the terminal displays a login screen for new employees. This system uses the "Employee-Portal."

[1521] The user (new employee) enters profile information (name, position, skill set, desired support content, etc.) on the login screen.

[1522] The terminal transmits the profile information entered by the user to the server.

[1523] The server uses the "Profile-Analyzer" to analyze the profile information of new employees and assign the optimal generative AI model. At this time, the parameters of the generative AI model are adjusted according to the employee's skill set and desired support content.

[1524] Correction of emails and presentation materials

[1525] The terminal displays an editor screen where users can create business emails and presentation materials. This editor uses "Business-Editor."

[1526] The user creates a draft of a business email or a presentation document on the editor screen.

[1527] The terminal transmits the completed email draft or presentation materials to the server.

[1528] The server sends the received materials to generative AI models, such as "Grammar-Checker AI" and "Pitch-Perfect AI," and requests them to analyze grammar, expression, design, and logical structure.

[1529] The generative AI model provides feedback on grammatical errors, appropriate phrasing, and areas for improvement in the design and logical structure of the presentation materials.

[1530] The server receives the generated feedback and transmits it to the terminal.

[1531] The device displays feedback to the user, allowing them to specifically understand areas for improvement and make corrections.

[1532] Communication Support

[1533] The terminal displays a consultation form where the user can enter their questions. This form uses the "Consultation-Form".

[1534] The user inputs a question into the consultation form. Specifically, the user inputs a question such as "Please tell me how to approach a new project."

[1535] The terminal transmits the entered question to the server.

[1536] The server uses "Question-Responder AI" to have a generative AI model generate answers to questions.

[1537] Generative AI models generate specific advice and answers to questions.

[1538] The server receives the generated response and transmits it to the terminal.

[1539] The terminal displays the provided answers to the user, who can receive advice in real time.

[1540] Providing Feedback

[1541] The server records the user's actions and interactions on the system (for example, what questions they asked, what feedback they received), and uses the "Activity-Logger" as the recording system.

[1542] The server periodically analyzes the recorded activity log using "Activity-Analyzer."

[1543] The server sends the analysis results to a generative AI model to generate overall feedback.

[1544] The generative AI model provides feedback on the user's progress and areas for improvement, such as "Your communication in a recent project has improved" or "Your presentation slides are now more logically structured."

[1545] The server provides the generated feedback to the user.

[1546] The device displays rich feedback to users, helping them track their progress, and also displays a feedback dashboard.

[1547] Examples of concrete examples and prompts

[1548] Example 1: Email editing

[1549] 1. The user inputs a draft of a business email into the terminal, for example, using "Business-Editor."

[1550] 2. The device sends the email draft to the server.

[1551] 3. The server sends the draft to a generative AI model for correction, specifically using "Grammar-Checker AI."

[1552] 4. The generative AI model suggests improvements to grammar, honorifics, and expressions.

[1553] 5. The server receives the feedback and sends it to the device.

[1554] 6. The device displays feedback to the user.

[1555] Example 2: Presentation correction

[1556] 1. The user uploads the presentation materials to the device, for example, using "Business-Editor."

[1557] 2. The device sends the materials to the server.

[1558] 3. The server sends the document to a generative AI model and requests corrections. Specifically, it uses "Pitch-Perfect AI."

[1559] 4. The generative AI model suggests improvements to the content, design, and logical structure of your slides.

[1560] 5. The server receives the feedback and sends it to the device.

[1561] 6. The device displays feedback to the user.

[1562] Examples of prompts include:

[1563] How do you approach a new project?

[1564] "Please check the grammar of this business email."

[1565] "How can I improve the design of my presentation materials?"

[1566] This concludes the detailed description of the embodiments of the invention, which will enable new employees to make the most of the generative AI mentor system, allowing them to smoothly adapt to the workplace and improve their skills.

[1567] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1568] Step 1: Initial Setup

[1569] The server collects book data on business skills and presentation skills.

[1570] As input, business books and materials are collected from external databases and the Internet.

[1571] As part of the data processing, the collected data is analyzed via the "Learning-Resource-Server."

[1572] The output provides training data for the generative AI model.

[1573] The server also collects document data about the company's unique mindset and rules.

[1574] As input, company policy documents and manuals are collected.

[1575] As part of the data processing, these documents are tokenized and text analyzed.

[1576] The output provides training data for the generative AI model.

[1577] Step 2: Register new employees

[1578] The terminal displays a login screen for new employees.

[1579] As input, a user attempts to log in.

[1580] As a result, the terminal launches the "Employee-Portal" and displays the login screen.

[1581] As an output, a login form is provided to the user.

[1582] The user (new employee) enters profile information.

[1583] As input, the user enters their name, job title, skill set, and desired support type.

[1584] In operation, the user enters the required information into each information field.

[1585] The terminal transmits the entered profile information to the server.

[1586] As input, the terminal receives the user's profile data.

[1587] In operation, the terminal issues a request to send these data to the server.

[1588] As an output, the profile data is sent to a server.

[1589] The server analyzes the profile information of the new employee.

[1590] The profile data received by the server as input.

[1591] For data calculation, the server uses "Profile-Analyzer" to analyze the data.

[1592] As an output, the new employee is assigned the best suited generative AI model.

[1593] Step 3: Editing emails and presentation materials

[1594] The terminal displays an editor screen for the user.

[1595] As input, the user accesses the editor screen.

[1596] As an operation, the editor "Business-Editor" is launched and displayed to the user.

[1597] As an output, an editor screen is provided.

[1598] Users create drafts of business emails and presentation materials on the editor screen.

[1599] As input, the user enters text and presentation slides into the editor screen.

[1600] In action, the user creates a draft or slide.

[1601] The terminal transmits the completed document to the server.

[1602] As input, the editor sends the completed draft or material.

[1603] In operation, the terminal issues a request to transmit material data to the server.

[1604] As an output, the document data is sent to the server.

[1605] The server sends the submitted materials to the generative AI model and requests it to analyze them.

[1606] Source data received by the server as input.

[1607] To calculate the data, the server requests "Grammar-Checker AI" and "Pitch-Perfect AI" to analyze the materials.

[1608] As an output, the analysis results are produced.

[1609] The generative AI model provides feedback on grammatical errors and areas for improvement in design and logical structure.

[1610] As input, material data.

[1611] As a data calculation, the generative AI model analyzes the material and generates specific feedback.

[1612] As an output, feedback data is generated.

[1613] The server sends the feedback data to the terminal for providing to the user.

[1614] As input, feedback data from the generative AI model.

[1615] In operation, the server transmits feedback data to the terminal.

[1616] As an output, feedback is sent to the terminal.

[1617] The terminal displays feedback to the user.

[1618] As input, the received feedback data.

[1619] As an action, feedback is displayed to the user.

[1620] As an output, feedback content is provided to the user.

[1621] Step 4: Communication support

[1622] The terminal displays a consultation form for the user.

[1623] As input, the user accesses a consultation form.

[1624] As a result, the "Consultation-Form" is displayed.

[1625] As output, a consultation form is provided.

[1626] The user enters their question into the consultation form.

[1627] As input, the user enters the consultation content into the form.

[1628] In action, the user enters a specific question.

[1629] The terminal transmits the entered question to the server.

[1630] As input, the question data entered by the user.

[1631] In operation, the query data is sent to the server.

[1632] As an output, the query data is sent to the server.

[1633] The server sends the question to a generative AI model, which generates an answer.

[1634] The query data received by the server as input.

[1635] As a data calculation, the "Question-Responder AI" analyzes the question and generates an answer.

[1636] As an output, response data is generated.

[1637] The server sends the generated response to the terminal.

[1638] As input, the generated response data.

[1639] As an operation, the response data is sent to the terminal.

[1640] As an output, the answer data is provided to the terminal.

[1641] The terminal displays the answer to the user.

[1642] As input, the received response data.

[1643] As an action, the answer is displayed to the user.

[1644] As an output, the answer content is presented to the user.

[1645] Step 5: Provide feedback

[1646] The server records the user's actions and interactions on the system.

[1647] As input, user activity log data.

[1648] In operation, "Activity-Logger" records user actions.

[1649] As an output, the activity log data is saved.

[1650] The server periodically analyzes the recorded activity log.

[1651] As input, the saved activity log data.

[1652] For data calculation, the server performs analysis using "Activity-Analyzer."

[1653] As an output, analysis result data is generated.

[1654] The server sends the analysis results to a generative AI model to generate overall feedback.

[1655] As input, the analysis result data.

[1656] As a data calculation, the generative AI model generates feedback based on the analysis results.

[1657] As an output, feedback data is generated.

[1658] The server transmits the generated feedback to the terminal.

[1659] As input, the generated feedback data.

[1660] As an operation, feedback data is transmitted to the terminal.

[1661] As an output, feedback data is provided to the terminal.

[1662] The terminal displays feedback to the user.

[1663] As input, the received feedback data.

[1664] As an action, feedback is displayed to the user.

[1665] As an output, feedback content is presented to the user.

[1666] (Application example 1)

[1667] 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."

[1668] In order for new employees to adapt quickly to the factory floor and perform their work smoothly, they need immediate support and appropriate feedback. However, traditional mentoring systems alone may not be able to provide sufficient support for each new employee, making it difficult for them to efficiently acquire skills and perform work safely. Therefore, a system is needed that can properly teach the factory's unique procedures and safety guidelines and quickly respond to any questions new employees may have.

[1669] 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.

[1670] In this invention, the server includes a means for learning the factory-specific procedures and safety guidelines, a means for providing real-time support for new employees to carry out on-site work procedures and safety checks, and a means for checking reports and memos created by new employees in real time and providing feedback, thereby enabling new employees to quickly adapt to the factory floor and perform their work safely and efficiently.

[1671] A "generative AI model" is an artificial intelligence system that helps new employees learn the skills and knowledge they need to perform their jobs and generates feedback and answers.

[1672] "Initial setup" refers to the preparatory work of teaching the generative AI model the business skills that new employees should learn, the company's unique rules, and factory procedures and safety guidelines.

[1673] "Profile Information" refers to personal data such as a new hire's name, job title, skill set, and desired support type.

[1674] "Feedback on areas for improvement" involves the generative AI model analyzing emails and presentation materials created by new employees and providing feedback and suggestions regarding grammar, structure, expression, etc.

[1675] "Means of sending questions to a generative AI model and obtaining answers" refers to a system in which new employees send questions that arise during work to a generative AI model and obtain solutions or explanations.

[1676] An "activity log" is a record of all operations and actions performed by new employees on the system.

[1677] The "means of providing feedback" refers to a system that analyzes new employees' profile information and activity logs and regularly provides advice for growth and improvement.

[1678] "Reports and memos to be prepared" refers to documents that new employees use to record the status and results of their work.

[1679] "Means for providing real-time support" refers to a system that instantly displays and provides necessary information and instructions to new employees when they perform their work on-site.

[1680] A "procedure manual" is a document that describes the specific steps and methods for properly carrying out work.

[1681] "Safety guidelines" are documents that list safety measures and precautions to take at work sites such as factories.

[1682] This invention provides a generative AI mentoring system that helps new employees quickly adapt to the workplace and perform their work safely and efficiently. The system consists of the following elements:

[1683] 1. Initial Setup

[1684] The server acquires the factory's specific procedures and safety guidelines and trains the generative AI model. It also trains the model on data related to the company's unique mindset, rules, and business skills. In this way, the generative AI model acquires the knowledge necessary for on-site work.

[1685] 2. New employee registration

[1686] New employees use their devices to access a login screen and enter their profile information, such as their name, job title, skill set, and desired support content. This information is sent to a server, which analyzes the new employee's profile and assigns the optimal generative AI model.

[1687] 3. Business support and real-time support

[1688] As new employees work on-site, the devices display real-time information on factory procedures and safety checks. If a new employee has a question, they can type it into the device, which sends it to the generative AI model via the server. The answer is then provided to the device via the server, where the new employee can immediately check it.

[1689] 4. Correction of emails and presentation materials

[1690] Reports and memos written by new employees are sent to a server via their device. The server then feeds the documents into a generative AI model and receives feedback on improvements to grammar, expression, and structure. The feedback is displayed on the device for the new employee to review.

[1691] 5. Providing Feedback

[1692] The server records and analyzes the activity logs of new employees. Periodically, a generative AI model evaluates the log data and generates feedback for growth and improvement, allowing new employees to continuously improve their skills.

[1693] Hardware and software used

[1694] Hardware: Factory robots (such as Universal Robots' UR series), data servers, and terminals (PCs or tablets)

[1695] Software: Generative AI models (e.g., GPT-4), databases (e.g., MongoDB)

[1696] Specific examples

[1697] Example 1: New employee asking a question

[1698] Question: "I don't know what to do today. What exactly should I do?"

[1699] process:

[1700] 1. The new employee types a question into the terminal.

[1701] 2. The server sends the question to the generative AI model, which generates an answer.

[1702] 3. The server sends the generated answer to the terminal, where the new employee can view it.

[1703] 4. This process ensures that new employees are immediately aware of the steps they need to take.

[1704] Example 2: New employee submitting a report

[1705] Report: "Please correct today's work report. Please let me know if there are any mistakes."

[1706] process:

[1707] 1. The new employee uploads the report to the terminal.

[1708] 2. The server sends the report to the generative AI model for correction.

[1709] 3. The generative AI model generates feedback suggesting improvements to grammar and expression.

[1710] 4. The server sends the feedback to the terminal, where the new employee can check it.

[1711] 5. This feedback will enable the new employee to improve the quality of their reports.

[1712] This completes the description of the preferred embodiment of the present invention. This system allows new employees to quickly adapt to the factory floor and perform their jobs safely and efficiently.

[1713] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1714] Step 1: Initial Setup

[1715] The server acquires data related to the factory's specific procedures and safety guidelines, the company's unique mindset and rules, and business skills. This data is trained into a generative AI model. The input is data related to the factory's procedures, safety guidelines, mindset, rules, and business skills. The output is the knowledge learned by the generative AI model. The server takes in this data and performs data analysis and model training.

[1716] Step 2: Register new employees

[1717] The user (new employee) uses a terminal to access the login screen and enters profile information such as name, job title, skill set, and desired support content. The input is the new employee's profile information. The terminal sends this to the server. The server analyzes the profile information and assigns the optimal generative AI model. The output is the assigned generative AI model.

[1718] Step 3: Operational and real-time support

[1719] When new employees work on-site, the terminal displays information on factory work procedures and safety checks in real time. The input is data on work procedures and safety checks. The output is the information displayed on the terminal. The terminal receives various data necessary for on-site work from the server and presents it to the user in a timely manner.

[1720] Step 4: Submit your question and get an answer

[1721] When a user (new employee) has a question, they input it into the terminal. The input is the user's question. The terminal sends the question to the server. The server sends the question to the generative AI model and generates an answer. The output is the generated answer. The server sends the answer received from the generative AI model to the terminal, and the user confirms it.

[1722] Step 5: Editing emails and presentation materials

[1723] The user (new employee) uploads a report or memo they created to the device. The input is the report or memo. The device sends it to the server. The server sends the report or memo to a generative AI model and receives feedback on improvements to grammar, expression, and structure. The output is feedback. The server sends the feedback to the device so that the user can review it.

[1724] Step 6: Provide feedback

[1725] The server records new employee activity logs and periodically analyzes them. The input is the activity log. The server evaluates the log data using a generative AI model and generates feedback for growth and improvement. The output is feedback for growth and improvement. The server sends the generated feedback to a terminal, where the new employee can check it.

[1726] The above is the specific flow of operations at each processing step of this system, which enables new employees to quickly adapt to the factory floor and perform their work safely and efficiently.

[1727] 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.

[1728] This invention relates to a generative AI mentoring system that complements traditional mentoring systems to help new employees smoothly commit to the workplace. This system includes an emotion engine that recognizes the user's emotions, and can analyze the user's emotional state and reflect it in the feedback of the generative AI model.

[1729] Overall overview

[1730] This generative AI mentor system performs the following processes using a server, terminals, a generative AI model, and an emotion engine.

[1731] 1. Initial Setup

[1732] 2. New employee registration

[1733] 3. Correction of emails and presentation materials

[1734] 4. Communication support

[1735] 5. Providing Feedback

[1736] 6. Sentiment Analysis and Adaptive Feedback

[1737] Program processing explanation

[1738] The program processing at each step will be explained below in order.

[1739] 1. Initial Setup

[1740] The server retrieves book data on business skills and presentation skills and trains the generative AI model.

[1741] The server collects data on the company's unique mindset and rules and trains the generative AI model.

[1742] 2. New employee registration

[1743] The terminal displays a login screen for new employees, where the user enters their profile information and registers the skills they need and the support they would like to receive.

[1744] The device transmits the entered profile information to the server.

[1745] The server analyzes the new employee's profile and assigns an appropriate generative AI model.

[1746] 3. Correction of emails and presentation materials

[1747] The terminal displays an editor screen for the user, and the user creates a draft of a business email or presentation materials.

[1748] The terminal transmits the created materials to the server.

[1749] The server sends the data to the generative AI model and requests it to analyze it.

[1750] The generative AI model analyzes the grammar and expressions of the document and suggests areas for improvement.

[1751] The server receives feedback from the generative AI model and sends it to the device, where it is displayed to the user.

[1752] 4. Communication support

[1753] The terminal displays a consultation form for the user, and the user inputs questions about workplace rules and business etiquette.

[1754] The terminal sends a question to the server.

[1755] The server sends the question to the generative AI model and requests an answer.

[1756] A generative AI model creates answers to questions.

[1757] The server receives the answer from the generated AI model, sends it to the device, and the device displays it to the user.

[1758] 5. Providing Feedback

[1759] The server logs the user's activity on the system.

[1760] The server analyzes the recorded activity logs and periodically generates feedback from the generative AI model.

[1761] The server sends the generated feedback to the terminal, which displays it to the user.

[1762] 6. Sentiment Analysis and Adaptive Feedback

[1763] The device sends the user's input text and voice data to the emotion engine.

[1764] An emotion engine analyzes the user's emotional state.

[1765] The emotion engine sends the analysis results to the generative AI model, which then uses them as feedback.

[1766] The server receives the analysis results from the emotion engine and adaptively changes the feedback content of the generative AI model.

[1767] The server sends adaptive feedback to the terminal, which displays it to the user.

[1768] Specific examples

[1769] Example 1: Email editing and sentiment analysis

[1770] 1. A user types a draft of a business email into a terminal.

[1771] 2. The device sends the email draft to the server, which then sends it to the generative AI model for editing.

[1772] 3. The generative AI model suggests improvements to grammar, honorifics, and expressions.

[1773] 4. The device sends the input text to the emotion engine, which analyzes the emotional state.

[1774] 5. The server sends the emotion analysis results to the generative AI model and adaptively changes the feedback content.

[1775] 6. The server sends adaptive feedback to the terminal, which displays it to the user.

[1776] Example 2: Presentation correction and sentiment analysis

[1777] 1. The user uploads the presentation materials to the device.

[1778] 2. The device sends the document to the server, which then sends it to the generative AI model for correction.

[1779] 3. The generative AI model suggests improvements to the content, design, and logical structure of your slides.

[1780] 4. The device sends the input text or voice to the emotion engine, which analyzes the emotional state.

[1781] 5. The server sends the emotion analysis results to the generative AI model and adaptively changes the feedback content.

[1782] 6. The server sends adaptive feedback to the terminal, which displays it to the user.

[1783] The above is a detailed description of the embodiment of the invention of a generative AI mentor system that combines an emotion engine. This enables new employees to receive appropriate support based on their emotions, improve their skills more efficiently, and adapt to the workplace.

[1784] The processing flow will be explained below.

[1785] 1. Initial Setup

[1786] Step 1:

[1787] The server acquires book data on business skills and presentation skills.

[1788] Step 2:

[1789] The data acquired by the server is trained into a generative AI model.

[1790] Step 3:

[1791] The server collects data on the company's unique mindset and rules.

[1792] Step 4:

[1793] The server trains the generative AI model on data about the company's mindset and rules.

[1794] 2. New employee registration

[1795] Step 1:

[1796] The terminal displays the login screen for new employees.

[1797] Step 2:

[1798] Users enter their profile information on the login screen and register the skills they need and the support they would like.

[1799] Step 3:

[1800] The device transmits the entered profile information to the server.

[1801] Step 4:

[1802] The server analyzes the new employee's profile and assigns an appropriate generative AI model.

[1803] 3. Correction of emails and presentation materials

[1804] Step 1:

[1805] The terminal displays the editor screen for the user.

[1806] Step 2:

[1807] The user creates a draft of a business email or a presentation in the editor.

[1808] Step 3:

[1809] The terminal transmits the drafts and documents created to the server.

[1810] Step 4:

[1811] The server sends the data to the generative AI model and requests it to analyze it.

[1812] Step 5:

[1813] The generative AI model analyzes the grammar and expressions of the document and suggests areas for improvement.

[1814] Step 6:

[1815] The server receives feedback from the generative AI model and sends it to the device.

[1816] Step 7:

[1817] The device displays the feedback to the user.

[1818] 4. Communication support

[1819] Step 1:

[1820] The terminal displays a consultation form for the user.

[1821] Step 2:

[1822] The user enters a question about workplace rules and business etiquette into a consultation form.

[1823] Step 3:

[1824] The terminal sends the entered question to the server.

[1825] Step 4:

[1826] The server sends the question to the generative AI model and requests an answer.

[1827] Step 5:

[1828] A generative AI model creates answers to questions.

[1829] Step 6:

[1830] The server receives the answer from the generated AI model and sends it to the device.

[1831] Step 7:

[1832] The terminal displays the provided answers to the user.

[1833] 5. Providing Feedback

[1834] Step 1:

[1835] The server logs the user's activity on the system.

[1836] Step 2:

[1837] The server analyzes the recorded activity logs and periodically generates feedback from the generative AI model.

[1838] Step 3:

[1839] The server transmits the generated feedback to the terminal.

[1840] Step 4:

[1841] The terminal displays the notified feedback to the user.

[1842] 6. Sentiment Analysis and Adaptive Feedback

[1843] Step 1:

[1844] The device sends the user's input text and voice data to the emotion engine.

[1845] Step 2:

[1846] An emotion engine analyzes the user's emotional state.

[1847] Step 3:

[1848] The emotion engine sends the analysis results to the server.

[1849] Step 4:

[1850] The server sends the emotion analysis results to the generative AI model and requests it to adaptively change the feedback content.

[1851] Step 5:

[1852] The generative AI model adjusts the feedback content based on the results of sentiment analysis.

[1853] Step 6:

[1854] The server sends the adapted feedback to the terminal.

[1855] Step 7:

[1856] The terminal displays adaptive feedback to the user.

[1857] The above is a detailed description of the embodiment of the invention of a generative AI mentor system that combines an emotion engine. This enables new employees to receive appropriate support according to their emotions, improve their skills more efficiently, and adapt to the workplace.

[1858] Example 2

[1859] 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."

[1860] Traditional mentoring systems often lack sufficient support to help new employees smoothly adapt to the workplace. In particular, they face the problem of not only failing to quickly learn business skills and company-specific rules, but also failing to receive appropriate feedback tailored to their individual emotional state. As a result, there are concerns that new employees may not be able to adapt to the workplace well and may find it difficult to improve their skills efficiently.

[1861] 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.

[1862] In this invention, the server includes: means for initializing a generative AI model for learning business skills and company-specific rules; means for inputting and registering new employee profile information; means for sending created emails and presentation materials to the generative AI model and receiving feedback on areas for improvement; means for sending questions to the generative AI model and receiving answers; means for analyzing the new employee's activity log and providing overall feedback; emotion engine means for analyzing user input text and voice data to obtain emotional states; and means for reflecting the emotional states from the emotion engine in the generative AI model and providing adaptive feedback. This allows new employees to quickly learn business skills and company-specific rules, receive appropriate feedback according to their individual emotional states, and smoothly adapt to the workplace.

[1863] "Business skills" refers to the knowledge and skills required to carry out work in the workplace, including communication skills, problem-solving skills, leadership skills, and time management skills.

[1864] "Company-specific rules" refer to internal rules and guidelines established by a specific company, including compliance, ethics rules, and internal company manuals, and are guidelines for behavior and operating procedures that employees must follow.

[1865] A "generative AI model" is a machine learning model that is trained using artificial intelligence techniques to perform a specific task (e.g., sentence generation, grammar checking, data analysis, etc.).

[1866] An "emotion engine" refers to a program or system that has the ability to analyze user input data (text or voice) and determine the user's emotional state based on that data.

[1867] "Feedback" refers to the information and suggestions that a generative AI model provides as a result of its analysis and judgment, suggesting areas for improvement or next steps based on user-entered data and actions.

[1868] "Activity logs" refer to data that records the operations and inputs that users make on the system. These are important data for analyzing and generating feedback.

[1869] "Adaptive feedback" refers to personalized feedback that is adjusted according to the user's emotional state and behavioral history. Its distinctive feature is that the content changes according to the user's state and needs.

[1870] This invention relates to a generative AI mentoring system that complements traditional mentoring systems to help new employees smoothly commit to the workplace. This system is equipped with an emotion engine that recognizes the user's emotions, analyzes the user's emotional state, and reflects this in the feedback of the generative AI model.

[1871] Hardware and software used

[1872] This system consists of the following hardware and software:

[1873] Server: A high-performance computer for running the generative AI models and emotion engine, and managing user data.

[1874] Device: A device such as a computer, tablet, or smartphone that the new employee will have access to.

[1875] Generative AI model: A machine learning model that uses natural language processing technology to correct business emails and answer questions.

[1876] Emotion engine: Software that uses speech recognition and natural language processing techniques to analyze a user's emotional state.

[1877] Program processing overview

[1878] The system's processing consists mainly of the following components:

[1879] Initial Setup

[1880] The server acquires book data on business skills and presentation skills and trains the generative AI model. It also acquires data on the company's unique mindset and rules and trains the generative AI model.

[1881] New employee registration

[1882] The device displays a login screen for new employees, and the user enters their profile information. The information is sent to the server for analysis, and the server assigns an appropriate generative AI model based on the analysis results.

[1883] Correction of emails and presentation materials

[1884] The device provides an editor screen for the user, who then creates business emails and presentation materials. The created materials are sent to a server and analyzed by a generative AI model. The generative AI model then suggests improvements to grammar and expression, and sends this feedback to the device via the server.

[1885] Communication Support

[1886] The device provides a consultation form for users, and users input questions about workplace rules and business etiquette. The questions are sent via the server to a generative AI model, which generates answers. The generated answers are then displayed on the device.

[1887] Providing Feedback

[1888] The server records the user's activity on the system in real time. These logs are analyzed and sent to the generative AI model, which then periodically generates feedback that is displayed on the device via the server.

[1889] Sentiment Analysis and Adaptive Feedback

[1890] The device sends the user's input text and voice data to the emotion engine. The emotion engine analyzes the user's emotional state and reflects the results in the generative AI model. The generative AI model generates adaptive feedback based on the user's emotional state and displays it on the device via the server.

[1891] Specific examples

[1892] Example 1: Email editing and sentiment analysis

[1893] 1. A user types a draft of a business email into a terminal.

[1894] 2. The device sends the email draft to the server, which then sends it to the generative AI model for editing.

[1895] 3. The generative AI model suggests improvements to grammar, honorifics, and expressions.

[1896] 4. The device sends the input text to the emotion engine, which analyzes the emotional state.

[1897] 5. The server sends the emotion analysis results to the generative AI model and adaptively changes the feedback content.

[1898] 6. The server sends adaptive feedback to the terminal, which displays it to the user.

[1899] Example prompt sentence:

[1900] "Please review the business email below and suggest improvements to grammar, honorifics, and expressions. We'd also like you to perform sentiment analysis and provide adaptive feedback."

[1901] Example 2: Presentation correction and sentiment analysis

[1902] 1. The user uploads the presentation materials to the device.

[1903] 2. The device sends the document to the server, which then sends it to the generative AI model for correction.

[1904] 3. The generative AI model suggests improvements to the content, design, and logical structure of your slides.

[1905] 4. The device sends the input text or voice to the emotion engine, which analyzes the emotional state.

[1906] 5. The server sends the emotion analysis results to the generative AI model and adaptively changes the feedback content.

[1907] 6. The server sends adaptive feedback to the terminal, which displays it to the user.

[1908] Example prompt sentence:

[1909] "Please review the presentation below and suggest improvements to the content, design, and logical structure. Please also conduct a sentiment analysis and provide adaptive feedback."

[1910] The above is an embodiment of the invention of a generative AI mentor system that combines an emotion engine. This system allows new employees to receive appropriate support based on their emotions, allowing them to improve their skills more efficiently and adapt to the workplace.

[1911] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1912] Step 1: Initial Setup

[1913] The server acquires book data related to business skills and presentation skills. Specifically, it accesses and downloads corporate databases and external specialized materials. The acquired data is in document format (PDF or text file) or audio format (audio file). The server preprocesses this data, performing tokenization and data cleaning. The server then supplies the data to the generative AI model, which then performs learning. This allows the generative AI model to acquire knowledge related to business skills and presentation skills.

[1914] Input: Book data, corporate databases, external specialist materials

[1915] Output: Preprocessed data, learning results of generative AI model

[1916] Step 2: Register new employees

[1917] The device displays a login screen for new employees. The user accesses the login screen and enters profile information such as their name, department, desired skills, and desired support content. The entered profile information is sent from the device to the server. The server stores the received profile information in a database and analyzes it. Based on the analysis results, the server assigns an appropriate generative AI model to the user.

[1918] Input: New employee profile information

[1919] Output: Analysis results, assignment of generative AI model

[1920] Step 3: Editing emails and presentation materials

[1921] The device provides an editor screen for the user, who then creates business emails and presentation materials. The completed materials are sent from the device to the server. The server analyzes the received materials and generates prompts for the generative AI model. The server sends the materials and prompts to the generative AI model, requesting analysis. The generative AI model analyzes the grammar and expressions of the materials and suggests improvements. The server receives feedback from the generative AI model, reflects this in the document, and sends it to the device. The device displays the improved document and feedback to the user.

[1922] Input: business emails, presentation materials, prompts

[1923] Output: Feedback for generative AI models, improved documentation

[1924] Step 4: Communication support

[1925] The device provides a consultation form for users, and the user inputs questions about workplace rules and business etiquette. The device then sends the input question to the server. The server analyzes the question and, if necessary, sends the question content to the generative AI model as a prompt. The server receives the answer from the generative AI model and sends it to the device. The device then displays the appropriate answer to the user.

[1926] Input: User question, prompt

[1927] Output: The answer of the generative AI model, displayed to the user

[1928] Step 5: Provide feedback

[1929] The server records the user's activity log on the system in real time. The recorded activity log is analyzed, and the server sends the log data to the generative AI model, requesting it to generate feedback. The generative AI model analyzes the activity log and periodically generates feedback. The server sends the generated feedback to the device, which then displays it to the user.

[1930] Input: Activity log, prompt

[1931] Output: Feedback from the generative AI model, displayed to the user

[1932] Step 6: Sentiment Analysis and Adaptive Feedback

[1933] The device sends the user's input text and voice data to the emotion engine. The emotion engine analyzes the text and voice data and obtains the user's emotional state. The emotion engine's analysis results are sent to the server, which then sends an emotion-reflecting prompt to the generative AI model. The generative AI model generates adaptive feedback that takes the emotional state into account. The server sends the adaptive feedback to the device, which displays it to the user.

[1934] Input: User input text, voice data, and sentiment analysis results

[1935] Output: Analysis results of the emotion engine, adaptive feedback by the generative AI model, and display to the user

[1936] (Application example 2)

[1937] 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."

[1938] Traditional mentoring systems alone have limitations in helping new employees and new store associates quickly and effectively adapt to their workplace or store operations, particularly in terms of emotional support. This increases the likelihood of delayed workplace adaptation and work errors. Furthermore, it is difficult to obtain adaptive feedback in real time when dealing with customers or performing work tasks in a physical store. Therefore, there is a need for a system that provides adaptive learning support and real-time feedback that takes into account the emotional state of new employees and new store associates.

[1939] 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.

[1940] In this invention, the server includes: means for initializing a generative AI model for new employees and store clerks to learn business skills and company-specific rules; means for inputting and registering profile information for new employees and store clerks; means for sending created emails and presentation materials to the generative AI model and receiving feedback on areas for improvement; means for sending questions to the generative AI model and receiving answers; means for analyzing the activity logs of new employees and store clerks and providing overall feedback; means for store clerks to view and study store operations manuals; means for store clerks to perform customer interaction simulations; means for providing real-time work feedback; and means for analyzing the emotional state of store clerks using an emotion analysis engine and providing adaptive feedback. This allows new employees and store clerks to receive appropriate support according to their emotional state, allowing them to improve their skills more efficiently and quickly adapt to work in the workplace or store.

[1941] A "generative AI model" is an artificial intelligence technology that learns from large amounts of data and generates appropriate answers and feedback for specific tasks or questions.

[1942] "Profile information" refers to information such as an individual's basic attributes, skills, experience, and desired support content.

[1943] "Feedback" refers to the evaluation and advice that the generative AI model provides to new employees and store associates on their actions, materials, and questions.

[1944] An "activity log" is data that records the history of operations and actions performed on the system by new employees and store clerks.

[1945] An "emotion analysis engine" is software or a system that analyzes a user's emotional state from voice, text, facial expression data, etc.

[1946] "Adaptive feedback" refers to appropriate advice and assessments provided by a generative AI model based on the user's emotional state and situation.

[1947] A "store operations manual" is a guidebook that summarizes store operations procedures, customer service methods, rules, etc.

[1948] "Customer service simulation" refers to a system or program that allows store staff to practice simulating actual customer service situations.

[1949] "Real-time feedback" refers to feedback that store employees receive immediately while they are working.

[1950] Overall overview

[1951] The present invention is a system that helps new employees and new store associates quickly adapt to their workplace or store and efficiently improve their skills. This system uses a server, a terminal, a generative AI model, and a sentiment analysis engine. Specifically, it is implemented through the following steps.

[1952] System Configuration

[1953] The system of the present invention includes the following elements:

[1954] 1. Initial Setup

[1955] server:

[1956] Obtain book data or electronic data related to business skills and presentation skills and train a generative AI model.

[1957] The generative AI model is trained on data related to a company's unique mindset, rules, store operations manuals, and customer service skills.

[1958] 2. Registering new employees and store associates

[1959] Device:

[1960] A login screen for new employees and new store clerks is displayed, and users enter their profile information, including the required skills and desired support content.

[1961] The entered profile information is sent to the server, where it is analyzed and an appropriate generative AI model is assigned.

[1962] 3. Correction of emails and presentation materials

[1963] Device:

[1964] An editor screen is displayed, and the user creates a draft of a business email or a presentation document.

[1965] The created materials are sent to the server and then sent to the generative AI model for analysis.

[1966] Generative AI models:

[1967] Analyze the grammar and expressions of the material and suggest areas for improvement.

[1968] server:

[1969] The proposed feedback is sent to the terminal and displayed to the user.

[1970] 4. Communication support

[1971] Device:

[1972] A consultation form is displayed, and the user inputs a question about workplace rules and business etiquette.

[1973] server:

[1974] Send your questions to a generative AI model and ask for an answer.

[1975] Generative AI models:

[1976] Create an answer to the question and send it to the server.

[1977] server:

[1978] The answer is sent to the terminal and displayed to the user.

[1979] 5. Customer response simulation

[1980] Device:

[1981] It provides customer interaction simulations for users to practice.

[1982] Generative AI models:

[1983] The simulation content is analyzed in real time and feedback is provided.

[1984] 6. Providing Feedback

[1985] server:

[1986] Record and analyze user activity logs.

[1987] Based on the analysis results, the generative AI model provides regular feedback.

[1988] 7. Sentiment Analysis and Adaptive Feedback

[1989] Device:

[1990] The user's input text and voice data are sent to the sentiment analysis engine.

[1991] Sentiment Analysis Engine:

[1992] It analyzes the emotional state and sends the results to a generative AI model.

[1993] Generative AI models:

[1994] Generate adaptive feedback that takes emotional state into account.

[1995] server:

[1996] The provided adaptive feedback is sent to the terminal and displayed to the user.

[1997] Specific examples

[1998] Example 1: Customer interaction simulation

[1999] 1. The user begins a simulated customer interaction practice session on the smartphone app.

[2000] 2. The app uses voice and facial recognition technology to evaluate the user's reactions and responses.

[2001] 3. The emotion analysis engine analyzes the user's emotional state in real time, and the generative AI model provides real-time feedback such as "Take a deep breath and speak calmly."

[2002] Specific generative AI model prompt examples:

[2003] "A new employee is handling a customer complaint. Please rate the following response and suggest improvements. The user is feeling a bit anxious. Please take this into consideration when providing feedback. Here's what the user said:"

[2004] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[2005] Step 1:

[2006] The server performs the initial setup.

[2007] Input: Data on business skills, presentation skills, company-specific rules, store operations manuals, and customer service skills.

[2008] Processing: The server trains the generative AI model on the collected book and electronic data, as well as the company's specific rules and mindset.

[2009] Output: A generative AI model with initial configuration completed.

[2010] Step 2:

[2011] The terminal registers new employees and store clerks.

[2012] Input: Profile information for new employees and store clerks (basic information, skills, experience, desired support).

[2013] Processing: The device displays a login screen, where the user enters their profile information. The information is sent to the server, which analyzes it and assigns an appropriate generative AI model.

[2014] Output: The assigned generative AI model and the user's profile data.

[2015] Step 3:

[2016] A user creates an email or presentation document and requests corrections.

[2017] Input: Business email drafts and presentation materials created by users.

[2018] Processing: The device displays the editor screen, the user creates a document, and sends it to the server. The server then sends the document to the generative AI model, which analyzes the document's grammar and expressions and suggests improvements.

[2019] Output: Suggested feedback.

[2020] Step 4:

[2021] Users can enter questions and receive communication support.

[2022] Input: User asks a question about workplace rules and business etiquette.

[2023] Processing: The device displays a consultation form, the user enters a question, and sends it to the server. The server sends the question to the generative AI model and requests an answer. The generative AI model creates an answer, which the server sends to the device.

[2024] Output: The generative AI model's answer to the question.

[2025] Step 5:

[2026] The user performs a customer interaction simulation.

[2027] Input: The command with which the user starts the simulation.

[2028] Processing: The terminal provides customer interaction simulations, and the generative AI model analyzes the simulation content in real time and provides appropriate feedback. It also uses an emotion analysis engine to analyze the user's emotional state.

[2029] Output: Feedback during the simulation.

[2030] Step 6:

[2031] The server provides feedback.

[2032] Input: User activity log.

[2033] Processing: The server records and analyzes the user's activity log, periodically generates feedback from the generative AI model, and sends the feedback to the device.

[2034] Output: Periodic feedback.

[2035] Step 7:

[2036] Providing adaptive feedback based on emotional state.

[2037] Input: User-entered text or voice data.

[2038] Processing: The device sends the input data to the emotion analysis engine to analyze the emotional state. The analysis results are sent to the generative AI model to generate adaptive feedback. The server sends the adaptive feedback to the device.

[2039] Output: Adaptive feedback taking into account emotional state.

[2040] 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.

[2041] 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.

[2042] 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.

[2043] [Fourth embodiment]

[2044] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[2045] 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.

[2046] 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).

[2047] 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.

[2048] 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.

[2049] 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).

[2050] 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.

[2051] 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.

[2052] 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.

[2053] 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.

[2054] 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.

[2055] 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.

[2056] 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."

[2057] This invention relates to a generative AI mentoring system that complements traditional mentoring systems to help new employees commit smoothly to the workplace and prevent early turnover. This system provides an environment where new employees can easily seek advice and receive individual feedback.

[2058] Overall overview

[2059] This generative AI mentor system performs the following processes using a server, terminals, and generative AI models.

[2060] 1. Initial Setup

[2061] 2. New employee registration

[2062] 3. Correction of emails and presentation materials

[2063] 4. Communication support

[2064] 5. Providing Feedback

[2065] Program processing explanation

[2066] The program processing at each step will be explained below in order.

[2067] 1. Initial Setup

[2068] The server first obtains book data on business skills and presentation skills and trains the generative AI model.

[2069] The server also acquires data on the company's unique mindset and rules, which it then uses to train the generative AI model.

[2070] 2. New employee registration

[2071] The terminal displays a login screen for new employees, where the user (new employee) enters profile information such as name, job title, skill set, desired support content, etc.

[2072] The terminal transmits the entered profile information to the server.

[2073] The server analyzes the new employee's profile and assigns the most appropriate generative AI model.

[2074] 3. Correction of emails and presentation materials

[2075] The terminal displays an editor screen for the user, and the user creates a draft of a business email or presentation materials.

[2076] The terminal transmits the completed draft or document to the server.

[2077] The server sends the submitted materials to a generative AI model, which analyzes grammar and expressions and provides feedback on areas for improvement.

[2078] The server receives the feedback and provides it to the user.

[2079] The terminal displays the feedback to the user.

[2080] 4. Communication support

[2081] The terminal displays a consultation form for the user, and the user inputs a question.

[2082] The terminal sends a query to the server.

[2083] The server sends the question to a generative AI model, which generates an answer.

[2084] The server receives the generative AI model's answer and provides it to the user.

[2085] The terminal displays the provided answers to the user.

[2086] 5. Providing Feedback

[2087] The server records and analyzes a log of the user's activities on the system.

[2088] The server periodically retrieves the analysis results from the generated AI model and generates overall feedback.

[2089] The server provides feedback to the user.

[2090] The terminal displays the provided feedback to the user.

[2091] Specific examples

[2092] Example 1: Email editing

[2093] 1. A user types a draft of a business email into a terminal.

[2094] 2. The device sends the email draft to the server.

[2095] 3. The server sends the draft to the generative AI model for editing.

[2096] 4. The generative AI model suggests improvements to grammar, honorifics, and expressions.

[2097] 5. The server receives the feedback and sends it to the device.

[2098] 6. The device displays feedback to the user.

[2099] Example 2: Presentation correction

[2100] 1. The user uploads the presentation materials to the device.

[2101] 2. The device sends the materials to the server.

[2102] 3. The server sends the document to the generative AI model and requests corrections.

[2103] 4. The generative AI model suggests improvements to the content, design, and logical structure of your slides.

[2104] 5. The server receives the feedback and sends it to the device.

[2105] 6. The device displays feedback to the user.

[2106] The above is the details of the mode for carrying out the invention. This enables new employees to use the generative AI mentor system to smoothly adapt to the workplace and improve their skills.

[2107] The processing flow will be explained below.

[2108] 1. Initial Setup

[2109] Step 1:

[2110] The server acquires book data on business skills and presentation skills.

[2111] Step 2:

[2112] The data acquired by the server is trained into a generative AI model.

[2113] Step 3:

[2114] The server collects data on the company's unique mindset and rules.

[2115] Step 4:

[2116] The server trains the generative AI model on data about the company's mindset and rules.

[2117] 2. New employee registration

[2118] Step 1:

[2119] The terminal displays the login screen for new employees.

[2120] Step 2:

[2121] Users enter their profile information on the login screen and register the skills they need and the support they would like.

[2122] Step 3:

[2123] The device transmits the entered profile information to the server.

[2124] Step 4:

[2125] The server analyzes the new employee's profile and assigns an appropriate generative AI model.

[2126] 3. Correction of emails and presentation materials

[2127] Step 1:

[2128] The terminal displays the editor screen for the user.

[2129] Step 2:

[2130] The user creates a draft of a business email or a presentation in the editor.

[2131] Step 3:

[2132] The terminal transmits the drafts and documents created to the server.

[2133] Step 4:

[2134] The server sends the data to the generative AI model and requests it to analyze it.

[2135] Step 5:

[2136] The generative AI model analyzes the grammar and expressions of the document and suggests areas for improvement.

[2137] Step 6:

[2138] The server receives feedback from the generative AI model and sends it to the device.

[2139] Step 7:

[2140] The device displays the feedback to the user.

[2141] 4. Communication support

[2142] Step 1:

[2143] The terminal displays a consultation form for the user.

[2144] Step 2:

[2145] The user enters a question about workplace rules and business etiquette into a consultation form.

[2146] Step 3:

[2147] The terminal sends the entered question to the server.

[2148] Step 4:

[2149] The server sends the question to the generative AI model and requests an answer.

[2150] Step 5:

[2151] A generative AI model creates answers to questions.

[2152] Step 6:

[2153] The server receives the answer from the generated AI model and sends it to the device.

[2154] Step 7:

[2155] The terminal displays the provided answers to the user.

[2156] 5. Providing Feedback

[2157] Step 1:

[2158] The server logs the user's activity on the system.

[2159] Step 2:

[2160] The server analyzes the recorded activity logs and periodically generates feedback from the generative AI model.

[2161] Step 3:

[2162] The server sends the generated feedback to the user.

[2163] Step 4:

[2164] The terminal displays the notified feedback to the user.

[2165] These are the specific steps in the programming process of the generative AI mentor system, which allows new employees to efficiently improve their skills and adapt to the workplace.

[2166] Example 1

[2167] 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."

[2168] An appropriate support environment is necessary to enable new employees to smoothly adapt to the workplace and prevent early attrition. However, traditional mentoring systems have problems with providing individual feedback and making it difficult to carry out effective follow-up. Furthermore, when many new employees join the company at once, the burden on mentors increases, and as a result, there is a risk that high-quality support will not be provided. To solve these problems, a system that utilizes generative AI models to provide appropriate support and feedback to new employees is needed.

[2169] 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.

[2170] In this invention, the server includes means for initializing a generative AI model that has learned business skills and company-specific rules, means for inputting and registering new employee profile information, means for sending created documents to the generative AI model and receiving feedback on areas for improvement, means for sending questions to the generative AI model and receiving answers, means for analyzing the new employee's activity log and providing overall feedback, means for sending information input at the terminal to the server, and means for receiving analysis results from the generative AI model at the server and sending them to the terminal. This allows new employees to not only receive individual feedback, but also quickly receive specific advice on how to adapt to the workplace in an appropriate support environment.

[2171] "Business skills" refers to a set of skills required in the workplace, such as business etiquette, communication skills, problem-solving skills, and presentation skills.

[2172] "Company-specific rules" refer to the unique mindset, code of conduct, ethical guidelines, internal rules, etc. established by a particular company.

[2173] A "generative AI model" is a system that uses artificial intelligence technology to learn and generate, and refers to a model that uses natural language processing technology to generate feedback and answers for users.

[2174] "Initialization means" refers to the process of providing the necessary training data for a generative AI model and configuring the model to suit the specific needs of the company.

[2175] "Profile Information" refers to personal information entered by a user, such as name, job title, skill set, and desired support type.

[2176] "Document" refers to text data such as a business email draft or presentation material created by a user.

[2177] "Activity log" refers to data that records a user's actions and interactions when using a system.

[2178] "Feedback" refers to specific advice provided by the generative AI model on areas for improvement such as grammar, expression, pay, design, and logical structure.

[2179] "Means for obtaining an answer" refers to the process by which a user sends a question to a generative AI model and obtains an answer to that question from the generative AI model.

[2180] "Analysis results" refers to the results of the analysis performed by the generative AI model based on the user's input data.

[2181] This invention relates to a generative AI mentoring system that complements traditional mentoring systems to help new employees smoothly adapt to the workplace and prevent early turnover. The system provides an environment where new employees can easily seek advice and receive individual feedback.

[2182] Hardware and software used

[2183] This system operates using a server, a terminal, and a generative AI model. The specific hardware and software used are as follows:

[2184] Server: Manages and processes data to learn business skills and company-specific rules.

[2185] Software used: "Learning-Resource-Server", "Profile-Analyzer", "Grammar-Checker AI", "Pitch-Perfect AI", "Question-Responder AI", "Activity-Analyzer", "Activity-Logger", etc.

[2186] Terminal: Provides an interface for users (new employees) to enter information and receive feedback and responses.

[2187] Software used: "Employee-Portal", "Business-Editor", "Consultation-Form", etc.

[2188] Program processing explanation

[2189] The program for this system consists of multiple processing steps that mainly involve the server, terminal, and generative AI model working together. Each processing step is explained below in natural language.

[2190] Initial Setup

[2191] The server collects book data on business skills and presentation skills and trains the generative AI model via the "Learning-Resource-Server."

[2192] The server also collects document data about the company's unique mindset and rules, and similarly trains the generative AI model. This process allows the generative AI model to respond to the specific needs of the company.

[2193] New employee registration

[2194] When a user accesses the system for the first time, the terminal displays a login screen for new employees. This system uses the "Employee-Portal."

[2195] The user (new employee) enters profile information (name, position, skill set, desired support content, etc.) on the login screen.

[2196] The terminal transmits the profile information entered by the user to the server.

[2197] The server uses the "Profile-Analyzer" to analyze the profile information of new employees and assign the optimal generative AI model. At this time, the parameters of the generative AI model are adjusted according to the employee's skill set and desired support content.

[2198] Correction of emails and presentation materials

[2199] The terminal displays an editor screen where users can create business emails and presentation materials. This editor uses "Business-Editor."

[2200] The user creates a draft of a business email or a presentation document on the editor screen.

[2201] The terminal transmits the completed email draft or presentation materials to the server.

[2202] The server sends the received materials to generative AI models, such as "Grammar-Checker AI" and "Pitch-Perfect AI," and requests them to analyze grammar, expression, design, and logical structure.

[2203] The generative AI model provides feedback on grammatical errors, appropriate phrasing, and areas for improvement in the design and logical structure of the presentation materials.

[2204] The server receives the generated feedback and transmits it to the terminal.

[2205] The device displays feedback to the user, allowing them to specifically understand areas for improvement and make corrections.

[2206] Communication Support

[2207] The terminal displays a consultation form where the user can enter their questions. This form uses the "Consultation-Form".

[2208] The user inputs a question into the consultation form. Specifically, the user inputs a question such as "Please tell me how to approach a new project."

[2209] The terminal transmits the entered question to the server.

[2210] The server uses "Question-Responder AI" to have a generative AI model generate answers to questions.

[2211] Generative AI models generate specific advice and answers to questions.

[2212] The server receives the generated response and transmits it to the terminal.

[2213] The terminal displays the provided answers to the user, who can receive advice in real time.

[2214] Providing Feedback

[2215] The server records the user's actions and interactions on the system (for example, what questions they asked, what feedback they received), and uses the "Activity-Logger" as the recording system.

[2216] The server periodically analyzes the recorded activity log using "Activity-Analyzer."

[2217] The server sends the analysis results to a generative AI model to generate overall feedback.

[2218] The generative AI model provides feedback on the user's progress and areas for improvement, such as "Your communication in a recent project has improved" or "Your presentation slides are now more logically structured."

[2219] The server provides the generated feedback to the user.

[2220] The device displays rich feedback to users, helping them track their progress, and also displays a feedback dashboard.

[2221] Examples of concrete examples and prompts

[2222] Example 1: Email editing

[2223] 1. The user inputs a draft of a business email into the terminal, for example, using "Business-Editor."

[2224] 2. The device sends the email draft to the server.

[2225] 3. The server sends the draft to a generative AI model for correction, specifically using "Grammar-Checker AI."

[2226] 4. The generative AI model suggests improvements to grammar, honorifics, and expressions.

[2227] 5. The server receives the feedback and sends it to the device.

[2228] 6. The device displays feedback to the user.

[2229] Example 2: Presentation correction

[2230] 1. The user uploads the presentation materials to the device, for example, using "Business-Editor."

[2231] 2. The device sends the materials to the server.

[2232] 3. The server sends the document to a generative AI model and requests corrections. Specifically, it uses "Pitch-Perfect AI."

[2233] 4. The generative AI model suggests improvements to the content, design, and logical structure of your slides.

[2234] 5. The server receives the feedback and sends it to the device.

[2235] 6. The device displays feedback to the user.

[2236] Examples of prompts include:

[2237] How do you approach a new project?

[2238] "Please check the grammar of this business email."

[2239] "How can I improve the design of my presentation materials?"

[2240] This concludes the detailed description of the embodiments of the invention, which will enable new employees to make the most of the generative AI mentor system, allowing them to smoothly adapt to the workplace and improve their skills.

[2241] The flow of the identification process in the first embodiment will be described with reference to FIG.

[2242] Step 1: Initial Setup

[2243] The server collects book data on business skills and presentation skills.

[2244] As input, business books and materials are collected from external databases and the Internet.

[2245] As part of the data processing, the collected data is analyzed via the "Learning-Resource-Server."

[2246] The output provides training data for the generative AI model.

[2247] The server also collects document data about the company's unique mindset and rules.

[2248] As input, company policy documents and manuals are collected.

[2249] As part of the data processing, these documents are tokenized and text analyzed.

[2250] The output provides training data for the generative AI model.

[2251] Step 2: Register new employees

[2252] The terminal displays a login screen for new employees.

[2253] As input, a user attempts to log in.

[2254] As a result, the terminal launches the "Employee-Portal" and displays the login screen.

[2255] As an output, a login form is provided to the user.

[2256] The user (new employee) enters profile information.

[2257] As input, the user enters their name, job title, skill set, and desired support type.

[2258] In operation, the user enters the required information into each information field.

[2259] The terminal transmits the entered profile information to the server.

[2260] As input, the terminal receives the user's profile data.

[2261] In operation, the terminal issues a request to send these data to the server.

[2262] As an output, the profile data is sent to a server.

[2263] The server analyzes the profile information of the new employee.

[2264] The profile data received by the server as input.

[2265] For data calculation, the server uses "Profile-Analyzer" to analyze the data.

[2266] As an output, the new employee is assigned the best suited generative AI model.

[2267] Step 3: Editing emails and presentation materials

[2268] The terminal displays an editor screen for the user.

[2269] As input, the user accesses the editor screen.

[2270] As an operation, the editor "Business-Editor" is launched and displayed to the user.

[2271] As an output, an editor screen is provided.

[2272] Users create drafts of business emails and presentation materials on the editor screen.

[2273] As input, the user enters text and presentation slides into the editor screen.

[2274] In action, the user creates a draft or slide.

[2275] The terminal transmits the completed document to the server.

[2276] As input, the editor sends the completed draft or material.

[2277] In operation, the terminal issues a request to transmit material data to the server.

[2278] As an output, the document data is sent to the server.

[2279] The server sends the submitted materials to the generative AI model and requests it to analyze them.

[2280] Source data received by the server as input.

[2281] To calculate the data, the server requests "Grammar-Checker AI" and "Pitch-Perfect AI" to analyze the materials.

[2282] As an output, the analysis results are produced.

[2283] The generative AI model provides feedback on grammatical errors and areas for improvement in design and logical structure.

[2284] As input, material data.

[2285] As a data calculation, the generative AI model analyzes the material and generates specific feedback.

[2286] As an output, feedback data is generated.

[2287] The server sends the feedback data to the terminal for providing to the user.

[2288] As input, feedback data from the generative AI model.

[2289] In operation, the server transmits feedback data to the terminal.

[2290] As an output, feedback is sent to the terminal.

[2291] The terminal displays feedback to the user.

[2292] As input, the received feedback data.

[2293] As an action, feedback is displayed to the user.

[2294] As an output, feedback content is provided to the user.

[2295] Step 4: Communication support

[2296] The terminal displays a consultation form for the user.

[2297] As input, the user accesses a consultation form.

[2298] As a result, the "Consultation-Form" is displayed.

[2299] As output, a consultation form is provided.

[2300] The user enters their question into the consultation form.

[2301] As input, the user enters the consultation content into the form.

[2302] In action, the user enters a specific question.

[2303] The terminal transmits the entered question to the server.

[2304] As input, the question data entered by the user.

[2305] In operation, the query data is sent to the server.

[2306] As an output, the query data is sent to the server.

[2307] The server sends the question to a generative AI model, which generates an answer.

[2308] The query data received by the server as input.

[2309] As a data calculation, the "Question-Responder AI" analyzes the question and generates an answer.

[2310] As an output, response data is generated.

[2311] The server sends the generated response to the terminal.

[2312] As input, the generated response data.

[2313] As an operation, the response data is sent to the terminal.

[2314] As an output, the answer data is provided to the terminal.

[2315] The terminal displays the answer to the user.

[2316] As input, the received response data.

[2317] As an action, the answer is displayed to the user.

[2318] As an output, the answer content is presented to the user.

[2319] Step 5: Provide feedback

[2320] The server records the user's actions and interactions on the system.

[2321] As input, user activity log data.

[2322] In operation, "Activity-Logger" records user actions.

[2323] As an output, the activity log data is saved.

[2324] The server periodically analyzes the recorded activity log.

[2325] As input, the saved activity log data.

[2326] For data calculation, the server performs analysis using "Activity-Analyzer."

[2327] As an output, analysis result data is generated.

[2328] The server sends the analysis results to a generative AI model to generate overall feedback.

[2329] As input, the analysis result data.

[2330] As a data calculation, the generative AI model generates feedback based on the analysis results.

[2331] As an output, feedback data is generated.

[2332] The server transmits the generated feedback to the terminal.

[2333] As input, the generated feedback data.

[2334] As an operation, feedback data is transmitted to the terminal.

[2335] As an output, feedback data is provided to the terminal.

[2336] The terminal displays feedback to the user.

[2337] As input, the received feedback data.

[2338] As an action, feedback is displayed to the user.

[2339] As an output, feedback content is presented to the user.

[2340] (Application example 1)

[2341] 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."

[2342] In order for new employees to adapt quickly to the factory floor and perform their work smoothly, they need immediate support and appropriate feedback. However, traditional mentoring systems alone may not be able to provide sufficient support for each new employee, making it difficult for them to efficiently acquire skills and perform work safely. Therefore, a system is needed that can properly teach the factory's unique procedures and safety guidelines and quickly respond to any questions new employees may have.

[2343] 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.

[2344] In this invention, the server includes a means for learning the factory-specific procedures and safety guidelines, a means for providing real-time support for new employees to carry out on-site work procedures and safety checks, and a means for checking reports and memos created by new employees in real time and providing feedback, thereby enabling new employees to quickly adapt to the factory floor and perform their work safely and efficiently.

[2345] A "generative AI model" is an artificial intelligence system that helps new employees learn the skills and knowledge they need to perform their jobs and generates feedback and answers.

[2346] "Initial setup" refers to the preparatory work of teaching the generative AI model the business skills that new employees should learn, the company's unique rules, and factory procedures and safety guidelines.

[2347] "Profile Information" refers to personal data such as a new hire's name, job title, skill set, and desired support type.

[2348] "Feedback on areas for improvement" involves the generative AI model analyzing emails and presentation materials created by new employees and providing feedback and suggestions regarding grammar, structure, expression, etc.

[2349] "Means of sending questions to a generative AI model and obtaining answers" refers to a system in which new employees send questions that arise during work to a generative AI model and obtain solutions or explanations.

[2350] An "activity log" is a record of all operations and actions performed by new employees on the system.

[2351] The "means of providing feedback" refers to a system that analyzes new employees' profile information and activity logs and regularly provides advice for growth and improvement.

[2352] "Reports and memos to be prepared" refers to documents that new employees use to record the status and results of their work.

[2353] "Means for providing real-time support" refers to a system that instantly displays and provides necessary information and instructions to new employees when they perform their work on-site.

[2354] A "procedure manual" is a document that describes the specific steps and methods for properly carrying out work.

[2355] "Safety guidelines" are documents that list safety measures and precautions to take at work sites such as factories.

[2356] This invention provides a generative AI mentoring system that helps new employees quickly adapt to the workplace and perform their work safely and efficiently. The system consists of the following elements:

[2357] 1. Initial Setup

[2358] The server acquires the factory's specific procedures and safety guidelines and trains the generative AI model. It also trains the model on data related to the company's unique mindset, rules, and business skills. In this way, the generative AI model acquires the knowledge necessary for on-site work.

[2359] 2. New employee registration

[2360] New employees use their devices to access a login screen and enter their profile information, such as their name, job title, skill set, and desired support content. This information is sent to a server, which analyzes the new employee's profile and assigns the optimal generative AI model.

[2361] 3. Business support and real-time support

[2362] As new employees work on-site, the devices display real-time information on factory procedures and safety checks. If a new employee has a question, they can type it into the device, which sends it to the generative AI model via the server. The answer is then provided to the device via the server, where the new employee can immediately check it.

[2363] 4. Correction of emails and presentation materials

[2364] Reports and memos written by new employees are sent to a server via their device. The server then feeds the documents into a generative AI model and receives feedback on improvements to grammar, expression, and structure. The feedback is displayed on the device for the new employee to review.

[2365] 5. Providing Feedback

[2366] The server records and analyzes the activity logs of new employees. Periodically, a generative AI model evaluates the log data and generates feedback for growth and improvement, allowing new employees to continuously improve their skills.

[2367] Hardware and software used

[2368] Hardware: Factory robots (such as Universal Robots' UR series), data servers, and terminals (PCs or tablets)

[2369] Software: Generative AI models (e.g., GPT-4), databases (e.g., MongoDB)

[2370] Specific examples

[2371] Example 1: New employee asking a question

[2372] Question: "I don't know what to do today. What exactly should I do?"

[2373] process:

[2374] 1. The new employee types a question into the terminal.

[2375] 2. The server sends the question to the generative AI model, which generates an answer.

[2376] 3. The server sends the generated answer to the terminal, where the new employee can view it.

[2377] 4. This process ensures that new employees are immediately aware of the steps they need to take.

[2378] Example 2: New employee submitting a report

[2379] Report: "Please correct today's work report. Please let me know if there are any mistakes."

[2380] process:

[2381] 1. The new employee uploads the report to the terminal.

[2382] 2. The server sends the report to the generative AI model for correction.

[2383] 3. The generative AI model generates feedback suggesting improvements to grammar and expression.

[2384] 4. The server sends the feedback to the terminal, where the new employee can check it.

[2385] 5. This feedback will enable the new employee to improve the quality of their reports.

[2386] This completes the description of the preferred embodiment of the present invention. This system allows new employees to quickly adapt to the factory floor and perform their jobs safely and efficiently.

[2387] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[2388] Step 1: Initial Setup

[2389] The server acquires data related to the factory's specific procedures and safety guidelines, the company's unique mindset and rules, and business skills. This data is trained into a generative AI model. The input is data related to the factory's procedures, safety guidelines, mindset, rules, and business skills. The output is the knowledge learned by the generative AI model. The server takes in this data and performs data analysis and model training.

[2390] Step 2: Register new employees

[2391] The user (new employee) uses a terminal to access the login screen and enters profile information such as name, job title, skill set, and desired support content. The input is the new employee's profile information. The terminal sends this to the server. The server analyzes the profile information and assigns the optimal generative AI model. The output is the assigned generative AI model.

[2392] Step 3: Operational and real-time support

[2393] When new employees work on-site, the terminal displays information on factory work procedures and safety checks in real time. The input is data on work procedures and safety checks. The output is the information displayed on the terminal. The terminal receives various data necessary for on-site work from the server and presents it to the user in a timely manner.

[2394] Step 4: Submit your question and get an answer

[2395] When a user (new employee) has a question, they input it into the terminal. The input is the user's question. The terminal sends the question to the server. The server sends the question to the generative AI model and generates an answer. The output is the generated answer. The server sends the answer received from the generative AI model to the terminal, and the user confirms it.

[2396] Step 5: Editing emails and presentation materials

[2397] The user (new employee) uploads a report or memo they created to the device. The input is the report or memo. The device sends it to the server. The server sends the report or memo to a generative AI model and receives feedback on improvements to grammar, expression, and structure. The output is feedback. The server sends the feedback to the device so that the user can review it.

[2398] Step 6: Provide feedback

[2399] The server records new employee activity logs and periodically analyzes them. The input is the activity log. The server evaluates the log data using a generative AI model and generates feedback for growth and improvement. The output is feedback for growth and improvement. The server sends the generated feedback to a terminal, where the new employee can check it.

[2400] The above is the specific flow of operations at each processing step of this system, which enables new employees to quickly adapt to the factory floor and perform their work safely and efficiently.

[2401] 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.

[2402] This invention relates to a generative AI mentoring system that complements traditional mentoring systems to help new employees smoothly commit to the workplace. This system includes an emotion engine that recognizes the user's emotions, and can analyze the user's emotional state and reflect it in the feedback of the generative AI model.

[2403] Overall overview

[2404] This generative AI mentor system performs the following processes using a server, terminals, a generative AI model, and an emotion engine.

[2405] 1. Initial Setup

[2406] 2. New employee registration

[2407] 3. Correction of emails and presentation materials

[2408] 4. Communication support

[2409] 5. Providing Feedback

[2410] 6. Sentiment Analysis and Adaptive Feedback

[2411] Program processing explanation

[2412] The program processing at each step will be explained below in order.

[2413] 1. Initial Setup

[2414] The server retrieves book data on business skills and presentation skills and trains the generative AI model.

[2415] The server collects data on the company's unique mindset and rules and trains the generative AI model.

[2416] 2. New employee registration

[2417] The terminal displays a login screen for new employees, where the user enters their profile information and registers the skills they need and the support they would like to receive.

[2418] The device transmits the entered profile information to the server.

[2419] The server analyzes the new employee's profile and assigns an appropriate generative AI model.

[2420] 3. Correction of emails and presentation materials

[2421] The terminal displays an editor screen for the user, and the user creates a draft of a business email or presentation materials.

[2422] The terminal transmits the created materials to the server.

[2423] The server sends the data to the generative AI model and requests it to analyze it.

[2424] The generative AI model analyzes the grammar and expressions of the document and suggests areas for improvement.

[2425] The server receives feedback from the generative AI model and sends it to the device, where it is displayed to the user.

[2426] 4. Communication support

[2427] The terminal displays a consultation form for the user, and the user inputs questions about workplace rules and business etiquette.

[2428] The terminal sends a question to the server.

[2429] The server sends the question to the generative AI model and requests an answer.

[2430] A generative AI model creates answers to questions.

[2431] The server receives the answer from the generated AI model, sends it to the device, and the device displays it to the user.

[2432] 5. Providing Feedback

[2433] The server logs the user's activity on the system.

[2434] The server analyzes the recorded activity logs and periodically generates feedback from the generative AI model.

[2435] The server sends the generated feedback to the terminal, which displays it to the user.

[2436] 6. Sentiment Analysis and Adaptive Feedback

[2437] The device sends the user's input text and voice data to the emotion engine.

[2438] An emotion engine analyzes the user's emotional state.

[2439] The emotion engine sends the analysis results to the generative AI model, which then uses them as feedback.

[2440] The server receives the analysis results from the emotion engine and adaptively changes the feedback content of the generative AI model.

[2441] The server sends adaptive feedback to the terminal, which displays it to the user.

[2442] Specific examples

[2443] Example 1: Email editing and sentiment analysis

[2444] 1. A user types a draft of a business email into a terminal.

[2445] 2. The device sends the email draft to the server, which then sends it to the generative AI model for editing.

[2446] 3. The generative AI model suggests improvements to grammar, honorifics, and expressions.

[2447] 4. The device sends the input text to the emotion engine, which analyzes the emotional state.

[2448] 5. The server sends the emotion analysis results to the generative AI model and adaptively changes the feedback content.

[2449] 6. The server sends adaptive feedback to the terminal, which displays it to the user.

[2450] Example 2: Presentation correction and sentiment analysis

[2451] 1. The user uploads the presentation materials to the device.

[2452] 2. The device sends the document to the server, which then sends it to the generative AI model for correction.

[2453] 3. The generative AI model suggests improvements to the content, design, and logical structure of your slides.

[2454] 4. The device sends the input text or voice to the emotion engine, which analyzes the emotional state.

[2455] 5. The server sends the emotion analysis results to the generative AI model and adaptively changes the feedback content.

[2456] 6. The server sends adaptive feedback to the terminal, which displays it to the user.

[2457] The above is a detailed description of the embodiment of the invention of a generative AI mentor system that combines an emotion engine. This enables new employees to receive appropriate support based on their emotions, improve their skills more efficiently, and adapt to the workplace.

[2458] The processing flow will be explained below.

[2459] 1. Initial Setup

[2460] Step 1:

[2461] The server acquires book data on business skills and presentation skills.

[2462] Step 2:

[2463] The data acquired by the server is trained into a generative AI model.

[2464] Step 3:

[2465] The server collects data on the company's unique mindset and rules.

[2466] Step 4:

[2467] The server trains the generative AI model on data about the company's mindset and rules.

[2468] 2. New employee registration

[2469] Step 1:

[2470] The terminal displays the login screen for new employees.

[2471] Step 2:

[2472] Users enter their profile information on the login screen and register the skills they need and the support they would like.

[2473] Step 3:

[2474] The device transmits the entered profile information to the server.

[2475] Step 4:

[2476] The server analyzes the new employee's profile and assigns an appropriate generative AI model.

[2477] 3. Correction of emails and presentation materials

[2478] Step 1:

[2479] The terminal displays the editor screen for the user.

[2480] Step 2:

[2481] The user creates a draft of a business email or a presentation in the editor.

[2482] Step 3:

[2483] The terminal transmits the drafts and documents created to the server.

[2484] Step 4:

[2485] The server sends the data to the generative AI model and requests it to analyze it.

[2486] Step 5:

[2487] The generative AI model analyzes the grammar and expressions of the document and suggests areas for improvement.

[2488] Step 6:

[2489] The server receives feedback from the generative AI model and sends it to the device.

[2490] Step 7:

[2491] The device displays the feedback to the user.

[2492] 4. Communication support

[2493] Step 1:

[2494] The terminal displays a consultation form for the user.

[2495] Step 2:

[2496] The user enters a question about workplace rules and business etiquette into a consultation form.

[2497] Step 3:

[2498] The terminal sends the entered question to the server.

[2499] Step 4:

[2500] The server sends the question to the generative AI model and requests an answer.

[2501] Step 5:

[2502] A generative AI model creates answers to questions.

[2503] Step 6:

[2504] The server receives the answer from the generated AI model and sends it to the device.

[2505] Step 7:

[2506] The terminal displays the provided answers to the user.

[2507] 5. Providing Feedback

[2508] Step 1:

[2509] The server logs the user's activity on the system.

[2510] Step 2:

[2511] The server analyzes the recorded activity logs and periodically generates feedback from the generative AI model.

[2512] Step 3:

[2513] The server transmits the generated feedback to the terminal.

[2514] Step 4:

[2515] The terminal displays the notified feedback to the user.

[2516] 6. Sentiment Analysis and Adaptive Feedback

[2517] Step 1:

[2518] The device sends the user's input text and voice data to the emotion engine.

[2519] Step 2:

[2520] An emotion engine analyzes the user's emotional state.

[2521] Step 3:

[2522] The emotion engine sends the analysis results to the server.

[2523] Step 4:

[2524] The server sends the emotion analysis results to the generative AI model and requests it to adaptively change the feedback content.

[2525] Step 5:

[2526] The generative AI model adjusts the feedback content based on the results of sentiment analysis.

[2527] Step 6:

[2528] The server sends the adapted feedback to the terminal.

[2529] Step 7:

[2530] The terminal displays adaptive feedback to the user.

[2531] The above is a detailed description of the embodiment of the invention of a generative AI mentor system that combines an emotion engine. This enables new employees to receive appropriate support according to their emotions, improve their skills more efficiently, and adapt to the workplace.

[2532] Example 2

[2533] 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."

[2534] Traditional mentoring systems often lack sufficient support to help new employees smoothly adapt to the workplace. In particular, they face the problem of not only failing to quickly learn business skills and company-specific rules, but also failing to receive appropriate feedback tailored to their individual emotional state. As a result, there are concerns that new employees may not be able to adapt to the workplace well and may find it difficult to improve their skills efficiently.

[2535] 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.

[2536] In this invention, the server includes: means for initializing a generative AI model for learning business skills and company-specific rules; means for inputting and registering new employee profile information; means for sending created emails and presentation materials to the generative AI model and receiving feedback on areas for improvement; means for sending questions to the generative AI model and receiving answers; means for analyzing the new employee's activity log and providing overall feedback; emotion engine means for analyzing user input text and voice data to obtain emotional states; and means for reflecting the emotional states from the emotion engine in the generative AI model and providing adaptive feedback. This allows new employees to quickly learn business skills and company-specific rules, receive appropriate feedback according to their individual emotional states, and smoothly adapt to the workplace.

[2537] "Business skills" refers to the knowledge and skills required to carry out work in the workplace, including communication skills, problem-solving skills, leadership skills, and time management skills.

[2538] "Company-specific rules" refer to internal rules and guidelines established by a specific company, including compliance, ethics rules, and internal company manuals, and are guidelines for behavior and operating procedures that employees must follow.

[2539] A "generative AI model" is a machine learning model that is trained using artificial intelligence techniques to perform a specific task (e.g., sentence generation, grammar checking, data analysis, etc.).

[2540] An "emotion engine" refers to a program or system that has the ability to analyze user input data (text or voice) and determine the user's emotional state based on that data.

[2541] "Feedback" refers to the information and suggestions that a generative AI model provides as a result of its analysis and judgment, suggesting areas for improvement or next steps based on user-entered data and actions.

[2542] "Activity logs" refer to data that records the operations and inputs that users make on the system. These are important data for analyzing and generating feedback.

[2543] "Adaptive feedback" refers to personalized feedback that is adjusted according to the user's emotional state and behavioral history. Its distinctive feature is that the content changes according to the user's state and needs.

[2544] This invention relates to a generative AI mentoring system that complements traditional mentoring systems to help new employees smoothly commit to the workplace. This system is equipped with an emotion engine that recognizes the user's emotions, analyzes the user's emotional state, and reflects this in the feedback of the generative AI model.

[2545] Hardware and software used

[2546] This system consists of the following hardware and software:

[2547] Server: A high-performance computer for running the generative AI models and emotion engine, and managing user data.

[2548] Device: A device such as a computer, tablet, or smartphone that the new employee will have access to.

[2549] Generative AI model: A machine learning model that uses natural language processing technology to correct business emails and answer questions.

[2550] Emotion engine: Software that uses speech recognition and natural language processing techniques to analyze a user's emotional state.

[2551] Program processing overview

[2552] The system's processing consists mainly of the following components:

[2553] Initial Setup

[2554] The server acquires book data on business skills and presentation skills and trains the generative AI model. It also acquires data on the company's unique mindset and rules and trains the generative AI model.

[2555] New employee registration

[2556] The device displays a login screen for new employees, and the user enters their profile information. The information is sent to the server for analysis, and the server assigns an appropriate generative AI model based on the analysis results.

[2557] Correction of emails and presentation materials

[2558] The device provides an editor screen for the user, who then creates business emails and presentation materials. The created materials are sent to a server and analyzed by a generative AI model. The generative AI model then suggests improvements to grammar and expression, and sends this feedback to the device via the server.

[2559] Communication Support

[2560] The device provides a consultation form for users, and users input questions about workplace rules and business etiquette. The questions are sent via the server to a generative AI model, which generates answers. The generated answers are then displayed on the device.

[2561] Providing Feedback

[2562] The server records the user's activity on the system in real time. These logs are analyzed and sent to the generative AI model, which then periodically generates feedback that is displayed on the device via the server.

[2563] Sentiment Analysis and Adaptive Feedback

[2564] The device sends the user's input text and voice data to the emotion engine. The emotion engine analyzes the user's emotional state and reflects the results in the generative AI model. The generative AI model generates adaptive feedback based on the user's emotional state and displays it on the device via the server.

[2565] Specific examples

[2566] Example 1: Email editing and sentiment analysis

[2567] 1. A user types a draft of a business email into a terminal.

[2568] 2. The device sends the email draft to the server, which then sends it to the generative AI model for editing.

[2569] 3. The generative AI model suggests improvements to grammar, honorifics, and expressions.

[2570] 4. The device sends the input text to the emotion engine, which analyzes the emotional state.

[2571] 5. The server sends the emotion analysis results to the generative AI model and adaptively changes the feedback content.

[2572] 6. The server sends adaptive feedback to the terminal, which displays it to the user.

[2573] Example prompt sentence:

[2574] "Please review the business email below and suggest improvements to grammar, honorifics, and expressions. We'd also like you to perform sentiment analysis and provide adaptive feedback."

[2575] Example 2: Presentation correction and sentiment analysis

[2576] 1. The user uploads the presentation materials to the device.

[2577] 2. The device sends the document to the server, which then sends it to the generative AI model for correction.

[2578] 3. The generative AI model suggests improvements to the content, design, and logical structure of your slides.

[2579] 4. The device sends the input text or voice to the emotion engine, which analyzes the emotional state.

[2580] 5. The server sends the emotion analysis results to the generative AI model and adaptively changes the feedback content.

[2581] 6. The server sends adaptive feedback to the terminal, which displays it to the user.

[2582] Example prompt sentence:

[2583] "Please review the presentation below and suggest improvements to the content, design, and logical structure. Please also conduct a sentiment analysis and provide adaptive feedback."

[2584] The above is an embodiment of the invention of a generative AI mentor system that combines an emotion engine. This system allows new employees to receive appropriate support based on their emotions, allowing them to improve their skills more efficiently and adapt to the workplace.

[2585] The flow of the identification process in the second embodiment will be described with reference to FIG.

[2586] Step 1: Initial Setup

[2587] The server acquires book data related to business skills and presentation skills. Specifically, it accesses and downloads corporate databases and external specialized materials. The acquired data is in document format (PDF or text file) or audio format (audio file). The server preprocesses this data, performing tokenization and data cleaning. The server then supplies the data to the generative AI model, which then performs learning. This allows the generative AI model to acquire knowledge related to business skills and presentation skills.

[2588] Input: Book data, corporate databases, external specialist materials

[2589] Output: Preprocessed data, learning results of generative AI model

[2590] Step 2: Register new employees

[2591] The device displays a login screen for new employees. The user accesses the login screen and enters profile information such as their name, department, desired skills, and desired support content. The entered profile information is sent from the device to the server. The server stores the received profile information in a database and analyzes it. Based on the analysis results, the server assigns an appropriate generative AI model to the user.

[2592] Input: New employee profile information

[2593] Output: Analysis results, assignment of generative AI model

[2594] Step 3: Editing emails and presentation materials

[2595] The device provides an editor screen for the user, who then creates business emails and presentation materials. The completed materials are sent from the device to the server. The server analyzes the received materials and generates prompts for the generative AI model. The server sends the materials and prompts to the generative AI model, requesting analysis. The generative AI model analyzes the grammar and expressions of the materials and suggests improvements. The server receives feedback from the generative AI model, reflects this in the document, and sends it to the device. The device displays the improved document and feedback to the user.

[2596] Input: business emails, presentation materials, prompts

[2597] Output: Feedback for generative AI models, improved documentation

[2598] Step 4: Communication support

[2599] The device provides a consultation form for users, and the user inputs questions about workplace rules and business etiquette. The device then sends the input question to the server. The server analyzes the question and, if necessary, sends the question content to the generative AI model as a prompt. The server receives the answer from the generative AI model and sends it to the device. The device then displays the appropriate answer to the user.

[2600] Input: User question, prompt

[2601] Output: The answer of the generative AI model, displayed to the user

[2602] Step 5: Provide feedback

[2603] The server records the user's activity log on the system in real time. The recorded activity log is analyzed, and the server sends the log data to the generative AI model, requesting it to generate feedback. The generative AI model analyzes the activity log and periodically generates feedback. The server sends the generated feedback to the device, which then displays it to the user.

[2604] Input: Activity log, prompt

[2605] Output: Feedback from the generative AI model, displayed to the user

[2606] Step 6: Sentiment Analysis and Adaptive Feedback

[2607] The device sends the user's input text and voice data to t...

Claims

1. A way to initially configure generative AI models that teach new employees business skills and company-specific rules. a means for entering and registering new employee profile information; A way to send created emails and presentations to the generative AI model and get feedback on improvements. A means of sending questions to the generative AI model and getting answers; A means of analyzing new hires' activity logs and providing overall feedback; A system including:

2. A means to train a generative AI model on data related to business skills and presentation skills, and A way to train generative AI models to understand a company's unique mindset and rules, and a means for receiving and providing feedback from the generative AI model to the user; and The system of claim 1 , comprising:

3. A means of analyzing new employee profile information and assigning an appropriate generative AI model; A means of providing generated feedback to new hires on a regular basis; The system of claim 1 , comprising:

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